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.

Is AI actually starting to ‘get smarter’?! The latest research from MIT indicates that the internal structure of large language models (LLMs) astonishingly resembles that of the human brain! This study utilized sparse autoencoder techniques to conduct an in-depth analysis of the activation space of LLMs, revealing three hierarchical structural features that are truly remarkable: first, at the microscopic level, researchers discovered the existence of structures akin to ‘crystals’. The surfaces of these ‘crystals’ are composed of parallelograms or trapezoids, similar to familiar lexical analogies such as ‘man:’

In the world of artificial intelligence, the reasoning capabilities of machine learning models, particularly large language models (LLMs), have been a focal point for scientists. Recently, Apple's AI research team published a paper titled 'Understanding the Limitations of Large Language Models in Mathematical Reasoning,' shedding light on these models' limitations when addressing logical problems. In the paper, researchers demonstrate this through a simple mathematical question. They first posed a question about Oliver picking kiwis: as follows: Oliver picked 44 on Friday.

Biotechnology company Chai Discovery has received nearly $30 million in investment from Thrive Capital and OpenAI within six months of its founding. The company focuses on the application of biotechnology in drug development, developing innovative technologies that can predict biochemical molecular structures and reprogram molecular interactions, aiming to transform biology into 'engineering.' Chai Discovery's goal is to accelerate and optimize the drug development process by enhancing drug molecule design through technologies such as artificial intelligence and machine learning.

MIT research reveals that AI, particularly chatbots, can implant "false memories" in the human brain. Experiments show that AI can lead volunteers to develop persistent incorrect memories through questioning. Individuals interested in but unfamiliar with AI are more susceptible to this influence. This finding poses challenges for AI applications in law, healthcare, and education, emphasizing the need to ensure AI acts as a tool for protecting and....

KAN2.0, an upgraded version of the neural network architecture, aims to deepen the integration of AI and classical physics. Researchers can customize the model to incorporate expertise for discovering key concepts in physical systems, such as the Lagrangian. The new framework enhances the model's expressiveness, practicality, and interpretability through three main features: MultKAN, kanpiler, and Tree Converter, addressing compatibility issues between AI and science. KAN2.0 excels in feature recognition, revealing module structures, and discovering symbolic formulas, particularly in fields that are difficult to represent with symbolic equations, such as chemistry.