Cars have become icons of style, mobility and luxury. Design teams around the globe are creating concept vehicles for the future that will drive themselves. Icona started in 2010 in Italy and has now has grown to a global design company with 180 employees and studios, in Turin, Italy, Shanghai, China and Santa Margarita, California. Auto Futures talked to Icona's Global Design Director, Samuel Chuffart, at AutoMobility LA ahead of the North American debut of the Icona Nucleus. "In the future of cars we have to reset our values and adapt to the needs of new technology such as Level 5 autonomous driving," says Chuffart who sees autonomous cars as s more like a private yacht on wheels and draws inspiration for the Concorde airplane.

In 2017, a Palestinian construction worker in the West Bank settlement of Beiter Illit, Jerusalem, posted a picture of himself on Facebook in which he was leaning against a bulldozer. Shortly after, Israeli police arrested him on suspicions that he was planning an attack, because the caption of his post read "attack them." The real caption of the post was "good morning" in Arabic. But for some unknown reason, Facebook's artificial intelligence–powered translation service translated the text to "hurt them" in English or "attack them" in Hebrew. The Israeli Defense Force uses Facebook's automated translation to monitor the accounts of Palestinian users for possible threats. In this case, they trusted Facebook's AI enough not to have the post checked by an Arabic-speaking officer before making the arrest. The Palestinian worker was eventually released after the mistake came to light--but not before he underwent hours of questioning. Facebook apologized for the mistake and said that it took steps to correct it.

Until technology allows us to upload our consciousness to a computer when our physical bodies start irreparably failing, death is going to remain a real thing. But what if you could continue communicating with loved ones -- or, at least, a reasonable facsimile of them -- long after they've shuffled off this mortal coil? It might sound like an episode of Black Mirror (it is!), but it's also the basis for a recently announced research project being carried out at India's Shree Devi Institute of Technology. Researchers Shriya Devadiga and Bhakthi Shetty have been investigating how a chatbot could be made to duplicate a person's personality digitally, granting users the ability to chat with an A.I. approximation of an individual, such as a family member, who is no longer around. For their study, the researchers used Replika A.I., an app created by Russian coder Euginia Kuyda.

Since the first version of this white paper in 2016, the range and impact of Artificial Intelligence (AI) has expanded at a dizzying pace as the area continues to capture an ever greater share of the business and popular imaginations. Along with the cloud, AI is emerging as the key driver of the'fourth industrial revolution', the term (after steam, electricity and computing) coined by Davos founder Klaus Schwab for the deep digital transformation now under way. This white paper is written from the perspective of the in-house lawyer working on the legal aspects of their organisation's adoption and use of AI. "artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment".4 "interdisciplinary field ... dealing with models and systems for the performance of functions generally associated with human intelligence, such as reasoning and learning." Most recently, in its January 2018 book, 'The Future: Computed', Microsoft thinks of AI as: "a set of technologies that enable computers to perceive, learn, reason and assist in decision- making to solve problems in ways that are similar to what people do."7

This week in Las Vegas, Amazon rolled out dozens of new features, upgrades, and new products at AWS re:Invent. Here's a quick roundup of news out of the annual conference that may matter to members of the AI community. A disproportionate amount of money is spent on inference versus training when it comes to AI models, AWS CEO Andy Jassy said, and GPUs can be terribly inefficient. To address these issues, Amazon custom-designed a chip named Inferentia due out next year and created Elastic Inference, a service that identifies parts of a neural network that can benefit from acceleration. To speed up training of AI models, Amazon introduced AWS-Optimized TensorFlow, which can train a model with the ResNet-50 benchmark in 14 minutes.

The growing use of AI will increase data usage exponentially. As part of Singapore's smart nation initiative, the government has planned to invest up to S$150m from the National Research Foundation on AI over five years through the AI Singapore programme. While first-generation AI architectures have historically been centralised, Equinix predicts that enterprises will enter the realm of distributed AI architectures, where AI model building and model inferencing will take place at the edge, physically closer to the origin source of the data. To access more external data sources for accurate predictions, enterprises will turn to secure data transaction marketplaces. They will also strive to leverage AI innovation in multiple public clouds without getting locked into a single cloud, further decentralising AI architectures.

The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) percept-based prediction of an object's likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.

Recently, compression and acceleration of deep neural networks are in critic need. Bayesian generalization of structured pruning represents an important research direction to solve the above problem. However, the existing Bayesian methods ignore the dependency among neurons and filters for computational simplicity. In this study, we explore, under Bayesian framework, a structured pruning method with layer-wise sequential dependency assumed, a more general learning setting. Based on the property of Dirac distribution, we further derive a new dropout noise, which makes it possible to approximate the posterior of dropout noise knowing that of the previous layer. With the Dirac-like dropout noise, we further propose a recursive strategy, named \emph{Recursive Bayesian Pruning} (RBP), to train and prune networks in a layer-by-layer fashion. The unimportant neurons and filters are directly targeted and removed, taking the influence from the previous layer. Experiments on typical neural networks LeNet-300-100, LeNet-5 and VGG-16 have demonstrated the proposed method are competitive with or even outperform the state-of-the-art methods in several compression and acceleration metrics.

Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures. In this paper, we learn a disease model for AD that combines genotypic and phenotypic profiles, and cognitive health metrics of patients. We propose a probabilistic generative subspace that describes the correlative, complementary and domain-specific semantics of the dependencies in multi-view, multi-modality medical data. Guided by domain knowledge and using the latent consensus between abstractions of multi-view data, we model the fusion as a data generating process. We show that our approach can potentially lead to i) explainable clinical predictions and ii) improved AD diagnoses.

Stanley Kubrick's 2001: A Space Odyssey will help launch the world's first super-high definition 8K television channel on Saturday. Japanese broadcaster NHK said it had asked Warner Bros to scan the original film negatives in 8K for its new channel. Super-high definition 8K pictures offer 16 times the resolution of HD TV. However, few people currently have the necessary television or equipment to receive the broadcasts. NHK says it has been developing 8K, which it calls super-hi vision, since 1995.