AI Daily - 2025-06-22(Evening)

Keywords：SurgeAI, ScaleAI, MiniMax-M1, Claude 3.7 Sonnet, model collapse, AI data services industry, long-context model performance, Mixture of Experts (MoE) design, model self-training, increased error rate

🔥 Highlights

SurgeAI’s revenue surpasses ScaleAI, sparking industry attention: SurgeAI, a company founded in 2019 without venture capital funding, reportedly has annual revenue exceeding $1 billion, surpassing its competitor ScaleAI (projected 2024 revenue of $870 million). SurgeAI has consistently been profitable, and its model performance in data annotation and post-training data outperforms ScaleAI, especially in certain specific domains. This news has sparked discussions about the competitive landscape and business models in the AI data services industry, particularly the potential of bootstrapped B2B startups. (Source: teortaxesTex, teortaxesTex, teortaxesTex)

MiniMax releases MiniMax-M1 model, challenging long-text processing and reasoning: MiniMax has launched its open long-text model, MiniMax-M1, which performs exceptionally well in the Fiction.liveBench long-text test, with performance comparable to Gemini 2.5 Pro-preview 05-06. MiniMax-M1 features a Mixture of Experts (MoE) design, ultra-fast Lightning Attention mechanism, supports a 1M token context window, and uses the new CISPO algorithm for reinforcement learning, with computational costs reportedly 4 times lower than DeepSeek-R1. Its training took only 3 weeks and 512 high-end GPUs. This signifies the immense potential of the original linear attention mechanism based on the Shazeer architecture for long-text understanding and efficient reasoning. (Source: teortaxesTex, TheTuringPost)

Jeff Clune reflects on early OpenAI: Felt like discovering aliens and helping them arrive: AI researcher Jeff Clune, in an interview, described his early experiences at OpenAI, saying it felt like astronomers discovering aliens heading towards Earth, and the team was not just observing but also providing information to these “aliens” (a metaphor for AI), helping them develop. This metaphor vividly reveals the awe, excitement, and complex feelings of early AI researchers facing the rapidly developing potential of artificial general intelligence, as well as hinting at the uncertainty of AI’s development direction and impact. (Source: Reddit r/artificial)

Jeff Clune says early OpenAI felt like being an astronomer and spotting aliens on their way to Earth: "We weren't just watching the aliens coming, we were also giving them information. We were helping them come."

“Model collapse” phenomenon sparks ongoing discussion, researchers hold differing views: Discussions continue regarding whether continuously retraining Large Language Models (LLMs) on their own generated content leads to “model collapse” (i.e., model outputs become repetitive, superficial, and error-prone). Critics argue that this self-training exposes models to increasingly narrow and biased language segments (their own high-probability clichés), leading to the loss of rare vocabulary, complex syntax, and subtle meanings. However, some researchers believe these concerns might be “naive” ideas from 2023, suggesting that solutions already exist or are emerging. (Source: teortaxesTex)

🎯 Developments

Essential AI founder discusses company vision, aims to be the “DeepSeek of the West”: Ashish Vaswani, the first author of the “Attention is All You Need” paper and founder of Essential AI, stated in an interview with The Economic Times that he hopes his company can become the “DeepSeek of the West.” This indicates that DeepSeek’s achievements in the AI field have gained recognition from top international researchers and may become a target for other AI companies to pursue and emulate, while also reflecting the coexisting global competition and cooperation in the AI landscape. (Source: teortaxesTex)

Huawei MateBook X90 chip confirmed to use SMIC’s N+2 process: After a teardown of the Huawei MateBook X90, TechInsights found that its chip, model H252G63, M0=40nm, uses SMIC’s N+2 process, not the rumored 5nm or N+3 node. This finding clarifies previous speculations about Huawei’s chip manufacturing process and provides new information for assessing China’s semiconductor industry’s independent R&D capabilities. Nevertheless, some users believe that if its multi-core benchmark performance (approximately equivalent to M3) is accurate, the MateBook Fold’s value proposition remains attractive. (Source: teortaxesTex, teortaxesTex)

Google Gemini API receives major cache infrastructure update, boosting video and PDF processing speeds: Google announced a significant update to its Gemini API caching infrastructure. Post-update, the Time to First Byte (TTFT) for cached videos is 3x faster, and for PDFs, it’s 4x faster. Additionally, the speed gap between implicit and explicit caching has been reduced, and ongoing optimizations are being made for large audio files. These improvements aim to enhance the Gemini API’s responsiveness and efficiency when processing multimedia content. (Source: jeremyphoward, demishassabis)

Arcee launches its first foundation model, AFM-4.5B-Preview: Arcee has released a preview version of its first foundation model, AFM-4.5B-Preview. The model, with fewer than 10B parameters, was developed over 5 months and aims to meet customer demand for better small foundation models. This reflects a trend in the AI field where, alongside the pursuit of larger parameter models, there is also a focus on the R&D and application of efficient, small models for specific needs. (Source: TheZachMueller)

PokéChamp: LLM achieves expert-level performance in competitive Pokémon battles: A new paper introduces PokéChamp, a minimax LLM agent, which achieved an Elo rating placing it in the top 10%-30% of human players in competitive battles on the Pokémon Showdown platform, without any Pokémon-specific training. This research demonstrates the potential of Large Language Models in complex strategy games, achieving high competitive levels through general reasoning abilities even without domain-specific data. The code has been open-sourced on GitHub. (Source: jpt401)

New open-source music generation model SongBloom released, supports vocals: An open-source music generation model named SongBloom has been launched, capable of generating music that includes vocals. This development provides new AI tools for music creation, lowering the barrier to music production and potentially driving the generation of personalized music content. (Source: DhruvBatraDB)

Apple publishes paper discussing Tokenizer evaluation beyond text compression: Apple’s Machine Learning Research team has published a paper titled “Beyond Text Compression: Evaluating Tokenizers Across Scales.” The research focuses on how to more comprehensively evaluate the performance of Tokenizers under different scales and conditions, which is crucial for Large Language Models to understand and generate text. (Source: dl_weekly)

WINGS: Dual-learner architecture to prevent pure-text forgetting in Multimodal Large Models: An AI paper introduces a dual-learner architecture called WINGS, designed to prevent Multimodal Large Language Models (MLLMs) from forgetting pure-text information during processing. This architecture likely uses specialized modules or mechanisms to maintain and strengthen the model’s understanding and memory of text semantics, which is significant for improving MLLM performance in cross-modal tasks. (Source: Reddit r/ArtificialInteligence)

Deep learning used for de novo design of high-affinity protein-binding macrocycles: A study demonstrates the use of deep learning techniques to accurately design high-affinity protein-binding macrocycles de novo. This indicates AI’s immense potential in drug discovery and molecular design, capable of accelerating the new drug development process and designing macromolecular drugs with specific binding properties. (Source: Reddit r/ArtificialInteligence)

🔥 Highlights

🎯 Developments

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