Recently, according to Bloomberg, Apple is developing a new voice assistant, Siri, which will utilize advanced large language models (LLMs) technology to achieve a more natural conversational experience. This move aims to close the gap with competitors, as products like Google's Gemini Live have demonstrated higher naturalness in conversational capabilities in recent years. According to sources, the new Siri assistant will completely replace the current Siri interface used by customers, with a planned release in 2026.

With the rapid development of artificial intelligence (AI) technologies in industrial sectors, experts indicate that high-quality data and data governance will be more important than generative technologies. By 2025, companies must focus more on scalable and flexible solutions when adopting AI, rather than solely relying on Generative AI (GenAI). Image note: The picture is generated by AI, authorized by the image service provider Midjourney, based on the analysis by Qlik, the key to fully harnessing AI's potential lies in companies' investments in high-quality, real-time data.

Microsoft has recently made significant progress in the field of enterprise AI intelligences, establishing the world's largest enterprise AI intelligent ecosystem. Since the launch of Copilot Studio, over 100,000 organizations have participated in the creation and editing of AI intelligences. This milestone signifies Microsoft's leading position in the enterprise technology sector. Microsoft executive Charles Lamanna stated that the growth rate exceeded expectations, being faster than the adoption rate of any cutting-edge technology previously released by the company.

Recently, AI large language models (LLMs) have been performing remarkably well in various tasks, effortlessly writing poetry, coding, and chatting; they seem to be capable of anything! But can you believe it? These 'genius' AIs are actually 'math novices'! They often fail dramatically when tackling simple arithmetic problems, leaving people astonished. A recent study unveiled the 'quirky' secret behind LLMs' arithmetic reasoning abilities: they rely neither on powerful algorithms nor solely on memory, but instead adopt a strategy known as 'heuristic hodgepodge'!

A recent study indicates that generative AI models, particularly large language models (LLMs), can be used to create a framework that accurately simulates human behavior across various scenarios. This finding provides a powerful new tool for social science research. The researchers initially recruited over 1,000 participants from diverse backgrounds in the United States and conducted in-depth interviews lasting up to two hours, collecting information about their life experiences, opinions, and values. Subsequently, they utilized these interview records along with a large language model to construct.

Recently, NVIDIA unveiled its brand-new Blackwell platform and showcased its initial performance in the MLPerf Training 4.1 benchmark tests. According to the test results, Blackwell achieved a doubling of performance in certain aspects compared to the previous Hopper platform, garnering widespread attention in the industry. In the MLPerf Training 4.1 benchmark tests, the Blackwell platform excels in LLM (Large Language Models).

According to the latest forecast from research firm IDC, global spending on artificial intelligence (AI) technologies is expected to reach $337 billion by 2025, and this figure is projected to double to $749 billion by 2028. As IT leaders across industries continue to drive the implementation of AI strategies, future application scenarios will be increasingly diverse. Image source credit: Image generated by AI, image provided by licensing service provider Midjourney. In line with this trend, more and more companies are starting to experiment with generative AI.

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.

The Massachusetts Institute of Technology (MIT) showcased a brand new robot training model this week that abandons the previous focus on training methods centered around specific datasets, opting instead for an approach that utilizes vast amounts of information similar to that used in training large language models (LLMs). Researchers noted that imitation learning—where agents learn by mimicking individuals performing tasks—can fail when facing minor challenges. These challenges may include varying lighting conditions, different environmental setups, or new obstacles. In such cases, the robots lack sufficient data to adapt to these changes.

This week, MIT showcased a new model for training robots, aimed at addressing the challenges faced by imitation learning when introduced to small obstacles. Researchers pointed out that imitation learning can fail in conditions such as varying lighting, different environments, or new obstacles because robots simply do not have enough data to adapt. The team sought powerful data methods, like those used in models such as GPT-4, to tackle the problem. They introduced a new architecture called the Heterogeneous Pretrained Transformer (HPT), which aggregates information from different sensors and environments.

Meta recently announced that its MobileLLM model is now open to researchers, allowing users to download and use these models for free on the Hugging Face platform, fully integrated with the Transformers library. This initiative aims to promote research and development of large language models (LLMs) on mobile devices, providing a broader range of tools and resources for developers and academia. MobileLLM represents a significant advancement for Meta in the field of mobile AI.

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:’

Recently, the Meta AI team introduced LongVU, a novel spatio-temporal adaptive compression mechanism aimed at enhancing the language understanding capabilities of long videos. Traditional multimodal large language models (MLLMs) face limitations in context length when processing long videos, and LongVU was created to address this challenge. LongVU operates primarily by filtering duplicate frames and employing inter-frame token compression techniques to efficiently utilize context length, allowing it to reduce video data while preserving visual details.

Large Language Models (LLMs) like the GPT series exhibit astonishing abilities in language understanding, reasoning, and planning, achieving human-level performance in various challenging tasks due to their vast datasets. Most research focuses on enhancing these models further by training them on larger datasets, aiming to develop more powerful foundational models. However, while training stronger foundational models is crucial, researchers believe that empowering models with the capability to continue evolving during the reasoning phase, known as AI self-evolution, is essential for the development of AI.

Recently, Google announced the open-sourcing of its text watermark tool SynthID, aimed at helping developers better identify AI-generated text. This tool is now publicly available through Google's 'Responsible AI Toolkit'. Pushmeet Kohli, Vice President of Research at Google DeepMind, stated that this technology will enable other generative AI developers to detect whether text outputs originate from their own Large Language Models (LLMs), which will help them be more responsible.

At the Honor MagicOS 9.0 launch event and developer conference on October 23, Honor CEO Zhao Ming announced the official release of the latest operating system, MagicOS 9.0. During his speech, Zhao reviewed the evolution of Honor MagicOS, which has positioned itself as an AI OS since its inception, continuously driving technological innovation.

Dow Jones, the parent company of The Wall Street Journal, and the New York Post are suing AI-driven search startup Perplexity for using their news content to train its large language model. The publications from these two news groups accuse Perplexity of copyright infringement by generating answers to people's queries using their articles, thereby diverting traffic from the publications' websites. 'This lawsuit is brought by news publishers seeking compensation as Perplexity openly plans to provide services for free.'

In the coming year of 2025, generative artificial intelligence (GenAI) is expected to reach a critical turning point, where we will gain a clearer understanding of its true potential. As analyses of its various applications across industries deepen, the GenAI that was once shrouded in noise will gradually reveal its true nature. Recently, Hieb noted that businesses investing in GenAI do not necessarily have to choose expensive and complex solutions. In fact, more and more companies are discovering that large language models (LLMs) don't always...