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[Взаимная подписка с адекватными @since1795]

«Пожалуйста, не кормите квокк — как бы сильно они ни просили»

https://t.co/yG9tJG7g35

[Milk Road AI @MilkRoadAI]

This is WILD!

Ray Kurzweil, the futurist who predicted the internet, smartphones, and AI says aging ends by 2032 (Save this)

Kurzweil, now 78 years old, told a live audience that humanity will reach longevity escape velocity by 2032 and he explained exactly what that means with mathematical precision.

Right now, for every year you live, you get back approximately five months of life expectancy from medical and scientific progress meaning you are losing roughly seven months of net life per calendar year.

Longevity escape velocity is the threshold where that ratio flips, for every year you live, you get back a full year or more from scientific progress, meaning your biological clock starts running backward.

Kurzweil's prediction is that threshold hits by 2032 and beyond that point, you do not simply stop dying of aging, you actively get younger every year.

The mechanism is AI-driven drug discovery at a scale that was physically impossible five years ago.

By 2030, Kurzweil argues, AI will be able to take a biological problem, generate millions of potential drug candidates, screen all of them, and run trials on simulated digital populations compressing decades of clinical research into weeks.

This is already happening.

David Sinclair's lab at Harvard used AI to virtually screen 8 billion molecules against aging targets and is now preparing human trials moving from $400,000 gene therapies toward a $100 pill that can reset biological age by 50 to 95% in four weeks.

Sinclair has already demonstrated the ability to reverse aging in mammals restoring sight in mice with optic nerve damage and reversing Alzheimer's symptoms in lab models.

Kurzweil's track record is what makes the 2032 claim impossible to dismiss.

He predicted the internet's global dominance in 1990, the defeat of a world chess champion by a computer in 1998, pocket-sized devices as primary communications tools in 1999, and AI passing professional exams in the mid-2020s, all before anyone else was saying it publicly.

If you are under 60 and in reasonable health, his message is stay alive, stay healthy, and get to 2032.

The tools on the other side of that date will be unlike anything medicine has ever produced.

[Andrew Curran @AndrewCurran_]

https://t.co/yUVogvdruU

[Saira @AiWithSaira]

GROK IS A STOCK TRADING GENIUS

MOST PEOPLE HAVE NO IDEA HOW TO USE IT.

HERE ARE 7 PROMPTS TO UNLOCK STOCK TRADING AUTOMATION:

[ahdpe @ahdpe]

@cryptocupra BTC - old Mercedes: reliable but slow
ETH - empty club, everyone left
SOL - scam neighborhood on every corner
XRP - corporate dinosaur waiting for regulators.
Meanwhile AI stole all the attention and money

[Manuel Aráoz @maraoz]

PSA: I now consider *all* of DeFi unsafe.

Coding agents are superhuman at finding vulnerabilities, and smart contract security is too asymmetric: defenders need to fix every bug while attackers need just one exploit to steal funds.

[Cultura Inquieta @culturainquieta]

El clip colaborativo del rapero sueco Yung Lean y GENER8ION supera los límites de un simple videoclip musical y ya es un festín visual extraordinario. 🔥

La coreografía, a cargo del gran coreógrafo francés Damien Jalet. 💥

Hoy, 29 de abril, Día internacional de la danza.

[Brian McDonald @BrianMcDonaldIE]

Mention the 1990s and a lot of Russians get that haunted look in their eyes.

That said, this AI nostalgia clip is clearly striking a chord.

[Movez @0xMovez]

This 8 minute video by Nassim Taleb will show you how a 70-year-old formula used by quants separates top traders from bankrupt ones

Bookmark & watch it. It will change the way you trade forever. Then read the post below

[ahdpe @ahdpe]

@GamewithDave

[am.will @LLMJunky]

The human brain is truly a marvel of nature.

If you horribly reductive, and boiled it down to a language model, you'd be looking at roughly 100 trillon parameters running as a sparse MoE architecture

Only about 1-5% of neurons fire at any given moment, meaning the brain "activates" maybe 1-5 trillion parameters per inference step.

For context, the largest AI models we've built probably top out around 5 trillion parameters.

The brain is roughly 100x larger. Even its active params at any given moment are larger than almost every model in existence today.

Here's what melts my brain (pun intnended) though

Your brain does all of this on about 20 watts of power, less than a dim light bulb.

Training a frontier AI model consumes enough electricity to power small cities for months. Running inference across data centers pulls megawatts.

Your brain runs 24/7 for 80+ years on the equivalent of a phone charger.

We haven't come close to matching the brain's scale. And we're not even in the same universe when it comes to efficiency.

Evolution spent 500 million yrs optimizing the most energy-efficient intelligence architecture ever known. we're trying to brute force our way there with compute and electricity.

Nature is still the best engineer in the room.

[Anthropic @AnthropicAI]

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.

It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
https://t.co/NQ7IfEtYk7

[Завтракаст • подкаст @zavtracast]

Милла Йовович стала разработчицей. Актриса, которая известна по адаптациям Resident Evil и «Пятому элементу», завела GitHub, где вместе с другом выложила инструмент MemPalace.

Он создан, чтобы улучшить память ИИ-агентов — воспоминания о пользователи хранятся локально, и система сама решает, когда их нужно применять.

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[ahdpe @ahdpe]

@MAXALEXHARDY Ты не прав, брат. Невесомость - это свободное падение, а не "притяжение исчезло". На 1-й косм. падаешь вокруг Земли, на 2-й - улетаешь нахой.
Орион тормозит движками против хода - в вакууме реактивная тяга заебись работает. Луна просто слабее тянет. Всё по физике )

[Elon Musk @elonmusk]

Here you go

[Derya Unutmaz, MD @DeryaTR_]

As unbelievably good as current AI models are, this is not even 1% of what AI will become in just a few years! Consider the advance from ChatGPT 3.5’s release more than 3 years ago to now, there will be even more progress than that within a year! The human brain can’t grasp this!

[Michael Andregg @michaelandregg]

We've uploaded a fruit fly. We took the @FlyWireNews connectome of the fruit fly brain, applied a simple neuron model (@Philip_Shiu Nature 2024) and used it to control a MuJoCo physics-simulated body, closing the loop from neural activation to action.

A few things I want to say about what this means and where we're going at @eonsys. 🧵

[Josh Kale @JoshKale]

Scientists just copied a Fruit Fly's biological brain and trapped it inside of a computer.
Not an AI model trained to act like a fly... A total digital copy of a fly !! This is some sick sci-fi stuff:

- They scanned and copied the brain, neuron by neuron, synapse by synapse, from electron microscopy data.
- Then dropped that brain into a simulated body in a video game like environment.

The fly walked. It groomed. It fed. Nobody taught it anything. The behavior was already in the wiring.

The entire premise of modern AI is that intelligence is something you train into a system. This is proof it's something you can transfer out of one. Wild times

[Derya Unutmaz, MD @DeryaTR_]

After what I’ve seen recently, I am now updating my prediction for the arrival of Artificial Superintelligence (ASI) to sometime in 2028 from 2030.

ASI as defined by Nick Bostrom: intellect that is much smarter than the best human brains in practically every field.