Recently, Meta AI open-sourced a foundational multimodal language model named SPIRIT LM, which can freely mix text and speech, opening new possibilities for multimodal tasks involving audio and text. SPIRIT LM is based on a pre-trained text language model with 7 billion parameters, which has been continuously trained on text and speech units, expanding into the speech modality. It can understand and generate text like a large text model, while also being capable of understanding and generating speech, and even mixing text and speech to create various forms of expression.

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.

In today's technology landscape, CLIP (Contrastive Language-Image Pre-training) is an important multimodal foundational model. It combines visual signals and text signals into a shared feature space using contrastive learning loss on a large-scale dataset of image-text pairs. As a retriever, CLIP supports various tasks such as zero-shot classification, detection, segmentation, and image-text retrieval. Meanwhile, as a feature extractor, it performs well in nearly all...

In the application of artificial intelligence, achieving real-time interaction with AI has always been a significant challenge for developers and researchers. Among these challenges, integrating multimodal information (such as text, images, and audio) to form a coherent dialogue system is particularly complex. Although advanced large language models like GPT-4 have made some progress, many AI systems still struggle with achieving smoothness in real-time conversations, context awareness, and multimodal understanding, which limits their effectiveness in practical applications.

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 startup in Seattle named Moondream launched a compact visual language model called moondream2. Despite its small size, the model has shown outstanding performance in various benchmark tests and has garnered significant attention. As an open-source model, moondream2 is expected to enable local image recognition capabilities on smartphones. Moondream2 was officially released in March and can process both text and image inputs, possessing functions such as answering questions, text extraction (OCR), object counting, and item classification.

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:’