The global artificial intelligence market is expected to top US$40 billion in 2020, with a compound annual growth rate (CAGR) of 43.39 percent, according to Market Insight Reports. AI’s remarkable ...
Just hours after making waves and triggering a backlash on social media, Genderify — an AI-powered tool designed to identify a person’s gender by analyzing their name, username or email address — has ...
The quality and fluency of AI bots’ natural language generation are unquestionable, but how well can such agents mimic other human behaviours? Researchers and practitioners have long considered the ...
The concept of AI self-improvement has been a hot topic in recent research circles, with a flurry of papers emerging and prominent figures like OpenAI CEO Sam Altman weighing in on the future of ...
Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting ...
Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate at the token level, generating output one word or subword at ...
DeepSeek AI has announced the release of DeepSeek-Prover-V2, a groundbreaking open-source large language model specifically designed for formal theorem proving within the Lean 4 environment. This ...
The recent and rapid development of powerful machine learning models for computer vision has boosted 2D and 3D human pose estimation performance from RGB cameras, LiDAR, and radar inputs. These ...
Speaking at the London Mathematical Society in 1947, Alan Turing seemed to anticipate the current state of machine learning research: “What we want is a machine that can learn from experience . . .
One of the intriguing puzzles within the realm of neural network generalization is a phenomenon known as “grokking.” It involves a neural network achieving perfect training accuracy but displaying ...
Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised ...
In-context learning has enabled pretrained language models to achieve impressive zero- and few-shot learning performance without parameter updates. Most conventional in-context learning techniques ...
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