OpenAI's text generating system GPT-3 has captured mainstream attention. GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. The output produced from this autocomplete bot can be used to manipulate people on social media and spew political propaganda, argue about the meaning of life (or lack thereof), disagree with the notion of what differentiates a hot-dog from a sandwich, take upon the persona of the Buddha or Hitler or a dead family member, write fake news articles that are indistinguishable from human written articles, and also produce computer code on the fly. There have also been colorful conversations about whether GPT-3 can pass the Turing test, or whether it has achieved a notional understanding of consciousness, even amongst AI scientists who know the technical mechanics. The chatter on perceived consciousness does have merit–it's quite probable that the underlying mechanism of our brain is a giant autocomplete bot that has learnt from 3 billion years of evolutionary data that bubbles up to our collective selves, and we ultimately give ourselves too much credit for being original authors of our own thoughts (ahem, free will).

A demonstration of virtual reality goggles used to access the Virtual Interactive Training Agent platform. From left: Sharon Mozgai and Arno Hartholt. The job interview can be a terrifying ordeal. The anxiety and stress of preparing for it, especially if you have never done one before, stops many people from bringing their best self or even showing up at all. It is also challenging to practice for.

Underwater vehicles haven't changed much since the submarines of World War II. They're rigid, fairly boxy and use propellers to move. And whether they are large manned vessels or small robots, most underwater vehicles have one cruising speed where they are most energy efficient. Fish take a very different approach to moving through water: Their bodies and fins are very flexible, and this flexibility allows them to interact with water more efficiently than rigid machines. Researchers have been designing and building flexible fishlike robots for years, but they still trail far behind real fish in terms of efficiency.

On 22 September 2021, the UK Government published its 10-year strategy on artificial intelligence ("AI"; the "UK AI Strategy"). The UK AI Strategy has three main pillars: (1) investing and planning for the long-term requirements of the UK's AI ecosystem; (2) supporting the transition to an AI-enabled economy across all sectors and regions of the UK; and (3) ensuring that the UK gets the national and international governance of AI technologies "right". The approach to AI regulation as set out in the UK AI Strategy is largely pro-innovation, in line with the UK Government's Plan for Digital Regulation published in July 2021. This pillar focuses on the need to invest in the skills and resources that lead to AI innovation with the aim of increasing the type, frequency and scale of AI discoveries in the UK. The Pillar has twelve action points, with an emphasis on the importance of access to and availability of data.

If you have been frustrated by the lack of interest your local representative shows during their work hours, here's a way to flag it now. Belgium-based developer, Dries Depoorter has created an artificial intelligence (AI) tool that calculates how much time are politicians distracted by their phones during meetings. Called the Flemish Scrollers, the tool is written in Python and uses machine learning and face recognition technologies. The law of the land requires that all meetings of the Flemish government be in the public domain. The government broadcasts it live on its YouTube channel.

Here's What You Need to Know: AI-capable drone defenses can already gather, pool, organize and analyze an otherwise disconnected array of threat variables. What if waves of hundreds of autonomous, integrated artificial intelligence (AI)-capable mini-drones were closing in upon a forward Army unit, Air Force base or Navy ship at staggering speeds, presenting unprecedented complexity for defenders? Perhaps they are programmed with advanced algorithms such that they operate in close coordination with one another? Perhaps hundreds of them are themselves engineered as explosives to close in upon and explode on target? Simply put, what happens when computerized swarms of enemy drone attacks exceed any human capacity to respond in time?

Sometimes in order to understand what something is, it's easier to start with what it is not. So, before we understand the unique aspects of securing an AI/ML system, we should first define what we mean by a non-AI/ML system. I am going to assume that the reader knows the basics of cybersecurity for a non-AI/ML system, which is why defining a non-AI/ML system is a good place to start. Once I do that, I can then provide some light on the "delta" between non-AI/ML systems and AI-ML systems when it comes to thinking about threats, vulnerabilities, and protective cybersecurity controls. Let us start by considering a human designing a system.

Accurate weather prediction is essential for many aspects of life, notably the early warning of extreme weather events such as rainstorms. Short-term predictions of these events rely on forecasts from numerical weather models, in which, despite much improvement in the past decades, outstanding issues remain concerning model uncertainties, and increasing demands for computation and storage resources. In recent years, the advance of deep learning offers a viable alternative approach. Here, we show that a 3D convolutional neural network using a single frame of meteorology fields as input is capable of predicting the precipitation spatial distribution. The network is developed based on 39-years (1980-2018) data of meteorology and daily precipitation over the contiguous United States. The results bring fundamental advancements in weather prediction. First, the trained network alone outperforms the state-of-the-art weather models in predicting daily total precipitation, and the superiority of the network extends to forecast leads up to 5 days. Second, combining the network predictions with the weather-model forecasts significantly improves the accuracy of model forecasts, especially for heavy-precipitation events. Third, the millisecond-scale inference time of the network facilitates large ensemble predictions for further accuracy improvement. These findings strongly support the use of deep-learning in short-term weather predictions.

In spite of the enormous success of neural networks, adversarial examples remain a relatively weakly understood feature of deep learning systems. There is a considerable effort in both building more powerful adversarial attacks and designing methods to counter the effects of adversarial examples. We propose a method to transform the adversarial input data through a mixture of generators in order to recover the correct class obfuscated by the adversarial attack. A canonical set of images is used to generate adversarial examples through potentially multiple attacks. Such transformed images are processed by a set of generators, which are trained adversarially as a whole to compete in inverting the initial transformations. To our knowledge, this is the first use of a mixture-based adversarially trained system as a defense mechanism. We show that it is possible to train such a system without supervision, simultaneously on multiple adversarial attacks. Our system is able to recover class information for previously-unseen examples with neither attack nor data labels on the MNIST dataset. The results demonstrate that this multi-attack approach is competitive with adversarial defenses tested in single-attack settings.

Frequent lane changes during congestion at freeway bottlenecks such as merge and weaving areas further reduce roadway capacity. The emergence of deep reinforcement learning (RL) and connected and automated vehicle technology provides a possible solution to improve mobility and energy efficiency at freeway bottlenecks through cooperative lane changing. Deep RL is a collection of machine-learning methods that enables an agent to improve its performance by learning from the environment. In this study, a decentralized cooperative lane-changing controller was developed using proximal policy optimization by adopting a multi-agent deep RL paradigm. In the decentralized control strategy, policy learning and action reward are evaluated locally, with each agent (vehicle) getting access to global state information. Multi-agent deep RL requires lower computational resources and is more scalable than single-agent deep RL, making it a powerful tool for time-sensitive applications such as cooperative lane changing. The results of this study show that cooperative lane changing enabled by multi-agent deep RL yields superior performance to human drivers in term of traffic throughput, vehicle speed, number of stops per vehicle, vehicle fuel efficiency, and emissions. The trained RL policy is transferable and can be generalized to uncongested, moderately congested, and extremely congested traffic conditions.