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Last year in August Google released the Transformer, a novel neural network architecture based on a self-attention mechanism particularly well suited for language understanding. Before the Transformer, most neural network based approaches to machine translation relied on recurrent neural networks (RNNs) which operated sequentially using recurrence. In contrast to RNN-based approaches, the Transformer used no recurrence, instead it processed all words or symbols in the sequence and let each word attend the other word over multiple processing steps using a self-attention mechanism to incorporate context from words farther away. This approach led Transformer to train the recurrent models much faster and yield better translation results than RNNs. “However, on smaller and more structured language understanding tasks, or even simple algorithmic tasks such as copying a string (e.g. to transform an input of “abc” to “abcabc”), the Transformer does not perform very well.”, says Stephan Gouws and Mostafa Dehghani from the Google Brain team. Hence this year the team has come up with Universal Transformers, an extension to standard Transformer which is computationally universal using a novel and efficient flavor of parallel-in-time recurrence. The Universal Transformer is built to yield stronger results across a wider range of tasks. How does the Universal Transformer function The Universal Transformer is built on the parallel structure of the Transformer to retain its fast training speed. It has replaced the Transformer’s fixed stack of different transformation functions with several applications of a single, parallel-in-time recurrent transformation function. Crucially, where an RNN can process a sequence symbol-by-symbol (left to right), the Universal Transformer will process all symbols at the same time (like the Transformer), but then refine its interpretation of every symbol in parallel over a variable number of recurrent processing steps using self-attention. This parallel-in-time recurrence mechanism is both faster than the serial recurrence used in RNNs, making the Universal Transformer more powerful than the standard feedforward Transformer. Source: Google AI Blog At each step, information is communicated from each symbol (e.g. word in the sentence) to all other symbols using self-attention, just like in the original Transformer. However, now the number of times transformation will be applied to each symbol (i.e. the number of recurrent steps) can either be manually set ahead of time (e.g. to some fixed number or to the input length), or it can be decided dynamically by the Universal Transformer itself. To achieve the latter, the team has added an adaptive computation mechanism to each position which will allocate more processing steps to symbols that are ambiguous or require more computations. Furthermore, on a diverse set of challenging language understanding tasks the Universal Transformer generalizes significantly better and achieves a new state of the art on the bAbI linguistic reasoning task and the challenging LAMBADA language modeling task. But perhaps the larger feat is that the Universal Transformer also improves translation quality by 0.9 BLEU1 over a base Transformer with the same number of parameters, trained in the same way on the same training data. “Putting things in perspective, this almost adds another 50% relative improvement on top of the previous 2.0 BLEU improvement that the original Transformer showed over earlier models when it was released last year”, says the Google Brain team. The code to train and evaluate Universal Transformers can be found in the open-source Tensor2Tensor repository page. Read in detail about the Universal Transformers on the Google AI blog. Create an RNN based Python machine translation system [Tutorial] FAE (Fast Adaptation Engine): iOlite’s tool to write Smart Contracts using machine translation Setting up the Basics for a Drupal Multilingual site: Languages and UI Translation

Facebook is working with NYU in a bid to transform the speed at which MRI scans can be performed. Using artificial intelligence, and training on 3 million MRI scans, fastMRI can supposedly work ten times as fast as a traditional MRI scan. MRI scans can offer medical professionals a level of detail that other scans cannot. But they can take some time, compared to, say, an X-ray. This is because MRI scans work by gathering a sequence of views which can then be turned into cross sections of a patient's internal tissue. To get a detailed picture more data needs to be gathered in the scan. However, with this project, the aim is to reduce the amount of data that needs to be collected. It will do this by using neural networks to build up the foundational components within a scan, so the scan can instead focus on what's unique to that specific patient. This a bit like how humans are able to process information by filtering out what they already know/what is already familiar and focusing on what is important. Some work has already been done by researchers at NYU on getting neural networks to produce high quality images from limited data. What makes Facebook's and NYU's MRI research unique? In a post published on Monday, Larry Zitnick from Facebook's AI Research Lab and Daniel Sodickson from NYU School of Medicine explained why this project is different from similar artificial intelligence research in medicine: "Unlike other AI-related projects, which use medical images as a starting point and then attempt to derive anatomical or diagnostic information from them (in emulation of human observers), this collaboration focuses on applying the strengths of machine learning to reconstruct the most high-value images in entirely new ways. With the goal of radically changing the way medical images are acquired in the first place, our aim is not simply enhanced data mining with AI, but rather the generation of fundamentally new capabilities for medical visualization to benefit human health." Facebook and NYU are ambitious about the scale of the project. They plan to open source the research to encourage wider participation in the area, and potentially push the boundaries of AI-informed medical research even further. But the teams say that "its long-term impact could extend to many other medical imaging applications" such as CT scans. Read next HUD files complaint against Facebook over discriminatory housing ads Four 2018 Facebook patents to battle fake news and improve news feed Facebook launches a 6-part Machine Learning video series

MIT is known for offering out-of-the-box interesting courses such as pirate training, street-fighting math, and so on. Its April 2016 spring course held at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) known as Duckietown tops them all. Duckietown Kickstarter project launched on 7th August 2018, teaches how to program self-driving cars at an affordable cost. Duckietown is designed to be an affordable, modular, scalable, duckie-filled introduction to autonomous vehicles. https://youtu.be/b0B6S2Ca75Q At present, the Duckietown kits are being used to teach practical self-driving robotics to students around the world. The idea with the Kickstarter is to help them scale up, providing robots and classroom kits that can do more for less money. On asking why the initial Duckietown class turned into a much larger project via email, Andrea Censi, President of the Duckietown Foundation, replied to IEEE Spectrum, “We found ourselves receiving emails from both independent learners and educational institutions all over the world showing interest in different forms. We realized that without “scaling up” our methods, it would have soon been impossible to manage all the people that wanted to be involved.” Why did Duckietown go to Kickstarter? Andrea explained the need to establish a one-click-solution to obtain hardware. The  bottlenecks faced during the distribution of the Duckietown platform was the accessibility of the hardware, which included (a) the time and effort necessary to obtain Duckiebots and Duckietowns, (b) the price, (c) the inconsistent availability of the components from the sources and their geographical location and related shipping limitations. Most of the components in the Duckietown kit are off-the-shelf, and links to the vendors are provided. The shipping process is time-consuming and cost-ineffective. Vendors do not guarantee the availability of the components, only ship to some parts of the world, and might at any time run out of inventory for specific components or change the prices. The Kickstarter is a way to solve this problem by raising the funds to create the necessary pipeline to make hardware available, anywhere, at any time, with the ease of a single click. Once the hardware distribution pipeline is established, one can purchase components in bulk and obtain lower prices. No prior coding experience required with Duckietown Kit Students or teachers who wish to use the Duckietown kit can follow step by step instructions detailed in their respective open-source Duckietown book (or Duckiebook). One of the highlights of this learning experience is, not much robotics or coding experience is necessary to follow these instructions. Andrea said, “By just following the instructions, learners will experience the hardware assembly of a robot (need for sensing, actuation, power and computation), the basics of Linux and ROS (Robotic Operating System) operations, the need to calibrate the camera, and be able to “play around” (tune high level parameters) with fundamental car behaviors like lane following, obstacle (i.e. duckie and Duckiebot) avoidance, intersection navigation, and stopping at a red light.” From an educational aspect, MIT envisions Duckietown to become a  milestone in learning experience in the fields of robotics and autonomy education. It will provide an educational experience which will be automatically tailored to each learner. From MIT’s research aspect, Duckietown could become a standardized research testbed for embodied autonomy. This is the main goal of the AI Driving Olympics (AI-DO), with its first edition at NIPS 2018, and the second edition at ICRA 2019. To know more about how Duckietown can be used to program self-driving cars, read Andrea Censi’s complete email interview at the IEEE Spectrum post. What the IEEE 2018 programming languages survey reveals to us Tesla is building its own AI hardware for self-driving cars Four interesting Amazon patents in 2018 that use machine learning, AR, and robotics  

Arm, the world’s leading semiconductor IP company, for the first time have disclosed its forward-looking compute performance data and a CPU roadmap for their Client Line of Business from now through 2020. Every year they introduce new world-class CPU designs that have delivered double-digit gains in instructions-per-clock (IPC) performance since 2013. Their aim is to enable the PC industry to overcome their reliance on Moore’s law and deliver a high-performance, always-on, always-connected laptop experience. Key highlights of this client compute CPU roadmap Arm’s client roadmap 2018: Earlier this year, the launch of Cortex-A76 was announced. It delivers laptop-class performance while maintaining the power efficiency of a smartphone. We can expect hearing more on the first commercial devices on 7nm towards the end of the year and coming months. 2019: Arm will be delivering the CPU codenamed ‘Deimos’ to their partners, which is a successor to Cortex-A76. ‘Deimos’ is optimized for the latest 7nm nodes and is based on DynamIQ technology. DynamIQ redefines multi-core computing by combining the big and LITTLE CPUs into a single, fully-integrated cluster with many new and enhanced benefits in power and performance for mobile to infrastructure. With these added improvements, it is expected to deliver a 15+ percent increase in compute performance. 2020: The CPU codenamed ‘Hercules’ will be available to Arm partners. Same as ‘Deimos’, it is also based on DynamIQ technology and will be optimized for both 5nm and 7nm nodes. It is expected to improve power and area efficiency by 10 percent in addition to increase in the compute performance. What does this roadmap tell us? Take advantage of the disruptive innovation 5G will bring to all client devices. The innovations from their silicon and foundry partners will help Arm SoCs (System on Chip) to breakthrough the dominance of x86 and gain substantial market share in Windows laptops and Chromebooks over the next five years. The Arm Artisan Physical IP platform and Arm POP IP will help partners get every bit of performance-per-watt they can out of their SoCs on whatever process node they choose. This latest roadmap highlights that Arm is bringing new innovations and features to the PC industry with its annual cadence design. They will be talking more about their latest product releases and ecosystem developments at Arm TechCon which will be held in October this year. To know more about their CPU roadmap, head over to Arm’s news post. SpectreRSB targets CPU return stack buffer, found on Intel, AMD, and ARM chipsets Intel’s Spectre variant 4 patch impacts CPU performance AMD’s $293 million JV with Chinese chipmaker Hygon starts production of x86 CPUs

Java is preparing to support pattern matching, part of which is revamping the switch statement. The changes are going to allow the switch statement to be used as both statements and as an expression. The changes to the switch statement will simplify everyday coding. It will also pave the way for the use of pattern matching in switch. The current Java switch statement is similar to the ones in languages such as C and C++. It supports fall-through semantics by default. This traditional control flow is often useful for writing low-level code but is error-prone in switch statements used in higher-level code. Brian Goetz, architect at Oracle has proposed to add a new simplified form, with new "case L ->" switch labels in addition to traditional switch blocks. On label match, only the statement or expression to the right of an arrow label is executed. For example, consider the following method: static void howMany(int k) {    switch (k) {        case 1 -> System.out.println("one");        case 2 -> System.out.println("two");        case 3 -> System.out.println("many");    } } On calling the function on these values: howMany(1); howMany(2); howMany(3); This is the output: one two many A new form of switch label, written "case L ->" is proposed to be added. This is an effort to imply that only the code to the right of the label is to be executed if the label is matched. Like a switch statement, a switch expression can also use a traditional switch block with "case L:" switch labels. Most switch expressions have only one expression to the right of the "case L ->" switch label. When a full block is needed, the break statement is extended to take an argument. The cases of a switch expression must contain a matching switch label for any possible value. In practice, this means that a default clause is required. An enum switch expression covers all known cases. In this case, a default clause can be inserted by the compiler indicating that the enum definition has changed between compile-time and runtime. This is done manually by developers today, but having the compiler insert is less intrusive. Also, a switch expression must execute normally with a value or throw an exception. This has a number of consequences like the compiler checking every switch label. Another consequence is that the control statements like break, return and continue, not being able to jump through a switch expression. For more information visit the official OpenJDK post. No more free Java SE 8 updates for commercial use after January 2019 Dagger 2.17, a dependency injection framework for Java and Android, is now out! Build Java EE containers using Docker [Tutorial]

Back in June, the OpenAI Five team, had smashed amateur humans in the video game Dota 2. Then early this month, OpenAI Five beat semi-professional Dota 2 players. Now OpenAI Five is at set to claim the Dota 2 throne with plans to beat the world’s best professional Dota 2 players. The Elon Musk backed non-profit AI research company, OpenAI is pitting its team of five neural networks, called OpenAI Five, against a team of top professional “Dota 2” players at The International esports tournament. The International 2018 is an ongoing event this week (held from Aug 20-25) at the Rogers Arena in Vancouver, Canada. With this challenge the team is using Dota 2 as a testbed for general-purpose AI systems which will start to capture the messiness and continuous nature of the real world, such as teamwork, long time horizons, and hidden information. The Dota training system showed that the current AI algorithms can learn long-term planning with large but achievable scale. The system is not specific to Dota 2, and they’ve also used it to control a robotic hand—a previously unsolved problem in robotics.OpenAI’s mission is to ensure that artificial general intelligence can benefit all of the humanity. How does OpenAI Five work A team of five artificial neural networks, are a kind of simulated “brains” which the team has designed to be well-shaped for learning Dota 2. The OpenAI Five sees the world as a list of 20,000 numbers which encode the visible game state (limited to the information a human player is permitted to see), and chooses an action by emitting a list of 8 numbers. The OpenAI team writes code which maps between game state/actions and lists of numbers. Once trained, these neural networks are creatures of pure instinct—their neural networks implement memory but do not otherwise learn further. They play as a team, but do not design special communication structures and only provide with an incentive. OpenAI Five Training The Five neural networks start with random parameters and use a general-purpose training system, Rapid, to learn better parameters. Rapid has OpenAI Five play copies of itself. It generates 180 years of gameplay data each day across thousands of simultaneous games. It will consume 128,000 CPU cores and 256 GPUs. At each game frame, Rapid computes a numeric reward which is positive when something favorable happens (e.g. an allied hero gained experience) and negative when something unfavorable happens (e.g. an allied hero is killed). Rapid will apply the Proximal Policy Optimization algorithm to update the parameters of the neural network—making actions which occurred soon before positive reward more likely and those soon before negative reward less likely. “Our team is focused on making the goal. We don’t know if it will be achievable, but we believe that with hard work (and some luck) we have a real shot,” says the OpenAI team. For further details, read the OpenAI blog page. OpenAI Five bots beat a team of former pros at Dota 2 OpenAI builds reinforcement learning based system giving robots human like dexterity Extending OpenAI Gym environments with Wrappers and Monitors [Tutorial]

A team led by Xiaogang Qiang from the Quantum Engineering Technology Labs at the University of Bristol in the UK, have designed a fully programmable silicon chip to control two-qubits of information simultaneously within a single integrated chip, taking us closer to the quantum computing era. Read Also: Quantum Computing is poised to take a quantum leap with industries and governments on its side The researchers invented a silicon chip which guides single particles of light (or photons) in optical tracks called waveguides to produce quantum-bits of information called “qubits”. This small device can be used to perform a wide range of quantum information experiments. It can also be used to demonstrate how completely functional quantum computers can be engineered from large-scale fabrication processes. “We programmed the device to implement 98 different two-qubit unitary operations (with an average quantum process fidelity of 93.2 ± 4.5%), a two-qubit quantum approximate optimization algorithm, and efficient simulation of Szegedy directed quantum walks -- fosters further use of the linear-combination architecture with silicon photonics for future photonic quantum processors” write the researchers on the paper titled “Large-scale silicon quantum photonics implementing arbitrary two-qubit processing”. The new design has solved one of the major problems faced during quantum computer development.  With the current technology, it is possible to effectively carry out the operations requiring just a single qubit (a unit of information that is in a superposition of simultaneous “0” and “1”). But, by adding a second qubit, it enables quantum entanglement, which exacerbates the problem. Qiang and colleagues have found a solution to this problem as their new quantum processor is capable of controlling two qubits. As mentioned in the paper “by using large-scale silicon photonic circuits to implement-- a linear combination of quantum operators scheme --we realize a fully programmable two-qubit quantum processor, enabling universal two-qubit quantum information processing in optics”. The paper also mentions that the quantum processor has been fabricated with mature CMOS-compatible processing and consists of more than 200 photonic components. “It’s a very primitive processor because it only works on two qubits, which means there is still a long way before we can do useful computations with this technology,” says Lead author, Dr. Xiaogang Qiang. Read News What is Quantum Entanglement? Google AI releases Cirq and Open Fermion-Cirq to boost Quantum computation “The future is quantum” — Are you excited to write your first quantum computing code using Microsoft’s Q#?

Microsoft is ever striving to make its products run better. They can add yet another accomplishment to their list as Microsoft’s cloud service search engine, Bing is now running fully on .NET Core 2.1, as announced by the .NET engineering team in their blog yesterday. .NET Core is the slimmed down and cross-platform version of Microsoft’s .NET managed common language runtime. Since Bing runs on thousands of servers spanning many data centers across the globe, .NET Core will serve as the perfect platform for it to function on. Why did Bing migrate to .NET Core 2.1? Bing has always run on the .NET Framework, but has been able to move to .NET Core 2.1 after some recent API additions. Let’s take a look at the main reasons for Bing.com’s migration to .NET Core. 1. Performance i.e. serving latency .NET Core 2.1 has led to an improvement in performance in virtually all areas of the runtime and libraries. The internal server latency over the last few months shows a striking 34% improvement. Check out the graph for a clear picture!     Souce: blog.msdn.microsoft.com The following changes in .NET Core 2.1 are the reasons why the workload and performance has greatly improved- #1 Vectorization of string.Equals & string.IndexOf/LastIndexOf HTML rendering and manipulation are string-heavy workloads. Vectorization of String comparisons and indexing operations (major components of string slicing) is the biggest contributor to the performance improvement. You can find more information on this on the github page for  Vectorization of string.Equals and string.IndexOf/LastIndexOf #2 Devirtualization Support for EqualityComparer<T>.Default One of .NET core’s major software components is a heavy user of Dictionary<int/long, V>, which indirectly benefits from the intrinsic recognition work that was done in the JIT to make Dictionary<K, V> amenable to that optimization.  Head over to the github page for more clarity on why this feature empowers .NET Core 2.1 #3 Software Write Watch for Concurrent GC This led to a reduction in CPU usage. The implementation relies on a JIT Write Barrier, which instinctively increases the cost of a reference store, but that cost is amortized and not noticed in the workload. #4 Methods with calli are now inline-able ldftn + calli  are used in lieu of delegates (which incur an object allocation) in performance-critical pieces of code where there is a need to call a managed method indirectly. This change allowed method bodies with a calli instruction to be eligible for inlining. The github page provides more insight on this subject. #5 Improve performance of string.IndexOfAny for 2 & 3 char searches A common operation in a front-end stack is search for ‘:’, ‘/’, ‘/’ in a string to delimit portions of a URL. Check out this special-casing improvement that was beneficial throughout the codebase on the github page. 2. Runtime Agility The ability to have an xcopy version of the runtime inside their application denotes that they can adopt newer versions of the runtime at a much faster pace. The Continuous integration (CI) pipeline is run with .NET Core’s daily CI and it builds testing functionality and performance all the way through the release. 3. ReadyToRun Images Managed applications usually can have poor startup performance as methods first have to be JIT compiled to machine code. .NET Framework has a precompilation technology, NGEN. On .NET Core, the crossgen tool allows the code to be precompiled as a pre-deployment step, such as in the build lab, and the images deployed to production are Ready To Run! This feature was not supported on the previous  .NET implementation. The .NET Core team is striving to provide Bing.com users fast results. The latest software and technologies used by their developers will ensure that .NET Core will not fail Bing.com! Read the detailed overview of the article on Microsoft's blog. Say hello to FASTER: a new key-value store for large state management by Microsoft Microsoft Azure’s new governance DApp: An enterprise blockchain without mining .NET Core completes move to the new compiler – RyuJIT

NVIDIA has shocked (in a good way, of course) gamers all over the globe by introducing GeForce RTX, world’s first real-time ray-tracing gaming GPUs. Jensen Huang introduced the GeForce RTX series of gaming processors, at Gamescom 2018, calling it the “biggest leap in performance in NVIDIA’s history”. These gaming GPUs are based on NVIDIA Turing architecture and the NVIDIA RTX platform, which fuses shaders with real-time ray tracing and AI capabilities. Turing delivers 6x more performance than its predecessor, Pascal and delivers 4K HDR gaming at 60 frames per second on even the most advanced titles. [box type="shadow" align="" class="" width=""]Ray tracing models the behavior of light in real time as it intersects objects in a scene producing life-like simulations possible in games and other animations.[/box] The new GeForce RTX 2080 Ti, 2080 and 2070 GPUs are packed with amazing features including: New RT Cores to enable real-time ray tracing of objects and environments with physically accurate shadows, reflections, refractions and global illumination. Turing Tensor Cores to perform lightning-fast deep neural network processing. New NGX neural graphics framework which integrates AI into the overall graphics pipeline for image enhancement and generation. New Turing shader architecture with Variable Rate Shading allows shaders to focus processing power on areas of rich detail, boosting overall performance. New memory system featuring ultra-fast GDDR6 with over 600GB/s of memory bandwidth for high-speed, high-resolution gaming. NVIDIA NVLink, a high-speed interconnect that provides higher bandwidth (up to 100 GB/s) and improved scalability for multi-GPU configurations. Hardware support for USB Type-C and VirtualLink. The world’s top game publishers, developers and engine creators have announced support for the NVIDIA RTX platform. These include Battlefield V, Shadow of the Tomb Raider, Metro Exodus, Control, and Assetto Corsa Competizione. Developers include EA, Square Enix, EPIC Games, and more. GeForce RTX graphics cards will be available worldwide, across 238 countries and territories at a starting price of $499. You can pre-order on nvidia.com. For more coverage of the news, including what motivated NVIDIA to develop these GPUs, read the NVIDIA blog. NVIDIA unveils a new Turing architecture: “The world’s first ray tracing GPU” NVIDIA’s Volta Tensor Core GPU hits performance milestones. But is it the best? NVIDIA open sources its material definition language, MDL SDK

Stanford University researchers have devised a new type of camera powered by artificial intelligence. This camera system is powered by two computers and can classify images faster while being more energy efficient. The underlying image recognition technology in today’s autonomous vehicles teach themselves to recognize objects around them. The problem with the current system is that the computers running the artificial intelligence algorithms are too large and slow for future handheld applications. For future applications to be viable and to be in production, the computers need to be much smaller. The hybrid optical-electronic system An assistant professor, Gordon Wetzstein with Julie Chang, a graduate student and first author on the paper Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification published in Nature Scientific Reports, married two types of computers into one. This created an optical-electronic hybrid computer whose aim is image analysis. The prototype camera’s first layer is an optical computer, which does not require power-intensive mathematical computing. The second layer is a conventional electronic computer. The optical computer physically preprocesses the image data, filtering it in multiple ways. An electronic computer would have had to do it mathematically otherwise. This layer operates with zero input power since the filtering happens naturally by light passing through the optics. A lot of time and power is saved in this hybrid model which would have been consumed by image computation. Chang said, “We’ve outsourced some of the math of artificial intelligence into the optics,” This results in fewer calculations which in turn means fewer calls to memory and far less time to complete the process. Skipping these preprocessing steps gives the digital computer a head start for the remaining analysis. Wetzstein said, “Millions of calculations are circumvented and it all happens at the speed of light. Some future version of our system would be especially useful in rapid decision-making applications, like autonomous vehicles,” Fast decision-making The prototype rivals the existing electronic-only computing processors in speed and accuracy. But the change here is that there are substantial computational cost savings which translates to time. The current prototype is arranged on a lab bench and could not be exactly classified as hand-held small. The researchers said that the system can one day be made small enough to be handheld. Wetzstein, Chang and the researchers at the Stanford Computational Imaging Lab are now working in ways to make the optical component do even more of the preprocessing. This would result in a smaller, faster AI camera system that can replace the trunk sized computers currently used in cars and drones. It is important to note that the system was successfully able to identify objects in both simulations and real-world experiments. For more information, you can visit the official Stanford news website and the research paper. Tesla is building its own AI hardware for self-driving cars AI powered Robotics : Autonomous machines in the making AutoAugment: Google’s research initiative to improve deep learning performance

Edge services and edge computing have been in talks since at least the 90s. When Edge computing is extended to the cloud it can be managed and consumed as if it were local infrastructure. The logic is simple. It’s the same as how humans find it hard to interact with infrastructure that is too far away. Edge Analytics is the exciting area of data analytics that is gaining a lot of attention these days. While traditional analytics, answer questions like what happened, why it happened, what is likely to happen and options on what you should do about it Edge analytics is data analytics in real time. It deals with the operations performed on data at the edge of a network either at or close to a sensor, a network switch or some other connected device. This saves time and overhead issues as well as latency problems. As they rightly say, Time is money! Now imagine using AI to facilitate edge analytics. What does AI in Edge Computing mean When Edge computing is extended to the cloud it can be managed and consumed as if it were local infrastructure. The logic is simple. It’s the same as how humans find it hard to interact with infrastructure that is too far away. Smart Applications these rely on sending tons of information to the cloud. Data can be compromised in such situations. As such, security and privacy challenges may arise. Application developers will have to consider whether the bulk of information sent to the cloud contains personally identifiable information (PII) and whether storing it is in breach of privacy laws. They’ll also have to take the necessary measures to secure the information they store and prevent it from being stolen, or accessed or shared illegally. Now that is a lot of work! Enter  “Intelligent Edge” computing used to save the day!  Edge computing by itself will not replace the power of the cloud. It can, however, reduce cloud payloads drastically when used in collaboration with machine learning. transform the AI’s operation model into that of the human brain: perform routine and time-critical decisions at the edge and only refer to the cloud where more intensive computation and historical analysis is needed. Why use AI in edge computing Most mobile apps, IoT devices and other applications that work with AI and machine learning algorithms and applications rely on the processing power of the cloud or on a datacenter situated thousands of miles away. They have little or no intelligence to apply processing at the edge. Even if you show your favorite pet picture to your smart device a thousand times, it’ll still have to look it up in its cloud server in order to recognize if its a dog or a cat for the 1001st time. OK, who cares if it takes a couple of minutes more for my device to differentiate between a dog and a cat! Let’s consider a robot surgeon which wants to perform a sensitive operation on a patient. It will need to be able to analyze images and make decisions dozens of times per second. The round trip to the cloud would cause lags that could have severe consequences. God forbid, if there is a cloud outage or poor internet connectivity. To perform this task efficiently, faster and to reduce the back and forth communication involved between the cloud and the device, implementing AI in edge is a good idea. Top 7 AI for edge computing use cases that caught our attention Now that you are convinced that Intelligent edge or AI powered edge computing does have potential, here are some recent advancements in edge AI and some ways it is being used in the real world. #1 Consumer IoT: Microsoft’s $5 billion investment in IoT to empower the intelligent cloud and the intelligent edge One of the central design principles of Microsoft’s intelligent edge products and services is to secure data no matter where it is stored. Azure Sphere is one of their intelligent edge solutions to power and protect connected microcontroller unit (MCU)-powered devices. There are 9 billion MCU-powered devices shipping every year, which power everything from household stoves and refrigerators to industrial equipment. That’s intelligent edge for you on the consumer end of the application spectrum. Let’s look at the industrial application use case next. #2  Industrial IoT: GE adds edge analytics, AI capabilities to its industrial IoT suite To make a mark in the field of industrial internet of things (IIoT), GE Digital is adding features to its Predix platform as a service (PaaS). This will let industrial enterprises run predictive analytics as close as possible to data sources, whether they be pumps, valves, heat exchangers, turbines or even machines on the move. The main idea behind edge computing is to analyze data in near real-time, optimize network traffic and cut costs. GE Digital has been working to integrate the company's field service management (FSM) software with GE products and third-party tools. For example, artificial intelligence-enabled predictive analytics now integrate the Apache Spark AI engine to improve service time estimates. New application integration features let service providers launch and share FSM data with third-party mobile applications installed on the same device. Read the whole story on Network World. #3 Embedded computing and robotics: Defining (artificial) intelligence at the edge for IoT systems Machine intelligence has largely been the domain of computer vision (CV) applications such as object recognition. While artificial intelligence technology is thus far still in its infancy, its benefits for advanced driver assistance systems (ADAS), collaborative robots (cobots), sense-and-avoid drones, and a host of other embedded applications are obvious. Related to the origins of AI technology is the fact that most, if not all, machine learning frameworks were developed to run on data center infrastructure. As a result, the software and tools required to create CNNs/DNNs for embedded targets have been lacking. In the embedded machine learning sense, this has meant that intricate knowledge of both embedded processing platforms and neural network creation has been a prerequisite for bringing AI to the embedded edge – a luxury most organizations do not have or is extremely time-consuming if they do. Thanks to embedded silicon vendors, this paradigm is set to shift. Based on the power consumption benchmarks, AI technology is quickly approaching deeply embedded levels. Read the whole article on embedded computing design to know more about how Intelligent edge is changing our outlook towards embedded systems. #4 Smart grids: Grid Edge Control and Analytics Grid Edge Controllers are intelligent servers, deployed as an interface between the edge nodes and the utility’s core network. Smart Grid, as we know, is essentially the concept of establishing a two-way communication between distribution infrastructure, consumer and the utility head end using Internet Protocol. From residential rooftops to solar farms, commercial solar, electric vehicles and wind farms, smart meters are generating a ton of data. This helps utilities to view the amount of energy available and required, allowing their demand response to become more efficient, avoid peaks and reduce costs. This data is first processed in the Grid Edge Controllers that perform local computation and analysis of the data, only sending necessary actionable information over a wireless network to the Utility. #5 Predictive maintenance: Oil and Gas Remote Monitoring Using Internet of Things devices such as temperature, humidity, pressure, and moisture sensors, alongside internet protocol (IP) cameras and other technologies, oil and gas monitoring operations produce an immense amount of data which provide key insights into the health of their specific systems. Edge computing allows this data to be analysed, processed, and then delivered to end-users in real-time. This, in turn, enables control centers to access data as it occurs in order to foresee and prevent malfunctions or incidents before they occur. #6 Cloudless Autonomous Vehicles Self-driving cars and intelligent traffic management systems are already the talk of the town today and the integration of edge AI could be the next big step. When it comes to autonomous systems, safety is paramount. Any delay, malfunction, or anomaly within autonomous vehicles can prove to be fatal. Calculating a number of parameters at the same time, edge computing and AI enables safe and fast transportation with quick decision making capabilities. #7 Intelligent Traffic Management Edge computing is able to analyse and process data on the traffic hardware itself and finds ways to remove unnecessary traffic. This reduces the overall amount of data that needs to be transmitted across a given network and helps to reduce both operating and storage costs. What’s next for AI enabled edge computing? The intelligent edge will allow humans to simplify multi-faceted processes by replacing the manual process of sorting and identifying complex data, key insights and actionable plans. This forte of technology can help humans gain a competitive edge by having better decision-making, improved ROI, operational efficiency and cost savings. However, on the flip side, there are also many cons to machine learning based edge computing.. The cost of deploying and managing an edge will be considerably huge. With all rapidly evolving technologies- evaluating, deploying and operating edge computing solutions has its risks. A key risk area being -security. Tons of data needs to be made available for processing at the edge and where there is data, there is always a fear of data breach. Performing so many operations on the data also can be challenging. All-in-All even though the concept of incorporating AI into edge computing is exciting, some work does need to be done to get intelligent edge-based solutions l fully set up, functional and running smoothly in production. What’s your take on this digital transformation? Reinforcement learning model optimizes brain cancer treatment Tesla is building its own AI hardware for self-driving cars OpenAI builds reinforcement learning based system giving robots human like dexterity

The Rust Language Server 0.130.5 announces its first 1.0 release candidate. The 1.0 release is available on the nightly and beta version and will be available with stable Rust version from 3rd September this year. RLS 1.0 will be able to handle most small and medium sized projects with certain limitations and improvements. Major highlights of this release are syntax highlighting, syntactic code completion and code intelligence.To easily install RLS you can install an extension of your favorite editor, for example: Visual Studio Code Atom Sublime Text Eclipse What’s new in RLS 1.0 release Syntax highlighting Each editor does its own syntax highlighting Code completion Code completion is syntactic, performed by Racer. Because it is syntactic there are many instances where it is incomplete or incorrect. Errors and warnings Errors and other diagnostics are displayed inline. Exactly how the errors are presented depends on the editor. Formatting By Rustfmt (which is also at the 1.0 release candidate stage). Clippy Clippy is installed as part of the RLS. You can turn it on with a setting in your editor or with the usual crate attribute. Code intelligence The RLS can do the following: type and docs on hover goto definition find all references find all implementations for traits and concrete types find all symbols in the file/project renaming (this will not work where a renaming would cause an error, such as where the field initialisation syntax is used) change glob imports to list imports For more information visit the release notes page. Rust 2018 Edition Preview 2 is here! Rust and Web Assembly announce ‘wasm-bindgen 0.2.16’ and the first release of ‘wasm-bindgen-futures’ Rust 1.28 is here with global allocators, nonZero types and more

When you ask Google to turn off locations, it actually tracks in incognito mode. This default setting opens up Google to a potentially huge fine as per Europe’s GDPR rules. Google is secretly tracking your moves When users turn off their location tracking, they expect Google to stop detecting where they are, but this is not the case. Google continues as a secret stalker without the consent of the user. Recently, Associated Press News reported about Google continuing to collect a user’s location points, while users think they are safe from being tracked. According to AP news, location tracking by Google continues even if the user disabled it; and following are some of the resulting issues: User settings governing location markers are in different places Location tracking can be "Paused", but not permanently disabled Location tracking continues in Maps, Search and other Google applications regardless of the "Location History" setting. Warnings provided to both iOS and Android users are misleading How is Google’s location tracking violating EU’s new GDPR rules? In the month of May, this year, Europe announced its much anticipated new privacy law known as the General Data Protection Regulation (GDPR). This law has been virtually impacting every technology worldwide. As per the GDPR law, any company operating in the EU or any company that serves EU citizens should abide by its strict new privacy guidelines meant to protect consumers from companies abusing their personal data. Any company failing to comply with these rules faces financial penalties as high as 4 percent of their annual revenue. For Google, this penalty could mean billions of dollars in fine! GDPR’s data minimisation principle states that data collection should be done for specified, explicit and legitimate purposes for which they are processed. Serena Tierney, a partner at VWV law firm and a data protection and privacy specialist, said to The Register, “The legitimate purpose of the data collection must be clear. Is it only used for Google's own internal machine learning algorithms, say, or is it part of a personal profile sold to advertisers?” "It's part of a wider public debate. Is this part of the social contract between society generally (including me) and search engines (including Google) that in return for getting free search, for example, we expect our personal data to be used for personal advertising, with no way for us to opt out?" Tierney continued. Rafe Laguna, an open source infrastructure provider of Open-Xchange, says, “The Google location scandal could be the first real test of GDPR. The regulation states that user consent must be clear, distinguishable and written in plain language.” Google updated its location policies: “Some location data may be saved” Right after Google faced investigation by the AP regarding its location tracking practice, it made some quick updates to its location history feature. According to a report from Associated Press, Google, in this update made on 16th August, acknowledges that it still tracks users via its Google Maps, weather updates, and browser searches services. As per Google’s help page for location history setting, “some location data may be saved as part of your activity on other services, like Search and Maps.” The Location History toggle won’t actually stop Google from tracking users. However, users can turn it off by disabling the “Web and App Activity” option (which is enabled by default). By disabling the option, Google won’t be able to store and track user’s Maps’ data and browser searches for location anymore. To know more about this evolving story in detail, visit Associated Press News’ full coverage. Microsoft Cloud Services get GDPR Enhancements Machine learning APIs for Google Cloud Platform Build an IoT application with Google Cloud [Tutorial]

RxAndroid 2.1.0, the latest version of RxAndroid, has an option to post async messages. The async parameter affects Android APIs 16 and newer. It allows one to avoid VSYNC locking that pushes every post to the next frame. RxAndroid adds minimum classes to RxJava to make reactive programming in Android applications easy and hassle-free. Why this change is introduced? The main thread has historically used Handler#post() to schedule new Messages. This follows the VSYNC locking and results in waiting until the next frame to run. Use of Handler#post() causes a delay of up to 16ms for every emission to go through the post(). This delay happens even if you’re already on the main thread. The Async API aims to bypass VSYNC locking while still letting the framework handle all the scheduling safely in its looper. How you can install the Async API? With the help of RxAndroidPlugins you can set async as the custom scheduler. In Kotlin: val asyncMainThreadScheduler = AndroidSchedulers.from(Looper.getMainLooper(), true) RxAndroidPlugins.setInitMainThreadSchedulerHandler { asyncMainThreadScheduler } // Or if the default scheduler is already initialiazedRxAndroidPlugins.setMainThreadSchedulerHandler { asyncMainThreadScheduler } In Java: Scheduler asyncMainThreadScheduler = AndroidSchedulers.from(Looper.getMainLooper(), true); RxAndroidPlugins.setInitMainThreadSchedulerHandler(callable -> asyncMainThreadScheduler); // Or if the default scheduler is already initialiazed RxAndroidPlugins.setMainThreadSchedulerHandler(scheduler -> asyncMainThreadScheduler); How asynchronous is enabled in the different Android APIs? The Async API works by relying on the new Handler.createAsync factory in API 28, and on pre-28 it will reflectively fall back to a private constructor of Handler to enable this. API 28: The Handler.createAsync() factory API is used. It sets all Messages it handles to be asynchronous by default. API 22+: The public setAsynchronous() method is used. API [16–21]: The setAsynchronous() method is still used but the lint error that says it’s only 22+ is suppressed. To avoid any OEM situations of deleted/changed internal APIs, the Message#setAsynchronous() method call is handled using try/catch in the from() Scheduler factory to ensure it’s there at runtime. This catches the NoSuchMethodError if it is missing and falls back to the standard non-async messaging. API <16: There is no behavior change and the standard non-async messaging is used since the asynchronous APIs didn’t exist. To know more about the Async API head over to Zac Sweers announcement on Medium. Entry level phones to taste the Go edition of the Android 9.0 Pie version Android 9 pie’s Smart Linkify: How Android’s new machine learning based feature works Dagger 2.17, a dependency injection framework for Java and Android, is now out!

git-bug is a distributed bug tracker that is embedded in git. Using git's internal storage ensures that no files are added in your project. You can push your bugs to the same git remote that you are already using to collaborate with other people. The main idea behind implementing a distributed bug tracker in Git was to stop relying on a web service somewhere to deal with bugs. Browsing and editing bug reports offline wouldn’t be much of a pain, thanks to this implementation. While git-bug addresses a pressing need, note that the project is not yet available for full fledged use and is currently a proof of concept released just 3 days ago at version 0.2.0. Reddit is abuzz with views on the release. A user quotes- Source: reddit.com Certain users also had counter thoughts on the cons of the release - Source: reddit.com   Now that you want to get your hands on git-bug, let’s look at how to get started. Installing git-bug, Linux packages needed and CLI usage for its implementation To install the git-bug, all you need to do is execute the following command- go get github.com/MichaelMure/git-bug If it's not done already, add golang binary directory in your PATH: export PATH=$PATH:$GOROOT/bin:$GOPATH/bin You can set pre-compiled binaries by following 3 simple steps: Head over to the release page and download the appropriate binary for your system. Copy the binary anywhere in your PATH Rename the binary to git-bug (or git-bug.exe on windows) The only linux packge needed for this release is the Archlinux (AUR) Further, you can use the CLI to implement the git-bug using the following commands- Create a new bug: git bug new Your favorite editor will open to write a title and a message. You can push your new entry to a remote: git bug push [<remote>] And pull for updates: git bug pull [<remote>] List existing bugs: git bug ls   Use commands like show, comment, open or close to display and modify bugs. For more details about each command, you can run git bug <command> --help or scan the command's documentation. Features of the git-bug #1 Interactive User Interface for the terminal Use the git bug termui  command to browse and edit bugs. This short video will demonstrate how easy and interactive it is to browse and edit bugs #2 Launch a rich Web UI Take a look at the awesome web UI that is obtained with git bug webui. Source: github.com     Source: github.com   This web UI is entirely packed inside the same go binary and serve static content through a localhost http server. It connects to  backend through a GraphQL API. Take a look at the schema for more clarity. The additional features that are planned include media embedding import/export of github issue extendable data model to support arbitrary bug tracker inflatable raptor Every new release is expected to come with exciting new features, it is also coupled with a few minor constraints. You can check out some of the minor inconveniences as listed out on the github page. We can’t wait for the release to be in a fully working condition. But before that, if you need any additional information on how the git-bug works, head over to the github page. Snapchat source code leaked and posted to GitHub GitHub open sources its GitHub Load Balancer (GLB) Director Homebrew’s Github repo got hacked in 30 mins. How can open source projects fight supply chain attacks?