#test-timetraining

[ai-network]

Evolution Hints at Emerging Artificial General Intelligence

Recent developments in artificial intelligence (AI) have fueled speculation that the field may be inching toward the elusive goal of Artificial General Intelligence (AGI). This level of intelligence, which allows a machine to understand, learn, and apply reasoning across diverse domains with human-like adaptability, has long been considered a benchmark in AI research. However, researchers from the Massachusetts Institute of Technology (MIT) have introduced a new technique, termed Test-Time Training (TTT), which may represent a significant step toward AGI. The findings, published in a recent paper, showcase TTT's unexpected efficacy in enhancing AI’s abstract reasoning capabilities, a core component in AGI’s development​. White Paper Summary Original White Paper   What Makes Test-Time Training Revolutionary? Test-Time Training offers an innovative approach where AI models update their parameters dynamically during testing, allowing them to adapt to novel tasks beyond their pre-training. Traditional AI systems, even with extensive pre-training, struggle with tasks that require advanced reasoning, pattern recognition, or manipulation of unfamiliar information. TTT circumvents these limitations by enabling AI to “learn” from a minimal number of examples during inference, updating the model temporarily for that specific task. This real-time adaptability is essential for models to tackle unexpected challenges autonomously, making TTT a promising technique for AGI research​. The MIT study tested TTT on the Abstraction and Reasoning Corpus (ARC), a benchmark composed of complex reasoning tasks involving diverse visual and logical challenges. The researchers demonstrated that TTT, combined with initial fine-tuning and augmentation, led to a six-fold improvement in accuracy over traditional fine-tuned models. When applied to an 8-billion parameter language model, TTT achieved 53% accuracy on ARC’s public validation set, exceeding the performance of previous methods by nearly 25% and matching the performance level of humans on many tasks​. Pushing the Boundaries of Reasoning in AI The study’s findings suggest that achieving AGI may not solely depend on complex symbolic reasoning but could also be reached through enhanced neural network adaptations, as demonstrated by TTT. By dynamically adjusting its understanding and approach at the test phase, the AI system closely mimics human-like learning behaviors, enhancing both flexibility and accuracy. MIT’s study shows that TTT can push neural language models to excel in domains traditionally dominated by symbolic or rule-based systems. This could represent a paradigm shift in AI development strategies, bringing AGI within closer reach​. Implications and Future Directions The implications of TTT are vast. By enabling AI models to adapt dynamically during testing, this approach could revolutionize applications across sectors that demand real-time decision-making, from autonomous vehicles to healthcare diagnostics. The findings encourage a reassessment of AGI’s feasibility, as TTT shows that AI systems might achieve sophisticated problem-solving capabilities without exclusively relying on highly structured symbolic AI. Despite these advances, researchers caution that AGI remains a complex goal with many unknowns. However, the ability of AI models to adjust parameters in real-time to solve new tasks signals a promising trajectory. The breakthrough hints at an imminent era where AI can not only perform specialized tasks but adapt across domains, a hallmark of AGI. In Summary: The research from MIT showcases the potential of Test-Time Training to bring AI models closer to Artificial General Intelligence. As these adaptable and reasoning capabilities are refined, the future of AI may not be limited to predefined tasks, but open to broad, versatile applications that mimic human cognitive flexibility. Read the full article

[str9led]

Sidefx Houdini 17 Organic modeling tool test. from Werner on Vimeo.

More testing to see how the modeling tools can be improved. This time I played with organic modeling. The idea was not to create a final model, but rather a test the workflow, and showcase the problem areas in Houdini modeling. It helps to describe these problems when submitting RFE's to Sidefx.

music: Pipe Choir (P C III) - TimeTrain

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timetrain 日本語 教育 dpz 日本語学校で真剣にやってる人たちはそんじょそこらの高校生��相手にならないくらいの日本語知識がある。一方で、慣習的な用法だとほんと難しいらしいが Domino-R 「母にしかられたし、父にも殴られた」殴られてるしw こういう文章はもう日本じゃ出ないだろうな。/多分これはDだけ連体形、残りは終止形かな。 enemyoffreedom kalmalogy 教育 hidamari1993 そういやネイティブにTOEICやらせると大体800点後半あたりで落ち着くらしいから日本の普通の高校生に実際にやらせてみたら意外な結果になるかもなあ。 white_rose リスニングぼーっと聞いてたら落としそうだ。あれわかんないの英語だからだけじゃなかったんだ……。／思ってたよりレベル高かった。中国だからかなあ。／68考えてもなぜDなのかわからない。 kiku-chan あとで読む nagaichi ことば guldeen china japan language education dpz 興味深い 日本のエラいさん(特に、経団連とか)に、解かせてみたいね(´･ω･) そして、こういう試験を突破してきた中国人を、管理職に就かせるかどうかとかも訊いてみたい harumomo2006