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Hot Chips 31, the premiere event for the biggest semiconductor vendors to highlight their latest architectural developments is held in August every year. The event this year was held at the Memorial Auditorium on the Stanford University Campus in California, from August 18-20, 2019. Since its inception it is co-sponsored by IEEE and ACM SIGARCH. Hot Chips is amazing for the level of depth it provides on the latest technology and the upcoming releases in the IoT, firmware and hardware space. This year the list of presentations for Hot Chips was almost overwhelming with a wide range of technical disclosures on the latest chip logic innovations. Almost all the major chip vendors and IP licensees involved in semiconductor logic designs took part: Intel, AMD, NVIDIA, Arm, Xilinx, IBM, were on the list. But companies like Google, Microsoft, Facebook and Amazon also took part. There are notable absences from the likes of Apple, who despite being on the Committee, last presented at the conference in 1994. Day 1 kicked off with tutorials and sponsor demos. On the cloud side, Amazon AWS covered the evolution of hypervisors and the AWS infrastructure. Microsoft described its acceleration strategy with FPGAs and ASICs, with details on Project Brainwave and Project Zipline. Google covered the architecture of Google Cloud with the TPU v3 chip.  And a 3-part RISC-V tutorial rounded off by afternoon, so the day was spent well with insights into the latest cloud infrastructure and processor architectures. The detailed talks were presented on Day 2 and Day 3, below are some of the important highlights of the event: IBM’s POWER10 Processor expected by 2021 IBM which creates families of processors to address different segments, with different models for tasks like scale-up, scale-out, and now NVLink deployments. The company is adding new custom models that use new acceleration and memory devices, and that was the focus of this year’s talk at Hot Chips. They also announced about POWER10 which is expected to come with these new enhancements in 2021, they additionally announced, core counts of POWER10 and process technology. IBM also spoke about focusing on developing diverse memory and accelerator solutions to differentiate its product stack with heterogeneous systems. IBM aims to reduce the number of PHYs on its chips, so now it has PCIe Gen 4 PHYs while the rest of the SERDES run with the company's own interfaces. This creates a flexible interface that can support many types of accelerators and protocols, like GPUs, ASICs, CAPI, NVLink, and OpenCAPI. AMD wants to become a significant player in Artificial Intelligence AMD does not have an artificial intelligence–focused chip. However, AMD CEO Lisa Su in a keynote address at Hot Chips 31 stated that the company is working toward becoming a more significant player in artificial intelligence. Lisa stated that the company had adopted a CPU/GPU/interconnect strategy to tap artificial intelligence and HPC opportunity. She said that AMD would use all its technology in the Frontier supercomputer. The company plans to fully optimize its EYPC CPU and Radeon Instinct GPU for supercomputing. It would further enhance the system’s performance with its Infinity Fabric and unlock performance with its ROCM (Radeon Open Compute) software tools. Unlike Intel and NVIDIA, AMD does not have a dedicated artificial intelligence chip or application-specific accelerators. Despite this, Su noted, “We’ll absolutely see AMD be a large player in AI.” AMD is considering whether to build a dedicated AI chip or not. This decision will depend on how artificial intelligence evolves. Lisa explained that companies have been improving their CPU (central processing unit) performance by leveraging various elements. These elements are process technology, die size, TDP (thermal design power), power management, microarchitecture, and compilers. Process technology is the biggest contributor, as it boosts performance by 40%. Increasing die size also boosts performance in the double digits, but it is not cost-effective. While AMD used microarchitecture to boost EPYC Rome server CPU IPC (instructions per cycle) by 15% in single-threaded and 23% in multi-threaded workloads. This IPC improvement is above the industry average IPC improvement of around 5%–8%. Intel’s Nervana NNP-T and Lakefield 3D Foveros hybrid processors Intel revealed fine-grained details about its much-anticipated Spring Crest Deep Learning Accelerators at Hot Chips 31. The Nervana Neural Network Processor for Training (NNP-T) comes with 24 processing cores and a new take on data movement that's powered by 32GB of HBM2 memory. The spacious 27 billion transistors are spread across a 688mm2 die. The NNP-T also incorporates leading-edge technology from Intel-rival TSMC. Intel Lakefield 3D Foveros Hybrid Processors Intel in another presentation talked about Lakefield 3D Foveros hybrid processors that are the first to come to market with Intel's new 3D chip-stacking technology. The current design consists of two dies. The lower die houses all of the typical southbridge features, like I/O connections, and is fabbed on the 22FFL process. The upper die is a 10nm CPU that features one large compute core and four smaller Atom-based 'efficiency' cores, similar to an ARM big.LITTLE processor. Intel calls this a "hybrid x86 architecture," and it could denote a fundamental shift in the company's strategy. Finally, the company stacks DRAM atop the 3D processor in a PoP (package-on-Package) implementation. Cerebras largest chip ever with 1.2 trillion transistors California artificial intelligence startup Cerebras Systems introduced its Cerebras Wafer Scale Engine (WSE), the world’s largest-ever chip built for neural network processing. Sean Lie the Co-Founder and Chief Hardware Architect at Cerebras Lie presented the gigantic chip ever at Hot Chips 31. The 16nm WSE is a 46,225 mm2 silicon chip which is slightly larger than a 9.7-inch iPad. It features 1.2 trillion transistors, 400,000 AI optimized cores, 18 Gigabytes of on-chip memory, 9 petabyte/s memory bandwidth, and 100 petabyte/s fabric bandwidth. It is 56.7 times larger than the largest Nvidia graphics processing unit, which accommodates 21.1 billion transistors on a 815 mm2 silicon base. NVIDIA’s multi-chip solution for deep neural networks accelerator NVIDIA which announced about designing a test multi-chip solution for DNN computations at a VLSI conference last year, the company explained chip technology at Hot Chips 31 this year. It is currently a test chip which involves a multi-chip DL inference. It is designed for CNNs and has a RISC-V chip controller. It has 36 small chips, 8 Vector MACs per PE, and each chip has 12 PEs and each package has 6x6 chips. Few other notable talks at Hot Chips 31 Microsoft unveiled its new product Hololens 2.0 silicone. It has a holographic processor and a custom silicone. The application processor runs the app, and the HPU modifies the rendered image and sends to the display. Facebook presented details on Zion, its next generation in-memory unified training platform. Zion which is designed for Facebook sparse workloads, has a unified BFLOAT 16 format with CPU and accelerators. Huawei spoke about its Da Vinci architecture, a single Ascend 310 which can deliver 16 TeraOPS of 8-bit integer performance, support real-time analytics across 16 channels of HD video, and consume less than 8W of power. Xiling Versal AI engine Xilinx, the manufacturer of FPGAs, announced its new Versal AI engine last year as a way of moving FPGAs into the AI domain. This year at Hot Chips they expanded on its technology and more. Ayar Labs, an optical chip making startup, showcased results of its work with DARPA (U.S. Department of Defense's Defense Advanced Research Projects Agency) and Intel on an FPGA chiplet integration platform. The final talk on Day 3 ended with a presentation by Habana, they discussed about an innovative approach to scaling AI Training systems with its GAUDI AI Processor. AMD competes with Intel by launching EPYC Rome, world’s first 7 nm chip for data centers, luring in Twitter and Google Apple advanced talks with Intel to buy its smartphone modem chip business for $1 billion, reports WSJ Alibaba’s chipmaker launches open source RISC-V based ‘XuanTie 910 processor’ for 5G, AI, IoT and self-driving applications

Open Data Institute posted a video titled “Regulating for responsible technology – is the UK getting it right?”, as a part of its ODI Fridays series last week. Jacob Ohrvik Scott, a researcher at Think-tank Doteveryone, a UK based organization that promotes ideas on responsible tech. In the video, Ohrvik talks about the state of digital regulation, systemic challenges faced by independent regulators and the need for an Office for responsible tech, an independent regulatory body, in the UK. Let’s look at the key takeaways from the video. Ohrvik started off the video talking about responsible tech and three main factors that fall under responsible tech. The factors include: unintended consequences of its applications kind of value that flows to and fro the technology kind of societal context in which it operates Ohrvik states that many people in the UK have been calling for an internet regulator to carry out different digital-safety related responsibilities. For instance, the NSPCC, National Society for the Prevention of Cruelty to Children, called for an internet regulator to make sure that children are safe online. Similarly, media and Sport Committee is called out to implement an ethical code of practice for social media platforms and big search engines. Given the fact that many people were talking about the independent internet regulatory body, Doteveryone decided to come out with their own set of proposals. It had previously carried out a survey that observed the public attitude and understanding of digital technologies. As per the survey results, one of the main things that people emphasized was greater accountability from tech companies. Also, people were supportive of the idea of an independent internet regulator. “We spoke to lots of people, we did some of our own thinking and we were trying to imagine what this independent internet regulator might look like. But..we uncovered some more sort of deep-rooted systemic challenges that a single internet regulator couldn't really tackle” said Ohrvik. Systemic Challenges faced by Independent Internet Regulator The systemic challenges presented by Ohrvik are the need for better digital capabilities, society needs an agency and the need for evidence. Better digital capabilities Ohrvik cites the example of Christopher Wiley, a “whistleblower” in the Cambridge Analytica scandal.  As per Wiley, one of the weak points of the system is the lack of tech knowledge. The fact that he was asked a lot of basic questions by the Information Commissioner’s Office (UK’s data regulator) that wouldn’t be normally asked by a database engineer is indicative of the overall challenges faced by the regulatory system. Tech awareness among the public is important The second challenge is that society needs an agency that can help bring back their trust in tech. Ohrvik states that as part of the survey that Doteveryone conducted, they observed that when people were asked to give their views on reading terms and conditions, 58 percent said that they don't read terms and conditions. 47% of people feel that they have no choice but to accept the terms and conditions on the internet. While 43% of people said that there's no point in reading terms and conditions because tech companies will do what they want anyway. This last area of voters especially signals towards a wider kind of trend today where the public feel disempowered and cynical towards tech. This is also one of the main reasons why Ohrvik believes that a regulatory system is needed to “re-energize” the public and give them “more power”. Everybody needs evidence Ohrvik states that it’s hard to get evidence around online harms and some of the opportunities that arise from digital technologies. This is because: a) you need a rigorous and kind of longitudinal evidence base b)  getting access to the data for the evidence is quite difficult (esp. from a large private multinational company not wanting to engage with government) and c) hard to look under the bonnet of digital technologies, meaning, dealing with thousands of algorithms and complexities that makes it hard to make sense of  what’s really happening. Ohrvik then discussed the importance of having a separate office for responsible technology if we want to counteract the systemic challenges listed above. Having an Office for responsible technology Ohrvik states that the office for responsible tech would do three broad things namely, empowering regulators, informing policymakers and public, and supporting people to seek redress. Empowering regulators This would include analyzing the processes that regulators have in-place to ensure they are up-to-date. Also, recommending the necessary changes required to the government to effectively put the right plan in action. Another main requirement is building up the digital capabilities of regulators. This would be done in a way where the regulators are able to pay for the tech talent across the whole regulatory system, which in turn, would help them understand the challenges related to digital technologies.                                         ODI: Regulating for responsible technology Empowering regulators would also help shift the role of regulators from being kind of reactive and slow towards being more proactive and fast moving. Informing policymakers and public This would involve communicating with the public and policymakers about certain developments related to tech regulation. This would further offer guidance and make longer-term engagements to promote positive long term change in the public relationship with digital technologies.                                                                              ODI: Regulating for responsible technology For instance, a long term campaign centered around media literacy can be conducted to tackle misinformation. Similarly, a long-term campaign around helping people better understand their data rights can also be implemented. Supporting people to seek redress This is aimed at addressing the power imbalance between the public and tech companies. This can be done by auditing the processes, procedures, and technologies that tech companies have in place, to protect the public from harms.                                                    ODI: Regulating for responsible technology For instance, a spot check can be carried out on algorithms or artificial intelligence to spot harmful content. While spot checking, handling processes and moderation processes can also be checked to make sure they’re working well. So, in case, certain processes for the public don't work, then this can be easily redressed. This approach of spotting harms at an early stage can further help people and make the regulatory system stronger. In all, an office for responsible tech is quite indispensable to promote the responsible design of technologies and to predict their digital impact on society. By working with regulators to come out with approaches that support responsible innovation, an office for responsible tech can foster healthy digital space for everyone.     Microsoft, Adobe, and SAP share new details about the Open Data Initiative Congress passes ‘OPEN Government Data Act’ to make open data part of the US Code Open Government Data Act makes non-sensitive public data publicly available in open and machine readable formats

If things seem too good to be true, they probably are. That's a pretty good motto to live by, and one that's particularly pertinent in the days of fake news and crypto-bubbles. However, it seems like advice many people haven't heeded with Initiative Q, a new 'payment system' developed by the brains behind PayPal technology. That's not to say that Initiative Q certainly is too good to be true. But when an organisation appears to be offering almost hundreds of thousands of dollars to users who simply offer an email and then encourage others to offer theirs, caution is essential. If it looks like a pyramid scheme, then do you really want to risk the chance that it might just be a pyramid scheme? What is Initiative Q? Initiative Q, is, according to its founders, "tomorrow's payment network." On its website it says that current methods of payment, such as credit cards, are outdated. They open up the potential for fraud and other bad business practices, as well as not being particularly efficient. Initiative Q claims that is it going to develop an alternative to these systems "which aggregate the best ideas, innovations, and technologies developed in recent years." It isn't specific about which ideas and technological innovations its referring to, but if you read through the payment model it wants to develop, there are elements that sound a lot like blockchain. For example, it talks about using more accurate methods of authentication to minimize fraud, and improving customer protection by "creating a network where buyers don’t need to constantly worry about whether they are being scammed" (the extent to which this turns out to be deliciously ironic remains to be seen). To put it simply, it's a proposed new payment system that borrows lots of good ideas that still haven't been shaped into a coherent whole. Compelling, yes, but alarm bells are probably sounding. Who's behind Initiative Q? There are very few details on who is actually involved in Initiative Q. The only names attached to the project are Saar Wilf, an entrepreneur who founded Fraud Sciences, a payment technology that was bought by PayPal in 2008, and Lawrence White, Professor of Monetary Theory and Policy and George Mason University. The team should grow, however. Once the number of members has grown to a significant level, the Initiative Q team say "we will continue recruiting the world’s top professionals in payment systems, macroeconomics, and Internet technologies." How is Initiative Q supposed to work? Initiative Q explains that for the world to adopt a new payment network is a huge challenge - a fair comment, because after all, for it to work at all, you need actors within that network who believe in it and trust it. This is why the initial model - which looks and feels a hell of a lot like a pyramid or Ponzi scheme - is, according to Initiative Q, so important. To make this work, you need a critical mass of users. Initiative Q actually defends itself from accusations that it is a Pyramid scheme by pointing out that there's no money involved at this stage. All that happens is that when you sign up you receive a specific number of 'Qs' (the name of the currency Initiative Q is proposing). These Qs obviously aren't worth anything at the moment. The idea is that when the project actually does reach critical mass, it will take on actual value. Isn't Initiative Q just another cryptocurrency? Initiative Q is keen to stress that it isn't a cryptocurrency. That said, on its website the project urges you to "think of it as getting free bitcoin seven years ago." But the website does go into a little more detail elsewhere, explaining that "cryptocurrencies have failed as currencies" because they "focus on ensuring scarcity" while neglecting to consider how people might actually use them in the real world." The implication, then, is that Initiative Q is putting adoption first. Presumably, it's one of the reasons that it has decided to go with such an odd acquisition strategy. Ultimately though, it's too early to say whether Initiative Q is or isn't a cryptocurrency in the strictest (ie. fully de-centralized etc.) sense. There simply isn't enough detail about how it will work. Of course, there are reasons why Initiative Q doesn't want to be seen as a cryptocurrency. From a marketing perspective, it needs to look distinctly different from the crypto-pretenders of the last decade. Initiative Q: pyramid scheme or harmless vaporware? Because no money is exchanged at any point, it's difficult to call Initiative Q a ponzi or pyramid scheme. In fact it's actually quite hard to know what to call it. As David Gerard wrote in a widely shared post from June, published when Initiative Q had a first viral wave, "the Initiative Q payment network concept is hard to critique — because not only does it not exist, they don’t have anything as yet, except the notion of “build a payment network and it’ll be awesome.” But while it's hard to critique, it's also pretty hard to say that it's actually fraudulent. In truth, at the moment it's relatively harmless. However, as David Gerard points out in the same post, if the data of those who signed up is hacked - or even sold (although the organization says it won't do that) - that's a pretty neat database of people who'll offer their details up in return for some empty promises of future riches.

Tomorrow, on Wednesday 12 September, the European Parliament will vote on amendments to the EU Copyright Bill, first proposed back in September 2016. This bill could have a huge impact on open source, software engineering, and even the future of the internet. Back in July, MEPs voted down a digital copyright bill that was incredibly restrictive. It asserted the rights of large media organizations to tightly control links to their stories, copyright filters on user generated content. https://twitter.com/EFF/status/1014815462155153408 The vote tomorrow is an opportunity to amend aspects of the directive - that means many of the elements that were rejected in July could still find their way through. What parts of the EU copyright directive are most important for software developers? There are some positive aspects of the directive. To a certain extent, it could be seen as evidence of the European Union continuing a broader project to protect citizens by updating digital legislation - a move that GDPR began back in May 2018. However, there are many unintended consequences of the legislation. It's unclear whether the negative impact is down to any level of malicious intent from law makers, or is simply reflective of a significant level of ignorance about how the web and software works. There are 3 articles within the directive that developers need to pay particular attention to. Article 13 of the EU copyright directive: copyright filters Article 13 of the directive has perhaps had the most attention. Essentially, it will require "information society service providers" - user-generated information and content platforms - to use "recognition technologies" to protect against copyright infringement. This could have a severe impact on sites like GitHub, and by extension, the very philosophy of open collaboration and sharing on which they're built. It's for this reason that GitHub has played a big part in educating Brussels law makers about the possible consequences of the legislation. Last week, the platform hosted an event to discuss what can be done about tomorrow's vote. In it, Marten Mickos, CEO of cybersecurity company Hacker One gave a keynote speech, saying that "Article 13 is just crap. It will benefit nobody but the richest, the wealthiest, the biggest - those that can spend tens of millions or hundreds of millions on building some amazing filters that will somehow know whether something is copyrighted or not." https://youtu.be/Sm_p3sf9kq4 A number MEPs in Brussels have, fortunately, proposed changes that would exclude software development platforms to instead focus the legislation on sites where users upload music and video. However, for those that believe strongly in an open internet, even these amendments could be a small compromise that not only places an unnecessary burden on small sites that simply couldn't build functional copyright filters, but also opens a door to censorship online. A better alternative could be to ditch copyright filters and instead opt for licensing agreements instead. This is something put forward by German politician Julia Reda - if you're interested in policy amendments you can read them in detail here. [caption id="attachment_22485" align="alignright" width="300"] Image via commons.wikimedia.org[/caption] Julia Reda is a member of the Pirate Party in Germany - she's a vocal advocate of internet freedoms and an important voice in the fight against many of the directive (she wants the directive to be dropped in its entirety). She's put together a complete list of amendments and alternatives here. Article 11 of the EU Copyright Directive: the "link tax" Article 11 follows the same spirit of article 13 of the bill. It gives large press organizations more control over how their content is shared and linked to online. It has been called the "link tax" - it could mean that you would need a license to link to content. According to news sites, this law would allow them to charge internet giants like Facebook and Google that link to their content. As Cory Doctorow points out in an article written for Motherboard in June, only smaller platforms would lose out - the likes of Facebook and Google could easily manage the cost. But there are other problems with article 11. It could, not only, as Doctorow also writes, "crush scholarly and encyclopedic projects like Wikipedia that only publish material that can be freely shared," but it could also "inhibit political discussions". This is because the 'link tax' will essentially allow large media organizations to fully control how and where their content is shared. "Links are facts" Doctorow argues, meaning that links are a vital component within public discourse, which allows the public to know who thinks what, and who said what. Article 3 of the EU Copyright Directive: restrictions on data mining Article 3 of the directive hasn't received as much attention as the two above, but it does nevertheless have important implications for the data mining and analytics landscape. Essentially, this proportion of the directive was originally aimed at posing restrictions on the data that can be mined for insights except in specific cases of scientific research. This was rejected by MEPs. However, it is still an area of fierce debate. Those that oppose it argue that restrictions on text and data mining could seriously hamper innovation and hold back many startups for whom data is central to the way they operate. However, given the relative success of GDPR in restoring some level of integrity to data (from a citizen's perspective), there are aspects of this article that might be worth building on as a basis for a compromise. With trust in a tech world at an all time low, this could be a stepping stone to a more transparent and harmonious digital domain. An open internet is worth fighting for - we all depend on it The difficulty unpicking the directive is that it's not immediately clear who its defending. On the one hand, EU legislators will see this as something that defends citizens from everything that they think is wrong with the digital world (and, let's be honest, there are things that are wrong with it). Equally, those organizations lobbying for the change will, as already mentioned, want to present this as a chance to knock back tech corporations that have had it easy for too long. Ultimately, though, the intention doesn't really matter. What really matters are the consequences of this legislation, which could well be catastrophic. The important thing is that the conversation isn't owned by well-intentioned law makers that don't really understand what's at stake, or media conglomerates with their own interests in protecting their content from the perceived 'excesses' of a digital world whose creativity is mistaken for hostility. If you're an EU citizen, get in touch with your MEP today. Visit saveyourinternet.eu to help the campaign. Read next German OpenStreetMap protest against “Article 13” EU copyright reform making their map unusable YouTube’s CBO speaks out against Article 13 of EU’s controversial copyright law

In 2003, when The New York Times announced that the human genome project was successfully complete two years ahead of its schedule (leave aside the conspiracy theory that the genome was never ‘completely’ sequenced), it heralded a new dawn in the history of modern science. The challenge thereafter was to make sense out of the staggering data that became available. The High Throughput Sequencing technology came to revolutionize the processing of genomic data in a way, but had its own limitations (such as the high rate of erroneous base calls produced). Google has now launched an artificial intelligence tool, DeepVariant, to analyze the huge data resulting from the sequencing of the genome. It took two years of research for Google to build DeepVariant. It's a combined effort from Google’s Brain team, a group that focuses on developing and applying AI techniques, and Verily Life Sciences, another Alphabet subsidiary that is focused on the life sciences. How the DeepVariant makes sense of your genome? DeepVariant uses the latest deep learning techniques to turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data. Ever since the high-throughput sequencing made genome sequencing more accessible, the data produced has at best offered error-prone snapshot of a full genome. Researchers have found it challenging to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. A number of tools and methods have come out to interpret these readouts (both public and private funded), but all of them have used simpler statistical and machine-learning approaches to identify mutations. Google claims DeepVariant offers significantly greater accuracy than all previous classical methods. DeepVariant transforms the task of variant calling (the process to identify variants from sequence data) into an image classification problem well-suited to Google's existing technology and expertise. Google's team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB) project, and fed the data to a deep-learning system that interpreted sequenced data with a high level of accuracy. “Using multiple replicates of GIAB reference genomes, we produced tens of millions of training examples in the form of multi-channel tensors encoding the HTS instrument data, and then trained a TensorFlow-based image classification model to identify the true genome sequence from the experimental data produced by the instruments.” Google said. The result has been remarkable. Within a year, DeepVariant went on to win first place in the PrecisionFDA Truth Challenge, outperforming all state-of-the-art methods in accurate genetic sequencing. “Since then, we've further reduced the error rate by more than 50%,” the team claims. Image Source: research.googleblog.com “The success of DeepVariant is important because it demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems,” says Brendan Frey, CEO of Deep Genomics, one of the several companies using AI on genomics for potential drugs. DeepVariant is ‘open’ for all The best thing about DeepVariant is that it has been launched as an open source software. This will encourage enthusiastic researchers for collaboration and possibly accelerate its adoption to solve real world problems. “To further this goal, we partnered with Google Cloud Platform (GCP) to deploy DeepVariant workflows on GCP, available today, in configurations optimized for low-cost and fast turnarounds using scalable GCP technologies like the Pipelines API,” Google said. This paired set of releases could facilitate a scalable, cloud-based solution to handle even the largest genomics datasets. The road ahead: What DeepVariant means for future According to Google, DeepVariant is the first of “what we hope will be many contributions that leverage Google's computing infrastructure and Machine learning expertise” to better understand the genome and provide deep learning-based genomics tools to the community. This is, in fact, all part of a “broader goal” to apply Google technologies to healthcare and other scientific applications. As AI starts to propel different branches of medicine take big leaps forward in coming years, there is a whole lot of medical data to mine and drive insights from. But with genomic medicine, the scale is huge. We are talking about an unprecedented set of data that is equally complex. “For the first time in history, our ability to measure our biology, and even to act on it, has far surpassed our ability to understand it,” says Frey. “The only technology we have for interpreting and acting on these vast amounts of data is AI. That’s going to completely change the future of medicine.” These are exciting times for medical research. In 1990, when the human genome project was initiated, it met with a lot of skepticism from many people, including scientists and non-scientists alike. But today, we have completely worked out each A, T, C, and G that makes up the DNA of all 23 pairs of human chromosomes. After high-throughput sequencing made the genomic data accessible, Google’s DeepVariant could just be the next big thing to take genetic sequencing to a whole new level.

We hope you had a great Thanksgiving and are having the time of your life shopping for your wishlist this weekend. The last thing you want to do this weekend is to spend your time you would rather spend shopping, scouring the web for content you would like to read. Here is a brief roundup of the best of what we published on the Datahub this week for your weekend reading. Thanksgiving Weekend Reading A mid-autumn Shopper’s dream – What an Amazon-fulfilled Thanksgiving would look like Data science folks have 12 reasons to be thankful for this Thanksgiving Black Friday Special - 17 ways in 2017 that online retailers use machine learning Through the customer’s eyes - 4 ways Artificial Intelligence is transforming e-commerce Expert in Focus Shyam Nath, director of technology integrations, Industrial IoT, GE Digital on  Why the Industrial Internet of Things (IIoT) needs Architects 3 Things that happened this week in Data Science News Amazon ML Solutions Lab to help customers “work backwards” and leverage machine learning Introducing Gluon- a powerful and intuitive deep learning interface New MapR Platform 6.0 powers DataOps Get hands-on with these Tutorials Visualizing 3D plots in Matplotlib 2.0 How to create 3D Graphics and Animation in R Implementing the k-nearest neighbors algorithm in Python Do you agree with these Insights & Opinions? Why you should learn Scikit-learn 4 ways Artificial Intelligence is leading disruption in Fintech 7 promising real-world applications of AI-powered Mixed Reality  

The weekend is here! You have got your laundry to do, binge on those Netflix episodes of your favorite show, catch up on that elusive sleep and go out with your friends and if you are married, then spending quality time with your family is also on your priority list. The last thing you want to do to spend hours shortlisting content that is worth your reading time. So here is a handpicked list of the best of Datahub published this week. Enjoy! 3 Things you should know that happened this week in News Introducing Tile: A new machine learning language with auto-generating GPU Kernels What we are learning from Microsoft Connect(); 2017 Tensorflow Lite developer preview is Here  Get hands-on with these Tutorials Implementing Object detection with Go using TensorFlow Machine Learning Algorithms: Implementing Naive Bayes with Spark MLlib Using R to implement Kriging – A Spatial Interpolation technique for Geostatistics data Do you agree with these Insights & Opinions? 3 ways JupyterLab will revolutionize Interactive Computing Of Perfect Strikes, Tackles and Touchdowns: How Analytics is Changing Sports 13 reasons why Exit Polls get it wrong sometimes Just relax and have fun reading these Date with Data Science Episode 04: Dr. Brandon explains ‘Transfer Learning’ to Jon Implementing K-Means Clustering in Python Scotland Yard style!      

The history of the Jupyter notebook is quite interesting. It started as a spin-off project to IPython in 2011, with support for the leading languages for data science such as R, Python, and Julia. As the project grew, Jupyter’s core focus shifted to being more interactive and user-friendly. It was soon clear that Jupyter wasn’t just an extension of IPython - leading to the ‘Big Split’ in 2014. Code reusability, easy sharing, and deployment, as well as extensive support for third-party extensions - these are some of the factors which have led to Jupyter becoming the popular choice of notebook for most data professionals. And now, Jupyter plan to go a level beyond with JupyterLab - the next-gen Jupyter notebook with strong interactive and collaborative computing features. [box type="info" align="" class="" width=""] What is JupyterLab? JupyterLab is the next-generation end-user version of the popular Jupyter notebook, designed to enhance interaction and collaboration among the users. It takes all the familiar features of the Jupyter notebook and presents them through a powerful, user-friendly interface.[/box] Here are 3 ways, or reasons shall we say, to look forward to this exciting new project, and how it will change interactive computing as we know it. [dropcap]1[/dropcap] Improved UI/UX One of Jupyter’s strongest and most popular feature is that it is very user-friendly, and the overall experience of working with Jupyter is second to none. With improvements in the UI/UX, JupyterLab offers a cleaner interface, with an overall feel very similar to the current Jupyter notebooks. Although JupyterLab has been built with a web-first vision, it also provides a native Electron app that provides a simplified user experience.The other key difference is that JupyterLab is pretty command-centric, encouraging users to prefer keyboard shortcuts for quicker tasks. These shortcuts are a bit different from the other text editors and IDEs, but they are customizable. [dropcap]2[/dropcap] Better workflow support Many data scientists usually start coding on an interactive shell and then migrate their code onto a notebook for building and deployment purposes. With JupyterLab, users can perform all these activities more seamlessly and with minimal effort. It offers a document-less console for quick data exploration and offers an integrated text editor for running blocks of code outside the notebook. [dropcap]3[/dropcap] Better interactivity and collaboration Probably the defining feature which propels JupyterLab over Jupyter and the other notebooks is how interactive and collaborative it is, as compared to the other notebooks. JupyterLab has a side by side editing feature and provides a crisp layout which allows for viewing your data, the notebook, your command console and some graphical display, all at the same time. Better real-time collaboration is another big feature promised by JupyterLab, where users will be able to share their notebooks on a Google drive or Dropbox style, without having to switch over to different tool/s. It would also support a plethora of third-party extensions to this effect, with Google drive extension being the most talked about. Popular Python visualization libraries such as Bokeh will now be integrated with JupyterLab, as will extensions to view and handle different file types such as CSV for interactive rendering, and GeoJSON for geographic data structures. JupyterLab has gained a lot of traction in the last few years. While it is still some time away from being generally available, the current indicators look quite strong. With over 2,500 stars and 240 enhancement requests on GitHub already, the strong interest among the users is pretty clear. Judging by the initial impressions it has had on some users, JupyterLab hasn’t made a bad start at all, and looks well and truly set to replace the current Jupyter notebooks in the near future.