Tech Guides - Data

Cybersecurity analysts have warned that spoofing using artificial intelligence is within the realm of possibility and that people should be aware of the possibility of getting fooled with such voice or picture-based deepfakes. What is Deepfake? Deepfakes rely on a branch of AI called Generative Adversarial Networks (GANs). It requires two machine learning networks that teach each other with an ongoing feedback loop. The first one takes real content and alters it. Then, the second machine learning network, known as the discriminator, tests the authenticity of the changes. As the machine learning networks keep passing the material back and forth and receiving feedback about it, they get smarter. GANs are still in the early stages, but people expect numerous potential commercial applications. For example, some can convert a single image into different poses. Others can suggest outfits similar to what a celebrity wears in a photo or turn a low-quality picture into a high-resolution snapshot. But, outside of those helpful uses, deepfakes could have sinister purposes. Consider the blowback if a criminal creates a deepfake video of something that would hurt someone's reputation — for instance, a deepfake video of a politician "admitting" to illegal activities, like accepting a bribe. Other instances of this kind of AI that are already possible include cases of misleading spoken dialogue. Then, the lips of someone saying something offensive get placed onto someone else. In one of the best-known examples of Deepfake manipulation, BuzzFeed published a clip now widely known as "ObamaPeele." It combined a video of President Obama with film director Jordan Peele's lips. The result made it seem as if Obama cursed and said things he never would in public. Deepfakes are real enough to cause action The advanced deepfake efforts that cybersecurity analysts warn about rely on AI to create something so real that it causes people to act. For example, in March of 2019, the CEO of a British energy firm received a call from what sounded like his boss. The message was urgent — the executive needed to transfer a large amount of funds to a Hungarian supplier within the hour. Only after the money was sent did it become clear the executive’s boss was never on the line. Instead, cybercriminals had used AI to generate an audio clip that mimicked his boss’s voice. The criminals called the British man and played the clip, convincing him to transfer the funds. The unnamed victim was scammed out of €220,000 — an amount equal to $243,000. Reports indicate it's the first successful hack of its kind, although it's an unusual way for hackers to go about fooling victims. Some analysts point out other hacks like this may have happened but have gone unreported, or perhaps the people involved did not know hackers used this technology. According to Rüdiger Kirsch, a fraud expert at the insurance company that covered the full amount of the claim, this is the first time the insurer dealt with such an instance. The AI technology apparently used to mimic the voice was so authentic that it captured the parent company leader's German accent and the melody of his voice. Deepfakes capitalize on urgency One of the telltale signs of deepfakes and other kinds of spoofing — most of which currently happen online — is a false sense of urgency. For example, lottery scammers emphasize that their victims must send personal details immediately to avoid missing out on their prizes. The deepfake hackers used time constraints to fool this CEO, as well. The AI technology on the other end of the phone told the CEO that he needed to send the money to a Hungarian supplier within the hour, and he complied. Even more frighteningly, the deceiving tech was so advanced that hackers used it for several phone calls to the victim. One of the best ways to avoid scams is to get further verification from outside sources, rather than immediately responding to the person engaging with you. For example, if you're at work and get a call or email from someone in accounting who asks for your Social Security number or bank account details to update their records, the safest thing to do is to contact the accounting department yourself and verify the legitimacy. Many online spoofing attempts have spelling or grammatical errors, too. The challenging thing about voice trickery, though, is that those characteristics don't apply. You can only go by what your ears tell you. Since these kinds of attacks are not yet widespread, the safest thing to do for avoiding disastrous consequences is to ignore the urgency and take the time you need to verify the requests through other sources. Hackers can target deepfake victims indefinitely One of the most impressive things about this AI deepfake case is that it involved more than one phone conversation. The criminals called again after receiving the funds to say that the parent company had sent reimbursement funds to the United Kingdom firm. But, they didn't stop there. The CEO received a third call that impersonated the parent company representative again and requested another payment. That time, though, the CEO became suspicious and didn't agree. As, the promised reimbursed funds had not yet come through. Moreover, the latest call requesting funds originated from an Austrian phone number. Eventually, the CEO called his boss and discovered the fakery by handling calls from both the real person and the imposter simultaneously. Evidence suggests the hackers used commercially available voice generation software to pull off their attack. However, it is not clear if the hackers used bots to respond when the victim asked questions of the caller posing as the parent company representative. Why do deepfakes work so well? This deepfake is undoubtedly more involved than the emails hackers send out in bulk, hoping to fool some unsuspecting victims. Even those that use company logos, fonts and familiar phrases are arguably not as realistic as something that mimics a person's voice so well that the victim can't distinguish the fake from the real thing. The novelty of these incidents also makes individuals less aware that they could happen. Although many people receive training that helps them spot some online scams, the curriculum does not yet extend to these advanced deepfake cases. Making the caller someone in a position of power increases the likelihood of compliance, too. Generally, if a person hears a voice on the other end of the phone that they recognize as their superior, they won't question it. Plus, they might worry that any delays in fulfilling the caller's request might get perceived as them showing a lack of trust in their boss or an unwillingness to follow orders. You've probably heard people say, "I'll believe it when I see it." But, thanks to this emerging deepfake technology, you can't necessarily confirm the authenticity of something by hearing or seeing it. That's an unfortunate development, plus something that highlights how important it is to investigate further before acting. That may mean checking facts or sources or getting in touch with superiors directly to verify what they want you to do. Indeed, those extra steps take more time. But, they could save you from getting fooled. Author Bio Kayla Matthews writes about big data, cybersecurity, and technology. You can find her work on The Week, Information Age, KDnuggets and CloudTweaks, or over at ProductivityBytes.com. Media manipulation by Deepfakes and cheap fakes require both AI and social fixes, finds a Data & Society report Terrifyingly realistic Deepfake video of Bill Hader transforming into Tom Cruise is going viral on YouTube Now there is a Deepfake that can animate your face with just your voice and a picture using Temporal GANs

The rise of the bots is nigh! If you can imagine a situation involving a dialog, there is probably a chatbot for that. Just look at the chatbot market - text-based email/SMS bots, voice-based bots, bots for customer support, transaction-based bots, entertainment bots and many others. A large number of enterprises, from startups to established organizations, are seeking to invest in this sector. This has also led to an increase in the number of platforms used for chatbot building. These frameworks incorporate AI techniques along with natural language processing capabilities to assist developers in building and deploying chatbots. Let’s start with how a chatbot typically works before diving into some of the frameworks. Understand: The first step for any chatbot is to understand the user input. This is made possible using pattern matching and intent classification techniques. ‘Intents’ are the tasks that users might want to perform with a chatbot. Machine learning, NLP and speech recognition techniques are typically used to identify the intent of the message and extract named entities. Entities are the specific pieces of information extracted from the user’s response i.e. the content associated with an intent. Respond: After understanding, the next goal is to generate a response. This is based on the current input message and the context of the conversation. After specifying the intents and entities, a dialog flow is constructed. This is basically the replies/feedback expected from a chatbot. Learn: Chatbots use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided, and elicit a suitable response for future replies. This is important because different requests might have different meanings depending on previous requests. Top chatbot development frameworks A bot development framework is a set of predefined classes, functions, and utilities that a developer can use to build chatbots easier and faster. They vary in the level of complexity, integration capabilities, and functionalities. Let us look at some of the development platforms utilized for chatbot building. API.AI API.AI, a code based framework with a simple web-based interface, allows users to build engaging voice and text-based conversational apps using a large number of libraries and SDKs including Android, iOS, Webkit HTML5, Node.js, and Python API. It also supports nearly 32 one-click platform integrations such as Google, Facebook Messenger, Twitter and Skype to name a few. API.AI makes use of an agent - a container that transforms natural language based user requests into actionable data. The software tries to find the intent behind a user’s reply and matches it to the default or the closest match. After intent matching, it executes the actions and responses the developer has defined for that intent. API.AI also makes use of entities. Once the intents and entities are specified, the bot is trained. API.AI’s training module efficiently tracks each user’s request and lets developers see how they are parsed and matched to an intent. It also allows for correction of any errors and change requests thus retraining the bot. API.AI streamlines the entire bot-creating process by helping developers provide domain-specific knowledge that is unique to a bot’s needs while working on speech recognition, intent and context management in the backend. Google has recently partnered with API.AI to help them build conversational tools like Apple’s Siri. Microsoft Bot Framework Microsoft Bot Framework allows building and deployment of chatbots across multiple platforms and services such as web, SMS, non-Microsoft platforms, Office 365, Skype etc. The Bot Framework includes two components - The Bot Builder and the Microsoft Cognitive Services. The Bot Builder comprises of two full-featured SDKs - for the.NET and the Node.js platforms along with an emulator for testing and debugging. There’s also a set of RESTful APIs for building code in other languages. The SDKs support features for simple and easy interactions between bots. They also have a large collection of prebuilt sample bots for the developer to choose from. The Microsoft Cognitive Services is a collection of intelligent APIs that simplify a variety of AI tasks such as allowing the system to understand and interpret the user's needs using natural language in just a few lines of code. These APIs allow integration to most modern languages and platforms and constantly improve, learn, and get smarter. Microsoft created the AI Inner Circle Partner Program to work hand in hand with industry to create AI solutions. Their only partner in the UK is ICS.AI who build conversational AI solutions for the UK's public sector. ICS are the first choice for many organisations due to their smart solutions that scale and serve to improve services for the general public. Developers can build bots in the Bot Builder SDK using C# or Node.js. They can then add AI capabilities with Cognitive Services. Finally, they can register the bots on the developer portal, connecting it to users across platforms such as Facebook and Microsoft Teams and also deploy it on the cloud like Microsoft Azure. For a step-by-step guide for chatbot building using Microsoft Bot Framework, you can refer to one of our books on the topic. Sabre Corporation, a customer service provider for travel agencies, have recently announced the development of an AI-powered chatbot that leverages Microsoft Bot Framework and Microsoft Cognitive Services. Watson Conversation IBM’s Watson Conversation helps build chatbot solutions that understand natural-language input and use machine learning to respond to customers in a way that simulates conversations between humans. It is built on a neural network of one million Wikipedia words. It offers deployment across a variety of platforms including mobile devices, messaging platforms, and robots. The platform is robust and secure as IBM allows users to opt out of data sharing. The IBM Watson Tone Analyzer service can help bots understand the tone of the user’s input for better management of the experience. The basic steps to create a chatbot using Watson Conversation are as follows. We first create a workspace - a place for configuring information to maintain separate intents, user examples, entities, and dialogues for each application. One workspace corresponds to one bot. Next, we create Intents. Watson Conversation makes use of multiple conditioned responses to distinguish between similar intents. For example, instead of building specific intents for locations of different places, it creates a general intent “location” and adds an entity to capture the response, like the “location- bedroom” - to the right, near the stairs, “location-kitchen”- to the left. The third step is entity establishment. This involves grouping entities that might trigger a similar response in the dialog. The dialog flow, thus generated after specifying the intents and entities, goes through testing followed by embedding this into an application. It is then connected with other services by using the conversation API. Staples, an office supply retailing firm, uses Watson Conversation in their “Easy Systems” to simplify the customer’s shopping experience. CXP Designer and Aspect NLU Aspect Customer Experience Platform is an application lifecycle management tool to build text and voice-based applications such as chatbots. It provides deployment options across multiple communication channels like text, voice, mobile web and social media networks. The Aspect CXP typically includes a CXP designer to build chatbots and the inbuilt Aspect NLU to provide advanced natural language capabilities. CXP designer works by creating dialog objects to provide a menu of options for frontend as well as backend. Menu items for the frontend are used to create intents and modules within those intents. The developer can then modify labels (of those intents and modules) manually or use the Aspect NLU to disambiguate similar questions for successful extraction of meaning and intent. The Aspect NLU includes tools for spelling correction, linguistic lexicons such as nouns, verbs etc. and options for detecting and extracting common data types such as date, time, numbers, etc. It also allows a developer to modify the meaning extraction based on how they want it if they want it! CXP designer also allows skipping of certain steps in chatbots. For instance, if the user has already provided their tracking id for a particular package, the chatbot will skip the prompt of asking them the tracking id again. With Aspect CXP, developers can create and deploy complex chatbots. Radisson Blu Edwardian, a hotel in London, has collaborated with Aspect software to build an SMS based, AI virtual host. Conclusion Another popular chatbot development platform worth mentioning is the Facebook messenger with over 100,000 monthly active bots, but without cross-platform deployment features. The above bot frameworks are typically used by developers to build chatbots from scratch and require some programming skills. However, there has been a rise in automated bot development tools of late. Some of these include Chatfuel and Motion AI and typically involve drag and drop functionalities. With such tools, beginners and non-programmers can create and deploy chatbots within few minutes. But, they lack the extended functionalities supported by typical code based frameworks such as the flexibility to store data, produce analytics or incorporate customized AI tasks. Every chatbot development system, whether framework or tool, serves a different purpose. Choosing the right one depends on the type of application to build, organizational needs, and the developer’s expertise.

Is data science important? It's a term that's talked about a lot but often misunderstood. Because it's a buzzword it's easy to dismiss; but data science is important. Behind the term lies very specific set of activities - and skills - that businesses can leverage to their advantage. Data science allows businesses to use the data at their disposal, whether that's customer data, financial data or otherwise, in an intelligent manner. It's results should be a key driver of growth. However, although it’s not wrong to see data science as a real game changer for business, that doesn’t mean it’s easy to do well. In fact, it’s pretty easy to do data science badly. A number of reports suggest that a large proportion of analytics projects fail to deliver results. That means a huge number of organizations are doing data science wrong. Key to these failures is a misunderstanding of how to properly utilize data science. You see it so many times - buzzwords like data science are often like hammers. They make all your problems look like nails. And not properly understanding the business problems you’re trying to solve is where things go wrong. What is data science? But what is data science exactly? Quite simply, it’s about using data to solve problems. The scope of these problems is huge. Here are a few ways data science can be used: Improving customer retention by finding out what the triggers of churn might be Improving internal product development processes by looking at points where faults are most likely to happen Targeting customers with the right sales messages at the right time Informing product development by looking at how people use your products Analyzing customer sentiment on social media Financial modeling As you can see data science is a field that can impact every department. From marketing to product management to finance, data science isn’t just a buzzword, it’s a shift in mindset about how we work. Data science is about solving business problems To anyone still asking is data science important, the answer is actually quite straightforward. It's important because it solves business problems. Once you - and management - recognise that fact, you're on the right track. Too often businesses want machine learning, big data projects without thinking about what they’re really trying to do. If you want your data scientists to be successful, present them with the problems - let them create the solutions. They won’t want to be told to simply build a machine learning project. It’s crucial to know what the end goal is. Peter Drucker once said “in God we trust… everyone else must bring data”. But data science didn’t really exist then - if it did it could be much simpler: trust your data scientists.

GDPR is an acronym that has been doing the rounds for a couple of years now. It’s become even more visible in the last few weeks, thanks to the Facebook and Cambridge Analytica data hijacking scandal. And with the deadline date looming - 25 May 2018 - every organization on the planet needs to make sure their on top of things. But what is GDPR exactly? And how is it going to affect you? What is GDPR? Before April, 2016, a data protection directive enforced in 1995 was in place. This governed all organisations that dealt with collecting, storing and processing data. This directive became outdated with rapidly evolving technological trends, which meant a revised directive was needed. In April 2016, the European Union drew up General Data Protection Regulation. It has been specifically created to to protect the personal data and privacy of European citizens. It's important to note at this point that the directive doesn't just apply to EU organizations - it applies to anyone who deals with data on EU citizens. A relatively new genre of crime involving stealing data, has cropped up over the past decade. Data is so powerful, that its misuse could be devastating, possibly resulting in another world war. GDPR aims to set a new benchmark for the protection of consumer data rights by making organisations more accountable. Governed by GDPR, organisations will now be responsible for guarding every quantum of information that is connected to an individual, including IP addresses and web cookies! Read more: Why GDPR is good for everyone. Why should organizations bother with GDPR? In December 2017, the RSA, one of the first cryptosystems and security organisations, surveyed 7,500 customers in France, Italy, Germany, the UK and the US, and the results were interesting. When asked what their main concern was, customers responded that lost passwords, banking information, passports and other important documents were their major concern. The more interesting part was that over 60% of the respondents said that in the event of a breach, they would blame the organisation that lost their data rather than the hacker. If you work for or own a company that deals with the data of EU citizens, you’ll probably have GDPR on your radar. If you don’t comply, you’ll face a hefty fine - more on that below. What kind of data are we talking about? The GDPR aims to protect data related to identity information like name, physical address, sexual orientation and more. It also covers any ID numbers; IP addresses, cookies and RFID tags; genetic and any data related to health; biometric data like fingerprints, retina scans, etc; racial or ethnic data; political opinions. Who must comply with GDPR? You’ll be governed by GDPR if: You’re a company located in the EU You’re not located in the EU but you still process data of EU citizens You have more than 250 employees You have lesser than 250 employees but process data that could impact the rights and freedom of EU citizens When does GDPR come into force? In case you missed it in the first paragraph, GDPR comes into effect on 25 May 2018. If you're not ready yet, now is the time to scramble to get things right and make sure you comply with GDPR regulations. What if you don’t make the date? Unlike an invitation to a birthday party, if you miss the date to comply with the GDPR, you’re likely to be fined to the tune of €20 million or 4% of the worldwide turnover of your company. A more relaxed fine includes €10 million or 2% of the worldwide turnover of your company, for misusing data in ways involving failure to report a data breach, failure to incorporate privacy by design and failure to ensure that data protection is applied at the initial stage of a project. It also includes the failure to hire a Data Protection Officer/Chief Data Officer, who has professional experience and knowledge of data protection laws that are proportionate to what the organisation carries out. If it makes you feel any better, you’re not the only one. A report from Ovum states that more than 50% of the companies feel they’re most likely to be fined for non compliance. How do you prepare for GDPR? Well, here are a few honest steps that you could perform to ensure a successful compliance: Prepare to shell out between $1 million to $10 million to meet GDPR requirements Hire a DPO or a CDO who’s capable of handling all your data policies and migration Fully understand GDPR and its requirements Perform a risk assessment, understand what kind of data you store and what implications it might have Strategize to mitigate that risk Review/Create your data protection plan Plan for a 72 hour incident response system Implement internal plans and policies to ensure employees follow For the third time then - time is running out! It’s imperative that you ensure your organisation complies with GDPR before the 25th of May, 2018. We’ll follow up with some more thoughts to help you make the shift, as well as give you more insight into this game changing regulation. If you own or are part of an organisation that has migrated to comply with GDPR, please share some tips in the comments section below to help others still in the midst of the transition.

Internet job portal ‘Indeed.com’ links potential employers with people who are looking to take the next step in their careers. The proportion of job posts on their site, relating to ‘Data Science’, a specific job in the AI category, is growing fast (see chart below). More broadly, Artificial Intelligence & machine learning skills, of which ‘Data Scientist’ is just one example, are in demand. No wonder that it has been termed as the sexiest job role of the 21st century. Interest comes from an explosion of jobs in the field from big companies and Start-Ups, all of which are competing to come up with the best AI business and to earn the money that comes with software that automates tasks. The skills shortage associated with Artificial Intelligence represents an opportunity for any developer. There has never been a better time to consider whether reskilling or upskilling in AI could be a lucrative path for you. Below : Indeed.com. Proportion of job postings containing Data Scientist or Data Science. [caption id="attachment_21240" align="aligncenter" width="1525"] Artificial Intelligence skills are increasingly in demand and create a real opportunity for those prepared to reskill or upskill.[/caption] Source: Indeed  The AI skills gap the market is experiencing comes from the difficulty associated with finding an individual demonstrating a competent mixture of the very disparate faculties that AI roles require. Artificial Intelligence and it’s near equivalents such as Machine Learning and Neural Networks operate at the intersection of what have mostly been two very different disciplines – statistics and software development. In simple terms, they are half coding, half maths. Hamish Ogilvy, CEO of AI based Internal Search company Sajari is all too familiar with the problem. He’s on the front line, hiring AI developers. “The hardest part”, says Ogilvy, “is that AI is pretty complex and the average developer/engineer does not have the background in maths/stats/science to actually understand what is happening. On the flip side the trouble with the stats/maths/science people is that they typically can't code, so finding people in that sweet spot that have both is pretty tough.” He’s right. The New York Times suggests that the pool of qualified talent is only 10,000 people, worldwide. Those who do have jobs are typically happily ensconced, paid well, treated appropriately and given no reason whatsoever to want to leave. [caption id="attachment_21244" align="aligncenter" width="1920"] Judged by $ investments in the area alone, AI skills are worth developing for those wishing to stay current on technology skills.[/caption] In fact, an instinct to develop AI skills will serve any technology employee well. No One can have escaped the many estimates, from reputable consultancies, suggesting that Automation will replace up to 30% of jobs in the next 10 years. No job is safe. Every industry is touched by AI in some form. Any responsible individual with a view to the management of their own skills could learn ML and AI skills to stay relevant in current times. Even if you don't want to move out of your current job, learning ML will probably help you adapt better in your industry. What is a typical AI job and what will it pay? OpenAI, a world class Artificial Intelligence research laboratory, revealed the salaries of some of its key Data Science employees recently. Those working in the AI field with a specialization can earn $300 to $500k in their first year out of university. Experts in Artificial Intelligence now command salaries of up to $1m. [caption id="attachment_21242" align="aligncenter" width="432"] The New York Times observes AI salaries[/caption] [caption id="attachment_21241" align="aligncenter" width="1121"] The New York Times observes AI salaries[/caption] Source: The New York times Indraneil Roy, an Expert in AI and Talent Acquisition who works for Edge Networks puts it this way when outlining the difficulties of hiring the right skills and to explain why wages in the field are so high. “The challenge is the quality of resources. As demand is high for this skill, we are already seeing candidates with fake experience and work pedigree not up to standards.” The phenomenon is also causing a ‘brain drain’ in Universities. About a third of jobs in the AI field will go to someone with a Ph.D., and all of those are drawn from universities working on the discipline, often lured by the significant pay packages which are available. So, with huge demand and the universities drained, where will future AI employees come from? 3 ways to skill up to become an AI expert (And earn all that money?) There is still not a lot of agreed terminology or even job roles and responsibility in the sector. However, some things are clear. Those wishing to evolve in to the field of AI must understand the conceptual thinking involved, as a starting point, whether that view is found on the job or as part of an informal / formal educational course. Specifically, most jobs in the specialty require a working knowledge of neural networks, data / analytics, predictive analytics, with some basic programming and database skills. There are some great resources available online to train you up. Most, as you’d expect, are available on your smartphone so there really is no excuse for not having a look. 1. Free online course: Machine Learning & Statistics and probability Hamish Ogilvy summed the online education which is available in the area well. There are “so many free courses now on AI from Stanford,” he said, “that people are able to educate themselves and make up for the failings of antiquated university courses. AI is just maths really,” he says “complex models and stats. So that's what people need grounding in to be successful.” Microsoft offer free AI courses for technical professionals: Microsoft’s training materials are second to none. They’re also provided free and provide a shortcut to a credible understanding in an area simply because it comes from a technical behemoth. Importantly, they also have a list of AI services which you can play with, again for free. For example, a Natural Language engine offers a facility for you to submit text from Instant Messaging conversations and establish the sentiment being felt by the writer. Practical experience of the tools, processes and concepts involved will set you apart. See below. [caption id="attachment_21245" align="aligncenter" width="1999"] Check out Microsoft’s free AI training program for developers.[/caption] Google are taking a proactive stance on Machine Learning. They see it’s potential to improve efficiency in every industry and also offer free ML training courses on their site. 2. Take courses on AI/ML Packt’s machine learning courses, books and videos: Packt is working towards a mission to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. It has published over 6,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. You can choose from a variety of Packt’s books, videos and courses for AI/ML. Here’s a list of top ones: Artificial Intelligence by Example [Book] Artificial Intelligence for Big data [Book] Learn Artificial Intelligence with TensorFlow [Video] Introduction to Artificial Intelligence with Java [Video] Advanced Artificial Intelligence Projects with Python [Video] Python Machine learning - Second Edition [Book] Machine Learning with R - Second Edition [Book] Coursera’s machine learning courses Coursera is a company which make training courses, for a variety of subjects, available online. Taken from actual University course content and delivered with tests, videos and training notes, all accessed online, each course is roughly a University Module. Students pick up an ‘up to under-graduate’ level of understanding of the content involved. Coursera’s courses are often cited as merit worthy and are recognizable in the industry. Costs vary but are typically between $2k and $5k per course. 3. Learn by doing Familiarize yourself with relevant frameworks and tools including Tensor Flow, Python and Keras. TensorFlow from Google is the most used open source AI software library. You can use existing code in your experiments and experiment with neural networks in much the same way as you can in Microsoft’s. Python is a programming language written for a big data world. Its proponents will tell you that Python saves developers hundreds of lines of code, allowing you to tie together information and systems faster than ever before. Python is used extensively in ML and AI applications and should be at the top of your study list. Keras, a deep learning library is similarly ubiquitous. It’s a high level Neural Network API designed to allow prototyping of your software as fast as possible. Finally, a lesser known but still valuable resources is the Accord.net. It is one final example of the many  software elements with which you can engage with to train yourself up. Accord Framework.net will expose you to image libraries, natural learning and real time facial recognition. Earn extra points with employers AI has several lighthouse tasks which are proving the potential of the technology in these still early stages. We’ve included a couple of examples, Natural Language processing and image recognition, above. Practical expertise in these areas specifically, image or voice recognition or pattern matching are valued highly by employers. Alternatively, have you patented something? A registered patent in your name is highly prized. Especially something to do with Machine Learning. Both will help you showcase Extra skills / achievements that will help your application.’ The specifics of how to apply for patents differ by country but you can find out more about the overall principles of how to submit an idea here. Passion and engagement in the subject are also, clearly appealing characteristics for potential employers to see in applicants. Participating in competitions like Kaggle, and having a portfolio of projects you can showcase on facilities like GitHub are also well prized. Of all of these suggestions, for those employed, any on the job experience you can get will stand you in the best stead. Indraneil says "Individual candidates need to spend more time doing relevant projects while in employment. Start ups involved in building products and platforms on AI seem to have better talent." The fact that there are not many AI specialists around is a bad sign There is a demand for employees with AI skills and an investment in relevant training may pay you well. Unfortunately, the underlying problem this situation reveals could be far worse than the problems experienced so far. Together, once found, all these AI scientists are going to automate millions of jobs, in every industry, in every country around the world. If Industry, Governments and Universities cannot train enough people to fill the roles being created by an evolving skills market, we may rightly be concerned to worry about how they will deal with retraining all those displaced by AI, for whom there may be no obvious replacement role available. 18 striking AI Trends to watch in 2018 – Part 1 DeepMind, Elon Musk, and others pledge not to build lethal AI Attention designers, Artificial Intelligence can now create realistic virtual textures

As a data developer, the concept or process of data analysis may be clear to your mind. However, although there happen to be similarities between the art of data analysis and that of statistical analysis, there are important differences to be understood as well. This article is taken from the book Statistics for Data Science by James D. Miller. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. In this article, we've broken things into the following topics: What is statistical analysis and it's best practices? How to establish the nature of data? What is Statistical analysis? Some in the study of statistics sometimes describe statistical analysis as part of statistical projects that involves the collection and scrutiny of a data source in an effort to identify trends within the data. With data analysis, the goal is to validate that the data is appropriate for a need, and with statistical analysis, the goal is to make sense of, and draw some inferences from, the data. There is a wide range of possible statistical analysis techniques or approaches that can be considered. How to perform a successful statistical analysis It is worthwhile to mention some key points, dealing with ensuring a successful (or at least productive) statistical analysis effort. As soon as you can, decide on your goal or objective. You need to know what the win is, that is, what the problem or idea is that is driving the analysis effort. In addition, you need to make sure that, whatever is driving the analysis, the result obtained must be measurable in some way. This metric or performance indicator must be identified early. Identify key levers. This means that once you have established your goals and a way to measure performance towards obtaining those goals, you also need to find out what has an effect on the performance towards obtaining each goal. Conduct a thorough data collection. Typically, the more data the better, but in the absence of quantity, always go with quality. Clean your data. Make sure your data has been cleaned in a consistent way so that data issues would not impact your conclusions. Model, model, and model your data. Modeling drives modeling. The more you model your data, the more questions you'll have asked and answered, and the better results you'll have. Take time to grow in your statistical analysis skills. It's always a good idea to continue to evolve your experiences and style of statistical analysis. The way to improve is to do it. Another approach is to remodel the data you may have on hand for other projects to hone your skills. Optimize and repeat. As always, you need to take the time for standardizing, following proven practices, using templates, and testing and documenting your scripts and models, so that you can re-use your best efforts over and over again. You will find that this time will be well spent and even your better efforts will improve with use. Finally, share your work with others! The more eyes, the better the product. Some interesting advice on ensuring success with statistical projects includes the following quote: It's a good idea to build a team that allows those with an advanced degree in statistics to focus on data modeling and predictions, while others in the team-qualified infrastructure engineers, software developers and ETL experts-build the necessary data collection infrastructure, data pipeline and data products that enable streaming the data through the models and displaying the results to the business in the form of reports and dashboards.                                                                                                            - G Shapira, 2017 Establishing the nature of data When asked about the objectives of statistical analysis, one often refers to the process of describing or establishing the nature of a data source. Establishing the nature of something implies gaining an understanding of it. This understanding can be found to be both simple as well as complex. For example, can we determine the types of each of the variables or components found within our data source; are they quantitative, comparative, or qualitative? A more advanced statistical analysis aims to identify patterns in data; for example, whether there is a relationship between the variables or whether certain groups are more likely to show certain attributes than others. Exploring the relationships presented in data may appear to be similar to the idea of identifying a foreign key in a relational database, but in statistics, relationships between the components or variables are based upon correlation and causation. Further, establishing the nature of a data source is also, really, a process of modeling that data source. During modeling, the process always involves asking questions such as the following (in an effort establish the nature of the data): What? Some common examples of this (what) are revenue, expenses, shipments, hospital visits, website clicks, and so on. In the example, we are measuring quantities, that is, the amount of product that is being moved (sales). Why? This (why) will typically depend upon your project's specific objectives, which can vary immensely. For example, we may want to track the growth of a business, the activity on a website, or the evolution of a selected product or market interest. Again, in our current transactional data example, we may want to identify over- and under-performing sales types, and determine if, new or repeat customers provide more or fewer sales? How? The how will most likely be over a period of time (perhaps a year, month, week, and so on) and then by some other related measure, such as a product, state, region, reseller, and so on. Within our transactional data example, we've focused on the observation of quantities by sale type. Another way to describe establishing the nature of your data is adding context to it or profiling it. In any case, the objective is to allow the data consumer to better understand the data through visualization. Another motive for adding context or establishing the nature of your data can be to gain a new perspective on the data. In this article, we explored the purpose and process of statistical analysis and listed the steps involved in a successful statistical analysis. Next, to learn about statistical regression and why it is important to data science, read our book Statistics for Data Science. Estimating population statistics with Point Estimation. Why You Need to Know Statistics To Be a Good Data Scientist. Why choose IBM SPSS Statistics over R for your data analysis project.

First, there was data. Data became database. Then came SQL. Next came NoSQL. And now comes NewSQL. NewSQL Origins For decades, relational database or SQL was the reigning data management standard in enterprises all over the world. With the advent of Big Data and cloud-based storage rose the need for a faster, more flexible and scalable data management system, which didn’t necessarily comply with the SQL standards of ACID compliance. This was popularly dubbed as NoSQL, and databases like MongoDB, Neo4j, and others gained prominence in no time. We can attribute the emergence and eventual adoption of NoSQL databases to a couple of very important factors. The high costs and lack of flexibility of the traditional relational databases drove many SQL users away. Also, NoSQL databases are mostly open source, and their enterprise versions are comparatively cheaper too. They are schema-less meaning they can be used to manage unstructured data effectively. In addition, they can scale well horizontally - i.e. you could add more machines to increase computing power and use it to handle high volumes of data. All these features of NoSQL come with an important tradeoff, however - these systems can’t simultaneously ensure total consistency. Of late, there has been a rise in another type of database systems, with the aim to combine ‘the best of both the worlds’. Popularly dubbed as ‘NewSQL’, this system promises to combine the relational data model of SQL and the scalability and speed of NoSQL. NewSQL - The dark horse in the databases race NewSQL is ‘SQL on Steroids’, say many. This is mainly because all NewSQL systems start with the relational data model and the SQL query language, but also incorporate the features that have led to the rise of NoSQL - addressing the issues of scalability, flexibility, and high performance. They offer the assurance of ACID transactions like in the relational models. However, what makes them really unique is that they allow the horizontal scaling functionality of NoSQL, and can process large volumes of data with high performance and reliability. This is why businesses really like the concept of NewSQL - the performance of NoSQL and the reliability and consistency of the SQL model, all packed in one. To understand what the hype surrounding NewSQL is all about, it’s worth comparing NewSQL database systems with the traditional SQL and NoSQL database systems, and see where they stand out: Characteristic Relational (SQL) NoSQL NewSQL ACID compliance Yes No Yes OLTP/OLAP support Yes No Yes Rigid Schema Structure Yes No In some cases Support for unstructured data No Yes In some cases Performance with large data Moderate Fast Very fast Performance overhead Huge Moderate Minimal Support from Community Very high High Low   As we can see from the table above, NewSQL really comes through as the best when you’re dealing with larger datasets with a desire to lower performance overheads. To give you a practical example, consider an organization that has to work with a large number of short transactions, access a limited amount of data, but executes those queries repeatedly. For such organizations, a NewSQL database system would be a perfect fit. These features are leading to the gradual growth of NewSQL systems. However, it will take some time for more industries to adopt them. Not all NewSQL databases are created equal Today, one has a host of NewSQL solutions to choose from. Some popular solutions are Clustrix, MemSQL, VoltDB and CockroachDB.  Cloud Spanner, the latest NewSQL offering by Google, became generally available in February 2017 - indicating Google’s interest in the NewSQL domain and the value a NewSQL database can offer to their existing cloud offerings. It is important to understand that there are significant differences among these various NewSQL solutions. As such you should choose a NewSQL solution carefully after evaluating your organization’s data requirements and problems. As this article on Dataconomy points out, while some databases handle transactional workloads well, they do not offer the benefit of native clustering - SAP HANA is one such example. NuoDB focuses on cloud deployments, but its overall throughput is found to be rather sub-par. MemSQL is a suitable choice when it comes to clustered analytics but falls short when it comes to consistency. Thus, the choice of the database purely depends on the task you want to do, and what trade-offs you are ready to allow without letting it affect your workflow too much. DBAs and Programmers in the NewSQL world Regardless of which database system an enterprise adopts, the role of DBAs will continue to be important going forward. Core database administration and maintenance tasks such as backup, recovery, replication, etc. will need to be taken care of. The major challenge for the NewSQL DBAs will be in choosing and then customizing the right database solution that fits the organizational requirements. Some degree of capacity planning and overall database administration skills might also have to be recalibrated. Likewise, NewSQL database programmers may find themselves dealing with data manipulation and querying tasks similar to those faced while working with traditional database systems. But NewSQL programmers will be doing these tasks at a much larger, or shall we say, at a more ‘distributed’ scale. In conclusion When it comes to solving a particular problem related to data management, it’s often said that 80% of the solution comes down to selecting the right tool, and 20% is about understanding the problem at hand! In order to choose the right database system for your organization, you must ask yourself these two questions: What is the nature of the data you will work with? What are you willing to trade-off? In other words, how important are factors such as the scalability and performance of the database system? For example, if you primarily work with mostly transactional data with a priority on high performance and high scalability, then NewSQL databases might fit your bill just perfectly. If you’re going to work with volatile data, NewSQL might help you there as well, however, there are better NoSQL solutions to tackle your data problem. As we have seen earlier, NewSQL databases have been designed to combine the advantages and power of both relational and NoSQL systems. It is important to know that NewSQL databases are not designed to replace either NoSQL or SQL relational models. They are rather intentionally-built alternatives for data processing, which mask the flaws and shortcomings of both relational and nonrelational database systems. The ultimate goal of NewSQL is to deliver a high performance, highly available solution to handle modern data, without compromising on data consistency and high-speed transaction capabilities.

[box type="note" align="" class="" width=""]This article is an excerpt taken from a book Big Data Analytics with Java written by Rajat Mehta. In this book, you will learn how to perform real-time streaming analytics on big data using machine learning algorithms and power of Java. [/box] From the article given below, you will learn why graph analytics is a favourable choice in order to analyze complex datasets. Graph analytics Vs Relational Databases The biggest advantage to using graphs is you can analyze these graphs and use them for analyzing complex datasets. You might ask what is so special about graph analytics that we can’t do by relational databases. Let’s try to understand this using an example, suppose we want to analyze your friends network on Facebook and pull information about your friends such as their name, their birth date, their recent likes, and so on. If Facebook had a relational database, then this would mean firing a query on some table using the foreign key of the user requesting this info. From the perspective of relational database, this first level query is easy. But what if we now ask you to go to the friends at level four in your network and fetch their data (as shown in the following diagram). The query to get this becomes more and more complicated from a relational database perspective but this is a trivial task on a graph or graphical database (such as Neo4j). Graphs are extremely good on operations where you want to pull information from one end of the node to another, where the other node lies after a lot of joins and hops. As such, graph analytics is good for certain use cases (but not for all use cases, relational database are still good on many other use cases): As you can see, the preceding diagram depicts a huge social network (though the preceding diagram might just be depicting a network of a few friends only). The dots represent actual people in a social network. So if somebody asks to pick one user on the left-most side of the diagram and see and follow host connections to the right-most side and pull the friends at the say 10th level or more, this is something very difficult to do in a normal relational database and doing it and maintaining it could easily go out of hand. There are four particular use cases where graph analytics is extremely useful and used frequently (though there are plenty more use cases too): Path analytics: As the name suggests, this analytics approach is used to figure out the paths as you traverse along the nodes of a graph. There are many fields where this can be used—simplest being road networks and figuring out details such as shortest path between cities, or in flight analytics to figure out the shortest time taking flight or direct flights. Connectivity analytics: As the name suggests, this approach outlines how the nodes within a graph are connected to each other. So using this you can figure out how many edges are flowing into a node and how many are flowing out of the node. This kind of information is very useful in analysis. For example, in a social network if there is a person who receives just one message but gives out say ten messages within his network then this person can be used to market his favorite products as he is very good in responding to messages. Community Analytics: Some graphs on big data are huge. But within these huge graphs there might be nodes that are very close to each other and are almost stacked in a cluster of their own. This is useful information as based on this you can extract out communities from your data. For example, in a social network if there are people who are part of some community, say marathon runners, then they can be clubbed into a single community and further tracked. Centrality Analytics: This kind of analytical approach is useful in finding central nodes in a network or graph. This is useful in figuring out sources that are single handedly connected to many other sources. It is helpful in figuring out influential people in a social network, or a central computer in a computer network. From the perspective of this article, we will be covering some of these use cases in our sample case studies and for this we will be using a library on Apache Spark called GraphFrames. GraphFrames GraphX library is advanced and performs well on massive graphs, but, unfortunately, it’s currently only implemented in Scala and does not have any direct Java API. GraphFrames is a relatively new library that is built on top of Apache Spark and provides support for dataframe (now dataset) based graphs. It contains a lot of methods that are direct wrappers over the underlying sparkx methods. As such it provides similar functionality as GraphX except that GraphX acts on the Spark SRDD and GraphFrame works on the dataframe so GraphFrame is more user friendly (as dataframes are simpler to use). All the advantages of firing Spark SQL queries, joining datasets, filtering queries are all supported on this. To understand GraphFrames and representing massive big data graphs, we will take small baby steps first by building some simple programs using GraphFrames before building full-fledged case studies. First, let’s see how to build a graph using Spark and GraphFrames on some sample dataset. Building a graph using GraphFrames Consider that you have as simple graph as shown next. This graph depicts four people Kai, John, Tina, and Alex and the relation they share whether they follow each other or are friends. We will now try to represent this basic graph using the GraphFrame library on top of Apache Spark and in the meantime, we will also start learning the GraphFrame API. Since GraphFrame is a module on top of Spark, let’s first build the Spark configuration and spark sql context for brevity: SparkConfconf= ... JavaSparkContextsc= ... SQLContextsqlContext= ... We will now build the JavaRDD object that will contain the data for our vertices or the people Kai, John, Alex, and Tina in this small network. We will create some sample data using the RowFactory class of Spark API and provide the attributes (ID of the person, and their name and age) that we need per row of the data: JavaRDD<Row>verRow = sc.parallelize(Arrays.asList(RowFactory.create(101L,”Kai”,27),        RowFactory.create(201L,”John”,45),   RowFactory.create(301L,”Alex”,32),        RowFactory.create(401L,”Tina”,23))); Next we will define the structure or schema of the attributes used to build the data. The ID of the person is of type long and the name of the person is a string, and the age of the person is an integer as shown next in the code: List<StructField>verFields = newArrayList<StructField>();  verFields.add(DataTypes.createStructField(“id”,DataTypes.LongType, true));  verFields.add(DataTypes.createStructField(“name”,DataTypes.StringType, true));      verFields.add(DataTypes.createStructField(“age”,DataTypes.IntegerType, true)); Now, let’s build the sample data for the relations between these people and this can basically be represented as the edges of the graph later. This data item of relationship will have the IDs of the persons that are connected together and the type of relationship they share (that is friends or followers). Again we will use the Spark provided RowFactory and build some sample data per row and create the JavaRDD with this data: JavaRDD<Row>edgRow = sc.parallelize(Arrays.asList(        RowFactory.create(101L,301L,”Friends”),        RowFactory.create(101L,401L,”Friends”),        RowFactory.create(401L,201L,”Follow”),        RowFactory.create(301L,201L,”Follow”),        RowFactory.create(201L,101L,”Follow”))); Again, define the schema of the attributes added as part of the edges earlier. This schema is later used in building the dataset for the edges. The attributes passed are the source ID of the node, destination ID of the other node, as well as the relationType, which is a string: List<StructField>EdgFields = newArrayList<StructField>();    EdgFields.add(DataTypes.createStructField(“src”,DataTypes.LongType,true));  EdgFields.add(DataTypes.createStructField(“dst”,DataTypes.LongType,true));  EdgFields.add(DataTypes.createStructField(“relationType”,DataTypes.StringType,true)); Using the schemas that we have defined for the vertices and edges, let’s now build the actual dataset for the vertices and the edges. For this, first create the StructType  object that holds the schema details for the vertices and the edges data and using this structure and the actual data we will next build the dataset of the verticles (verDF) and the dataset for the edges (edgDF): StructTypeverSchema = DataTypes.createStructType(verFields); StructTypeedgSchema = DataTypes.createStructType(EdgFields);    Dataset<Row>verDF = sqlContext.createDataFrame(verRow, verSchema); Dataset<Row>edgDF = sqlContext.createDataFrame(edgRow, edgSchema); Finally, we will now use the vertices and the edges dataset and pass it as a parameter to the GraphFrame constructor and build the GraphFrame instance: GraphFrameg = newGraphFrame(verDF,edgDF); Time has now come to see some mild analytics on the graph we just created. Let’s first visualize our data for the graphs; let’s see the data on the vertices. For this, we will invoke the vertices method on the GraphFrame instance and invoke the standard show method on the generated vertices dataset (GraphFrame would generate a new dataset when the vertices method is invoked). g.vertices().show(); This would print the output as follows: Let’s also see the data on the edges: g.edges().show(); This would print as the output as follows: Let’s also see the number of edges and the number of vertices: System.out.println(“Number of Vertices : “ + g.vertices().count()); System.out.println(“Number of Edges : “ + g.edges().count()); This would print the result as follows: Number of Vertices : 4 Number of Edges : 5 GraphFrame has a handy method to find all the indegrees (out degree or degree) g.inDegrees().show(); This would print the in degrees of all the vertices as shown next: Finally, let’s see one more small thing on this simple graph. As GraphFrames work on the datasets, all the dataset handy methods such as filtering, map, and so on can be applied on them. We will use the filter method and run it on the vertices dataset to figure out the people in the graph with age greater than thirty: g.vertices().filter(“age > 30”).show(); This would print the result as follows: From this post, we learned about graph analytics. We saw how graphs can be built from massive big datasets in order to derive quick insights. You will understand when to implement graph analytics or relational database based on the growing challenges in your organization. To know more about preparing and refining big data and to perform smart data analytics using machine learning algorithms you can refer to the book Big Data Analytics with Java.

The ethics of artificial intelligence seems to have found its way into just about every corner of public life. From law enforcement to justice, through to recruitment, artificial intelligence is both impacting both the work we do and the way we think. But if you really want to get into the ethics of artificial intelligence you need to go further than the public realm and move into the bedroom. Sex robots have quietly been a topic of conversation for a number of years, but with the rise of artificial intelligence they appear to have found their way into the mainstream - or at least the edges of the mainstream. There’s potentially some squeamishness when thinking about sex robots, but, in fact, if we want to think seriously about the consequences of artificial intelligence - from how it is built to how it impacts the way we interact with each other and other things - sex robots are a great place to begin. Read next: Introducing Deon, a tool for data scientists to add an ethics checklist Sexualizing artificial intelligence It’s easy to get caught up in the image of a sex doll, plastic skinned, impossible breasts and empty eyes, sad and uncanny, but sexualized artificial intelligence can come in many other forms too. Let’s start with sex chatbots. These are, fundamentally, a robotic intelligence that is able to respond to and stimulate a human’s desires. But what’s significant is that they treat the data of sex and sexuality as primarily linguistic - the language people use to describe themselves, their wants, their needs their feelings. The movie Her is a great example of a sexualised chatbot. Of course, the digital assistant doesn’t begin sexualised, but Joaquin Phoenix ends up falling in love with his female-voiced digital assistant through conversation and intimate interaction. The physical aspect of sex is something that only comes later. https://youtu.be/3n5muEWaE_Q Ai Furuse - the Japanese sex chatbot But they exist in real life too. The best example out these is Ai Furuse, a virtual girlfriend that interacts with you in an almost human-like manner. Ai Furuse is programmed with a dictionary of more than 30,000 words, and is able to respond to conversational cues. But more importantly, AI Furuse is able to learn from conversations. She can gather information about her interlocutor and, apparently, even identify changes in their mood. The more you converse with the chatbot, the more intimate and closer your relationship should be (in theory). Immediately, we can begin to see some big engineering questions. These are primarily about design, but remember - wherever you begin to think about design we’re starting to move towards the domain of ethics as well. The very process of learning through interaction requires the AI to be programmed in certain ways. It's a big challenge for engineers to determine what’s really important in these interactions. The need to make judgements on how users behave. The information that’s passed to the chatbot needs to be codified and presented in a way that can be understood and processed. That requires some work in itself. The models of desire on which Ai Furuse are necessarily limited. They bear the marks of the engineers that helped to create 'her'. It becomes a question of ethics once we start to ask if these models might be normative in some way. Do they limit or encourage certain ways of interacting? Desire algorithms In the context of one chatbot that might not seem like a big deal. But if (or as) the trend moves into the mainstream, we start to enter a world where the very fact of engineering chatbots inadvertently engineers the desires and sexualities that are expressed towards them. In this instance, not only do we shape the algorithms (which is what’s meant to happen), we also allow these ‘desire algorithms’ to shape our desires and wants too. Storing sexuality on the cloud But there’s another more practical issue as well. If the data on which sex chatbots or virtual lovers runs on the cloud, we’re in a situation where the most private aspects of our lives are stored somewhere that could easily be accessed by malicious actors. This a real risk of Ai Furuse, where cloud space is required for your ‘virtual girlfriend’ to ‘evolve’ further. You pay for additional cloud space. It’s not hard to see how this could become a problem in the future. Thousands of sexual and romantic conversations could be easily harvested for nefarious purposes. Sex robots, artificial intelligence and the problem of consent Language, then, is the kernel of sexualised artificial intelligence. Algorithms, when made well, should respond, process, adapt to and then stimulate further desire. But that's only half the picture. The physical reality of sex robots - both as literal objects, but also the physical effects of what they do - only adds a further complication into the mix. Questions about what desire is - why we have it, what we should do with it - are at the forefront of this debate. If, for example, a paedophile can use a child-like sex robot as a surrogate object of his desires, is that, in fact, an ethical use of artificial intelligence? Here the debate isn’t just about the algorithm, but how it should be deployed. Is the algorithm performing a therapeutic purpose, or is it actually encouraging a form of sexuality that fails to understand the concept of harm and consent? This is an important question in the context of sex robots, but it’s also an important question for the broader ethics of AI. If we can build an AI that is able to do something (ie. automate billions of jobs) should we do it? Who’s responsibility is it to deal with the consequences? The campaign against sex robots These are some of the considerations that inform the perspective of the Campaign Against Sex Robots. On their website, they write: "Over the last decades, an increasing effort from both academia and industry has gone into the development of sex robots – that is, machines in the form of women or children for use as sex objects, substitutes for human partners or prostituted persons. The Campaign Against Sex Robots highlights that these kinds of robots are potentially harmful and will contribute to inequalities in society. We believe that an organized approach against the development of sex robots is necessary in response the numerous articles and campaigns that now promote their development without critically examining their potentially detrimental effect on society." For the campaign, sex robots pose a risk in that they perpetuate already existing inequalities and forms of exploitation in society. They prevent us from facing up to these inequalities. They argue that it will “reduce human empathy that can only be developed by an experience of mutual relationship.” Consent and context Consent is the crucial problem when it comes to artificial intelligence. And you could say that it points to one of the limitations of artificial intelligence that we often miss - context. Algorithms can’t ever properly understand context. There will, undoubtedly be people who disagree with this. Algorithms can, for example, understand the context of certain words and sentences, right? Well yes, that may be true, but that’s not strictly understanding context. Artificial intelligence algorithms are set a context, one from which they cannot deviate. They can’t, for example, decide that actually encouraging a pedophile to act out their fantasies is wrong. It is programmed to do just that. But the problem isn’t simply with robot consent. There’s also an issue with how we consent to an algorithm in this scenario. As journalist Adam Rogers writes in this article for Wired, published at the start of 2018: "It’s hard to consent if you don’t know to whom or what you’re consenting. The corporation? The other people on the network? The programmer?" Rogers doesn’t go into detail on this insight, but it gets to the crux of the matter when discussing artificial intelligence and sex robots. If sex is typically built on a relationship between people, with established forms of communication that establish both consent and desire, what happens when this becomes literally codified? What happens when these additional layers of engineering and commerce get added on top of basic sexual interaction? Is the problem that we want artificial intelligence to be human? Towards the end of the same piece, Rogers finds a possible solutions from privacy researcher Sarah Jamie Lewis. Lewis wonders whether one of the main problems with sex robots is this need to think in humanoid terms. “We’re already in the realm of devices that look like alien tech. I looked at all the vibrators I own. They’re bright colors. None of them look like a penis that you’d associate with a human. They’re curves and soft shapes.” Of course, this isn’t an immediate solution - sex robots are meant to stimulate sex in its traditional (arguably heteronormative) sense. What Lewis suggests, and Rogers seems to agree with, is really just AI-assisted masturbation. But their insight is still useful. On reflection, there is a very real and urgent question about the way in which we deploy artificial intelligence. We need to think carefully about what we want it to replicate and what we want it to encourage. Sex robots are the starting point for thinking seriously about artificial intelligence It’s worth noting that when discussing algorithms we end up looping back onto ourselves. Sex robots, algorithms, artificial intelligence - they’re a problem insofar as they pose questions about what we really value as humans. They make us ask what we want to do with our time, and how we want to interact with other people. This is perhaps a way forward for anyone that builds or interacts with algorithms. Whether they help you get off, or find your next purchase. Consider what you’re algorithm is doing - what’s it encouraging, storing , processing, substituting. We can’t prepare for a future with artificial intelligence without seriously considering these things.

After more than five decades of incremental innovation, the manufacturing sector is finally ready to pivot thanks to Industry 4.0. Self-consciousness of technology plays a key role in ushering in Industry 4.0. AI in the manufacturing sector aims to achieve just that i.e. the creation of systems that can perceive their environment and take action consequently. One of the prominent minds in AI, Andrew Ng believes Factories to be AI's next frontier! Andrew is on a mission to AI-ify manufacturing with its new start-up called Landing.AI. For this initiative he partnered with Foxconn, world's largest contract manufacturer and makers of Apple iPhones. Together they aim to develop a wide range of AI transformation programs, from introduction of new technologies, operational processes, automated quality control and much more. In this AI-powered industrial revolution, machines are becoming smarter and interconnected. Manufacturers are using embedded intelligence of machines for collecting and analyzing data to generate meaningful insights. These are then used to run equipment efficiently, optimize workflows of operations and supply chains, among other things. Thus, AI is leaving an indelible impact across the manufacturing cycle. Further, a new wave of automation is transforming the role of human workforce wherein AI-driven robots are empowering production 24-hours a day. This is helping industrial environments gear up for the shift towards the smart factory environment. Below are some ways AI is revolutionizing manufacturing. Predictive analytics for increased production output Smart manufacturing systems are leveraging the power of predictive analysis and machine learning algorithms to enhance the production capacity. Predictive analytics derives its power from the data collected from the devices or sensors embedded in a manufacturer’s industrial equipments. These sensors become part of the IoT (Internet of Things) which collects and shares data with data scientists on the cloud. This setup is helping manufacturing industries to move from repair-and-replace to a predict-and-fix maintenance model. They do this by enabling these businesses to retrieve the right information at the right time to make the right decisions. For instance, in a pump manufacturing company, data scientists could collect, store and analyze sensor data based on machine attributes like heat, vibration, noise etc. This data can be stored in the cloud allowing for an array of analyses to be performed from understanding machine performance to predicting and monitoring disruption in processes and equipment remotely. Further, syncing up production schedules with parts availability can ensure enhanced production output. Enhancing product and service quality with machine and deep learning algorithms Manufacturers can deploy supervised/unsupervised ML, DL and reinforcement learning algorithms to monitor quality issues in the manufacturing process. For instance, researchers at Lappeenranta University of Technology in Finland have developed an innovative welding system for high-strength steel. They used unsupervised learning to allow the system to learn to mimic human’s ability to self-explore and self-correct. This welding system detects imperfections and self-corrects during the welding process using a new kind of sensor system controlled by a neural network program. Further, it also calculates other faults that may arise during the entire process. Visual inspection technology in an industrial environment identifies both functional and cosmetic defects. IBM has developed a new offering for manufacturing clients to automate visual quality inspections. Rooted in deep learning, a centralized ‘learning service’ collects images of all products - normal and abnormal. Next, it builds analytical models to recognize and classify different characteristics of machine parts and components into OK or NG. Characteristics that meet quality specifications are considered as OK while those that don't are classified as ‘NG’. Predictive maintenance for enhanced MRO (Maintenance, Repair and Overhaul) performance Manufacturing industries strive to achieve excellence throughout the production process. To ensure this, machinery embedded with sensors generate real-time performance and workload data. This helps in diagnosing faults and in predicting the need for equipment maintenance. For instance, a machine may break down due to lack of maintenance in the long run, incurring losses to business. With predictive maintenance, businesses can be better equipped to handle equipment malfunction by identifying significant causal factors like weather, temperature etc. Targeted predictive maintenance generates critical information such as which machine parts will need replacing and when. This helps in reducing equipment downtime, lowering maintenance costs and pre-emptively addressing aging equipment. Reinforcement Learning for managing warehouses Large warehouses face challenging times in streamlining space, managing inventories and reducing transit time. Manufacturing industries are employing reinforcement learning for efficient warehouse management. RL approach uses trial and error iterations within an environment to achieve a particular goal. Imagine what a breeze warehousing could be and the associated cost savings, if robots could pick up the right products from various lots and move them to the right destinations with great precision. Here, reinforcement learning based algorithms can improve the efficiency of such intelligent warehouses with multirobot systems by addressing task scheduling and path planning issues. Fanuc, a Tokyo based company, employs robots having reinforced learning ability to perform such tasks with great agility and precision. AI in supply chain management AI is helping manufacturers gain an in-depth understanding of the complex variables at play in the supply chain and in predicting future scenarios. To enable seamless insights generation, businesses are opting for more flexible and efficient cyber-physical systems. These intelligent systems are self-configurative and self-optimizing structures that can predict problems and minimize losses. Thus they help businesses to innovate rapidly by reducing the time to market, foresee uncertainties and deal with them promptly. Siemens, for example, is creating a self-organizing factory that aims to automate the entire supply chain by generating work orders using the demand and order information. Implication of AI in Industry 4.0 Industry 4.0 is the new way of manufacturing using automation, devices connected on the IoT, cloud and cognitive computing. It propagates the concept of the “smart factory” in which cyber-physical systems observe the physical processes of the factory and make discrete decisions accordingly. As AI finds its application in Industry 4.0, computers will merge together with robotics to automate and maximize the efficiency of the industrial processes. Powered by machine learning algorithms, the computer systems could control the robots with minimum human intervention. For instance, in a manufacturing setup, AI can work alongside systems like SCADA - to control industrial processes in an efficient manner. These systems can monitor, collect and process real-time data by directly interacting with devices such as sensors, pumps, motors etc. through human-machine interface (HMI) software. These machine-to-machine communication systems give new direction to the human-machine collaboration potential thus changing the way we see workforce management. Industry 4.0 will favor those who can build software, hardware, and firmware - those who can adapt and maintain new equipment and those who can design automation and robotics. Within Industry 4.0, augmented reality and virtual reality are other cutting edge production ready technologies that are making the idea of a smart factory a reality. The recent relaunch of Google Glass especially designed for the factory floor is worth a mention here. The Wi-Fi-enabled glasses allow factory workers, mechanics, and other technicians to view instructional videos, manuals, training videos etc., all in their line of sight. This helps in maintaining higher standards of work while ensuring safety with agility. In Conclusion Manufacturing industries are gearing themselves to harness AI along with IoT, AR/VR to create an agile manufacturing environment and to make smarter and real-time decisions. AI is helping realize the full potential of Industrial Internet of Things (IIoT) by applying machine learning, deep learning and other evolutionary algorithms to the sensor data. Human-machine collaboration is transforming the scenario at the fulfillment centers creating a win-win situation for both humans and robots. Robots employed at the fulfillment centers having motion sensors move on to the field of QR codes with precision and agility withouting running into each other creating a fascinating view. Imagine a real-life JARVIS from the movie Iron Man managing entire supply chains or factory spaces. The day is not far away when we can see a JARVIS like advanced virtual assistant uses sensors to collect real-time data, AI to process data,and blockchain to securely transmit the information while using AR to interact with us visually. It could take care of system and mechanical failures remotely while ceasing control of the factory for efficient energy management. Manufacturers could go save the world or unveil new products, Iron Man style!

Artificial intelligence might seem intimidating, but it isn’t actually as complex as you might think. Many of the tools that have been developed over the last decade or so have all helped to make artificial intelligence and machine learning more accessible to engineers with varying degrees of experience and knowledge. Today, we’ve got to a stage where it’s now accessible even to people who have barely written a line of code in their life! Pretty exciting, right? But if you’re completely new to the field, it can be challenging to know how to get started - fortunately, we’re about to help you overcome that first hurdle. If you are an AI denier, then be sure to first read ‘why learn Machine Learning as a non-techie’ before you move forward. A strong purpose and belief is the first step to learning anything new. Alright, now here’s how you can get started with artificial intelligence and machine learning techniques quickly. 0. Use a free MLaaS or a no code interactive machine learning tool to experience first hand what is possible with learning machine learning: Some popular examples of no code machine learning as a service option are Microsoft Azure, BigML, Orange, and Amazon ML. Read Q2 under the FAQ section below to know more on this topic. 1. Learn Linear Algebra: Linear Algebra is the elementary unit for ML. It helps you effectively comprehend the theory behind the Machine learning algorithms and how they work. It also improves your math skills such as statistics, programming skills, which are all other skills that helps in ML. Learning Resources: Linear Algebra for Beginners: Open Doors to Great Careers Linear algebra Basics 2. Learn just enough Python or any programming: Now, you can get started with any language of your interest, but we suggest Python as  it’s great for people who are new to programming. It’s easy to learn due to its simple syntax. You’ll be able to quickly implement the ML algorithms. Also,  It has a rich development ecosystem that offers a ton of libraries and frameworks in Machine Learning such as Scikit Learn, Lasagne, Numpy, Scipy, Theano, Tensorflow, etc. Learning Resources: Python Machine Learning Learn Python in 7 Days Python for Beginners 2017 [Video] Learn Python with codecademy Python editor for beginner programmers 3. Learn basic Probability Theory and statistics: A lot of fundamental Statistical and Probability Theories form the basis for ML. You’ve probably already learned Probability and statistics in school, it easy to dive into advanced statistics for ML. Machine learning in its currently widely used form is a way to predict odds and see patterns. Knowing statistics and probability is important as it will help you with better understanding of why any machine learning algorithm works. For example, your grounding in this area, will help to ask the right questions, choose the right set of algorithms and know what to expect as answers from your ML model on questions such as: What are the odds of this person also liking this movie given their current movie watching choices ( Collaborative filtering and content-based filtering) How similar is this user to that group of users who brought a bunch of stuff on my site (clustering, collaborative filtering, and classification) Could this person be at risk of cancer given a certain set of traits and health indicator observations (logistic regression) Should you buy that stock (decision tree) Also, check out our interview with James D. Miller to know more about why learning stats is important in this field. Learning resources: Statistics for Data Science [Video] 4. Learn machine learning algorithms: Do not get intimidated!  You don’t have to be an expert to learn ML algorithms. Knowing basic ML algorithms that are majorly used in the real world applications like linear regression, naive Bayes, and decision trees, are enough to get you started. Learn what they do and how they are used in Machine Learning. 5. Learn numpy sci-kit learn,Keras or any other popular machine learning framework: It can be confusing initially to decide which framework to learn. Each one has its own advantages and disadvantages. Numpy is a linear algebra library which is useful for performing mathematical and logical operations. You can easily work with large multidimensional arrays using Numpy. Sci-kit learn helps with quick implementation of popular algorithms on datasets as just one line of code makes different algorithms available for you. Keras is minimalistic and straightforward with high-levels of extensibility, so it is easier to approach. Learning Resources:  Hands-on Machine Learning with TensorFlow [Video]  Hands-on Scikit-learn for Machine Learning [Video] If you have reached till here, it is time to put your learning into practice. Go ahead and create a simple linear regression model using some publicly available dataset in your area of interest. Kaggle, ourworldindata.org, UC Irvine Machine Learning repository, elitedatascience, all have a rich set of clean datasets in varied fields. Now, it is necessary to commit and put in daily efforts to practise these skills. Quora, Reddit, Medium, and stackoverflow will be your best friends when it comes to solving doubts regarding any of these skills. Data Helpers is another great resource that provides newcomers with help on queries regarding entering the ML field and related topics. Additionally, once you start getting hang of these skills, identify your strengths and interests, to realign your career goals. Research on the kind of work you want to put your newly gained Machine Learning skill to use. It needn’t be professional or serious, it just needs to be something that you deeply care about or are passionate about. This will pull you through your learning milestones, should you feel low at some point. Also, don’t forget to collaborate with other people and learn from them. You can work with web developers, software programmers, data analysts, data administrators, game developers etc. Finally, keep yourself updated with all the latest happenings in the ML world. Follow top experts and influencers on social media, top blogs on Machine Learning, and conferences. Once you are done checking off these steps off your list, you’ll be ready to start off with your ML project.                                                  Now, we’ll be looking at the most frequently asked questions by beginners in the field of Machine learning. Frequently asked questions by Beginners in ML As a beginner, it’s natural to have a lot of questions regarding ML. We’ll be addressing the top three frequently asked questions by beginners or non-programmers when it comes to Machine learning: Q.1 I am looking to make a career in Machine learning but I have no prior programming experience. Do I need to know programming for Machine learning? In a nutshell, Yes. If you want a career in Machine learning then having some form of programming knowledge really helps. As mentioned earlier in this article, learning a programming language can really help you with implementing ML algorithms. It also lets you know the internal mechanism behind Machine learning. So, having programming as a prior skill is great. Again, as mentioned before, you can get started with Python which is the easiest and the most common languages for ML. However, programming is just a part of Machine learning. For instance, “machine learning engineers” typically write more code than develop models, while “research scientists” work more on modelling and analyzing different models. Now, ML is based on the principles of statistical inference and for talking statistically to the computer, we need a language, there comes Coding. So, even though the nature of your job in ML might not require you to code as much, there’s still some amount of coding required. Read Also: Why is Python so good for AI and ML? 5 Python Experts Explain Top languages for Artificial Intelligence development Q.2 Are there any tools that can help me with Machine learning without touching a single line of code? Yes. With the rise of MLaaS (Machine learning as a service), there are certain tools that help you get started with machine learning right-away. These are especially useful for business applications of ML, such as predictive modelling and clustering. Read Also: How MLaaS is transforming cloud Some of the most popular ones are: BigML:  This cloud based web-service lets you upload your data, prepare it and run algorithms on it. It’s great for people with not so extensive data science backgrounds. It offers a clean and easy to use interfaces for configuring algorithms (decision trees) and reviewing the results. Being focused “only” on Machine Learning, it comes with a wide set of features, all well integrated within a usable Web UI. Other than that, it also offers an API so that if you like it you can build an application around it. Microsoft Azure: The Microsoft Azure ML studio is a “GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure”. So, via an integrated development environment called ML Studio, people without data science background or non-programmers can also build data models with the help of drag-and-drop gestures and simple data flow diagrams. This also saves a lot of time through ML Studio's library of sample experiments. Learning resources: Microsoft Azure Machine Learning Machine Learning In The Cloud With Azure ML[Video] Orange: This is an open source machine learning and data visualization studio for novice and experts alike. It provides a toolbox comprising of text mining (topic modelling) and image recognition. It also offers a design tool for visual programming which allows you to connect together data preparation, algorithms, and result evaluation, thereby, creating machine learning “programs”. Apart from that, it provides over 100 widgets for the environment and there’s also a Python API and library available which you can integrate into your application. Amazon ML: Amazon ML is a part of Amazon Web Services ( AWS ) that combines powerful machine learning algorithms with interactive visual tools to guide you towards easily creating, evaluating, and deploying machine learning models. So, whether you are a data scientist or a newbie, it offers ML services and tools tailored to meet your needs and level of expertise. Building ML models using Amazon ML consists of three operations: data analysis, model training, and evaluation. Learning Resources: Effective Amazon Machine Learning Q.3  Do I need to know advanced mathematics ( college graduate level ) to learn Machine learning? It depends. As mentioned earlier, understanding of the following mathematical topics: Probability, Statistics and Linear Algebra can really make your machine learning journey easier and also help simplify your code. These help you understand the “why” behind the working of the machine learning algorithms, which is quite fundamental to understanding ML. However, not knowing advanced mathematics is not an excuse to not learning Machine Learning. There a lot of libraries which makes the task of applying an ML algorithm to solve a task easier. One such example is the widely used Python’s scikit-learn library. With scikit-learn, you just need one line of code and you’ll have the most common algorithms there for you, ready to be used. But, if you want to go deeper into machine learning then knowing advanced mathematics is a prerequisite as it will help you understand the algorithms, the formulas, how the learning is done and many other Machine Learning concepts. Also, with so many courses and tutorials online, you can always learn advanced mathematics on the side while exploring Machine learning. So, we looked at the three most asked questions by beginners in the field of Machine Learning. In the past, machine learning has provided us with self-driving cars, effective web search, speech recognition, etc. Machine learning is extremely pervasive, in fact, many researchers believe that ML is the best way to make progress towards human-level AI. Learning ML is not an easy task but its not next to impossible either. In the end, it all depends on the amount of dedication and efforts that you’re willing to put in to get a grasp of it. We just touched the tip of the iceberg in this article, there’s a lot more to know in Machine Learning which you will get a hang of as you get your feet dirty in it. That being said, all the best for the road ahead! Facebook launches a 6-part ML video series 7 of the best ML conferences for the rest of 2018 Google introduces Machine Learning courses for AI beginners

NoSQL has seen a sharp rise in both adoption and migration from the tried and tested relational database management systems. The open source world has accepted it with open arms, which wasn’t the case with large enterprise organisations that still prefer and require ACID-compliant databases. However, as there are so many NoSQL databases, it’s difficult to keep track of them all! Let’s explore the most popular and different ones available to us: 1 - Apache Cassandra Apache Cassandra is an open source NoSQL database. Cassandra is a distributed database management system that is massively scalable. An advantage of using Cassandra is its ability to manage large amounts of structured, semi-structured, and unstructured data. What makes Cassandra more appealing as a database system is its ability to ‘Scale Horizontally’, and it’s one of the few database systems that can process data in real time and generate high performance and maintain high availability. The mixture of a column-oriented database with a key-value store means not all rows require a column, but the columns are grouped, which is what makes them look like tables. Cassandra is perfect for ‘mission critical’ big data projects, as Cassandra offers ‘no single point of failure’ if a data node goes down. 2 - MongoDB MongoDBis an open source schemaless NoSQL database system; its unique appeal is that it’s a ‘Document database’ as opposed to a relational database. This basically means it’s a ‘data dumpster’ that’s free for all. The added benefit in using MongoDB is that it provides high performance, high availability, and easy scalability (auto-sharding) for large sets of unstructured data in JSON-like files. MongoDB is the ultimate opposite to the popular MySQL. MySQL data has to be read in rows and columns, which has its own set of benefits with smaller sets of data. 3 - Neo4j Neo4j is an open source NoSQL ‘graph-based database’. Neo4j is the frontrunner of the graph-based model. As a graph database, it manages and queries highly connected data reliably and efficiently. It allows developers to store data more naturally from domains such as social networks and recommendation engines. The data collected from sites and applications are initially stored in nodes that are then represented as graphs. 4 - Hadoop Hadoop is easy to look over as a NoSQL database due to its ecosystem of tools for big data. It is a framework for distributed data storage and processing, designed to help with huge amounts of data while limiting financial and processing-time overheads. Hadoop includes a database known as HBase, which runs on top of HDFS and is a distributed, column-oriented data store. HBase is also better known as a distributed storage system for Hadoop nodes, which are then used to run analytics with the use of MapReduce V2, also known as Yarn. 5 - OrientDB OrientDB has been included as a wildcard! It’s a very interesting database and one that has everything going for it, but has always been in the shadows of Neo4j. Orient is an open source NoSQL hybrid graph-document database that was developed to combine the flexibility of a document database with the complexity of a graph database (Mongo and Neo4j all in one!). With the growth of complex and unstructured data (such as social media), relational databases were not able to handle the demands of storing and querying this type of data. Document databases were developed as one solution and visualizing them through nodes was another solution. Orient has combined both into one, which sounds awesome in theory but might be very different in practice! Whether the Hybrid approach works and is adopted remains to be seen.

At the wee hours of the night, on January 4 this year, over 9.8 million people experienced a magnitude 4.4 earthquake that rumbled across the San Francisco Bay Area. This was followed by a magnitude 7.6 earthquake in the Caribbean sea on January 9, following which a tsunami advisory was in effect for Puerto Rico and the U.S. and British Virgin Islands. In the past 6 months, the United States alone has witnessed four back-to-back storms from one brutal hurricane season, and a massive wildfire with almost 2 million acres of land ablaze. Natural Disasters across the globe are increasingly becoming more damaging and frequent. Since 1970, the number of disasters worldwide has more than quadrupled to around 400 a year. These series of natural disasters have strained the emergency services and disaster relief operations beyond capacity. We now need to look for newer ways to assist the affected people and automate the recovery process. Artificial intelligence and machine learning have advanced to the state where they are highly proficient in making predictions, and in identification and classification tasks. These use cases of AI can also be applied to prevent disasters or respond quickly, in case of an emergency.   Here are 5 ways how AI can lend a helping hand during emergency situations. 1. Machine Learning for targeted disaster relief management In case of any disaster, the first step is to formulate a critical response team to help those in distress. Before the team goes into action, it is important to analyze and assess the extent of damage and to ensure that the right aid goes first to those who need it the most. AI techniques such as image recognition and classification can be quite helpful in assessing the damage as they can analyze and observe images from the satellites. They can immediately and efficiently filter these images, which would have required months to be sorted manually. AI can identify objects and features such as damaged buildings, flooding, blocked roads from these images. They can also identify temporary settlements which may indicate that people are homeless, and so the first care could be directed towards them.   Artificial intelligence and machine learning tools can also aggregate and crunch data from multiple resources such as crowd-sourced mapping materials or Google maps. Machine learning approaches then combine all this data together, remove unreliable data, and identify informative sources to generate heat maps. These heat maps can identify areas in need of urgent assistance and direct relief efforts to those areas. Heat maps are also helpful for government and other humanitarian agencies in deciding where to conduct aerial assessments. DigitalGlobe provides space imagery and geospatial content. Their Open Data Program is a special program for disaster response. The software learns how to recognize buildings on satellite photos by learning from the crowd. DigitalGlobe releases pre- and post-event imagery for select natural disasters each year, and their crowdsourcing platform, Tomnod, will prioritize micro-tasking to accelerate damage assessments. Following the Nepal Earthquakes in 2015, Rescue Global and academicians from the Orchid Project used machine learning to carry out rescue activities. They took pre and post-disaster imagery and utilized crowd-sourced data analysis and machine learning to identify locations affected by the quakes that had not yet been assessed or received aid. This information was then shared with relief workforces to facilitate their activities. 2. Next Generation 911 911 is the first source of contact during any emergency situation. 911 dispatch centers are already overloaded with calls on a regular day. In case of a disaster or calamity, the number gets quadrupled, or even more. This calls for augmenting traditional 911 emergency centers with newer technologies for better management. Traditional 911 centers rely on voice-based calls alone. Next-gen dispatch services are upgrading their emergency dispatch technology with machine learning to receive more types of data. So now they can ingest the data from not just calls but also from text, video, audio, and pictures, to analyze them to make quick assessments. The insights gained from all this information can be passed on to the emergency response teams out in the field to efficiently carry out critical tasks. The Association of Public-Safety Communications (APCO) have employed IBM’s Watson to listen to 911 calls. This initiative is to help emergency call centers improve operations and public safety by using Watson’s speech-to-text and analytics programs. Using Watson's speech-to-text function, the context of each call is fed into the AI's analytics program allowing improvements in how call centers respond to emergencies. It also helps in reducing call times, provide accurate information, and help accelerate time-sensitive emergency services. 3. Sentiment analysis on social media data for disaster management and recovery Social media channels are a major source of news in present times. Some of the most actionable information, during a disaster, comes from social media users. Real-time images and comments from Facebook, Twitter, Instagram, and YouTube can be analyzed and validated by AI to filter real information from fake ones. These vital stats can help on-the-ground aid workers to reach the point of crisis sooner and direct their efforts to the needy. This data can also help rescue workers in reducing the time needed to find victims. In addition, AI and predictive analytics software can analyze digital content from Twitter, Facebook, and Youtube to provide early warnings, ground-level location data, and real-time report verification. In fact, AI could also be used to view the unstructured data and background of pictures and videos posted to social channels and compare them to find missing people. AI-powered chatbots can help residents affected by a calamity. The chatbot can interact with the victim, or other citizens in the vicinity via popular social media channels and ask them to upload information such as location, a photo, and some description. The AI can then validate and check this information from other sources and pass on the relevant details to the disaster relief committee. This type of information can assist them with assessing damage in real time and help prioritize response efforts. AI for Digital Response (AIDR) is a free and open platform which uses machine intelligence to automatically filter and classify social media messages related to emergencies, disasters, and humanitarian crises. For this, it uses a Collector and a Tagger. The Collector helps in collecting and filtering tweets using keywords and hashtags such as "cyclone" and "#Irma," for example. The Collector works as a word-filter. The Tagger is a topic-filter which classifies tweets by topics of interest, such as "Infrastructure Damage," and "Donations," for example. The Tagger automatically applies the classifier to incoming tweets collected in real-time using the Collector. 4. AI answers distress and help-calls Emergency relief services are flooded with distress and help calls in the event of any emergency situation. Managing such a huge amount of calls is time-consuming and expensive when done manually. The chances of a critical information being lost or unobserved is also a possibility. In such cases, AI can work as a 24/7 dispatcher. AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location. They can not only interact with callers naturally and process those calls, but can also instantly transcribe and translate languages. AI systems can analyze the tone of voice for urgency, filtering redundant or less urgent calls and prioritizing them based on the emergency. Blueworx is a powerful IVR platform which uses AI to replace call center officials. Using AI technology is especially useful when unexpected events such as natural disasters drive up call volume. Their AI engine is well suited to respond to emergency calls as unlike a call center agent, it can know who a customer is even before they call. It also provides intelligent call routing, proactive outbound notifications, unified messaging, and Interactive Voice Response. 5. Predictive analytics for proactive disaster management Machine learning and other data science approaches are not limited to assisting the on-ground relief teams or assisting only after the actual emergency. Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities. A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events. For instance, clustering algorithms can classify disaster data on the basis of severity. They can identify and segregate climatic patterns which may cause local storms with the cloud conditions which may lead to a widespread cyclone. Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people. In addition, predictive analytics techniques can also provide insight for understanding the economic and human impact of natural calamities. Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters. Recent advances in cloud technologies and numerous open source tools have enabled predictive analytics with almost no initial infrastructure investment. So agencies with limited resources can also build systems based on data science and develop more sophisticated models to analyze disasters.   Optima Predict, a suite of software by Intermedix collects and reads information about disasters such as viral outbreaks or criminal activity in real time. The software spots geographical clusters of reported incidents before humans notice the trend and then alerts key officials about it. The data can also be synced with FirstWatch, which is an online dashboard for an EMS (Emergency Medical Services) personnel. Thanks to the multiple benefits of AI, government agencies and NGOs can start utilizing machine learning to deal with disasters. As AI and allied fields like robotics further develop and expand, we may see a fleet of drone services, equipped with sophisticated machine learning. These advanced drones could expedite access to real-time information at disaster sites using video capturing capabilities and also deliver lightweight physical goods to hard to reach areas. As with every progressing technology, AI will also build on its existing capabilities. It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

In recent years, we have experienced an exponential growth of data. As the amount of data grows, the need for developers with knowledge of data analytics and especially data visualization spikes. Data visualizations help in getting a clear and concise view of the data, making it more tangible for (non-technical) audiences. MATLAB and R are the two available languages that have been traditionally used for data science and data visualization. However, Python is the most requested and used language in the industry. Its ease of use and the speed at which you can manipulate and visualize data combined with the number of available libraries makes Python the best choice. So Data visualization seems easy, doesn’t it? However, there are a lot of myths surrounding it. Let us have a look at some of them. Myth 1: Data visualizations are just for data scientists Today's data visualization libraries are very convenient, so any person can create meaningful visualizations in just a few minutes. Myth 2: Data visualization technologies are difficult to learn Of course, building and designing sophisticated data visualizations will take some work and learning but with very little knowledge of the libraries and what they are capable of, you can create simple visualizations that will help you get valuable insights into your data. Python is a comparably easy language. The “pythonic” approach is also used when building visualization libraries for Python which makes them easy to understand and use. Myth 3: Data visualization isn’t needed for data insights Imagine having a table of data with 20 columns and several thousand rows. What do you think will give you better insights into this data? Just looking at the table and trying to make sense of all the columns and values in them, or creating some simple plots that visualize the content of this table? Of course, you could force yourself to get insights without visualizations, but the key is to work smarter, not harder. Myth 4: Data visualization takes a lot of time If you have a basic understanding of your data, you can create some basic visualizations in no time. There are a lot of libraries, which will be covered in this course, that allow you to simply import some data and build visualizations in a few lines of code. The more difficult part is creating visualizations which are descriptive and display the concepts you wanted to show but don’t worry, this will be discussed in the course in detail as well. Amidst all the myths, Data visualization in combination with Python is an essential skill when working with data. When properly utilized, it is a powerful combination that not only enables you to get better insights into your data but also gives you the tool to communicate results better. Head over to our course titled ‘Data Visualization with Python’, to use Python with NumPy, Pandas, Matplotlib, and Seaborn to create impactful data visualizations with the real world, public data. About Tim and Mario Tim Großmann is a CS student with interest in diverse topics ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of big data engineering. He’s highly involved in different Open Source projects and actively speaks at meetups and conferences about his projects and experiences. Mario Döbler is a graduate student with a focus in deep learning and AI. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning. Currently, he dedicates himself to apply deep learning to medical data to make health care accessible to everyone. 4 tips for learning Data Visualization with Python Setting up Apache Druid in Hadoop for Data visualizations [Tutorial] 8 ways to improve your data visualizations  

Now that you’ve opened this article, I’ll assume you’re a web developer who is all excited with the prospect of building a machine learning project. You may be here for one of these reasons. Either you have been in a circle of people who find web development is dying? (Is it really dying or just unwell?). Or maybe you are stagnating in your current trajectory. And so, you want to learn something different, something trending, something like Artificial Intelligence. Or you/your employer/your client is aware of the capabilities of machine learning and want to include it in some part of your web app to make it more powerful. Or like the majority of the folks, you just want to see first hand if all the fuss about artificial intelligence is really worth all the effort to switch gears, by building a side toy ML project. Either way, there are different approaches to fulfill these needs. Learning Machine Learning for the Web with Javascript Learning machine learning coming from a web development background comes with its own constraints. You might worry about having to learn entirely different concepts from scratch - from different algorithms to programming languages like Python to mathematical concepts like linear algebra, calculus, and statistics. However, chances are you can skip learning a new language. You probably know some Javascript in some form or the other thanks to your web development experience. As such, you can learn Machine Learning in JavaScript (You don’t have to learn another programming language from scratch) and take it right to your browsers with WebGL. There are some advantages to using JavaScript for ML. Its popularity is one; while ML in JavaScript is not as popular as Python’s ML ecosystem, at the moment, the language itself is. As demand for ML applications rises, and as hardware becomes faster and cheaper, it's only natural for machine learning to become more prevalent in the JavaScript world. The JavaScript ecosystem offers a rich set of libraries suited for most Machine Learning tasks. Math: math.js Data Analysis: d3.js Server: node.js (express, koa, hapi) Performance: Tensorflow.js (e.g. GPU accelerated via WebGL API in the browser), Keras.js etc. Read also: 5 JavaScript machine learning libraries you need to know BRIIM is a good collection of materials to get you started as web developer or JavaScript enthusiast in machine learning. In case you’re interested in learning Python instead of Javascript, here are the set of libraries you should pick. Math: numpy Data Analysis: Pandas Data Mining: PySpark Server: Flask, Django Performance: TensorFlow (because it is written with a Python API over a C/C++ engine) or Keras (sits on top of TensorFlow). Using Machine Learning as a service If you don’t want to spend your time learning frameworks, tools, and languages suited for machine learning, you can adopt Machine Learning as a service or MLaaS. These services provide machine learning tools as part of cloud computing services. So basically, you can benefit from machine learning without the allied cost, time and risk of establishing an in-house internal machine learning team. All you need is sufficient knowledge of incorporating APIs. All Machine Learning tasks including data pre-processing, model training, model evaluation, and predictions can be completed through MLaaS. Read also: How machine learning as a service is transforming cloud A large number of companies provide Machine Learning as a service. Most prominent ones include: Amazon Machine Learning Amazon ML makes it easy for web developers to build smart applications using simple APIs. This includes applications for fraud detection, demand forecasting, targeted marketing, and click prediction. They provide a Developer Guide, which provides a conceptual overview of Amazon ML and includes detailed instructions for using the service. They also have a API reference, which describes all the API operations and provides sample requests and responses for supported web service protocols. Azure ML web app templates The web app templates available in the Azure Marketplace can build a custom web app that knows your web service's input data and expected results. All you need to do is give the web app access to your web service and data, and the template does the rest. There are two available templates: Azure ML Request-Response Service Web App Template Azure ML Batch Execution Service Web App Template Each template creates a sample ASP.NET application by using the API URI and key for your web service. The template then deploys the application as a website to Azure. No coding is required to use these templates. You just supply the API key and URI, and the template builds the application for you. Google Cloud based APIs Google also provides machine learning services, with pre-trained models and a service to generate your own tailored models. Google’s Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Cloud AutoML is used by Disney on their website shopDisney to enhance guest experience through more relevant search results, expedited discovery, and product recommendations. Building Conversational Interfaces As a web developer, another thing you might be looking into, is developing conversational interfaces or chatbots to enhance your web apps. Amazon, Google, and Microsoft provide Machine learning powered tools to help developers with building their own chatbots. Amazon Lex You can embed chatbots in your web apps with the Amazon Lex featuring ASR (Automatic Speech Recognition) and NLP (Natural Language Processing) capabilities. The API can recognize written and spoken text and the Lex interface allows you to hook the recognized inputs to various back-end solutions. Lex currently supports deploying chatbots for Facebook Messenger, Slack, and Twilio. Google Dialogflow Google’s Dialogflow can build voice and text-based conversational interfaces, such as voice apps and chatbots, powered by AI. Dialogflow incorporates Google's machine learning expertise and products such as Google Cloud Speech-to-Text. The API can be tweaked and customized for needed intents using Java, Node.js, and Python. It is also available as an enterprise edition. Microsoft Azure Cognitive Services Microsoft Cognitive Services simplify a variety of AI-based tasks, giving you a quick way to add intelligence technologies to your bots with just a few lines of code. It provides tools and APIs for aiding the development of conversational interfaces. These include: Translator Speech API Bing Speech API to convert text into speech and speech into text Speaker Recognition API for voice verification tasks Custom Speech Service to apply Azure NLP capacities using own data and models Language Understanding Intelligent Service (LUIS) is an API that analyzes intentions in text to be recognized as commands Text Analysis API for sentiment analysis and defining topics Bing Spell Check Translator Text API Web Language Model API that estimates probabilities of words combinations and supports word autocompletion Linguistic Analysis API used for sentence separation, tagging the parts of speech, and dividing texts into labeled phrases Read also: Top 4 chatbot development frameworks for developers These tools should be enough to get your feet off the ground quickly and move into the specific area of machine learning. Ultimately your choice of tool relies on the kind of application you want to build, your level of expertise, and how much time and effort you’re willing to put to learn. Obviously, depending on your area of choice, you would have to do more research and develop yourself in those areas. How should web developers learn machine learning? 5 examples of Artificial Intelligence in Web apps The most valuable skills for web developers to learn in 2018