Self-supervised learning reduces annotation costs by generating labels from raw data, limiting manual effort. As models scale across business units, pretraining on unlabeled datasets shortens fine-tuning cycles and frees skilled teams for higher-value work.
π¨ JUST IN: Treasury Secretary Scott Bessent CONFIRMS the $250 bill with President Trump's face on it is ALREADY in the works
"There is proposed legislation in front of the House, in front of the Senate to change the [law] so that a living person, Donald J. Trump, could be on a $250 bill."
"At Treasury, we prepare things in advance. So we have prepared in advance that if the legislation is passed, but we will stick to the law."
LLM = Smart, but works only with what it was trained on. Its knowledge is fixed after training, and when it lacks relevant information, it may generate plausible-sounding but incorrect answers ("hallucinations").
RAG = Smart + can "look things up" in real time. It retrieves specific, relevant information from an external source and combines it with the LLMβs reasoning to give more accurate, up-to-date answers. Still, if retrieval is poor or ignored, hallucinations can happen.
Agent = Smart + can "decide what to do next" to reach a goal. It reasons through problems, chooses and uses the right tools (like web search or APIs), and can optionally maintain memory of its steps. This enables it to handle complex tasks that require multiple actions.
Chinese researchers just dropped ASI-Evolve. An AI agent researcher that reads papers, designs experiments, runs them, analyzes results, and keeps improving itself.
100% open source.
GPT Image 2 is now live in Lovart. The quality is insane. But one brief β full campaign unlocks many use cases I turned one prompt into a Bloomberg-style AI Atlas carousel
https://t.co/PzQJSDbCOc
GPT Image 2 is now live in Lovart.
The quality is insane.
But one brief β full campaign unlocks many use cases
I turned one prompt into a Bloomberg-style AI Atlas carousel
Hereβs how.
I'm late to the game here, but WOW Code Interpreter is cool! Especially for brainstorming algorithms, it can just handle all the details and visualization
Combee: Scaling Prompt Learning for Self-Improving Agents Current prompt learning methods degrade when running many agents in parallel. Combee fixes this with parallel scans and an augmented shuffle mechanism, scaling to 80+ agents with 17Γ speedup.
Microsoft just released Phi-Ground-Any on Hugging Face A 4B parameter vision model for GUI grounding that achieves SOTA results on ScreenSpot-pro and UI-Vision, enabling AI agents to precisely click screen elements.
@IAmSophiaNelson@SpecialOlympics Made a token and we can donate all the fees to special olympics!
Fees are already directed to them to support them @IAmSophiaNelson
p9JLQfRsdiTmi2zDTMpAoKfwNzB6kiA6JfhtEmApump
Hunter Peterson wants Spirit Airlines to function like the Green Bay Packers. Even if he can't pull it off, his strategy is one that every leader should pay attention to https://t.co/dVqk28yTBp
This is a good way for the Goverment to step in and really protect the people from Ai taking over jobs that are feeding their famlies but on the other hand if we learn to work with Ai it could earn yourself a way better living