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Last week, researchers from the University of Montreal, University of Oregon, and IIT Bombay published a paper, titled, ‘BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop’ that introduces, BabyAI platform. This platform provides a heuristic expert agent for the purpose of simulating a human teacher and also supports humans in the loop for grounded language learning. BabyAI platform uses synthetic Baby Language for giving instructions to the agent. The researchers have implemented a 2D gridworld environment called MiniGrid for convenience, as it’s simple and easy to work on. The BabyAI platform includes a verifier that checks if an agent performing a sequence of actions, in a given environment, has successfully achieved its goal or not. Why BabyAI platform is introduced? It’s difficult for humans to train an intelligent agent for understanding natural language instructions. No matter how advanced AI technology becomes, human users would always want to customize their intelligent helpers to be able to understand their desires and needs, better. The main obstacle in language learning with a human in the loop is the amount of data that would be required. Deep learning methods, used in the context of imitation learning or reinforcement learning paradigms, could be effective but even they require enormous amounts of data. This data could be in the form of millions of reward function queries or thousands of demonstrations. BabyAI platform works on data efficiency, as the researchers measure the minimum number of samples required to solve several levels with imitation and reinforcement learning baselines. Also, the platform and pretrained models will be available online. This surely will improve data efficiency of grounded language learning. The Baby Language Baby Language is a combinatorially rich subset of English, designed to be easily understood by humans. In this language, the agent can be instructed to go to the objects, pick them up, open doors, and put objects next to the other ones. The language can also express the combination of several such tasks. For example “put a red ball next to the green box after you open the door". In order to keep the instructions readable by humans, the researchers have kept the language simple. But the language still exhibits interesting combinatorial properties and contains 2.48 × 1019 possible instructions. There a couple of structural restrictions on this language including: The ‘and connector’ can only appear inside the ‘then and after forms’ Instructions can contain only one ‘then’ or ‘after’ word. MiniGrid: The environment that supports the BabyAI platform Since the data-efficiency studies are very expensive as multiple runs are required for different amounts of data, the researchers have aimed to keep the design of the environment, minimalistic. The researchers have implemented MiniGrid, an open source, partially observable 2D gridworld environment. This environment is fast and lightweight. It is available online and supports integration with OpenAI Gym. Experiments conducted and the results The researchers assess the difficulty of BabyAI levels by training an imitation learning baseline for each level. Moreover, they have estimated as to how much data is required to solve some of the simpler levels. They have also studied to what extent can the data demands be reduced with the help of basic curriculum learning and interactive teaching methods. The results suggest that the current imitation learning and reinforcement learning methods, both scale and generalise poorly when it comes to learning tasks with a compositional structure. Thousands of demonstrations are needed to learn tasks which seem trivial by human standards. Methods like curriculum learning and interactive learning can provide measurable improvements in terms of data efficiency. But for involving an actual human in the loop, an improvement of at least three orders of magnitude is required. Future Scope The direction of future research is towards finding strategies to improve data efficiency of language learning. Tackling such a challenge might require new models and teaching methods. Approaches that involve an explicit notion of modularity and subroutines, such as Neural Module Networks or Neural Programming Interpreters also seems to be a promising direction. To know more about BabyAI platform, check out the research paper published by Yoshua Bengio et. al. Facebook open sources QNNPACK, a library for optimized mobile deep learning Facebook’s Child Grooming Machine Learning system helped remove 8.7 million abusive images of children Optical training of Neural networks is making AI more efficient

Xilinx Inc., has reportedly won orders from Microsoft Corp.’s Azure cloud unit to account for half of the co-processors currently used on Azure servers to handle machine-learning workloads. This will replace the chips made by Intel Corp, according to people familiar with Microsoft's’ plans as reported by Bloomberg Microsoft’s decision comes with effect to add another chip supplier in order to serve more customers interested in machine learning. To date, this domain was run by Intel’s Altera division. Now that Xilinx has bagged the deal, does this mean Intel will no longer serve Microsoft? Bloomberg reported Microsoft’s confirmation that it will continue its relationship with Intel in its current offerings. A Microsoft spokesperson added that “There has been no change of sourcing for existing infrastructure and offerings”. Sources familiar with the arrangement also commented on how Xilinx chips will have to achieve performance goals to determine the scope of their deployment. Cloud vendors these days are investing heavily in research and development centering around the machine learning field. The past few years has seen an increase in need of flexible chips that can be configured to run machine-learning services. Companies like Microsoft, Google and Amazon are massive buyers of server chips and are always looking for alternatives to standard processors to increase the efficiency of their data centres. Holger Mueller, an analyst with Constellation Research Inc., told SiliconANGLE that “Programmable chips are key to the success of infrastructure-as-a-service providers as they allow them to utilize existing CPU capacity better, They’re also key enablers for next-generation application technologies like machine learning and artificial intelligence.” Earlier this year, Xilinx CEO Victor Peng made clear his plans to focus on data center customers, saying “data center is an area of rapid technology adoption where customers can quickly take advantage of the orders of magnitude performance and performance per-watt improvement that Xilinx technology enables in applications like artificial intelligence (AI) inference, video and image processing, and genomics” Last month, Xilinx made headlines with the announcement of a new breed of computer chips designed specifically for AI inference. These chips combine FPGAs with two higher-performance Arm processors, plus a dedicated AI compute engine and relates to the application of deep learning models in consumer and cloud environments. The chips promise higher throughput, lower latency and greater power efficiency than existing hardware. Looks like Xilinx is taking noticeable steps to make itself seen in the AI market. Head over to Bloomberg for the complete coverage of this news. Microsoft Ignite 2018: New Azure announcements you need to know Azure DevOps outage root cause analysis starring greedy threads and rogue scale units Microsoft invests in Grab; together aim to conquer the Southeast Asian on-demand services market with Azure’s Intelligent Cloud    

Google researchers have come up with a new AutoML framework, which can automatically learn high-quality models with minimal expert intervention. Google AdaNet is a fast, flexible, and lightweight TensorFlow-based framework for learning a neural network architecture and learning to ensemble to obtain even better models. How Google Adanet works? As machine learning models increase in number, Adanet will automatically search over neural architectures, and learn to combine the best ones into a high-quality model. Adanet implements an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks. It can add subnetworks of different depths and widths to create a diverse ensemble, and trade off performance improvement with the number of parameters. This saves ML engineers the time spent selecting optimal neural network architectures. Source: Google Adanet: Built on Tensorflow AdaNet implements the TensorFlow Estimator interface. This interface simplifies machine learning programming by encapsulating training, evaluation, prediction and export for serving. Adanet also integrates with open-source tools like TensorFlow Hub modules, TensorFlow Model Analysis, and Google Cloud’s Hyperparameter Tuner. TensorBoard integration helps to monitor subnetwork training, ensemble composition, and performance. Tensorboard is one of the best TensorFlow features for visualizing model metrics during training. When AdaNet is done training, it exports a SavedModel that can be deployed with TensorFlow Serving. How to extend AdaNet to your own projects Machine learning engineers and enthusiasts can define their own AdaNet adanet.subnetwork.Builder using high level TensorFlow APIs like tf.layers. Users who have already integrated a TensorFlow model in their system can use the adanet.Estimator to boost model performance while obtaining learning guarantees. Users are also invited to use their own custom loss functions via canned or custom tf.contrib.estimator.Heads in order to train regression, classification, and multi-task learning problems. Users can also fully define the search space of candidate subnetworks to explore by extending the adanet.subnetwork.Generator class. Experiments: NASNet-A versus AdaNet Google researchers took an open-source implementation of a NASNet-A CIFAR architecture and transformed it into a subnetwork. They were also able to improve upon CIFAR-10 results after eight AdaNet iterations. The model achieves this result with fewer parameters: [caption id="attachment_23810" align="aligncenter" width="640"] Performance of a NASNet-A model versus AdaNet learning to combine small NASNet-A subnetworks on CIFAR-10[/caption] Source: Google You can checkout the Github repo, and walk through the tutorial notebooks for more details. You can also have a look at the research paper. Top AutoML libraries for building your ML pipelines. Anatomy of an automated machine learning algorithm (AutoML) AmoebaNets: Google’s new evolutionary AutoML

“Capital is dead labor, which, vampire-like, lives only by sucking living labor, and lives the more, the more labor it sucks,” Karl Marx An explanation is due after the introductory statement. I am not going to whine about how capitalism is a necessary evil and socialism or similar ‘isms’ promise a better future. Let’s agree with one basic fact. We are living in one of the most peaceful and technologically advanced era of human civilization on earth (that we know of yet). No major worldwide wars have taken place for around 75 years. We have developed tech miracles, with smartphones and social media becoming the new age essential commodities. It would be naive on my part to start blaming the burgeoning social media business and the companies involved to the decreasing happiness index and the gaping hole in income of the rich and the poor. But then facts don’t lie. A recent study conducted by UC Berkely, (Chris Benner, Gabriela Giusta, Louise Auerhahn, Bob Brownstein, Jeffrey Buchanan) highlights the growing income inequality in Silicon Valley. Source: UC Berkley This chart shows the dip in income of employees belonging to different income brackets. The X-axis shows the percentile of total income earners. We can clearly see that the biggest dip has happened at the middle-income earner section (14.2%). The top tier has seen a rise of 1% and the bottom end is also pretty much stagnant at with a dip of 1%. The biggest impact has been on those slap bang in the middle of the average income bracket. This is particularly alarming because from 1997 to 2017 the owners of some of the planets biggest tech companies have seen a massive jump in their earnings. Companies like Amazon, Facebook, Google have earned enormous wealth and control over the tech landscape. Amazon’s Jeff Bezos is presently sitting on top of a $150 Billion fortune. The anticipation of the majority of the population was that tech will improve the average quality of life. It was believed that it would increase the minimum wage for the low rung workers, and improve the economic status of the middle class. The existing situation seems to point to the exact opposite direction. The reasons can be summed up as below: The competition is immense among the tech companies to survive. Hence profits are largely invested in R&D and in developing ‘better’ futuristic solutions - or new novel products for consumers and users. Advertisement and promotional campaigns have also become significant factors in the survival strategies. That’s why we don’t see companies building affordable housing for their workforce anymore, bonuses have become rare events. The money comes in and goes back into the wheel, the employees are given the reasoning that to survive, the company will have to innovate. The survival rate of start-ups are very low in the tech domain. Hence, more and more startups are shrinking their budget and ensuring that they can breach the profit margin early. To a certain extent this makes sense. But it is damaging for the people who join to explore new domains in their career. If the startup fails, they have to start afresh - and if it is even moderately successful they may find they are working astonishingly long hours for very little reward. The modern-day tech workforce is not organized in any manner. The top tech companies discourage their employees from creating any form of labour union. While activism at work has not been exactly a good influence, but the complete absence of it has often proved to be a disadvantage for the workforce, and is a particular disadvantage given the tumultuous conditions of working in the tech field. The race to reinvent the wheel even when the system is running at a decent progressive pace is what has brought the human civilization to its present state. Global wealth distribution is skewed in favor of the rich few as badly as it possibly can. Monopoly in the tech market is not helping the cause. The internet is slowly becoming a playground for rich kids who own everything, right from the ground itself to the tools to keep it in shape. The rules are determined by them. The frenzy over yearly new tech releases is so huge and so marketable, that people have stopped asking the fundamental question of it all - what’s actually new? This is what capitalism stood for during most of the 20th century - even if it wasn’t immediately clear as it is now. It made people believe that consumerism can make their daily lives better, it’s perfectly ok to let go of a few basic humanitarian values in the pursuit of wealth, and most importantly that everyone can achieve it if they work hard and put their heart and soul into it. Today, technology continues to show us such dreams. Artificial Intelligence and Machine Learning can make our lives better, self-driving cars should ease traffic issues, and halt the march of global warming. But just because we see these dreams, doesn’t mean they’re becoming true. For many people at the heart of this innovation should feel the positive impact of these changes in their own lives, not longer working hours and precarious employment. The constant urge to win the race shouldn’t make the rich richer and the poor poorer. If that pattern starts to emerge and is upheld in the next 4-5 years, then we can surely conclude that Capitalism 2.0 - the type of capitalism that benefits from vulnerability and not from the power and creativity we share  - has finally taken its full form. We might have only ourselves to blame for. OK Google, why are you ok with mut(at)ing your ethos for Project DragonFly? Facebook finds ‘no evidence that hackers accessed third party Apps via user logins Is YouTube’s AI Algorithm evil?

Tableau 2019.1 beta was announced at the Tableau Conference 2018 held in New Orleans last week. This release brings features like the ability to ask more sophisticated data questions, a better mobile app, export to PowerPoint and more. Ask questions in plain language with Ask Data A new feature called Ask Data allows you to use plain natural language and ask data questions in Tableau 2019.1 beta. The answers are returned in the form of a viz on typing a question. Ask Data makes it easy to refine a question as your explore your data more. The query can be simple as “monthly TV sales” and aggregation is done behind the scenes based on the current month filtering for the product type. Ask Data also understands vague terms like “latest” or “most popular”. Ask Data is integrated into Tableau Server and Tableau Online and works with any live or extract data sources. Schedule and run Prep flows in Tableau 2019.1 beta From Tableau 2019.1 you’ll be able to use a secure server environment to run Tableau Prep flows via Tableau Prep Conductor. The Tableau Prep conductor is an add-on to Tableau Online and Tableau Server with an additional cost. It lets scheduling and running the flows authored by  you with the Tableau Prep application right from the browser. Teams can receive prepped data and run history to help understand the scheduling history. A redesigned mobile app with Tableau 2019.1 beta Both the iOS and Android apps for Tableau have been redesigned for a more intuitive experience. You can easily find the most important dashboards with favorites, and interact with your favorite dashboards even without internet. There are options to scroll, highlight, and view tooltips on vizzes regardless of internet connectivity. There is a powerful, integrated search which allows you to browse projects and find specific content. The new app is even more secure with fingerprint or facial recognition authentications. Export to PowerPoint, alerts, and Google AdWords You can now export from Tableau to PowerPoint presentations. Vizzes from Tableau Server or Tableau Online can be exported to PowerPoint as high-resolution images which are complete with a link back to the original workbook. The dashboards can be integrated directly into presentations with a single click. Data-driven alerts were originally introduced in Tableau 10.3. It notifies users of activities like sales team exceeding their allowed limit or when the IT team needs to respond to an event. This now has a new view displaying all the existing alerts for the viz you are viewing. Users can also add themselves with an “Add Me” option from the side panel. You can now analyze your ad metrics by connecting Google AdWords to Tableau. The Beta is available for existing customers. For more details, visit the Tableau website. How to do data storytelling well with Tableau [Video] A tale of two tools: Tableau and Power BI “Tableau is the most powerful and secure end-to-end analytics platform”: An interview with Joshua Milligan

It was only last week when a report by The New York Times brought to light the shocking allegations against Andy Rubin’s (creator of Android) sexual misconduct at Google. Now, more than 200 engineers at Google are organizing a “women’s walk” walkout this week to protest against the company's response to the reports of sexual misconduct, as per Buzzfeed news. According to the report by the New York Times, after Rubin was accused of misbehavior in 2014 and the allegations were confirmed by Google, he was asked to leave by former Google CEO, Mr.Page, but received $90 million as an exit package. He also received a high profile well-respected farewell by Google in October 2014. But Rubin isn’t the only one, at least four senior executives have been protected by Google in the past, despite them being accused of sexual misconduct, as per the NY Times report. Andy Rubin spoke out about the allegation on Twitter where he denied the NY Times report: https://twitter.com/Arubin/status/1055632398509985792 https://twitter.com/Arubin/status/1055632399172755456 As per the Buzzfeed reports, Google executives had hosted an all-hands meeting last Thursday, during which they tried explaining their behavior towards Rubin and apologized to employees. Google CEO Sundar Pichai also sent an email to all Google employees on Thursday clarifying how the company has fired 48 people over the last two years for sexual harassment where 13 of them were “senior managers and above”. He also mentioned how none of the accused employees received any exit packages. “We are dead serious about making sure we provide a safe and inclusive workplace. We want to assure you that we review every single complaint about sexual harassment or inappropriate conduct, we investigate and we take action”, read the email. The protest is a response to Google’s handling of sexual misconduct within the workplace in the recent past, that employees found as inadequate. Moreover, Google employees participating in the planned protest are dissatisfied that senior executives such as Drummond, Chief Legal Officer, Alphabet, and Chairman, CapitalG, mentioned in the NY times report for indulging in “inappropriate relationships” within the organization continue to work in highly placed positions at Google and have not faced any real punitive action by Google for their action. In April this year, Google employees protested against Project Maven, with petitions, and better demands for more transparency within the organization. The demands of the upcoming protest haven’t been made specified yet, but, shares similar sentiments. The planning for the walkout was done on an internal online forum by the Google employees to map out the details regarding the protest. By yesterday, that post had upvotes, as per a current Google employee who wishes to remain anonymous. The day and timing of the walkout haven’t been fixed yet but is likely to take place this Thursday, as reported by BuzzFeed. One of the Google employee, who wishes to remain anonymous, told BuzzFeed, 'Personally, I’m furious. I feel like there’s a pattern of powerful men getting away with awful behavior towards women at Google‚ or if they don’t get away with it, they get a slap on the wrist, or they get sent away with a golden parachute, like Andy Rubin. Public reaction towards the protest is largely positive: https://twitter.com/iamjono/status/1057120231074611200 https://twitter.com/womensmarch/status/1057067265324183552 https://twitter.com/ryancarson/status/1057003377777930240 Our take on this development If this protest manages to get a response from Google on the veracity of the claims made by the NYT article, it would be a good place to start healing. Openly acknowledging issues is the first step towards working on them. The protest could be more effective had the organizers has a clear set of goals to achieve from the walkout. Currently, it appears more like an emotional response to the revelation than as a way to move the company in the right direction on the topic of making the workplace safe and treating everyone fairly. Of late Google, employees seem to increasingly place the role of the companies moral compass on contentious and sensitives topics. Holding Google accountable for its role in enabling workplace misconduct is a worthy cause to stand up for. However, doing this via continuous protests or through media leaks does not seem to be an effective long-term approach to dealing with organizational issues - for both employees and for Google. There is the risk of employees becoming jaded and distrusting or management simply taking the easy way out by choosing to leave behind those that don’t align with its new vision only to become a monolithic thinking machine.   Frequent employee protests is a symptom of a deeper value-misalignment problem that Google must reflect on. Ex-googler who quit Google on moral grounds writes to Senate about company’s “Unethical” China censorship plan OK Google, why are you ok with mut(at)ing your ethos for Project DragonFly? Google takes steps towards better security, introduces new API policies for 3rd parties and a Titan Security system for mobile devices

Google released version 4.0 of its OCR engine, Tesseract, yesterday. Tesseract 4.0 comes with a new neural net (LSTM) based OCR engine, updated build system, other improvements, and bug fixes. Tesseract is an OCR engine that offers support for unicode (a specification that supports all character set) and comes with an ability to recognize more than 100 languages out of the box. It can be trained to recognize other languages and is used for text detection on mobile devices, videos, and in Gmail image spam detection. Let’s have a look at what's new in Tesseract 4.0. New neural net (LSTM) based OCR engine The new OCR engine uses a neural network system based on LSTMs, with major accuracy gains. This consists of new training tools for the LSTM OCR engine. You can train a new model from scratch or by fine-tuning an existing model. Trained data including LSTM models and 123 languages have been added to the new OCR engine. Optional accelerated code paths have been added for the LSTM recognizer: Moreover, a new parameter lstm_choice_mode that allows including alternative symbol choices in the hOCR output has been added. Updated Build System Tesseract 4.0 uses semantic versioning and requires Leptonica 1.74.0 or a higher version. In case you want to build Tesseract from source code then a compiler with strong C++ 11 support is necessary. Unit tests have been added to the main repo. Tesseract's source tree has been reorganized in version 4.0. A new option has been added that lets you compile Tesseract without the code of the legacy OCR engine. Bug Fixes Issues in trainingdata rendering have been fixed. Damage caused to binary images when processing PDFs has been fixed. Issues in the OpenCL code have been fixed. OpenCL now works fine for the legacy Tesseract OCR engine but the performance hasn’t improved yet. Other Improvements Multi-page TIFF handling is improved in Tesseract 4.0. Improvements are made to PDF rendering. The version information and improved help texts have been added to the training tools. tessedit_pageseg_mode 1 has been removed from hocr, pdf, and tsv config files. The user has to now explicitly use --psm 1 if that is desired. For more information, check out the official release notes. Tesla v9 to incorporate neural networks for autopilot Neural Network Intelligence: Microsoft’s open source automated machine learning toolkit

Yesterday, Facebook open-sourced QNNPACK that stands for Quantized Neural Networks PACKage. It is a mobile-optimized library for low-intensity convolutions used in state-of-the-art neural networks. It comes with implementations of convolutional, deconvolutional, and fully connected neural network operators on quantized 8-bit tensors. This library makes it possible to bring advanced computer vision tasks such as running Mask R-CNN and DensePose on phones in real time and image classification in less than 100 ms. Currently, QNNPACK is integrated into PyTorch1.0 with Caffe2 graph representation and is usable via Caffe2 model representation. Why QNNPACK is introduced? Running state-of-the-art artificial intelligence on mobile phones is not very easy as it requires several adaptations to get optimized performance from its hardware. Earlier, there wasn’t a performant open source implementation for several common neural network primitives. Because of this reason, promising research models such as ResNeXt, CondenseNet, and ShuffleNet were underused. QNNPACK enables developers to use these research models by providing high-performance implementations of convolutional, deconvolutional, and fully connected operations on quantized tensors. QNNPACK-based Caffe2 operators were approximately 2x faster than TensorFlow Lite on quantized state-of-the-art MobileNet v2 architecture, says the Facebook research blog. The library speeds up many operations, such as depthwise convolutions, that advanced neural network architectures use. Along with QNNPACK, they have also open-sourced Caffe2 quantized MobileNet v2 model that gives 1.3 percent higher accuracy than the corresponding TensorFlow model. To know more in detail about QNNPACK, check out the official announcement on the Facebook blog. Facebook introduces two new AI-powered video calling devices “built with Privacy + Security in mind” Facebook’s Glow, a machine learning compiler, to be supported by Intel, Qualcomm and others Facebook launches LogDevice: An open source distributed data store designed for logs

As per the UK budget report 2018, chancellor Philip Hammond is planning to introduce a digital services tax from April 2020 on tech giants. The chancellor is proposing a 2% tax rate against the sales that large digital companies make in the UK. The digital services tax is to be levied against social media platforms, internet marketplaces and search engines (read Facebook, Amazon, and Google). At the moment, digital media firms used to pay taxes on UK profits, which is a much smaller figure than revenues. The new digital services tax would be levied on companies that are profitable and generate "at least £500m a year in global revenue". Which clearly indicates that the chancellor is targeting tech giants instead of startups. The UK seems to have become frustrated with the slow movement of international lawmaking in this space. Both the 36 member OECD (Organisation for Economic Co-operation and Development) and the European commission have been working on this issue but the progress has been quite slow. Taking matters into their hands, the UK Govt has earmarked April 2020 for the new levy. The Treasury forecasts that the tax will generate £275m in 2019-20, rising to £370m in the following year. It then expects that digital services tax receipts will reach £400m in 2021-22 and £440m the year after. However, Julian David, chief executive of industry body TechUK, said the proposed £500m threshold was "low" and "and risks capturing much smaller companies than anticipated". Although the digital services tax is going to be effective from April 2020, it may not move ahead if the EU intervenes before that. Europe may also potentially move ahead with its own digital levy which would tax firms at a 3% rate. The OECD also intends to provide an update on its plans in 2019 with the aim of producing a final report the following year. People all over the internet have varied thoughts over this proposal. While some are happy: One hacker news user commented, “With business rate slashes to help struggling retailers (retaliation against Amazon) and mental health funding increases for schools (retaliation against Facebook/Google), I'm quite happy to see something happen at last. I expect other governments to follow suit.” Another user said, “This is a pretty good idea. Tech giants make money in Europe paying little to no tax incorporating in Ireland, while all small to medium businesses incorporated in the UK pay the usual tax. It’s simply unfair.” Some are not: https://twitter.com/Corporategang1/status/1057118872099319809 https://twitter.com/ConsultantsUnlt/status/1057104669418242048 We can only speculate as to how the tax will unfold. The implementation details are bound to be tricky, and surely companies will look for loopholes to avoid taxes. U.S Government Accountability Office (GAO) reports U.S weapons can be easily hacked. 5 reasons government should regulate technology Tech Titans, Acquisitions and Regulation – Trick or Treat?

Honda has come up with a program called Curious Minded Machine (CMM) to expand cognitive robotics research. This is a program to create artificial intelligence that enables ‘learning’ with a human-like sense of curiosity. What is the Curious Minded Machine program? The idea is to build a model based on how children ‘learn to learn’. By observing human interactions and how they perform tasks, CMM can learn better ways to achieve goals. This initiative explores Cooperative Intelligence (CI), AI embedded in a social context enabling people to confidence and trust with AI systems. Soshi Iba, a principal scientist at Honda Research Institute USA, Inc says: “Our ultimate goal is to create new types of machines that can acquire an interest in learning and knowledge, and the ability to interact with the world and others. We want to develop Curious Minded Machines that use curiosity to serve the common good by understanding people's needs, empowering human capability, and ultimately addressing complex societal issues.” Who is in the program by Honda? This three-year program will include efforts from the Computer Science & Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT), the School of Engineering and Applied Science at the University of Pennsylvania (Penn), and the Paul G. Allen School of Computer Science & Engineering at the University of Washington. The areas tackled by the research teams participating with Honda are: MIT CSAIL: They are addressing a key limitation in robotic action planning. The focus is on establishing a causal theory of sensor percepts, which will help in predicting future percepts and the effect of future actions. Penn Engineering: This team from Pennsylvania is focusing on challenges in machine perception by learning from biological systems. Then applying an embodied, active and efficient approach towards acquiring representations of the surrounding world and actions. University of Washington: They are addressing the challenges to enable robots working effectively in human environments. Similar to a human child learning through exploration and curiosity, they aim to build a mathematical model of curiosity. After three years, the participating universities will have to show demonstrations of working systems that will be the foundation of CMM. To know more about the initiative, visit the Curious mind machine website. SingularityNET and Mindfire unite talents to explore artificial intelligence MIT plans to invest $1 billion in a new College of computing that will serve as an interdisciplinary hub for computer science, AI, data science “Deep meta reinforcement learning will be the future of AI where we will be so close to achieving artificial general intelligence (AGI)”, Sudharsan Ravichandiran

SQLite adopted Mozilla Community Participation Guidelines as its new Code of Conduct on Saturday. After being nagged by clients and businesses to implement a code of conduct, SQLite founder, D. Richard Hipp, had come out with a code of conduct based on "instruments of good works" from chapter 4 of The Rule of St. Benedict, last Monday. But, it faced major criticism as the majority of developers, across the world, did not approve of it. SQLite then adopted the Mozilla guidelines. “The original document we put here was more of a Code of Ethics of the Project Founder. While we stand by those principles, they are not in line with the modern technical meaning of a Code of Conduct and have hence been renamed”, reads the SQLite Code of conduct page. SQLite is one of the most used database engines across the world. It is a self-contained, high-reliability, embedded, full-featured, and a public-domain, SQL database engine. Earlier Hipp stated that the former CoC “was created (in a slightly different format) for the purpose of filling in a box on "supplier registration" forms submitted to the SQLite developers by various minor clients. But it is not a Code of Conduct in the same sense that many communities mean a Code of Conduct. Rather, the foundational ethical principles upon which SQLite is based...a succinct description of the SQLite Founder's idea of what it means to be virtuous”. The former code of conduct comprised an overview, instruments of Good works, the scope of application, and “The Rule”. It has received a lot of criticism from developers. In fact, many of them were confused about whether the new CoC is a sarcastic reply to the clients asking SQLite to set up CoC, or if SQLite is serious about it. Here’s the former Code of conduct by SQLite. When asked by users if the CoC was legit, D. Richard Hipp, replied on the SQLite forum with, “Yes. Clients were encouraging me to have a code of conduct. (Having a CoC seems to be a trendy thing nowadays.)  So I looked around and came up with what you found, submitted the idea to the whole staff, and everybody approved”. Public reaction regarding the former CoC varied. Some believed that the CoC was impractical and excludes people based on religion, while others loved it. https://twitter.com/DarrenPMeyer/status/1054364170232258562 https://twitter.com/panzertime/status/1054407789257330688 https://twitter.com/geek/status/1054423437249253376 https://twitter.com/brionv/status/1054371935629373440 https://twitter.com/aaronbieber/status/1054403524686200838 https://twitter.com/_sagesharp_/status/1054404033518043137 The CoC also comprised of 72 rules such as  “Do not murder”, “Do not commit adultery”, “Do not steal”, “Do not covet”,”Do not bear false witness”, “Chastise the body”, “Do not become attached to pleasures”, “Love fasting”, “Clothe the naked”, and so forth. “No one is required to follow The Rule, to know The Rule, or even to think that The Rule is a good idea… anyone who follows The Rule will live a happier and more productive life, but individuals are free to dispute or ignore that advice if they wish”, reads the former CoC. SQLite’s new code of conduct based on Mozilla’s community participation guideline preaches rules such as being respectful, being direct but professional, being inclusive, understanding different perspectives, appreciating differences, leading by example, and so forth. This change, however, is not the result of public criticism received as the SQLite team states, “While we are not doing so in reaction to any current or ongoing issues, we believe that this will be a helpful part of maintaining the long-term sustainability of the project”. Here’s what people feel about SQLite’s decision to adopt Mozilla’s community participation guidelines. Some approve of it while others liked the former CoC better. https://twitter.com/ZanBaldwin/status/1055994596369547264 https://twitter.com/ann_arcana/status/1055399132230246400 https://twitter.com/TheQuQu/status/1055170161425240064 SQLite 3.25.0 is out with better query optimizer and support for windows functions How to use SQLite with Ionic to store data? Introduction to SQL and SQLite

“The harvesting and sharing of data by mobile phone apps is out of control” -Researchers at Oxford University A paper published on 18th October by researchers at Oxford University revealed that 90% of Google play store apps are harvesting user data and subsequently sharing it with companies like Alphabet, Twitter, Facebook, and many others. The study points out the presence of third-party trackers on nearly one million (959,000) apps from the US and UK Google Play stores. The statistics are unsettling. Around 88% of this data is handed over to ‘Alphabet’- Google’s parent company. Microsoft, Twitter, Facebook and others follow suite. Here is how they fair: The most prevalent root parent tracking companies and their subsidiaries These third-party trackers were mostly prevalent in news apps and apps aimed at children and young adults. By tracking users data- which includes information like age, location, gender, buying habits, and other miscellaneous information- companies can form a profile of users. This can then be used to send target specific ads, influence a user’s buying habits or even send political campaign messages. Considering that these trackers were hugely present in apps related to children, the paper states that allowing profiling of children without attempting to obtain parental consent, is downright unlawful. Even though there are tracker blocking software available for mobile and web, these primarily cannot control the tracking software embedded on an app’s OS. The privacy settings for an app are focussed on more specific app permissions like contact sharing, location sharing etc. In response to this research,  a Google spokesman said in a statement to Business Insider “Across Google and in Google Play, we have clear policies and guidelines for how developers and third-party apps can handle data and we require developers to be transparent and ask for user permission.” Further, they added, “If an app violates our policies, we take action.” Google also added that the researchers had “mischaracterized” some of the app’s basic functions to reach their conclusion. Head over to the research paper to obtain more information about this study. Alternatively, you can visit the dailmail.co.uk for more insights to this news. Google is missing out $50 million because of Fortnite’s decision to bypass Play Store A multimillion-dollar ad fraud scheme that secretly tracked user affected millions of Android phones. This is how Google is tackling it. All new Android apps on Google Play must target API Level 26 (Android Oreo) or higher, to publish

Yesterday, a shocking report by The New York Times shared its investigation on sexual misconduct at Google. It alleged that Google had protected at least four senior executives over the past decade after they were accused of sexual misconduct. They obtained corporate and court documents and spoke to more than three dozen current and former Google executives and employees about these episodes. Here is a summary of the three incidents that the New York Times article reported on.. The controversy with Andy Rubin, Creator of Android Andy Rubin, the creator of Android, often exhibited unprofessional behavior towards his co-workers. He was involved in a consensual relationship with a woman employee from 2011, who reported to one of his direct reports on the Android team. Google’s human resources department was not informed about this relationship despite a policy in place to do so. In 2013 when she wanted to cool things off, she agreed to meet Rubin at a hotel, where she was pressured to perform a non-consensual sexual activity. The woman filed a complaint to Google’s human resources department in 2014 and informed officials about the relationship. Amidst Google’s investigation, in September 2014, Mr. Rubin was awarded a stock grant worth $150 million approved by Google board’s leadership development and compensation committee. Google’s inquiry found the claims to be credible and the relationship inappropriate.   Mr. Page, the then CEO of Google, decided Mr. Rubin should leave and Google paid him $90 million as an exit package with an agreement to not work with Google’s rival companies. The company then proceeded to give Mr. Rubin’s a high profile well-respected farewell in October 2014. A civil suit filed later this month by Mr. Rubin’s ex-wife, Rie Rubin, includes a screenshot on an email (dated August 2015) that Mr. Rubin sent to a woman which said: “You will be happy being taken care of, Being owned is kinda like you are my property, and I can loan you to other people.” Mr. Rubin released a statement calling the allegations “false” and “part of a smear campaign by my ex-wife to disparage me during a divorce and custody battle.” The controversy with Richard DeVaul, Director at Google X In 2013, Richard DeVaul, director at Google X, interviewed Star Simpson, a hardware engineer. After the job interview, he invited her to an annual festival in the Nevada desert, the following week. On getting back to his encampment, he asked her to remove her shirt and offered a back rub. When she refused, he insisted and she relented to a neck rub. Why you ask? “I didn’t have enough spine or backbone to shut that down as a 24-year-old” -Ms. Simpson Later she was informed by Google that she did not land the job, without any explanation. After finally reporting the episode to Google after 2 years, human resources told her that her account was “more likely than not” true and that “appropriate action” was taken. She was asked to stay quiet about the whole incident. Chelsea Bailey, the head of human resources at X, declined Simpson's allegations in a statement, adding that officials investigated and “took appropriate corrective action.” declining to say what the action was, owing to employee confidentiality. The controversy with Amit Singhal, former SVP of Search In 2005, an employee alleged that Amit Singhal, a senior vice president who headed search, groped her at an off-site event attended by dozens of colleagues. Google investigated and found that Mr. Singhal was inebriated and there were no witnesses to corroborate the incident. Google did not fire Mr. Singhal. They accepted his resignation and negotiated an exit package that paid him millions and prevented him from working for a competitor. The controversy with Drummond, Chief Legal Officer, Alphabet, and Chairman, CapitalG “Google felt like I was the liability.” - Jennifer Blakely, ex- senior contract manager David C. Drummond, joined as general counsel in 2002, started dating Jennifer Blakely (senior contract manager) in 2004. They had a son in 2007, after which Mr. Drummond disclosed their relationship to the company. Soon after, Google took action and Ms. Blakely had to leave the legal department as only one of them could work there and transferred to sales in 2007. She eventually left Google in 2008. While resigning, she was asked to sign paperwork saying she had departed voluntarily. Drummond left her in late 2008. Since the affair, Mr. Drummond’s has moved up the rungs within Alphabet. As Alphabet’s chief legal officer and chairman of CapitalG, he has reaped about $190 million from stock options and awards since 2011. Google’s response to the New York Times story Following the report by the New York Times, Google CEO Sundar Pichai sent an email to all Google employees on Thursday clarifying that the company has fired 48 people over the last two years for sexual harassment 13 of them were "senior managers and above". None of them received any exit packages. The email opened “We are dead serious about making sure we provide a safe and inclusive workplace. We want to assure you that we review every single complaint about sexual harassment or inappropriate conduct, we investigate and we take action.” It also stated that there are “confidential channels” available for employees to report incidents of sexual harassment. He further informed they have updated their policies to demand all VPs and SVPs to disclose any relationship with a co-worker irrespective of whether they work on the same projects or not.  You can head over to CNBC to read the entire email. Our take on this story The email seems to have deliberately excluded the timelines during which the incidents reported in the New York Times article took place. Also, it neither denies nor confirms those incidents which hints at them being true, in most likelihood. While Mr. Pichai assures his people that Google is doing everything to ensure it is a safe place to work, he does not address any of the red flags satisfactorily the NYT article raised such as: All the above incidents point to weak policy implementation by HR and Google leadership. Just amending policies is clearly not enough. The ‘hard line on inappropriate conduct by people in positions of authority’ that Pichai references in his response seem to vary based on how valuable the perpetrator is to Google or its board. What measures are they taking to ensure an impartial assessment happens? The incidents also highlight that executives brazenly misbehave with their victims. There is no mention of how that aspect of Google is being tackled. Specifically, for example, would Mr. Page take a different decision today if had a chance to go back in time or if Mr. Pichai, as Google CEO personally taken a public stance on specific incidents of sexual misconduct without hiding behind aggregate numbers and figures. The report throws light on the pervasive sexist culture in male-dominated Silicon Valley and the growing chorus denouncing it.  It is traumatic enough to experience such harassment, imagine the pressure that one has to deal with when such incidents go public. It is also sad that the tech giant that everyone looks up to- Google- decided to sweep matters under the carpet to save itself from public attention. These recurring stories seem to have led to the release of Brotopia: Breaking Up The Boys Club of Silicon Valley, a book by Emily Chang, Bloomberg reporter, that dives into the stories of women who say they have been sexually harassed at tech companies and venture capital firms. You can head over to The New York Times for the entire news coverage as well as similar incidents documented. NIPS Foundation decides against name change as poll finds it an unpopular superficial move; instead increases ‘focus on diversity and inclusivity initiatives’ Python founder resigns – Guido van Rossum goes ‘on a permanent vacation from being BDFL’ Ex-googler who quit Google on moral grounds writes to Senate about company’s “Unethical” China censorship plan

The Google Brain team with DeepMind and ETH Zurich have introduced an episodic memory-based curiosity model which allows Reinforcement Learning (RL) agents to explore environments in an intelligent way. This model was the result of a study called Episodic Curiosity through Reachability, the findings of which Google AI shared yesterday. Why this episodic curiosity model is introduced? In real-world scenarios, the rewards required in reinforcement learning are sparse and most of the current reinforcement learning algorithms struggle with such sparsity. Wouldn’t it be better if the agent is capable of creating its own rewards? That’s what this model does. This makes the rewards denser and more suitable for learning. Many researchers have worked on some curiosity-driven learning approaches before, one of them is Intrinsic Curiosity Module (ICM). This method is explored in the recent paper Curiosity-driven Exploration by Self-supervised Prediction published by Ph.D. students at the University of California, Berkeley. ICM builds a predictive model of the dynamics of the world. The agent is rewarded when the model fails to make good predictions. Exploring unvisited locations is not directly a part of the ICM curiosity formulation. In the ICM method, visiting them is only a way to obtain more “surprise” and thus maximize overall rewards. As a result, in some environments there could be other ways to cause self-surprise, leading to unforeseen results. The authors of the ICM method along with researchers at OpenAI, in their research Large-Scale Study of Curiosity-Driven Learning, show a hidden danger of surprise maximization. Instead of doing something useful for the task at hand, agents can learn to indulge procrastination-like behavior. The episodic memory-based curiosity model overcomes this “procrastination” issues. What is episodic memory-based curiosity model? This model uses a deep neural network trained to measure how similar two experiences are. For training the model, the researchers made it guess whether two observations were experienced close together in time, or far apart in time. Temporal proximity is a good proxy for whether two experiences should be judged to be part of the same experience. This training gives a general concept of novelty via reachability, which is shown a follows: Source: Google AI How this model works Inspired by curious behavior in animals, this model rewards the agent with a bonus when it observes something novel. This bonus is summed up with the real task reward making it possible for the RL algorithm to learn from the combined reward. To calculate the bonus of the agent, the current observation is compared with the observation in memory. This comparison is done based on how many environment steps it takes to reach the current observation from those in memory. Source: Google AI This method follows these steps: The agent's observations of the environment are stored in an episodic memory. The agents are also rewarded for reaching observations that are not yet represented in memory. In this method, being “not in memory” is the definition of novelty in our method. Such a behavior of seeking the unfamiliar will lead the artificial agent to new locations, thus keeping it from wandering in circles and ultimately help it stumble on the goal. Experiment and results Different approaches to curiosity were tested in two visually rich 3D environments: ViZDoom and DMLab. The agent was given various tasks such as searching for a goal in a maze or collecting good objects and avoiding bad objects. The standard setting in previous formulations, such as ICM, on DMLab, was to provide the agent a laser-like science fiction gadget. If the agent does not need a gadget for a particular task, it was free not to use it. In this test, the surprise-based ICM method used this gadget a lot even when it is useless for the task at hand. The newly introduced method instead learns reasonable exploration behavior under the same conditions. This is because it does not try to predict the result of its actions, but rather seeks observations which are “harder” to achieve from those already in the episodic memory. In short, the agent implicitly pursues goals which require more effort to reach from memory than just a single tagging action. This approach penalizes an agent running in circles because after completing the first circle the agent does not encounter new observations other than those in memory, and thus receives no rewards. In the experimental environment, the model was able to achieve: In ViZDoom, the agent learned to successfully navigate to a distant goal at least two times faster than the state-of-the-art curiosity method ICM. In DMLab, the agent generalized well to new procedurally generated levels of the game. It was able to reach the goal at least two times more frequently than ICM on test mazes with very sparse reward. To know more in detail about the episodic memory-based curiosity model, check out Google AI’s post and also the paper: Episodic Curiosity Through Reachability. DeepMind open sources TRFL, a new library of reinforcement learning building blocks Google open sources Active Question Answering (ActiveQA), a Reinforcement Learning based Q&A system Understanding Deep Reinforcement Learning by understanding the Markov Decision Process [Tutorial]

Mozilla announced funding for the seven projects of its 2018 Creative Media Awards, earlier this week. These projects aimed at promoting art and advocacy to highlight the unintended and indirect consequences of artificial intelligence in our everyday lives. Mozilla’s Creative Media Awards is an initiative taken by Mozilla to support and promote a healthy internet ecosystem. Mozilla announced, in June this year,  that it will be awarding $225,000 to the winner technologists and media makers. “We’re seeking projects that explore artificial intelligence and machine learning. In a world where biased algorithms, skewed data sets, and broken recommendation engines can radicalize YouTube users, promote racism, and spread fake news, it’s more important than ever to support artwork and advocacy work that educates and engages internet users”, reads the Mozilla awards page. The creative media awards are a part of the NetGain Partnership. NetGain Partnership is a collaboration between Mozilla, Ford Foundation, Knight Foundation, MacArthur Foundation, and the Open Society Foundation. The winners of the seven projects come from five different countries, namely, the U.S, the U.K, Netherlands, India, and the United Arab Emirates (UAE). The winners used science fiction, short documentaries, games, and other media to make the” impact of Artificial Intelligence on the society understandable”. These seven projects will be launched by June 2019. Let’s have a look at these projects. Stealing Ur Feelings Stealing Ur Feelings will be an interactive documentary by Noah Levenson in the U.S. Levenson, has been awarded $50,000 as a prize. The documentary will be exploring how an emotion recognition AI tracks whether you’re happy or sad. It will also reveal how companies use that data to influence your behavior. Do Not Draw a Penis Do Not Draw a Penis by Moniker in the Netherlands aims at addressing automated censorship and algorithmic content moderation. Moniker has also been awarded $50,000 as a prize. In Do Not Draw a Penis, users will have to visit a web page and will be met with a blank canvas. On that blank canvas, users can draw whatever they want, and an AI voice will comment on their drawings ( such as “nice landscape!”). However, in case the drawing resembles a penis or other explicit content, the AI will scold the user and destroy the image. A Week With Wanda A week with Wanda by Joe Hall from the UK will be a web-based simulation of risks and rewards attached to Artificial Intelligence. Hall has been awarded $25,000 as a prize. Wanda is an AI assistant that interacts with users over the course of one week to “improve” their lives. So, Wanda might send “uncouth” messages to Facebook friends or order you anti-depressants. It might even freeze your bank account, however, Wanda’s actions are simulated, not real. Survival of the Best Fit Survival of the Best Fit by Alia ElKattan in the United Arab Emirates is a web-based simulation of how blinding use of AI during the hiring process reinforces workplace inequality. ElKattan has been awarded $25000 as a prize. Survival of the Best Fit presents users with an algorithm to experience how white-sounding names are prioritized, among other related biases. The Training Commission The Training Commission is a web-based fiction by Ingrid Burrington and Brendan Byrne in the U.S. This team was awarded $25,000 as a prize. The Training Commission tells stories of AI’s unintended consequences and harms to public life. What Do You See? What do you see| by Suchana Seth from India highlights and explores how differently humans and algorithms “see” the same image, and how easily bias can kick in. Seth has been awarded $25,000 as a prize. What do you see involves humans having to visit a website and describe an image in their own words, without the help of prompts. Then, humans will see how an image captioning algorithm explaining the same image. Mate Me or Eat Me Mate Me or Eat Me is a dating simulator by Benjamin Berman in the U.S. Berman has also been awarded $25,000 as the prize. Mate Me or Eat Me examines how exclusionary real dating apps can be. Users will have to create a monster and mingle with others, swiping right and left to either mate with or eat others. Users are also presented with an insight on how their choice impacts who they see next as well as who all have been excluded from their pool of potential paramours. These seven awardees were selected depending on the quantitative scoring of their applications by a review committee. Committee members comprise the Mozilla staff, current, and alumni Mozilla Fellows, as well as the outside experts.  Diversity in applicant background, past work, and medium were also considered during the selection process. For more information, read the official Mozilla Blog. Mozilla announces $3.5 million award for ‘Responsible Computer Science Challenge’ to encourage teaching ethical coding to CS graduates Is Mozilla the most progressive tech organization on the planet right now? To bring focus on the impact of tech on society, an education in humanities is just as important as STEM for budding engineers, says Mozilla co-founder