How-To Tutorials - Data

BrainNet provides the first multi-person brain-to-brain interface which allows a nonthreatening direct collaboration between human brains. It can help small teams collaborate to solve a range of tasks using direct brain-to-brain communication. How does BrainNet operate? The noninvasive interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver the required information to the brain. For now, the interface allows three human subjects to collaborate, handle and solve a task using direct brain-to-brain communication. Two out of three human subjects are “Senders”. The senders’ brain signals are decoded using real-time EEG data analysis. This technique allows extracting decisions which are vital in communicating in order to solve the required challenges. Let’s take an example of a Tetris-like game--where you need quick decisions to decide whether to rotate a block or drop as it is in order to fill a line. The senders’ signals (decisions) are transmitted to the third subject human brain via the Internet, the “Receiver” in this case. The decisions are sent to the receiver brain via magnetic stimulation of the occipital cortex. The receiver can’t see the game screen to decide if the rotation of the block is required. The receiver integrates the decisions received and makes an informed call using an EEG interface regarding turning the position of the block or keeping it in the same position. The second round of the game allows the senders to validate the previous move and provide the necessary feedback to the receiver’s action. How did the results look? The group of researchers has implemented this technique for the Tetris game to evaluate the performance of BrainNet considering the following factors: Group-level performance during the game True/False positive rates of subject’s decisions Mutual information between subjects This was implemented among five groups of three human brain subjects to perform the Tetris task using BrainNet interface. The average accuracy result for the task was 0.813. Furthermore, they also tried varying the information reliability by injecting artificially generated noise into one of the senders’ signals. However, the receiver was able to classify which sender is more reliable based on the information transmitted to their brains. These positive results have open the gates and the possibilities of future brain-to-brain interfaces which holds the power of enabling cooperative problem solving by humans using a "social network" of connected brains. To know more, you can refer to the research paper. Read more Diffractive Deep Neural Network (D2NN): UCLA-developed AI device can identify objects at the speed of light Baidu announces ClariNet, a neural network for text-to-speech synthesis Optical training of Neural networks is making AI more efficient

Facebook is constantly under the spotlight these days when it comes to controversies regarding user’s data and privacy. A new research paper published by the Princeton University researchers states that Facebook shares the contact information you handed over for security purposes, with their advertisers. This study was first brought to light by a Gizmodo writer, Kashmir Hill. “Facebook is not content to use the contact information you willingly put into your Facebook profile for advertising. It is also using contact information you handed over for security purposes and contact information you didn’t hand over at all, but that was collected from other people’s contact books, a hidden layer of details Facebook has about you that I’ve come to call “shadow contact information”, writes Hill. Recently, Facebook introduced a new feature called custom audiences. Unlike traditional audiences, the advertiser is allowed to target specific users. To do so, the advertiser uploads user’s PII (personally identifiable information) to Facebook. After the uploading is done, Facebook then matches the given PII against platform users. Facebook then develops an audience that comprises the matched users and allows the advertiser to further track the specific audience. Essentially with Facebook, the holy grail of marketing, which is targeting an audience of one, is practically possible; nevermind whether that audience wanted it or not. In today’s world, different social media platforms frequently collect various kinds of personally identifying information (PII), including phone numbers, email addresses, names and dates of birth. Majority of this PII often represent extremely accurate, unique, and verified user data. Because of this, these services have the incentive to exploit and use this personal information for other purposes. One such scenario includes providing advertisers with more accurate audience targeting. The paper titled ‘Investigating sources of PII used in Facebook’s targeted advertising’ is written by Giridhari Venkatadri, Elena Lucherini, Piotr Sapiezynski, and Alan Mislove. “In this paper, we focus on Facebook and investigate the sources of PII used for its PII-based targeted advertising feature. We develop a novel technique that uses Facebook’s advertiser interface to check whether a given piece of PII can be used to target some Facebook user and use this technique to study how Facebook’s advertising service obtains users’ PII,” reads the paper. The researchers developed a novel methodology, which involved studying how Facebook obtains the PII to provide custom audiences to advertisers. “We test whether PII that Facebook obtains through a variety of methods (e.g., directly from the user, from two-factor authentication services, etc.) is used for targeted advertising, whether any such use is clearly disclosed to users, and whether controls are provided to users to help them limit such use,” reads the paper. The paper uses size estimates to study what sources of PII are used for PII-based targeted advertising. Researchers used this methodology to investigate which range of sources of PII was actually used by Facebook for its PII-based targeted advertising platform. They also examined what information gets disclosed to users and what control users have over PII. What sources of PII are actually being used by Facebook? Researchers found out that Facebook allows its users to add contact information (email addresses and phone numbers) on their profiles. While any arbitrary email address or phone number can be added, it is not displayed to other users unless verified (through a confirmation email or confirmation SMS message, respectively). This is the most direct and explicit way of providing PII to advertisers. Researchers then further moved on to examine whether PII provided by users for security purposes such as two-factor authentication (2FA) or login alerts are being used for targeted advertising. They added and verified a phone number for 2FA to one of the authors’ accounts. The added phone number became targetable after 22 days. This proved that a phone number provided for 2FA was indeed used for PII-based advertising, despite having set the privacy controls to the choice. What control do users have over PII? Facebook allows users the liberty of choosing who can see each PII listed on their profiles, the current list of possible general settings being: Public, Friends, Only Me.   Users can also restrict the set of users who can search for them using their email address or their phone number. Users are provided with the following options: Everyone, Friends of Friends, and Friends. Facebook provides users a list of advertisers who have included them in a custom audience using their contact information. Users can opt out of receiving ads from individual advertisers listed here. But, information about what PII is used by advertisers is not disclosed. What information about how Facebook uses PII gets disclosed to the users? On adding mobile phone numbers directly to one’s Facebook profile, no information about the uses of that number is directly disclosed to them. This Information is only disclosed to users when adding a number from the Facebook website. As per the research results, there’s very little disclosure to users, often in the form of generic statements that do not refer to the uses of the particular PII being collected or that it may be used to allow advertisers to target users. “Our paper highlights the need to further study the sources of PII used for advertising, and shows that more disclosure and transparency needs to be provided to the user,” says the researchers in the paper. For more information, check out the official research paper. Ex-employee on contract sues Facebook for not protecting content moderators from mental trauma How far will Facebook go to fix what it broke: Democracy, Trust, Reality Mark Zuckerberg publishes Facebook manifesto for safeguarding against political interference

Blockchain is reshaping the world as we know it. And we are not talking metaphorically because the new technology is really influencing everything from online security and data management to governance and smart contracting. Statistical reports support these claims. According to the study, the blockchain universe grows by over 40% annually, while almost 70% of banks are already experimenting with this technology. IT experts at the Editing AussieWritings.com Services claim that the potential in this field is almost limitless: “Blockchain offers a myriad of practical possibilities, so you definitely want to get acquainted with it more thoroughly.” Developers who are curious about blockchain can turn it into a lucrative career opportunity since it gives them the chance to master the art of cryptography, hierarchical distribution, growth metrics, transparent management, and many more. There were 5,743 mostly full-time job openings calling for blockchain skills in the last 12 months - representing the 320% increase - while the biggest freelancing website Upwork reported more than 6,000% year-over-year growth. In this post, we will recommend our 9 best blockchain online courses. Let’s take a look! Udemy Udemy offers users one of the most comprehensive blockchain learning sources. The target audience is people who have heard a little bit about the latest developments in this field, but want to understand more. This online course can help you to fully understand how the blockchain works, as well as get to grips with all that surrounds it. Udemy breaks down the course into several less complicated units, allowing you to figure out this complex system rather easily. It costs $19.99, but you can probably get it with a 40% discount. The one downside, however, is that content quality in terms of subject scope can vary depending on the instructor, but user reviews are a good way to gauge quality. Each tutorial lasts approximately 30 minutes, but it also depends on your own tempo and style of work. Pluralsight Pluralsight is an excellent beginner-level blockchain course. It comes in three versions: Blockchain Fundamentals, Surveying Blockchain Technologies for Enterprise, and Introduction to Bitcoin and Decentralized Technology. Course duration varies from 80 to 200 minutes depending on the package. The price of Pluralsight is $29 a month or $299 a year. Choosing one of these options, you are granted access to the entire library of documents, including course discussions, learning paths, channels, skill assessments, and other similar tools. Packt Publishing Packt Publishing has a wide portfolio of learning products on Blockchain for varying levels of experience in the field from beginners to experts. And what’s even more interesting is that you can choose your learning format from books, ebooks to videos, courses and live courses. Or you could simply subscribe to MAPT, their library to gain access to all products at a reasonable price of $29 monthly and $150 annually.  It offers several books and videos on the leading blockchain technology. You can purchase 5 blockchain titles at a discounted rate of $50. Here’s the list of top blockchain courses offered by Packt Publishing: Exploring Blockchain and Crypto-currencies: You will gain the foundational understanding of blockchain and crypto-currencies through various use-cases. Building Blockchain Projects: In this, you will be able to develop real-time practical DApps with Ethereum and JavaScript. Mastering Blockchain - Second Edition: You can learn about cryptography and cryptocurrencies, so you can build highly secure, decentralized applications and conduct trusted in-app transactions. Hands-On Blockchain with Hyperledger: This book will help you leverage the power of Hyperledger Fabric to develop Blockchain-based distributed ledgers with ease. Learning Blockchain Application Development [video ]: This interactive video will help you learn build smart contracts and DApps on Ethereum. Create Ethereum and Blockchain Applications using Solidity [video ]: This video will help you learn about Ethereum, Solidity, DAO, ICO, Bitcoin, Altcoin, Website Security, Ripple, Litecoin, Smart Contracts, and Apps. Cryptozombies Cryptozombies is an online blockchain course based on gamification elements. The tool teaches you to write smart contracts in Solidity through building your own crypto-collectibles game. It is entirely Ethereum-focused, but you don’t need any previous experience to understand how Solidity works. There is a step by step guide that explains to you even the smallest details, so you can quickly learn to create your own fully-functional blockchain-based game. The best thing about Cryptozombies is that you can test it for free and give up in case you don’t like it. Coursera The blockchain is the epicenter of the cryptocurrency world, so it’s necessary to study it if you want to deal with Bitcoin and other digital currencies. Coursera is the leading online resource in the field of virtual currencies, so you might want to check it out. After this course like Blockchain Specialization, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer to secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin in your own projects. The course is a 4-part course spanning a duration 4 weeks, but you can take each part separately. The price depends on the level and features you choose. LinkedIn Learning (formerly known as Lynda) LinkedIn Learning (what used to be Lynda) doesn't offer a specific blockchain course, but it does have a wide range of industry-related learning sources. A search for ‘blockchain’ will present you with almost 100 relevant video courses. You can find all sorts of lessons here, from beginner to expert levels. Lynda allows you to customize selection according to video duration, authors, software, subjects, etc. You can access the library for $15 a month. B9Lab B9Lab ETH-25 Certified Online Ethereum Developer Course is another course that promotes blockchain technology aimed at the Ethereum platform. It’s a 12-week in-depth learning solution that targets experienced programmers. B9Lab introduces everything there is to know about blockchain and how to build useful applications. Participants are taught about the Ethereum platform, the programming language Solidity, how to use web3 and the Truffle framework, and how to tie everything together. The price is €1450 or about $1700. IBM IBM made a self-paced blockchain course, titled Blockchain Essentials that lasts over two hours. The video lectures and lab in this course help you learn about blockchain for business and explore key use cases that demonstrate how the technology adds value. You can learn how to leverage blockchain benefits, transform your business with the new technology, and transfer assets. Besides that, you get a nice wrap-up and a quiz to test your knowledge upon completion. IBM’s course is free of charge. Khan Academy Khan Academy is the last, but certainly not the least important online course on our list. It gives users a comprehensive overview of blockchain-powered systems, particularly Bitcoin. Using this platform, you can learn more on cryptocurrency transactions, security, proof of work, etc. As an online education platform, Khan Academy won’t cost you a dime. [dropcap]B[/dropcap]lockchain is the groundbreaking technology that opens new boundaries in almost every field of business. It directly influences financial markets, data management, digital security, and a variety of other industries. In this post, we presented 9 best blockchain online courses you should try. These sources can teach you everything there is to know about the blockchain basics. Take some time to check them out and you won’t regret it! Author Bio: Olivia is a passionate blogger who writes on topics of digital marketing, career, and self-development. She constantly tries to learn something new and to share this experience on various websites. Connect with her on Facebook and Twitter. Google introduces Machine Learning courses for AI beginners Microsoft start AI School to teach Machine Learning and Artificial Intelligence.

According to information obtained by Bloomberg, The White House is reportedly making a draft executive order against online platform bias in Social Media firms. Per this draft, federal antitrust and law enforcement agencies are instructed to investigate into the practices of Google, Facebook, and other social media companies. The existence of the draft was first reported by Capital Forum. Federal law enforcers are required to investigate primarily against two violations. First, if an online platform has acted in violation of the antitrust laws. Second, to remove anti-competitive spirit among online platforms and address online platform bias. Per the sources by Capital Forum, the draft is written in two parts. The first part is a policy statement stating that online platforms are central to the flow of information and commerce and need to be held accountable through competition. The second part instructs agencies to investigate bias and anticompetitive conduct in online platforms where they have the authority. In case of lack of authorization, they are required to report concerns or issues to the Federal Trade Commission or the Department of Justice. No online platforms are mentioned by name in the draft. It’s unclear when or if the White House will decide to issue the order. Donald Trump and the White House have always been vocal about the prevalent bias in Social media platforms. In August, Trump tweeted about Social Media discriminating against Republican and Conservative voices. Source: Twitter He also went on to claim that Google search results for “Trump News” reports fake news. He accused the search engines’ algorithms of being rigged. However, that allegation having not been backed by evidence, let Google slam Trump’s accusations, asserting that its search engine algorithms do not favor any political ideology. Earlier this month, Facebook COO Sheryl Sandberg and Twitter CEO Jack Dorsey faced the Senate Select Intelligence Committee, to discuss foreign interference through social media platforms in US elections. Google, Facebook, and Twitter also released a Testimony ahead of appearing before the committee. As reported by the Wall Street Journal, Google CEO Sundar Pichai also plans to meet privately with top Republican lawmakers this Friday to discuss a variety of topics, including the company's alleged political bias in search results. This meeting is organized by the House Majority Leader, Kevin McCarthy. Pichai said on Tuesday that “I look forward to meeting with members on both sides of the aisle, answering a wide range of questions, and explaining our approach." Google is also facing public scrutiny over a report that it intends to launch a censored search engine in China. Google’s custom search engine would link Chinese users’ search queries to their personal phone numbers, thus making it easier for the government to track their searches. About a thousand Google employees frustrated with a series of controversies involving Google have signed a letter to demand transparency on building the alleged search engine. Google’s new Privacy Chief officer proposes a new framework for Security Regulation. Amazon is the next target on EU’s antitrust hitlist. Mark Zuckerberg publishes Facebook manifesto for safeguarding against political interference.  

What is a convolutional neural network, exactly? Well, let's start with the basics: a convolutional neural network (CNN) is a type of neural network that is most often applied to image processing problems. You've probably seen them in action anywhere a computer is identifying objects in an image. But you can also use convolutional neural networks in natural language processing projects, too. The fact that they are useful for these fast growing areas is one of the main reasons they're so important in deep learning and artificial intelligence today. What makes a convolutional neural network unique? Once you understand how a convolutional neural network works and what makes it unique from other neural networks, you can see why they're so effective for processing and classifying images. But let’s first take a regular neural network. A regular neural network has an input layer, hidden layers and an output layer. The input layer accepts inputs in different forms, while the hidden layers perform calculations on these inputs. The output layer then delivers the outcome of the calculations and extractions. Each of these layers contains neurons that are connected to neurons in the previous layer, and each neuron has its own weight. This means you aren’t making any assumptions about the data being fed into the network - great usually, but not if you’re working with images or language. Convolutional neural networks work differently as they treat data as spatial. Instead of neurons being connected to every neuron in the previous layer, they are instead only connected to neurons close to it and all have the same weight. This simplification in the connections means the network upholds the spatial aspect of the data set. It means your network doesn’t think an eye is all over the image. The word ‘convolutional’ refers to the filtering process that happens in this type of network. Think of it this way, an image is complex - a convolutional neural network simplifies it so it can be better processed and ‘understood.’ What's inside a convolutional neural network? Like a normal neural network, a convolutional neural network is made up of multiple layers. There are a couple of layers that make it unique - the convolutional layer and the pooling layer. However, like other neural networks, it will also have a ReLu or rectified linear unit layer, and a fully connected layer. The ReLu layer acts as an activation function, ensuring non-linearity as the data moves through each layer in the network - without it, the data being fed into each layer would lose the dimensionality that we want to maintain. The fully connected layer, meanwhile, allows you to perform classification on your dataset. The convolutional layer The convolutional layer is the most important, so let’s start there. It works by placing a filter over an array of image pixels - this then creates what’s called a convolved feature map. "It’s a bit like looking at an image through a window which allows you to identify specific features you might not otherwise be able to see. The pooling layer Next we have the pooling layer - this downsamples or reduces the sample size of a particular feature map. This also makes processing much faster as it reduces the number of parameters the network needs to process. The output of this is a pooled feature map. There are two ways of doing this, max pooling, which takes the maximum input of a particular convolved feature, or average pooling, which simply takes the average. These steps amount to feature extraction, whereby the network builds up a picture of the image data according to its own mathematical rules. If you want to perform classification, you'll need to move into the fully connected layer. To do this, you'll need to flatten things out - remember, a neural network with a more complex set of connections can only process linear data. How to train a convolutional neural network There are a number of ways you can train a convolutional neural network. If you’re working with unlabelled data, you can use unsupervised learning methods. One of the best popular ways of doing this is using auto-encoders - this allows you to squeeze data in a space with low dimensions, performing calculations in the first part of the convolutional neural network. Once this is done you’ll then need to reconstruct with additional layers that upsample the data you have. Another option is to use generative adversarial networks, or GANs. With a GAN, you train two networks. The first gives you artificial data samples that should resemble data in the training set, while the second is a ‘discriminative network’ - it should distinguish between the artificial and the 'true' model. What's the difference between a convolutional neural network and a recurrent neural network? Although there's a lot of confusion about the difference between a convolutional neural network and a recurrent neural network, it's actually more simple than many people realise. Whereas a convolutional neural network is a feedforward network that filters spatial data, a recurrent neural network, as the name implies, feeds data back into itself. From this perspective recurrent neural networks are better suited to sequential data. Think of it like this: a convolutional network is able to perceive patterns across space - a recurrent neural network can see them over time. How to get started with convolutional neural networks If you want to get started with convolutional neural networks Python and TensorFlow are great tools to begin with. It’s worth exploring MNIST dataset too. This is a database of handwritten digits that you can use to get started with building your first convolutional neural network. To learn more about convolutional neural networks, artificial intelligence, and deep learning, visit Packt's store for eBooks and videos.

Facebook, along with other tech media giants, like Twitter and Google, broke the democratic process in 2016. Facebook also broke the trust of many of its users as scandal after scandal kept surfacing telling the same story in different ways - the story of user data and trust abused in exchange for growth and revenue. The week before last, Mark Zuckerberg posted a long explanation on Facebook titled ‘Preparing for Elections’. It is the first of a series of reflections by Zuckerberg that ‘address the most important issues facing Facebook’. That post explored what Facebook is doing to avoid ending up in a situation similar to the 2016 elections when the platform ‘inadvertently’ became a super-effective channel for election interference of various kinds. It follows just weeks after Facebook COO, Sheryl Sandberg appeared in front of a Senate Intelligence hearing alongside Twitter CEO, Jack Dorsey on the topic of social media’s role in election interference. Zuckerberg’s mobile-first rigor oversimplifies the issues Zuckerberg opened his post with a strong commitment to addressing the issues plaguing Facebook using the highest levels of rigor the company has known in its history. He wrote, “I am bringing the same focus and rigor to addressing these issues that I've brought to previous product challenges like shifting our services to mobile.”  To understand the weight of this statement we must go back to how Facebook became a mobile-first company that beat investor expectations wildly. Suffice to say it went through painful years of restructuring and reorientation in the process. Those unfamiliar with that phase of Facebook, please read the section ‘How far did Facebook go to become a mobile-first company?’ at the end of this post for more details. To be fair, Zuckerberg does acknowledge that pivoting to mobile was a lot easier than what it will take to tackle the current set of challenges. He writes, “These issues are even harder because people don't agree on what a good outcome looks like, or what tradeoffs are acceptable to make. When it comes to free expression, thoughtful people come to different conclusions about the right balances. When it comes to implementing a solution, certainly some investors disagree with my approach to invest so much on security. We have a lot of work ahead, but I am confident we will end this year with much more sophisticated approaches than we began, and that the focus and investments we've put in will be better for our community and the world over the long term.” However, what Zuckerberg does not acknowledge in the above statement is that the current set of issues is not merely a product challenge, but a business ethics and sustainability challenge. Unless ‘an honest look in the mirror’ kind of analysis is done on that side of Facebook, any level of product improvements will only result in cosmetic changes that will end in an ‘operation successful, patient dead’ scenario. In the coming sections, I attempt to dissect Zuckerberg’s post in the context of the above points by reading between the lines to see how serious the platform really is about changing its ways to ‘be better for our community and the world over the long term’. Why does Facebook’s commitment to change feel hollow? Let’s focus on election interference in this analysis as Zuckerberg limits his views to this topic in his post. Facebook has been at the center of this story on many levels. Here is some context on where Zuckerberg is coming from.   Facebook’s involvement in the 2016 election meddling Apart from the traditional cyber-attacks (which they had even back then managed to prevent successfully), there were Russia-backed coordinated misinformation campaigns found on the platform. Then there was also the misuse of its user data by data analytics firm, Cambridge Analytica, which consulted on election campaigning. They micro-profiled users based on their psychographics (the way they think and behave) to ensure more effective ad spending by political parties. There was also the issue of certain kinds of ads, subliminal messages and peer pressure sent out to specific Facebook users during elections to prompt them to vote for certain candidates while others did not receive similar messages. There were also alleged reports of a certain set of users having been sent ‘dark posts’ (posts that aren’t publicly visible to all, but visible only to those on the target list) to discourage them from voting altogether. It also appears that Facebook staff offered both the Clinton and the Trump campaigns to assist with Facebook advertising. The former declined the offer while the latter accepted. We don’t know which of the above and to what extent each of these decisions and actions impacted the outcome of the 2016 US presidential elections. But one thing is certain, collectively they did have a significant enough impact for Zuckerberg and team to acknowledge these are serious problems that they need to address, NOW! Deconstructing Zuckerberg’s ‘Protecting Elections’ Before diving into what is problematic about the measures that are taken (or not taken) by Facebook, I must commend them for taking ownership of their role in election interference in the past and for attempting to rectify the wrongs. I like that Zuckerberg has made himself vulnerable by sharing his corrective plans with the public while it is a work in progress and is engaging with the public at a personal level. Facebook’s openness to academic research using anonymized Facebook data and their willingness to permit publishing findings without Facebook’s approval is also noteworthy. Other initiatives such as the political ad transparency report, AI enabled fake account & fake news reduction strategy, doubling the content moderator base, improving their recommendation algorithms are all steps in the right direction. However, this is where my list of nice things to say ends. The overall tone of Zuckerberg’s post is that of bargaining rather than that of acceptance. Interestingly this was exactly the tone adopted by Sandberg as well in the Senate hearing earlier this month, down to some very similar phrases. This makes one question if everything isn’t just one well-orchestrated PR disaster management plan. Disappointingly, most of the actions stated in Zuckerberg's post feel like half-measures; I get the sense that they aren’t willing to go the full distance to achieve the objectives they set for themselves. I hope to be wrong. 1. Zuckerberg focuses too much on ‘what’ and ‘how’, is ignoring the ‘why’ Zuckerberg identifies three key issues he wants to address in 2018: preventing election interference, protecting the community from abuse, and providing users with better control over their information. This clarity is a good starting point. In this post, he only focuses on the first issue. So I will reserve sharing my detailed thoughts on the other two for now. What I would say for now is that the key to addressing all issues on Facebook is taking a hard look at Facebook policies, including privacy, from a mission statement perspective. In other words, be honest about ‘Why Facebook exists’. Users are annoyed, advertisers are not satisfied and neither are shareholders confident about Facebook’s future. Trying to be everyone’s friend is clearly not working for Facebook. As such, I expected this in the opening part of the series. ‘Be better for our community and the world over the long term’ is too vague of a mission statement to be of any practical use. 2. Political Ad transparency report is necessary, but not sufficient In May this year, Facebook released its first political ad transparency report as a gesture to show its commitment to minimizing political interference. The report allows one to see who sponsored which issue advertisement and for how much. This was a move unanimously welcomed by everyone and soon others like Twitter and Google followed suit. By doing this, Facebook hopes to allow its users to form more informed views about political causes and other issues.   Here is my problem with this feature. (Yes, I do view this report as a ‘feature’ of the new Facebook app which serves a very specific need: to satisfy regulators and media.) The average Facebook user is not the politically or technologically savvy consumer. They use Facebook to connect with friends and family and maybe play silly games now and then. The majority of these users aren’t going to proactively check out this ad transparency report or the political ad database to arrive at the right conclusions. The people who will find this report interesting are academic researchers, campaign managers, and analysts. It is one more rich data point to understand campaign strategy and thereby infer who the target audience is. This could most likely lead to a downward spiral of more and more polarizing ads from parties across the spectrum. 3. How election campaigning, hate speech, and real violence are linked but unacknowledged Another issue closely tied with political ads is hate speech and violence-inciting polarising content that aren’t necessarily paid ads. These are typical content in the form of posts, images or videos that are posted as a response to political ads or discourses. These act as carriers that amplify the political message, often in ways unintended by the campaigners themselves. The echo chambers still exist. And the more one's ecosystem or ‘look-alike audience’ responds to certain types of ads or posts, users are more likely to keep seeing them, thanks to Facebook's algorithms. Seeing something that is endorsed by one’s friends often primes one to trust what is said without verifying the facts for themselves thus enabling fake news to go viral. The algorithm does the rest to ensure everyone who will engage with the content sees it. Newsy political ads will thrive in such a setup while getting away with saying ‘we made full disclosure in our report’. All of this is great for Facebook’s platform as it not only gets great engagement from the content but also increased ad spendings from all political parties as they can’t afford to be missing from action on Facebook. A by-product of this ultra-polarised scenario though is more protectionism and less free, open and meaningful dialog and debate between candidates as well as supporters on the platform. That’s bad news for the democratic process. 4. Facebook’s election interference prevention model is not scalable Their single-minded focus on eliminating US election interference on Facebook’s platforms through a multipronged approach to content moderation is worth appreciating. This also makes one optimistic about Facebook’s role in consciously attempting to do the right thing when it comes to respecting election processes in other nations as well. But the current approach of creating an ‘election war room’ is neither scalable nor sustainable. What happens everytime a constituency in the US has some election or some part of the world does? What happens when multiple elections take place across the world simultaneously? Who does Facebook prioritize to provide election interference defense support and why? Also, I wouldn’t go too far to trust that they will uphold individual liberties in troubled nations with strong regimes or strong divisive political discourses. What happens when the ruling party is the one interfering with the elections? Who is Facebook answerable to? 5. Facebook’s headcount hasn’t kept up with its own growth ambitions  Zuckerberg proudly states in his post that they’ve deleted a billion fake accounts with machine learning and have double the number of people hired to work on safety and security. "With advances in machine learning, we have now built systems that block millions of fake accounts every day. In total, we removed more than one billion fake accounts -- the vast majority within minutes of being created and before they could do any harm -- in the six months between October and March. ....it is still very difficult to identify the most sophisticated actors who build their networks manually one fake account at a time. This is why we've also hired a lot more people to work on safety and security -- up from 10,000 last year to more than 20,000 people this year." ‘People working on safety and security’ could have a wide range of job responsibilities from network security engineers to security guards hired at Facebook offices. What is missing conspicuously in the above picture is a breakdown of the number of people hired specifically to fact check, moderate content and resolve policy related disputes and review flagged content. With billions of users posting on Facebook, the job of content moderators and policy enforcers, even when assisted by algorithms, is massive. It is important that they are rightly incentivized to do their job well and are set clear and measurable goals. The post neither talks of how Facebook plans to reward moderators and neither does it talk about what the yardsticks for performance in this area would be. Facebook fails to acknowledge that it is not fully prepared, partly because it is understaffed. 6. The new Product Policy Director, human rights role is a glorified Public Relations job The weekend following Zuckerberg’s post, a new job opening appeared on Facebook’s careers page for the position of ‘Product policy director, human rights’. Below snippet is taken from that job posting. Source: Facebook careers The above is typically what a Public relations head does as well. Not only are the responsibilities cited above heavily communication and public perception building based, there’s not much given in terms of authority to this role to influence how other teams achieve their goals. Simply put, this role ‘works with, coordinates or advises teams’, it does not ‘guide or direct teams’. Als,o another key point to observe is that this role aims to add another layer of distance to further minimize exposure for Zuckerberg, Sandberg and other top key executives in public forums such as congressional hearings or press meets. Any role/area that is important to a business typically finds a place at the C-suite table. Had this new role been one of the c-suite roles it would have been advertised so, and it may have had some teeth. Of the 24 key executives in Facebook, only one is concerned with privacy and policy, ‘Chief Privacy Officer & VP of U.S. Public Policy’. Even this role does not have a global directive or public welfare in mind. On the other hand, there are multiple product development, creative and business development roles on Facebook’s c-suite. There is even a separate watch product head, a messaging product head, and one just dedicated to China called ‘Head of Creative Shop - Greater China’. This is why Facebook’s plan to protect elections will fail I am afraid Facebook’s greatest strength is also it’s Achilles heel. The tech industry’s deified hacker culture is embodied perfectly by Facebook. Facebook’s ad revenue based flawed business model is the ingenious creation of that very hacker culture. Any attempts to correct everything else is futile without correcting the issues with the current model. The ad revenue based model is why the Facebook app is designed the way it is: with ‘relevant’ news feeds, filter bubbles and look-alike audience segmentation. It is the reason why viral content gets rewarded irrespective of its authenticity or the impact it has on society. It is also the reason why Facebook has a ‘move fast and break things’ internal culture where growth at all costs is favored and idolized. Facebook’s Q2 2018 Earnings summary highlights the above points succinctly. Source: Facebook's SEC Filing The above snapshot means that even if we assume all 30k odd employees do some form of content moderation (the probability of which is zero), every employee is responsible for 50k users’ content daily. Let’s say every user only posts 1 post a day. If we assume Facebook’s news feed algorithms are super efficient and only find 2% of the user content questionable/fake (as speculated by Sandberg in her Senate hearing this month), that would still mean nearly 1k posts per person to review every day!   What can Facebook do to turn over a new leaf? Unless Facebook attempts to sincerely address at least some of the below, I will continue to be skeptical of any number of beautifully written posts by Zuckerberg or patriotically orated speeches by Sandberg. A content moderation transparency report that shares not just the number of posts moderated, the number of people working to moderate content on Facebook but also the nature of content moderated, the moderators’ job satisfaction levels, their tenure, qualifications, career aspirations, their challenges, and how much Facebook is investing in people, processes and technology to make its platform safe and objective for everyone to engage with others. A general Ad transparency report that not only lists advertisers on Facebook but also their spendings and chosen ad filters for the public and academia to review or analyze any time. Taking responsibility for the real-world consequences of actions enabled by Facebook. Like the recent gender and age discrimination employment ads shown on Facebook. Really banning hate speech and fake viral content. Bring in a business/AI ethics head who is only next to Zuckerberg and equal to Sandberg’s COO role. Exploring and experimenting with other alternative revenue channels to tackle the current ad-driven business model problem. Resolving the UI problem so that users can gain back control over their data and make it easy to choose to not participate in Facebook’s data experiments. This would mean a potential loss in some ad revenue. The ‘grow hacker’ culture problem that is a byproduct of years of moving fast and breaking things. This would mean a significant change in behavior by everyone starting from the top and probably restructuring the way teams are organized and business is done. It would also mean a different definition and measurement of success which could lead to shareholder backlash. But Mark is uniquely placed to withstand these pressures given his clout over the board voting powers. Like Augustus Caesar his role model, Zuckerberg has a chance to make history. But he might have to put the company through hard and sacrificing times in exchange for the proverbial 200 years of world peace. He’s got the best minds and limitless resources at his disposal to right what he and his platform wronged. But he would have to make enemies with the hands that feed him. Would he rise to the challenge? Like Augustus who is rumored to have killed his grandson, will Zuckerberg ever be prepared to kill his ad revenue generating brainchild? In the meanwhile, we must not underestimate the power of good digital citizenry. We must continue to fight the good fight to move tech giants like Facebook in the right direction. Just as persistent trickling water droplets can erode mountains and create new pathways, so can our mindful actions as digital platform users prompt major tech reforms. It could be as bold as deleting one's Facebook account (I haven’t been on the platform for years now, and I don’t miss it at all). You could organize groups to create awareness on topics like digital privacy, fake news, filter bubbles, or deliberately choose to engage with those whose views differ from yours to understand their perspective on topics and thereby do your part in reversing algorithmically accentuated polarity. It could also be by selecting the right individuals to engage in informed dialog with tech conglomerates. Not every action needs to be hard though. It could be as simple as customizing your default privacy settings or choosing to only spend a select amount of time on such platforms, or deciding to verify the authenticity and assessing the toxicity of a post you wish to like, share or forward to your network. Addendum How far did Facebook go to become a mobile-first company? Following are some of the things Facebook did to become the largest mobile advertising platform in the world, surpassing Google by a huge margin. Clear purpose and reason for the change: “For one, there are more mobile users. Second, they’re spending more time on it... third, we can have better advertising on mobile, make more money,” said Zuckerberg at TechCrunch Disrupt back in 2012 on why they were becoming mobile first. In other words, there was a lot of growth and revenue potential in investing in this space. This was a simple and clear ‘what’s in it for me’ incentive for everyone working to make the transition as well for stockholders and advertisers to place their trust in Zuckerberg’s endeavors. Setting company-wide accountability: “We realigned the company around, so everybody was responsible for mobile.”, said the then President of Business and Marketing Partnerships David Fischer to Fortune in 2013. Willing to sacrifice desktop for mobile: Facebook decided to make a bold gamble to lose its desktop users to grow its unproven mobile platform. Essentially it was willing to bet its only cash cow for a dark horse that was dependent on so many other factors to go right. Strict consequences for non-compliance: Back in the days of transitioning to a mobile-first company Zuckerberg famously said to all his product teams that when they went in for reviews: “Come in with mobile. If you come in and try to show me a desktop product, I’m going to kick you out. You have to come in and show me a mobile product.” Expanding resources and investing in reskilling: They grew their team of 20 mobile engineers to literally all engineers at Facebook undergoing training courses on iOS and Android development. “we’ve completely changed the way we do product development. We’ve trained all our engineers to do mobile first.”, said Facebook’s VP of corporate development, Vaughan Smith to TechCrunch by the end of 2012. Realigning product design philosophy: Designed custom features for the mobile-first interface instead of trying to adapt the features for the web to mobile. In other words, they began with mobile as their default user interface. Local and global user behavior sensitization: Some of their engineering teams even did field visits to developing nations like the Philippines to see first hand how mobile apps are being used there. Environmental considerations in app design: Facebook even had the foresight to consider scenarios where mobile users may not have quality internet signals or poor quality mobile battery related issues. They designed their apps keeping these future needs in mind.

Facebook is playing a huge role in artificial intelligence research. It’s not only a core part of the Facebook platform, it’s central to how the organization works. The company launched its AI research lab - FAIR - back in 2013. Today, led by some of the best minds in the field, it's not only helping Facebook to leverage artificial intelligence, it's also making it more accessible to researchers and engineers around the world. Let’s take a look at some of the tools built by Facebook that are doing just that. PyTorch: Facebook's leading artificial intelligence tool PyTorch is a hugely popular deep learning framework (rivalling Google's TensorFlow) that, by combining flexiblity and dynamism with stability, bridges the gap between research and production. Using a tape-based auto-differentiation system, PyTorch can be modified and changed by engineers without losing speed. That’s good news for everyone. Although PyTorch steals the headlines, there are a range of supporting tools that are making artificial intelligence and deep learning more accessible and achievable for other engineers. Read next: Is PyTorch better than Google’s TensorFlow? Find PyTorch eBooks and videos on the Packt website.  Facebook's computer vision tools Another field that Facebook has revolutionized is computer vision and image processing. Detectron, Facebook’s state-of-the-art object detection software system, has powered many research projects including Mask R-CNN - a simple and flexible way of developing Convolution Neural Networks for image processing. Mask R-CNN has also helped to power DensePose, a tool that map all human pixels of an RGB image to a 3D surface-based representation of the human body. Facebook has also heavily contributed to research in detecting and recognizing Human-Object interactions as well. Their contribution to the field of generative modeling is equally very important, with tasks such as minimizing variations in the quality of images, JPEG compression as well as image quantization now becoming easier and more accessible. Facebook, language and artificial intelligence We share updates, we send messages - language is a cornerstone of Facebook. This is why it's such an important area for Facebook’s AI researchers. There are a whole host of libraries and tools that are built for language problems. FastText is a library for text representation and classification, while ParlAI is a platform pushing the boundaries of dialog research. The platform is focused on tackling 5 key AI tasks: question answering, sentence completion, goal-oriented dialog, chit-chat dialog, and visual dialog. The ultimate aim for ParlAI is to develop a general dialog AI. There are also a few more language tools in Facebook’s AI toolkit - Fairseq and Translate are helping with translation and text generation, while Wav2Letter is an Automatic Speech Recognition system that can be used for transcription tasks. Rational artificial intelligence for gaming and smart decision making Although Facebook isn’t known for gaming, its interest in developing artificial intelligence that can reason could have an impact on the way games are built in the future. ELF is a tool developed by Facebook that allows game developers to train and test AI algorithms in a gaming environment. ELF was used by Facebook researchers to recreate DeepMind’s AlphaGo Zero, the AI bot that has defeated Go champions. Running on a single GPU, the ELF OpenGo bot defeated four professional Go players 14-0. Impressive, right? There are other tools built by Facebook that aim to build AI into game reasoning. Torchcraft is probably the most notable example - its a library that’s making AI research on Starcraft - a strategy game - accessible to game developers and AI specialists alike. Facebook is defining the future of artificial intelligence As you can see, Facebook is doing a lot to push the boundaries of artificial intelligence. However, it’s not just keeping these tools for itself - all these tools are open source, which means they can be used by anyone.

Amazon Machine Learning is useful for building ML models and generating predictions. It also enables the development of robust and scalable smart applications. The process of building ML models with Amazon Machine Learning consists of three operations: data analysis model training evaluation. The code files for this article are available on Github. This tutorial is an excerpt from a book written by Alexis Perrier titled Effective Amazon Machine Learning. The Amazon Machine Learning service is available at https://console.aws.amazon.com/machinelearning/. The Amazon ML workflow closely follows a standard Data Science workflow with steps: Extract the data and clean it up. Make it available to the algorithm. Split the data into a training and validation set, typically a 70/30 split with equal distribution of the predictors in each part. Select the best model by training several models on the training dataset and comparing their performances on the validation dataset. Use the best model for predictions on new data. As shown in the following Amazon ML menu, the service is built around four objects: Datasource ML model Evaluation Prediction The Datasource and Model can also be configured and set up in the same flow by creating a new Datasource and ML model. Let us take a closer look at each one of these steps. Understanding the dataset used We will use the simple Predicting Weight by Height and Age dataset (from Lewis Taylor (1967)) with 237 samples of children's age, weight, height, and gender, which is available at https://v8doc.sas.com/sashtml/stat/chap55/sect51.htm. This dataset is composed of 237 rows. Each row has the following predictors: sex (F, M), age (in months), height (in inches), and we are trying to predict the weight (in lbs) of these children. There are no missing values and no outliers. The variables are close enough in range and normalization is not required. We do not need to carry out any preprocessing or cleaning on the original dataset. Age, height, and weight are numerical variables (real-valued), and sex is a categorical variable. We will randomly select 20% of the rows as the held-out subset to use for prediction on previously unseen data and keep the other 80% as training and evaluation data. This data split can be done in Excel or any other spreadsheet editor: By creating a new column with randomly generated numbers Sorting the spreadsheet by that column Selecting 190 rows for training and 47 rows for prediction (roughly a 80/20 split) Let us name the training set LT67_training.csv and the held-out set that we will use for prediction LT67_heldout.csv, where LT67 stands for Lewis and Taylor, the creator of this dataset in 1967. As with all datasets, scripts, and resources mentioned in this book, the training and holdout files are available in the GitHub repository at https://github.com/alexperrier/packt-aml. It is important for the distribution in age, sex, height, and weight to be similar in both subsets. We want the data on which we will make predictions to show patterns that are similar to the data on which we will train and optimize our model. Loading the data on S3 Follow these steps to load the training and held-out datasets on S3: Go to your s3 console at https://console.aws.amazon.com/s3. Create a bucket if you haven't done so already. Buckets are basically folders that are uniquely named across all S3. We created a bucket named aml.packt. Since that name has now been taken, you will have to choose another bucket name if you are following along with this demonstration. Click on the bucket name you created and upload both the LT67_training.csv and LT67_heldout.csv files by selecting Upload from the Actions drop-down menu: Both files are small, only a few KB, and hosting costs should remain negligible for that exercise. Note that for each file, by selecting the Properties tab on the right, you can specify how your files are accessed, what user, role, group or AWS service may download, read, write, and delete the files, and whether or not they should be accessible from the Open Web. When creating the datasource in Amazon ML, you will be prompted to grant Amazon ML access to your input data. You can specify the access rules to these files now in S3 or simply grant access later on. Our data is now in the cloud in an S3 bucket. We need to tell Amazon ML where to find that input data by creating a datasource. We will first create the datasource for the training file ST67_training.csv. Declaring a datasource Go to the Amazon ML dashboard, and click on Create new... | Datasource and ML model. We will use the faster flow available by default: As shown in the following screenshot, you are asked to specify the path to the LT67_training.csv file {S3://bucket}{path}{file}. Note that the S3 location field automatically populates with the bucket names and file names that are available to your user: Specifying a Datasource name is useful to organize your Amazon ML assets. By clicking on Verify, Amazon ML will make sure that it has the proper rights to access the file. In case it needs to be granted access to the file, you will be prompted to do so as shown in the following screenshot: Just click on Yes to grant access. At this point, Amazon ML will validate the datasource and analyze its contents. Creating the datasource An Amazon ML datasource is composed of the following: The location of the data file: The data file is not duplicated or cloned in Amazon ML but accessed from S3 The schema that contains information on the type of the variables contained in the CSV file: Categorical Text Numeric (real-valued) Binary It is possible to supply Amazon ML with your own schema or modify the one created by Amazon ML. At this point, Amazon ML has a pretty good idea of the type of data in your training dataset. It has identified the different types of variables and knows how many rows it has: Move on to the next step by clicking on Continue, and see what schema Amazon ML has inferred from the dataset as shown in the next screenshot: Amazon ML needs to know at that point which is the variable you are trying to predict. Be sure to tell Amazon ML the following: The first line in the CSV file contains te column name The target is the weight We see here that Amazon ML has correctly inferred the following: sex is categorical age, height, and weight are numeric (continuous real values) Since we chose a numeric variable as the target Amazon ML, will use Linear Regression as the predictive model. For binary or categorical values, we would have used Logistic Regression. This means that Amazon ML will try to find the best a, b, and c coefficients so that the weight predicted by the following equation is as close as possible to the observed real weight present in the data: predicted weight = a * age + b * height + c * sex Amazon ML will then ask you if your data contains a row identifier. In our present case, it does not. Row identifiers are useful when you want to understand the prediction obtained for each row or add an extra column to your dataset later on in your project. Row identifiers are for reference purposes only and are not used by the service to build the model. You will be asked to review the datasource. You can go back to each one of the previous steps and edit the parameters for the schema, the target and the input data. Now that the data is known to Amazon ML, the next step is to set up the parameters of the algorithm that will train the model. Understanding the model We select the default parameters for the training and evaluation settings. Amazon ML will do the following: Create a recipe for data transformation based on the statistical properties it has inferred from the dataset Split the dataset (ST67_training.csv) into a training part and a validation part, with a 70/30 split. The split strategy assumes the data has already been shuffled and can be split sequentially. The recipe will be used to transform the data in a similar way for the training and the validation datasets. The only transformation suggested by Amazon ML is to transform the categorical variable sex into a binary variable, where m = 0 and f = 1 for instance. No other transformation is needed. The default advanced settings for the model are shown in the following screenshot: We see that Amazon ML will pass over the data 10 times, shuffle splitting the data each time. It will use an L2 regularization strategy based on the sum of the square of the coefficients of the regression to prevent overfitting. We will evaluate the predictive power of the model using our LT67_heldout.csv dataset later on. Regularization comes in 3 levels with a mild (10^-6), medium (10^-4), or aggressive (10^-02) setting, each value stronger than the previous one. The default setting is mild, the lowest, with a regularization constant of 0.00001 (10^-6) implying that Amazon ML does not anticipate much overfitting on this dataset. This makes sense when the number of predictors, three in our case, is much smaller than the number of samples (190 for the training set). Clicking on the Create ML model button will launch the model creation. This takes a few minutes to resolve, depending on the size and complexity of your dataset. You can check its status by refreshing the model page. In the meantime, the model status remains pending. At that point, Amazon ML will split our training dataset into two subsets: a training and a validation set. It will use the training portion of the data to train several settings of the algorithm and select the best one based on its performance on the training data. It will then apply the associated model to the validation set and return an evaluation score for that model. By default, Amazon ML will sequentially take the first 70% of the samples for training and the remaining 30% for validation. It's worth noting that Amazon ML will not create two extra files and store them on S3, but instead create two new datasources out of the initial datasource we have previously defined. Each new datasource is obtained from the original one via a Data rearrangement JSON recipe such as the following: { "splitting": { "percentBegin": 0, "percentEnd": 70 } } You can see these two new datasources in the Datasource dashboard. Three datasources are now available where there was initially only one, as shown by the following screenshot: While the model is being trained, Amazon ML runs the Stochastic Gradient algorithm several times on the training data with different parameters: Varying the learning rate in increments of powers of 10: 0.01, 0.1, 1, 10, and 100. Making several passes over the training data while shuffling the samples before each path. At each pass, calculating the prediction error, the Root Mean Squared Error (RMSE), to estimate how much of an improvement over the last pass was obtained. If the decrease in RMSE is not really significant, the algorithm is considered to have converged, and no further pass shall be made. At the end of the passes, the setting that ends up with the lowest RMSE wins, and the associated model (the weights of the regression) is selected as the best version. Once the model has finished training, Amazon ML evaluates its performance on the validation datasource. Once the evaluation itself is also ready, you have access to the model's evaluation. Evaluating the model Amazon ML uses the standard metric RMSE for linear regression. RMSE is defined as the sum of the squares of the difference between the real values and the predicted values: Here, ŷ is the predicted values, and y the real values we want to predict (the weight of the children in our case). The closer the predictions are to the real values, the lower the RMSE is. A lower RMSE means a better, more accurate prediction. Making batch predictions We now have a model that has been properly trained and selected among other models. We can use it to make predictions on new data. A batch prediction consists in applying a model to a datasource in order to make predictions on that datasource. We need to tell Amazon ML which model we want to apply on which data. Batch predictions are different from streaming predictions. With batch predictions, all the data is already made available as a datasource, while for streaming predictions, the data will be fed to the model as it becomes available. The dataset is not available beforehand in its entirety. In the Main Menu select Batch Predictions to access the dashboard predictions and click on Create a New Prediction: The first step is to select one of the models available in your model dashboard. You should choose the one that has the lowest RMSE: The next step is to associate a datasource to the model you just selected. We had uploaded the held-out dataset to S3 at the beginning of this chapter (under the Loading the data on S3 section) but had not used it to create a datasource. We will do so now.When asked for a datasource in the next screen, make sure to check My data is in S3, and I need to create a datasource, and then select the held-out dataset that should already be present in your S3 bucket: Don't forget to tell Amazon ML that the first line of the file contains columns. In our current project, our held-out dataset also contains the true values for the weight of the students. This would not be the case for "real" data in a real-world project where the real values are truly unknown. However, in our case, this will allow us to calculate the RMSE score of our predictions and assess the quality of these predictions. The final step is to click on the Verify button and wait for a few minutes: Amazon ML will run the model on the new datasource and will generate predictions in the form of a CSV file. Contrary to the evaluation and model-building phase, we now have real predictions. We are also no longer given a score associated with these predictions. After a few minutes, you will notice a new batch-prediction folder in your S3 bucket. This folder contains a manifest file and a results folder. The manifest file is a JSON file with the path to the initial datasource and the path to the results file. The results folder contains a gzipped CSV file: Uncompressed, the CSV file contains two columns, trueLabel, the initial target from the held-out set, and score, which corresponds to the predicted values. We can easily calculate the RMSE for those results directly in the spreadsheet through the following steps: Creating a new column that holds the square of the difference of the two columns. Summing all the rows. Taking the square root of the result. The following illustration shows how we create a third column C, as the squared difference between the trueLabel column A and the score (or predicted value) column B: As shown in the following screenshot, averaging column C and taking the square root gives an RMSE of 11.96, which is even significantly better than the RMSE we obtained during the evaluation phase (RMSE 14.4): The fact that the RMSE on the held-out set is better than the RMSE on the validation set means that our model did not overfit the training data, since it performed even better on new data than expected. Our model is robust. The left side of the following graph shows the True (Triangle) and Predicted (Circle) Weight values for all the samples in the held-out set. The right side shows the histogram of the residuals. Similar to the histogram of residuals we had observed on the validation set, we observe that the residuals are not centered on 0. Our model has a tendency to overestimate the weight of the students: In this tutorial, we have successfully performed the loading of the data on S3 and let Amazon ML infer the schema and transform the data. We also created a model and evaluated its performance. Finally, we made a prediction on the held -out dataset. To understand how to leverage Amazon's powerful platform for your predictive analytics needs,  check out this book Effective Amazon Machine Learning. Four interesting Amazon patents in 2018 that use machine learning, AR, and robotics Amazon Sagemaker makes machine learning on the cloud easy Amazon ML Solutions Lab to help customers “work backwards” and leverage machine learning    

This article presents an overview of the workflow of a simple Amazon Machine Learning (Amazon ML) project. Amazon Machine Learning is an online service by Amazon Web Services (AWS) that does supervised learning for predictive analytics. Launched in April 2015 at the AWS Summit, Amazon ML joins a growing list of cloud-based machine learning services, such as Microsoft Azure, Google prediction, IBM Watson, Prediction IO, BigML, and many others. These online machine learning services form an offer commonly referred to as Machine Learning as a Service or MLaaS following a similar denomination pattern of other cloud-based services such as SaaS, PaaS, and IaaS respectively for Software, Platform, or Infrastructure as a Service. The Amazon ML workflow closely follows a standard Data Science workflow with steps: Extract the data and clean it up. Make it available to the algorithm. Split the data into a training and validation set, typically a 70/30 split with equal distribution of the predictors in each part. Select the best model by training several models on the training dataset and comparing their performances on the validation dataset. Use the best model for predictions on new data. This article is an excerpt taken from the book 'Effective Amazon Machine Learning' written by Alexis Perrier. As shown in the following Amazon ML menu, the service is built around four objects: Datasource ML model Evaluation Prediction The Datasource and Model can also be configured and set up in the same flow by creating a new Datasource and ML model. We will take a closer look at the Datasource and ML model. Amazon ML  dataset For the rest of the article, we will use the simple Predicting Weight by Height and Age dataset (from Lewis Taylor (1967)) with 237 samples of children's age, weight, height, and gender, which is available at https://v8doc.sas.com/sashtml/stat/chap55/sect51.htm. This dataset is composed of 237 rows. Each row has the following predictors: sex (F, M), age (in months), height (in inches), and we are trying to predict the weight (in lbs) of these children. There are no missing values and no outliers. The variables are close enough in range and normalization is not required. In short, we do not need to carry out any preprocessing or cleaning on the original dataset. Age, height, and weight are numerical variables (real-valued), and sex is a categorical variable. We will randomly select 20% of the rows as the held-out subset to use for the prediction of previously unseen data and keep the other 80% as training and evaluation data. This data split can be done in Excel or any other spreadsheet editor: By creating a new column with randomly generated numbers Sorting the spreadsheet by that column Selecting 190 rows for training and 47 rows for prediction (roughly a 80/20 split) Let us name the training set LT67_training.csv and the held-out set that we will use for prediction LT67_heldout.csv, where LT67 stands for Lewis and Taylor, the creator of this dataset in 1967. Note that it is important for the distribution in age, sex, height, and weight to be similar in both subsets. We want the data on which we will make predictions to show patterns that are similar to the data on which we will train and optimize our model. Loading the data on Amazon S3 Follow these steps to load the training and held-out datasets on S3: Go to your s3 console at https://console.aws.amazon.com/s3. Create a bucket if you haven't done so already. Buckets are basically folders that are uniquely named across all S3. We created a bucket named aml.packt. Since that name has now been taken, you will have to choose another bucket name if you are following along with this demonstration. Click on the bucket name you created and upload both the LT67_training.csv and LT67_heldout.csv files by selecting Upload from the Actions drop-down menu: Both files are small, only a few KB, and hosting costs should remain negligible for that exercise. Note that for each file, by selecting the Properties tab on the right, you can specify how your files are accessed, what user, role, group or AWS service may download, read, write, and delete the files, and whether or not they should be accessible from the Open Web. When creating the datasource in Amazon ML, you will be prompted to grant Amazon ML access to your input data. You can specify the access rules to these files now in S3 or simply grant access later on. Our data is now in the cloud in an S3 bucket. We need to tell Amazon ML where to find that input data by creating a datasource. We will first create the datasource for the training file ST67_training.csv. Declaring a datasource Go to the Amazon ML dashboard, and click on Create new... | Datasource and ML model. We will use the faster flow available by default: As shown in the following screenshot, you are asked to specify the path to the LT67_training.csv file {S3://bucket}{path}{file}. Note that the S3 location field automatically populates with the bucket names and file names that are available to your user: Specifying a Datasource name is used to organize your Amazon ML assets. By clicking on Verify, Amazon ML will make sure that it has the proper rights to access the file. In case it needs to be granted access to the file, you will be prompted to do so as shown in the following screenshot: Just click on Yes to grant access. At this point, Amazon ML will validate the datasource and analyze its contents. Creating the datasource An Amazon ML datasource is composed of the following: The location of the data file: The data file is not duplicated or cloned in Amazon ML but accessed from S3 The schema that contains information on the type of the variables contained in the CSV file: Categorical Text Numeric (real-valued) Binary It is possible to supply Amazon ML with your own schema or modify the one created by Amazon ML. At this point, Amazon ML has a pretty good idea of the type of data in your training dataset. It has identified the different types of variables and knows how many rows it has: Move on to the next step by clicking on Continue, and see what schema Amazon ML has inferred from the dataset as shown in the next screenshot: Amazon ML needs to know at that point which is the variable you are trying to predict. Be sure to tell Amazon ML the following: The first line in the CSV file contains te column name The target is the weight We see here that Amazon ML has correctly inferred the following: sex is categorical age, height, and weight are numeric (continuous real values) Since we chose a numeric variable as the target Amazon ML, will use Linear Regression as the predictive model. For binary or categorical values, we would have used Logistic Regression. This means that Amazon ML will try to find the best a, b, and c coefficients so that the weight predicted by the following equation is as close as possible to the observed real weight present in the data: predicted weight = a * age + b * height + c * sex Amazon ML will then ask you if your data contains a row identifier. In our present case, it does not. Row identifiers are used when you want to understand the prediction obtained for each row or add an extra column to your dataset later on in your project. Row identifiers are for reference purposes only and are not used by the service to build the model. You will be asked to review the datasource. You can go back to each one of the previous steps and edit the parameters for the schema, the target, and the input data. Now that the data is known to Amazon ML, the next step is to set up the parameters of the algorithm that will train the model. The machine learning model We select the default parameters for the training and evaluation settings. Amazon ML will do the following: Create a step for data transformation based on the statistical properties it has inferred from the dataset Split the dataset (ST67_training.csv) into a training part and a validation part, with a 70/30 split. The split strategy assumes the data has already been shuffled and can be split sequentially. The step will be used to transform the data in a similar way for the training and the validation datasets. The only transformation suggested by Amazon ML is to transform the categorical variable sex into a binary variable, where m = 0 and f = 1 for instance. No other transformation is needed. The default advanced settings for the model are shown in the following screenshot: We see that Amazon ML will pass over the data 10 times, shuffle splitting the data each time. It will use an L2 regularization strategy based on the sum of the square of the coefficients of the regression to prevent overfitting. We will evaluate the predictive power of the model using our LT67_heldout.csv dataset later on. Regularization comes in 3 levels with a mild (10^-6), medium (10^-4), or aggressive (10^-02) setting, each value stronger than the previous one. The default setting is mild, the lowest, with a regularization constant of 0.00001 (10^-6) implying that Amazon ML does not anticipate much overfitting on this dataset. This makes sense when the number of predictors, three in our case, is much smaller than the number of samples (190 for the training set). Clicking on the Create ML model button will launch the model creation. This takes a few minutes to resolve, depending on the size and complexity of your dataset. You can check its status by refreshing the model page. In the meantime, the model status remains pending. At that point, Amazon ML will split our training dataset into two subsets: a training and a validation set. It will use the training portion of the data to train several settings of the algorithm and select the best one based on its performance on the training data. It will then apply the associated model to the validation set and return an evaluation score for that model. By default, Amazon ML will sequentially take the first 70% of the samples for training and the remaining 30% for validation. It's worth noting that Amazon ML will not create two extra files and store them on S3, but instead create two new datasources out of the initial datasource we have previously defined. Each new datasource is obtained from the original one via a Data rearrangement JSON recipe such as the following: { "splitting": { "percentBegin": 0, "percentEnd": 70 } } You can see these two new datasources in the Datasource dashboard. Three datasources are now available where there was initially only one, as shown by the following screenshot: While the model is being trained, Amazon ML runs the Stochastic Gradient algorithm several times on the training data with different parameters: Varying the learning rate in increments of powers of 10: 0.01, 0.1, 1, 10, and 100. Making several passes over the training data while shuffling the samples before each path. At each pass, calculating the prediction error, the Root Mean Squared Error (RMSE), to estimate how much of an improvement over the last pass was obtained. If the decrease in RMSE is not really significant, the algorithm is considered to have converged, and no further pass shall be made. At the end of the passes, the setting that ends up with the lowest RMSE wins, and the associated model (the weights of the regression) is selected as the best version. Once the model has finished training, Amazon ML evaluates its performance on the validation datasource. Once the evaluation itself is also ready, you have access to the model's evaluation. The Amazon ML flow is smooth and facilitates the inherent data science loop: data, model, evaluation, and prediction. We looked at an overview of the workflow of a simple Amazon Machine Learning (Amazon ML) project. We discussed two objects of the Amazon ML menu: Datasource and ML model. If you found this post useful, be sure to check out the book 'Effective Amazon Machine Learning' to learn about evaluation and prediction in Amazon ML along with other AWS ML concepts. Integrate applications with AWS services: Amazon DynamoDB & Amazon Kinesis [Tutorial] AWS makes Amazon Rekognition, its image recognition AI, available for Asia-Pacific developers

The media has gone into a frenzy over Google’s latest facial recognition patent that shows an algorithm can track you across social media and gather your personal details. We thought, we’d dive further into what other patents Google has applied for in facial recognition tehnology in 2018. What we discovered was an eye opener (pun intended). Google is only the 3rd largest applicant with IBM and Samsung leading the patents race in facial recognition. As of 10th Aug, 2018, 1292 patents have been granted in 2018 on Facial recognition. Of those, IBM received 53. Here is the summary comparison of leading companies in facial recognition patents in 2018. Read Also: Top four Amazon patents in 2018 that use machine learning, AR, and robotics IBM has always been at the forefront of innovation. Let’s go back about a quarter of a century, when IBM invented its first general-purpose computer for business. It built complex software programs that helped in launching Apollo missions, putting the first man on the moon. It’s chess playing computer, Deep Blue, back in 1997,  beat Garry Kasparov, in a traditional chess match (the first time a computer beat a world champion). Its researchers are known for winning Nobel Prizes. Coming back to 2018, IBM unveiled the world’s fastest supercomputer with AI capabilities, and beat the Wall Street expectations by making $20 billion in revenue in Q3 2018 last month, with market capitalization worth $132.14 billion as of August 9, 2018. Its patents are a major part of why it continues to be valuable highly. IBM continues to come up with cutting-edge innovations and to protect these proprietary inventions, it applies for patent grants. United States is the largest consumer market in the world, so patenting the technologies that the companies come out with is a standard way to attain competitive advantage. As per the United States Patent and Trademark Office (USPTO), Patent is an exclusive right to invention and “the right to exclude others from making, using, offering for sale, or selling the invention in the United States or “importing” the invention into the United States”. As always, IBM has applied for patents for a wide spectrum of technologies this year from Artificial Intelligence, Cloud, Blockchain, Cybersecurity, to Quantum Computing. Today we focus on IBM’s patents in facial recognition field in 2018. Four IBM facial recognition innovations patented in 2018 Facial recognition is a technology which identifies and verifies a person from a digital image or a video frame from a video source and IBM seems quite invested in it. Controlling privacy in a face recognition application Date of patent: January 2, 2018 Filed: December 15, 2015 Features: IBM has patented for a face-recognition application titled “Controlling privacy in a face recognition application”. Face recognition technologies can be used on mobile phones and wearable devices which may hamper the user privacy. This happens when a "sensor" mobile user identifies a "target" mobile user without his or her consent. The present mobile device manufacturers don’t provide the privacy mechanisms for addressing this issue. This is the major reason why IBM has patented this technology. Editor’s Note: This looks like an answer to the concerns raised over Google’s recent social media profiling facial recognition patent.   How it works? Controlling privacy in a face recognition application It consists of a privacy control system, which is implemented using a cloud computing node. The system uses a camera to find out information about the people, by using a face recognition service deployed in the cloud. As per the patent application “the face recognition service may have access to a face database, privacy database, and a profile database”. Controlling privacy in a face recognition application The facial database consists of one or more facial signatures of one or more users. The privacy database includes privacy preferences of target users. Privacy preferences will be provided by the target user and stored in the privacy database.The profile database contains information about the target user such as name, age, gender, and location. It works by receiving an input which includes a face recognition query and a digital image of a face. The privacy control system then detects a facial signature from the digital image. The target user associated with the facial signature is identified, and profile of the target user is extracted. It then checks the privacy preferences of the user. If there are no privacy preferences set, then it transmits the profile to the sensor user. But, if there are privacy preferences then the censored profile of the user is generated omitting out the private elements in the profile. There are no announcements, as for now, regarding when this technology will hit the market. Evaluating an impact of a user's content utilized in a social network Date of patent: January 30, 2018 Filed: April 11, 2015 Features:  IBM has patented for an application titled “Evaluating an impact of a user's content utilized in a social network”.  With so much data floating around on social network websites, it is quite common for the content of a document (e.g., e-mail message, a post, a word processing document, a presentation) to be reused, without the knowledge of an original author. Evaluating an impact of a user's content utilised in a social network Evaluating an impact of a user's content utilized in a social network Because of this, the original author of the content may not receive any credit, which creates less motivation for the users to post their original content in a social network. This is why IBM has decided to patent for this application. Evaluating an impact of a user's content utilized in a social network As per the patent application, the method/system/product  “comprises detecting content in a document posted on a social network environment being reused by a second user. The method further comprises identifying an author of the content. The method additionally comprises incrementing a first counter keeping track of a number of times the content has been adopted in derivative works”. There’s a processor, which generates an “impact score” which  represents the author's ability to influence other users to adopt the content. This is based on the number of times the content has been adopted in the derivative works. Also, “the method comprises providing social credit to the author of the content using the impact score”. Editor’s Note: This is particularly interesting to us as IBM, unlike other tech giants, doesn’t own a popular social network or media product. (Google has Google+, Microsoft has LinkedIn, Facebook and Twitter are social, even Amazon has stakes in a media entity in the form of Washington Post). No information is present about when or if this system will be used among social network sites. Spoof detection for facial recognition Date of patent: February 20, 2018 Filed: December 10, 2015 Features: IBM patented an application named “Spoof detection for facial recognition”.  It provides a method to determine whether the image is authentic or not. As per the patent “A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.” Editor’s Note: This seems to have a direct impact on the work around tackling deepFakes, which incidentally is something DARPA is very keen on. Could IBM be vying for a long term contract with the government? How it works? The patent consists of a system that helps detect “if a face in a facial recognition authentication system is a three-dimensional structure based on multiple selected images from the input video”.                                      Spoof detection for facial recognition There are four or more two-dimensional feature points which are located via an image processing device connected to the camera. Here the two-dimensional feature points do not lie on the same two-dimensional plane. The patent reads that “one or more additional images of the user's face can be received with the camera; and, the at least four two-dimensional feature points can be located on each additional image with the image processor. The image processor can identify displacements between the two-dimensional feature points on the additional image and the two-dimensional feature points on the first image for each additional image” Spoof detection for facial recognition There is also a processor connected to the image processing device that helps figure out whether the displacements conform to a three-dimensional surface model. The processor can then determine whether to authenticate the user depending on whether the displacements conform to the three-dimensional surface model. Facial feature location using symmetry line Date of patent: June 5, 2018 Filed: July 20, 2015 Features: IBM patented for an application titled “Facial feature location using symmetry line”. As per the patent, “In many image processing applications, identifying facial features of the subject may be desired. Currently, location of facial features require a search in four dimensions using local templates that match the target features. Such a search tends to be complex and prone to errors because it has to locate both (x, y) coordinates, scale parameter and rotation parameter”. Facial feature location using symmetry line Facial feature location using symmetry line The application consists of a computer-implemented method that obtains an image of the subject’s face. After that it automatically detects a symmetry line of the face in the image, where the symmetry line intersects at least a mouth region of the face. It then automatically locates a facial feature of the face using the symmetry line. There’s also a computerised apparatus with a processor which performs the steps of obtaining an image of a subject’s face and helps locate the facial feature.  Editor’s note: Atleast, this patent makes direct sense to us. IBM is majorly focusing on bring AI to healthcare. A patent like this can find a lot of use in not just diagnostics and patient care, but also in cutting edge areas like robotics enabled surgeries. IBM is continually working on new technologies to provide the world with groundbreaking innovations. Its big investments in facial recognition technology speaks volumes about how IBM is well-versed with its endless possibilities. With the facial recognition technological progress,  come the privacy fears. But, IBM’s facial recognition application patent has got it covered as it lets the users set privacy preferences. This can be a great benchmark for IBM as no many existing applications are currently doing it. The social credit score evaluating app can really help bring the voice back to the users interested in posting content on social media platforms. The spoof detection application will help maintain authenticity by detecting forged images. Lastly, the facial feature detection can act as a great additional feature for image processing applications. IBM has been heavily investing in facial recognition technology. There are no guarantees by IBM as to whether these patents will ever make it to practical applications, but it does say a lot about how IBM thinks about the technology. Four interesting Amazon patents in 2018 that use machine learning, AR, and robotics Facebook patents its news feed filter tool to provide more relevant news to its users Google’s new facial recognition patent uses your social network to identify you!  

A series can be defined as a number of events, objects, or people of a similar or related kind coming one after another; if we add the dimension of time, we get a time series. A time series can be defined as a series of data points in time order. In this article, we will understand what time series is and why it is one of the essential characteristics for forecasting. This article is an excerpt from a book written by Harish Gulati titled SAS for Finance. The importance of time series What importance, if any, does time series have and how will it be relevant in the future? These are just a couple of fundamental questions that any user should find answers to before delving further into the subject. Let's try to answer this by posing a question. Have you heard the terms big data, artificial intelligence (AI), and machine learning (ML)? These three terms make learning time series analysis relevant. Big data is primarily about a large amount of data that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interaction. AI is a kind of technology that is being developed by data scientists, computational experts, and others to enable processes to become more intelligent, while ML is an enabler that is helping to implement AI. All three of these terms are interlinked with the data they use, and a lot of this data is time series in its nature. This could be either financial transaction data, the behavior pattern of individuals during various parts of the day, or related to life events that we might experience. An effective mechanism that enables us to capture the data, store it, analyze it, and then build algorithms to predict transactions, behavior (and life events, in this instance) will depend on how big data is utilized and how AI and MI are leveraged. A common perception in the industry is that time series data is used for forecasting only. In practice, time series data is used for: Pattern recognition Forecasting Benchmarking Evaluating the influence of a single factor on the time series Quality control For example, a retailer may identify a pattern in clothing sales every time it gets a celebrity endorsement, or an analyst may decide to use car sales volume data from 2012 to 2017 to set a selling benchmark in units. An analyst might also build a model to quantify the effect of Lehman's crash at the height of the 2008 financial crisis in pushing up the price of gold. Variance in the success of treatments across time periods can also be used to highlight a problem, the tracking of which may enable a hospital to take remedial measures. These are just some of the examples that showcase how time series analysis isn't limited to just forecasting. In this chapter, we will review how the financial industry and others use forecasting, discuss what a good and a bad forecast is, and hope to understand the characteristics of time series data and its associated problems. Forecasting across industries Since one of the primary uses of time series data is forecasting, it's wise that we learn about some of its fundamental properties. To understand what the industry means by forecasting and the steps involved, let's visit a common misconception about the financial industry: only lending activities require forecasting. We need forecasting in order to grant personal loans, mortgages, overdrafts, or simply assess someone's eligibility for a credit card, as the industry uses forecasting to assess a borrower's affordability and their willingness to repay the debt. Even deposit products such as savings accounts, fixed-term savings, and bonds are priced based on some forecasts. How we forecast and the rationale for that methodology is different in borrowing or lending cases, however. All of these areas are related to time series, as we inevitably end up using time series data as part of the overall analysis that drives financial decisions. Let's understand the forecasts involved here a bit better. When we are assessing an individual's lending needs and limits, we are forecasting for a single person yet comparing the individual to a pool of good and bad customers who have been offered similar products. We are also assessing the individual's financial circumstances and behavior through industry-available scoring models or by assessing their past behavior, with the financial provider assessing the lending criteria. In the case of deposit products, as long as the customer is eligible to transact (can open an account and has passed know your customer (KYC), anti-money laundering (AML), and other checks), financial institutions don't perform forecasting at an individual level. However, the behavior of a particular customer is primarily driven by the interest rate offered by the financial institution. The interest rate, in turn, is driven by the forecasts the financial institution has done to assess its overall treasury position. The treasury is the department that manages the central bank's money and has the responsibility of ensuring that all departments are funded, which is generated through lending and attracting deposits at a lower rate than a bank lends. The treasury forecasts its requirements for lending and deposits, while various teams within the treasury adhere to those limits. Therefore, a pricing manager for a deposit product will price the product in such a way that the product will attract enough deposits to meet the forecasted targets shared by the treasury; the pricing manager also has to ensure that those targets aren't overshot by a significant margin, as the treasury only expects to manage a forecasted target. In both lending and deposit decisions, financial institutions do tend to use forecasting. A lot of these forecasts are interlinked, as we saw in the example of the treasury's expectations and the subsequent pricing decision for a deposit product. To decide on its future lending and borrowing positions, the treasury must have used time series data to determine what the potential business appetite for lending and borrowing in the market is and would have assessed that with the current cash flow situation within the relevant teams and institutions. Characteristics of time series data Any time series analysis has to take into account the following factors: Seasonality Trend Outliers and rare events Disruptions and step changes Seasonality Seasonality is a phenomenon that occurs each calendar year. The same behavior can be observed each year. A good forecasting model will be able to incorporate the effect of seasonality in its forecasts. Christmas is a great example of seasonality, where retailers have come to expect higher sales over the festive period. Seasonality can extend into months but is usually only observed over days or weeks. When looking at time series where the periodicity is hours, you may find a seasonality effect for certain hours of the day. Some of the reasons for seasonality include holidays, climate, and changes in social habits. For example, travel companies usually run far fewer services on Christmas Day, citing a lack of demand. During most holidays people love to travel, but this lack of demand on Christmas Day could be attributed to social habits, where people tend to stay at home or have already traveled. Social habit becomes a driving factor in the seasonality of journeys undertaken on Christmas Day. It's easier for the forecaster when a particular seasonal event occurs on a fixed calendar date each year; the issue comes when some popular holidays depend on lunar movements, such as Easter, Diwali, and Eid. These holidays may occur in different weeks or months over the years, which will shift the seasonality effect. Also, if some holidays fall closer to other holiday periods, it may lead to individuals taking extended holidays and travel sales may increase more than expected in such years. The coffee shop near the office may also experience lower sales for a longer period. Changes in the weather can also impact seasonality; for example, a longer, warmer summer may be welcome in the UK, but this would impact retail sales in the autumn as most shoppers wouldn't need to buy a new wardrobe. In hotter countries, sales of air-conditioners would increase substantially compared to the summer months' usual seasonality. Forecasters could offset this unpredictability in seasonality by building in a weather forecast variable. We will explore similar challenges in the chapters ahead. Seasonality shouldn't be confused with a cyclic effect. A cyclic effect is observed over a longer period of generally two years or more. The property sector is often associated with having a cyclic effect, where it has long periods of growth or slowdown before the cycle continues. Trend A trend is merely a long-term direction of observed behavior that is found by plotting data against a time component. A trend may indicate an increase or decrease in behavior. Trends may not even be linear, but a broad movement can be identified by analyzing plotted data. Outliers and rare events Outliers and rare events are terminologies that are often used interchangeably by businesses. These concepts can have a big impact on data, and some sort of outlier treatment is usually applied to data before it is used for modeling. It is almost impossible to predict an outlier or rare event but they do affect a trend. An example of an outlier could be a customer walking into a branch to deposit an amount that is 100 times the daily average of that branch. In this case, the forecaster wouldn't expect that trend to continue. Disruptions Disruptions and step changes are becoming more common in time series data. One reason for this is the abundance of available data and the growing ability to store and analyze it. Disruptions could include instances when a business hasn't been able to trade as normal. Flooding at the local pub may lead to reduced sales for a few days, for example. While analyzing daily sales across a pub chain, an analyst may have to make note of a disruptive event and its impact on the chain's revenue. Step changes are also more common now due to technological shifts, mergers and acquisitions, and business process re-engineering. When two companies announce a merger, they often try to sync their data. They might have been selling x and y quantities individually, but after the merger will expect to sell x + y + c (where c is the positive or negative effect of the merger). Over time, when someone plots sales data in this case, they will probably spot a step change in sales that happened around the time of the merger, as shown in the following screenshot: In the trend graph, we can see that online travel bookings are increasing. In the step change and disruptions chart, we can see that Q1 of 2012 saw a substantive increase in bookings, where Q1 of 2014 saw a substantive dip. The increase was due to the merger of two companies that took place in Q1 of 2012. The decrease in Q1 of 2014 was attributed to prolonged snow storms in Europe and the ash cloud disruption from volcanic activity over Iceland. While online bookings kept increasing after the step change, the disruption caused by the snow storm and ash cloud only had an effect on sales in Q1 of 2014. In this case, the modeler will have to treat the merger and the disruption differently while using them in the forecast, as disruption could be disregarded as an outlier and treated accordingly. Also note that the seasonality chart shows that Q4 of each year sees almost a 20% increase in travel bookings, and this pattern continues each calendar year. In this article, we defined time series and learned why it is important for forecasting. We also looked at the characteristics of time series data. To know more how to leverage the analytical power of SAS to perform financial analysis efficiently, you can check out the book SAS for Finance. Read more Getting to know SQL Server options for disaster recovery Implementing a simple Time Series Data Analysis in R Training RNNs for Time Series Forecasting

Researchers at the University of California, Los Angeles (UCLA) have developed a 3D-printed all-optical deep learning architecture called Diffractive Deep Neural Network (D2NN). D2NN is a deep learning neural network physically formed by multiple layers of diffractive surfaces that work in collaboration to optically perform an arbitrary function. While the inference/prediction of the physical network is all-optical, the learning part that leads to its design is done through a computer. How does D2NN work? A computer-simulated design was created first, then the researchers with the help of a 3D printer created very thin polymer wafers. The uneven surface of the wafers helped diffract light coming from the object in different directions. The layers are composed of tens of thousands of artificial neurons or tiny pixels from which the light travels through. These layers together, form an “optical network” that shapes how incoming light travels through them. The network is able to identify an object because the light coming from the object is diffracted mostly toward a single pixel that is assigned to that type of object. The network was then trained using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produced as the light from that object passes through the device. What are its advantages? Scalable: It can easily be scaled up using numerous high-throughput and large-area 3D fabrication methods, such as, soft-lithography, additive manufacturing, and wide-field optical components and detection systems. Easily reconfigurable: D2NN can be easily improved by additional 3D printed layers or replacing some of the existing layers with newly trained ones. Lightening speed: Once the device is trained, it works at the speed of light. Efficient: No energy is consumed to run the device. Cost-effective: The device can be reproduced for less than $50, making it very cost-effective. What are the areas it can be used in? Image analysis Feature detection Object classification Can also enable new microscope or camera designs that can perform unique imaging tasks This new AI device could find applications in the area of medical technologies, data intensive tasks, robotics, security, and or any application where image and video data are essential. Refer to UCLA’s official news article to know more in detail. Also, you can refer to this paper  All-optical machine learning using diffractive deep neural Networks. OpenAI builds reinforcement learning based system giving robots human like dexterity Datasets and deep learning methodologies to extend image-based applications to videos AutoAugment: Google’s research initiative to improve deep learning performance

Adopting agile ways of working is easier said than done. Firms like Barclays, C.H.Robinson, Ericsson, Microsoft, and Spotify are considered as agile enterprises and are operating entrepreneurially on a large scale. Do you think the leadership of these firms have something in common? Let us take a look at it in this article. The leadership of a firm has a very high bearing on the extent of Enterprise Agility which the company can achieve. Leaders are in a position to influence just about every aspect of a business, including vision, mission, strategy, structure, governance, processes, and more importantly, the culture of the enterprise and the mindset of the employees. This article is an extract from the Enterprise Agility written by Sunil Mundra. In this article we’ll explore the personal traits of leaders that are critical for Enterprise Agility. Personal traits are by definition intrinsic in nature. They enable the personal development of an individual and are also enablers for certain behaviors. We explore the various personal traits in detail. #1 Willingness to expand mental models Essentially, a mental model is an individual's perception of reality and how something works in that reality. A mental model represents one way of approaching a situation and is a form of deeply-held belief. The critical point is that a mental model represents an individual's view, which may not be necessarily true. Leaders must also consciously let go of mental models that are no longer relevant today. This is especially important for those leaders who have spent a significant part of their career leading enterprises based on mechanistic modelling, as these models will create impediments for Agility in "living" businesses. For example, using monetary rewards as a primary motivator may work for physical work, which is repetitive in nature. However, it does not work as a primary motivator for knowledge workers, for whom intrinsic motivators, namely, autonomy, mastery, and purpose, are generally more important than money. Examining the values and assumptions underlying a mental model can help in ascertaining the relevance of that model. #2 Self-awareness Self-awareness helps leaders to become cognizant of their strengths and weaknesses. This will enable the leaders to consciously focus on utilizing their strengths and leveraging the strengths of their peers and teams, in areas where they are not strong. Leaders should validate the view of strengths and weaknesses by seeking feedback regularly from people that they work with. According to a survey of senior executives, by Cornell's School of Industrial and Labor Relations: "Leadership searches give short shrift to 'self-awareness,' which should actually be a top criterion. Interestingly, a high self-awareness score was the strongest predictor of overall success. This is not altogether surprising as executives who are aware of their weaknesses are often better able to hire subordinates who perform well in categories in which the leader lacks acumen. These leaders are also more able to entertain the idea that someone on their team may have an idea that is even better than their own." Self-awareness, a mostly underrated trait, is a huge enabler for enhancing other personal traits. #3 Creativity Since emergence is a primary property of complexity, leaders will often be challenged to deal with unprecedented circumstances emerging from within the enterprise and also in the external environment. This implies that what may have worked in the past is less likely to work in the new circumstances, and new approaches will be needed to deal with them. Hence, the ability to think creatively, that is, "out of the box," for coming up with innovative approaches and solutions is critical. The creativity of an individual will have its limitations, and hence leaders must harness the creativity of a broader group of people in the enterprise. A leader can be a huge enabler to this by ideating jointly with a group of people and also by facilitating discussions by challenging status quo and spurring the teams to suggest improvements. Leaders can also encourage innovation through experimentation. With the fast pace of change in the external environment, and consequently the continuous evolution of businesses, leaders will often find themselves out of their comfort zone. Leaders will therefore have to get comfortable with being uncomfortable. It will be easier for leaders to think more creatively once they accept this new reality. #4 Emotional intelligence Emotional intelligence (EI), also known as emotional quotient (EQ), is defined by Wikipedia as "the capability of individuals to recognize their own emotions and those of others, discern between different feelings and label them appropriately, use emotional information to guide thinking and behavior, and manage and/or adjust emotions to adapt to environments or achieve one's goal/s". [iii] EI is made up of four core skills: Self-awareness Social awareness Self-management Relationship management The importance of EI in people-centric enterprises, especially for leaders, cannot be overstated. While people in a company may be bound by purpose and by being a part of a team, people are inherently different from each other in terms of personality types and emotions. This can have a significant bearing on how people in a business deal with and react to circumstances, especially adverse ones. Having high EI enables leaders to understand people "from the inside." This helps leaders to build better rapport with people, thereby enabling them to bring out the best in employees and support them as needed. #5 Courage An innovative approach to dealing with an unprecedented circumstance will, by definition, carry some risk. The hypothesis about the appropriateness of that approach can only be validated by putting it to the test against reality. Leaders will therefore need to be courageous as they take the calculated risky bets, strike hard, and own the outcome of those bets. According to Guo Xiao, the President and CEO of ThoughtWorks, "There are many threats—and opportunities—facing businesses in this age of digital transformation: industry disruption from nimble startups, economic pressure from massive digital platforms, evolving security threats, and emerging technologies. Today's era, in which all things are possible, demands a distinct style of leadership. It calls for bold individuals who set their company's vision and charge ahead in a time of uncertainty, ambiguity, and boundless opportunity. It demands courage." Taking risks does not mean being reckless. Rather, leaders need to take calculated risks, after giving due consideration to intuition, facts, and opinions. Despite best efforts and intentions, some decisions will inevitably go wrong. Leaders must have the courage and humility to admit that the decision went wrong and own the outcomes of that decision, and not let these failures deter them from taking risks in the future. #6 Passion for learning Learnability is the ability to upskill, reskill, and deskill. In today's highly dynamic era, it is not what one knows, or what skills one has, that matters as much as the ability to quickly adapt to a different skill set. It is about understanding what is needed to optimize success and what skills and abilities are necessary, from a leadership perspective, to make the enterprise as a whole successful. Leaders need to shed inhibitions about being seen as "novices" while they acquire and practice new skills. The fact that leaders are willing to acquire new skills can be hugely impactful in terms of encouraging others in the enterprise to do the same. This is especially important in terms of bringing in and encouraging the culture of learnability across the business. #7 Awareness of cognitive biases Cognitive biases are flaws in thinking that can lead to suboptimal decisions. Leaders need to become aware of these biases so that they can objectively assess whether their decisions are being influenced by any biases. Cognitive biases lead to shortcuts in decision-making. Essentially, these biases are an attempt by the brain to simplify information processing. Leaders today are challenged with an overload of information and also the need to make decisions quickly. These factors can contribute to decisions and judgements being influenced by cognitive biases. Over decades, psychologists have discovered a huge number of biases. However, the following biases are more important from decision-making perspective: Confirmation bias This is the tendency of selectively seeking and holding onto information to reaffirm what you already believe to be true. For example, a leader believes that a recently launched product is doing well, based on the initial positive response. He has developed a bias that this product is successful. However, although the product is succeeding in attracting new customers, it is also losing existing customers. The confirmation bias is making the leader focus only on data pertaining to new customers, so he is ignoring data related to the loss of existing customers. Bandwagon effect bias Bandwagon effect bias, also known as "herd mentality," encourages doing something because others are doing it. The bias creates a feeling of not wanting to be left behind and hence can lead to irrational or badly-thought-through decisions. Enterprises launching the Agile transformation initiative, without understanding the implications of the long and difficult journey ahead, is an example of this bias. "Guru" bias Guru bias leads to blindly relying on an expert's advice. This can be detrimental, as the expert could be wrong in their assessment and therefore the advice could also be wrong. Also, the expert might give advice which is primarily furthering his or her interests over the interests of the enterprise. Projection bias Projection bias leads the person to believe that other people have understood and are aligned with their thinking, while in reality this may not be true. This bias is more prevalent in enterprises where employees are fearful of admitting that they have not understood what their "bosses" have said, asking questions to clarify or expressing disagreement. Stability bias Stability bias, also known as "status quo" bias, leads to a belief that change will lead to unfavorable outcomes, that is, the risk of loss is greater than the possibility of benefit. It makes a person believe that stability and predictability lead to safety. For decades, the mandate for leaders was to strive for stability and hence, many older leaders are susceptible to this bias. Leaders must encourage others in the enterprise to challenge biases, which can uncover "blind spots" arising from them. Once decisions are made, attention should be paid to information coming from feedback. #8 Resilience Resilience is the capacity to quickly recover from difficulties. Given the turbulent business environment, rapidly changing priorities, and the need to take calculated risks, leaders are likely to encounter difficult and challenging situations quite often. Under such circumstances, having resilience will help the leader to "take knocks on the chin" and keep moving forward. Resilience is also about maintaining composure when something fails, analyzing the failure with the team in an objective manner and leaning from that failure. The actions of leaders are watched by the people in the enterprise even more closely in periods of crisis and difficulty, and hence leaders showing resilience go a long way in increasing resilience across the company. #9 Responsiveness Responsiveness, from the perspective of leadership, is the ability to quickly grasp and respond to both challenges and opportunities. Leaders must listen to feedback coming from customers and the marketplace, learn from it, and adapt accordingly. Leaders must be ready to enable the morphing of the enterprise's offerings in order to stay relevant for customers and also to exploit opportunities. This implies that leaders must be willing to adjust the "pivot" of their offerings based on feedback, for example, the journey of Amazon Web Services, which was an internal system but has now grown into a highly successful business. Other prominent examples are Twitter, which was an offshoot of Odeo, a website focused on sound and podcasting, and PayPal's move from transferring money via PalmPilots to becoming a highly robust online payment service. We discovered that leaders are the primary catalysts for any enterprise aspiring to enhance its Agility. Leaders need specific capabilities, which are over and above the standard leadership capabilities, in order to take the business on the path of enhanced Enterprise Agility. These capabilities comprise of personal traits and behaviors that are intrinsic in nature and enable leadership Agility, which is the foundation of Enterprise Agility. Want to know more about how an enterprise can thrive in a dynamic business environment, check out the book Enterprise Agility. Skill Up 2017: What we learned about tech pros and developers 96% of developers believe developing soft skills is important Soft skills every data scientist should teach their child

Elasticsearch is much more than just a search engine; it supports complex aggregations, geo filters, and the list goes on. Best of all, you can run all your queries at a speed you have never seen before.  Elasticsearch, like any other open source technology, is very rapidly evolving, but the core fundamentals that power Elasticsearch don't change. In this article, we will briefly discuss how Elasticsearch works internally and explain the basic query APIs.  All the data in Elasticsearch is internally stored in  Apache Lucene as an inverted index. Although data is stored in Apache Lucene, Elasticsearch is what makes it distributed and provides the easy-to-use APIs. This Elasticsearch tutorial is an excerpt taken from the book,'Learning Elasticsearch' written by Abhishek Andhavarapu. Inverted index in Elasticsearch Inverted index will help you understand the limitations and strengths of Elasticsearch compared with the traditional database systems out there. Inverted index at the core is how Elasticsearch is different from other NoSQL stores, such as MongoDB, Cassandra, and so on. We can compare an inverted index to an old library catalog card system. When you need some information/book in a library, you will use the card catalog, usually at the entrance of the library, to find the book. An inverted index is similar to the card catalog. Imagine that you were to build a system like Google to search for the web pages mentioning your search keywords. We have three web pages with Yoda quotes from Star Wars, and you are searching for all the documents with the word fear. Document1: Fear leads to anger Document2: Anger leads to hate Document3: Hate leads to suffering In a library, without a card catalog to find the book you need, you would have to go to every shelf row by row, look at each book title, and see whether it's the book you need. Computer-based information retrieval systems do the same. Without the inverted index, the application has to go through each web page and check whether the word exists in the web page. An inverted index is similar to the following table. It is like a map with the term as a key and list of the documents the term appears in as value. Term Document Fear 1 Anger 1,2 Hate 2,3 Suffering 3 Leads 1,2,3 Once we construct an index, as shown in this table, to find all the documents with the term fear is now just a lookup. Just like when a library gets a new book, the book is added to the card catalog, we keep building an inverted index as we encounter a new web page. The preceding inverted index takes care of simple use cases, such as searching for the single term. But in reality, we query for much more complicated things, and we don't use the exact words. Now let's say we encountered a document containing the following: Yosemite national park may be closed for the weekend due to forecast of substantial rainfall We want to visit Yosemite National Park, and we are looking for the weather forecast in the park. But when we query for it in the human language, we might query something like weather in yosemite or rain in yosemite. With the current approach, we will not be able to answer this query as there are no common terms between the query and the document, as shown: Document Query rainfall rain To be able to answer queries like this and to improve the search quality, we employ various techniques such as stemming, synonyms discussed in the following sections. Stemming Stemming is the process of reducing a derived word into its root word. For example, rain, raining, rained, rainfall has the common root word "rain". When a document is indexed, the root word is stored in the index instead of the actual word. Without stemming, we end up storing rain, raining, rained in the index, and search relevance would be very low. The query terms also go through the stemming process, and the root words are looked up in the index. Stemming increases the likelihood of the user finding what he is looking for. When we query for rain in yosemite, even though the document originally had rainfall, the inverted index will contain term rain. We can configure stemming in Elasticsearch using Analyzers. Synonyms Similar to rain and raining, weekend and sunday mean the same thing. The document might not contain Sunday, but if the information retrieval system can also search for synonyms, it will significantly improve the search quality. Human language deals with a lot of things, such as tense, gender, numbers. Stemming and synonyms will not only improve the search quality but also reduce the index size by removing the differences between similar words. More examples: Pen, Pen[s] -> Pen Eat, Eating  -> Eat Phrase search As a user, we almost always search for phrases rather than single words. The inverted index in the previous section would work great for individual terms but not for phrases. Continuing the previous example, if we want to query all the documents with a phrase anger leads to in the inverted index, the previous index would not be sufficient. The inverted index for terms anger and leads is shown below: Term Document Anger 1,2 Leads 1,2,3 From the preceding table, the words anger and leads exist both in document1 and document2. To support phrase search along with the document, we also need to record the position of the word in the document. The inverted index with word position is shown here: Term Document Fear 1:1 Anger 1:3, 2:1 Hate 2:3, 3:1 Suffering 3:3 Leads 1:2, 2:2, 3:2 Now, since we have the information regarding the position of the word, we can search if a document has the terms in the same order as the query. Term Document anger 1:3, 2:1 leads 1:2, 2:2 Since document2 has anger as the first word and leads as the second word, the same order as the query, document2 would be a better match than document1. With the inverted index, any query on the documents is just a simple lookup. This is just an introduction to inverted index; in real life, it's much more complicated, but the fundamentals remain the same. When the documents are indexed into Elasticsearch, documents are processed into the inverted index. Scalability and availability in Elasticsearch Let's say you want to index a billion documents; having just a single machine might be very challenging. Partitioning data across multiple machines allows Elasticsearch to scale beyond what a single machine do and support high throughput operations. Your data is split into small parts called shards. When you create an index, you need to tell Elasticsearch the number of shards you want for the index and Elasticsearch handles the rest for you. As you have more data, you can scale horizontally by adding more machines. We will go in to more details in the sections below. There are type of shards in Elasticsearch - primary and replica. The data you index is written to both primary and replica shards. Replica is the exact copy of the primary. In case of the node containing the primary shard goes down, the replica takes over. This process is completely transparent and managed by Elasticsearch. We will discuss this in detail in the Failure Handling section below. Since primary and replicas are the exact copies, a search query can be answered by either the primary or the replica shard. This significantly increases the number of simultaneous requests Elasticsearch can handle at any point in time. As the index is distributed across multiple shards, a query against an index is executed in parallel across all the shards. The results from each shard are then gathered and sent back to the client. Executing the query in parallel greatly improves the search performance. Now, we will discuss the relation between node, index and shard. Relation between node, index, and shard Shard is often the most confusing topic when I talk about Elasticsearch at conferences or to someone who has never worked on Elasticsearch. In this section, I want to focus on the relation between node, index, and shard. We will use a cluster with three nodes and create the same index with multiple shard configuration, and we will talk through the differences. Three shards with zero replicas We will start with an index called esintroduction with three shards and zero replicas. The distribution of the shards in a three node cluster is as follows: In the above screenshot, shards are represented by the green squares. We will talk about replicas towards the end of this discussion. Since we have three nodes(servers) and three shards, the shards are evenly distributed across all three nodes. Each node will contain one shard. As you index your documents into the esintroduction index, data is spread across the three shards. Six shards with zero replicas Now, let's recreate the same esintroduction index with six shards and zero replicas. Since we have three nodes (servers) and six shards, each node will now contain two shards. The esintroduction index is split between six shards across three nodes. The distribution of shards for an index with six shards is as follows: The esintroduction index is spread across three nodes, meaning these three nodes will handle the index/query requests for the index. If these three nodes are not able to keep up with the indexing/search load, we can scale the esintroduction index by adding more nodes. Since the index has six shards, you could add three more nodes, and Elasticsearch automatically rearranges the shards across all six nodes. Now, index/query requests for the esintroduction index will be handled by six nodes instead of three nodes. If this is not clear, do not worry, we will discuss more about this as we progress in the book. Six shards with one replica Let's now recreate the same esintroduction index with six shards and one replica, meaning the index will have 6 primary shards and 6 replica shards, a total of 12 shards. Since we have three nodes (servers) and twelve shards, each node will now contain four shards. The esintroduction index is split between six shards across three nodes. The green squares represent shards in the following figure. The solid border represents primary shards, and replicas are the dotted squares: As we discussed before, the index is distributed into multiple shards across multiple nodes. In a distributed environment, a node/server can go down due to various reasons, such as disk failure, network issue, and so on. To ensure availability, each shard, by default, is replicated to a node other than where the primary shard exists. If the node containing the primary shard goes down, the shard replica is promoted to primary, and the data is not lost, and you can continue to operate on the index. In the preceding figure, the esintroduction index has six shards split across the three nodes. The primary of shard 2 belongs to node elasticsearch 1, and the replica of the shard 2 belongs to node elasticsearch 3. In the case of the elasticsearch 1 node going down, the replica in elasticsearch 3 is promoted to primary. This switch is completely transparent and handled by Elasticsearch. Distributed search One of the reasons queries executed on Elasticsearch are so fast is because they are distributed. Multiple shards act as one index. A search query on an index is executed in parallel across all the shards. Let's take an example: in the following figure, we have a cluster with two nodes: Node1, Node2 and an index named chapter1 with two shards: S0, S1 with one replica: Assuming the chapter1 index has 100 documents, S1 would have 50 documents, and S0 would have 50 documents. And you want to query for all the documents that contain the word Elasticsearch. The query is executed on S0 and S1 in parallel. The results are gathered back from both the shards and sent back to the client. Imagine, you have to query across million of documents, using Elasticsearch the search can be distributed. For the application I'm currently working on, a query on more than 100 million documents comes back within 50 milliseconds; which is simply not possible if the search is not distributed. Failure handling in Elasticsearch Elasticsearch handles failures automatically. This section describes how the failures are handled internally. Let's say we have an index with two shards and one replica. In the following diagram, the shards represented in solid line are primary shards, and the shards in the dotted line are replicas: As shown in preceding diagram, we initially have a cluster with two nodes. Since the index has two shards and one replica, shards are distributed across the two nodes. To ensure availability, primary and replica shards never exist in the same node. If the node containing both primary and replica shards goes down, the data cannot be recovered. In the preceding diagram, you can see that the primary shard S0 belongs to Node 1 and the replica shard S0 to the Node 2. Next, just like we discussed in the Relation between Node, Index and Shard section, we will add two new nodes to the existing cluster, as shown here: The cluster now contains four nodes, and the shards are automatically allocated to the new nodes. Each node in the cluster will now contain either a primary or replica shard. Now, let's say Node2, which contains the primary shard S1, goes down as shown here: Since the node that holds the primary shard went down, the replica of S1, which lives in Node3, is promoted to primary. To ensure the replication factor of 1, a copy of the shard S1 is made on Node1. This process is known as rebalancing of the cluster. Depending on the application, the number of shards can be configured while creating the index. The process of rebalancing the shards to other nodes is entirely transparent to the user and handled automatically by Elasticsearch. We discussed inverted indexes, relation between nodes, index and shard, distributed search and how failures are handled automatically in Elasticsearch. Check out this book, 'Learning Elasticsearch' to know about handling document relationships, working with geospatial data, and much more. How to install Elasticsearch in Ubuntu and Windows Working with Kibana in Elasticsearch 5.x CRUD (Create Read, Update and Delete) Operations with Elasticsearch

Humans have been excellent players in most of the gameplays be it indoor or outdoors. However, over the recent years we have been increasingly coming across machines that are playing and winning popular board games Go and Chess against humans using machine learning algorithms. If you think machines are only good at solving the black and whites, you are wrong. The recent achievement of a machine trying to solve a complex game (a Rubik’s cube) is DeepCube. Rubik cube is a challenging piece of puzzle that’s captivated everyone since childhood. Solving it is a brag-worthy accomplishment for most adults. A group of UC Irvine researchers have now developed a new algorithm (used by DeepCube) known as Autodidactic Iteration, which can solve a Rubik’s cube with no human assistance. The Erno Rubik’s cube conundrum Rubik’s cube, a popular three-dimensional puzzle was developed by Erno Rubik in the year 1974. Rubik worked for a month to figure out the first algorithm to solve the cube. Researchers at the UC Irvine state that “Since then, the Rubik’s Cube has gained worldwide popularity and many human-oriented algorithms for solving it have been discovered. These algorithms are simple to memorize and teach humans how to solve the cube in a structured, step-by-step manner.” After the cube became popular among mathematicians and computer scientists, questions around how to solve the cube with least possible turns became mainstream. In 2014, it was proved that the least number of steps to solve the cube puzzle was 26. More recently, computer scientists have tried to find ways for machines to solve the Rubik’s cube. As a first step, they tried and tested ways to use the same successful approach tried in the games Go and Chess. However, this approach did not work well for the Rubik’s cube. The approach: Rubik vs Chess and Go Algorithms used in Go and Chess are fed with rules of the game and then they play against themselves. The deep learning machine here is rewarded based on its performance at every step it takes. Reward process is considered as important as it helps the machine to distinguish between a good and a bad move. Following this, the machine starts playing well i.e it learns how to play well. On the other hand, the rewards in the case of Rubik’s cube are nearly hard to determine. This is because there are random turns in the cube and it is hard to judge whether the new configuration is any closer to a solution. The random turns can be unlimited and hence earning an end-state reward is very rare. Both Chess and Go have a large search space but each move can be evaluated and rewarded accordingly. This isn’t the case for Rubik’s cube! UC Irvine researchers have found a way for machines to create its own set of rewards in the Autodidactic Iteration method for DeepCube. Autodidactic Iteration: Solving the Rubik’s Cube without human Knowledge DeepCube’s Autodidactic Iteration (ADI) is a form of deep learning known as deep reinforcement learning (DRL). It combines classic reinforcement learning, deep learning, and Monte Carlo Tree Search (MCTS). When DeepCube gets an unsolved cube, it decides whether the specific move is an improvement on the existing configuration. To do this, it must be able to evaluate the move. The algorithm, Autodidactic iteration starts with the finished cube and works backwards to find a configuration that is similar to the proposed move. Although this process is imperfect, deep learning helps the system figure out which moves are generally better than others. Researchers trained a network using ADI for 2,000,000 iterations. They further reported, “The network witnessed approximately 8 billion cubes, including repeats, and it trained for a period of 44 hours. Our training machine was a 32-core Intel Xeon E5-2620 server with three NVIDIA Titan XP GPUs.” After training, the network uses a standard search tree to hunt for suggested moves for each configuration. The researchers in their paper said, “Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.” Researchers also wrote, “DeepCube is able to teach itself how to reason in order to solve a complex environment with only one reward state using pure reinforcement learning.” Furthermore, this approach will have a potential to provide approximate solutions to a broad class of combinatorial optimization problems. To explore Deep Reinforcement Learning check out our latest releases, Hands-On Reinforcement Learning with Python and Deep Reinforcement Learning Hands-On. How greedy algorithms work Creating a reference generator for a job portal using Breadth First Search (BFS) algorithm Anatomy of an automated machine learning algorithm (AutoML)