Tech News - Data

Facebook has been trending in the news because of its ethics and data privacy issues. Right from the Cambridge Analytica scandal to multiple hearings and fine against the company, Facebook has been surrounded by these controversies since quite some time now. Lately,  the Canadian and British Columbia privacy commissioners decided to take Facebook to Federal Court to seek an order because of its privacy practices. And once again, the company makes the headline for tracking users across Stack Overflow. Well, to explain this better, Stack Overflow directly links to Facebook profile pictures. You must be wondering many third-party platforms allow such tracking, then what’s the big deal in this one? So, the trap is, that this linking unintentionally allows user activity throughout Stack Exchange to be tracked by Facebook and surprisingly, it also tracks the topics you are interested in! To explain this further, let’s take an example from a Stack Overflow user. Image source: Stack Overflow The user says, “Have a look: when I load a page containing any avatars hot-linked from Facebook, my browser automatically sends a request including a Facebook identifying cookie and the URL of the page I'm viewing on Stack Exchange. They don't just know that I'm visiting the site, they also get to know which topics I'm interested on throughout the network.” Another user commented on the thread, “Facebook creates 'shadow' accounts for many people who don't have actual accounts (or at least, for people they can't find an actual account for) in order to consistently/reliably track/gather data to sell.” Few others are complaining about their profile pictures being attributed directly to facebook.com domains. The browser is basically making a request to Facebook and the Facebook session cookie identifies the user as well as a referrer header. This header tells Facebook what page the users were on at the time they check the image. How to save yourself from such creepy activity by Facebook? A lot of users have suggested selecting the cookies they should be accepting on each of the sites they visit. Also, blocking third-party cookies and setting the browser to remove cookies while closing the browser as a viable option. Manual removal of cookies is advisable while quitting a browser. Few others have suggested using an ad blocker which will refrain the users from going on fishy sites. It is suggested to enable Strict Content Blocking in Firefox for security concerns. But the matter of concern is that even other tech companies must be involved in collecting the user data and manipulating them and basically playing around our privacy. Just a few years ago, Google was trying to patent the collection of user data. It’s surprising to see how is the world changing around us and we are forced to live in an era where the tech giants are data minded. To know more about this news, check out the Stack Overflow thread. Facebook bans six toxic extremist accounts and a conspiracy theory organization Facebook open-sources F14 algorithm for faster and memory-efficient hash tables Facebook shareholders back a proposal to oust Mark Zuckerberg as the board’s chairperson

Crypto-ML is a machine learning powered cryptocurrency price prediction service for Cryptocurrency Traders.  It currently supports Bitcoin, Litecoin, and Bitcoin Cash trading. Individuals at level with Enterprises Individuals have relied on outdated and speculative technical indicators, as opposed to enterprises who have sophisticated machine-learning technologies at their disposal to enhance their trading results. Outdated methods have reduced reliability due to human error, emotional inputs, selection bias, lag, and changing market dynamics. Crypto-ML focuses on bringing newer machine learning technology to individuals, helping to level the playing field. How does Crypto-ML work? Traditional technical indicators generally provide mediocre results, particularly in crypto markets, which are often hectic. Additionally, they are subject to interpretation, can conflict with other indicators, and often lag rather than making an accurate prediction. Crypto-ML uses vast data sets to generate proprietary models for predicting future price movement. It uses machine learning to generate triggers or signals. These signals belong to three categories - buy, sell, or hold. These signals are generated by an end-to-end systematic machine-learning mechanism. Crypto-ML has historically opened an average of 12 trades per year (24 buy/sell signals). Since the models are continuously optimizing, the frequency of triggers may change. The use of ML algorithms eliminates human emotion and error. Moreover, as the crypto markets undergo constant change and flux, the Crypto-ML models are trained and evaluated every day. However, this service predicts future outcomes using past data. Changes in market conditions, including, but not limited to, micro, macro, and global conditions, may invalidate existing models or cause exceptions for one or more days. Thus the team at Crypto-ML says that the tool is to be “used for informational purposes only. Each individual has unique risk tolerances and is responsible for their own investment decisions. It provides no warranties of any kind.” Crypto's ML service early access is currently $19 per month. Users may cancel at any time. This service is based on a month-to-month membership with no commitment. Users can cancel their account from the membership site. To know more, visit Crypto-ML official website.

Today, ID R&D, the biometric solutions provider offering proprietary AI-based behavioral, voice, and anti-spoofing user authentication capabilities, releases SafeMessage, the industry’s first biometric authentication technology for messaging. ID R&D will be demoing SafeMessage, as well as its other award-winning voice and behavioral biometric products, today at FinovateFall. SafeMessage, offers multi-layer continuous authentication of verified users when integrated across messaging platforms (WhatsApp, Telegram, Skype, Slack, and others) without impacting user’s experience. It combines voice, behavioral, and facial recognition, along with voice and facial “liveness” analysis to provide unmatched authentication and security. Requiring a mere 1-2 seconds of free speech, keystrokes, or facial scans, SafeMessage adds to ID R&D’s suite of comprehensive, frictionless biometric solutions. SafeMessage provides frictionless, yet continuous authentication, and can also differentiate between an authorized user and a voice recording or a computer-generated simulation (for voice input). Alexey Khitrov, CEO of ID R&D says, “ID R&D is thrilled to be the first to introduce frictionless authentication to messaging apps and platforms. Now secure and seamless communication is a possibility for all end users, whether over text, mobile app, instant message, chat, or the internet.” To know more about SafeMessage, visit ID R&D website. Microsoft Edge introduces Web Authentication for passwordless web security Machine learning APIs for Google Cloud Platform Google updates biometric authentication for Android P, introduces BiometricPrompt API

UK professors and researchers have developed the first general framework for safeguarding privacy in deep learning built over Pytorch. They have reported their findings in the paper “A generic framework for privacy preserving deep learning.” Using constructs that preserve privacy This paper introduces a transparent framework to preserve privacy while using deep learning in PyTorch. This framework puts a premium on data ownership and its securing processing. It introduces a value representation which is based on chains of commands and tensors. The resulting abstraction allows implementation of complex constructs that preserve privacy. Constructs like federated learning, secure multiparty computation, and differential privacy are used. Boston Housing and Pima Indian Diabetes datasets are used in the paper to show early results. Except for differential privacy, other privacy features do not affect prediction accuracy. The current framework implementation introduces a significant overhead which is to be addressed in a later development stage. Deep learning operations in untrusted environments To perform operations in untrusted environments without disclosing data, Secure Multiparty Computation (SMPC) is used, which is a popular approach. In machine learning, SMPC can protect the model weights while allowing multiple worker nodes to participate in training with their own datasets. This is known as federated learning (FL). These securely trained models are still vulnerable to reverse engineering attacks. This vulnerability is addressed by differentially private (DP) methods. The standardized PyTorch framework contains: A chain structure in which performing transformations or sending tensors to other workers can be shown as a chain of operations. For a virtual to real context of federated learning, a concept called Virtual Workers is introduced. These Virtual Workers reside in the same machine and do not communicate over the network. Results and conclusion A reasonably small overhead is observed when using Web Socket workers in place of Virtual Workers. This overhead is due to the low network latency when communication takes place between different local tabs. When using the Pima Indian Diabetes dataset, the same overhead in performance is observed. The design in this paper relies on chains of tensors exchanged between the local and remote workers. Decreasing training time is an issue to be addressed. Another concern is securing MPC to avoid malicious attempts targeted at corrupting the data or the model. For more details, read the research paper. PyTorch 1.0 preview release is production ready with torch.jit, c10d distributed library, C++ API OpenAI launches Spinning Up, a learning resource for potential deep learning practitioners NVIDIA leads the AI hardware race. But which of its GPUs should you use for deep learning?

Recently, Vertex.AI announced a simple and compact machine learning language for its PlaidML framework. Tile is a tensor manipulation language built to bring the PlaidML framework to a wider developer audience. PlaidML is their open source and portable deep learning framework developed for deploying neural networks on any device. A key obstacle the developers of PlaidML faced was scalability. In order for any framework to be adopted across a wide variety of platforms, software support is required. By software support we mean the implementation of software kernels which is a glue between frameworks and the underlying system. Tile comes as a rescue here because it can automatically generate these kernels. This addresses the problem of compatibility by making it easier to add support for different NVIDIA GPUs as well as other new types of processors such as those from AMD and Intel. Tile runs on the backend of PlaidML to produce custom kernels for each specific operation for each GPU. As these kernels are machine generated they are highly accelerated. A high acceleration leads to easily adding support for different processors. Using Tile, machine learning operations can be methodically implemented on parallel computing architectures. It can also be easily converted into optimized GPU kernels. Another key feature of Tile is the fact that the code is very easy to write and understand. This is because coding in Tile is similar to writing a mathematical notation. In addition to this, all machine learning operations expressed in this language can be automatically differentiated. The fact that it is so easy to understand makes it easily adoptable by both machine learning practitioners as well as software engineers and mathematicians. This is an example for writing a Tile matrix multiply : function (A[M, L], B[L, N]) -> (C) { C[i, j: M, N] = +(A[i, k] * B[k, j]); } Note how closely it resembles linear algebra operations with an easy syntax. This syntax is demonstrative as well as optimized for covering all operations required to build neural networks. PlaidML uses Tile as the intermediate language while integration with Keras. This reduces significant writing of backend Keras code. So, it gets easy to support and implement new operations such as dilated convolutions. Tile can also address and analyze issues such as cache coherency, shared memory usage, and memory bank conflicts. According to the official blog of Vertex AI, Tile is characterized by: Control-flow & side-effect free operations on n-dimensional tensors Mathematically oriented syntax resembling tensor calculus N-Dimensional, parametric, composable, and type-agnostic functions Automatic Nth-order differentiation of all operations Suitability for both JITing and pre-compilation Transparent support for resizing, padding & transposition The developers are currently working to bring the language to a formal specification. In the future, they intend to use a similar approach to make TensorFlow, PyTorch, and other frameworks compatible with PlaidML. If you’re interested in learning how to write code in Tile, you can check the Tile tutorial on their GitHub.

Throughout this year, we saw many data breaches and security issues involving Facebook. Adding to this list, last week, some hackers were able to gain access to 120 million accounts and posted private posts of Facebook users. As reported by the BBC News, the hackers also put an advert selling access to these compromised accounts for 10 cents per account. What this Facebook hack was about? This case of data breach seems to be different from the ones we saw previously. While the previous attacks took advantage of vulnerabilities in Facebook’s code, this breach happened due to malicious extensions. This breach was first spotted in September, when a user nicknamed as “FBSaler” appeared on an English-language internet forum. This user was selling personal information of Facebook users: "We sell personal information of Facebook users. Our database includes 120 million accounts.” BBC contacted Digital Shadows, a cyber-security company to investigate the case. The cyber-security company confirmed that more than 81,000 of the profiles posted online contained private messages. Also, the data from 176,000 accounts were made available online, but BBC added that this data may have been scraped from members who had not hidden it. To confirm that these private posts and messages were actually of real users BBC also contacted five Russian Facebook users. These users confirmed that the posts were theirs. Who exactly is responsible for this hack? Going by Facebook’s statement to BBC, this hack happened because of malicious browser extensions. This malicious extension tracked victims’ activity on Facebook and shared their personal details and private conversations with the hackers. Facebook has not yet disclosed any information about the extension. One of the Facebook’s executive, Guy Rosen told BBC: "We have contacted browser-makers to ensure that known malicious extensions are no longer available to download in their stores. We have also contacted law enforcement and have worked with local authorities to remove the website that displayed information from Facebook accounts." On deeper investigation by BBC News, one of the websites where the data was published appeared to have been set up in St Petersburg. In addition to taking the website down, its IP address has also been flagged by the Cybercrime Tracker service. According to the Cybercrime Tracker service this address was also used to spread the LokiBot Trojan. This trojan allows attacker to gain access to user passwords. Cyber experts told BBC that if malicious extensions were the root cause of this data breach, then browsers are also responsible for it: “Independent cyber-experts have told the BBC that if rogue extensions were indeed the cause, the browsers' developers might share some responsibility for failing to vet the programs, assuming they were distributed via their marketplaces.” This news has led to a big discussion on Hacker News. One of the users on the discussion shared how these kind of attacks could be mitigated by browser policies: “Maybe it's time for the browsers to put more effort into extension network security. 1) Every extension has to declare up front what urls it needs to communicate to. 2) Every extension has to provide schema of any data it intends to send out of browser. 3) Browser locally logs all this comms. 4) Browser blocks anything which doesn't match strict key values & value values and doesn't leave browser in plain text.” We will have to wait and see how these browsers will be able to stop the use of malicious extensions and also, how Facebook makes itself much stronger against all these data breaches. Read the full report on this Facebook hack on BBC News. Facebook’s CEO, Mark Zuckerberg summoned for hearing by UK and Canadian Houses of Commons Facebook’s Machine Learning system helped remove 8.7 million abusive images of children Facebook says only 29 million and not 50 million users were affected by last month’s security breach

On Wednesday the Members of European Parliament (MEPs) passed a resolution on banning autonomous weapon systems. They emphasized that weapons like these, without proper human control over selecting and attacking targets should be banned before it is too late. Reportedly, some countries and industries are developing lethal autonomous weapon systems, which are also known as killer robots, ranging from missiles capable of selective targeting to learning machines with cognitive skills to decide whom, when, and where to fight. These might also include armed quadcopters that can search for and eliminate people meeting certain predefined criteria. According to the MEPs, giving machines so much power raises fundamental ethical and legal questions of human control, in particular with regard to critical functions such as target selection and engagement. They want the EU policy chief Federica Mogherini, the member states, and the Council to agree on a common position on lethal autonomous weapon systems, which will ensure meaningful human control over their critical functions and to speak with one voice in different international forums. Federica said during the debate at the European Parliament: "I know this might look like a debate about some distant future or about science fiction. It's not." A further discussion is scheduled at the United Nations in November, where it is hoped an agreement on an international ban can be reached. AI is growing, and it is growing fast. It has reached to a stage where building such systems is feasible in few years and could put power in wrong hands. This needs to stop because any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable. UN meetings ended with US & Russia avoiding formal talks to ban AI enabled killer robots 15 millions jobs in Britain at stake with Artificial Intelligence robots set to replace humans at workforce 6 powerful microbots developed by researchers around the world

According to a latest research paper and demonstration from researchers at Northeastern University in Boston, hackers can hijack the systems used to guide aeroplanes by spoofing and compromising the radio signals used during landing. By using a $600 software defined radio, the researchers can now spoof airport signals that cause a pilot’s navigation instruments to falsely indicate that a plane is off course. Attackers can attack by sending a signal that causes a pilot’s course deviation indicator in order to show that a plane is slightly too far to the left of the runway, even when the plane is perfectly aligned. The pilot will react by guiding the plane to the right and inadvertently steer over the centerline. The spoofed signals can also be used to indicate that a plane’s angle of descent is more gradual than it actually is. The spoofed message can also generate a “fly down” signal that instructs the pilot to steepen the angle of descent, possibly causing the aircraft to touch the ground before reaching the start of the runway. In this paper, the researchers have investigated and demonstrated the vulnerability of aircraft instrument landing systems to wireless attacks. The researchers have further analyzed the instrument landing system (ILS) waveforms’ and have shown the feasibility of spoofing radio signals. This might lead to last-minute go around decisions, and in worst case scenarios, it can even lead to missing the landing zone in low-visibility scenarios. The researchers have first shown that it is possible to fully and in fine-grain control the course deviation indicator, as displayed by the ILS receiver, in real time, and further demonstrate it on aviation-grade ILS receivers. They have also analyzed the potential of both an overshadowing attack, and a lower-power single-tone attack. Note: The overshadowing attack involves sending specific ILS signals at a high power level to overpower legitimate ILS signals. The single-tone attack interferes with a legitimate ILS signal through the transmission of a lower power frequency tone which alters the plane's course deviation indicator needle. For evaluating the complete attack, the researchers have developed a tightly-controlled closed-loop ILS spoofer. This spoofer adjusts the adversary’s transmitted signals as a function of the aircraft GPS location which maintains power and keeps the deviation consistent with the adversary’s target position, causing an undetected off-runway landing. They have also demonstrated the integrated attack on an FAA (Federal Aviation Administration) certified flight-simulator (XPlane) by incorporating a spoofing region detection mechanism. This mechanism triggers the controlled spoofing on entering the landing zone to reduce detectability. The researchers have evaluated the performance of the attack against X-Plane’s AI-based autoland feature, and demonstrated a systematic success rate with offset touchdowns of 18 meters to over 50 meters. The researchers have investigated the security of aircraft instrument landing system against wireless attacks. For both these attacks, the researchers have generated specially crafted radio signals that are similar to the legitimate ILS signals using low-cost software-defined radio hardware platform. They have successfully induced aviation-grade ILS receivers, in real time, to lock and display arbitrary alignment to both horizontal and vertical approach path. This also demonstrates the potential for an adversary to trigger multiple aborted landings that would cause air traffic disruption and might let the aircraft to overshoot the landing zone or miss the runway entirely. The researchers then discuss potential countermeasures including failsafe systems such as GPS and show that these systems also do not provide sufficient security guarantees. They have also highlighted that implementing cryptographic authentication on ILS signals is not enough as the system could be vulnerable to record and replay attacks. Therefore, the researchers highlight on an open research challenge of building secure, scalable and efficient aircraft landing systems. To know more about this, check out the research paper. Researchers from China introduced two novel modules to address challenges in multi-person pose estimation AI can now help speak your mind: UC researchers introduce a neural decoder that translates brain signals to natural sounding speech OpenAI researchers have developed Sparse Transformers, a neural network which can predict what comes next in a sequence

It’s been just a month since the release of TensorFlow 1.10, and the TensorFlow community introduces the newer version 1.11 with few major additions, lots of bug fixes and numerous performance improvements. Major Features of TensorFlow 1.11.0: Prebuilt binaries built for Nvidia GPU Experimental tf.data integration for Keras Preview support for eager execution on Google Cloud TPUs Added multi-GPU DistributionStrategy support in tf.keras for model distribution Added multi-worker DistributionStrategy support in Estimator C, C++, and Python functions added for querying kernels Added simple Tensor and DataType classes to TensorFlow Lite Java Bug Fixes and Other Changes: Default values for tf.keras RandomUniform, RandomNormal, and TruncatedNormal initializers changed Added pruning mode for boosted trees Old checkpoints do not get deleted by default Total disk space for dumped tensor data limited to 100 GB. Added experimental IndexedDatasets Performance Improvements: Enhanced performance for StringSplitOp & StringSplitV2Op Regex replace operations improvised with max performance. Toco compilation/execution fixed for Windows Added GoogleZoneProvider class for detecting Google Cloud Engine zone tensorflow Import enabled for tensor.proto.h Added documentation clarifying the differences between tf.fill and tf.constant Added selective registration target using the lite proto runtime Support for bitcasting to and from uint32 and uint64 Estimator subclass added and can be created from a SavedModelEstimator Added argument leaf index modes Please see the full release notes for complete details on added features and changes. You can also check the GitHub repository to find various interesting use cases of TensorFlow. Top 5 Deep Learning Architectures A new Model optimization Toolkit for TensorFlow can make models 3x faster Intelligent mobile projects with TensorFlow: Build your first Reinforcement Learning model on Raspberry Pi

PostgreSQL team announces the first beta release of PostgreSQL 11 which contains sneak peek into all the features that will be available in the release candidate of PostgreSQL 11 which is likely to be released in late 2018. The major features are centered around database simplicity, handling large datasets, and various performance bottlenecks. We can expect some minor changes before the final release is out. Since it is still in beta release, it is strongly advised you don't run them in the production environment to avoid any hassle. PostgreSQL is an open source relational database management system which has grown in popularity over the years. With the constant development of more than 30 years, PostgreSQL is one of the popular database used today. It has been called the DBMS of 2017 because of its powerful database management system that offers better reliability, robustness, and performance measures. Some of the noteworthy changes available in PostgreSQL 11 Beta 1: Partitioning improvements Partitioning plays an integral part in splitting a large dataset into smaller pieces in order to carry out complex operations with ease. PostgreSQL 11 contains several new features and improvements to working with data in partitions: New feature, hash partitioning, allows you to partition using a hash key You can now use UPDATE statements to a partition key in order to move the affected rows to the appropriate partitions PostgreSQL 11 supports enhanced partition elimination during query processing and execution thus leading to improved SELECT query performance Complete support for PRIMARY KEY, FOREIGN KEY, triggers, and indexes on partitions A new feature has been added which allows the query to distribute grouping and aggregation to partitioned tables before the final aggregation. However, in order to enable the settings, you need to set enable_partitionwise_aggregate = on in your configuration file, since it is disabled by default. Parallelism improvements New features have been added to build a smooth parallel query infrastructure to manage and carry out workloads efficiently and effectively thus providing significant performance enhancements. We now have parallelized hash joins and CREATE INDEX for B-tree indexes We can use parallelized features on certain queries with UNION SQL stored procedures A new feature SQL stored procedures is introduced by the PostgreSQL team which allows users to use embedded transactions such as BEGIN, COMMIT/ROLLBACK and more within a procedure. Just-In-Time compilation Now you can optimize the execution of code, and operations; and even make required changes during the run time. Thus it stands out as a perfect framework which gives you a leeway to allow future optimizations in the workflow. In case you are building PostgreSQL 11 from source, you can enable JIT compilation using the --with-llvm flag. Window functions In PostgreSQL 11, window functions will support all options in SQL:2011 standard SCRAM authentication PostgreSQL 11 supports channel binding for SCRAM authentication, thus providing the required security feature to prevent man-in-the-middle attacks. PostgreSQL team has upgraded this feature since SCRAM authentication was already available. This was used to improve the storage and transmission of passwords on the basis of standard protocol. Simplicity and user experience improvements Although PostgreSQL provides a healthy set of features, not all of them can be easy-to-use in development and production environments. The PostgreSQL team has therefore brought many new improvements to better the user experience. Now you can quit the PostgreSQL command-line (psql) using keywords like quit and exit. Additional improvements and features Many other new improvements and features have been added to the PostgreSQL 11. You can refer the release notes for complete list of new and changed features in the roadmap. If you want to contribute to the project and want to test this new release in order to find bugs and issues, download PostgreSQL 11 Beta 1, from their official page. You can find existing open issues in the PostgreSQL wiki. In case you want to report any bugs or issues you can use report bugs form available on the PostgreSQL website. How to perform data partitioning in PostgreSQL 10 New updates to Microsoft Azure services for SQL Server, MySQL, and PostgreSQL 2018 is the year of graph databases. Here’s why

The Linux Foundation, nonprofit organization enabling mass innovation through open source, on Tuesday announced the formation of the Urban Computing Foundation (UCF). UCF will accelerate open source software to improve mobility, safety, road infrastructure, traffic congestion and energy consumption in connected cities. UCF’s mission is to enable developers, data scientists, visualization specialists and engineers to improve urban environments, human life quality, and city operation systems to build connected urban infrastructure. The founding members of UCF are Facebook, Google, IBM, UC San Diego, Interline Technologies, Uber etc. The executive director of Linux Foundation, Jim Zemlin spoke to Venturebeat, and said the Foundation will adopt an open governance model developed by the Technical Advisory Council (TAC), which will include technical and IP stakeholders in urban computing who’ll guide its work through projects by review and curation. The intent, added Zemlin, is to provide platforms to developers who seek to address traffic congestion, pollution, and other problems plaguing modern metros. Here’s the list of TAC members: Drew Dara-Abrams, principal, Interline Technologies Oliver Fink, director Here XYZ, Here Technologies Travis Gorkin, engineering manager of data visualization, Uber Shan He, project leader of Kepler.gl, Uber Randy Meech, CEO, StreetCred Labs Michal Migurski, engineering manager of spatial computing, Facebook Drishtie Patel, product manager of maps, Facebook Paolo Santi, senior researcher, MIT Max Sills, attorney, Google On Tuesday, Facebook announced their participation as a founding member of the Urban Computing Foundation (UCF). https://twitter.com/fb_engineering/status/1125783991452180481 Facebook mentions in its post that, “We are using our expertise — including a predictive model for mapping electrical grids, disaster maps , and more accurate population density maps — to improve access to this type of technology”. Further Facebook mentions that UCF will establish a neutral space for the critical work. It will include adapting geospatial and temporal machine learning techniques for urban environments and developing simulation methodologies for modeling and predicting citywide phenomena. Uber also reported about their joining and announced their contribution of Kepler.gl as the initiative’s first official project. Kepler is Uber’s open source, no-code geospatial analysis tool for creating large-scale data sets. It was released in 2018, and is currently used by Airbnb, Atkins Global, Cityswifter, Lime, Mapbox, Sidewalk Labs, and UBILabs, among others to generate visualizations of location data. While all of this set a path towards making of smarter cities, it also raises an alarm to another way of violating privacy and mishandling user data as per the history in tech. Moreover when recently Amnesty International in Canada regarded the Google Sidewalk Labs project in Toronto to normalize mass surveillance and a direct threat to human rights. Questions are raised as to the tech companies forming foundation to address traffic congestion issue but not to address the privacy violation or online extremism. https://twitter.com/shannoncoulter/status/1126199285530238976 The Linux Foundation announces the CHIPS Alliance project for deeper open source hardware integration Mapzen, an open-source mapping platform, joins the Linux Foundation project Uber becomes a Gold member of the Linux Foundation

Last week, Netflix announced the open source launch of Polynote which is a polyglot notebook. It comes with a full scale Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, SQL, and provides IDE-like features such as interactive autocomplete, a rich text editor with LaTeX support, and more. Polynote renders a seamless integration of Netflix’s Scala employed JVM-based ML platform with Python’s machine learning and visualization libraries. It is currently used by Netflix’s personalization and recommendation teams and is also being integrated with the rest of the Netflix research platform. The Netflix team says, “Polynote originated from a frustration with the shortcomings of existing notebook tools, especially with respect to their support of Scala.” Also, “we found that our users were also frustrated with the code editing experience within notebooks, especially those accustomed to using IntelliJ IDEA or Eclipse.”  Key features supported by Polynote Reproducibility A traditional notebook generally relies on a Read–eval–print loop (REPL) environment to build an interactive environment with other users. According to Netflix, the expressions and the results of a REPL evaluation is quite rigid. Thus, Netflix built the Polynote’s code interpretation from scratch, instead of relying on a REPL. This helps Polynote to keep track of the variables defined in each cell by constructing the input state for a given cell based on the cells that have run above it. By making the position of a cell important in its execution semantics, Polynote allows the users to read the notebook from top to bottom. This ensures reproducibility in Polynote by increasing the chances of running the notebook sequentially. Editing Improvements Polynote provides editing enhancements like: It integrates code editing with the Monaco editor for interactive auto-complete. It highlights errors internally to help users rectify it quickly. A rich text editor for text cells which allows users to easily insert LaTeX equations. Visibility One of the major guiding principles of Polynote is its visibility. It enables live view of what the kernel is doing at any given time, without requiring logs. A single glance at a user interface imparts with many information like- The notebook view and task list displays the current running cell, and also shows the queue to be run. The exact statement running in the system is highlighted in colour. Job and stage level Spark progress information is shown in the task list. The kernel status area provides information about the execution status of the kernel. Polyglot Currently, Polynote supports Scala, Python, and SQL cell types and enables users to seamlessly move from one language to another within the same notebook. When a cell is running in the system, the kernel handovers the typed input values to the cell’s language interpreter. Successively, the interpreter provides the resulted typed output values back to the kernel. This enables the cell in a Polynote notebook to run irrespective of the language with the same context and the same shared state. Dependency and Configuration Management In order to ease reproducibility, Polynote yields configuration and dependency setup within the notebook itself. It also provides a user-friendly Configuration section where users can set dependencies for each notebook. This allows Polynote to fetch the dependencies locally and also load the Scala dependencies into an isolated ClassLoader. This reduces the chances of a class conflict of Polynote with the Spark libraries. When Polynote is used in Spark mode, it creates a Spark Session for the notebook, where the Python and Scala dependencies are automatically added to the Spark Session. Data Visualization One of the most important use cases of a notebook is its ability to explore and visualize data. Polynote integrates with two open source visualization libraries- Vega and Matplotlib. It also has a native support for data exploration such as including a data schema view, table inspector and  plot constructor. Hence, this feature helps users to learn about their data without cluttering their notebooks. Users have appreciated Netflix efforts of open sourcing their Polynote notebook and have liked its features https://twitter.com/SpirosMargaris/status/1187164558382845952 https://twitter.com/suzatweet/status/1187531789763399682 https://twitter.com/SpirosMargaris/status/1187164558382845952 https://twitter.com/julianharris/status/1188013908587626497 Visit the Netflix Techblog for more information of Polynote. You can also check out the Polynote website for more details. Netflix security engineers report several TCP networking vulnerabilities in FreeBSD and Linux kernels Netflix adopts Spring Boot as its core Java framework Netflix’s culture is too transparent to be functional, reports the WSJ Linux foundation introduces strict telemetry data collection and usage policy for all its projects Fedora 31 releases with performance improvements, dropping support for 32 bit and Docker package

After the release of RxDB 8.0.0-beta.1 earlier this month, the RxDB community released RxDB 8.0.0 yesterday. The focus of this release is better defaults and improved performance with broadcast-channel for communication. RxDB is a reactive, offline-first, multiplatform database for JavaScript. What’s new in RxDB 8.0.0? Breaking changes RxDB has upgraded to pouchdb 7.0.0, its latest version As disableKeyCompression was not used by many users, it is now disabled by default and has been renamed as keyCompression RxDatabase.collection() now only takes the json-schema as schema-attribute In order to comply with the json-schema-standard, it is not allowed to set the required fields using required: true, instead you can use required: ['myfield'] Setters and save() are no more allowed on non-temporary documents. To change document-data, use RxDocument.atomicUpdate(), RxDocument.atomicSet(), or RxDocument.update(). The document methods, RxDocument.synced$ and RxDocument.resync() are removed middleware-hooks contain plain json as first parameter and RxDocument as second You can now set QueryChangeDetection by adding the boolean field queryChangeDetection: true when creating the database Additional Improvements RxDocument.atomicSet() RxCollection.awaitPersistence() Option for CORS to server-plugin All methods of RxDocument are bound to the instance Added RxReplicationState.denied$, which emits when a document failed to replicate Added RxReplicationState.alive$, which emits true or false depending if the replication is alive - data is transmitting properly between databases Miscellaneous changes Performance is improved by enabling cross-instance communication with broadcast-channel Upgraded to eslint 5 and babel 7 To read the full list of changes, check out RxDB’s GitHub repository. Introducing TimescaleDB 1.0, the first OS time-series database with full SQL support Introducing Watermelon DB: A new relational database to make your React and React Native apps highly scalable MongoDB 4.0 now generally available with support for multi-platform, mobile, ACID transactions and more

NVIDIA team has announced a new set of deep learning updates on their cloud computing software and hardware front during Computer Vision and Pattern Recognition Conference (CVPR 2018) held in Salt Lake City. Some of the key announcements made during the CVPR conference include Apex, an early release of a new open-source PyTorch extension, NVIDIA DALI and NVIDIA nvJPEG for efficient data optimization and image decoding, Kubernetes on NVIDIA GPUs release candidate, and runtime engine TensorRT version 4. Let’s look at some noteworthy updates made during CVPR conference: Apex Apex is an open-source PyTorch extension that includes all the required NVIDIA-maintained utilities to provide optimized and efficient mixed precision results and distributed training in PyTorch. This new extension helps machine learning engineers and data scientists to maximize deep learning training performance on NVIDIA Volta GPUs. The core promise of Apex is to provide up-to-date utilities to users as quickly as possible. Some of the notable features included are: NVIDIA PyTorch team has been inspired by the state of the art mixed precision training in tasks such as sentiment analysis, translational networks, and image classification. This has allowed them to create a set of tools to bring these methods to all levels of PyTorch users. Apex provides mixed precision utilities which are designed to improve training speed while maintaining the accuracy and stability of training in single precision. With Apex, you will now only require four or fewer line changes to the existing code to provide automatic loss scaling, automated execution of operations on FP16 or FP32, and automatic handling of master parameter conversion. In order to install/use Apex in your own development environment, you will require CUDA 9, PyTorch 0.4 or later, and Python 3. The extension is still in their early release, so we can expect the modules and utilities to undergo changes. If you want to download the code and get started with the tutorials and examples, you can visit the GitHub page. You can visit the official announcement page for more details. NVIDIA DALI and NVIDIA nvJPEG NVIDIA is using the power of GPUs with NVIDIA DALI, which utilizes the NVIDIA nvJPEG library to work on images at greater speed. This allows one to deal with performance bottleneck issues faced during image recognition and while decoding in deep learning powered computer vision applications. NVIDIA DALI is an open-source GPU-accelerated data augmentation and image loading library which can be used to optimize data pipelines (data optimization) of deep learning frameworks. You can refer to the GitHub page to learn more. NVIDIA nvJPEG is a GPU-accelerated library for JPEG decoding. You can download the release candidate for feedback and testing. This new update allows deep learning practitioners and researchers to have optimized training performance on image classification models such as ResNet-50 with MXNet, TensorFlow, and PyTorch across Amazon Web Services P3 8 GPU instances or DGX-1 systems with Volta GPUs. You can refer to the official announcement page for more details. Kubernetes on NVIDIA GPUs NVIDIA team has announced a release candidate of Kubernetes on NVIDIA GPUs which is freely available to developers for testing. This allows the enterprise to scale up training and ease up deployment to multi-cloud GPU clusters smoothly. This will ensure automated deployment, maintenance, and proper scheduling and operations of multiple GPU accelerated containers across clusters of nodes. You can arrange the growing resources on heterogeneous GPU clusters. To know more about this update, you can refer to the official announcement page. TensorRT 4 This new release of inference optimizer and runtime engine adds new layers such as recurrent neural networks, multilayer perceptrons, ONNX parser, and integration with TensorFlow to ease deep learning tasks. Moreover, it also provides the ability to execute custom neural network layers using FP16 precision and support for the Xavier SoC through NVIDIA DRIVE AI platforms. TensorRT ensures speeding up deep learning tasks such as machine translation, speech and image processing, recommender systems on GPUs. Using TensorRT across these application areas speed up the process 45x to 190x. All members of NVIDIA registered developer program can use TensorRT 4 for free. For more detailed information about the new features and updates, you can visit the developer’s official page. Read more NVIDIA open sources NVVL, library for machine learning training Nvidia’s Volta Tensor Core GPU hits performance milestones. But is it the best? Nvidia Tesla V100 GPUs publicly available in beta on Google Compute Engine and Kubernetes Engine

The Ellen MacArthur Foundation and Google, with research and analytical support provided by McKinsey & Company have released an interesting paper talking about the intersection of two emerging megatrends: artificial intelligence and circular economy. This paper is based on the insights from over 40 interviews with experts, taking a closer look at how Artificial Intelligence can accelerate the transition to a circular economy. The paper also highlights how artificial intelligence is being used in the food and consumer electronics industries. What is Circular Economy? Circular economy is based on creating value from consuming finite resources. It is based around three principles: Design out waste and pollution Keep products and materials at their highest value Regenerate natural systems A circular economy approach encourages manufacturers to extend the usability of products, by designing products for durability, repair or refurbishment. Figure: Circular economy diagram Why AI for circular economy? The paper highlights three circular economic opportunities where AI can potentially help. These are: “Design circular products, components, and materials. AI can enhance and accelerate the development of new products, components, and materials fit for a circular economy through iterative machine-learning-assisted design processes that allow for rapid prototyping and testing. Operate circular business models. AI can magnify the competitive strength of circular economy business models, such as product-as-a-service and leasing. By combining real-time and historical data from products and users, AI can help increase product circulation and asset utilization through pricing and demand prediction, predictive maintenance, and smart inventory management. Optimize circular infrastructure. AI can help build and improve the reverse logistics infrastructure required to “close the loop” on products and materials, by improving the processes to sort and disassemble products, re-manufacture components, and recycle materials.” For each of the three use cases, the paper also highlights three case studies where Artificial Intelligence was used to create circular value within current business models. First, project ‘Accelerated Metallurgy’, funded by the European Space Agency which used AI algorithms to analyse vast amounts of data on existing materials and their properties to design and test new alloy formulations. The second case study talks about software company ZenRobotics was the first company which uses an AI software ZenBrain to recover recyclables from waste. The paper also talks about two other case studies where AI was used to grow food regeneratively and make better use of its by-products. The paper points that “the potential value unlocked by AI in helping design out waste in a circular economy for food is up to $127 billion a year in 2030.”  In another case study, AI helped in circulating consumer electronics products, components, and materials. “The equivalent AI opportunity in accelerating the transition towards a circular economy for consumer electronics is up to $90 billion a year in 2030.” The paper urges stakeholders and industrialists to take inspiration from the use cases and case studies explored in the paper to create and define new opportunities for circular economy applications of AI. It suggests three ways: “Creating greater awareness and understanding of how AI can support a circular economy is essential to encourage applications in design, business models, and infrastructure Exploring new ways to increase data accessibility and sharing will require new approaches and active collaboration between stakeholders As with all AI development efforts, those that accelerate the transition to a circular economy should be fair and inclusive, and safeguard individuals’ privacy and data security” Circular economy coupled with AI is still in its early stages. The true impact of AI in creating sustainable economy can only be realized with proper funding, investment, and awareness. Reports like these do help in creating awareness among the VCs, stakeholders, software engineers, and tech companies, but it’s up to them, how they actually translate it to implementation. You can view the full report here. Do you need artificial intelligence and machine learning expertise in house? How is Artificial Intelligence changing the mobile developer role? The ethical dilemmas developers working on Artificial Intelligence products must consider