A new AI control system enables soft robotic arms to learn a wide repertoire of motions and tasks once, then adjust to new scenarios on the fly without needing retraining or sacrificing functionality. The work was co-led by researchers at the Singapore-MIT Alliance for Research and Technology (SMART).
How Exactly does Panpsychism Help Explain Consciousness?
In this episode, we explore a provocative new theory in the philosophy of mind—hybrid cosmopsy-chism. This hybrid form of panpsychism claims that conscious experience is rooted in the universe itself and distributed through emergent subjects like humans. By combining strong and weak emergence, this view promises to overcome the limitations of both physicalism and dualism, offering a radical yet elegant solution to the hard problem of consciousness.
Disclaimer:
In this video, we use Google’s NotebookLM to assist in the analysis and understanding of complex doc-uments. NotebookLM is a research and writing tool that allows us to generate summaries directly from uploaded documents. The podcast like audio overview you will hear is generated by Google’s AI based on the content of the published paper on the topic.
Please note that the interpretations and summaries generated by NotebookLM are automated and may not capture every detail or nuance. They are intended to aid in understanding but should not be consid-ered a substitute for professional advice or a legal interpretation of the documents.
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Robinson’s Podcast #219 — Consciousness, Artificial Intelligence, and the Threat of AI Apocalypse.
Joscha Bach is a computer scientist and artificial intelligence researcher currently working with Liquid AI. He has previously done research at Harvard, MIT, Intel, and the AI Foundation. In this episode, Joscha and Robinson discuss the nature of consciousness—both in humans and synthetic—various theories of consciousness like panpsychism, physicalism, dualism, and Roger Penrose’s, the distinction between intelligence and artificial intelligence, the next developments of ChatGPT and other LLMs, OpenAI, and whether advances in AI will spell the end of humankind.
Joscha’s X: https://twitter.com/Plinz
The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 possible variants—more combinations than atoms in the observable universe. Traditional engineering methods might test hundreds of variants but limit exploration to narrow regions of the sequence space. Recent machine learning approaches enable broader searches through computational screening. However, these approaches still require tens of thousands of measurements, or 5–10 iterative rounds.
With the advent of these foundational protein models, the bottleneck for protein engineering swings back to the lab. For a single protein engineering campaign, researchers can only efficiently build and test hundreds of variants. What is the best way to choose those hundreds to most effectively uncover an evolved protein with substantially increased function? To address this problem, researchers have developed MULTI-evolve, a framework for efficient protein evolution that applies machine learning models trained on datasets of ~200 variants focused specifically on pairs of function-enhancing mutations.
Published in Science, this work represents Arc Institute’s first lab-in-the-loop framework for biological design, where computational prediction and experimental design are tightly integrated from the outset, reflecting a broader investment in AI-guided research.
Researchers have discovered the first known Android malware to use generative AI in its execution flow, using Google’s Gemini model to adapt its persistence across different devices.
In a report today, ESET researcher Lukas Stefanko explains how a new Android malware family named “PromptSpy” is abusing the Google Gemini AI model to help it achieve persistence on infected devices.
“In February 2026, we uncovered two versions of a previously unknown Android malware family,” explains ESET.
Artificial intelligence (AI) is transforming healthcare, with large language models emerging as important tools for clinical practice, education, and research. To use it safely and effectively, healthcare professionals need to understand how it works, and how it fails. Using practical clinical examples, the authors explain the subset of AI called large language models, highlighting their capabilities and their limitations.
Key Points
AI is trained on vast amounts of data, which can itself be biased, leading to biased results.