Tech News

Yesterday (11th of September), the OpenSSL team announced the stable release of OpenSSL 1.1.1. With work being in progress for two years along with more than 500 commits, the release comes with many notable upgrades. The most important new feature in OpenSSL 1.1.1 is TLSv1.3, which was published last month as RFC 8446 by the Internet Engineering Task Force. Applications working with OpenSSL1.1.0 can gain the benefits of TLSv1.3 by upgrading to the new OpenSSL version. TLS 1.3 features Reduction in the number of round trips required between the client and server to improve connection times 0-RTT or “early data” feature - which is the ability  for clients to start sending encrypted data to the server straight away without any round trips with the server Removal of various obsolete and insecure cryptographic algorithms and encryption of more of the connection handshake has improved security For more details on TLS 1.3 read: Introducing TLS 1.3, the first major overhaul of the TLS protocol with improved security and speed Updates in OpenSSL 1.1.1 A complete rewrite of the OpenSSL random number generator The OpenSSL random number generator has been completely rewritten to introduce capabilities such as: The default RAND method now utilizes an AES-CTR DRBG according to NIST standard SP 800-90Ar1. Support for multiple DRBG instances with seed chaining. Public and private DRBG instance. DRBG instances are made fork-safe. Keep all global DRBG instances on the secure heap if it is enabled. The public and private DRBG instance are per thread for lock free operation Support for various new cryptographic algorithms The different algorithms that are now supported by OpenSSL 1.1.1 include: SHA3, SHA512/224 and SHA512/256 EdDSA (including Ed25519 and Ed448) X448 (adding to the existing X25519 support in 1.1.0) Multi-prime RSA SM2,SM3,SM4 SipHash ARIA (including TLS support) Side-Channel attack security improvements This upgrade also introduces significant Side-Channel attack security improvements, maximum fragment length TLS extension support and a new STORE module, implementing a uniform and URI based reader of stores containing keys, certificates, CRLs and numerous other objects. OpenSSL 1.0.2 will receive full support only until the end of 2018 and security fixes only till the end of 2019. The team advises users of OpenSSL 1.0.2 to upgrade to OpenSSL 1.1.1 at the earliest. Head over to the OpenSSL blog for further details on the news. GNU nano 3.0 released with faster file reads, new shortcuts and usability improvements Haiku, the open source BeOS clone, to release in beta after 17 years of development Ripgrep 0.10.0 released with PCRE2 and multi-line search support  

In their commitment to become the go-to platform for Artificial Intelligence, Unity has released a new version of their ML-Agents Toolkit.  ML-Agents toolkit v0.5 comes with more flexible action specification, a Gym interface for researchers to more easily integrate ML-Agents environments into their training workflows, and a new suite of learning environments replicating some of the Continuous Control benchmarks used in Deep Reinforcement Learning. They have also released a research paper on ML-Agents which the Unity platform has titled “Unity: A General Platform for Intelligent Agent.” Changes to the ML-Agents toolkit v0.5 A lot of changes have been made pertaining to ML-Agents toolkit v0.5. Highlighted changes to repository structure The python folder has been renamed ml-agents. It now contains a python package called mlagents. The unity-environment folder, containing the Unity project, has been renamed UnitySDK. The protobuf definitions used for communication have been added to a new protobuf-definitions folder. Example curricula and the trainer configuration file have been moved to a new config sub-directory. New features New package gym-unity which provides gym interface to wrap UnityEnvironment. The ML-Agents toolkit v0.5 can now run multiple concurrent training sessions with the --num-runs=<n> command line option. Added Meta-Curriculum which supports curriculum learning in multi-brain environments. Action Masking for Discrete Control which makes it possible to mask invalid actions each step to limit the actions an agent can take. Fixes & Performance Improvements Replaced some activation functions to swish. Visual Observations use PNG instead of JPEG to avoid compression losses. Improved python unit tests. Multiple training sessions are available on single GPU. Curriculum lessons are now tracked correctly. Developers can now visualize value estimates when using models trained with PPO from Unity with GetValueEstimate(). It is now possible to specify which camera the Monitor displays to. Console summaries will now be displayed even when running inference mode from python. Minimum supported Unity version is now 2017.4. You can read all about the new version of ML-Agents Toolkit on the Unity Blog. Unity releases ML-Agents v0.3: Imitation Learning, Memory-Enhanced Agents and more. Unity Machine Learning Agents: Transforming Games with Artificial Intelligence. Unite Berlin 2018 Keynote: Unity partners with Google, launches Ml-Agents ToolKit 0.4, Project MARS and more.

Last Friday, a group of Spanish researchers have published a research paper titled ‘Towards a distributed and real-time framework for robots: evaluation of ROS 2.0 communications for real-time robotic applications’. This paper talks about an experimental setup exploring the suitability of ROS 2.0 for real-time robotic applications. In this paper, ROS 2.0 communications is evaluated in a robotic inter-component communication hardware case running on top of Linux. The researchers have benchmarked and studied the worst case latencies and characterized ROS 2.0 communications for real-time applications. The results indicate that a proper real-time configuration of the ROS 2.0 framework reduces jitter making soft real-time communications possible but there were also some limitations that prevented hard real-time communications. What is ROS? ROS is a popular framework that provides services for the development of robotic applications. It has utilities like a communication infrastructure, drivers for a variety of software and hardware components, libraries for diagnostics, navigation, manipulation, and other things. ROS simplifies the process of creating complex and robust robot behavior across many robotic platforms. ROS 2.0 is the new version which extends the concepts of the first version. Data Distribution Service (DDS) middleware is used in ROS 2.0 due to its characteristics and benefits as compared to other solutions. Need for real-time applications in robotic systems In all robotic systems, tasks need to be time responsive. While moving at a certain speed, robots must be able to detect an obstacle and stop to avoid collision. These robot systems often have timing requirements to execute tasks or exchange data. By not meeting the timing requirements, the system behavior will degrade or the system will fail. With ROS being the standard software infrastructure for robotic applications development, demands rose in the ROS community to include real-time capabilities. Hence, ROS 2.0 was created for delivering real-time performance. But to deliver a complete, distributed and real-time solution for robots, ROS 2.0 needs to be surrounded with appropriate elements. These elements are described in the papers Time-sensitive networking for robotics and Real-time Linux communications: an evaluation of the Linux communication stack for real-time robotic applications. ROS 2 uses DDS as its communication middleware. DDS contains Quality of Service (QoS) parameters which can be configured and tuned for real-time applications. The results of the experiment In the research paper, a setup was made to measure the real-time performance of ROS 2.0 communications over Ethernet in a PREEMPT-RT patched kernel. The end-to-end latencies between two ROS 2.0 nodes in different machines was measured. A Linux PC and an embedded device which could represent a robot controller (RC) and a robot component (C) were used for the setup. An overview of the setup can be seen as follows: Source: LinkedIn Some of the results are as follows: Source: LinkedIn The image describes the Impact of RT settings under different system load. They are a) System without additional load without RT settings. b) is system under load without RT settings. c) is system without additional load and RT settings. d) is system under load and RT settings. The results from the experiment showed that a proper real-time configuration of the ROS 2.0 framework and DDS threads greatly reduces the jitter andworst-casee latencies. This mean a smooth and fast communication. However, there were also some limitations when there is noncritical traffic in the Linux Network Stack is in picture. By configuring the network interrupt threads and using Linux traffic control QoS methods, some of the problems could be avoided. The researchers conclude that it is possible to achieve soft real-time communications with mixed-critical traffic using the Linux Network stack. However hard real-time is not possible due to the aforementioned limitations. For a more detailed understanding of the experiments and results, you can read the research paper. Shadow Robot joins Avatar X program to bring real-world avatars into space 6 powerful microbots developed by researchers around the world Boston Dynamics’ ‘Android of robots’ vision starts with launching 1000 robot dogs in 2019

Yesterday, Diane Greene, the CEO of Google Cloud, announced in a blog post that Chief Artificial Intelligence Scientist Dr. Fei-Fei Li will be   replaced by Dr. Andrew Moore, dean of the school of computer science at Carnegie Mellon University at the end of this year. The blog further mentions that, as originally planned, Dr. Fei-Fei Li will be returning to her professorship at Stanford and in the meanwhile, she will transition to being an AI/ML Advisor for Google Cloud. The timing of the transition following the controversies surrounding Google and Pentagon Project Maven is not lost on many. Flashback on ‘Project Maven’ protest and its outcry On March 2017 it was revealed that Google Cloud, headed by Greene, signed a secret $9m contract with the United States Department of Defense called as 'Project Maven'. The project aimed to develop an AI system that could help recognize people and objects captured in military drone footage. The contract was crucial to the Google Cloud Platform gaining a key US government FedRAMP authorization. This project was expected to assist Google in finding future government work worth potentially billions of dollars. Planned for use for non-offensive purposes only,  project Maven also had the potential to expand to a $250m deal. Google provided the Department of Defense with its TensorFlow APIs to assist in object recognition, which the Pentagon believed would eventually turn its stores of video into "actionable intelligence". In September 2017, in a leaked email reviewed by The New York Times, Scott Frohman, Google’s head of defense and intelligence sales asked Dr. Li ,Google Cloud AI’s leader and Chief Scientist, for directions on the “burning question” of how to publicize this news to the masses. To which she replied back- “Avoid at ALL COSTS any mention or implication of AI. Weaponized AI is probably one of the most sensitized topics of AI — if not THE most. This is red meat to the media to find all ways to damage Google.” As predicted by Dr. Li, the project was met with outrage by more than 3000 Google employees who believed that Google shouldn't be involved in any military work and that algorithms have no place in identifying potential targets. This caused a rift in Google’s workforce, fueled heated staff meetings and internal exchanges, and prompted some employees to resign. Many employees were "deeply concerned" that the data collected by Google integrated with military surveillance data for targeted killing. Fast forward to June 2018 where Google stated that it would not renew its contract (to expire in 2019) with the Pentagon. Dr. Li’s timeline at Google During her two year tenure, Dr. Li oversaw some remarkable work in accelerating the adoption of AI and ML by developers and Google Cloud customers. Considered as one of the most talented machine learning researchers in the world, Dr. Li has published more than 150 scientific articles in top-tier journals and conferences including Nature, Journal of Neuroscience, New England Journal of Medicine and many more. Dr. Li is the inventor of ImageNet and the ImageNet Challenge, a large-scale effort contributing to the latest developments in computer vision and deep learning in AI. Dr. Li has been a keynote or invited speaker at many conferences. She has been in the forefront of receiving prestigious awards for innovation and technology while being an acclaimed feature in many magazines. In addition to her contributions in the world of tech, Dr Li also is a co-founder of Stanford’s renowned SAILORS outreach program for high school girls and the national non-profit AI4ALL. The controversial email from Dr.Li can lead to one thinking if the transition was made as a result of the events of 2017. However, no official statement has been released by Google or Dr. Li on why she is moving on. Head over to Google’s Blog for the official announcement of this news. Google CEO Sundar Pichai won’t be testifying to Senate on election interference Bloomberg says Google, Mastercard covertly track customers’ offline retail habits via a secret million dollar ad deal Epic games CEO calls Google “irresponsible” for disclosing the security flaw in Fortnite Android Installer before patch was ready      

Last Friday (7th September, 2018), DARPA announced a multi-year investment of more than $2 billion in a new program called the ‘AI Next’ campaign. DARPA’s Agency director, Dr. Steven Walker, officially unveiled the large-scale effort during D60,  DARPA’s 60th Anniversary Symposium held in Maryland. This campaign seeks contextual reasoning in AI systems in order to create deeper trust and collaborative partnerships between humans and machines. The key areas the AI Next Campaign may include are: Automating critical DoD (Department of Defense) business processes, such as security clearance vetting in a week or accrediting software systems in one day for operational deployment. Improving the robustness and reliability of AI systems; enhancing the security and resiliency of machine learning and AI technologies. Reducing power, data, and performance inefficiencies. Pioneering the next generation of AI algorithms and applications, such as ‘explainability’ and commonsense reasoning. The Next-Generation Nonsurgical Neurotechnology (N3) program In the conference, DARPA officials also described the next frontier of neuroscience research: technologies for able-bodied soldiers that give them super abilities. Following this, they introduced the Next-Generation Nonsurgical Neurotechnology (N3) program, which was announced in March. This program aims at funding research on tech that can transmit high-fidelity signals between the brain and some external machine without requiring that the user is cut open for rewiring or implantation. Al Emondi, manager of N3, said to IEEE Spectrum that he is currently picking researchers who will be funded under the program and can expect an announcement in early 2019. The program has two tracks: Completely non-invasive: The N3 program aims for new non-invasive tech that can match the high performance currently achieved only with implanted electrodes that are nestled in the brain tissue and therefore have a direct interface with neurons—either recording the electrical signals when the neurons “fire” into action or stimulating them to cause that firing. Minutely invasive: DARPA says it doesn’t want its new brain tech to require even a tiny incision. Instead, minutely invasive tech might come into the body in the form of an injection, a pill, or even a nasal spray. Emondi imagines “nanotransducers” that can sit inside neurons, converting the electrical signal when it fires into some other type of signal that can be picked up through the skull. Justin Sanchez, director of DARPA’s Biological Technologies Office, said that making brain tech easy to use will open the floodgates. He added, “We can imagine a future of how this tech will be used. But this will let millions of people imagine their own futures”. To know more about the AI Next Campaign and the N3 program in detail, visit DARPA blog. Skepticism welcomes Germany’s DARPA-like cybersecurity agency – The federal agency tasked with creating cutting-edge defense technology DARPA on the hunt to catch deepfakes with its AI forensic tools underway  

Today the TensorFlow team announced the launch of TensorFlow Data Validation (TFDV), an open-source library that enables developers to understand, validate, and monitor their machine learning data at scale. Why is TensorFlow Data Validation introduced? While building machine learning algorithms a lot of attention is paid on improving their performance. However, if the input data is wrong, all this optimization effort goes to waste. Understanding and validating small amount of data is easy, you can do it manually as well. However, in the real-world this is not the case. Data in production is huge and often arrives continuously and in big chunks. This is why, it is necessary to automate and scale the tasks of data analysis, validation, and monitoring. What are some features of TFDV? TFDV is part of the TensorFlow Extended (TFX) platform, a TensorFlow-based general-purpose machine learning platform. It is already being used by Google every day to analyze and validate petabytes of data. TFDV provides some of the following features: It can compute descriptive statistics that provide a quick overview of the data in terms of the features that are present and the shapes of their value distributions. It includes tools such as Facets Overview, which provides a visualization of the computed statistics for easy browsing. Data-schema can be generated automatically to describe expectations about data such as required values, ranges, and vocabularies. Since writing a schema can be a tedious task for datasets with lots of features, TFDV provides a method to generate an initial version of the schema based on the descriptive statistics. You can inspect the schema with the help of schema viewer. You can identify anomalies such as missing features, out-of-range values, or wrong feature types with Anomaly detection. Provides an anomalies viewer so that you can see what features have anomalies and learn more in order to correct them. To learn more on how it is used in production, read the official announcement by TensorFlow on Medium and also check out TFDV’s GitHub repository. Why TensorFlow always tops machine learning and artificial intelligence tool surveys TensorFlow 2.0 is coming. Here’s what we can expect. Can a production ready Pytorch 1.0 give TensorFlow a tough time?

Project Jupyter released Jupytext, last week, a new project which allows you to convert Jupyter notebooks to and from Julia, Python or R scripts (extensions .jl, .py and .R), markdown documents (extension .md), or R Markdown documents (extension .Rmd). It comes with features such as writing notebooks as plain text, paired notebooks, command line conversion, and round-trip conversion. It is available from within Jupyter. It allows you to work as you would usually do on your notebook in Jupyter, and save and read it in the formats you select. Let’s have a look at its major features.  Writing notebooks as plain text Jupytext allows plain scripts that you can draft and test in your favorite IDE and open naturally as notebooks in Jupyter. You can run the notebook in Jupyter for generating output, associating a .ipynb representation, along with saving and sharing your research. Paired Notebooks Paired notebooks let you store a .ipynb file alongside the text-only version. Paired notebooks can be enabled by adding a jupytext_formats entry to the notebook metadata with Edit/Edit Notebook Metadata in Jupyter's menu. On saving the notebook, both the Jupyter notebook and the python scripts are updated. Command line conversion There’s a jupytext script present for command line conversion between the various notebook extensions: jupytext notebook.ipynb --to md --test      (Test round-trip conversion) jupytext notebook.ipynb --to md --output      (display the markdown version on screen) jupytext notebook.ipynb --to markdown           (create a notebook.md file) jupytext notebook.ipynb --to python               (create a notebook.py file) jupytext notebook.md --to notebook              (overwrite notebook.ipynb) (remove outputs) Round-trip conversion Round-trip conversion is also possible with Jupytext. Converting Script to Jupyter notebook to script again is identity, meaning that on associating a Jupyter kernel with your notebook, the information will go to a yaml header at the top of your script. Converting Markdown to Jupyter notebook to Markdown is again identity. Converting Jupyter to script, then back to Jupyter preserves source and metadata. Similarly, converting Jupyter to Markdown, and Jupyter again preserves source and metadata (cell metadata available only for R Markdown). For more information on, check out the official release notes. 10 reasons why data scientists love Jupyter notebooks Is JupyterLab all set to phase out Jupyter Notebooks? How everyone at Netflix uses Jupyter notebooks from data scientists, machine learning engineers, to data analysts

Now you can store your data in Watermelon! Yesterday, Nozbe released Watermelon DB v0.6.1-1, a new addition to the database world. It aims to help you build powerful React and React Native apps that scale to large number of records and remain fast. Watermelon architecture is database-agnostic, making it cross-platform. It is a high-level layer for dealing with data, but can be plugged in to any underlying database, depending on platform needs. Why choose Watermelon DB? Watermelon DB is optimized for building React and React Native complex applications. Following are the factors that help in ensuring high speed of applications: It makes your application highly scalable by using lazy loading, which means Watermelon DB loads data only when it is requested. Most queries resolve in less than 1ms, even with 10,000 records, as all querying is done on SQLite database on a separate thread. You can launch your app instantly irrespective of how much data you have. It is supported on iOS, Android, and the web. It is statically typed keeping Flow, a static type checker for JavaScript, in mind. It is fast, asynchronous, multi-threaded, and highly cached. It is designed to be used with a synchronization engine to keep the local database up to date with a remote database. Currently, Watermelon DB is in active development and cannot be used in production. Their roadmap states that, migrations will soon be added to allow the production use of Watermelon DB. Schema migrations is the mechanism by which you can add new tables and columns to the database in a backward-compatible way. To know how you can install it and to try few examples, check out Watermelon DB on GitHub. React Native 0.57 coming soon with new iOS WebViews What’s in the upcoming SQLite 3.25.0 release: windows functions, better query optimizer and more React 16.5.0 is now out with a new package for scheduling, support for DevTools, and more!

The Tor Project released its first mobile browser on the Google Play Store, called the Tor Browser, last week. The browser is currently an Alpha release i.e. you can expect few bugs while using the browser. The stable version is expected to be released by early 2019. It comes packed with privacy-enhancing features such as block trackers, defense against surveillance, resist fingerprinting, browse freely, and multi-layered protection. The Tor Browser for Android comes with the highest privacy protections available. The Alpha release of this browser can be downloaded on GooglePlay, or you can use the apk directly from their download page. The Alpha release of this browser requires the installation of a proxy application named Orbot. Orbot is used for connecting the browser to the Tor network. This dependency on Orbot will be deprecated in the future stable release of the this Browser. Let’s now have a look at the features of this new Browser. Block Trackers Tor Browser prevents third-party trackers and ads from following you as it isolates each website that you visit. Also, all the cookies clear away on their own once you are done browsing. Defend against Surveillance This feature in the Tor Browser prevents others watching your connection from knowing what websites you visit. All that the people monitoring your browsing habits would see is that you’re using Tor. Resist Fingerprinting Tor Browser focuses on making all users look the same, and makes it difficult for you to be fingerprinted depending on your browser and device information. Multi-layered Encryption Using Tor Browser for Android, your traffic gets relayed and encrypted three times on passing over the Tor network. The network comprises thousands of volunteer-run servers called Tor relays. Browse Freely Tor Browser lets you access sites that your local internet service provider may have blocked. Currently, there is no official Tor Browser for iOS devices, and there’s one known issue i.e. the Security Slider is under ‘Security Settings,’ but because of a small issue, it shows up only after you restart the app. For more information, check out the official Tor Project Blog post. Tor Browser 8.0 powered by Firefox 60 ESR released Mozilla releases Firefox 62.0 with better scrolling on Android, a dark theme on macOS, and more Airbnb introduces MvRx, a new Android framework for easier, faster, and high-quality product development

Microsoft is rebranding Visual Studio Team Services(VSTS) to Azure DevOps along with  Azure DevOps Server, the successor of Team Foundation Server (TFS). Microsoft understands that DevOps has become increasingly critical to a team’s success. The re-branding is done to achieve the aim of shipping higher quality software in a short span of time. Azure DevOps supports both public and private cloud configurations. The services are open and extensible and designed to work with any type of application, framework, platform, or cloud. Since Azure DevOps services work great together, users can gain more control over their projects. Azure DevOps is free for open source projects and small projects including up to five users. For larger teams, the cost ranges from $30 per month to $6,150 per month, depending upon the number of users. VSTS users will be upgraded into Azure DevOps projects automatically without any loss of functionally. URLs will be changed from abc.visualstudio.com to dev.azure.com/abc. Redirects from visualstudio.com URLs will be supported to avoid broken links. New users will get the update starting 10th September 2018, and existing users can expect the update in coming months. Key features in Azure DevOps: #1 Azure Boards Users can keep track of their work at every development stage with Kanban boards, backlogs, team dashboards, and custom reporting. Built-in scrum boards and planning tools help in planning meetings while gaining new insights into the health and status of projects with powerful analytics tools. #2 Azure Artifacts Users can easily manage Maven, npm, and NuGet package feeds from public and private sources. Code storing and sharing across small teams and large enterprises is now efficient thanks to Azure Artifacts. Users can Share packages, and use built-in CI/CD, versioning, and testing. They can easily access all their artifacts in builds and releases. #3 Azure Repos Users can enjoy unlimited cloud-hosted private Git repos for their projects.  They can securely connect with and push code into their Git repos from any IDE, editor, or Git client. Code-aware searches help them find what they are looking for. They can perform effective Git code reviews and use forks to promote collaboration with inner source workflows. Azure repos help users maintain a high code quality by requiring code reviewer sign off, successful builds, and passing tests before pull requests can be merged. #4 Azure Test Plans Users can improve their code quality using planned and exploratory testing services for their apps. These Test plans help users in capturing rich scenario data, testing their application and taking advantage of end-to-end traceability. #5 Azure Pipelines There’s more in store for VSTS users. For a seamless developer experience, Azure Pipelines is also now available in the GitHub Marketplace. Users can easily configure a CI/CD pipeline for any Azure application using their preferred language and framework. These Pipelines can be built and deployed with ease. They provide users with status reports, annotated code, and detailed information on changes to the repo within the GitHub interface. The pipelines Work with any platform- like Azure, Amazon Web Services, and Google Cloud Platform. They can run on apps with operating systems, including Android, iOS, Linux, macOS, and Windows systems. The Pipelines are free for open source projects. Microsoft has tried to update user experience by introducing these upgrades. Are you excited yet? You can learn more at the Microsoft live Azure DevOps keynote today at 8:00 a.m. Pacific and a workshop with Q&A on September 17 at 8:30 a.m. Pacific on Microsoft’s events page. You can read all the details of the announcement on Microsoft’s official Blog. Real clouds take out Microsoft’s Azure Cloud; users, developers suffer indefinite Azure outage Machine Learning as a Service (MLaaS): How Google Cloud Platform, Microsoft Azure, and AWS are democratizing Artificial Intelligence 8 ways Artificial Intelligence can improve DevOps  

Rigetti Computing have launched Quantum Cloud Services, bringing together the best of classical and quantum computing on a single cloud platform. “What this platform achieves for the very first time is an integrated computing system that is the first quantum cloud services architecture,” says Chad Rigetti, founder and CEO. Rigetti Computing has been competing head to head with behemoths like Google and IBM to grab the quantum computing market. Last month, Rigetti unveiled plans to deploy 128 qubit chip quantum computing system, challenging Google, IBM, and Intel for leadership in this emerging technology. Prior to that, last year, in December, Rigetti developed a new quantum algorithm to supercharge unsupervised Machine Learning. Now the startup says, “the first Quantum computing is almost ready for business.” With QCS you can build and run programs combining real quantum hardware in a virtual development environment. Quantum Cloud Services will be used to address fundamental challenges in medicine, energy, business, and science. Quantum cloud Services will offer a combination of a cloud-based classical computer, its Forest development platform and access to Rigetti’s quantum backends. Chemistry: QCS can be used for predicting the properties of complex molecules and materials to design more effective medicines, energy technologies and resilient crops. Machine Learning: QCS can be used for training advanced AI on quantum computers. These will help in computer vision, pattern recognition, voice recognition and machine translation. Optimization: QCS can solve complex optimizations such as ‘job shop’ scheduling and traveling salesperson problems. This will drive critical efficiencies in businesses, military and public sector logistics, scheduling, shipping and resource allocation. Rigetti is now inviting customers to apply for free access to these systems. They have invited developers to build a real-world application that achieves quantum advantage and the first to make it wins a $1 million prize. “What we want to do is focus on the commercial utility and applicability of these machines, because ultimately that’s why this company exists,” says Rigetti. Rigetti is also partnering with a number of leading quantum computing startups including Entropica Labs, Horizon Quantum Computing, OTI Lumionics, ProteinQure, QC Ware and Riverlane Research. They have collaborated with Rigetti to build and distribute the applications through the Rigetti QCS platform. You can read more details on the Rigetti Computing official website. Quantum Computing is poised to take a quantum leap with industries and governments on its side. Did quantum computing just take a quantum leap? A two-qubit chip by UK researchers makes controlled quantum entanglements possible. Rigetti plans to deploy 128 qubit chip Quantum computer.

If you have time to read only one thing from this post, read the essay by Kate Crawford and Vladan Joler on the Anatomy of an AI System: The Amazon Echo as an anatomical map of human labor, data and planetary resources. It guarantees that you’ll never see AI the same way again, irrespective of who you are.   Here are five other developments that happened in tech this weekend that is worth pondering over. Quantum computing is leaping forward Rigetti computing launched a project in the mold of Amazon Web Services (AWS) called Quantum Cloud Services. “What this platform achieves for the very first time is an integrated computing system that is the first quantum cloud services architecture,” says Chad Rigetti, founder and CEO of his namesake company. (Fast Company) Quantum computing has moved out of the realm of theoretical physics and into the real world. For both Dario Gil,  the chief operating officer of IBM Research and the company’s vice president of artificial intelligence and quantum computing, and Chad Rigetti, a former IBM  researcher who founded Rigetti Computing and serves as its chief executive, the moment that a quantum computer will be able to perform operations better than a classical computer is only three years away. (Tech Crunch) Google’s ‘world domination plans’, a hot topic of disdain discussion Google has a monopoly on search rankings. We can't let them obtain a monopoly on websites says Charlie Owen. Google wants websites to adopt AMP as the default approach to building web pages. Tell them no, says a redditor. (Reddit) Google's move to strip out the www in domains typed into the address bar, beginning with version 69 of its Chrome browser, has drawn an enormous amount of criticism from developers who see the move as a bid to cement the company's dominance of the Web. (iTWire) Google requiring phone number to log into Chromebook. “Am I crazy or does this seem like an extremely cynical attempt to get more phone numbers? I don't even understand how giving them my phone number proves anything as I definitely did not ever give them one previously,” asks a hacker news user. (Hacker News) Alibaba’s gradual change of guard begins. Tesla may follow, unwittingly. Jack Ma to Retire from Alibaba (New York Times) Alibaba appoints Daniel Zhang to succeed Jack Ma in a 12-month succession plan. Ma, the co-founder of Asia’s most valuable company and one of China’s most recognizable names, will remain Alibaba’s executive chairman for 12 months until September 10, 2019. (South China Morning Post) Tesla wrapped up an interesting week -- CEO Elon Musk took a puff or two from a joint live on Joe Rogan's podcast, its recently-hired chief accounting officer quit after less than a month on the job and its HR chief announced she would not return from a leave of absence. Tesla now has a new President, Automotive. In a move that may take some direct responsibilities and pressure off of Musk, Jerome Guillen "will oversee all automotive operations and program management, as well as coordinate our extensive automotive supply chain. (End Gadget)   What’s Apple been upto Apple Has Permanently Banned Alex Jones' Infowars App From The App Store. Apple's App Store guidelines for developers forbid apps with "content that is offensive, insensitive, upsetting, intended to disgust, or in exceptionally poor taste." (BuzzFeed) Apple is building an online portal for police to make data requests. The tech giant is also upgrading its program that trains law enforcement in digital forensics. (CNET) “Apple prices may increase because of the massive Tariffs we may be imposing on China - but there is an easy solution where there would be ZERO tax, and indeed a tax incentive. Make your products in the United States instead of China. Start building new plants now. Exciting! #MAGA”, @realDonalTrump on Twitter. Thoughts on Diversity, Inclusion and Ethics “Balancing motherhood with my work as a data scientist was exciting and strenuous. It meant working during my commute, coming home to feed the kids and put them to sleep, then falling into bed. I worked until the day my daughter was born. Then I had to make the hardest decision of my life. I had to choose between my dream job and my baby girl”, Eliza Khuner writes on Wired. In the wake of public allegations that Riot Games has fostered a sexist workplace culture, two longtime employees exited the company yesterday under hazy circumstances. Systems designer Daniel Klein and communications associate Mattias Lehman—both outspoken advocates for gender diversity at Riot—are no longer working at the company following a contentious weekend involving a controversial PAX West panel. (Kotaku) A year later, Equifax has faced little fallout from losing data. It was “one of the most egregious examples of corporate malfeasance since Enron,” said Senate Democratic leader Chuck Schumer at the time. Yet, a year on from following the devastating hack that left the company reeling from a breach of almost every American adult, the company has faced little to no action or repercussions. (Tech Crunch) Other salient stories DARPA Announces $2 Billion Campaign to Develop Next Wave of AI Technologies Python enters the TIOBE index top 3 for the first time Like newspapers, Google algorithms are protected by the First amendment making them hard to legally regulate Joseph Stiglitz on artificial intelligence: 'We’re going towards a more divided society' Facebook's AI Just Set a New Record in Translation and Why It Matters PWA moving mainstream as Twitter makes its mobile site the main one New law in Belgium: right to open science regardless of contract with a publisher YouTube shuts down official Syrian government channels YouTube pulls ads by Russian Putin critic New tool releases and announcements Snort 3 beta available now! Haiku, the open source BeOS clone, to release in beta after 17 years of development OpenZeppelin 2.0 RC 1, a framework for writing secure smart contracts on Ethereum, is out! Ripgrep 0.10.0 released with PCRE2 and multi-line search support GNU nano 3.0 released with faster file reads, new shortcuts and usability improvements Tagmatic – automated content tagging using machine learning AsmBB v2.4 released: File attachments, PMs on steroids and performance boost CNCF Adopts Database Project, TiKV, from PingCAP KDE Frameworks 5.50.0 released Mesa 3D 18.2.0 released

Reportedly, Google wants all the web developers to adopt the AMP approach for their websites. The AMP project was announced by Google on October 7, 2015 and AMP pages first became available to web users in February 2016. Nowadays, mobile search is getting more popular as compared to desktop search. It is important for web pages to appear in Google’s mobile search results, and this is why AMP is not optional for web publishers. Without AMP, a publisher’s articles will be extremely unlikely to appear in the Top Stories carousel on mobile search in Google. What is AMP? AMP is short for Accelerated Mobile Pages. As the name suggests, this open-source project aims to provide a straightforward way to create web pages that are compelling, smooth, and load near instantaneously for users. AMP consists of three components: AMP HTML: This component consists of regular HTML with some custom AMP properties. AMP JS: This component is responsible for fast rendering of your page. It implements all of AMP's best performance practices, manages resource loading, and provides the custom tags. AMP Cache: It is used to serve cached AMP HTML pages. It is a proxy-based content delivery network for delivering all valid AMP documents. Why are web developers annoyed with AMP? This is the part which infuriates developers, because they have to follow the rules set by Google. Developing a website in itself is a difficult job and on top of that AMP adds the extra burden of creating separate AMP versions of articles. Following are some of the rules that AMP pages need to follow: To avoid delay caused by JavaScript in page rendering, AMP only allows asynchronous JavaScript. Resources such as images, ads, or iframes should mention their size in the HTML to enable AMP to determine each element’s size and position before resources are downloaded. CSS must be inline and the upper limit for the size of inline style sheet is 50 kilobytes. All resource downloads are controlled by AMP. It optimizes downloads so that the currently most important resources are downloaded first and prefetches lazy-loaded resources. Web font optimization should be kept in mind as web fonts are super large. Google Search Console checks your AMP pages and shares feedback stating what all improvements you can make to better align it with the restrictions set by Google. It basically wants full equivalency between the regular website and the AMP versions of the pages. It is not very easy to follow these restrictive rules. Many developers feel they have to do all the work they already put in to build the normal version of the site all over again specifically for the AMP version. Instead of creating two different versions, developers would be forced to build the whole site in AMP. Why Google wants web developers to accept AMP? It's very rare to find websites that look good, have great performance, and fully follow the web standards. This becomes a huge challenge for search engines. Google's crawlers and indexers have to process a lot of junk to find and index content on the web. Website built entirely in AMP are fast to load, fast to crawl, easy to understand, and in short makes Google's life so much easier. One redditor stated in a long discussion thread, that the main problem is not “AMP” itself, but “Google treating it special” is. “The problems you're describing I believe are problems with implementation not AMP itself. The only issue I really have with AMP is actually that Google treats it special. If you treat it like a web framework where you write slightly different html and get lazy loading and tons of integrations as built in components for free, it's actually quite nice both for the user and for the programmer. The problems are that people want to put in all their normal functionality, continue trying to game SEO and ad revenue, and that Google wants to serve it themselves. If Google stopped trying to integrate AMP directly into their search results/CDN system, I'd be much more willing to support and use it. AMP itself is basically just a predefined set of web components and a limitation to not use taxing JS. You can even be partially AMP compliant and still leverage all the benefits with none of the negatives (including the fact that Google won't host it if you aren't fully compliant, I believe).” To know more on why Google wants developers to embrace AMP, read this article: Google AMP Can Go To Hell. If you are interested in reading about how AMP makes content loading quicker, check out this article: What is Google AMP and how does it work?. Like newspapers, Google algorithms are protected by the First amendment making them hard to legally regulate them Google launches a Dataset Search Engine for finding Datasets on the Internet Google Chrome’s 10th birthday brings in a new Chrome 69

At the end of last month, Google denied U.S President Donald Trump’s accusatory tweet which said it’s algorithms favor liberal media outlets over right-wing ones. Trump’s accusations hinted at Google regulating the information that comes up in Google searches. However, governing or regulating algorithms and the decisions they make about which information should be provided and prioritized is a bit tricky. Eugene Volokh, a University of California-Los Angeles law professor and author of a 2012 white paper on the constitutional First Amendment protection of search engines, said, “Each search engine’s editorial judgment is much like many other familiar editorial judgments.” A similar scenario of a newspaper case from 1974 sheds light on what the government can control under the First Amendment, companies’ algorithms and how they produce and organize information. On similar lines, Google too has the right to protect its algorithms from being regulated by the law. Google has the right to protect algorithms, based on a 1974 case According to Miami Herald v. Tornillo 1974 case, the Supreme Court struck down a Florida law that gave political candidates the “right of reply” to criticisms they faced in newspapers. The law required the newspaper to publish a response from the candidate, and to place it, free of charge, in a conspicuous place. The candidate’s lawyers contended that newspapers held near monopolistic roles when it came to reaching audiences and that compelling them to publish responses was the only way to ensure that candidates could have a comparable voice. The 1974 case appears similar to the current scenario. Also, if Google’s algorithms are manipulated, those who are harmed will have comparatively limited tools through which to be heard. Back then, Herald refused to comply with the law. Its editors argued that the law violated the First Amendment because it allowed the government to compel a newspaper to publish certain information. The Supreme Court too agreed with the Herald and the Justices explained that the government cannot force newspaper editors “to publish that which reason tells them should not be published.” Why Google cannot be regulated by law Similar to the 1974 case, Justices used the decision to highlight that the government cannot compel expression. They also emphasized that the information selected by editors for their audiences is part of a process and that the government has no role in that process. The court wrote, “The choice of material to go into a newspaper and the decisions as to limitations on size and content of the paper, and treatment of public issues and public officials—fair or unfair—constitute the exercise of editorial control and judgment.” According to two federal court decisions, Google is not a newspaper and algorithms are not human editors. Thus, a search engine or social media company’s algorithm-based content decisions should not be protected in similar ways as those made by newspaper editors. The judge explained, “Here, the process, which involves the . . . algorithm, is objective in nature. In contrast, the result, which is the PageRank—or the numerical representation of relative significance of a particular website—is fundamentally subjective in nature.” Ultimately, the judge compared Google’s algorithms to the types of judgments that credit-rating companies make. These firms have a right to develop their own processes and to communicate the outcomes. Comparison of both journalistic protections and algorithms, was conducted in a Supreme Court’s ruling in Citizens United v. FEC in 2010. The case focused on the parts of the Bipartisan Campaign Reform Act that limited certain types of corporate donations during elections. Citizens United, which challenged the law, is a political action committee. Chief Justice John Roberts explained that the law, because of its limits on corporate spending, could allow the government to halt newspapers from publishing certain information simply because they are owned by corporations. This can also harm public discourse. Any attempt to regulate Google’s and other corporations’ algorithmic outputs would have to overcome: The hurdles the Supreme Court put in place in the Herald case regarding compelled speech and editorial decision-making, The Citizens United precedent that corporate speech, which would also include a company’s algorithms, is protected by the First Amendment. Read more about this news in detail on Columbia Journalism Review. Google slams Trump’s accusations, asserts its search engine algorithms do not favor any political ideology North Korean hacker charged for WannaCry ransomware and for infiltrating Sony Pictures Entertainment California’s tough net neutrality bill passes state assembly vote

A paper titled “Video-to-Video Synthesis”, introduces a new model using the generative adversarial learning framework. This model is capable of performing video to video synthesis to achieve high-resolution, photorealistic, and temporally coherent video results on a diverse set of inputs. These inputs include segmentation masks, sketches, and poses. https://www.youtube.com/watch?v=GRQuRcpf5Gc Video-to-video synthesis What problem is the paper trying to solve? The paper focuses on a mapping function which can effectively convert an input video to an output video. Although image-to-image translation methods are quite popular, a general-purpose solution for video-to-video synthesis has not yet been explored. The paper considers the video-to-video synthesis problem as a distribution matching problem. This involves training a model in such a way that conditional distribution of the synthesized videos makes sure that the input videos resembles that of real videos. Given a set of aligned input and output videos, the model maps the input videos to the output domain at the test time. This approach is also capable of generating photorealistic 2K resolution videos which can be up to 30 seconds long. How does the model work? The network is trained in a spatio-temporally progressive manner. “We start with generating low-resolution and few frames, and all the way up to generating full resolution and 30 (or more) frames. Our coarse-to-fine generator consists of three scales, which operates on 512 × 256, 1024 × 512, and 2048 × 1024 resolutions, respectively” reads the paper. The model is trained for 40 epochs and uses the ADAM [36] optimizer with lr = 0.0002 and (β1, β2) = (0.5, 0.999) on an NVIDIA DGX1 machine. All the GPUs in DGX1 (8 V100 GPUs, each with 16GB memory) are used for training purposes. A generator computation task is distributed to 4 GPUs and the discriminator computation task is distributed to the other 4 GPUs. Training the model takes somewhere around 10 days for 2K resolution. There are several datasets which are used for training the model such as Cityscapes, Apollo Scape, Face video dataset, FaceForensics dataset, and Dance video dataset. Apart from this, the researchers compared the approach to two baselines trained on the same data, namely, pix2pixHD ( the state-of-the-art image-to-image translation approach) and COVST. For evaluating the model’s performance, both subjective and objective metrics are used. First is the Human preference score that performs a human subjective test for evaluation of the visual quality of synthesized videos. Second is the Fréchet Inception Distance (FID), a widely used metric for implicit generative models. Limitations of the model This model fails in situations when synthesizing turning cars because of insufficient information in label maps. This can be addressed by adding 3D cues, such as depth maps. Also, the model doesn’t guarantee that an object will have a consistent appearance across the whole video. This means that there can be instances where a car may change its color gradually. Lastly, by performing semantic manipulations such as turning trees into buildings, visible artifacts may appear i.e. building and trees can have different label shapes. However, this can be resolved by using coarser semantic labels to train the model since that would make it less sensitive to label shapes. “Extensive experiments demonstrate that our results are significantly better than the results by state-of-the-art methods. Its extension to the future video prediction task also compares favorably against the competing approaches” reads the paper. The paper has received public criticism from a few over the concern that it can be used to create deepfakes or tampered videos which can deceive people for illegal and exploitation purposes. While others view it as a great step into the AI-driven future. For more information, be sure to check out the official research paper. This self-driving car can drive in its imagination using deep reinforcement learning Introducing Deon, a tool for data scientists to add an ethics checklist Baidu releases EZDL – a platform that lets you build AI and machine learning models without any coding knowledge