Physicists debut a new method to detect gravitational waves with unprecedented precision, providing insights into black hole mergers.
A team of researchers from Peking University and the Eastern Institute of Technology (EIT) in China has developed a new framework to train machine learning models with prior knowledge, such as the laws of physics or mathematical logic, alongside data.
Chinese researchers are on the brink of pioneering a groundbreaking approach to developing ‘AI scientists capable of conducting experiments and solving scientific problems.
Recent advances in deep learning models have revolutionized scientific research, but current models still struggle to simulate real-world physics interactions accurately.
Erasing key information during training allows machine learning models to learn new languages faster and more easily.
“Without a fundamental understanding of the world, a model is essentially an animation rather than a simulation,” said Chen Yuntian, study author and a professor at the Eastern Institute of Technology (EIT).
Deep learning models are generally trained using data and not prior knowledge, which can include things such as the laws of physics or mathematical logic, according to the paper.
But the scientists from Peking University and EIT wrote that when training the models, prior knowledge could be used alongside data to make them more accurate, creating “informed machine learning” models capable of incorporating this knowledge into their output.
Inflection AI has recently launched Inflection-2.5, a model that competes with all the world’s leading LLMs, including GPT-4 and Gemini. Inflection-2.5 approaches the performance level of GPT-4 but utilizes only 40% of the computing resources for training.
Inflection-2.5 is available to all Pi’s users today, at pi.ai, on iOS, on Android, and via the new desktop app.
Inflection AI’s previous model, Inflection-1, utilized about 4% of the training FLOPs of GPT-4 and exhibited an average performance of around 72% compared to GPT-4 across various IQ-oriented tasks.
Techstars has currently invested about two-thirds of the fund, Gavet recently told TechCrunch, adding that the bank is “an amazing partner” and “very active in our program.”
However, J.P. Morgan has yet to tell Techstars whether it will renew the partnership for an Advancing Cities 2 Fund once the initial contract expires in December, sources say. That decision was supposed to be handed down last summer so that Techstars could start fundraising and begin deploying capital in 2025.
This means the fate of the Advancing Cities programs — and some of the around 20 people who work at Techstars in this program — is up in the air.
TikTok faces an uncertain fate in the U.S. once again.
After a surprise flurry of activity in the House this week, TikTok is the target of a new government push to separate the company from its Chinese ownership or force it out of the country.
TikTok is based in Los Angeles and Singapore, but is owned by Chinese tech giant ByteDance. That relationship that has raised eyebrows among U.S. officials, who warn that the app could be leveraged to further the interests of an adversary.
Science unites against artificial intelligence bio-weapon risks with new policies, emphasizing responsible research and ethical practices.
Researchers emulate snail-like movement in robots, a significant leap in bio-inspired engineering that could shape the future of robotics.