The best book ever written about IT work or the computer industry will be 40 years old in August. Tracy Kidder's The Soul of a New Machine describes the work of Data General engineers to prototype a minicomputer, codenamed "Eagle," intended to halt the advance of the Digital Equipment Corporation's hugely successful VAX range. It won both the Pulitzer Prize and National Book Award for non-fiction, perhaps the two highest honors available for book-length journalism. Year after year, the book continues to sell and win new fans. Developers born since it was published often credit it with shaping their career choices or helping them appreciate the universal aspects of their own experiences. Soul's appeal has endured, even though what started out as a dispatch from a fast-growing firm building a piece of the future now reads as a time capsule from a lost world. Back in 1991 I read the book for an undergraduate class, typing my paper on a PC that was already more capable than Eagle yet cost 100 times less. So why are so many people still excited to relive the creation of a pitifully obsolete computer, designed by a team of obscure engineers for a long-forgotten company that never mattered very much anyway? Having spent almost 30 years now trying to take the book apart and figure out how it works, I think I have some answers. Paradoxically, the obscurity of Data General helps to explain the book's enduring power.

In some stores, sophisticated systems are tracking customers in almost every imaginable way, from recognizing their faces to gauging their age, their mood, and virtually gussying them up with makeup. The systems rarely ask for people's permission, and for the most part they don't have to. In our season 1 finale, we look at the explosion of AI and face recognition technologies in retail spaces, and what it means for the future of shopping. This episode was reported and produced by Jennifer Strong, Anthony Green, Tate Ryan-Mosley, Emma Cillekens and Karen Hao. Strong: Retailers have been using face recognition and AI tracking technologies for years. And what if you could know about the presence of violent criminals before they act? With Face First you can stop crime before it starts.] It detects faces, voices, objects and claims it can analyze behavior. But face recognition systems have a well-documented history of misidentifying women and people of color. And they're trying to sell it and impose it on the entirety of the country?] Strong: This is Representative Alexandria Ocasio-Cortez at a 2019 congressional hearing on facial recognition.

We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, "pointwise" and "distributional" constraints over the target LM -- to our knowledge, this is the first approach with such generality -- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study we show the effectiveness of our adaptive technique for obtaining faster convergence.

When answering questions about an image, it not only needs knowing what -- understanding the fine-grained contents (e.g., objects, relationships) in the image, but also telling why -- reasoning over grounding visual cues to derive the answer for a question. Over the last few years, we have seen significant progress on visual question answering. Though impressive as the accuracy grows, it still lags behind to get knowing whether these models are undertaking grounding visual reasoning or just leveraging spurious correlations in the training data. Recently, a number of works have attempted to answer this question from perspectives such as grounding and robustness. However, most of them are either focusing on the language side or coarsely studying the pixel-level attention maps. In this paper, by leveraging the step-wise object grounding annotations provided in the GQA dataset, we first present a systematical object-centric diagnosis of visual reasoning on grounding and robustness, particularly on the vision side. According to the extensive comparisons across different models, we find that even models with high accuracy are not good at grounding objects precisely, nor robust to visual content perturbations. In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy. To reconcile these different aspects, we further develop a diagnostic model, namely Graph Reasoning Machine. Our model replaces purely symbolic visual representation with probabilistic scene graph and then applies teacher-forcing training for the visual reasoning module. The designed model improves the performance on all three metrics over the vanilla neural-symbolic model while inheriting the transparency. Further ablation studies suggest that this improvement is mainly due to more accurate image understanding and proper intermediate reasoning supervisions.

These Visionary companies have a big idea and are well on their way to achieving it, although it isn't always an easy road for any really innovative technology. In the case of Cruise, that meant testing self driving vehicles on the streets of San Francisco, one of the hardest driving environments in the world. Some of our Visionary Awards go to companies who are opening up new market applications for robotics, such as Built Robotics in construction, Dishcraft in food services, Embark in self-driving trucks, Iron Ox in urban agriculture and Zipline in drone delivery. Some are building tools or platforms that the entire robotics industry can benefit from, such as Agility Robotics, Covariant, Formant, RobustAI and Zoox. The companies in our Good Robot Awards also show that'technologies built for us, have to be built by us'.

Self-driving vehicles would not be possible without sensors and so it's not surprising to see two small new sensors in the 2020 Silicon Valley Robotics'Good Robot' Innovation Awards, the Velabit from Velodyne and the nanoScan3 from SICK. Our other Innovation Awards go to companies with groundbreakingly new robots; from the tensegrity structure of Squishy Robotics, which will help in both space exploration and disaster response on earth, to the Dusty Robotics full scale FieldPrinter for the construction industry, and Titan from FarmWise for agriculture, which was also named one of Time's Best Inventions for 2020. Finally, we're delighted to see innovation in robotics that is affordable and collaborative enough for home robot applications, with Stretch from Hello Robot and Eve from Halodi Robotics. The Velabit, a game-changing lidar sensor, leverages Velodyne's innovative lidar technology and manufacturing partnerships for cost optimization and high-volume production, to make high-quality 3D lidar sensors readily accessible to everyone. The Velabit is smaller than a deck of playing cards, and it shatters the price barrier, costing $100.00 per sensor.

His idea unfolded amid a massive shift in the world of business as companies as diverse as JPMorgan Chase and General Motors became more reliant on computers as part of their infrastructure. It happened in incremental steps, from big mainframes to personal computers to the era of smartphones and cloud computing. Understanding all these shifts was critical for IT executives, and Mr. Gartner positioned his firm as a vital guide to understanding and embracing the idea that those changing systems were a critical part of the corporate equation.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.

The following week, she took part in several workshops at NeurIPS, the largest annual AI research conference, which over 20,000 people attended this year. It was "therapeutic," she says, to see how the community she'd helped build showed up and supported one another. Now, another week later, she's just winding down and catching her breath--and trying to make sense of it all. On Monday, December 14, I caught up with Gebru via Zoom. She recounted what happened during her time at Google, reflected on what it meant for the field and AI ethics research, and gave parting words of advice to those who want to keep holding tech companies accountable.

While impressive, the technology wasn't yet capable of replacing the existing expensive and time-consuming experimental methods for determining what these proteins look like. However, its latest software comes close. In November, AlphaFold again outperformed all the other competing groups at CASP. The technology solved protein structures other labs had been working on for years. Scientists think the technology could have immense implications for the way proteins are studied.