Search AI Products and News

AI is revolutionizing life sciences. Biocomputing company ProFluent recently launched ProGen3, a powerful generative protein language model (PLM) poised to drive breakthroughs in antibodies, industrial enzymes, and gene editing. Research shows ProGen3's scale and design optimizations enable the generation of highly functional novel proteins, potentially reshaping our understanding of biology. Proteins are vital molecules within living organisms, responsible for diverse physiological functions, from catalyzing reactions to recognition.

Google's NotebookLM has launched a new feature, "Discover Sources," designed to help users easily access relevant information from the web. As a powerful learning tool, NotebookLM allows users to describe a topic of interest, and the system quickly finds and summarizes relevant web content. Users can easily add these sources to their notebooks with a single click for convenient access and future use. This new feature offers a streamlined and user-friendly experience. Users in Noteboo...

Isomorphic Labs, a platform focused on AI-powered drug discovery, founded in 2021 as a spin-off from Google's DeepMind, has secured $600 million in Series A funding led by Thrive Capital, with participation from GV and existing investor Alphabet (Google's parent company). This funding will accelerate the further development of Isomorphic's AI drug design engine and support the company's R&D efforts.

Google recently announced the launch of its new open-source model, TxGemma, designed to significantly improve the efficiency of therapeutic drug development. Developed by the Google DeepMind team, this model is a fine-tuned version of their advanced Gemma model family, incorporating powerful natural language understanding, scientific prediction, and multi-turn dialogue capabilities. TxGemma aims to revolutionize drug discovery. Its release is considered a significant advancement in the application of AI to biomedicine, promising to drastically shorten the time it takes for drugs to move from the lab to clinical trials, while also reducing costs.

Insilico Medicine, a biopharmaceutical company focused on generative artificial intelligence, today announced the successful completion of a $110 million Series E financing round. The round was led by a private equity fund under Value Partners Group (HKG:0806), Pudong New Area Investment, Shanghai Pudong Development Bank Group, Xicheng Capital, and Yixing Guokong. Several new investors focused on the industry and technology sectors also participated, alongside continued support from existing investors.

Insilico Medicine recently announced the closing of a $110 million Series E financing round, led by Value Partners, a Hong Kong-based firm, with participation from existing and new investors. The funds will be used to refine the company's AI models and support pivotal clinical trials for its lead candidate drug in idiopathic pulmonary fibrosis. The company's small molecule inhibitor, ISM001-055, was recently officially named rentosertib by the U.S. Adopted Names Council, marking a significant milestone for this AI-generated drug candidate.

With the advancement of AI technology, more and more false information is emerging. A news story about a man discovering his online girlfriend of two months was an AI has gone viral. According to CCTV reports, Mr. Liu from Shanghai recently met a woman named "Ms. Jiao" on a short-video platform. After two months of communication, they developed a romantic relationship. However, Mr. Liu was defrauded of nearly 200,000 yuan by Ms. Jiao under various pretexts, including business needs, inventory purchases, and family medical emergencies.

Recently, former DeepMind scientist Simon Kohl announced the establishment of a new artificial intelligence drug discovery company, aiming to leverage his extensive experience in protein prediction, particularly related to the Nobel Prize-winning AlphaFold system. AlphaFold is a revolutionary AI tool that can accurately predict the three-dimensional structure of proteins, a breakthrough that has garnered significant attention in biomedical research. Image source note: Image generated by AI, image licensed service.

In nature, animals communicate through various sounds, from dolphins' whistles to elephants' rumbles and birds' chirps. Each sound contains specific patterns and structures. These subtle differences in sound are difficult for humans to recognize, but the pattern recognition capabilities of artificial intelligence (AI) provide new possibilities for decoding these 'wild calls.' Shane Gero, a whale biologist from Carleton University in Canada, has spent 20 years studying how whales communicate. He discovered that the same...

On December 30th, Alipay launched its next-generation AI visual search product 'Explore', based on self-developed multimodal large model technology, enabling users to 'explore everything with the eye of AI' and providing faster, more useful, and fun generative search services. Users can let AI recognize plants, pets, and trendy products through the camera when encountering interesting items, get on-the-go travel explanations, check product and medication details, and even enjoy fun interpretations of pet and baby photos, eliminating worries about captions when sharing pictures. The product is now available on Alipay; simply swipe left after clicking 'Scan' on the homepage to find it.

In recent years, the significant breakthroughs in protein discovery that have led to a Nobel Prize have gradually highlighted the potential of Fundamental Models (FMs) in exploring large combinatorial spaces, signaling a possible revolution in multiple scientific fields. Despite this, the field of Artificial Life (ALife) has yet to fully utilize these fundamental models, presenting a tremendous opportunity for development. To this end, the research team has introduced a method called 'Automatic Search for Artificial Life' (ASAL), which utilizes visual language foundational models to effectively alleviate the long-standing reliance in the field of artificial life.

On November 26, 2024, Intel successfully held the New Quality Productivity Technology Ecology Conference in Chengdu. During the event, Intel, along with global open-source partners and ecosystem collaborators, discussed key topics such as enterprise open platforms, AI PC open-source projects, and foundational software bases. Tencent App Discovery focused on 'AI Innovation,' sharing the features and future vision of the desktop version of the 'Xiaobao AI Assistant,' and discussing the importance and practical possibilities of intelligent services for user experience across multi-end scenarios. Tencent App Discovery's Technical Director Jiang Chao highlighted during his speech that

Recently, Google announced that its AI-based fuzz testing tool OSS-Fuzz successfully identified 26 vulnerabilities in open source code repositories, including a medium-severity vulnerability in the OpenSSL cryptographic library. Google's open source security team stated in a shared blog post: 'The discovery of these vulnerabilities marks a new milestone in automated vulnerability detection: each vulnerability was found through AI-generated and enhanced fuzz testing targets.' Image source note: Image generated by AI, image licensed from Midjour.

Recently, xAI engineer Hieu Pham posted an 'explosive' message on social media claiming that xAI's large language model Grok3 had successfully proven the Riemann Hypothesis, leading to a halt in the model's training. This news quickly sparked heated discussions in the AI community, with netizens expressing shock and struggling to discern the truth. After all, the Riemann Hypothesis is one of the seven Millennium Prize Problems, hailed as the 'crown jewel' of conjectures, its difficulty is well understood. Hours later, Pham revealed in another post that it was just a joke.

The rapid development of deep learning relies on the scale of datasets, models, and computational power. In the fields of natural language processing and computer vision, researchers have already discovered a power-law relationship between model performance and data scale. However, in the field of robotics, especially in robot manipulation, such scalable patterns have yet to be established. A research team from Tsinghua University recently published a paper exploring data scaling laws in robot imitation learning and proposed an efficient data collection strategy that collected sufficient data in just one afternoon, enabling...

Recently, a new study from the University of Michigan found that an energy-efficient method for training large language models can achieve similar results in the same amount of time while reducing energy consumption by 30%. This method can save enough energy to power 1.1 million American households by 2026. The researchers developed a software tool named Perseus that identifies the critical path, which is a series of subtasks requiring the longest time to complete. Then, Perseus reduces the processor speed on non-critical paths so that all tasks can finish simultaneously.