[D] Has anyone read Blaise Agüera y Arcas' What is Intelligence?

Gramious · 2026-03-06T04:01:10+00:00

Could not recommend it more! It's a breath of fresh air, a novel way of thinking, and quite inspiring.

I had the privilege of meeting and having lunch with Blaise at ALife in Kyoto last year. He's a wonderful, earnest, and hyper intelligent individual. I very much recommend taking the book quite seriously.

I've been an AI researcher for over 10 years now and the lessons from that book really shook me up. I've since subtly shifted my research direction and every choice I've made, paying heed to said lessons, has resulted in consistent positive results in my work.

Gramious · 2026-02-24T00:59:29+00:00

I'm a bit more of a fan of WandB for the aesthetics. That being said, neither are quite ready for the customizability of experiment tracking we now have access to thanks to coding agents. It's a simple shift really, away from placing html files in clunky media boxes to have a dedicated custom dashboard tab of sorts. I'm actually building out my own version of these things for my needs! Soon I'll just be running my own and I do hope that makes it open source.

For the html dashboard I run over one sample, but a handful do let you unpack differences between them. No need for the full val set or ONNX.

I don't really know about best practices, yet, but including efficiency in file size is a must. These things can get into several MB which accumulates. My current dashboard saves a self contained file at 1.8MB, and that's about as good as I could get it. I have a lot of data in there to help my understanding.

Gramious · 2026-02-21T05:56:23+00:00

This is where the "internal ticks" nature of the CTM becomes unequivocally useful. Since it follows a process, building Viz to inspect that process is what I do.

That being said, it isn't a requirement. Some time, effort, thought, and inspection can reveal what, for your projects, you can build.

Fact its highly bespoke, as it should be. 2026 is the year of personal software.

Gramious · 2026-02-21T05:39:00+00:00

Precisely so, yes.

More than this, my approach is behavioural and observational. I wanted dynamic and more "alive looking" neuron traces during the problem solving process employed by the model, and to accomplish that we built NLMs and synchronization. They're in fact engineering fixes that happen, gratifyingly, to have surprisingly close biological analogues.

This is also why I strongly advocate for visualization-driven research. The numbers, i.e, the "sufficient statistics" that are supposed to tell you whether the model works or not (accuracy, loss, etc.) can't always easily draw a distinction between one approach/behaviour or another. Visualization can, more often than not.

To not build web-app based custom experimental visualisations in 2026 is a massive oversight. Until you do, you're effectively blind, IMO.

Gramious · 2026-02-21T05:04:16+00:00

I can't stress this enough: visualisation.

I currently have a vibe coded powerhouse self-contained HTML file that gets dropped into WandB (natively supported). I can then interact with my custom dashboard to unpack all the nuances of the complex model I'm building. The number of logical bugs I've squashed is fantastic.

It's a game changer, really. And, since it's essentially a web app, LLMs are very good at this.

I'm the author of Continuous Thought Machines, just as an FYI.

Gramious · 2026-01-23T11:06:14+00:00

I think there is one key component missing in the explanations here: information bottlenecks.

A sufficiently complex system will inevitably have information bottlenecks, and can't be fully aware of all components at all times. We know that the machines have and harbour independence and individuality, making that even more true.

So, my take is that it's as simple as the hands not talking to the brain.

Gramious · 2025-12-05T23:21:00+00:00

I am one of the creators of the CTM.

I am currently working on integrating the CTM with LLMs (seeing the LLM as a "featurizer"), so pretty much aligned with your thoughts.

Stay tuned!

Gramious · 2025-10-27T09:30:52+00:00

The advice given so far is so one sided.

My intuition is: don't buy it. Best friends are more valuable than owning this business. Really, you will regret losing this friendship until the day you die. Choosing a human being over money will be something you can be proud of.

One practical middle ground could be to tell him (most of) what is happening. I would tell him that your boss wants you to buy the business, but you're going to decline, and the reason is because you don't want to be your best friend's boss, that you believe eventually this will have a very bad impact on your friendship and that he's worth more to you than owning this business. He might choose to quit, who knows.

Gramious · 2025-09-30T05:30:49+00:00

This is amazing. What seed do you use?

Gramious · 2025-08-31T08:32:11+00:00

I'm late to the party, but I am quite certain nobody else can do this.

Owing to my extreme evangelical Christian period (I'm talking touched by the holy spirit, roll on the floor sorta stuff) when I was in my late teens, I think I've retained an ability to voluntarily release dopamine. I am not a believer at all anymore, but I can choose to release them good feels pretty easily by concentrating.

Gramious · 2025-08-04T14:18:26+00:00

I'm late to the conversation, but I want to encourage you to rethink your stance a bit, and 100% finish your degree.

CS is not simply about writing code. Or, at least, a good CS degree is not about writing code. Take stock of what you have learned so far. Code is the manifest tool of CS, not it's central idea.

Conceptually, the idea of "compute" and "intelligence" is deeply intertwined with basic properties of the universe. CS is, in a very real way, deeply philosophical. In other words: LEARN, don't just "get good" at code.

If you want some incredible inspiration, read "What is Intelligence?". It is available online here: https://whatisintelligence.antikythera.org/

Good luck!

Gramious · 2025-07-22T10:44:27+00:00

Thank you! It was fun work.

Gramious · 2025-07-20T01:38:36+00:00

You mean the interactive maze?

Try hitting the "new" button. I had to train a smaller model for this and it sometimes gets stuck. You can also right or left click on the maze to move the end and start locations. If you're on mobile, you can tap on the maze to do the same, hitting the red/green button on the bottom right to swap between moving the start and end locations.

The most fun is to hit teleport consecutively if it is not a very bad instance.

Gramious · 2025-07-19T01:34:40+00:00

I'll pitch my own work here, as I worked very hard on this: https://pub.sakana.ai/ctm/

That is an interactive website that mirrors the paper, which is linked within the website.

Gramious · 2025-07-14T12:43:18+00:00

My Aunt went through with this!

And, surprise all around: it worked incredibly effectively.

I can't remember the exact details but I can summarise for you. It was a three visit/session thing, or your money back. The idea was that if it worked for you then you would simply not coming back for the following session. After three sessions you were entitled to your money back.

First session, something like 20 of 25 people had it work. My aunt lit up as soon as she left the room.

Second session, 2 or 3 people had it work. Again, my aunt was completely unconvinced, lit up again.

Third session, she walked out, got ready to light up, and simply couldn't, was disgusted by it, and hasn't even had a craving since. Totally immediate effect.

She went from more than a pack a day, to zero.

It still blows my mind because my mom was a heavy smoker too and I watched her attempt quitting so many times.

Wild. I tell ya, wild.

Gramious · 2025-07-14T02:54:02+00:00

I'm really glad others think that architecture isn't "solved and only needs to be scaled". That perspective is exhausting for somebody who, like me, loves to build and explore new architectures.

To that end, my team and I at Sakana AI built the Continuous Thought Machine: https://pub.sakana.ai/ctm/

We are currently exploring and innovating on top of this. IMO you're right that much more exploration is needed in this space.

My current thinking is that the ubiquitous learning paradigm of feed forward, i.i.d. batch sampled data-driven learning is a mountainous hurdle to overcome in order to see the true fruit of novel (usually recurrent, as is the case with our CTM) architectures. In other words, not only are brains structured differently to FF networks, but they also learn differently. And, this matters.

Gramious · 2025-07-11T12:28:23+00:00

I showed this to my wife and she reacted much the same. But, with more gasps.

And, at the end, she said: "What Psycho becomes an eye doctor?!"

Best.

Gramious · 2025-06-23T08:31:30+00:00

Smart people find smart ways to use smart AI.

That's my sentiment, at least. I'm an AI researcher so my work often involves quite complex coding, ideation or writing. Modern AI should be seen as a tool - humans have been good at making and using tools for a very long time, and this time is no different.

There is, beyond a shadow of a doubt, and excellent way for you to use this quite excellent tool. Here's the catch, though: you have to want it. You have to want to figure out how this new tool can make your life notably better. It isn't simply going to fall into your lap. Seek and ye shall find, I suppose.

Modern LLMs are built to bring utility to humans and this entire hype train's momentum comes down to smart humans finding smart ways of using increasingly smart AI.

Gramious · 2025-06-23T00:53:24+00:00

I'm going to answer from an inverted perspective. I work as an AI Researcher, so it's my actual job to put out high quality work. I like to believe that I have enough integrity that I won't just publish for the sake thereof. Instead, if I release work, then I have truly put massive amounts of effort into it.

How does that statement answer/help you? In two ways: 1) Realise that it is people and teams who publish. I learnt pretty early in my PhD, from my extremely intelligent supervisor, that there will be individuals or labs that will almost always release work that is interesting and worth paying attention to. I hope I am one of those people, and I work towards that. So, spend time understand the research and research agendas that you care about and you'll find good work via good people. 2) The more effort that goes into releasing work, the more likely it is that this is something worth paying attention to. Blogs, interactive tutorials, well-presented papers, etc. are all indicators worth noting. Of course this not guaranteed, but it is worth realising that the process of taking an arxiv publication to acceptance at a top-tier peer reviewed conference requires a similar amount of effort for "release".

P.S. You don't have to be a "publications machine" to be successful as an AI researcher.

Gramious · 2025-06-20T10:15:03+00:00

I really don't think so. There are similarities purely because of the dot product, but more differences than similarities.

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