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Last week, Microsoft open sourced Infer.NET, the cross-platform framework used for model-based machine learning. This popular machine learning engine used in Office, Xbox and Azure, will be available on GitHub under the permissive MIT license for free use in commercial applications. Features of  Infer.NET The team at Microsoft Research in Cambridge initially envisioned Infer.NET as a research tool and released it for academic use in 2008. The framework has served as a base to publish hundreds of papers across a variety of fields, including information retrieval and healthcare. The team then started using the framework as a machine learning engine within a wide range of Microsoft products. A model-based approach to machine learning Infer.NET allows users to incorporate domain knowledge into their model. The framework can be used to build bespoke machine learning algorithms directly from their model. To sum it up, this framework actually constructs a learning algorithm for users based on the model they have provided. Facilitates interpretability Infer.NET also facilitates interpretability. If users have designed the model themselves and the learning algorithm follows that model, they can understand why the system behaves in a particular way or makes certain predictions. Probabilistic Approach In Infer.NET, models are described using a probabilistic program. This is used to describe real-world processes in a language that machines understand. Infer.NET compiles the probabilistic program into high-performance code for implementing something cryptically called deterministic approximate Bayesian inference. This approach allows a notable amount of scalability. For instance, it can be used in a system that automatically extracts knowledge from billions of web pages, comprising petabytes of data. Additional Features The framework also supports the ability of the system to learn as new data arrives. The team is also working towards developing and growing it further. Infer.NET will become a part of ML.NET (the machine learning framework for .NET developers). They have already set up the repository under the .NET Foundation and moved the package and namespaces to Microsoft.ML.Probabilistic.  Being cross platform, Infer.NET supports .NET Framework 4.6.1, .NET Core 2.0, and Mono 5.0. Windows users get to use Visual Studio 2017, while macOS and Linux folks have command-line options, which could be incorporated into the code wrangler of their choice. Download the framework to learn more about Infer.NET. You can also check the documentation for a detailed User Guide. To know more about this news, head over to Microsoft’s official blog. Microsoft announces new Surface devices to enhance user productivity, with style and elegance Neural Network Intelligence: Microsoft’s open source automated machine learning toolkit Microsoft’s new neural text-to-speech service lets machines speak like people

In a hearing on March 12, the sub-committee on emerging threats and capabilities received testimonies on Artificial Intelligence Initiatives within the Department of Defense(DoD). The panel included Peter Highnam, Deputy Director of the Defense Advanced Research Projects Agency; Michael Brown, DoD Defense Innovation Unit Director; and Lieutenant General John Shanahan, director of the Joint Artificial Intelligence Center (JAIC). The panel broadly testified to senators that AI will significantly transform DoD’s capabilities and that it is critical the U.S. remain competitive with China and Russia in developing AI applications. Dr. Peter T. Highnam on DARPA’s achievements and future goals Dr. Peter T. Highnam, Deputy Director, Defense Advanced Research Projects Agency talked about DARPA’s significant role in the development of AI technologies that have produced game-changing capabilities for the Department of Defense and beyond. In his testimony, he mentions, “DARPA’s AI Next effort is simply a continuing part of its 166 historic investment in the exploration and advancement of AI technologies.” Dr. Highnam highlighted different waves of AI technologies. The first wave, which was nearly 70 years ago, emphasized handcrafted knowledge, and computer scientists constructed so-called expert systems that captured the rules that the system could then apply to situations of interest. However, handcrafting rules was costly and time-consuming. The second wave that brought in machine learning that applies statistical and probabilistic methods to large data sets to create generalized representations that can be applied to future samples. However, this required training deep learning (artificial) neural networks with a variety of classification and prediction tasks when adequate historical data. Therein lies the rub, however, as the task of collecting, labelling, and vetting data on which to train. Such a process is prohibitively costly and time-consuming too. He says, “DARPA envisions a future in which machines are more than just tools that execute human programmed rules or generalize from human-curated data sets. Rather, the machines DARPA envisions will function more as colleagues than as tools.” Towards this end, DARPA is focusing its investments on a “third wave” of AI technologies that brings forth machines that can reason in context. Incorporating these technologies in military systems that collaborate with warfighters will facilitate better decisions in complex, time-critical, battlefield environments; enable a shared understanding of massive, incomplete, and contradictory information; and empower unmanned systems to perform critical missions safely and with high degrees of autonomy. DARPA’s more than $2 billion “AI Next” campaign, announced in September 2018, includes providing robust foundations for second wave technologies, aggressively applying the second wave AI technologies into appropriate systems, and exploring and creating third wave AI science and technologies. DARPA’s third wave research efforts will forge new theories and methods that will make it possible for machines to adapt contextually to changing situations, advancing computers from tools to true collaborative partners. Furthermore, the agency will be fearless about exploring these new technologies and their capabilities – DARPA’s core function – pushing critical frontiers ahead of our nation’s adversaries. To know more about this in detail, read Dr. Peter T. Highnam’s complete statement. Michael Brown on (Defense Innovation Unit) DIU’s efforts in Artificial Intelligence Michael Brown, Director of the Defense Innovation Unit, started the talk by highlighting on the fact how China and Russia are investing heavily to become dominant in AI.  “By 2025, China will aim to achieve major breakthroughs in AI and increase its domestic market to reach $59.6 billion (RMB 400 billion) To achieve these targets, China’s National Development and Reform Commission (China’s industrial policy-making agency) funded the creation of a national AI laboratory, and Chinese local governments have pledged more than $7 billion in AI funding”, Brown said in his statement. He said that these Chinese firms are in a way leveraging U.S. talent by setting up research institutes in the state, investing in U.S. AI-related startups and firms, recruiting U.S.-based talent, and commercial and academic partnerships. Brown said that DIU will engage with DARPA and JAIC(Joint Artificial Intelligence Center) and also make its commercial knowledge and relationships with potential vendors available to any of the Services and Service Labs. DIU also anticipates that with its close partnership with the JAIC, DIU will be at the leading edge of the Department’s National Mission Initiatives (NMIs), proving that commercial technology can be applied to critical national security challenges via accelerated prototypes that lay the groundwork for future scaling through JAIC. “DIU looks to bring in key elements of AI development pursued by the commercial sector, which relies heavily on continuous feedback loops, vigorous experimentation using data, and iterative development, all to achieve the measurable outcome, mission impact”, Brown mentions. DIU’s AI portfolio team combines depth of commercial AI, machine learning, and data science experience from the commercial sector with military operators. However, they have specifically prioritized projects that address three major impact areas or use cases which employ AI technology, including: Computer vision The DIU is prototyping computer vision algorithms in humanitarian assistance and disaster recovery scenarios. “This use of AI holds the potential to automate post-disaster assessments and accelerate search and rescue efforts on a global scale”, Brown said in his statement. Large dataset analytics and predictions DIU is prototyping predictive maintenance applications for Air Force and Army platforms. For this DIU plans to partner with JAIC to scale this solution across multiple aircraft platforms, as well as ground vehicles beginning with DIU’s complementary predictive maintenance project focusing on the Army’s Bradley Fighting Vehicle. Brown says this is one of DIU’s highest priority projects for FY19 given its enormous potential for impact on readiness and reducing costs. Strategic reasoning DIU is prototyping an application from Project VOLTRON that leverages AI to reason about high-level strategic questions, map probabilistic chains of events, and develop alternative strategies. This will make DoD owned systems more resilient to cyber attacks and inform program offices of configuration errors faster and with fewer errors than humans. Know more about what more DIU plans in partnership with DARPA and JAIC, in detail, in Michael Brown’s complete testimony. Lieutenant General Jack Shanahan on making JAIC “AI-Ready” Lieutenant General Jack Shanahan, Director, Joint Artificial Intelligence Center, touches upon  how the JAIC is partnering with the Under Secretary of Defense (USD) Research & Engineering (R&E), the role of the Military Services, the Department’s initial focus areas for AI delivery, and how JAIC is supporting whole-of-government efforts in AI. “To derive maximum value from AI application throughout the Department, JAIC will operate across an end-to-end lifecycle of problem identification, prototyping, integration, scaling, transition, and sustainment. Emphasizing commerciality to the maximum extent practicable, JAIC will partner with the Services and other components across the Joint Force to systematically identify, prioritize, and select new AI mission initiatives”, Shanahan mentions in his testimony. The AI capability delivery efforts that will go through this lifecycle will fall into two categories including National Mission Initiatives (NMI) and Component Mission Initiatives (CMI). NMI is an operational or business reform joint challenge, typically identified from the National Defense Strategy’s key operational problems and requiring multi-service innovation, coordination, and the parallel introduction of new technology and new operating concepts. On the other hand, Component Mission Initiatives (CMI) is a component-level challenge that can be solved through AI. JAIC will work closely with individual components on CMIs to help identify, shape, and accelerate their Component-specific AI deployments through: funding support; usage of common foundational tools, libraries, cloud infrastructure; application of best practices; partnerships with industry and academia; and so on. The Component will be responsible for identifying and implementing the organizational structure required to accomplish its project in coordination and partnership with the JAIC. Following are some examples of early NMI’s by JAIC to deliver mission impact at speed, demonstrate the proof of concept for the JAIC operational model, enable rapid learning and iterative process refinement, and build their library of reusable tools while validating JAIC’s enterprise cloud architecture. Perception Improve the speed, completeness, and accuracy of Intelligence, Surveillance, Reconnaissance (ISR) Processing, Exploitation, and Dissemination (PED). Shanahan says Project Maven’s efforts are included here. Predictive Maintenance (PMx) Provide computational tools to decision-makers to help them better forecast, diagnose, and manage maintenance issues to increase availability, improve operational effectiveness, and ensure safety, at a reduced cost. Humanitarian Assistance/Disaster Relief (HA/DR) Reduce the time associated with search and discovery, resource allocation decisions, and executing rescue and relief operations to save lives and livelihood during disaster operations. Here, JAIC plans to apply lessons learned and reusable tools from Project Maven to field AI capabilities in support of federal responses to events such as wildfires and hurricanes—where DoD plays a supporting role. Cyber Sensemaking Detect and deter advanced adversarial cyber actors who infiltrate and operate within the DoD Information Network (DoDIN) to increase DoDIN security, safeguard sensitive information, and allow warfighters and engineers to focus on strategic analysis and response. Shanahan states, “Under the DoD CIO’s authorities and as delineated in the JAIC establishment memo, JAIC will coordinate all DoD AI-related projects above $15 million annually.” “It does mean that we will start to ensure, for example, that they begin to leverage common tools and libraries, manage data using best practices, reflect a common governance framework, adhere to rigorous testing and evaluation methodologies, share lessons learned, and comply with architectural principles and standards that enable scale”, he further added. To know more about this in detail, read Lieutenant General Jack Shanahan’s complete testimony. To know more about this news in detail, watch the entire hearing on 'Artificial Intelligence Initiatives within the Department of Defense' So, you want to learn artificial intelligence. Here’s how you do it. What can happen when artificial intelligence decides on your loan request Mozilla partners with Ubisoft to Clever-Commit its code, an artificial intelligence assisted assistant

Open source licenses allow software to be freely distributed, modified, and used. These licenses give developers an additional advantage of allowing others to use their software as per their own rules and conditions. Recently, software developer and open-source advocate Coraline Ada Ehmke has caused a stir in the software engineering community with ‘The Hippocratic License.’ Ehmke was also the original author of Contributor Covenant, a “code of conduct" for open source projects that encourages participants to use inclusive language and to refrain from personal attacks and harassment. In a tweet posted in September last year, following the code of conduct, she mentioned, “40,000 open source projects, including Linux, Rails, Golang, and everything OSS produced by Google, Microsoft, and Apple have adopted my code of conduct.” [box type="shadow" align="" class="" width=""]The term ‘Hippocratic’ is derived from the Hippocratic Oath, the most widely known of Greek medical texts. The Hippocratic Oath in literal terms requires a new physician to swear upon a number of healing gods that he will uphold a number of professional ethical standards.[/box] Ehmke explained the license in more detail in a post published on Sunday. In it, she highlights how the idea that writing software with the goals of clarity, conciseness, readability, performance, and elegance are limiting, and potentially dangerous.“All of these technologies are inherently political,” she writes. “There is no neutral political position in technology. You can’t build systems that can be weaponized against marginalized people and take no responsibility for them.”The concept of the Hippocratic license is relatively simple. In a tweet, Ehmke said that it “specifically prohibits the use of open-source software to harm others.” Open source software and the associated harm Out of the many privileges that open source software allows such as free redistribution of the software as well as the source code, the OSI also defines there is no discrimination against who uses it or where it will be put to use. A few days ago, a software engineer, Seth Vargo pulled his open-source software, Chef-Sugar, offline after finding out that Chef (a popular open source DevOps company using the software) had recently signed a contract selling $95,000-worth of licenses to the US Immigrations and Customs Enforcement (ICE), which has faced widespread condemnation for separating children from their parents at the U.S. border and other abuses. Vargo took down the Chef Sugar library from both GitHub and RubyGems, the main Ruby package repository, as a sign of protest. In May, this year, Mijente, an advocacy organization released documents stating that Palantir was responsible for the 2017 ICE operation that targeted and arrested family members of children crossing the border alone. Also, in May 2018, Amazon employees, in a letter to Jeff Bezos, protested against the sale of its facial recognition tech to Palantir where they “refuse to contribute to tools that violate human rights”, citing the mistreatment of refugees and immigrants by ICE. Also, in July, the WYNC revealed that Palantir’s mobile app FALCON was being used by ICE to carry out raids on immigrant communities as well as enable workplace raids in New York City in 2017. Founder of OSI responds to Ehmke’s Hippocratic License Bruce Perens, one of the founders of the Open Source movement in software, responded to Ehmke in a post titled “Sorry, Ms. Ehmke, The “Hippocratic License” Can’t Work” . “The software may not be used by individuals, corporations, governments, or other groups for systems or activities that actively and knowingly endanger harm, or otherwise threaten the physical, mental, economic, or general well-being of underprivileged individuals or groups,” he highlights in his post. “The terms are simply far more than could be enforced in a copyright license,” he further adds.  “Nobody could enforce Ms. Ehmke’s license without harming someone, or at least threatening to do so. And it would be easy to make a case for that person being underprivileged,”  he continued. He concluded saying that, though the terms mentioned in Ehmke’s license were unagreeable, he will “happily support Ms. Ehmke in pursuit of legal reforms meant to achieve the protection of underprivileged people.” Many have welcomed Ehmke's idea of an open source license with an ethical clause. However, the license is not OSI approved yet and chances are slim after Perens’ response. There are many users who do not agree with the license. Reaching a consensus will be hard. https://twitter.com/seannalexander/status/1175853429325008896 https://twitter.com/AdamFrisby/status/1175867432411336704 https://twitter.com/rishmishra/status/1175862512509685760 Even though developers host their source code on open source repositories, a license may bring certain level of restrictions on who is allowed to use the code. However, as Perens mentions, many of the terms in Ehmke’s license hard to implement. Irrespective of the outcome of this license’s approval process, Coraline Ehmke has widely opened up the topic of the need for long overdue FOSS licensing reforms in the open source community. It would be interesting to see if such a license would boost ethical reformation by giving more authority to the developers in imbibing their values and preventing the misuse of their software. Read the Hippocratic license to know more in detail. Other interesting news Tech ImageNet Roulette: New viral app trained using ImageNet exposes racial biases in artificial intelligent system Machine learning ethics: what you need to know and what you can do Facebook suspends tens of thousands of apps amid an ongoing investigation into how apps use personal data

Mention McDonald’s to someone today, and they're more likely to think about Big Mac than Big Data. But that could soon change. As the fast-food giant embraced machine learning, with plans to become a tech-innovator in a fittingly super-sized way. McDonald's stunned a lot of people when it announced its biggest acquisition in 20 years, one that reportedly cost it over $300 million. It plans to acquire Dynamic Yield, a New York based startup that provides retailers with algorithmically driven "decision logic" technology. When you add an item to an online shopping cart, “decision logic” is the tech that nudges you about what other customers bought as well. Dynamic Yield’s client list includes blue-chip retail clients like Ikea, Sephora, and Urban Outfitters. McDonald’s vetted around 30 firms offering similar personalization engine services, and landed on Dynamic Yield. It has been recently valued in the hundreds of millions of dollars; people familiar with the details of the McDonald’s offer put it at over $300 million. This makes the company's largest purchase as per a tweet by the McDonald’s CEO Steve Easterbrook. https://twitter.com/SteveEasterbrk/status/1110313531398860800 The burger giant can certainly afford it; in 2018 alone it tallied nearly $6 billion of net income, and ended the year with a free cash flow of $4.2 billion. McDonalds, a food-tech innovator from the start Over the last several years, McDonalds has invested heavily in technology by bringing stores up to date with self-serve kiosks. The company also launched an app and partnered with Uber Eats in that time, in addition to a number of infrastructure improvements. It even relocated its headquarters less than a year ago from the suburbs to Chicago’s vibrant West Town neighborhood, in a bid to attract young talent. Collectively, McDonald’s serves around 68 million customers every single day. And the majority of those people are at their drive-thru window who never get out of their car, instead place and pick up their orders from the window. And that’s where McDonalds is planning to deploy Dynamic Yield tech first. “What we hadn’t done is begun to connect the technology together, and get the various pieces talking to each other,” says Easterbrook. “How do you transition from mass marketing to mass personalization? To do that, you’ve really got to unlock the data within that ecosystem in a way that’s useful to a customer.” Here’s what that looks like in practice: When you drive up to place your order at a McDonald’s today, a digital display greets you with a handful of banner items or promotions. As you inch up toward the ordering area, you eventually get to the full menu. Both of these, as currently implemented, are largely static, aside from the obvious changes like rotating in new offers, or switching over from breakfast to lunch. But in a pilot program at a McDonald’s restaurant in Miami, powered by Dynamic Yield, those displays have taken on new dexterity. In the new McDonald’s machine-learning paradigm, that particular display screen will show customers what other items have been popular at that location, and prompt them with potential upsells. Thanks for your Happy Meal order; maybe you’d like a Sprite to go with it. “We’ve never had an issue in this business with a lack of data,” says Easterbrook. “It’s drawing the insight and the intelligence out of it.” Revenue aspects likely to double with the acquisition McDonald’s hasn’t shared any specific insights gleaned so far, or numbers around the personalization engine’s effect on sales. But it’s not hard to imagine some of the possible scenarios. If someone orders two Happy Meals at 5 o’clock, for instance, that’s probably a parent ordering for their kids; highlight a coffee or snack for them, and they might decide to treat themselves to a pick-me-up. And as with any machine-learning system, the real benefits will likely come from the unexpected. While customer satisfaction may be the goal, the avenues McDonald’s takes to get there will increase revenues along the way. Customer personalization is another goal to achieve As you may think, McDonald’s didn’t spend over $300 million on a machine-learning company to only juice up its drive-thru sales. An important part is to figure how to leverage the “personalization” part of a personalization engine. Fine-tuned insights at the store level are one thing, but Easterbrook envisions something even more granular. “If customers are willing to identify themselves—there’s all sorts of ways you can do that—we can be even more useful to them, because now we call up their favorites,” according to Easterbrook, who stresses that privacy is paramount. As for what form that might ultimately take, Easterbrook raises a handful of possibilities. McDonald’s already uses geofencing around its stores to know when a mobile app customer is approaching and prepare their order accordingly. On the downside of this tech integration When you know you have to change so much in your company, it's easy to forget some of the consequences. You race to implement all new things in tech and don't adequately think about what your employees might think of it all. This seems to be happening to McDonald's. As the fast-food chain tries to catch up to food trends that have been established for some time, their employees seem to be not happy about the fact. As Bloomberg reports, the more McDonald's introduces, fresh beef, touchscreen ordering and delivery, the more its employees are thinking: "This is all too much work." One of the employees at the McDonalds franchisee revealed at the beginning of this year. "Employee turnover is at an all-time high for us," he said, adding "Our restaurants are way too stressful, and people do not want to work in them." Workers are walking away rather than dealing with new technologies and menu options. The result: customers will wait longer. Already, drive-through times at McDonald’s slowed to 239 seconds last year -- more than 30 seconds slower than in 2016, according to QSR magazine. Turnover at U.S. fast-food restaurants jumped to 150% meaning a store employing 20 workers would go through 30 in one year. Having said that it does not come to us as a surprise that McDonalds on Tuesday announced to the National Restaurant Association that it will no longer participate in lobby efforts against minimum-wage hikes at the federal, state or local level. It does makes sense when they are already paying low wages and an all time high attrition rate hail as a bigger problem. Of course, technology is supposed to solve all the world's problems, while simultaneously eliminating the need for many people. Looks like McDonalds has put all its eggs in the machine learning and automation basket. Would it not be a rich irony, if people saw technology being introduced and walked out, deciding it was all too much trouble for just a burger? 25 Startups using machine learning differently in 2018: From farming to brewing beer to elder care An AI startup now wants to monitor your kids’ activities to help them grow ‘securly’ Microsoft acquires AI startup Lobe, a no code visual interface tool to build deep learning models easily

Yesterday, at the PyTorch Developer Conference, Facebook announced the release of PyTorch 1.3. This release comes with three experimental features: named tensors, 8-bit model quantization, and PyTorch Mobile. Along with these exciting features, Facebook also announced the general availability of Google Cloud TPU support and a newly launched integration with Alibaba Cloud. Key updates in PyTorch 1.3 Named Tensors for more readable and maintainable code Though tensors are the building blocks of modern machine learning, researchers have argued that they are “broken.” Tensors have their own share of shortcomings: they expose private dimensions, broadcast based on absolute position, and keep the type information in the documentation. PyTorch 1.3 tries to solve this problem by introducing experimental support for named tensors, which was proposed by Sasha Rush, an Associate Professor at Cornell Tech. He has built a library called NamedTensor, which serves as a “thin-wrapper” on Torch tensor. This update introduces a few changes to the API. Dimension access and reduction now use a ‘dim’ argument instead of an index. Constructing and adding dimensions requires a “name” argument. Functions now broadcast based on set operations, not through heuristic ordering rules. 8-bit model quantization for mobile-optimized AI Quantization in deep learning is the method of approximating a neural network that uses 32-bit floating-point numbers by a neural network that uses a lower-precision numerical format. It is used to reduce the bandwidth and compute requirements of deep learning models. This is extremely essential for on-device applications that have limited memory size and number of computations. PyTorch 1.3 brings experimental support for 8-bit model quantization with the eager mode Python API for efficient deployment on servers and edge devices. This feature includes techniques like post-training quantization, dynamic quantization, and quantization-aware training. Moving from 32-bits to 8-bits can result in two to four times faster computations with one-quarter the memory usage. PyTorch Mobile for more efficient on-device machine learning Running machine learning models directly on edge devices is of great importance as it reduces latency. This is why PyTorch 1.3 introduces PyTorch Mobile that enables “an end-to-end workflow from Python to deployment on iOS and Android.” The current release is experimental. In the future releases, we can expect PyTorch Mobile to come with build-level optimization, selective compilation, support for QNNPACK quantized kernel libraries and ARM CPUs, further performance improvements, and more. Model interpretability and privacy tools in PyTorch 1.3 Captum and Captum Insights Captum is an easy-to-use model interpretability library for PyTorch. It is backed by state-of-the-art interpretability algorithms such as Integrated Gradients, DeepLIFT, and Conductance to help developers improve and troubleshoot their models. Developers can identify different features that contribute to a model’s output and improve its design. Facebook has also released an early release of Captum Insights. It is an interpretability visualization widget built on top of Captum. It works across images, text, and other features to help users understand feature attribution. Check out Facebook’s announcement to know more about Captum. CrypTen Machine learning via cloud-based platforms poses various security and privacy challenges. Facebook writes, “In particular, users of these platforms may not want or be able to share unencrypted data, which prevents them from taking full advantage of ML tools.” PyTorch 1.3 comes with CrypTen, a framework for privacy-preserving machine learning. It aims to make secure computing techniques accessible to machine learning practitioners. You can find more about CrypTen on GitHub. Libraries for multimodal AI systems Detectron2: It is an object detection library implemented in PyTorch. It features support for the latest models and tasks and increased flexibility to aid computer vision research. There are also improvements in maintainability and scalability to support production use cases. Fairseq gets speech extensions: With this release, Fairseq, a framework for sequence-to-sequence applications such as language translation includes support for end-to-end learning for speech and audio recognition tasks. The release of PyTorch 1.3 started a discussion on Hacker News and naturally many developers compared it with TensorFlow 2.0. Here’s what a user commented, “This is a common trend for being second in the market when we see Pytorch and TensorFlow 2.0, TF 2.0 was created to compete directly with Pytorch pythonic implementation (Keras based, Eager execution).” They further added, “Facebook at least on PyTorch has been delivering a quality product. Although for us running production pipelines TF is still ahead in many areas (GPU, TPU implementation, TensorRT, TFX and other pipeline tools) I can see Pytorch catching up on the next couple of years which by my prediction many companies will be running serious and advanced workflows and we may be able to see a winner there.” The named tensors implementation is being well-received by the PyTorch community: https://twitter.com/leopd/status/1182342855886376965 https://twitter.com/rasbt/status/1182647527906140161 These were some of the updates in PyTorch 1.3. Check out the official announcement by Facebook to know more. PyTorch 1.2 is here with a new TorchScript API, expanded ONNX export, and more PyTorch announces the availability of PyTorch Hub for improving machine learning research reproducibility Sherin Thomas explains how to build a pipeline in PyTorch for deep learning workflows Facebook AI open-sources PyTorch-BigGraph for faster embeddings in large graphs Facebook open-sources PyText, a PyTorch based NLP modeling framework

Last week, the team at Julia Computing published a research based on cutting edge cryptographic techniques. The research involved cryptography techniques to practically perform computation on data without ever decrypting it. For example, the user would send encrypted data (e.g. images) to the cloud API, which would run the machine learning model and then return the encrypted answer. Nowhere is the user data decrypted and in particular the cloud provider does not have access to either the original image nor is it able to decrypt the prediction it computed. The team made this possible by building a machine learning service for handwriting recognition of encrypted images (from the MNIST dataset). The ability to compute on encrypted data is generally referred to as “secure computation” and is a fairly large area of research, with many different cryptographic approaches and techniques for a plethora of different application scenarios. For their research, Julia team focused on using a technique known as “homomorphic encryption”. What is homomorphic encryption Homomorphic encryption is a form of encryption that allows computation on ciphertexts, generating an encrypted result which, when decrypted, matches the result of the operations as if they had been performed on the plaintext. This technique can be used for privacy-preserving outsourced storage and computation. It allows data to be encrypted and out-sourced to commercial cloud environments for processing, all while encrypted. In highly regulated industries, such as health care, homomorphic encryption can be used to enable new services by removing privacy barriers inhibiting data sharing. In this research, the Julia Computing team used a homomorphic encryption system which involves the following operations: pub_key, eval_key, priv_key = keygen() encrypted = encrypt(pub_key, plaintext) decrypted = decrypt(priv_key, encrypted) encrypted′ = eval(eval_key, f, encrypted) So the first three are fairly straightforward and are familiar to anyone who has used asymmetric cryptography before. The last one is important as it evaluates some function f on the encryption and returns another encrypted value corresponding to the result of evaluating f on the encrypted value. It is this property that gives homomorphic computation its name. Further the Julia Computing team talks about CKKS (Cheon-Kim-Kim-Song), a homomorphic encryption scheme that allowed homomorphic evaluation on the following primitive operations: Element-wise addition of length n vectors of complex numbers Element-wise multiplication of length n complex vectors Rotation (in the circshift sense) of elements in the vector Complex conjugation of vector elements But they also mentioned that computations using CKKS were noisy, and hence they tested to perform these operations in Julia. Which convolutional neural network did the Julia Computing team use As a starting point the Julia Computing team used the convolutional neural network example given in the Flux model zoo. They kept training the loop, prepared the data and tweaked the ML model slightly. It is essentially the same model as the one used in the paper “Secure Outsourced Matrix Computation and Application to Neural Networks”, which uses the same (CKKS) cryptographic scheme. This paper also encrypts the model, which the Julia team neglected for simplicity and they involved bias vectors after every layer (which Flux does by default). This resulted in a higher test set accuracy of the model used by Julia team which was (98.6% vs 98.1%). An unusual feature in this model are the x.^2 activation functions. More common choices here would have been tanh or relu or something more advanced. While those functions (relu in particular) are cheap to evaluate on plaintext values, they would however, be quite expensive to evaluate on encrypted values. Also, the team would have ended up evaluating a polynomial approximation had they adopted these common choices. Fortunately  x.^2 worked fine for their purpose. How was the homomorphic operation carried out The team performed homomorphic operation on Convolutions and Matrix Multiply assuming a batch size of 64. They precomputed each convolution window of 7x7 extraction from the original images which gave them 64 7x7 matrices per input image. Then they collected the same position in each window into one vector and got a 64-element vector for each image, (i.e. a total of 49 64x64 matrices), and encrypted these matrices. In this way the convolution became a scalar multiplication of the whole matrix with the appropriate mask element, and by summing all 49 elements later, the team got the result of the convolution. Then the team moved to Matrix Multiply by rotating elements in the vector to effect a re-ordering of the multiplication indices. They considered a row-major ordering of matrix elements in the vector. Then shifted the vector by a multiple of the row-size, and got the effect of rotating the columns, which is a sufficient primitive for implementing matrix multiply. The team was able to get everything together and it worked. You can take a look at the official blog post to know the step by step implementation process with codes. Further they also executed the whole encryption process in Julia as it allows powerful abstractions and they could encapsulate the whole convolution extraction process as a custom array type. The Julia Computing team states, “Achieving the dream of automatically executing arbitrary computations securely is a tall order for any system, but Julia’s metaprogramming capabilities and friendly syntax make it well suited as a development platform.” Julia co-creator, Jeff Bezanson, on what’s wrong with Julialang and how to tackle issues like modularity and extension Julia v1.3 released with new multithreading features, and much more! The Julia team shares its finalized release process with the community Julia announces the preview of multi-threaded task parallelism in alpha release v1.3.0 How to make machine learning based recommendations using Julia [Tutorial]

Deepfakes are becoming scaringly and indistinguishably real. A YouTube clip of Bill Hader in conversation with David Letterman on his late-night show in 2008 is going viral where Hader’s face subtly shifts to Cruise’s as Hader does his impression. This viral Deepfake clip has been viewed over 3 million times and is uploaded by Ctrl Shift Face (a Slovakian citizen who goes by the name of Tom), who has created other entertaining videos using Deepfake technology. For the unaware, Deepfake uses Artificial intelligence and deep neural networks to alter audio or video to pass it off as true or original content. https://www.youtube.com/watch?v=VWrhRBb-1Ig Deepfakes are problematic as they make it hard to differentiate between fake and real videos or images. This gives people the liberty to use deepfakes for promoting harassment and illegal activities. The most common use of deepfakes is found in revenge porn, political abuse, and fake celebrities videos as this one. The top comments on the video clip express dangers of realistic AI manipulation. “The fade between faces is absolutely unnoticeable and it's flipping creepy. Nice job!” “I’m always amazed with new technology, but this is scary.” “Ok, so video evidence in a court of law just lost all credibility” https://twitter.com/TheMuleFactor/status/1160925752004624387 Deepfakes can also be used as a weapon of misinformation since they can be used to maliciously hoax governments, populations and cause internal conflict. Gavin Sheridan, CEO of Vizlegal also tweeted the clip, “Imagine when this is all properly weaponized on top of already fractured and extreme online ecosystems and people stop believing their eyes and ears.” He also talked about future impact. “True videos will be called fake videos, fake videos will be called true videos. People steered towards calling news outlets "fake", will stop believing their own eyes. People who want to believe their own version of reality will have all the videos they need to support it,” he tweeted. He also tweeted whether we would require A-list movie actors at all in the future, and could choose which actor will portray what role. His tweet reads, “Will we need A-list actors in the future when we could just superimpose their faces onto the faces of other actors? Would we know the difference?  And could we not choose at the start of a movie which actors we want to play which roles?” The past year has seen accelerated growth in the use of deepfakes. In June, a fake video of Mark Zuckerberg was posted on Instagram, under the username, bill_posters_uk. In the video, Zuckerberg appears to give a threatening speech about the power of Facebook. Facebook had received strong criticism for promoting fake videos on its platform when in May, the company had refused to remove a doctored video of senior politician Nancy Pelosi. Samsung researchers also released a deepfake that could animate faces with just your voice and a picture using temporal GANs. Post this, the House Intelligence Committee held a hearing to examine the public risks posed by “deepfake” videos. Tom, the creator of the viral video told The Guardian that he doesn't see deepfake videos as the end of the world and hopes his deepfakes will raise public awareness of the technology's potential for misuse. “It’s an arms race; someone is creating deepfakes, someone else is working on other technologies that can detect deepfakes. I don’t really see it as the end of the world like most people do. People need to learn to be more critical. The general public are aware that photos could be Photoshopped, but they have no idea that this could be done with video.” Ctrl Shift Face is also on Patreon offering access to bonus materials, behind the scenes footage, deleted scenes, early access to videos for those who provide him monetary support. Now there is a Deepfake that can animate your face with just your voice and a picture. Mark Zuckerberg just became the target of the world’s first high profile white hat deepfake op. Worried about Deepfakes? Check out the new algorithm that manipulate talking-head videos by altering the transcripts.

This Monday the Department of Commerce, Bureau of Industry and Security (BIS) published a proposal to control the export of AI from USA. This move seems to lean towards restricting AI tech going out of the country to protect the national security of USA. The areas that come under the licensing proposal Artificial intelligence, as we’ve seen in recent years has great potential for both good and harm. The DoC in the United States of America is not taking any chances with it. The proposal lists many areas of AI that could potentially require a license to be exported to certain countries. Other than computer vision, natural language processing, military-specific products like adaptive camouflage and faceprint for surveillance is also listed in the proposal to restrict the export of AI. The areas major areas listed in the proposal are: Biotechnology including genomic and genetic engineering Artificial intelligence (AI) and machine learning including neural networks, computer vision, and natural language processing Position, Navigation, and Timing (PNT) technology Microprocessor technology like stacked memory on chip Advanced computing technology like memory-centric logic Data analytics technology like data analytics by visualization and analysis algorithms Quantum information and sensing technology like quantum computing, encryption, and sensing Logistics technology like mobile electric power Additive manufacturing like 3D printing Robotics like micro drones and molecular robotics Brain-computer interfaces like mind-machine interfaces Hypersonics like flight control algorithms Advanced Materials like adaptive camouflage Advanced surveillance technologies faceprint and voiceprint technologies David Edelman, a former adviser to ex-US president Barack Obama said: “This is intended to be a shot across the bow, directed specifically at Beijing, in an attempt to flex their muscles on just how broad these restrictions could be”. Countries that could be affected with regulation on export of AI To determine the level of export controls, the department will consider the potential end-uses and end-users of the technology. The list of countries is not clear but ones to which exports are restricted like embargoed countries will be considered. Also, China could be one of them. What does this mean for companies? If your organization creates products in ‘emerging technologies’ then there will be restrictions on the countries you can export to and also on disclosure of technology to foreign nationals in United States. Depending on the criteria, non-US citizens might even need licenses to participate in research and development of such technology. This will restrict non-US citizens to participate and take back anything from, say an advanced AI research project. If the new regulations go into effect, it will affect the security review of foreign investments across these areas. When the list of technologies is finalized, many types of foreign investments will be subject to a review and deals could be halted or undone. Public views on academic research In addition to commercial applications and products, this regulation could also be bad news for academic research. https://twitter.com/jordanbharrod/status/1065047269282627584 https://twitter.com/BryanAlexander/status/1064941028795400193 Even Google Home, Amazon Alexa, iRobot Roomba could be affected. https://twitter.com/R_D/status/1064511113956655105 But it does not look like research papers will be really affected. The document states that the commerce does not intend to expand jurisdiction on ‘fundamental research’ for ‘emerging technologies’ that is intended to be published and not currently subject to EAR as per § 734.8. But will this affect open-source technologies? We really hope not. Deadline for comments is less than 30 days away BIS has invited comments to the proposal for defining and categorizing emerging technologies, the impact of the controls in US technology leadership among other topics. However the short deadline of December 19, 2018 indicates their haste to implement licensing export of AI quickly. For more details, and to know where you can submit your comments, read the proposal. The US Air Force lays groundwork towards artificial general intelligence based on hierarchical model of intelligence Google open sources BERT, an NLP pre-training technique Teaching AI ethics – Trick or Treat?

Behind the heels of Stanford’s HCAI Institute ( which, mind you, received public backlash for non-representative faculty makeup). Amazon is collaborating with the National Science Foundation (NSF) to develop systems based on fairness in AI. The company will be investing $10M each in artificial intelligence research grants over a three-year period. The official announcement was made by Prem Natarajan, VP of natural understanding in the Alexa AI group, who wrote in a blog post “With the increasing use of AI in everyday life, fairness in artificial intelligence is a topic of increasing importance across academia, government, and industry. Here at Amazon, the fairness of the machine learning systems we build to support our businesses is critical to establishing and maintaining our customers’ trust.” Per the blog post, Amazon will be collaborating with NSF to build trustworthy AI systems to address modern challenges. They will explore topics of transparency, explainability, accountability, potential adverse biases and effects, mitigation strategies, validation of fairness, and considerations of inclusivity. Proposals will be accepted from March 26 until May 10, to result in new open source tools, publicly available data sets, and publications. The two organizations plan to continue the program with calls for additional proposals in 2020 and 2021. There will be 6 to 9 awards of type Standard Grant or Continuing Grant. The award size will be $750,000 - up to a maximum of $1,250,000 for periods of up to 3 years. The anticipated funding amount is $7,600,000. “We are excited to announce this new collaboration with Amazon to fund research focused on fairness in AI,” said Jim Kurose, NSF's head for Computer and Information Science and Engineering. “This program will support research related to the development and implementation of trustworthy AI systems that incorporate transparency, fairness, and accountability into the design from the beginning.” The insidious nexus of private funding in public research: What does Amazon gain from collab with NSF? Amazon’s foray into fairness system looks more of a publicity stunt than eliminating AI bias. For starters, Amazon said that they will not be making the award determinations for this project. NSF would solely be awarding in accordance with its merit review process. However, Amazon said that Amazon researchers may be involved with the projects as an advisor only at the request of an awardee, or of NSF with the awardee's consent. As advisors, Amazon may host student interns who wish to gain further industry experience, which seems a bit dicey. Amazon will also not participate in the review process or receive proposal information. NSF will only be sharing with Amazon summary-level information that is necessary to evaluate the program, specifically the number of proposal submissions, number of submitting organizations, and numbers rated across various review categories. There was also the question of who exactly is funding since VII.B section of the proposal states: "Individual awards selected for joint funding by NSF and Amazon will be   funded through separate NSF and Amazon funding instruments." https://twitter.com/nniiicc/status/1110335108634951680 https://twitter.com/nniiicc/status/1110335004989521920 Nic Weber, the author of the above tweets and Assistant Professor at UW iSchool, also raises another important question: “Why does Amazon get to put its logo on a national solicitation (for a paltry $7.6 million dollars in basic research) when it profits in the multi-billions off of AI that is demonstrably unfair and harmful.” Twitter was abundant with tweets from those in working tech questioning Amazon’s collaboration. https://twitter.com/mer__edith/status/1110560653872373760 https://twitter.com/patrickshafto/status/1110748217887649793 https://twitter.com/smunson/status/1110657292549029888 https://twitter.com/haldaume3/status/1110697325251448833 Amazon has already been under the fire due to its controversial decisions in the recent past. In June last year, when the US Immigration and Customs Enforcement agency (ICE) began separating migrant children from their parents, Amazon came under fire as one of the tech companies that aided ICE with the software required to do so. Amazon has also faced constant criticisms since the news came that Amazon had sold its facial recognition product Rekognition to a number of law enforcement agencies in the U.S. in the first half of 2018. Amazon is also under backlash after a study by the Massachusetts Institute of Technology in January, found Amazon Rekognition incapable of reliably determining the sex of female and darker-skinned faces in certain scenarios. Amazon is yet to fix this AI-bias anomaly, and yet it has now started a new collaboration with NSF that ironically focusses on building bias-free AI systems. Amazon’s Ring (a smart doorbell company) also came under public scrutiny in January, after it gave access to its employees to watch live footage from cameras of the customers. In other news, yesterday, Google also formed an external AI advisory council to help advance the responsible development of AI. More details here. Amazon won’t be opening its HQ2 in New York due to public protests Amazon admits that facial recognition technology needs to be regulated Amazon’s Ring gave access to its employees to watch live footage of the customers, The Intercept reports

MongoDB, the open source NoSQL database, is going to acquire mobile database platform Realm. The purchase is certainly one with clear technological and strategic benefits for both companies - and with MongoDB paying $39 million for a company that has up to now raised $40 million since its launch in 2011, it's clear that this is a move that isn't about short term commercial gains. It's important to note that the acquisition is not yet complete. It's expected to close in January 2020 at the end of the second quarter MongoDB's fiscal year. Further details about the acquisition and what it means for both products, will be revealed at MongoDB World in June. Why is MongoDB acquiring Realm? In the materials that announce the launch there's a lot of talk about the alignment between the two projects. "The best thing in the world is when someone just gets you, and you get them" MongoDB CTO Eliot Horowitz wrote in a blog post accompanying the release, "because when you share a vision of the world like that, you can do incredible things together. That’s exactly the case with MongoDB and Realm." At a more fundamental level the acquisition allows MongoDB to do a number of things. It can reach a new community of developers  working primarily in mobile development (according to the press release Realm has 100,000 active users), but it also allows MongoDB to strengthen its capabilities as cloud evolves to become the dominant way that applications are built and hosted. According to Dev Ittycheria, MongoDB President and CEO, Realm "is a natural fit for our global cloud database, MongoDB Atlas, as well as a complement to Stitch, our serverless platform." Serverless might well be a nascent trend at the moment, but the level of conversation and interest around it indicates that it's going to play a big part in application developers lives in the months and years to come. What's in it for Realm? For Realm, the acquisition will give the project access to a new pool of users. With backing from MongoDB, is also provides robust foundations for the project to extend its roadmap and even move faster than it previously would have been able to. Realm CEO David Ratner wrote yesterday (April 24) that: "The combination of MongoDB and Realm will establish the modern standard for mobile application development and data synchronization for a new generation of connected applications and services. MongoDB and Realm are fully committed to investing in the Realm Database and the future of data synchronization, and taking both to the next phase of their evolution. We believe that MongoDB will help accelerate Realm’s product roadmap, go-to-market execution, and support our customers’ use cases at a whole new level of global operational scale." A new chapter for MongoDB? 2019 hasn't been the best year for MongoDB so far. The project withdrew its submission for its controversial Server Side Public License last month following news that Red Hat was dropping it from Enterprise Linux and Fedora. This brought an initiative that the leadership viewed as strategically important in defending MongoDB's interests to a dramatic halt. However, the Realm acquisition sets up a new chapter and could go some way in helping MongoDB bolster itself for a future that it has felt uncertain about.

The data science community is reeling after data science learning startup DataCamp penned a blog post acknowledging that an unnamed company executive made "uninvited physical contact" with one of its employees. DataCamp, which operates an e-platform where aspiring data scientists can take courses in coding and data analysis is a startup valued at $184 million. It has additionally raised over $30 million in funding. The company disclosed in a blog post published on 4th April that this incident occurred at an "informal employee gathering" at a bar in October 2017. The unnamed DataCamp executive had "danced inappropriately and made uninvited physical contact" with the employee on the dance floor, the post read. The company didn't name the executive involved in the incident in its post. But called the executive's behavior on the dance floor "entirely inappropriate" and "inconsistent" with employee expectations and policies. When Buisness Insider reached out to one of the course instructors OS Keyes familiar with this matter, Keyes said that the executive in question is DataCamp's co-founder and CEO Jonathan Cornelissen. Yesterday Motherboard also reported that the company did not adequately address sexual misconduct by a senior executive there and instructors at DataCamp have begun boycotting the service and asking the company to delete their courses following allegations. What actually happened and how did DataCamp respond? On April 4, DataCamp shared a statement on its blog titled “a note to our community.” In it, the startup addressed the accusations against one of the company’s executives: “In October 2017, at an informal employee gathering at a bar after a week-long company offsite, one of DataCamp’s executives danced inappropriately and made uninvited physical contact with another employee while on the dance floor.” DataCamp got the complaint reviewed by a “third party not involved in DataCamp’s day-to-day business,” and said it took several “corrective actions,” including “extensive sensitivity training, personal coaching, and a strong warning that the company will not tolerate any such behavior in the future.” DataCamp only posted its blog a day after more than 100 DataCamp instructors signed a letter and sent it to DataCamp executives. “We are unable to cooperate with continued silence and lack of transparency on this issue,” the letter said. “The situation has not been acknowledged adequately to the data science community, leading to harmful rumors and uncertainty.” But as instructors read the statement from DataCamp following the letter, many found the actions taken to be insufficient. https://twitter.com/hugobowne/status/1120733436346605568 https://twitter.com/NickSolomon10/status/1120837738004140038 Motherboard reported this case in detail taking notes from Julia Silge, a data scientist who co-authored the letter to DataCamp. Julia says that going public with our demands for accountability was the last resort. Julia spoke about the incident in detail and says she remembered seeing the victim of the assault start working at DataCamp and then leave abruptly. This raised “red flags” but she did not reach out to her. Then Silge heard about the incident from a mutual friend and she began to raise the issue with internal people at DataCamp. “There were various responses from the rank and file. It seemed like after a few months of that there was not a lot of change, so I escalated a little bit,” she said. DataCamp finally responded to Silge by saying “I think you have misconceptions about what happened,” and they also mentioned that “there was alcohol involved” to explain the behavior of the executive. DataCamp further explained that “We also heard over and over again, ‘This has been thoroughly handled.’” But according to Silge and other instructors who have spoken out, say that DataCamp hasn’t properly handled the situation and has tried to sweep it under the rug. Silge also created a private Slack group to communicate and coordinate their efforts to confront this issue. She along with the group got into a group video conference with DataCamp, which was put into “listen-only” mode for all the other participants except DataCamp, meaning they could not speak in the meeting, and were effectively silenced. “It felt like 30 minutes of the DataCamp leadership saying what they wanted to say to us,” Silge said. “The content of it was largely them saying how much they valued diversity and inclusion, which is hard to find credible given the particular ways DataCamp has acted over the past.” Following that meeting, instructors began to boycott DataCamp more blatantly, with one instructor refusing to make necessary upgrades to her course until DataCamp addressed the situation. Silge and two other instructors eventually drafted and sent the letter, at first to the small group involved in accountability efforts, then to almost every DataCamp instructor. All told, the letter received more than 100 signatures (of about 200 total instructors). A DataCamp spokesperson said in response to this, “When we became aware of this matter, we conducted a thorough investigation and took actions we believe were necessary and appropriate. However, recent inquiries have made us aware of mischaracterizations of what occurred and we felt it necessary to make a public statement. As a matter of policy, we do not disclose details on matters like this, to protect the privacy of the individuals involved.” “We do not retaliate against employees, contractors or instructors or other members of our community, under any circumstances, for reporting concerns about behavior or conduct,” the company added. The response received from DataCamp was not only inadequate, but technologically faulty, as per one of the contractors Noam Ross who pointed out in his blog post that DataCamp had published the blog with a “no-index” tag, meaning it would not show up in aggregated searches like Google results. Thus adding this tag knowingly represents DataCamp’s continued lack of public accountability. OS Keyes said to Business Insider that at this point, the best course of action for DataCamp is a blatant change in leadership. “The investors need to get together and fire the [executive], and follow that by publicly explaining why, apologising, compensating the victim and instituting a much more rigorous set of work expectations,” Keyes said. #Rstats and other data science communities and DataCamp instructors take action One of the contractors Ines Montani expressed this by saying, “I was pretty disappointed, appalled and frustrated by DataCamp's reaction and non-action, especially as more and more details came out about how they essentially tried to sweep this under the rug for almost two years,” Due to their contracts, many instructors cannot take down their DataCamp courses. Instead of removing the courses, many contractors for DataCamp, including Montani, took to Twitter after DataCamp published the blog, urging students to boycott the very courses they designed. https://twitter.com/noamross/status/1116667602741485571 https://twitter.com/daniellequinn88/status/1117860833499832321 https://twitter.com/_tetration_/status/1118987968293875714 Instructors put financial pressures on the company by boycotting their own courses. They also wanted to get the executive responsible for such misbehaviour account for his actions, compensate the victim and compensate those who were fired for complaining—this may ultimately undercut DataCamp’s bottom line. Influential open-source communities, including RStudio, SatRdays, and R-Ladies, have cut all ties with DataCamp to show disappointment with the lack of serious accountability.. CEO steps down “indefinitely” from his role and accepts his mistakes Today Jonathan Cornelissen, accepted his mistake and wrote a public apology for his inappropriate behaviour. He writes, “I want to apologize to a former employee, our employees, and our community. I have failed you twice. First in my behavior and second in my failure to speak clearly and unequivocally to you in a timely manner. I am sorry.” He has also stepped down from his position as the company CEO indefinitely until there is complete review of company’s environment and culture. While it is in the right direction, unfortunately this apology comes to the community very late and is seen as a PR move to appease the backlash from the data science community and other instructors. https://twitter.com/mrsnoms/status/1121235830381645824 9 Data Science Myths Debunked 30 common data science terms explained Why is data science important?

Google AI Quantum team announced two releases at the First International Workshop on Quantum Software and Quantum Machine Learning(QSML) yesterday. Firstly the public alpha release of Cirq, an open source framework for NISQ computers. The second release is OpenFermion-Cirq, an example of a Cirq-based application enabling near-term algorithms. Noisy Intermediate Scale Quantum (NISQ) computers are devices including ~50 - 100 qubits and high fidelity quantum gates enhance the quantum algorithms such that they can understand the power that these machines uphold. However, quantum algorithms for the quantum computers have their limitations such as A poor mapping between the algorithms and the machines Also, some quantum processors have complex geometric constraints These and other nuances inevitably lead to wasted resources and faulty computations. Cirq comes as a great help for researchers here. It is focussed on near-term questions, which help researchers to understand whether NISQ quantum computers are capable of solving computational problems of practical importance. It is licensed under Apache 2 and is free to be either embedded or modified within any commercial or open source package. Cirq highlights With Cirq, researchers can write quantum algorithms for specific quantum processors. It provides a fine-tuned user control over quantum circuits by, specifying gate behavior using native gates, placing these gates appropriately on the device, and scheduling the timing of these gates within the constraints of the quantum hardware. Other features of Cirq include: Allows users to leverage the most out of NISQ architectures with optimized data structures to write and compile the quantum circuits. Supports running of the algorithms locally on a simulator Designed to easily integrate with future quantum hardware or larger simulators via the cloud. OpenFermion-Cirq highlights Google AI Quantum team also released OpenFermion-Cirq, which is an example of a CIrq-based application that enables the near-term algorithms.  OpenFermion is a platform for developing quantum algorithms for chemistry problems. OpenFermion-Cirq extends the functionality of OpenFermion by providing routines and tools for using Cirq for compiling and composing circuits for quantum simulation algorithms. An instance of the OpenFermion-Cirq is, it can be used to easily build quantum variational algorithms for simulating properties of molecules and complex materials. While building Cirq, the Google AI Quantum team worked with early testers to gain feedback and insight into algorithm design for NISQ computers. Following are some instances of Cirq work resulting from the early adopters: Zapata Computing: simulation of a quantum autoencoder (example code, video tutorial) QC Ware: QAOA implementation and integration into QC Ware’s AQUA platform (example code, video tutorial) Quantum Benchmark: integration of True-Q software tools for assessing and extending hardware capabilities (video tutorial) Heisenberg Quantum Simulations: simulating the Anderson Model Cambridge Quantum Computing: integration of proprietary quantum compiler t|ket> (video tutorial) NASA: architecture-aware compiler based on temporal-planning for QAOA (slides) and simulator of quantum computers (slides) The team also announced that it is using Cirq to create circuits that run on Google’s Bristlecone processor. Their future plans include making the Bristlecone processor available in cloud with Cirq as the interface for users to write programs for this processor. To know more about both the releases, check out the GitHub repositories of each Cirq and OpenFermion-Cirq. Q# 101: Getting to know the basics of Microsoft’s new quantum computing language Google Bristlecone: A New Quantum processor by Google’s Quantum AI lab Quantum A.I. : An intelligent mix of Quantum+A.I.

Earlier this month, the AI Now Institute published a report, authored by Sarah Myers West, Meredith Whittaker, and Kate Crawford, highlighting the link between the diversity issue in the current AI industry and the discriminating behavior of AI systems. The report further recommends some solutions to these problems that companies and the researchers behind these systems need to adopt to address these issues. Sarah Myers West is a postdoc researcher at the AI Now Institute and an affiliate researcher at the Berkman-Klein Center for Internet and Society. Meredith Whittaker is the co-founder of the AI Now Institute and leads Google's Open Research Group and the Google Measurement Lab.  Kate Crawford is a Principal Researcher at Microsoft Research and the co-founder and Director of Research at the AI Now Institute. Kate Crawford tweeted about this study. https://twitter.com/katecrawford/status/1118509988392112128 The AI industry lacks diversity, gender neutrality, and bias-free systems In recent years, we have come across several cases of “discriminating systems”. Facial recognition systems miscategorize black people and sometimes fails to work for trans drivers. When trained in online discourse, chatbots easily learn racist and misogynistic language. This type of behavior by machines is actually a reflection of society. “In most cases, such bias mirrors and replicates existing structures of inequality in the society,” says the report. The study also sheds light on gender bias in the current workforce. According to the report, only 18% of authors at some of the biggest AI conferences are women. On the other side of the spectrum are men who cover 80%. The tech giants, Facebook and Google, have a meager 15% and 10% women as their AI research staff. The situation for black workers in the AI industry looks even worse. While Facebook and Microsoft have 4% of their current workforce as black workers, Google stands at just 2.5%. Also, vast majority of AI studies assume gender is binary, and commonly assigns people as ‘male’ or ‘female’ based on physical appearance and stereotypical assumptions, erasing all other forms of gender identity. The report further reveals that, though there have been various “pipeline studies” to check the flow of diverse job candidates, they have failed to show substantial progress in bringing diversity in the AI industry. “The focus on the pipeline has not addressed deeper issues with workplace cultures, power asymmetries, harassment, exclusionary hiring practices, unfair compensation, and tokenization that are causing people to leave or avoid working in the AI sector altogether,” the report reads. What steps can industries take to address bias and discrimination in AI Systems The report lists 12 recommendations that AI researchers and companies should employ to improve workplace diversity and address bias and discrimination in AI systems. Publish compensation levels, including bonuses and equity, across all roles and job categories, broken down by race and gender. End pay and opportunity inequality, and set pay and benefit equity goals that include contract workers, temps, and vendors. Publish harassment and discrimination transparency reports, including the number of claims over time, the types of claims submitted, and actions taken. Change hiring practices to maximize diversity: include targeted recruitment beyond elite universities, ensure more equitable focus on under-represented groups, and create more pathways for contractors, temps, and vendors to become full-time employees. Commit to transparency around hiring practices, especially regarding how candidates are leveled, compensated, and promoted. Increase the number of people of color, women and other under-represented groups at senior leadership levels of AI companies across all departments. Ensure executive incentive structures are tied to increases in hiring and retention of underrepresented groups. For academic workplaces, ensure greater diversity in all spaces where AI research is conducted, including AI-related departments and conference committees. Remedying bias in AI systems is almost impossible when these systems are opaque. Transparency is essential, and begins with tracking and publicizing where AI systems are used, and for what purpose. Rigorous testing should be required across the lifecycle of AI systems in sensitive domains. Pre-release trials, independent auditing, and ongoing monitoring are necessary to test for bias, discrimination, and other harms. The field of research on bias and fairness needs to go beyond technical debiasing to include a wider social analysis of how AI is used in context. This necessitates including a wider range of disciplinary expertise. The methods for addressing bias and discrimination in AI need to expand to include assessments of whether certain systems should be designed at all, based on a thorough risk assessment. AI-related departments and conference committees. Credits: AI Now Institute Bringing diversity in the AI workforce In order to address the diversity issue in the AI industry, companies need to make changes in the current hiring practices. They should have a more equitable focus on under-represented groups. People of color, women, and other under-represented groups should get fair chance to get into senior leadership level of AI companies across all departments. Further opportunities should be created for contractors, temps, and vendors to become full-time employees. To bridge the gender pay gap in the AI industry, it is important that companies maintain transparency regarding the compensation levels, including bonuses and equity, regardless of gender or race. In the past few years, several cases of sexual misconducts involving some of the biggest companies like Google, Microsoft, have come into light because of movements like #MeToo, Google Walkout, and more. These movements gave the victims and other supporting employees  the courage to speak against employees at higher positions who were taking undue advantage of their power. There are cases were the sexual harassment complaints were not taken seriously by the HRs and victims were told to just “get over it”. This is why, companies should  publish harassment and discrimination transparency reports that include information like the number and types of claims made and the actions taken by the company. Academic workplaces should ensure diversity in all AI-related departments and conference committees. In the past, some of the biggest AI conferences like Neural Information Processing Systems conference has failed to provide a welcoming and safer environment for women. In a survey conducted last year, many respondents shared that they have experienced sexual harassment. Women reported persistent advances from men at the conference. The organizers of such conferences should ensure an inclusive and welcoming environment for everyone. Addressing bias and discrimination in AI systems To address bias and discrimination in AI systems, the report recommends to do rigorous testing across the lifecycle of these systems. These systems should have pre-release trials, independent auditing, and monitoring to check bias, discrimination, and other harms. Looking at the social implications of AI systems, just addressing the algorithmic bias is not enough. “The field of research on bias and fairness needs to go beyond technical debiasing to include a wider social analysis of how AI is used in context. This necessitates including a wider range of disciplinary expertise,” says the report. While assessing a AI system, researchers and developers should also check whether designing a certain system is required at all, considering the risks it poses. The study calls for re-evaluating the current AI systems used for classifying, detecting, and predicting the race and gender. The idea of identifying a race or gender just by appearance is flawed and can be easily abused. Especially, systems that use physical appearance to find interior states, for instance, those that claim to detect sexuality from headshots. These systems are urgently in need to be checked. To know more in detail, read the full report: Discriminating Systems. Microsoft’s #MeToo reckoning: female employees speak out against workplace harassment and discrimination Desmond U. Patton, Director of SAFElab shares why AI systems should be a product of interdisciplinary research and diverse teams Google’s Chief Diversity Officer, Danielle Brown resigns to join HR tech firm Gusto

Last week, a team of researchers from Stanford University, Max Planck Institute for Informatics, Princeton University and Adobe Research published a paper titled “Text-based Editing of Talking-head Video”. This paper proposes a method to edit a talking-head video based on its transcript to produce a realistic output video, in which the dialogue of the speaker has been modified. Basically, the editor modifies a video using a text transcript, to add new words, delete unwanted ones or completely rearrange the pieces by dragging and dropping. This video will maintain a seamless audio-visual flow, without any jump cuts and will look almost flawless to the untrained eye. The researchers want this kind of text-based editing approach to lay the foundation for better editing tools, in post production of movies and television. Actors often botch small bits of performance or leave out a critical word. This algorithm can help video editors fix that, which has until now involves expensive reshoots. It can also help in easy adaptation of audio-visual video content to specific target audiences. The tool supports three types of edit operations- add new words, rearrange existing words, delete existing words. Ohad Fried, a researcher in the paper says that “This technology is really about better storytelling. Instructional videos might be fine-tuned to different languages or cultural backgrounds, for instance, or children’s stories could be adapted to different ages.” https://youtu.be/0ybLCfVeFL4 How does the application work? The method uses an input talking-head video and a transcript to perform text-based editing. The first step is to align phonemes to the input audio and track each input frame to construct a parametric head model. Next, a 3D parametric face model with each frame of the input talking-head video is registered. This helps in selectively blending different aspects of the face. Then, a background sequence is selected and is used for pose data and background pixels. The background sequence allows editors to edit challenging videos with hair movement and slight camera motion. As Facial expressions are an important parameter, the researchers have tried to preserve the retrieved expression parameters as much as possible, by smoothing out the transition between them. This provides an output of edited parameter sequence which describes the new desired facial motion and a corresponding retimed background video clip. This is forwarded to a ‘neural face rendering’ approach. This step changes the facial motion of the retimed background video to match the parameter sequence. Thus the rendering procedure produces photo-realistic video frames of the subject, appearing to speak the new phrase.These localized edits seamlessly blends into the original video, producing an edited result. Lastly to add the audio, the resulted video is retimed to match the recording at the level of phones. The researchers have used the performers own voice in all their synthesis results. Image Source: Text-based Editing of Talking-head Video The researchers have tested the system with a series of complex edits including adding, removing and changing words, as well as translations to different languages. When the application was tried in a crowd-sourced study with 138 participants, the edits were rated as “real”, almost 60% of the time. Fried said that “The visual quality is such that it is very close to the original, but there’s plenty of room for improvement.” Ethical considerations: Erosion of truth, confusion and defamation Even though the application is quite useful for video editors and producers, it raises important and valid concerns about its potential for misuse. The researchers have also agreed that such a technology might be used for illicit purposes. “We acknowledge that bad actors might use such technologies to falsify personal statements and slander prominent individuals. We are concerned about such deception and misuse.” They have recommended certain precautions to be taken to avoid deception and misuse such as using watermarking. “The fact that the video is synthesized may be obvious by context, directly stated in the video or signaled via watermarking. We also believe that it is essential to obtain permission from the performers for any alteration before sharing a resulting video with a broad audience.” They urge the community to continue to develop forensics, fingerprinting and verification techniques to identify manipulated video. They also support the creation of appropriate regulations and laws that would balance the risks of misuse of these tools against the importance of creative, consensual use cases. The public however remain dubious pointing out valid arguments on why the ‘Ethical Concerns’ talked about in the paper, fail. A user on Hacker News comments, “The "Ethical concerns" section in the article feels like a punt. The author quoting "this technology is really about better storytelling" is aspirational -- the technology's story will be written by those who use it, and you can bet people will use this maliciously.” https://twitter.com/glenngabe/status/1136667296980701185 Another user feels that such kind of technology will only result in “slow erosion of video evidence being trustworthy”. Others have pointed out how the kind of transformation mentioned in the paper, does not come under the broad category of ‘video-editing’ ‘We need more words to describe this new landscape’ https://twitter.com/BrianRoemmele/status/1136710962348617728 Another common argument is that the algorithm can be used to generate terrifyingly real Deepfake videos. A Shallow Fake video was Nancy Pelosi’s altered video, which circulated recently, that made it appear she was slurring her words by slowing down the video. Facebook was criticized for not acting faster to slow the video’s spread. Not just altering speeches of politicians, altered videos like these can also, for instance, be used to create fake emergency alerts, or disrupt elections by dropping a fake video of one of the candidates before voting starts. There is also the issue of defaming someone on a personal capacity. Sam Gregory, Program Director at Witness, tweets that one of the main steps in ensuring effective use of such tools would be to “ensure that any commercialization of synthetic media tools has equal $ invested in detection/safeguards as in detection.; and to have a grounded conversation on trade-offs in mitigation”. He has also listed more interesting recommendations. https://twitter.com/SamGregory/status/1136964998864015361 For more details, we recommend you to read the research paper. OpenAI researchers have developed Sparse Transformers, a neural network which can predict what comes next in a sequence ‘Facial Recognition technology is faulty, racist, biased, abusive to civil rights; act now to restrict misuse’ say experts to House Oversight and Reform Committee Now there’s a CycleGAN to visualize the effects of climate change. But is this enough to mobilize action?

At Recode by Vox’s 2019 Code Conference on Tuesday, Bond partner Mary Meeker made her presentation onstage, covering everything on the internet's latest trends. Meeker had first started presenting these reports in 1995, underlining the most important statistics and technology trends on the internet. Last year in September, Meeker quit Kleiner Perkins to start her own firm Bond and is popularly known as the Queen of the Internet. Mary Meeker’s 2019 Internet trends report highlighted that the internet is continuing to grow, slowly, as more users come online, especially with mobile devices. She also talked about increased internet ad spending, data growth, as well as the rise of freemium subscription business models, interactive gaming, the on-demand economy and more. https://youtu.be/G_dwZB5h56E The internet trends highlighted by Meeker include: Internet Users E-commerce and advertising Internet Usage Freemium business models Data growth Jobs and Work Online Education Immigration and Healthcare Internet Users More than 50% of the world’s population now has access to the internet. There are 3.8 billion internet users in the world with Asia-pacific leading in both users and potential. China is the largest market with 21% of total internet users and India is at 12%. However, the growth is slowing by 6% in 2018 versus 7% in 2017 because so many people have come online that new users are harder to come by. New smartphone unit shipments actually declined in 2018. Per the global internet market cap leaders, the U.S. is stable at 18 of the top 30 and China is stable at 7 of the top 30. These are the two leading countries where internet innovation is at an especially high level. If we look at revenue growth for the internet market cap leaders it continues to slow - 11 percent year-on-year in Q1 versus 13 percent in Q4. Internet usage Internet usage had a solid growth, driven by investment in innovation. The digital media usage in the U.S. is accelerating up 7% versus 5% growth in 2017. The average US adult spends 6.3 hours each day with digital media, over half of which is spent on their mobiles. Wearables had 52 million users which doubled in four years. Roughly 70 million people globally listen to podcasts in the US, a figure that’s doubled in about four years. Outside the US, there's especially high innovation in data-driven and direct fulfillment that's growing very rapidly in China. Innovation outside the US is also especially strong in financial services. Images are also becoming an increasingly relevant way to communicate. More than 50% of the tweets of impressions today are images, video or other forms of media. Interactive gaming innovation is rising across platforms as interactive games like Fortnite become the new social media for certain people. It is accelerating with 2.4 billion users up, 6 percent year-on-year in 2018. On the flip side Almost 26% of adults are constantly online versus 21% three years ago. That number jumped to 39% for 18 to 29 year-olds surveyed. However, digital media users are taking action to reduce their usage and businesses are also taking actions to help users monitor their usage. Social media usage has decelerated up 1% in 2018 versus 6% in 2017. Privacy concerns are high but they're moderating. Regulators and businesses are improving consumer privacy control. In digital media encrypted messaging and traffic are rising rapidly. In Q1, 87 percent of global web traffic was encrypted, up from 53 percent three years ago. Another usage concern is problematic content. Problematic content on the Internet can be less filtered and more amplified. Images and streaming can be more powerful than text. Algorithms can amplify users on patterns  and social media can amplify trending topics. Bad actors can amplify ideologies, unintended bad actors can amplify misinformation and extreme views can amplify polarization. However internet platforms are indeed driving efforts to reduce problematic content as do consumers and businesses. 88% percent of people in the U.S. believe the Internet has been mostly good for them and 70% believe the Internet has been mostly good for society. Cyber attacks have continued to rise. These include state-sponsored attacks, large-scale data provider attacks, and monetary extortion attacks. E-commerce and online advertising E-commerce is now 15 percent of retail sales. Its growth has slowed — up 12.4 percent in Q1 compared with a year earlier — but still towers over growth in regular retail, which was just 2 percent in Q1. In online advertising, on comparing the amount of media time spent versus the amount of advertising dollars spent, mobile hit equilibrium in 2018 while desktop hit that equilibrium point in 2015. The Internet ads spending on an annual basis accelerated a little bit in 2018 up 22 percent.  Most of the spending is still on Google and Facebook, but companies like Amazon and Twitter are getting a growing share. Some 62 percent of all digital display ad buying is for programmatic ads, which will continue to grow. According to the leading tech companies the internet average revenue has been decelerating on a quarterly basis of 20 percent in Q1. Google and Facebook still account for the majority of online ad revenue, but the growth of US advertising platforms like Amazon, Twitter, Snapchat, and Pinterest is outstripping the big players: Google’s ad revenue grew 1.4 times over the past nine quarters and Facebook’s grew 1.9 times, while the combined group of new players grew 2.6 times. Customer acquisition costs — the marketing spending necessary to attract each new customer — is going up. That’s unsustainable because in some cases it surpasses the long-term revenue those customers will bring. Meeker suggests cheaper ways to acquire customers, like free trials and unpaid tiers. Freemium business models Freemium business models are growing and scaling. Freemium businesses equals free user experience which enables more usage, engagement, social sharing and network effects. It also equals premium user experience which drives monetization and product innovation. Freemium business evolution started in gaming, evolving and emerging in consumer and enterprise. One of the important factors for this growth is cloud deployment revenue which grew about 58% year-over-year. Another enabler of freemium subscription business models is efficient digital payments which account for more than 50% of day-to-day transactions around the world. Data growth Internet trends indicate that a number of data plumbers are helping a lot of companies collect data, manage connections, and optimize data. In a survey of retail customers, 91% preferred brands that provided personalized offers and recommendations. 83% were willing to passively share data in exchange for personalized services and 74% were willing to actively share data in exchange for personalized experiences. Data volume and utilization is also evolving rapidly. Enterprise surpassed consumer in 2018 and cloud is overtaking both. More data is now stored in the cloud than on private enterprise servers or consumer devices. Jobs and Work Strong economic indicators, internet enabled services, and jobs are helping work. If we look at global GDP. China, the US and India are rising, but Europe is falling. Cross-border trade is at 29% of global GDP and has been growing for many years. Global relative unemployment concerns are very high outside the US and low in itself. Consumer confidence index is high and rising. Unemployment is at a 19-year low but job openings are at an all-time high and wages are rising. On-demand work is creating internet-enabled opportunities and efficiencies. There are 7 million on-demand workers up 22 percent year-on-year. Remote work is also creating internet enabled work opportunities and efficiency. Americans working remotely have risen from 5 percent versus 3 percent in 2000. Online education Education costs and student debt are rising in the US whereas post-secondary education enrollment is slowing. Online education enrollment is high across a diverse base of universities - public, private for-profit, and private not-for-profit.  Top offline institutions are ramping their online offerings at a very rapid rate - most recently University of Pennsylvania, University of London, University of Michigan and UC Boulder. Google's growth in creating certificates for in-demand jobs is growing rapidly which they are doing in collaboration with Coursera. Immigration and Healthcare In the U.S. 60% of the most highly valued tech companies are founded by first or second generation Americans. They employed 1.9 million people last year. USA entitlements account for 61% of government spending versus 42% 30 years ago, and shows no signs of stopping. Healthcare is steadily digitizing, driven by consumers and the trends are very powerful. You can expect more telemedicine and on-demand consultations. For details and infographics, we recommend you to go through the slide deck of the Internet trends report. What Elon Musk and South African conservation can teach us about technology forecasting. Jim Balsillie on Data Governance Challenges and 6 Recommendations to tackle them Experts present the most pressing issues facing global lawmakers on citizens’ privacy, democracy and the rights to freedom of speech.