Chief Scientist at @UmnaiBase. Unleashing limitless human ingenuity through a revolutionary machine learning approach: Hybrid Intelligence. AI PhD

London, England
Totally agree with @AndrewYNg view
The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): deeplearning.ai/the-batch/is… ]
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Amazing to see speech being formed by analyzing signals from the motor cortex. So many people can benefit from this and really improve the quality of their life. This positive aspect of AI is why I chose my career to be in AI in the first place
A beautiful surprise
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Good post on why all the hype about “AI is about to doom us all within six months” should be taken with a large pinch of salt… This is an obvious play at achieving dominance by forcing regulation to be introduced in a hurry in the US
Could swarms of AI bots actually take over the internet in six months, as Dario Amodei just suggested? @nathanhamiel @ZackKorman and I take a look. garymarcus.substack.com/p/co…
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What if your brain works more like ChatGPT than we thought? A new neuroscience study found that bilingual brains store the meaning of words in one shared "mental map." That's surprisingly similar to how today's AI models organize knowledge. The overlap between neuroscience and AI is getting harder to ignore. cell.com/cell/fulltext/S0092…
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Most AI architectures today look like: Input → Model → Output Future AI architectures will increasingly look like: Input → Understanding → Reasoning → Decision → Explanation
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A useful test for any AI architecture: Can it explain why it reached a conclusion? Not with a post-hoc explanation. With the actual reasoning process.
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Why neurosymbolic AI matters AI has spent decades moving between two extremes: Symbolic AI and Statistical AI. Each solved problems the other could not. Neural systems learn well. Symbolic systems reason well. Real-world intelligence requires both. The challenge is not choosing. The challenge is integration. Cobbling up together a system that combines both is possible, like @GaryMarcus has outlined many years ago. Creating an elegant and scalable solution that combines both is where some of the most interesting work in AI is happening today.
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The biggest misconception in AI: People assume intelligence and reasoning are the same thing. They are not. Modern AI is highly intelligent in many respects. That doesn’t mean it reasons the way we think it does. @UmnaiBase
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Neurosymbolic AI is not a niche research topic. It is the natural evolution of AI. Neural systems learn. Symbolic systems reason. Real-world decision systems require both. @UmnaiBase
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Predictions inform. Decisions act. Most AI today is still optimized for the former.
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AI without structure struggles with reasoning. AI without adaptability struggles with reality. The future requires both. @UmnaiBase
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Now we know why Apple didn’t make the Apple Car: Jony was saving the Pherrari. Italian translation: Madonna! 😱🤌🏻
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Scale improves capability. It does not automatically produce reasoning. That distinction is becoming increasingly important in AI system design. @UmnaiBase
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Neural systems approximate.
Symbolic systems explain. Modern AI increasingly requires both.
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Symbolic AI is excellent at: •logic •constraints •explicit reasoning
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One gives flexibility.
The other gives structure. This is why purely statistical systems struggle with: •traceability •causality •defensibility The future of AI is not neural versus symbolic. It is neural plus symbolic.
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Most AI “reasoning” isn’t reasoning at all.
It’s just statistical compression. Here’s how real decision-making actually works in reliable systems 1. Identify
Spot the situation and pull out the key signals.   2. Assess
Analyze risks, constraints, trade-offs, and options.   3. Resolve
Choose an action — and clearly justify it. Today’s LLMs try to mash all three steps into ONE forward pass.
It feels magical.
But it’s a black box.   Speed? Massive win.   Accountability, traceability & safety? Big loss. The strongest AI systems today (advanced agents, thinking-style models, production-grade setups) keep these stages deliberately separate. Result:
→ Debuggable
→ Auditable
→ Trustworthy   This is what separates toy demos from systems you can actually bet your company (or life) on.   Do you want your AI to be fast… or actually reliable?  Quote RT with your take #AI #Reasoning #AgenticAI #xAI
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The uncomfortable truth about today’s AI: Most models jump straight from input → output. No assessment phase. No guaranteed progress. No real reasoning. They’re just extremely good predictors.
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This is why we still see confident hallucinations, brittle agents, and systems that sound smart but fail when it matters. We’re optimizing for fluency, not reliability.
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So the real question: Is test-time compute + process supervision enough… or do we need a deeper architectural shift? What do you think?
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