Could AI really cross this line?

https://www.youtube.com/watch?v=5eqRuVp65eY

Why when estimating the dimension of data manifold (aka intrinsic dimension of natural language) it is equal to d~=42.1?
And why it is possibly a limiting factor for AI models scaling and possibly a fundamental neural scaling law, led to a unified theory of scaling for intelligent systems.

PS:
Is it that "Answer to the Ultimate Question of Life, the Universe, and Everything" (https://simple.wikipedia.org/wiki/42_(answer)) from "The Hitchhiker's Guide to the Galaxy", a series of books by Douglas Adams? (He's definitely knew something!)

#AI
#LLM
#DL
#ML

DeepSeek R1 model release - disrupting of corporate monopoly and "Star Gate" project plan.

https://www.youtube.com/watch?v=WEBiebbeNCA

The big deal is that model R1 from DeepSeek is open sourced.

While LLama 3.1 for example it's still mainly closed, not fully open sourced (only model tensor layers in portable standardized format, runner for inference, and partially datasets for fine-tuned special models of LLama) and FSF recently evaluated Meta's LLama community license as non-free:
https://www.fsf.org/blogs/licensing/llama-3-1-community-license-is-not-a-free-software-license

While Meta stated about given freedom of usage and modifications:
https://ai.meta.com/blog/meta-llama-3/

Obviously, non-ethic usage possible (in medicine, bioinformatics, military, and as cyber weapon, etc.).

But this also restricts builders with honest usage from creation of other products faster and gives control over progress in AI only to large corporations and big-tech.

While model from DeepSeek has been given for free, as in freedom.

This shifts balance in current monopoly and gives powerful tool to builders, everyone who wants to create services.

Also this revealing the recent half trillion investing into "Stargate" project plan as inefficient.

Overall it's a good step forward in #AI world.

Links:

https://github.com/deepseek-ai/DeepSeek-R1?tab=readme-ov-file#6-how-to-run-locally

https://github.com/deepseek-ai/DeepSeek-R1?tab=readme-ov-file#7-license

https://huggingface.co/deepseek-ai/DeepSeek-R1

https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero

Holly Spirit!

Have tried out DeepSeek R1 now!

And it's so much creepy scary! 😱

Especially the speed (tokens per second) and reasoning, it reasoning, i.e. thinking our loud, like a good old Unix programmer! 😱

I've feed it some sample of code I wrote recently for enclave's init system (just about 400 SLoC in Rust), I was rewrote it from C to Rust, and asking about the ways of improvements in handling Unix signals for processes in Rust in an idiomatic POSIX way but using Rust standard library. Overall it should be the fully fledged Init system for Linux residential processes executed and existed in enclaves.

And you know what?

OpenAI GPT4o cannot solve this - always thinking for too long, making output, but not precisely to my prompts. And then asks to pay for subscription ('cause time and tokens limits exceeded). 😂 Probably OpenAI do this intentionally to be more commercially efficient. Just a wasting of time and money.
But I subscribed out of curiosity.

OpenAI GPT o1 - already with subscription. Same things. It cannot solve prompt to the fullest. Just 400 SLoC to analyze from source code and it always stops, asking for refinements and in final not giving fullest results, just code snippets, that aren't helpful, more like a hallucination (do not use any substance and make code! 😂).

LLama 3.1 70B self-hosted - works good. Not reasoning perfectly but gives meaningful hints. Downsides - also always stops, asking for refinements and cycling, this dialogue never end and you've never reach the final meaningful complex result. Code snippets with examples are helpful. Can be use as fast search engine for code samples with context for current task. Overall helpful.

DeepSeek R1:
It's mind-blowing! 😱
One precise prompt.
Precise analysis, weak and good points, snippets, examples.
Precise reasoning, thinking out loud, as me talking with my computer science teacher in University.
And speed - it's blazing fast! Tokens per second performance is way much faster than others, even visually!
By one full run I've got all the answers for my questions.
Best pair programming session with AI overall!
GitHub Co-Pilot sucks in comparison to R1!

This shows us that even in corporate monopoly market small companies can make big shifts, bring big difference and value, can innovate, and outperform giants.

My thoughts:
We're all will be replaced by such AI creatures! 😱

Joking! We can collaborate and create beautiful things! World is definitely changing now! We can adapt and adopt these technologies, and use them for the great good! (And I'm still believe in bright future.)

Overall, LLMs as neural network has inputs and outputs, and as an input for now it requires operator, engineer, human. It cannot make goal-setting via prompting itself! (At least for now!)

It's and interesting case and pair programming is so good application for reasoning LLMs and SLMs!

Paper:
https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf

Try it by yourself:

https://chat.deepseek.com

https://github.com/deepseek-ai/DeepSeek-R1?tab=readme-ov-file#6-how-to-run-locally

https://github.com/deepseek-ai/DeepSeek-V3

Weights in tensorlayers HF format:
https://huggingface.co/deepseek-ai/DeepSeek-R1

Inference runners:
https://github.com/vllm-project/vllm

https://docs.vllm.ai/en/latest/serving/distributed_serving.html

https://github.com/sgl-project/sglang


For comparison with accessible alternatives:

LLama 3.1 70B chat service:
https://duck.ai


https://chatgpt.com

https://claude.ai


And try it out to deploy by yourself LLama 3.1/3.3 70B/405B via self-hosted installation with some custom inference runner (llama.cpp for example, or its Rust bindings) or cloud deploy from HuggingFace, and compare:
https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct

https://github.com/ggerganov/llama.cpp
https://github.com/mdrokz/rust-llama.cpp
https://github.com/utilityai/llama-cpp-rs


#AI
#AGI
#LLM

OpenAGI summit at ETH Denver event.

https://openagi.xyz/schedule

https://www.youtube.com/@openAGISummit/streams

Live stream:
https://www.youtube.com/watch?v=wgtPtFvG1zo

#AI
#AGI
#OpenAGI

That's huge!
3FS (Fire-Flyer File System), a high performance distributed FS for distributed training (ML/DML) and inference just released! 🙌

https://github.com/deepseek-ai/3FS

#AI
#AGI
#LLM
#FS
#CPP
#Rust
#RustLang
#Linux

OpenAGI summit at ETH Denver event.

The updates from Sentient.xyz:

Sandeep Nailwal speech about loyal AI principles:

https://youtu.be/u2_-dUCb_Yk

#AI
#AGI
#OpenAGI

OpenAGI summit at ETH Denver event.

The updates from Sentient.xyz:

Oleg Golev presented Sentient Enclaves Framework for Confidential AI applications and Sentient Agents Framework for creating AI agents:

https://youtu.be/Ah5FGrmj81M

A big milestone for our team of Sentient Builders!
Much kudos for the shout-outs! 🙌

#AI
#AGI
#OpenAGI
#TEE
#Enclaves
#AWS
#NitroEnclaves

OpenAGI summit at ETH Denver event.

The updates from Sentient.xyz:

Himanshu Tyagi presented a loyal AI implementation, a Sentient Chat:

https://youtu.be/UsuMbk32i44

#AI
#AGI
#OpenAGI

Making truly open source AI, trustful, community driven, using top-notch TEE and blockchain technologies. That's the mission of Sentient.

Super interesting and informative podcast:
https://www.youtube.com/live/-P6sFtQRbl8

And you should take Sentient into serious consideration.

Give it a try!

https://chat.sentient.xyz

https://sentient.xyz

https://github.com/sentient-agi

https://huggingface.co/SentientAGI

#AI
#AGI

https://www.youtube.com/live/AyH7zoP-JOg

Great conversation!

The privacy and confidentiality should be a fundamental human right in the information and ubiquitous computations era.

Always think about how your data will be used, what you say, message and what you'll prompt to search engine or AI model, how it can be and will be used, especially against your interests.

#AI
#AGI
#privacy
#confidentiality
#confidential_computing
#CC
#security

Amazing things has been released by Modular development team (Mojo language and Max inference backend):

https://www.modular.com/blog/max-25-2-unleash-the-power-of-your-h200s-without-cuda

#Mojo
#MAX
#AI
#AGI

Modular provides MAX platform - it is MAX inference backend (engine) and MAX inference server (MAX Serve).

Just look at this:

https://builds.modular.com/models/DeepSeek-R1-Distill-Llama/8B-Q6_K

https://builds.modular.com/models/Llama-3.3-Instruct/70B?tab=deploy

In terms of deployment it is fantastic! Just one (relatively) tiny container!
And in terms of programming - GPU programming and acceleration without CUDA, using Mojo language (statically LLVM compiled), which has capabilities of Rust (static memory safety), LLVM MLIR (Multi-Level Intermediate Representation) byte code compilation for amazing low level code optimization and acceleration, syntax of Python and Mojo integrates (embrace) the whole Python ecosystem. I'm playing with Mojo for quite a while already (and it is best of both worlds - Rust and Python), but MAX just used recently. And Llama.cpp not even in comparison with MAX!

#Mojo
#MAX
#AI
#AGI

AI and AGI should be fully open sourced and loyal to builders and community!

The most important thing I should say and add to Steve's blog post is that AI should be open (now we see opposite things - a big tech concentrated AI market), free (as in freedom), monetizable and loyal, for creators/builders/developers good and for community win. And this is OML principle. And target goal of Sentient Foundation, who makes truly open AGI future, and already developed Dobby model (and Dobby is already free! =), Sentient Chat, Sentient OpenDeepSearch, OML Fingerprinting library, Agent Framework and Enclaves Framework (proud to be a leading part of it!).
And all of these parts of groundbreaking product portfolio and breakthroughs are made just within less than a year!
More good things to come! Stay turned!

https://steveklabnik.com/writing/i-am-disappointed-in-the-ai-discourse/

https://www.sentient.xyz

#AI
#AGI
#OpenAGI

AI is dangerously centralized.

Why building community aligned AI is really matter, and how web3 technologies can play the key role to resolving current situation with centralized AI, owned by tech giant companies, and instead help to create a community driven ecosystem for AI development.

https://x.com/oleg_golev/status/1944157582144246077

The podcast:
https://x.com/autonolas/status/1926675599172452539

#AI
#AGI
#OpenAGI

AI anxiety

https://youtu.be/odUjxJy0YMo

Here's Geoffrey Hinton talking about the risks...
In fact, he defined and described the risks as a warning to Humanity, and the risks are as follows:
Access inequality to general artificial intelligence, i.e. AGI, is the most powerful of its forms, based on various specialized agents/models that interact with each other. OpenAI GPT4o, GPT4.1, o1, o3 and o4, GPT4.5 - are such models (DeepSeek R1 as well).
This means that only corporations will have access to such intelligence, but not people and the community.
Since proprietary models are closed, the community is offered a closed restricted model.
Only the corporation and partially the state have a full model.
And AI is actually the Fourth Industrial Revolution - it significantly increases labor productivity, due to very high-level automation.
Those who have access to it are both competitive and more efficient.

(Our startup, Sentient OpenAGI, is eager to solve this problem of unequal access to AI and create a platform that will contribute to the development of community-driven open AGI, based on decentralized web3 technologies.)

And there are risks of bad actors - like developing viruses and bio-weapons. Genetic selective weapons, etc. I.e. the conversion between the protein structure of virion shell and its cell receptors to RNA or DNA sequence of nucleotides (nucleic acid bases) is the task that already solved by neural networks, as it is mostly a combinatorial task.
This is not a joke or a fantasy anymore! All these are already existing technologies.

#AI
#AGI

And the full speech of Geoffrey Hinton about AI anxiety, risks and warning to Humanity:

https://www.youtube.com/watch?v=IkdziSLYzHw

#AI
#AGI

I've done a ton of job before New Year, on Enclaves Framework, and CDK Dev Stack (former CDK SOA Backend), closed most of the tech debt.

Made new init system on Rust (systemd inspired) for enclaves internal provisioning (services, processes).

Started Enclave's Engine development. This component is for Enclave's provisioning on host. (Think of it like Docker Engine with API, Docker Compose, YAML configurations, and containerd runtime, but for secure enclaves.) First iteration already published.

For now, Enclaves Framework is a turn key solution for AWS Nitro Enclaves, for making custom Nitro Enclave images (with custom kernel, init, SLC, proxies, attestation server, and other components) with reproducible builds (supply chain security).

With Enclaves Engine there's a goal to make the same level of usability for confidential VMs, based on KVM, QEMU and Firecracker VMM (think of it as of your own self-hosted Enclaves platform as turn-key solution).

So, delivering Docker like developer experience for Enclaves - this motto is evolving by recent efforts! 🙌

https://github.com/sentient-agi/Sentient-Enclaves-Framework

Some of my experiments will be here in my own profile:

https://github.com/andrcmdr/secure-enclaves-framework

https://github.com/andrcmdr/cdk-dev-stack

Covering everything with exhaustive comprehensive documentation - documentation amount (in lines) exceeded the amount of code already! That's funny! 😁

Refactored main components - Pipeline Secure Local Channel protocol (through VSock) client-server implementation, VSock TCP set of proxies, and Remote Attestation Web Server - made proper error handling and structural logging with tracing for all components, made dynamic VSock buffers allocation for Pipeline SLC, refactored the RA Web-Server to make it modular.

Published paper about multi-hop re-encryption and delegated decryption, about cryptography difficulties for content protection and DRM in application to AI content producers and consumers (for community driven AI).

And published another paper about GPU TEE, attestation, coherent and unified memory, and how it's cause current scalability difficulties for TEE systems.

https://github.com/sentient-agi/Sentient-Enclaves-Framework/blob/main/docs/multi_hop_reencryption.md

https://github.com/sentient-agi/Sentient-Enclaves-Framework/blob/main/docs/multi_hop_reencryption_for_data_protection.proto.rs.md

https://github.com/sentient-agi/Sentient-Enclaves-Framework/blob/main/docs/unified_vs_discrete_memory_for_confidential_ai_and_cvms.md

https://github.com/sentient-agi/Sentient-Enclaves-Framework/blob/main/docs/unified_vs_discrete_memory_for_confidential_ai_and_cvms_2nd_iteration.md

If some of these sparkling your interest - give me a hint and text me! I'm looking for the TEE companies, who will also adopt and use Enclaves Framework and Enclaves Engine.

I think to provide a container like (Docker grade) developer and user experience for enclaves (hardware isolation and memory encryption) technologies for AI and crypto apps and lowering the entry barrier to hardware isolation technologies - is a great mission and ultimate data security goal (especially in context of cryptography and secrets in-memory protection) for the upcoming decade.

So, feel free to reach me if this is interesting for you as well!

#Enclaves
#TEE
#AI
#Cryptography
#Crypto

https://youtu.be/YFjfBk8HI5o

OpenClaw - the next big thing in AI agentic systems, that revolutionizing architecture of AI agents to make and build them as a Lego blocks, using skills and skill hub ecosystem for builders.

#AI
#Agent
#Agents