This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
The Alliance is building a framework that gives content creators a method to retain control over their data, along with mechanisms for fair reward should they choose to share their material with AI model makers. It’s a more ethical basis for AIdevelopment, and 2025 could be the year it gets more attention.
Training and running AI programs is resource intensive endeavour, and as things stand, big tech seems to have an upper hand which creates the risk of AI centralisation. Another recent study by Epoch AI confirms this trajectory, with projections showing that it will soon cost billions of dollars to train or run AI programs.
We are in the midst of an AI revolution where organizations are seeking to leverage data for business transformation and harness generative AI and foundation models to boost productivity, innovate, enhance customer experiences, and gain a competitive edge. Watsonx.data on AWS: Imagine having the power of data at your fingertips.
By 2011, AIresearchers had discovered NVIDIA GPUs and their ability to handle deep learning’s immense processing needs. Researchers at Google, Stanford and New York University began using NVIDIA GPUs to accelerate AIdevelopment, achieving performance that previously required supercomputers.
AI improves video and audio quality and adds unique effects to make virtual interactions smoother and collaboration more efficient. In 2016, NVIDIA hand-delivered to OpenAI the first NVIDIA DGX AI supercomputer — the engine behind the LLM breakthrough powering ChatGPT.
The Major Hidden Change It was 2016. There wasn't any significant AI product launch that year, but AI was gradually capturing the world's interest. Here's the paradox, though: that same year saw the publication of one of the most impactful AIresearch papers ever—" Attention Is All You Need."
Google DeepMind Google DeepMind is another tech giant that is making waves in the world of Generative AI. Founded in 2010, DeepMind was acquired by Google in 2014 and has since become one of the most respected AIresearch companies in the world.
In the News Next DeepMind's Algorithm To Eclipse ChatGPT IN 2016, an AI program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. Defense Department has worked for over a decade to ensure AI's responsible use. Powered by pluto.fi
Here, we explore key milestones in AI's journey, examining its technological breakthroughs and growing impact on the world. 1956 – The Inception of AI The journey began in 1956 when the Dartmouth Conference marked the official birth of AI.
A new database includes empirical examples of evidence for different AI behaviors, such as power-seeking, goal misspecification, and deception. Jeffrey wrote a blogpost explaining why he believes that strict regulation of AIdevelopment is plausible without disrupting progress in other areas of society.
We sometimes develop technology without fully understanding its mechanisms of action, e.g. in medicine, and so proceed cautiously. See also: Full wiki page on the catastrophic tools argument Powerful black boxes A volunteer and a nurse in a Phase 1 clinical trial.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content