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Day 2 of the International Consumer Electronics Show (CES 2018) witnessed major contributions by Intel, Samsung, Qualcomm, Udacity, and LG in incorporating AI in their technologies and products. Here are the top highlights: Intel shows off its neuromorphic AI chip and a 49 qubit quantum chip Intel announced that its Loihi AI chip, launched on September 17, is now fully-functional and ready to be shared with research partners. Loihi is Intel’s first neuromorphic chip, designed to mimic the functioning of neurons and synapses in the brain. They are less flexible and more powerful than most general-purpose chips. The Loihi chips don’t require a huge amount of training data to learn a process and are energy efficient. Currently, its functionality is limited to simple object recognition. However further applications of these chips are likely to be in robotics and self-driving cars. Intel also unveiled its Tangle Lake chip—a superconducting quantum test chip of 49 qubits at CES 2018. The 49-qubit chip builds upon their earlier work with 17-qubit arrays. Intel has also developed packaging to prevent radio-frequency interference with the qubits. They use a flip chip technology that enables smaller and denser connections to get signals on and off the chips. This new announcement has put Intel in a good position with IBM and Google as far as the quantum computing race goes. Qualcomm plans to make a smart speaker development kit and extends its support to popular voice assistants Qualcomm announced their plans for the first half of 2018 at the ongoing CES event. First, they plan to make a smart speaker development kit based on the Qualcomm Smart Audio Platform. The development kit is engineered to help developers and audio manufacturers simplify the development of smart speaker products. The development kit will feature a Wi-Fi certified System-on-Module (SoM) that integrates the key system components. The kit also includes schematics and design files to support easier customization and differentiation in the manufacturers’ products. Additionally, the development kit offers a reference design for smart speaker devices. The company also announced that its Smart Audio Platform will now allow developers to choose which assistant they want to incorporate into their smart speakers. A choice of voice assistants from Amazon’s Alexa, Microsoft’s Cortana, and Google assistant will also allow other hardware manufacturers to more easily build devices that support these virtual assistants. LG plans to intelligently enhance TV images using computer vision LG announced its plans to apply AI to enhance TV images using state-of-the-art computer vision at the ongoing CES 2018. They will apply object-based enhancement to TV images. Applying object recognition AI will help in smoothing color banding and also help in identifying faces in a picture or distinguishing between, say a cat from a dog. Thus every image or scene will be parsed more intelligently. However, this announcement is at a very early stage, as more progress is still to happen. Apart from this, LG also talked about the potential of its new AI platform, ThinQ, for bringing deep learning and interoperability to the company's smart products. LG smart appliances will have the ability to learn habits over time and communicate with each other. Samsung plans to connect all its products with the IoT cloud by 2020 Samsung announced plans to connect 90 percent of their products with the IoT cloud at the CES 2018.  Additionally, the products will have artificial intelligence capabilities through Samsung’s virtual assistant Bixby. Samsung’s SmartThings Cloud service will also be available this spring. The SmartThings Cloud would allow people to control IoT devices from a single app, instead of having one for each gadget. Apart from working with Samsung products, it will also connect with cars running on Harman's Ignite cloud and other products that work with SmartThings. Samsung also unveiled its modular TV, called The Wall with customizable size configurations. It will also have built-in Bixby support for searching for TV shows, movies, weather reports, play songs, show photos from the cloud etc. Udacity and Baidu partner to come up with AI courses for building self-driving cars Baidu, one of the leading AI organizations has partnered with Udacity, an online education platform for building courses together. This collaboration was announced by Baidu’s COO Qi Liu and Udacity founder Sebastian Thrun at the ongoing CES 2018. According to Thrun, the AI expertise required to build self-driving cars is depleting and thus courses and programs like these are necessary to bring more talent in this area. Apart from these, Udacity will also make contributions in Baidu’s Apollo which is an open platform for autonomous driving. In regards to this Udacity will offer an Introduction to Apollo program and help Apollo with talent identification and acquisition. The program will be free of cost to the aspirants and will cover the entire Apollo software and simulation environment with hands-on learning opportunities. More advancements and announcements are bound to continue for the next 3 days as more organizations showcase their innovative products. Keep an eye on our website for further updates.

The world’s biggest consumer technology show is here! Presenting CES 2018 that commenced last weekend. This new year, multiple tech firms such as LG, Sony, Samsung have launched brand new OLED screen televisions, smart laptops, speakers, and so on with next-gen technologies for their consumers. To know about these in detail, you can visit the link here. In this article, we explore how tech giants such as Nvidia, Intel, and AMD have leveraged AI and ML to launch next-gen products. Let’s take a brief look at each one’s contribution at the CES 2018. Nvidia Highlights at CES 2018 Nvidia unveiled their Xavier SoC(System on a Chip) autonomous machine intelligence processors at CES this year. The Xavier has over 9 billion transistors with a custom 8-core CPU, a 512-core Volta GPU, an 8K HDR video processor, a deep-learning accelerator and new computer-vision accelerators. With all these huge figures, Xavier can crunch more sensor and vehicle data for the AI systems that will power self-driving vehicles. The other striking features of this SoC are, it can perform 30 trillion operations per second using only 30 watts of power, and is 15 times more efficient than the previous architecture. Nvidia also announced three new variants of its DRIVE AI platform. These new variants are based around Xavier SoCs. The three variants include: Drive AR focuses on getting Augmented Reality into vehicles, which can enhance and transform the driving experience. It offers developers with an SDK, which will further enable them to build experiences that leverage computer vision, graphics and artificial intelligence capabilities to do things like overlay information about road conditions, points of interest and other real-world locations using interactive in-car displays. Drive IX would formulate an easy way to build and deploy in-car AI assistants. These assistants will be capable of incorporating both interior and exterior sensor data to interact not only with drivers but also with passengers on the road. The third DRIVE AI-based platform is a revision of its existing autonomous taxi brain, Pegasus. This new version improves on the previously revealed preproduction edition by compiling two Xavier SoCs with two Nvidia GPUs into a package that’s roughly the size of a license plate – down from the trunk-filling physical footprint of the original. Nvidia also announced that it is partnering with two Chinese companies Baidu and automaker ZF, for bringing autonomous driving to roads. Nvidia’s CEO Jensen Huang stated that Nvidia’s Drive Xavier auto compute platform would be used for Baidu’s Apollo Project. The Apollo project offers an open platform for self-driving cars in partnership with a wide variety of automakers, suppliers and tech companies. Huang also revealed that Nvidia will be supplying its self-driving computer hardware to Aurora, a Google start-up. Aurora would build self-driving systems for both Volkswagen and Hyundai, the startup revealed last week. Also, Uber has chosen Nvidia as one of its key technology partners in its fleet of self-driving, specifically to provide the AI computing aspects of its autonomous software. Uber has used Nvidia’s GPUs in both its self-driving ride-hailing test fleet and in its self-driving transport trucks, which are also developed by its Advanced Technologies Group. Intel Highlights at CES 2018 Intel in collaboration with AMD has unveiled new processors with the help of AMD’s Radeon RX Vega M graphics. These new core processors are Intel’s first CPU with discrete graphics included in a single package. This leads to an incredibly thin and lightweight laptops and desktops that are able to provide an impressive gaming performance with an added 4K media streaming. As per Intel, these chips would be the first example of power-sharing across CPU and GPU, the first consumer mobile chips to use HBM2 (the second-generation high bandwidth memory, a faster type of graphics memory), and also the first consumer solution to use Intel EMIB(Embedded Multi-die Interconnect Bridge). To know more about this in detail please visit the link given here. At CES 2018, Intel unveiled its new mini-PC NUC system, formerly codenamed Hades Canyon. This system aims at premium virtual reality (VR) applications. The system comes in two versions, the NUC8i7HVK and the NUC8i7HNK. The NUC8i7HVK: comes with Radeon RX Vega M GH graphics can operate from 1,063MHz to 1,190MHz It has an 8th-gen quad-core 100W Intel Core i7-8809G 3.1GHz with 4.2GHz turbo mode, and is "unlocked and VR-capable". The NUC8i7HNK: comes with Radeon RX Vega M GL graphics with an operating range of 931MHz-1,011MHz. It also has a 65W quad-core 8th-gen Intel Core i7-8705G 3.1GHz CPU with 4.1GHz turbo mode. To know more about this news in detail, visit the link here. AMD Highlights at CES 2018 AMD announced its brand new Ryzen 3 2300U APU chips specifically designed for affordable laptops and Chromebooks. The Ryzen 3 2300U is a full-featured chip featuring 4 cores and 4 threads clocked at a base 2.0GHz and boost 3.4GHz. Its APU comes with full-on Radeon RX Vega graphics powered by six compute units. In addition to the dual-core, Ryzen 3 2200U runs with 4 threads at a standard 2.5GHz frequency that boosts up to 3.4GHz. It also features Radeon RX Vega graphics similar to other APUs in the family but requires only three compute units to power it. AMD announced a new set of Ryzen chips for desktops i.e desktop Ryzen APUs in order to replace its ongoing Athlon chips. AMD’s new APUs are based on the Raven Ridge Architecture, and is a combination of an updated version of Ryzen processor with “discrete-class” Radeon RX Vega graphics. AMD has introduced two chips: Ryzen 5 2400G APU includes 4 cores and 8 threads clocked at a base 3.6Ghz and is boosted with 3.9GHz. On top of the processor, this new chip features Radeon RX Graphics with 11 compute units for playable gaming experiences at 1080p and high-quality settings.  Ryzen 3 2200G is rated for 3.5GHz base and 3.7GHz boost clock speeds. This entry-level APU also comes outfitted with 4 cores, but only 4 threads, as well as just 8, compute units attached to its Radeon RX Vega GPU. AMD also spoke about its new Ryzen 2 which would hit the market around April this year, which would have: A new 12nm Zen architecture, which out-smalls the 14nm transistors of Intel Coffee Lake. This upcoming chip brings higher clock speeds and Precision Boost 2 technology for greater performance and efficiency. To know more about this news in detail, click on the link here. Apart from well-known names such as Nvidia, Intel, and AMD, Ceva, the leading licensor of Signal processing platforms and AI processors, unveiled NeuPro. NeuPro is a powerful and specialized Artificial Intelligence (AI) processor family for deep learning inference at the edge. It is designed for edge device vendors who can quickly take advantage of the significant possibilities that deep neural network technologies offer. NeuPro extends the use of AI beyond machine vision to new edge-based applications including natural language processing, real-time translation, authentication, workflow management, and many other learning-based applications. With 4 more days to go, many such advancements are expected to be announced at the CES 2018. Watch this space in the coming days for more.

Nvidia’s new AI platforms, AI processors by Ceva, a new platform for many agent reinforcement learning, and more in today’s top stories around artificial intelligence, blockchain, and data science news. 1. Ethereum Foundation is looking for outside developers to help them solve Blockchain’s scaling problem Ethereum creators are exploring newer ways to fix the inability of blockchains to effectively scale. They are inviting outside developers to help solve the scaling problem. Until now, Ethereum has explored two possible fixes for the problem. The first solution is sharding which would require a small percentage of nodes to see and process every transaction, allowing many more transactions to be processed in parallel at the same time. The second solution involves creating data-link layers or layer 2 protocols that send most transactions off-chain and only interact with the underlying blockchain in order to enter and exit from the layer-2 system or in case of attacks on the system. A specification for an initial prototype is close to finalized and the next step involves building a reference implementation in python on top of Py-EVM, and a testnet in python. Outside developers are now invited to get involved in this sharding testnet and then the sharding mainnet steps. Ethereum is offering subsidies ranging from $50,000 to $1 million to programmers who can help find the fixes. Interested developers can send their proposals to [email protected]. For more information visit here. 2. Nvidia announces three new variants of the DRIVE AI platform based around Xavier SoCs At the ongoing CES 2018, Nvidia has announced three new variants of its DRIVE AI platform, which are based around Xavier SoCs. The Xavier autonomous machine intelligence processors are now shipping out to customers, after being unveiled last year. Most of Nvidia’s initiatives, this year,  revolve around self-driving cars and its platform for allowing car manufacturers to build their own. DRIVE AR, the first of the DRIVE AI offerings, aims at enhancing and transforming the driving experience by adding augmented reality into vehicles leveraging computer vision, graphics, and artificial intelligence capabilities. DRIVE IX, the second platform, helps developers build and deploy in-car AI assistants. These AI assistants will interact with drivers as well as passengers on the road by incorporating both interior and exterior sensor data. Apart, from these, Nvidia has launched a revision of Pegasus, it’s autonomous taxi brain. According to them, "it delivers the performance of a trunk full of PCs in an auto-grade form factor the size of a license plate" Nvidia is currently working with at least 25 customers using Pegasus to power their self-driving robotaxi fleet. 3. Volkswagen joins forces with Nvidia to use AI in its new electric microbus Volkswagen joins forces with Nvidia to use it’s Drive IX platform in some of its upcoming vehicles, including the I.D. Buzz electric bus. Drive IX, announced at CES 2018, is a software developer kit that Nvidia created to tap into the power of Xavier. Volkswagen will use it to build features like facial recognition, gesture control, natural language processing, and more in their microbus. Volkswagen will initially focus on building Intelligent Co-Pilot features and using sensor data to make driving easier, safer and more convenient for drivers. Volkswagen will also work with Drive AR, a new augmented reality-based SDK from Nvidia to incorporate augmented reality into vehicles. The partnership between the two companies is also likely to be extended to future vehicles. 4. MAgent: A new platform for Many-Agent Reinforcement Learning MAgent is a new platform to support research and development of many agent reinforcement learning. Instead of using single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents' optimal policies but more importantly, the observation and understanding of individual agent's behaviors and social phenomena emerging from the AI society, including communication languages, leadership, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. You can read the AAAI 2018 demo paper here. You can also watch the demo video for some interesting showcases here. 5. CEVA brings deep learning at the edge with NeuPro, a family of AI Processors Ceva Inc has unveiled NeuPro, an AI processor family for deep learning inference at the edge, at the CES 2018. It is designed for edge device vendors looking to quickly take advantage of the significant possibilities that deep neural network technologies offer. The AI processors offer performance ranging from 2 Tera Ops Per Second (TOPS) for the entry-level processor and 12.5 TOPS for the most advanced configuration. The NeuPro processor line extends the use of AI to new edge-based applications such as natural language processing, real-time translation, authentication, workflow management, etc. The NeuPro family comprises four AI processors offering different levels of parallel processing: NP500 the smallest processor, targeting IoT, wearables, and cameras. NP1000 targeting mid-range smartphones, ADAS, industrial applications and AR/VR headsets. NP2000 for high-end smartphones, surveillance, robots, and drones. NP4000 for high-performance edge processing in enterprise surveillance and autonomous driving.

Ethereum’s new high, a new blockchain venture, Chrome’s counter deceptive extension and more in today’s top stories around machine learning, blockchain, and data science news. 1. Cryptocurrencies on the rise: Ethereum crosses $1,000 for the first time Ethereum, the third largest cryptocurrency by market value soared to new records on 5th January ’18. Ethereum prices rose above $1,000 per unit on early Thursday for the first time ever. Bitcoin investors are increasingly looking towards alternative currencies such as Ethereum, Ripple, and Litecoin, which could be one of the reasons for the rise in price. Following this news, renowned bankers such as Credit Suisse, Barclays, and UBS announced plans to test the Ethereum blockchain in the hopes of making it easier to meet new European Union reporting standards under the Markets in Financial Instruments Directive II. Apart from this, investors have also helped push up the price of another rival cryptocurrency, Ripple, in part because more banks and institutions agreed to partner up with its community in a bid to speed up transactions, and growing investor interest. Ripple has overshadowed Ethereum’s recent surge outranking the latter’s $100 billion value by nearly $40 billion and now ranks as the second largest cryptocurrency by market capitalization, according to CoinMarketCap. 2. Google Chrome brings machine learning to counter deceptive extension installs Google Chrome plans to expand its abuse protection capabilities to further reduce user harm. Chrome will upgrade their automated inline installation abuse detection to improve their detection speed which would better detect extensions using deceptive or confusing installation flows. This implementation is expected to start in a few weeks. Additionally, this expanded enforcement will also use machine learning to evaluate each inline installation request for signs of deceptive, confusing, or malicious ads or web pages. On finding any of those malicious signals, Chrome would selectively disable that one inline installation request and redirect the user to the extension’s page on the Chrome Web Store. This selective enforcement will not impact inline installation of that extension from other, non-deceptive sources. To know more about this new implementation by Chrome visit Inline Installation Enforcement FAQ. 3. Datametrex AI and Bitnine form a Graph Blockchain Joint Venture Datametrex AI Ltd and its San Francisco based joint venture partner Bitnine Global Inc. plan to spin out a joint venture entity Graph Blockchain Limited. Graph Blockchain leverages graph database and blockchain technology and provides a unique way of organizing, analyzing and displaying blockchain transactional data in real-time. It presents Blockchain data up to 1,000 times faster than traditional methods from 7- 7000 transactions per second. It can effectively store, manage and present Blockchain transactions specifically in peer to peer networks making it ideal for Fintech, Banking and other mission-critical environments. Graph Blockchain has a contract with Revive Therapeutics Ltd. to develop the blockchain component in Revive’s proprietary patient-focused program. 4. Google Cloud platform announces the beta release of GPUs attached to Preemptible VMs Google Cloud announced the beta release of preemptible GPUs on 5th January 2018. Users can now attach NVIDIA K80 and NVIDIA P100 GPUs to Preemptible VMs for $0.22 and $0.73 per GPU hour, respectively. This is 50% cheaper than GPUs attached to on-demand instances. Preemptible VMs are highly affordable, short-lived compute instances suitable for batch jobs and fault-tolerant workloads. Last year, Google introduced lower pricing for Local SSDs attached to Preemptible VMs, expanding preemptible cloud resources to high-performance storage. Preemptible GPUs should be a good fit for any fault-tolerant machine learning workloads and other computation-heavy workloads. With Preemptible GPUs customers can now harness the power of GPUs to run distributed batch workloads at predictably affordable prices. Resources attached to Preemptible VMs have two key differences as compared to the on-demand resources. The Preemptible VMs can be used for a maximum of 24 hours and the compute Engine may shut them down after a 30-second warning. Customers can simply append --preemptible to the instance create command in the cloud to get started. Alternatively, they can specify scheduling.preemptible to true in the REST API or set Preemptibility to "On" in the Google Cloud Platform Console and then attach a GPU as usual. 5. Deepsense.ai trains United Nations in image recognition and deep Learning Deepsense.ai have trained a team of UN analysts in image recognition and other deep learning areas to help UN leverage its competencies in the field of artificial intelligence. The workshop consisted of a theoretical part and a hands-on coding session to teach participants advanced topics in the field of image recognition. This session was a follow-up of the workshop on text analysis which happened in 2016. UN and its stakeholders want to leverage data analytics, NLP, translation and cognitive computing to gain deeper insights into global macroeconomic and social trends. Which was why they invited deepsense.ai to share its expertise in the fast-evolving AI field.

Samsung’s Exynos 9, TensorFlow 1.5 RC, Baidu’s collaboration with Blackberry and more in today’s top stories around machine learning, deep learning, and data science news. 1. Samsung announces Exynos 9810 with sophisticated image recognition and deep learning Samsung unveiled its latest premium application processor, the Exynos 9 Series 9810 on 4th Jan,2018. The powerful third-generation custom CPU is equipped with a ultra-fast 1.2Gbps LTE modem and sophisticated image processing with deep learning-based software. Samsung’s new processor follows at the heels of Apple’s revelation of it’s neural chip for iphone X, a few months back. The neural network-based deep learning software of Exynos 9810 accurately recognizes people or items in photos for fast image searching or categorization. It also uses depth-sensing to scan a user’s face in 3D for hybrid face detection. Hybrid face detection enables realistic face-tracking filters as well as stronger security when unlocking a device with one’s face. Apart from this, the intelligent image processing feature allows for a real-time out-of-focus feature and filming and capturing of high-quality images in the dark and while on the move. Samsung’s Exynos 9 Series 9810 has been selected as a CES 2018 Innovation Awards Honoree in the Embedded Technologies product category. 2. TensorFlow 1.5 RC is out Introducing TensorFlow 1.5 release candidate with some breaking changes and other exciting major features and improvements. The breaking changes include: Prebuilt binaries are now built against CUDA 9 and cuDNN 7. Linux binaries are built using ubuntu 16 containers, potentially introducing glibc incompatibility issues with ubuntu 14. Starting from 1.6 release, prebuilt binaries will use AVX instructions. This may break TF on older CPUs. Other major features and improvements in the list are: It is prebuilt for CUDA9, cuDNN7 eager execution mode TensorFlow Lite’s dev preview is now available To know about this release in detail, visit the GitHub link. 3. Baidu teams up with BlackBerry on self-driving and connected car tech Chinese internet tech giant Baidu partners with Canada’s BlackBerry. This partnership will result in QNX being used as the basis for Baidu’s Apollo self-driving platform, popularly called by the press as the “Android” of the autonomous driving industry. The two companies also said they will integrate Baidu’s CarLife (a leading smartphone integration software for connected cars in China), DuerOS AI assistant and HD maps into BlackBerry’s QNX infotainment software. This partnership is a big one for BlackBerry, since it provides a way for the auto industry supplier to offer key features important in the Chinese market, which is the leading industry driving force in the automotive world today. For Baidu, it also means picking up a platform foundation for its open self-driving software project that has all the necessary vehicle industry safety certifications, and a proven track record. 4. U.S. Cellular to leverage Nokia’s machine learning and analytics to plan small cell build-outs U.S.Cellular to purchase Nokia’s innovative machine learning technology for planning its cell site and small cell build-outs more efficiently. In a release, Michael Irizarry, Ph.D., the operator’s EVP and CTO said that by using Nokia’s technology, this system will revolutionize the way they apply their various network data inputs to gain insights, predict outcomes and align resources to directly impact how their customers experience their network. She further added, “We know that the network is the backbone of our customers’ experience, and this collaboration with Nokia will allow us to better understand our customers’ data demands in order to exceed their expectations.” U.S. Cellular would deploy Nokia’s Customer Experience Management portfolio, including its “Cognitive Analytics for Customer Insight,” which Nokia describes as a machine learning-powered application. This would provide a complete view of customer satisfaction, revenue, and device and network performance. The service will alert U.S. Cellular engineers to unusual network activity and allow them to better predict and determine network performance, the companies said, as well as potentially allow the operator to create new services. 5. Microsoft acquires Avere Systems to further hybrid computing mission Microsoft acquires Avere Systems, a leading provider of high-performance NFS and SMB file-based storage for Linux and Windows clients running in the cloud, hybrid and on-premises environments. Avere makes use of an innovative combination of file system and caching technologies to support the performance requirements for customers who run large-scale compute workloads. By combining Avere’s storage expertise with the power of Microsoft’s cloud, customers will benefit from industry-leading innovations which will enable the largest, most complex high-performance workloads to run in Microsoft Azure. 6. Opera launches version 50 of its desktop browser with new cryptojacking feature   Opera launched the version 50 of its popular web browser with built-in cryptocurrency mining protection. The innovative anti-Bitcoin mining feature eliminates cryptocurrency mining scripts that overuse a device’s computing ability. Users can activate this feature by enabling Opera’s ad blocker to prevent cryptocurrency mining websites from hijacking a user’s CPU to mine for virtual currency without their knowledge. Users can find and change NoCoin in Settings (Preferences on macOS) > Basic > Block ads and under the Recommended lists of ad filters. Unlike Firefox and Chrome, which use extensions, the new cryptojacking feature in Opera is automatically enabled when you turn on the browser’s ad blocking tool. Opera’s v50 browser also includes support for streaming videos to Chromecast and a built-in VR player that lets Oculus Rift users view 360-degree videos in their headsets.

XGBoost 0.7 release, Travis Oliphant’s new startup, Deepmind’s dm_control, Community connectors and more in today’s top stories around machine learning, deep learning, and data science news.   DeepMind releases dm_control, a Control Suite and Control Package for reinforcement learning DeepMind released dm_control, the Deepmind Control Suit designed as performance benchmarks for reinforcement learning agents. The DeepMind Control Suite is a set of python reinforcement learning environments powered by the MuJoCo physics engine. It also includes libraries that provide Python bindings to the MuJoCo physics engine. The continuous control tasks are used for the design and performance comparison of reinforcement learning algorithms and are easy to use and modify. It includes a standardized structure and interpretable rewards, serving as performance benchmarks for RL agents. The uniform reward structure allows for robust suite-wide performance measures. The Control Suite is publicly available at their GitHub repository. A video summary of all tasks is available on youtube. PostgreSQL is the DBMS of the Year 2017 PostgreSQL was declared the DBMS of the year 2017 by DB-Engines, an information portal on database management systems. The DB-Engines Ranking is a monthly list of DBMS ranked by their current popularity. In 2017, PostgreSQL gained the most popularity from a pool of 341 monitored systems and was ranked number 1. DB-Engines derive popularity scores for each DBMS by subtracting the popularity scores of the previous scores from the latest scores. These scores are calculated based on a number of parameters. In 2017, PostgreSQL gained 55.81 scoring points outperforming all other systems in 2017. The new release of PostgreSQL 10 is considered to be a major factor behind this ranking. PostgreSQL 10 specifically focused on enhancements for effectively distributing data across many nodes. Google Data Studio introduces Community Connectors to channel data in a better way A new feature rolled out with Google Data studio named as community connectors. Google Data studio allows users to build free live, interactive dashboards. Using this, users can fetch their data from multiple sources and create unlimited reports in data studio, with full editing and sharing capabilities. The addition of community connectors lets users explore the Apps Script to build connectors to any internet accessible data source. Users can also share the community connectors with others in order to access their own data from within Data Studio. Some of the reasons why you should use Google’s Community Connectors are, to leverage Data Studio as a reporting platform for your customers, to reach a larger audience, and to develop customized enterprise solutions. To know more about how to build your community connectors visit the link here. Travis Oliphant, the Anaconda founder plans to unveil a new startup for helping organizations apply AI and ML to enterprise problems. Travis Oliphant, the founder of Anaconda and creator of popular python libraries Numpy, Scipy and Numba, tweeted that he is leaving full-time employment in Anaconda as of 01-01-2018. Anaconda is an open Data Science platform powered by Python. Travis was responsible for directing Community Innovation across the company while providing oversight over the data science platform. In 2018, he will be working on improving OSS sustainability through non-profit work and is starting a new services/product incubation company to help organizations make better use of OSS and AI/ML. His new company, Quansight is a services firm that helps companies take advantage of Open Source Software (OSS) for obtaining quantitative insight on their data. They advise organizations on using OSS effectively (including all the libraries in Anaconda) and help them in applying the capabilities of artificial intelligence and modern machine learning to their biggest problems. They also connect domain experts to companies and ideas and incubate enterprise products. XGBoost v0.7 is officially here! XGBoost released the 0.7 version of their popular open-source gradient boosting framework. XGBoost is short for Extreme Gradient Boosting. The new 0.7 version represents a major change from the last release (v0.6), which was released 18 months ago. The new features include Updated Sklearn API Refactored gbm to allow more friendly cache strategy Robust DMatrix construction from a sparse matrix Faster construction of DMatrix from 2D NumPy matrices: elide copies, use of multiple threads Automatic removal of nan from input data when it is sparse. Fixing of single-instance prediction function to obtain correct predictions Factoring out of Predictor interface (in a manner similar to the updater interface). Makefile support for Solaris and ARM Test code coverage using Codecov CPP tests added Dockerfile and Jenkinsfile to support continuous integration for GPU code New parameters in Python and R package Updated Documentation More details about the new version update can be found at their github. Numba 0.36.2 patch release: Support for CUDA 9 and import fix for Python versions prior to 2.7.9. The Numba version 0.36.2 patch release was announced by Stanley Seibert. This release has only two minor patches. Firstly, it adds support for the CUDA 9.x toolkit, and secondly, it fixes the syntax error with the exec causing import errors in Python 2.7.0 through 2.7.8. Numba will now work on the new toolkit--if present, though the community has not still released the cudatoolkit 9 conda packages into the Anaconda default channel, as it is still under some testing. There are also some CUDA compatibility changes to look at. The community will drop the support for CUDA 7.5 and older in Numba once it officially starts supporting CUDA 9 in Anaconda. Also, NVIDIA has dropped support for compute capability 2.x from CUDA 9, so Numba will not work with these older cards when the CUDA 9 toolkit is present. Note that newer NVIDIA GPU drivers are backward compatible with older toolkits. One cannot use the CUDA 9.1 toolkit unless the GPU drivers are new enough. Since GPU drivers can only be upgraded by system administrators, this is why the community supports a range of CUDA toolkits in Anaconda (and Numba). Atonix Digital™ to provide data-driven infrastructure solutions Atonix Digital focuses on software development, sales, and delivery, as well as innovative analytics through the proprietary ASSET360® data analytics platform developed by Black & Veatch. The cloud-based ASSET360®   captures integrate and analyzes data to generate actionable information while providing comprehensive system awareness to businesses and municipalities, boosting operational performance. Atonix’s software solutions aim at removing guesswork by easily modeling future scenarios and predicting outcomes often within seconds. It provides assistance for sharp planning, risk analysis, and budget control. Atonix also gives businesses a data-driven edge that brings into focus trends, issues and opportunities while maximizing efficiency, revenue, and performance.

Laceli AI compute stick, Parris, automated ML training tool, EZPOS ICO and Ripple’s effect on Ethereum in today’s top stories around machine learning, blockchain and data science news. The new USB stick race is on! Gryfaclon launches Laceli™ AI compute stick to counter Intel Movidius’s Neural Compute Stick Gyrfalcon Technology Inc, a Silicon Valley AI chip startup, has launched its Laceli™ AI Compute Stick after Intel Movidius announced its deep learning Neural Compute Stick in July, 2017. The Laceli™ AI Compute Stick, built on top of  Lightspeeur® 2801S, Gyrfalcon’s ultra-low power high performance AI processor, runs a 2.8 TOPS performance within 0.3 Watt of power. This is 90 times more efficient than the Movidius USB Stick, claims the company. Lightspeeur® is based on Gyrfalcon Technology Inc's APiM architecture, which uses memory as the AI processing unit. The standard USB 3.0 Stick can be connected to a range of devices from desktops to mobile devices, and can enhance their image-based deep learning capabilities to unprecedented levels without the need to connect to a cloud based system. With Caffe and TensorFlow support, the Stick enables various application development backgrounds. It can be used in many smart applications such as image and video recognition, natural speech understanding, natural language processing, and day-to-day AI applications. Expect to see the Laceli™ AI Compute Stick in January 2018 at CES 2018 in Las Vegas, Nevada where there showcase it to the public for the first time. Automated machine learning advances in news Introducing Parris: a new automated training tool for machine learning algorithms Parris is a free and open source tool that attempts to provide a one-stop training solution for building  machine learning algorithms. It makes no assumptions about the environment that you run, except that it can be installed programmatically on the launch of a server. You choose the environment into which your training algorithm will run, and this presumes that your chosen environment is available with that service. However, since Parris is an independently developed tool and just launched, it is missing many of the nice features that other training tools such as FloydHub offer. Features such as version control, having a proper CLI utility, and graphing capabilities, are currently not on the roadmap for Parris. You can check out the tool on GitHub here. On blockchains and cryptocurrencies EZPOS ICO released today; its cryptocurrency EZT uses crowdsale of tokens as rewards for customer loyalty EZPOS Holding Singapore, a cloud-based point-of-sale systems provider, today began an initial coin offering crowdsale of tokens for a product designed to help merchants maintain the loyalty of customers. The EZPOS platform uses a blockchain that makes its system difficult to tamper with and also provides value to its tokens. Using a digital currency, called EZTokens or EZT, merchants using EZPOS' point-of-sale network can assign, distribute and track loyalty points "Owned" by customers. A customer with a history of purchasing a particular brand will receive loyalty points that can then be exchanged for a discount on that brand in the future. Every time the customer buys a product from that brand and points are generated, the EZPOS blockchain would build up a historical record that could be plumbed later by marketers and brand advocates. EZPOS is the newest addition to a growing like of companies looking into using loyalty systems and tokens to keep customers coming back to their stores. Should Ethereum better watch its back? Ripple seems all set to put up a fight for the second spot in the cryptocurrency hierarchy Ripple, aka XRP, a cryptocurrency established by Ripple Labs Inc., surpassed Ethereum in trading New Year's Eve to become the second-largest token behind bitcoin by market capitalization. Ripple Labs was founded in 2012 with XRP being released in August 2013 as a cryptocurrency that could be used to facilitate global interbank transfers as an alternative to the SWIFT (Society for Worldwide Interbank Financial Telecommunication) network. Unlike many new coins and tokens entering the market during a surge of initial coin offering, XRP actually had a provable business case and an established company behind it. "Ripple does away with the idea of XRP as any kind of investment asset and instead focuses on making the blockchain as strong as possible for the good of the institutional entities that Ripple serves, like American Express or Santander Bank," the site noted. Because XRP is not on an open blockchain, it sits on a set allocation as determined by Ripple Labs, so far 100 billion XRP. And the company itself holds the majority of the coins.

Nvidia’s bye-bye to 32-bit operating systems, New SVM library ThunderSVM, ClustrixDB 9, and Microsoft-Litbit AI partnership on Kubernetes among today's top stories in data science. The last remnants of 32-bit machines.. NVIDIA to end support for 32-Bit operating system drivers The transition of the PC industry from 32-bit to 64-bit is almost in the final stages of completion. NVIDIA has announced that it will stop developing drivers supporting 32-bit operating systems for any GPU architecture in the near future. NVIDIA driver version 390 will be the final drivers from the company that will support 32-bit Windows 7/8/8.1/10, Linux or FreeBSD. Whatever version comes after, it will only run on 64-bit versions of OSes. The company will continue to release 32-bit drivers containing security fixes till January 2019, but has no plans to improve the performance or add features to such releases. Announcing ThunderSVM ThunderSVM: A fast library on GPUs and CPUs for SVM use-cases SVM is a short for Support Vector Machine, a machine learning technique used typically for classification and regression. SVMs have been used in various applications including spam filtering, document classification, network attack detection etc. ThunderSVM is a library that has been created to help users apply SVMs to solve problems. It exploits GPUs and multi-core CPUs to achieve high efficiency. Key features Support all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. Use same command line options as LibSVM. Support Python, R and Matlab interfaces. Prerequisites Supported Operating Systems: Linux, Windows and MacOS CUDA 7.5 or above | cmake 2.8 or above | gcc 4.8 or above Download git clone [email protected]:zeyiwen/thundersvm.git ThunderSVM uses the same command line options as LibSVM, so existing users of LibSVM can use ThunderSVM quickly. For new users of SVMs, the user guide provided in the LibSVM website can be helpful for training. So if you are one of the few still holding on to your 32-bit version of Windows (and specially if you're a gamer) it's time to upgrade! Microsoft bets on Kubernetes to push the boundaries of cloud-based artificial intelligence Microsoft collaborates with AI specialist Litbit on a system that uses Kubernetes to automatically scale unpredictable machine learning workloads Microsoft unveiled a new auto-scaling system that uses Kubernetes, the popular open-source container orchestration platform, to expand or shrink the amount of cloud-computing resources required for learning training workloads. The system was developed in partnership with Litbit, a California based technology startup that uses Internet of Things data to create "AI Personas" that workplaces can use to augment the capabilities of their employees based on their collective experiences and know-how. For instance, an organization can create and train a persona that helps its field technicians detect and diagnose equipment problems before jumping in a work truck and physically visit machinery that is acting up to save time and expense. “Some of these training jobs like Spark ML make heavy use of CPUs, while others like TensorFlow make heavy use of GPUs. In the latter case, some jobs retrain a single layer of the neural net and finish very quickly, while others need to train an entire new neural net and can take several hours or even days," Microsoft representatives said in a blog post. Newest ClustrixDB Supports Modern Data ClustrixDB 9 supports modern data features like JSON, Fractional-second Events and Generated Columns without sacrificing performance, scalability and ACID compliance Clustrix has announced that its most recent generally-available release, ClustrixDB 9.0, handles sophisticated, modern data including semi-structured data, fractional-second events, and generated columns. ClustrixDB 9 will continue to deliver the advantages of performance, scalability without sharding or replication, and ACID compliance. "Making the application developer's job easier by putting functionality and logic into the database instead of application code has always been a significant part of the Clustrix mission," said Mike Azevedo, CEO of Clustrix. "With ClustrixDB 9, developers who want to innovate do not have to choose between an RDBMS that has these features but does not scale well and a NoSQL database that has to relax availability and consistency in order to scale." Read more about ClustrixDB here: https://www.clustrix.com/scaleout-database/ The first US-China blockchain conference Next month’s Blockchain Connect Conference will bring together the two powerhouses in the fast-moving blockchain space The Blockchain Connect Conference will be held in San Francisco on Jan 26, 2018. This will bring together over 1000 scientists, entrepreneurs, investors and developers from all over the world for a day of blockchain discussion. Click here to register for the conference. The most important use case: AI, Machine Learning, Predictive Analytics for patient’s resque CLEW Medical unveils AI Predictive Platform, leveraging untapped patient data to provide actionable tracking for patients at risk Previously known as Intensix, CLEW Medical is launching its artificial intelligence powered predictive analytics platform to prevent life threatening complications in all care settings, using real-time data and machine learning technology. Already proven in the ICU, CLEW’s platform provides medical staff and healthcare administrators with actionable clinical, operational and financial insights to streamline medical care. The average digital footprint of a patient includes 300 unique data elements, some of which are measured every few milliseconds. The amount of data is sometimes too big to analyze in the time that critical decisions need to be made by medical professionals. This is where CLEW’s centralized AI platform offers healthcare providers with predictive insights for the health conditions of all admitted patients in different departments. “More and more we’re seeing hospitals around the world adapting to the digital age of medical technology, and we’d like to make this digital transformation in healthcare as efficient as possible by leveraging data that’s already available to us,” Founder and CEO Gal Salomon said. “With our advanced clinical ICU-tested algorithms that customizes physiological models and predicts patients deterioration before it happens, our goal is to bring hospitals into the future of medical care, and redefine healthcare delivery.”

Cats 1.0, first savings system in cryptocurrency called Peculium, a 'modular' style quantum computer, SAP's refocus on streaming analytics, and a new AI breakthrough in cancer detection in today's trending news around artificial intelligence and data science. The “cat” is out of the bag. And production ready. Announcing Cats 1.0.0 Cats, a library which provides abstractions for functional programming in Scala, has announced its version 1.0. Cats 1.0.0 indicates a stage where its API is robust and stable enough to start guarantee backward binary compatibility—going forward until Cats 2.0. “We expect the Cats 1.x series to be fully backwards compatible for at least one year,” the Cats maintainer team said. While Scala supports both object-oriented and functional programming, Cats strives to provide functional programming abstractions that are core, binary compatible, modular, approachable and extensible. Its name is actually a playful shortening of the word category. “After 1.0.0 release, we’ll use the MAJOR.MINOR.PATCH Semantic Versioning 2.0.0 going forward, which is different from the EPOCH.MAJOR.MINOR scheme common among Java and Scala libraries (including the Scala lang). In this semantic versioning, backward breaking change is ONLY allowed between MAJOR versions. We will maintain backward binary compatibility between PATCH and MINOR versions. For example, when we release cats 1.1.0, it will be backward binary compatible with the previous 1.0.x versions,” Cats team said in the official announcement. Most of the changes since 1.0.0-RC1 are API compatible. For the rests that are not, scalafix scripts are ready in v1.0.0. Here is the change list and migration guide. The first savings system in cryptocurrency, with an ‘automated’ financial advisor Announcing Peculium – First Decentralized Savings Management Platform Powered By AI & Machine Learning If you are a regular follower of our platform, you will recall that we earlier published about a robot financial advisor that is being used for trading in Toronto Stock Exchange in Canada. But that was related to Exchange-traded funds. Now, we have an automated machine-learning financial advisor that could predict the fluctuations of the cryptocurrency market. Peculium, as the new project is named, uses machine-learning, Big Data, and AI-based technologies to effortlessly manage cryptocurrency portfolios, learning ‘intelligently’ from the analysis of historical data. The decentralized savings platform aims to maximize profits and savings, while helping its users overcome the common investment risks using advanced algorithms. The key component of Peculium is AIEVE, an automated next-gen financial advisor, which is a short for Artificial Intelligence, Ethics, Values, and Equilibrium. AIEVE can direct or give real-time trading suggestions to platform users on their assets as well as giving them directions to execute on the best possible positions in the cryptocurrency market by pulling a significant amount of data from all over the web from sources such as OpenData, database, cloud networks, social networks, blockchains, and more. A new chapter in cancer detection through artificial intelligence! Indian researchers develop groundbreaking AI that detects Cancer in early stages Last month we posted about Japanese scientists developing an AI to successfully detect bowel cancer. Now a team of researchers in India, from the IISER Kolkata and IIT Kanpur, have claimed to develop an AI that detects cancer tissues in its primitive stages within minutes. According to the research, differences in the structure and formation of healthy and precancerous tissues change their refractive index and make them scatter light in discernible variations. Although this minute difference cannot be detected by the naked eye, these researchers have developed algorithms that can. In fact, the algorithm even identifies the stage of cancer, with more than 95% accuracy. “The microstructure of normal tissue is uniform, but as the disease progresses, the tissue microstructure becomes complex and different. Based on this correlation, we created a novel light scattering-based method to identify these unique microstructures for detecting cancer progression,” said Sabyasachi Mukhopadhyay, who published a paper in the Journal of Biomedical Optics detailing the ground-breaking algorithm. The team has developed two AI-based algorithms—Hidden Markov Model (HMM) and Support Vector Machine (SVM)—to differentiate healthy and precancerous tissues. Tighter integration across SAP solutions to simplify data management and leverage digital transformation SAP HANA refocuses on Streaming Analytics as SAP updates its enterprise information management (EIM) portfolio SAP has updated its Enterprise Information Management (EIM) portfolio, refocusing on a tighter integration between the data management tools and various backend platforms. As part of that broader plan, SAP HANA Smart Data Streaming has been renamed SAP HANA Streaming Analytics. SAP EIM tools have also been extended to include data quality, master data management, content management and information lifecycle management capabilities, in addition to the support for SAP S/4HANA ERP software. Integration between SAP EIM software and the SAP BW/4HANA data warehouse; an instance of Apache Spark dubbed SAP Vora; SAP Leonardo software for Internet of Things (IoT) environments; SAP Cloud Platform Big Data Services; and the SAP Asset Intelligence Network has also been enhanced. SAP has also added support for a range of cloud services, including Amazon Redshift and Amazon Elastic Compute Cloud (Amazon EC2), Microsoft Azure Cloud and SQL Data Warehouse, and Google Cloud Platform. In addition, EIM portfolio offers new and expanded support for Impala, Cassandra, OData, Hive data stores, IoT device connectivity; Parquet, Avro and ORC file types; and Kerberos and Knox Gateway security software. A new approach to quantum computing.. Sussex researchers working to create a different “Modular” quantum computing system At Sussex University, researchers are preparing to build a new modular type of quantum computer that they claim is going to be the most powerful ever. The prototype is not yet operational, but scientists are working in full gear. The work features a new invention permitting actual quantum bits to be transmitted between individual quantum computing modules in order to obtain a fully modular large-scale machine capable of reaching nearly arbitrary large computational processing powers. Previously, scientists had proposed using fibre optic connections to connect individual computer modules. The new invention introduces connections created by electric fields that allow charged atoms (ions) to be transported from one module to another. This new approach allows 100,000 times faster connection speeds between individual quantum computing modules compared to current state-of-the-art fibre link technology. We earlier posted about Microsoft’s quantum computing advancements and IBM’s 50Q processor, signifying how the race for quantum computing got heated up in 2017.

New Google Analytics add-on from Fastbase, a blockchain technology to disrupt shipping and logistics, and a new method 'lexical acquisition through implicit confirmation' for dialogue systems among today's top stories in artificial intelligence and data science. Now turn your web traffic into real business contacts! Fastbase offers Google Analytics users comprehensive details about 200 million businesses and employees, with an option to download the results with rich insights If you have an account in Google Analytics, you will now have full access to the contact and company database of Fastbase, which includes approximately 200 million businesses and the employees' details. So sign up for free and start searching the database, downloading all company information directly to Excel or CSV format or export it into a CRM database, such as Salesforce, Hubspot, and Zoho. Users automatically receive a "500 free leads" credit each month. The leads tool contains over 700 million email addresses and the key contact information of business owners, management, marketing, sales, finance, and IT personnel to help businesses search, discover, and engage with the right prospects at the right time. Apart from the business database, Google analytics users will also have access to Fastbase’s Web Leads tool, which combines a website's analytics data with real-time visitor information, allowing businesses to eliminate the guesswork around who is visiting their website. Disrupting transport and logistics on the Blockchain ShipChain using Blockchain to change the Shipping & Logistics market, public sale commences from 2018 ShipChain, a platform that intends to solve some of the biggest problems related to the logistics and transportation industry using blockchain technology, announced the start of its public sale of tokens effective Jan. 1, 2018. Under the sale, 29.17 million SHIP tokens will be sold, in a bid to raise about $10 million in ETH. The ICO price is $0.342 per token. To introduce their project to a larger community, the ShipChain team is also organizing the bounty campaign where participants will be awarded with ShipChain Tokens. One ShipChain token will have a value of $0.34 during the ICO. There will be a total of 1,500,000 ShipChain Tokens allocated for the whole bounty campaign, which has a projected value of $510,000. If you want to be a part of the facebook campaign, you should have at least 300 friends to get qualified. Shipchain is a fully integrated system that tracks delivery across the entire supply chain–from the moment it leaves the factory, field, or farm–to delivering the finished product to the customer’s doorstep. It uses an Ethereum smart contract that can be used by anyone to organize a shipping escrow on the distributed ledger. With a shortage of more than 100,000 truck drivers in the US alone (and that number is expected to grow to over 250,000 by 2025), there are significantly more shipments than there are drivers to handle it. In the ShipChain ecosystem, drivers are rewarded with SHIP tokens for not breaking the speed limit, on-time delivery, log compliance, eco-friendly driving, and a high customer rating. This attracts new drivers to join the industry, while also helping carriers maintain margins and benefit the operators overall. Using Deep Learning for ‘intelligent’ household products LG distributes AI development tool to all divisions to speed up growth In a bid to speed up the release of new products equipped with the latest technology, South Korean tech giant LG Electronics has distributed its Artificial Intelligence development platform "DeepThinQ 1.0" to all its business divisions. The DeepThinQ 1.0 platform could help researchers easily apply deep-learning technologies to electronics products. LG said the DeepThinQ platform supports voice and video recognition, as well as other top-notch AI technologies, which can potentially be applied to all products from home appliances to mobile devices in the future. "Products based on the DeepThinQ platform will transmit various kinds of information to cloud servers, and will become smarter as time passes, as they can educate themselves," the company said in a statement. "The DeepThinQ AI platform has been evolving based on the data gathered from AI-powered home appliances and other commercial robots." New technique allows AI to learn words in the flow of dialogue Lexical acquisition through implicit confirmation: Researchers develop method by which the computer acquires the category of an unknown word during conversation with humans A group of researchers at Japan’s Osaka University has developed a new method, lexical acquisition through implicit confirmation, for a computer to acquire the category of an unknown word over multiple dialogues by confirming whether or not its predictions are correct in the flow of conversation. The method aims for the system to predict the category of an unknown word from user input during conversation, to make implicit confirmation requests to the user, and to have the user respond to these requests. In this way, the system acquires knowledge about words during dialogues. This new research could actually be a new approach towards the realization of dialogue systems in which a computer can become ‘smarter’ through conversation with humans. In future, it may lead to the development of dialogue systems with the ability to customize responses according to the user's situation. The system uses machine learning to decide whether the prediction is correct or not taking into consideration the user response and its context. Gone in 60 seconds: This AI blockchain recorded one of the fastest selling ICO ever SingularityNET ICO sold out within 60 seconds of going public, secures $36 million SingularityNET, a decentralized marketplace for artificial intelligence, took the cryptocurrency market by storm raising $36 million in 60 seconds in their initial coin offering for an AI marketplace blockchain. The team at SingularityNET reported that the crowdfunding was capped after receiving $361 million in investor interest on its whitelist from more than 20,000 previous investors. The significance of this ICO is that the AGI (Artificial General Intelligence) tokens will let anyone buy and sell AI/machine learning services from around the world via SingularityNET marketplace. SingularityNET says it wants to open the AI market to the entire world be it the individuals, developers, small companies, or big organizations. Its overall vision is to combine AI and blockchain to create a decentralized marketplace for different types of AI, becoming the key open-source protocol for networking AI on the internet.

PyPy2.7 and PyPy3.5 v5.10 dual release, Microsoft-Fujitsu AI partnership, Photosynth re-addition into Microsoft Pix, and Baidu-Huawei open AI platform for mobiles in today's top stories around artificial intelligence and data science. v5.10 — an incremental release for PyPy 2.7 and PyPy 3.5 Announcing PyPy2.7 v5.10 and a ‘final’ PyPy3.5 v5.10 PyPy has released the version 5.10 of PyPy2.7 and PyPy3.5 (which will be its final version). The v5.10 is an incremental release with few new features, and compared to 5.9, the 5.10 version contains mostly bugfixes. However, one standout feature in the final PyPy3.5 release is that it works on linux and OS X with beta windows support. It also includes fixes for vmprof cooperation with greenlets. “We have in the pipeline big new features coming for PyPy 6.0 that did not make the release cut and should be available within the next couple months. There are quite a few important changes that are in the pipeline that did not make it into the 5.10 release. Most important are speed improvements to cpyext (which will make numpy and pandas a bit faster) and utf8 branch that changes internal representation of unicode to utf8, which should help especially the Python 3.5 version of PyPy,” the PyPy team said in its official announcement. Earlier in August, Mozilla had announced grant for PyPy 3.5 support. PyPy2.7 is an interpreter supporting Python 2.7 syntax and PyPy3.5 is an interpreter for Python 3.5 syntax. You can download the v5.10 releases here: http://pypy.org/download.html Fujitsu, Microsoft team up on AI Fujitsu, Microsoft collaborate further to develop Artificial Intelligence powered workplace solutions Japanese MNC Fujitsu and Microsoft have expanded the scope of their partnership into the field of artificial intelligence to ‘transform’ the ways people work in companies. Based on the Microsoft 365 integrated cloud service, the new solutions will be driven by Fujitsu's AI technology, Fujitsu Human Centric AI Zinrai, and Microsoft AI platform services on Azure. The two companies aim to develop a new $2 billion of new businesses in the global market by 2020. The new solutions will be available in the Japanese market from the second quarter of 2018 before they are rolled out globally. Photosynth returns as a feature in Microsoft Pix Microsoft adds Photosynth and Pix Comix features in its AI camera app Pix Earlier in February, Microsoft shut down Photosynth. Now the feature has been brought back in Microsoft Pix, the iOS camera app that leverages AI to help you take better photos. While the new Photosynth feature uses some of the technology behind the original platform, it is actually now faster and allows for smoother capture, Microsoft said. Photosynth is now making use of the built-in Pix features like auto-enhancements for white balance, tone and sharpness. In addition, the updated Pix app now also includes Pix Comix, which uses a machine learning model to select the most interesting frames in a video and turn them into a comic strip. The AI model will look for things such as faces with eyes open and interesting scenes, but will avoid blurry frames. This feature is somewhat similar to Storyboard for Android (Google’s recent experimental app). Chinese tech giants and their continued AI rally.. Baidu and Huawei are building an open AI ecosystem for smartphones China-based Huawei and Baidu have announced they will work together on developing and building an open AI mobile ecosystem, including devices, technology, internet services, and content. The new ecosystem will combine Baidu’s Brain (that’s how its suite of AI tools has been dubbed), with Huawei’s HiAI mobile computing platform. The project will also take advantage of the AI-focused hardware components that Huawei installs in its latest smartphone models. “The future is all about smart devices that will actively serve us, not just respond to what we tell them to do,” said Richard Yu, chief executive of Huawei’s Consumer Business Group. “With a strong background in R&D, Huawei will work with Baidu to accelerate innovation in the industry, develop the next generation of smartphones, and provide global consumers with AI that knows you better.” In addition, Baidu and Huawei will also work together to build an augmented reality ecosystem, extending their collaboration into other areas such as mobile search.

Facebook has open-sourced MUSE, Multilingual Unsupervised and Supervised Embeddings. It is a Python library to align embedding spaces in a supervised or unsupervised way. The supervised method uses a bilingual dictionary or identical character strings. The unsupervised approach does not use any parallel data. Instead, it builds a bilingual dictionary between two languages by aligning monolingual word embedding spaces in an unsupervised way. Facebook MUSE has state-of-the-art multilingual word embeddings for over 30 languages based on fastText. fastText is a library for efficient learning of word representations and sentence classification. fastText can be used for making word embeddings using Skipgram, word2vec or CBOW (Continuous Bag of Words) and use it for text classification. For downloading the English (en) and Spanish (es) embeddings, you can use: # English fastText Wikipedia embeddings curl -Lo data/wiki.en.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.en.vec # Spanish fastText Wikipedia embeddings curl -Lo data/wiki.es.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.es.vec Facebook MUSE also has 110 large-scale high-quality, truth, bilingual dictionaries to ease the development and evaluation of cross-lingual word embeddings and multilingual NLP. These dictionaries are created using an internal translation tool. The dictionaries handle the polysemy (the coexistence of many possible meanings for a word) of words well. As mentioned earlier, MUSE has two ways to obtain cross-lingual word embeddings. The Supervised approach uses a training bilingual dictionary (or identical character strings as anchor points) to learn a mapping from the source to the target space using Procrustes alignment. To learn a mapping between the source and the target space, simply run: python supervised.py --src_lang en --tgt_lang es --src_emb data/wiki.en.vec --tgt_emb data/wiki.es.vec --n_iter 5 --dico_train default The unsupervised approach learns a mapping from the source to the target space using adversarial training and Procrustes refinement without any parallel data or anchor point. To learn a mapping using adversarial training and iterative Procrustes refinement, run: python unsupervised.py --src_lang en --tgt_lang es --src_emb data/wiki.en.vec --tgt_emb data/wiki.es.vec Facebook MUSE also has a simple script to evaluate the quality of monolingual or cross-lingual word embeddings on several tasks: Monolingual python evaluate.py --src_lang en --src_emb data/wiki.en.vec --max_vocab 200000 Cross-lingual python evaluate.py --src_lang en --tgt_lang es --src_emb data/wiki.en-es.en.vec --tgt_emb data/wiki.en-es.es.vec --max_vocab 200000 To know more about the functionalities of this library and to download other resources, you can go through the official GitHub repo here.

Blockchain mania grips Amazon, Facebook’s Python library MUSE, Taplytics AI-powered Experience Cloud, and LinkedIn’s 2017 U.S. Emerging Jobs Report among today’s top stories around machine learning, artificial intelligence and data science news. Amazon joins Blockchain bandwagon Amazon Web Services announces AWS Blockchain Partners Portal Amazon Web Services is investing in blockchain though its partner ecosystem. You can visit the AWS Blockchain Partners Portal here. The portal supports customers’ integration of blockchain solutions with systems built on AWS. “We invite you to check out current blockchains and review reference architecture, deployment strategies, and development tools on our new portal page,” Amazon said in a statement. “We will be launching an AWS Blockchain Competency in 2018, and you can sign up for our Blockchain for AWS Partners mailing list to learn more.” The following Blockchain Partner Solutions are available now as one-click deploy: Sawtooth Supply Chain, Sawtooth 1.0, R3 Corda, PokitDok, and Blockapps Strato. “Expect to see Samsung SDS, Tibco, Quorum, and Virtusa solutions in 2018 along with reference architecture from our partners,” Amazon said, adding that the products will have native AWS integrations to allow plug-and-play access to the portfolio of services. Ahead of Christmas, Facebook announces MUSE! MUSE: A Python library for multilingual unsupervised or supervised word embeddings Facebook has just open-sourced MUSE. It is a Python library for multilingual word embeddings, whose goal is to provide the community with: state-of-the-art multilingual word embeddings based on fastText large-scale high-quality bilingual dictionaries for training and evaluation “We include two methods, one supervised that uses a bilingual dictionary or identical character strings, and one unsupervised that does not use any parallel data (read about Word Translation without Parallel Data for more details),” Facebook said in its official release. MUSE is available on CPU or GPU, in Python 2 or 3. “Faiss is optional for GPU users - though Faiss-GPU will greatly speed up nearest neighbor search - and highly recommended for CPU users. Faiss can be installed using conda install faiss-cpu -c pytorch or conda install faiss-gpu -c pytorch,” Facebook commented on the dependencies. For more details please visit the Github page. Redefining marketing cloud with the power of Artificial Intelligence Optimization startup Taplytics integrates “intelligent” Experience Cloud, expands beyond mobile apps Taplytics has announced ‘intelligent’ Experience Cloud—a set of experimentation, messaging, engagement and analytics tools that unify cross-channel optimization for brands. In a way, the Taplytics Experience Cloud is a scenario where artificial intelligence sits at the center of an integrated portfolio of experimentation, engagement and analytics solutions to help brands create holistic, data-driven experiences. As part of the new Experience Cloud, Taplytics is also announcing the release of its Visual Web Experimentation Engine and Dexter, Taplytics’ new AI Smart Assistant. Dexter surfaces smart, contextual recommendations on key areas of opportunity within a user’s journey to take the guesswork out experimentation and personalization. “The traditional marketing cloud concept falls short of connecting with an individual – it gets companies in the habit of viewing people as numbers. The Taplytics Experience Cloud changes the way that brands create experiences that are personalized, relevant and ultimately engaging,” commented Ashley Lewis, VP, Dollar Shave Club. Yes. Machine Learning is the most sought after job category. LinkedIn analyzes emerging job categories since 2012 and founds machine learning, data science related jobs growing the fastest LinkedIn has released data on the jobs that have been experiencing the most growth in numbers over the past 5 years. As expected, tech and data oriented jobs are among the fastest growing categories. The role of machine learning engineer is at the top, the job category growing 10-fold between 2012 and 2017. This is followed by data scientist, multiplying by a factor of seven during this same time. There are also six times as many big data developers, as well full-stack engineers. LinkedIn's top-10 leading categories are as follows: Machine learning engineer (9.8x as many jobholders as in 2012) Data scientist (6.5x) Sales development represenative (5.7x) Customer suvcess manaer (5.6x) Big Data developer (5.5x) Full stack engineer (5.5x) Utility developer (5.1x) Director of data science (4.9x) Brand partner (4.5x) Full-stack developer (4.5x) You can find the complete data here on LinkedIn’s 2017 U.S. Emerging Jobs Report.

DeepMind introduces NarrativeQA, a data repository setup for understanding complex narratives. Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. The question answering technique is traditionally used to assess the abilities of RC, both in AI agents and in children who are learning to read. However, DeepMind surveyed that the existing RC datasets such as MCTest, Children’s Book Test(CBT), CNN/Daily Mail, NewsQA, SearchQA, and so on and found out certain limitations which include, presence of small datasets, unnatural data, requirement of a single sentence of information to answer the questions, and so on. Hence, these RC datasets are unable to test an important integrative aspect of machine’s Reading Comprehension. In order to encourage deeper comprehension of language, DeepMind presents a brand new dataset and a set of tasks, known as the NarrativeQA. This dataset includes fictional stories, which are 1,567 complete stories from books and movie scripts, with human written questions and answers based solely on human-generated abstract summaries. The dataset is divided into three parts: non-overlapping training validation and testing There are 46,765 pairs of answers to questions written by humans and includes mostly the more complicated variety of questions such as "when / where / who / why". This dataset permits the training of neural network-based models over word embeddings and provide decent lexical coverage and diversity.Thus, this dataset would test and reward agents that approach human level of competency. Having given a quantitative and qualitative analysis of the difficulty of the more complex tasks, DeepMind suggests research directions that may help bridge the gap between existing models and human performance. DeepMind also hopes that this dataset will serve not only as a challenge for the machine reading community, but also as a driver for the development of a new class of neural models which will take a significant step beyond the level of complexity which existing datasets and tasks permit. To have a detailed understanding on the working of NarrativeQA dataset, you can have a look at the research paper here.

Juniper’s advancement on self-driving networks, Chain's new compiler, DeepMind's NarrativeQA, and Microsoft SQL Operation Studio new release among today's top stories in machine learning, artificial intelligence, and data science news. An ‘intelligent’ CRM to tap China’s new retail wave Gridsum announces AI-powered Intelligent CRM Solution Web analytics company Gridsum Holding said it’s launching an AI-driven, Software as a Service (SaaS), Intelligent CRM Solution for the China market. The Intelligent CRM Solution is a cloud-based, marketing-centric CRM solution tailor-made for both multinational and local companies operating in China. The solution leverages Gridsum's artificial intelligence engine "Gridsum Prophet" and its marketing automation suite, combining the company’s expertise in big data analytics and AI. The solution will also look to leverage Blockchain technologies in a number of specific areas, the company said. CEO Guosheng Qi said Gridsum is launching the solution after “substantial research, development and real world operation.” He noted how the Chinese consumer market is undergoing a ‘new retail’ bubble, and that needs to be ably backed with an automated machine learning-driven CRM solution. “By converging a client's business intelligence data with Gridsum Prophet and our marketing automation suite, we will be able to substantially drive sales efficiency with significant, immediate and quantifiable KPI enhancement for our clients,” he said. Bringing Self-Driving Network closer to reality—crawl, walk and run to a fully automated environment! Juniper introduces AI bots to “intent-based” networks In what could translate intent into automated workflows, Juniper Networks has announced the first set of ‘intelligent’ software bots in its bid towards “self-driving networks” – autonomous enterprise networks that can configure, monitor, and manage themselves automatically, with little human intervention. The three Juniper Bots are: a Contrail TestBot, which allows operators to test network changes before they are applied; AppFormix HealthBot, which uses machine learning to analyze network health and provide suggestions for improvements; and Contrail PeerBot, which automates network peering. Contrail PeerBot automates the process of network peering. This makes it easier to manage multiple Border Gateway Protocol (BGP) domains, simplifies policy enforcement and enables on-demand scaling. Contrail TestBot enables network professionals to shift to a DevOps approach for continuous integration/continuous deployment of network resources. The Bots can be used to automate auditing and provisioning modifications of the network. Whereas AppFormix HealthBot uses machine learning to track the fitness and health of the network by leveraging AppFormix to collect real-time network data used to discover new insights. Simplifying Smart Contracts on Bitcoin Blockchain Chain launches new open-source compiler and developer environment for writing Bitcoin smart contracts using Ivy Blockchain tech startup Chain has released an open-source compiler that translates between Ivy, Chain's own high-level smart contract language, and Bitcoin Script, the low-level programming language of the world's first and largest blockchain. Breaking the announcement in its official blog post, Chain said Ivy aims to help developers "write custom, SegWit-compatible bitcoin addresses that enforce arbitrary combinations of conditions supported by the bitcoin protocol, including signature checks, hash commitments, and timelocks." Chain has put a note of caution that the Ivy language is more for educational and research purposes as it’s an untested prototype software as of now. Ivy was introduced in a public demo in December 2016. Amazon expands geographical scope of AMIs AWS Deep Learning AMIs now available in 4 new regions across China, Europe and Asia Pacific Amazon Web Services has announced that its Deep Learning AMIs are now available in four new AWS Regions: Beijing, Frankfurt, Singapore, and Mumbai. The AMIs, elaborated as Amazon Machine Images, provide machine learning practitioners with the necessary infrastructure and tools to quickly start experimenting with deep learning models. The AMIs come with pre-built packages of popular deep learning frameworks including Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, and Keras. In addition, to expedite development and model training, the AMIs are pre-configured with NVIDIA CUDA and cuDNN drivers, and are optimized for GPU acceleration on Amazon EC2 P2 and P3 instances. We wrote about AWS AMIs last month. SQL Operations Studio: First major update The December Public Preview of SQL Operations Studio is now available This year at Connect(), SQL Operations Studio was announced for Public Preview, and now Microsoft has announced its December release. SQL Operations Studio is a data management tool that enables users to work with SQL Server, Azure SQL DB and SQL DW from Windows, macOS and Linux. More details about it is available on GitHub. The December release includes several major repo updates and feature releases, including: Migrating SQL Ops Studio Engineering to public GitHub repo Azure Integration with Create Firewall Rule Windows Setup and Linux DEB/RPM installation packages Manage Dashboard visual layout editor “Run Current Query with Actual Plan” command For complete updates, refer to the Release Notes. DeepMind’s NarrativeQA for complex narratives.. Introducing NarrativeQA: human questions & answers about entire books, plays and movies to help improve understanding of complicated narratives DeepMind has launched a new dataset that reads comprehensions and solves challenging questions. It is called NarrativeQA. This dataset includes fictional stories from books and movie scripts, with human written questions and answers based solely on human-generated abstract summaries. The NarrativeQA dataset is divided into three parts: non-overlapping training Validation testing For complete details on the NarrativeQA dataset, please refer to the research paper at arXiv.