Popular | Synced

AI Technology & Industry Review
56 Temperance St, #700 Toronto, ON M5H 3V5
By combining State-Space Models (SSMs) for efficient long-range dependency modeling with dense local attention for coherence, and using training strategies like diffusion forcing and frame local attention, researchers from Adobe Research successfully overcome the long-standing challenge of long-term memory in video generation.
A newly released 14-page technical paper from the team behind DeepSeek-V3, with DeepSeek CEO Wenfeng Liang as a co-author, sheds light on the “Scaling Challenges and Reflections on Hardware for AI Architectures.”
DeepSeek AI releases DeepSeek-Prover-V2, an open-source LLM for Lean 4 theorem proving. It uses recursive proof search with DeepSeek-V3 for training data and reinforcement learning, achieving top results on MiniF2F.
Kwai AI’s SRPO framework slashes LLM RL post-training steps by 90% while matching DeepSeek-R1 performance in math and code. This two-stage RL approach with history resampling overcomes GRPO limitations.
DeepSeek AI, a prominent player in the large language model arena, has recently published a research paper detailing a new technique aimed at enhancing the scalability of general reward models (GRMs) during the inference phase.
Meta AI’s recent research introduces the BLT architecture, eliminating tokenizers for improved multimodal processing, and the Large Concept Model (LCM), which operates on semantic “concepts” instead of tokens for more human-like reasoning and better cross-lingual generalization. These innovations challenge the traditional “next-token prediction” paradigm in LLMs.
Nvidia will launch Jetson Thor for humanoid robots in H1 2025, entering a growing market where Google is also active. The robotics sector is projected for substantial growth. Nvidia offers integrated hardware and software solutions. Simultaneously, China’s rapidly developing domestic humanoid robot market presents emerging competition.
In a recent technical report, OpenAI introduces Sora, a groundbreaking text-to-video model. Sora stands out for its ability to generate videos and images spanning a wide range of durations, aspect ratios, and resolutions, producing up to a minute of high-definition video content.
A Google DeepMind research team introduces a groundbreaking family of multimodal models Gemini, which showcase exceptional proficiency across image, audio, video, and text comprehension, pushing the boundaries of large-scale language modeling, image interpretation, audio processing, and video understanding.
In a new paper Llama 2: Open Foundation and Fine-Tuned Chat Model, a Meta AI research team presents and releases Llama 2 and Llama 2-Chat, the former one is a family of pretrained and fine-tuned LLMs and the later one is a fine-tuned version of Llama 2 that is optimized for dialogue, paving the way to develop more responsible LLMs.
In the new paper Language Is Not All You Need: Aligning Perception with Language Models, a Microsoft research team presents KOSMOS-1, a multimodal large language model (MLLM) that can perceive general modalities, learn in context, and follow instructions.
Turing Award winner and deep learning pioneer Geoffrey Hinton, one of the original proponents of backpropagation, has argued in recent years that backpropagation does not explain how the brain works. In his NeurIPS 2022 keynote speech, Hinton proposes a new approach to neural network learning: the Forward-Forward algorithm.
In the new paper Locating and Editing Factual Associations in GPT, a research team from MIT CSAIL, Northeastern University and Technion IIT examines how information flows during knowledge recall in large autoregressive transformers and introduces Rank-One Model Editing (ROME), a simple, zero-shot principled model editor capable of locating and editing factual associations in such models.
While today’s deep neural networks (DNNs) are driving AI’s deep-learning revolution, determining a DNN’s appropriate complexity remains challenging. If aContinue Reading
In the new paper Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks, a Microsoft research team presents BEiT-3, a general-purpose state-of-the-art multimodal foundation model for both vision and vision-language tasks that advances the big convergence of backbone architectures, pretraining tasks, and model scaling.
In the new paper YOLOv7: Trainable Bag-Of-Freebies Sets New State-Of-The-Art for Real-Time Object Detectors, an Academia Sinica research team releases YOLOv7. This latest YOLO version introduces novel “extend” and “compound scaling” methods that effectively utilize parameters and computation; and surpasses all known real-time object detectors in speed and accuracy.
In the new paper An Improved One millisecond Mobile Backbone, an Apple research team presents MobileOne, a novel mobile backbone that cuts inference time to under one millisecond on an iPhone12 and reaches 75.9 percent top-1 accuracy on ImageNet.
The BAAI Conference 2022 kicked off on May 31 in Beijing and ran through June 2. AI experts, industry leaders, young talents and international delegates joined the virtual gathering and live stream for three busy days of high-level keynotes, tech talks, parallel forums and networking.
In the new paper A Modern Self-Referential Weight Matrix That Learns to Modify Itself, a research team from The Swiss AI Lab, IDSIA, University of Lugano (USI) & SUPSI, and King Abdullah University of Science and Technology (KAUST) presents a scalable self-referential weight matrix (SRWM) that leverages outer products and the delta update rule to update and improve itself.
A DeepMind research team argues that the mathematical description of symmetries in group theory is an important foundation that determines the structure of the universe, constrains the nature of natural tasks, and consequently shapes both biological and artificial intelligence. The study proposes symmetry transformations as a fundamen