pubmed.ncbi.nlm.nih.gov/38454007
Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to many different sectors. Within healthcare there has been in…
hbr.org/2023/07/how-generative-ai-can-augment-human-creativity
There is tremendous apprehension about the potential of generative AI—technologies that can create new content such as text, images, and video—to replace people in many jobs. But one of the biggest opportunities generative AI offers is to augment human creativ…
doi.org/10.1038%2Fs41586-021-04357-7
Using the game Gran Turismo, an agent was trained with a combination of deep reinforcement learning algorithms and specialized training scenarios, demonstrating success against championship-level human racers.
web.archive.org/web/20241005165649/https://venturebeat.com/ai/why-jensen-huang-and-marc-benioff-see-gigantic-opportunity-for-agentic-ai
Already, progress in agentic AI is “spectacular and surprising,” moving faster and faster and getting into the “flywheel zone," Huang says.
pubmed.ncbi.nlm.nih.gov/35140384
Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres t…
ui.adsabs.harvard.edu/abs/2022Natur.602..223W
Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres t…
venturebeat.com/ai/microsofts-new-rstar-math-technique-upgrades-small-models-to-outperform-openais-o1-preview-at-math-problems
Phi-4 and an rStar-Math paper suggest that compact, specialized models can provide powerful alternatives to the industry’s largest systems.
arxiv.org/abs/2404.03592
Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing represe…
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