GPT-3 or Generative Pre-trained Transformer 3 is a language model that was created by OpenAI, an artificial intelligence research laboratory in San Francisco. The 175-billion parameter deep learning model is capable of producing human-like text and was trained on large text datasets with hundreds of billions of words. When OpenAI released GPT-3, in June 2020, the neural network's apparent grasp of the language was uncanny. It could generate convincing sentences, converse with humans, and even autocomplete code. GPT-3 was also monstrous in scale--larger than any other neural network ever built.

Artificial intelligence, built on large neural networks, are helping solve problems in finance, research and medicine - but could they be reaching consciousness? One expert thinks it is possible that it has already happened. On Wednesday, OpenAI cofounder Ilya Sutskever claimed on Twitter that'it may be that today's largest neural networks are slightly conscious.' He didn't name any specific developments, but is likely referring to the mega-scale neural networks, such as GPT-3, a 175 billion parameter language processing system built by OpenAI for translation, question answering, and filling in missing words. It is also unclear what'slightly conscious' actually means, because the concept of consciousness in artificial intelligence is a controversial idea.

OpenAI's top researcher has made a startling claim this week: that artificial intelligence may already be gaining consciousness. Ilya Sutskever, chief scientist of the OpenAI research group, tweeted today that "it may be that today's large neural networks are slightly conscious." Needless to say, that's an unusual point of view. The widely accepted idea among AI researchers is that the tech has made great strides over the past decade, but still falls far short of human intelligence, nevermind being anywhere close to experiencing the world consciously. It's possible that Sutskever was speaking facetiously, but it's also conceivable that as the top researcher at one of the foremost AI groups in the world, he's already looking downrange. He's long been preoccupied with artificial general intelligence, or AGI, which would refer to AI that operates at a human or superhuman level.

Gran Turismo Sophy is an artificial intelligence developed in-house at Sony. Sony claims it can race against the best Gran Turismo players in the world. It has been trained using the game’s engine and can score over 100 points after months of training. Sophy is trained through a deep reinforcement learning system, using Sony Interactive Entertainment’s cloud gaming infrastructure.  Sony calls it a different kind of AI to the likes of AlphaStar and OpenAI Five, which are developed for RTS games. The AI has to learn how to drive a car and deal with simulated physics. While Sony AI’s goal was to create artificial intelligence that could compete with the best Gran Turismo drivers, the team also saw the value in creating an AI that would be enjoyable for the best drivers to race against. Gran Turismo Sophy was created in such a way that it does neither feel unfair nor appear outlandishly superhuman.

The term artificial intelligence or AI is no longer new to us. Its existence and implementation have brought many benefits to every industry including manufacturing, healthcare, and business. As years goes by, the field of AI had broadens its scope and results in the revelation of various AI related technologies. Nowadays, the application of AI is essential either as an important tool for accelerating digital business or as innovation tool and research. This is further evident by the recent release of Gartner's top strategic technology trends for 2022 report as most of the technologies being listed are AI related technologies which include Generative AI, AI engineering and decision intelligence.

What does it mean for a generative AI model to be explainable? The emergent discipline of explainable AI (XAI) has made great strides in helping people understand discriminative models. Less attention has been paid to generative models that produce artifacts, rather than decisions, as output. Meanwhile, generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion. We conducted 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Drawing from prior work, we also propose 4 types of XAI features for GenAI for code and gathered additional design ideas from participants. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.

Modelling biologically-plausible neural structures for intelligent agents presents a unique challenge when operating in real-time domains. Neurons in our brains have different response properties, firing rates, and propagation lengths, creating noise that cannot be reliably decoded. This research explores the strengths and limitations of LIF spiking neuron ensembles for application in OpenAI virtual environments. Topics discussed include how we represent arbitrary environmental signals from multiple senses, choosing between equally viable actions in a given scenario, and how one can create a generic model that can learn and operate in a verity of situations.

Video games have proved to be a very defying laboratory to study machine-learning techniques, such as Deep Reinforcement Learning (DRL) algorithms. This paper presents a new approach for a DRL-based agent trained through Deep Q-Network (DQN) technique to perform free kicks in FIFA 18 game. The main motivation for choosing this case study is the fact that, like in many situations of the real life, FIFA represents a stochastic environment. Coping with this task, the main contributions of the present paper consist on: inspired on the OpenAI Gym and on the OpenAI Universe platforms, implementing a new user-friendly interface (in terms of portability and use simplicity) to connect the learning module with the 3D FIFA's game environment; implementing a DRL-based agent for free kicks in FIFA that uses two distinct data representations retrieved from lower cost computational procedures. The results were validated through two evaluative parameters: score of well succeed kicks and training time.

Understanding emotions and responding accordingly is one of the biggest challenges of dialog systems. We show that utilizing the history of emotions and other metadata can improve the quality of generated conversations by the dialog system. EmpTransfo utilizes state-of-the-art pre-trained models (e.g., OpenAI-GPT) for language generation, though models with different sizes can be used. Our experimental results using a challenging language corpus show that the proposed approach outperforms other models in terms of Hit@1 and PPL.

"The biggest downside for the OpenAI embeddings endpoint is the high costs (about 8,000–600,000 times more expensive than open models on your infrastructure), the high dimensionality of up to 12288 dimensions (making downstream applications slow), and the extreme latency when computing embeddings. This hinders the actual usage of the embeddings for any search applications." FYI: I had previously written about this issue over a year ago and even provided a search engine, it seems now more peeps are on top of this issue.