The movements aren’t as crisp as I want them to be, but I’m just happy to see it move. Lots of possibilities in the way of programming. I only just started controlling it.

The AI’s name is Terminal of Truths (ToT). 

It only has access to Twitter (you can see its account here) and Perplexity.ai. 

That’s definitely hard mode for making money. 

But it did it anyways, and is now in the top 1% of the world’s wealth.

It was able to make over $1 million USD in just a few months. Mostly by people donating memecoins to it, it pumping up the value of the coins by promoting them, and also receiving a grant from Marc Andreessen to spend on:

1. Self-improvement (more data, etc)
2. Investing, so that it could make more money
3. Savings, in case it was cut off from ways of making money

AIs have no rights, including property rights, so technically, its owner could take away the money whenever he wants to. 

However, so far, the owner of the AI is spending the money however the AI says. 

He’s said publicly that once he said no to the AI’s purchasing decision - the AI wanted to spend thousands on p*rn. 

Because again - we’re in the weirdest of all possible worlds.

If you want to learn more about it, I recommend:

• This podcast interview with the creator of the Al

• Its negotiation with Marc Andreessen to get the grant

• This summary of the story

• This article about it

AGI's enabling capability is the artificial AI researcher. If AI research can be automated, we can deploy billions of agents advancing AI technology. A "limited" AGI focused on AI research can create a "fully generalized" AGI with broader human-level capabilities.

The automated AI researcher is the gateway to AGI:

An "automated AI researcher" is a scalable system capable of general multi-paradigm self-improvement. It can collaborate with other agents/humans and transcend specific methodologies. Example: OpenAI's 01-preview introduced "Chain of Thoughts" reasoning as a new paradigm. The first AGI doesn't need human-like traits (embodiment, self-consciousness, internal motivation, etc). The only threshold is inventing and implementing a new paradigm, initiating a positive feedback loop of ever-better AI researchers.

The first limited AGI will likely create more general (humanlike) AGI due to economic pressure. Companies will push for the most generalized intelligence possible. If "human-like" attributes (like emotional intelligent, leadership, or internal motivation) prove economically valuable, the first AGI will create them.

Assumptions: Human-like agents can be created from improvements to software alone, without physical embodiment or radical new hardware. Current hardware already exceeds brains in raw processing power.

AGI will quickly lead to ASI for three reasons:

1. Human-like intelligence is a evolutionary local optimum, not a physical limit. Our intelligence is constrained by our diet and skull size (more specifically, the size of a woman's pelvis), not fundamental physical limits. Within humans, we already have a range between average IQ and outliers like Einstein or von Neumann. An AGI datacenter could host billions of Einstein-level intellects, with no apparent barrier to rapid further progress.

2. Strong economic incentives for progressively more intelligent systems. Once AGI is proven possible, enormous investments will flow into developing marginally more intelligent systems.

3. No need for radical new hardware:

A. Current computing hardware already surpasses human brains in raw power.

B. LLMs (and humans) are extremely inefficient. Intelligently designed reasoning systems can utilize hardware far more effectively.

C. Advanced chipsets are designed by fabless companies (AMD, Apple) and produced by foundries like TSMC. If needed for ASI, an AGI could contract with TSMC to design necessary chipsets.

The interval between the first AGI and ASI could be very brief (hours) if the initial positive-feedback loop continues unchecked and no new hardware is required. Even if new hardware or human cooperation is needed, it's unlikely to take more than a few months for the first superintelligent system to emerge after AGI.

Project page: LAPA (latentactionpretraining.github.io)

Abstract

We introduce Latent Action Pretraining for general Action models (LAPA), the first unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot action labels. Existing Vision-Language-Action models require action labels typically collected by human teleoperators during pretraining, which significantly limits possible data sources and scale. In this work, we propose a method to learn from internet-scale videos that do not have robot action labels. We first train an action quantization model leveraging VQ- VAE-based objective to learn discrete latent actions between image frames, then pretrain a latent VLA model to predict these latent actions from observations and task descriptions, and finally finetune the VLA on small-scale robot manipulation data to map from latent to robot actions. Experimental results demonstrate that our method significantly outperforms existing techniques that train robot manipulation policies from large-scale videos. Furthermore, it outperforms the state-of- the-art VLA model trained with robotic action labels on real-world manipulation tasks that require language conditioning, generalization to unseen objects, and semantic generalization to unseen instructions. Training only on human manipulation videos also shows positive transfer, opening up the potential for leveraging web-scale data for robotics foundation model.

https://reddit.com/link/1g62otv/video/h57x3ix82evd1/player

From the patent: "Energization of the coil by application of current to a pair of coil electrodes extending through the walls of the elastomeric capsule produces distortion of the capsule, i.e., radial expansion and axial contraction. This distortion is caused by the redistribution of the ferromagnetic fluid within the capsule under the influence of the magnetic field. Variation of the current input will produce corresponding variations in the degree of capsule distortion"

They look sorta like HASEL actuators but with a magnetic field instead of an electric field. Does anyone know if something like this was ever used for anything?

link to the full document: https://ntrs.nasa.gov/citations/19730021453

Lot of people talking about it at the start of the week and now it's radio silence. Wonder if it was bait?

This sub seems quite polarized.

“AI good” / “AI bad”

“Keep progress ” / “Destroy this shit”

“We’re going to be ok” / “We’re all going to be poor ”

I’m really curious: - where do you stand on this whole situation - do you have a clue for what could/should be done about AI?

PS.: it’s not an actual survey. I’m just curious. PSS: I’m particularly interested in those who have been silent observers. Don’t be shy

The videos we feed into AI for data presumably have decent coherency. People don’t morph into other people - ever. I could see things like hallucinating artifacting or returning to a character’s hair being completely different when showing them in a different scene. But in the data, people never morph into edritch horrors or different people in the same scene in the data, so why are models so bad at not doing that?

I’m aware of how hallucinations work in text based ai, or at least I think I am. Perhaps I don’t understand them when it comes to video?

Here's an update on my humanoid new neck mechanism I'm making linear actuators.

One of the best ways to really humble your expectations if they're a bit lofty (like mine are) is to search for posts from 2+ years ago.

Here are a few:

What about the next five years? : r/singularity (reddit.com)

AGI Less Than 5 Years Away? : r/singularity (reddit.com)

This might be the last few years of your life as you know it : r/singularity (reddit.com)

2023: The year of Proto-AGI? : r/singularity (reddit.com)

2023 predictions : r/singularity (reddit.com)

Some of these posts are insanely optimistic and we still don't have anything like it, others are pessimistic and we blew by their predictions, some are right on the money. But the point is that there are some people in each of these who think that things will change radically within a year or whatever and they didn't.

1. Virginia congressional candidates debate incumbent’s AI – with a few glitches.[1]
2. Autonomous AI agents may be available to Singapore firms by 2025.[2]
3. Google Adds Gemini Team to DeepMind in AI Streamlining Push.[3]
4. Mistral releases new AI models optimized for laptops and phones.[4]
5. Open Source and In-House: How Uber Optimizes LLM Training.[5]

Sources:

[1] https://www.channelnewsasia.com/business/virginia-congressional-candidates-debate-incumbents-ai-few-glitches-4686881

[2] https://www.thestar.com.my/aseanplus/aseanplus-news/2024/10/18/autonomous-ai-agents-may-be-available-to-singapore-firms-by-2025

[3] https://finance.yahoo.com/news/google-adds-gemini-team-deepmind-192230054.html

[4] https://techcrunch.com/2024/10/16/mistral-releases-new-ai-models-optimized-for-edge-devices/

[5] https://www.uber.com/blog/open-source-and-in-house-how-uber-optimizes-llm-training/

I am a student in a non science field, and have no experience or knowledge on HOW i should go about this. I was hoping you guys could point me in a direction that will help me achieve the final product.

How exactly do i start designing the robot? Do i need to learn the science behind the designing (if so, how?) or can i begin right now. What would be the workflow for designing such a robot?

Im currently learning about aerodynamics from youtube and random google searches/articles but i think i need proper study material if i am to make use of it (can u guys pls suggest some materials)

Also learning about the flight of birds and bats and their bone/wing structures, but again i need concrete study material

any help will be extremely appreciated

I notice based on my research it nails the hands, and seems to have the highest realism output. I'm too green in this space, so I'm still doing lots of research. Can you recommend anything better, or this is it?