part of an ongoing effort to think about what ASI could actually mean from a scientific perspective. > is superintelligence just some form of LLMs?
> what is the learning algorithm? > what is the data? read more here:
MACHINE LEARNING
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ASI Scientific Foundations: Beyond LLMs and Learning Algorithms
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Superintelligence: Supervised or Reinforcement Learning Path
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# Superintelligence from first principles some have said that superintelligence is "an engineering problem"; that we know how to build it now. but there are only two ways to learn: supervised and reinforcement learning. which one will get us there? i wrote about this:
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Superposition Reasoning: Novel Approach to Chain of Continuous Thought
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Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought Zhu et al.: https://
arxiv.org/abs/2505.12514 #ArtificialIntelligence #DeepLearning #MachineLearning -
Evaluating Post-hoc Explanations: Benchmarking XAI Methods for Governance
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Learn: ✅ Why post-hoc explanations often fall short without evaluation ✅ How to benchmark SHAP, LIME, DLBacktrace, & more across data modalities ✅ Integrating xai_evals into governance, audit & compliance workflows 💬 Stay for a live Q&A with Pratinav
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xai_evals Webinar: Evaluating AI Explanations Framework July 3rd
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Join our webinar on xai_evals — a framework to evaluate AI explanations using metrics like faithfulness & monotonicity. 📅 Thursday, 3rd July 🕒 4:00 PM BST 🔗 Register here: hubs.la/Q03sC89s0 📄 : hubs.la/Q03sC6tv0 #XAI #ExplainableAI #MachineLearning #AIWebinar
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Fine-tuning data scarcity limits model improvement potential
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It would if we had infinite fine-tuning data.
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Human Reasoning Beyond Token Prediction: Mental Models
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Human reasoning is not based on auto-regressive discrete symbol (token) prediction.
It is based on the manipulation of mental models in continuous representations spaces.
It is based on *searching* for a set of manipulations of this model to arrive at a particular result. -
Beyond Autoregressive: Rethinking Sequential Symbol Prediction in AI
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This is a fundamental flaw of sequential (auto-regressive) symbol prediction.
The fix is simple: don't do auto-regressive symbol prediction. -
Continuous Embedding Space Superior for Reasoning: Theoretical Proof
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It is intuitively obvious that reasoning in continuous embedding space is dramatically more powerful than reasoning in discrete token space.
This paper from @tydsh and team show that it is the case theoretically. -
II-Medical-8B-1706: Efficient Open Medical Model Outperforms Larger Competitors
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Intelligent Internet introduced II-Medical-8B-1706, an updated version of its open medical model Capable of running on <8GB RAM, the AI outperformed Google’s MedGemma 27B across benchmarks despite 70% fewer parameters