There's been a great deal of hype and excitement in the artificial intelligence (AI) world around a newly developed technology known as GPT-3. Put simply; it's an AI that is better at creating content that has a language structure – human or machine language – than anything that has come before it. GPT-3 has been created by OpenAI, a research business co-founded by Elon Musk and has been described as the most important and useful advance in AI for years. But there's some confusion over exactly what it does (and indeed doesn't do), so here I will try and break it down into simple terms for any non-techy readers interested in understanding the fundamental principles behind it. I'll also cover some of the problems it raises, as well as why some people think its significance has been overinflated somewhat by hype.

Artificial intelligence chip company Hailo Technologies Ltd. said today it's launching two new acceleration modules that will boost the processing capabilities of edge devices that run its specialist hardware. Hailo burst onto the AI scene in 2019 with a customized processor for running deep learning workloads at the edge of the network. The company, which is primarily focused on the automotive sector, said at the time that its Hailo-8 Deep Learning chip enables devices such as autonomous vehicles, smart cameras, drones and AR/VR platforms to run sophisticated deep learning applications at the edge that could previously be hosted only in cloud data centers. The Hailo-8 processor, which is smaller than a penny, was built from the ground up with completely redesigned memory, control and compute architecture components that enable "higher performance, lower power and minimal latency." Hailo also provides a software development kit for developers to build apps customized for the hardware.

This dataset was originally developed and described here, and it contains 10000 sequences each of length 20 with frame size 64 x 64 showing 2 digits moving in various trajectories (and overlapping). Something to note beforehand is the inherent randomness of the digit trajectories. We do expect that this will become a major hurdle for the model we are about to describe, and we also note that newer approaches such as Variational Autoencoders might be a more efficient model for this type of task. The specific model type we will be using is called a seq2seq model, which is typically used for NLP or time-series tasks (it was actually implemented in the Google Translate engine in 2016). The original papers on seq2seq are Sutskever et al., 2014 and Cho et al., 2014.

Olaf Witkowski is the Chief Scientist at Cross Labs, which aims to bridge the divide between intelligence science and AI technology. A researcher of artificial life, Witkowski started in artificial intelligence by exploring the replication of human speech through machines. He founded Commentag in 2007, and in 2009 moved to Japan to continue research, where he first became interested in artificial life. In his own words, Witkowski says, "artificial intelligence means that you are trying to copy human intelligence as best as possible. Artificial life says, okay, that's good, but let's try to understand human intelligence and recreate it from the fundamental knowledge we have acquired. It's a bit like the Richard Feynman quote: what I cannot create, I do not understand."

In this short note, we describe a Jump Unit that can be used to fit a step function with a simple neural network. Our motivation comes from quantitative finance problems where discontinuities often appear. Note from Towards Data Science's editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author's contribution. You should not rely on an author's works without seeking professional advice. See our Reader Terms for details.

Kirk Ouimet article is a dialogue between himself and GPT-3, which is referred to as'Wise Being'. The content of the dialogue is around the origin of the Big Bang and other associated topics such as time, space and the Universe. I was truly expecting to be bored or at the very least be a little bit impressed by the output of the'Wise Being' in the dialogue. After reading the entirety of the dialogue and article, I would have to admit that the responses from the'Wise Being' felt almost human-like, and surpassed my initial expectations. The responses were well put together and had some form of logic, well as much logic as possible when answering questions that are outside the realms of human imagination.

Airports uniquely demand both a very high passenger throughput and a very high degree of security underpinned by the positive identity confirmation of those passengers. At multiple points throughout the air travel experience, traveler identity must be confirmed to meet commercial policy, physical security, or national security requirements. This uncommon set of demands has forced innovation in the form of automated identity confirmation, primarily using biometrics. For two decades, some combination of face, fingerprint, and iris recognition has been deployed in an effort to speed up identity confirmation, with the goal of creating a secure and frictionless passenger experience. Thanks to rapid advances in Artificial Intelligence and specific technologies like Deep Learning and Convolutional Neural Networks, face recognition, in particular, has dramatically improved in the last few years.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. If artificial intelligence will destroy humanity, it probably won't be through killer robots and the incarnation--it will be through a thousand paper cuts. In the shadow of the immense benefits of advances in technology, the dark effects of AI algorithms are slowly creeping into different aspects of our lives, causing divide, unintentionally marginalizing groups of people, stealing our attention, and widening the gap between the wealthy and the poor. While we're already seeing and discussing many of the negative aspects of AI, not enough is being done to address them. And the reason is that we're looking in the wrong place, as futurist and Amy Webb discusses in her book The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity. Many are quick to blame large tech companies for the problems caused by artificial intelligence.

In recent years, the prosperity of deep learning has revolutionized the Artificial Neural Networks. However, the dependence of gradients and the offline training mechanism in the learning algorithms prevents the ANN for further improvement. In this study, a gradient-free training framework based on data assimilation is proposed to avoid the calculation of gradients. In data assimilation algorithms, the error covariance between the forecasts and observations is used to optimize the parameters. Feedforward Neural Networks (FNNs) are trained by gradient decent, data assimilation algorithms (Ensemble Kalman Filter (EnKF) and Ensemble Smoother with Multiple Data Assimilation (ESMDA)), respectively. ESMDA trains FNN with pre-defined iterations by updating the parameters using all the available observations which can be regard as offline learning. EnKF optimize FNN when new observation available by updating parameters which can be regard as online learning. Two synthetic cases with the regression of a Sine Function and a Mexican Hat function are assumed to validate the effectiveness of the proposed framework. The Root Mean Square Error (RMSE) and coefficient of determination (R2) are used as criteria to assess the performance of different methods. The results show that the proposed training framework performed better than the gradient decent method. The proposed framework provides alternatives for online/offline training the existing ANNs (e.g., Convolutional Neural Networks, Recurrent Neural Networks) without the dependence of gradients.

This paper focuses on cooperative value-based multi-agent reinforcement learning (MARL) in the paradigm of centralized training with decentralized execution (CTDE). Current state-of-the-art value-based MARL methods leverage CTDE to learn a centralized joint-action value function as a monotonic mixing of each agent's utility function, which enables easy decentralization. However, this monotonic restriction leads to inefficient exploration in tasks with nonmonotonic returns due to suboptimal approximations of the values of joint actions. To address this, we present a novel MARL approach called Universal Value Exploration (UneVEn), which uses universal successor features (USFs) to learn policies of tasks related to the target task, but with simpler reward functions in a sample efficient manner. UneVEn uses novel action-selection schemes between randomly sampled related tasks during exploration, which enables the monotonic joint-action value function of the target task to place more importance on useful joint actions. Empirical results on a challenging cooperative predator-prey task requiring significant coordination amongst agents show that UneVEn significantly outperforms state-of-the-art baselines.