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million public models across various sectors and serves seven million users, proposes an AI Action Plan centred on three interconnected pillars: Hugging Face stresses the importance of strengthening open-source AI ecosystems. The company prioritises efficient and reliable adoption of AI. Hugging Face, which hosts over 1.5
They are used in medicine, research, business, law and a variety of other areas. theconversation.com Robotics AI Robotics Market to Reach US$39 Billion by 2028 The AI Robotics market is expected to rise from US$11.89 billion by 2028, with a CAGR of 27.10% over the forecast period. billion in 2023 to US$39.43
Looking even further ahead, NVIDIA teased the Feynman architecture (arriving in 2028), which will take things up another notch with photonics-enhanced designs. 📶AI Eval of the Weeek ( Courtesy of LayerLens ) Mistral Small 3.1 Blackwell’s output. Rubin Ultra scales that to 576 GPUs per rack.
What happened this week in AI by Louie The ongoing race between open and closed-source AI has been a key theme of debate for some time, as has the increasing concentration of AIresearch and investment into transformer-based models such as LLMs. This would be its 5th generation AI training cluster.
Dubbed ‘The AI Alliance,’ the group — which also includes academic institutions — was formed to promote an open, voluntary sharing of AIresearch in an effort to move the tech forward in a cooperative way.
Global Market and Impact so far ReportLinker, a market research provider, predicts that the machine learning as a service market will grow to $36.2 billion globally by 2028, expanding at an annual growth rate (CAGR) of 31.6% between 2018 and 2028. The post What is Machine Learning as a Service?
Morgan Stanley categorizes these industries into three tiers: Tier 1 includes sectors such as forestry, farming, food preparation, and personal care, where adoption is expected to begin around 2028. Sign up for more AIresearch updates. Enjoy this article?
(Click to expand) How "digital neuroscience" could help I’ve argued that it could be inherently difficult to measure whether AI systems are safe, for reasons such as: AI systems that are not deceptive probably look like AI systems that are so good at deception that they hide all evidence of it , in any way we can easily measure.
(As a bonus, it doesn’t seem out of the question that transformative AI will be developed extremely soon - 10 years from now or faster. ↩ E.g., Ajeya Cotra gives a 15% probability of transformative AI by 2030; eyeballing figure 1 from this chart on expert surveys implies a >10% chance by 2028.
In 2023, that number increased by 593,000, and will rise yet again by another 718,000 by 2028, according to Statista. forbes.com Research A guide to AI for cancer researchersAI has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers.
Data is the fuel of AI applications, but the magnitude and scale of enterprise data often make it too expensive and time-consuming to use effectively. Because of the extremely high volume and various data types, most generative AI applications use a fraction of the total amount of data being stored and generated.
The World Economic Forum and VivaTech launched the European Centre for AI Excellence (CAIE) to develop responsible AI solutions. theguardian.com AI at the Brink: Preventing the Subversion of Democracy The year is 2028. techpolicy.press Robotics Boston Dynamics Led a Robot Revolution. You can also subscribe via email.
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