At the main forum of the 2024 Inclusion Bund Conference, Michael Jordan emphasized three key points missing from the current discussions on artificial intelligence: collectivity, uncertainty, and incentive mechanisms. He pointed out that AI systems need to form collaborative collectives and introduce an economic perspective on incentives to build collaborative systems. He noted that AI performs inadequately when facing uncertainty and that humanity's ability to cope with uncertainty through collective collaboration is worth emulating. To enhance the collective collaboration capability of AI systems, Michael Jordan suggested constructing a 'three-layer data market' model.

Researchers at Binghamton University in New York, led by Ahmed Abdeen Hammed, have developed a machine learning algorithm called xFakeSci that can identify fabricated academic papers with up to 94% accuracy. Hammed and his team created 50 fake papers on topics including Alzheimer's disease, cancer, and depression, and conducted comparative analyses with genuine papers. They found that the number of bigrams in the fake papers was significantly lower than in the real papers, and the relevance of the content was much stronger. Hammed plans to expand the algorithm to more fields in response to A

Sports technology startup SportAI has completed a $1.8 million seed funding round aimed at leveraging artificial intelligence to enhance athlete performance. Its system focuses on coaching, commentary, and analysis of athletic techniques through machine learning, computer vision, and biometrics, primarily serving coaches, training facilities, media broadcasts, and sports brands. SportAI's cloud platform can analyze athletes' technical performance in real-time, providing personalized feedback to help improve levels in sports such as golf and tennis. By comparing with a vast amount of athlete data, the system achieves automation and data-driven insights.

Reddit, known for its vast user community and rich data resources, has opened up new revenue streams through data licensing. The platform hosts over 100,000 active communities, attracting 76 million users daily and generating a massive amount of data that includes real-time discussions, opinions, and interactions, which is highly valuable for AI and machine learning model development. With the rapid advancement of AI technology, Reddit's data has become a key resource for training AI models. In 2023, Reddit launched its data licensing program, which is expected to grow significantly in 2024.

Noetik, a biotechnology company focused on artificial intelligence, announced that it has successfully completed a $40 million Series A funding round. The round was led by Polaris Partners, with participation from new investors including Khosla Ventures, Wittington Ventures, Breakout Ventures, as well as existing investors DCVC, Zetta Venture Partners, and Catalio Capital Management.

Geekbench AI is a brand new cross-platform tool designed to evaluate device performance under AI-intensive workloads. It tests the CPU, GPU, and NPU to determine the device's ability to handle machine learning applications. It supports various AI frameworks such as ONNX, CoreML, TensorFlow Lite, and OpenVINO, and evaluates performance from both speed and accuracy dimensions, providing scores in full precision, half precision, and quantized precision. Additionally, Geekbench AI's testing results...

Geekbench AI is a new cross-platform tool designed to evaluate the performance of devices under compute-intensive workloads, with a particular focus on machine learning tasks. It provides full precision, half precision, and quantized scores by measuring the performance of CPUs, GPUs, and NPUs, and includes accuracy assessments. It supports various machine learning frameworks, including ONNX, CoreML, TensorFlow Lite, and OpenVINO. This tool is available on Windows, macOS, Linux, Android, and iOS.

Sakana AI has announced the launch of the 'AI Scientist' system, a comprehensive AI platform capable of independently conducting scientific research, from concept generation, coding, experiment execution, result summarization to paper writing, including peer review. Its design aims to mimic the human scientific research process. The system has demonstrated innovative contributions in several subfields of machine learning, such as diffusion models, transformers, and deep understanding, showcasing high computational efficiency and low costs (approximately $15 per paper). Although the initial version of the system may have some limitations.

Hugging Face has acquired XetHub to accelerate the process of AI democratization. Founded by former Apple engineers, XetHub focuses on simplifying the building of machine learning and AI applications. The team secured $7.5 million in funding from Madrona Ventures in 2021 to accelerate their development. This acquisition signifies Hugging Face, an influential platform in the AI development space, further expanding its technological capabilities and talent pool. The inclusion of XetHub reflects the industry's growth.

Reddit has successfully acquired cognitive processing technology startup Memorable AI, marking its first major move since going public. Memorable AI leverages machine learning and generative AI to analyze human cognitive responses to advertisements, optimizing ad targeting and creativity to improve advertising effectiveness. The company's founders will join Reddit's leadership team, bringing a deep integration of cognitive science and AI technology. This acquisition will enable Reddit to offer more personalized and effective advertising solutions.

Recently, Google has made a remarkable breakthrough in the field of weather forecasting. They have developed a new atmospheric circulation model called NeuralGCM, which boasts a computational efficiency that is 100,000 times higher than traditional physical models, equivalent to the progress made in high-performance computing over the past 25 years.By combining machine learning with physical modeling, NeuralGCM performs both rapidly and accurately in simulating Earth's atmosphere. The results of

The research team at UC Berkeley developed a language model system that approaches human-level prediction capabilities. The system automates key prediction processes using large-scale data and the rapid processing abilities of language models. The study shows an average Brier score of 0.179, indicating potential for increased prediction accuracy and efficiency. This research provides strong evidence for integrating language models into the prediction field.

Scientists at Dalhousie University have utilized artificial intelligence technology to decode chicken language, revealing that chicken calls are not random sounds, but rather a complex communication system. By analyzing audio data and employing natural language processing techniques, researchers are able to identify the chickens' emotions and non-verbal cues, leading to a deeper understanding of how these animals communicate. The findings contribute to improving poultry business practices, enhancing the living conditions and health of chickens, and have practical implications for agriculture.

Apple, in collaboration with Cornell University, has released the open source multimodal machine learning model Ferret. Ferret is a system that can reference and locate any point in an image at any precision using any shape of area. It can identify elements within images and respond as part of the query. This open source release demonstrates Apple's commitment to impactful AI research and its willingness to collaborate more openly in AI work.

Ant Group has open-sourced the full-chain causal learning system OpenASCE at NeurIPS. OpenASCE supports distributed causal discovery and causal effect estimation. OpenASCE is widely used in credit risk control, marketing optimization, and recommendation scenarios. OpenASCE includes over 20 industrial-grade causal learning algorithms and lowers the application threshold for causal technologies.

ByteDance recently showcased its technology for optimizing the Linux kernel using AI and machine learning at the Linux Plumbers Conference. Through AI, ByteDance successfully increased the efficiency and performance of the Linux kernel across various hardware, reducing memory usage by 30%. The demonstration highlighted the universality of AI in kernel optimization, lowering development difficulty compared to hardware-specific optimizations. AI optimization also improved network latency by 12%, balanced CPU usage, utilized cache more effectively, and has potential.

An Australian research team has successfully shortened the manufacturing cycle of perovskite solar cells using AI technology, breaking the traditional manufacturing time constraints. Perovskite solar cells are low-cost and stable, with the new manufacturing method achieving a power conversion efficiency of 16.9%, setting the best record without human intervention. The research team has successfully predicted and manufactured thousands of solar cells using machine learning models, demonstrating commercial application potential. Scientists stated that this technology could be used to test and prototype other types of solar cells and are willing to collaborate further with industry partners.

Recently, MLPerf has added tests for training large language models and generating images based on text. Nvidia, Intel, and Google have all invested in large systems to participate in the new benchmark tests. Nvidia's 10,000 GPU system completed GPT-3 training in the shortest time. Intel's Gaudi 2 has significantly improved its 8-bit floating-point computing performance but still lags behind Nvidia's system. Nvidia and Microsoft's systems based on tens of thousands of GPUs are engaged in fierce competition for GPT-3 training. Training generative AI systems requires immense computing power, and companies are competing vigorously.

In recent years, artificial intelligence has been widely applied in the scientific field, but caution is needed to avoid misleading or erroneous results. A new statistical technique called PPI helps researchers safely use machine learning models to validate scientific hypotheses. The PPI technique corrects outputs from large generalized models to fit specific scientific problems, avoiding biases in machine learning. This technology can be applied across various domains, such as protein structure analysis and estimating deforestation in the Amazon rainforest.

The first CPAL Academic Conference awarded the Rising Star Prize, exploring minimal learning and the frontier of AI. Sixteen scholars received the Rising Star Prize, encouraging PhD students, postdoctoral researchers, junior faculty, and industry researchers. The research topics included optimization problems, graph neural networks, gradient noise attraction, heuristic methods in machine learning, and high-dimensional data analysis. CPAL aims to explore the application of minimal, low-dimensional structures in emerging fields such as smart hardware and interdisciplinary sciences. The academic conference provided a platform for researchers in the fields of science and engineering to exchange ideas and collaborate.