After many years of academia leading the way in the development of artificial intelligence, the tides have shifted and industry has taken over, according to the 2023 AI Index, a report created by Stanford University with help from companies including Google, Anthropic and Hugging Face. “In 2022, there were 32 significant industry-produced machine learning models compared to just three produced by academia,” the report says. The shift in influence is attributed mainly to the large resource demands — in staff, computing power and training data — required to create state of the art AI systems. Continue reading Report: Enterprise Supplants Academia as Driving Force of AI

ChatGPT came too late (end of November) to make a significant impact on CES this year, but the cacophony of opinions about the generative AI model definitely made its way to Vegas. The timing was perfect. Just as the crypto crash left the hype industry paralyzed, OpenAI launched ChatGPT in what now feels like a nerdy and frustrating tech version of the Rolling Stones’ Altamont concert in ’69 (with computer scientists as the Hells Angels). Make no mistake: this is a landmark achievement in machine learning — perhaps the single greatest since the 2006 paper by Hinton, Salakhutdinov, Osindero and Teh on backpropagation in deep neural networks. However, it’s critical that industries, including M&E, distinguish between hype and reality. Continue reading CES: Generative AI Is Having Its ‘War of the Worlds’ Moment

OpenAI’s natural language processing (NLP) model GPT-3 offers 175 billion parameters, compared with its predecessor, GPT-2’s mere 1.5 billion parameters. The result of GPT-3’s immense size has enabled it to generate human-like text based on only a few examples of a task. Now, many users have gained access to the API, and the result has been some interesting use cases and applications. But the ecosystem is still nascent and how it matures — or whether it’s superseded by another NLP model — remains to be seen. Continue reading GPT-3: New Applications Developed for OpenAI’s NLP Model

OpenAI unveiled DALL-E, which generates images from text using two multimodel AI systems that leverage computer vision and NLP. The name is a reference to surrealist artist Salvador Dali and Pixar’s animated robot WALL-E. DALL-E relies on a 12-billion parameter version of GPT-3. OpenAI demonstrated that DALL-E can manipulate and rearrange objects in generated imagery and also create images from scratch based on text prompts. It has stated that it plans to “analyze how models like DALL·E relate to societal issues.” Continue reading OpenAI Unveils AI-Powered DALL-E Text-to-Image Generator

At the beginning of its SC20 supercomputing conference, Nvidia unveiled its 80GB version of the A100 GPU (graphics processing unit) based on its Ampere graphics architecture and aimed at AI and graphics for supercomputing. The chip is intended to enable faster real-time data analysis for business and government applications. This new version doubles the memory of the predecessor, debuted six months ago. Nvidia executive Paresh Kharya noted that 90 percent of the world’s data was created in the last two years. Continue reading Nvidia Debuts New Version of A100 GPU for Supercomputers

To fully examine the inner workings and potential impact of deep learning language model GPT-3 on media, ETC’s project on AI & Neuroscience in Media is hosting a virtual event on November 10 from 11:00 am to 12:15 pm. RSVP here to join moderator Yves Bergquist of ETC@USC and presenter Dr. Mark Riedl of Georgia Tech as they present, “Machines That Can Write: A Deep Look at GPT-3 and its Implications for the Industry.” The launch last June of OpenAI’s GPT-3, a language model that uses deep learning to generate human-like text, has raised many questions in the creative community and the world at large.  Continue reading Virtual Event: GPT-3 and Its Implications for the M&E Industry

OpenAI’s Generative Pre-trained Transformer (GPT), a general-purpose language algorithm for using machine learning to answer questions, translate text and predictively write it, is currently in its third version. GPT-3, first described in a research paper published in May, is now in a private beta with a select group of developers. The goal is to eventually launch it as a commercial cloud-based subscription service. Its predecessor, GPT-2, released last year, was able to create convincing text in several styles. Continue reading Beta Testers Give Thumbs Up to New OpenAI Text Generator

Artificial intelligence research institute OpenAI, after collecting trillions of words, debuted its first commercial product, the API. Its goal is to create the “most flexible general-purpose AI language system” in existence. Currently, the API’s skills include translating between languages, writing news stories, and answering everyday questions. The API is engaged in limited testing and, said chief executive Sam Altman, will be released broadly for use in a range of tasks, such as customer support, education and games. Continue reading OpenAI Tests Commercial Version of Its AI Language System

Meet Meena, Google’s new chatbot powered by a neural network. According to the tech giant, Meena was trained on 341 gigabytes of public social-media chatter (8.5 times as much data as OpenAI’s GPT-2) and can talk about anything and even make jokes. With Meena, Google hopes to have made a chatbot that feels more human, always a challenge for AI-enabled media, whether it’s a chatbot or a character in a video game. To do so, Google created the Sensibleness and Specificity Average (SSA) as a metric for natural conversations. Continue reading Google Debuts Chatbot with Natural Conversational Ability

We’re not going to lie: the annual “heads up CES” piece on artificial intelligence is a major exercise in hit or miss. This is because technology rarely evolves on an annual time scale, and certainly not advanced technology like AI. Yet, here we are once again. Sure, 2019 was as fruitful as it gets in the AI research community. The raw debate between Neural Networks Extremists (those pushing for an “all neural nets all the time” approach to intelligence) and the Fanatical Symbolists (those advocating a more hybrid approach between knowledge bases, expert systems and neural nets) took an ugly “Mean Girl” turn, with two of the titans of the field (Gary Marcus and Yann LeCun) trading real insults on Twitter just a few days ago.  Continue reading The Human Interface: What We Expect From AI at CES 2020