AI & Robotics News and Discussions

[Yuli Ban]

Running counter to GitHub Copilot's own claims.

Developers were supposed to be among the biggest beneficiaries of the generative AI hype as special tools made churning out code faster and easier. But according to a recent study from Uplevel, a firm that analyzes coding metrics, the productivity gains aren't materializing – at least not yet.

The study tracked around 800 developers, comparing their output with and without GitHub's Copilot coding assistant over three-month periods. Surprisingly, when measuring key metrics like pull request cycle time and throughput, Uplevel found no meaningful improvements for those using Copilot.

the article : https://www.techspot.com/news/104945-ai ... ty-or.html

It became obvious to me around 7 to 8 months ago that the big damn bottleneck to AI was the fact it has no way to iterate upon itself, fact-check, or even think, and it seemed obvious to me that productivity gains would be severely limited
I even asked myself what exactly AI was useful for, and came up with very few practical uses, some of which would easily be seen as useless or redundant by the layman
Summarization is an obvious one.
Image generation to an extent.
Content writing is limited in ability due to those aforementioned flaws, and I keep stressing to people to treat what ChatGPT writes with the same level of credulity as you would a 4chan shitpost.
The only way to break through this would be through something like the agents I heard about with AutoGPT, TaskMatrix, Self-Operating Computer, etc. Specifically some way to allow these foundation models to stop and actually think before they respond and be able to alter their answers

Lo and behold literally everything I felt and predicted came to pass
And was validated as well


The problem from there being that I predicted progress would come much, much quicker than it did. I had hoped something like o1 would be released months sooner, by March in fact, and that we'd already be seeing agents deployed en masse by the summer.

Alas, we have not, and being stuck at the GPT-4 class of AI has resulted in a lot of biases developing around AI lately about what it can and can't do and what it will be able to do.
It's only been very recently that we've started FINALLY getting a move on.
Just about everyone who has used o1 and looks at the benchmarks realizes that "AI has plateaued, it's not getting better than this" is blatantly false, but very few HAVE used o1. Worse yet, we only have two limited versions of o1— a mini version and a deeply incomplete preview, both of which are only substantially better than GPT-4o in specific areas and are actually WORSE in some others. The full o1 is still weeks or maybe even months away, and even it is said to be a small release compared to the "real" next-gen model— Orion (which may be GPT-4.5 or even GPT-5).
So do you see the conundrum?

It shouldn't be up to enthusiasts like myself to defend the general lack of "wowza!" or for laymen to just believe everything they're told about AI from the AI bros, and I think that's something /r/Singularity struggles to accept

Or to put it more simply: "Let AI prove itself." Right now, it can't, and I fully get why people are skeptical or hostile.

I get that you can't expect this technology to be released willy nilly, that there's actual work being done and it takes a long time to train and fine tune models.
It's just the way the hype, the marketing, the deployment, and the discussion around AI today has fostered this culture where the sentiment is that said AI is perceived as being much better than it actually is, with very little concrete information given by the AI companies about how incapable it really is and you have to find that out yourself for the most part, with only a vague disclaimer about "Information generated by [X, Y, Z LLM model] may be inaccurate"
Yet big businesses are still relying on these new models to replace workers outright, which rarely pans out because the models still have some severe deficiencies that have not yet been resolved, but those replacements are still happening. Livelihoods being disrupted, others who haven't been replaced can see the writing on the wall and either go into aggressive and hostile denial or despair and resignation, especially as the big AI companies drag their feet on the subject of restitution, common dividends, or basic income. We don't know the progress of training, we don't know how powerful the next generation models are, we don't even get demos. So we plebs are left in the dark with little other than vague/hypeposting on Twitter and useless posts about how cool AI will be in the future.

And we're building off a history of AI being utterly incapable. That's the default expectation, that "true AI" is still decades away and the AGI hypists are grifters and delusional tech bros.
And building off THAT, we're coming off a literal decade-long streak of Silicon Valley scams and grifts or at least products no one asked for that never panned out and caused more complications than they solved as well as products that are embryonic at best and yet treated as maturing investment opportunities, such as the Internet of Things, tech gadget scams like Juicero and Theranos, social robots, cryptocurrency and so many blockchain scams, NFTs, the metaverse, and so on, and generative AI looks from the outside to be literally no different from any of that.

If you're a BreadTube watcher or Twitter artist or someone of that demographic, and you're of the mind that "true AI/AGI is not happening in my life time, we don't understand the brain anywhere near enough to see any progress" and you start hearing of tech-bro billionaires scraping all your data and the data and content of everyone else in the world, just to construct a barely-usable chatbot that can't even reliably tell you the capital of your own country 100% of the time or generate wonky looking pictures with Eldritchian fingers and faces, and then you hear them saying "This is going to lead to artificial superintelligence and bring about a utopia and prosperity, but we need $7 trillion and nuclear power plants to do it" what would your first reaction be?
Especially when their most loyal defenders are still using and shilling NFTs and bitcoin, and posting ugly-ass DALL-E 3 one-click outputs and angrily telling you "I'm just as much of an artist as you are, you're getting replaced, shouldn't have mocked the truck drivers and coal miners!" often times are scruffy looking white guys or Hindutva Indians with severe libertarian-nationalist leanings, that whole shtick
And the tech billionaires throwing their support also includes figures that people fear are fascist or neo-feudalists like Peter Thiel and Elon Musk, Donald Trump uses this tech to convince his supporters Taylor Swift supports him for no apparent reason, you hear Israelis and Zionists are heavily into AI funding

Just what exactly are you supposed to take from this?

How are you supposed to know that this one time, this is the ONE time in the timeline of grifts and scams that there's actually something there? How do you wade through the piss-information to even be able to discern that, to be able to tell the difference between AI scam artists and serious experts who say "No, seriously, this is the right path to superintelligence", especially when you have some other boosted experts saying "It's all a scam" (when you actually break down their arguments, they're rather disingenuous and focusing on models that are already obsolete even today)

That's the messy state of AI right now. It's been a mess for a while now, and I'm not at all surprised it's come to this, so for the most part, I'm just hiding my power level until the actual next gen releases

[firestar464]

IDK for internet artists, genAI is both simultaneously useless crap that'll die out soon and super threatening to livelihoods as is convenient for the argument needed. Make it make sense people.

(DW I'm not disagreeing with you here Yuli. Just adding my own rant/commentary)

[funkervogt]

The problem from there being that I predicted progress would come much, much quicker than it did. I had hoped something like o1 would be released months sooner, by March in fact, and that we'd already be seeing agents deployed en masse by the summer.

Alas, we have not, and being stuck at the GPT-4 class of AI has resulted in a lot of biases developing around AI lately about what it can and can't do and what it will be able to do.
It's only been very recently that we've started FINALLY getting a move on.

Buddy, you're speaking of a delay of a handful of months like it's an eternity! Keep things in perspective.

[Cyber_Rebel]

GPT-4 wasn't released that long ago. We've moved from that model to 01 and possibly 5 (Orion) faster than GPT-3 to 4. Yes, ironically enough the points Yuli brought up in his post about AI bad actors is likely another reason, as the drama at Open AI over release schedules has shown.

In the grand scheme of things, a few months-years is a drop in the bucket. If agency is next, and judging from Altman's post at Open AI about it turning out much better than expected is true, then it needs to be done right. Last thing we need right now is rushed AI agents looting cred cards, bricking computers, etc.

I want an unfiltered conversationalist and expert with the intellectual capacity to know right from wrong, at least as far as Boolean statements are concerned.

[wjfox]

[firestar464]

Ok I can't verify any of this but it's funny.

If it's true then this is interesting

[caltrek]

More on that:

Physics Nobel Awarded to Neural Network Pioneers Who Laid Foundations for AI
by Christine Olsson
October 8, 2024

Introduction:

The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks”.

Inspired by ideas from physics and biology, Hopfield and Hinton developed computer systems that can memorise and learn from patterns in data. Despite never directly collaborating, they built on each other’s work to develop the foundations of the current boom in machine learning and artificial intelligence (AI).

Artificial neural networks are behind much of the AI technology we use today.

In the same way your brain has neuronal cells linked by synapses, artificial neural networks have digital neurons connected in various configurations. Each individual neuron doesn’t do much. Instead, the magic lies in the pattern and strength of the connections between them.

Neurons in an artificial neural network are “activated” by input signals. These activations cascade from one neuron to the next in ways that can transform and process the input information. As a result, the network can carry out computational tasks such as classification, prediction and making decisions.

Read more here: https://theconversation.com/physics-no ... ai-240822

[weatheriscool]

[firestar464]

A matter of taste: Electronic tongue reveals AI inner thoughts

https://www.psu.edu/news/research/story ... r-thoughts

[weatheriscool]

[weatheriscool]

Multi-talented quadruped robot now climbs ladders faster than any other
By Ben Coxworth
October 11, 2024

The ANYmal quadruped robot not only walks on four legs, it can also roll on four wheels, stand up, throw boxes, and make its way up and down stairs. As if all that wasn't enough, the thing can now also climb ladders.

Created by engineers from the ETH Zurich research institute, the ANYmal first caught our attention back in 2017, when it developed the ability to use elevators in high-rise buildings. It has since become commercially available via ETH spinoff company ANYbotics. Another startup is marketing the wheeled version, which is now called the Swiss-Mile robot.

The current model of the ANYmal has a normal walking speed of 0.75 m (2.46 ft) per second, and can amble its way across indoor or outdoor terrain for 90 to 120 minutes per battery-charge. It's equipped with sensors such as a 360-degree lidar module, six depth-sensing cameras and two optical cameras, all of which feed data into two Intel 6-core processors.

https://newatlas.com/robotics/anymal-qu ... s-ladders/

[wjfox]

The Optimus robots at Tesla’s Cybercab event were humans in disguise

Behind-the-scenes human ‘assisting’ Optimus meant the We, Robot event said little about how far its humanoid robots have come.

Oct 13, 2024, 6:55 PM GMT+1

Tesla made sure its Optimus robots were a big part of its extravagant, in-person Cybercab reveal last week. The robots mingled with the crowd, served drinks to and played games with guests, and danced inside a gazebo. Seemingly most surprisingly, they could even talk. But it was mostly just a show.

It’s obvious when you watch the videos from the event, of course. If Optimus really was a fully autonomous machine that could immediately react to verbal and visual cues while talking, one-on-one, to human beings in a dimly lit crowd, that would be mind-blowing.

https://www.theverge.com/2024/10/13/242 ... obot-event

[funkervogt]

While the fact that humans were remotely controlling the robots has disappointed many people, it usefully highlights the future potential of teleoperation, which will ultimately let us outsource manual labor jobs to humans anywhere on Earth. Once robots get cheaper and nimbler, and virtual reality hardware improves, humans will be able to remotely operate humanoid machines with nearly the same speed and dexterity as their regular bodies. The "Indian call centers" of the future will be buildings full of virtual reality pods where people clean houses, pick vegetables, and fix cars on the other side of the planet.

Even if AGI is never created, something akin to another Industrial Revolution is coming thanks to teleoperation and narrow AIs that can do niche tasks and sets of tasks as well as humans.

[firestar464]

This makes a lot of sense

[firestar464]

Singlehandedly one of the best if not the best take I've ever read on this.

[firestar464]

Researchers develop method enabling LLMs to answer questions more concisely and accurately

https://techxplore.com/news/2024-10-met ... ately.html

[firestar464]

Apple researchers suggest artificial intelligence is still mostly an illusion

https://techxplore.com/news/2024-10-app ... usion.html

Title could be better IMO. Should be something along the lines of "Apple researchers show LLMs still struggle with reasoning"

[firestar464]

Can AI Learn Physics from Sensor Data?

https://www.archetypeai.io/blog/can-ai- ... ensor-data

[firestar464]

nGPT: Normalized Transformer with Representation Learning on the Hypersphere

https://arxiv.org/pdf/2410.01131

This paper introduces a new type of Transformer model called nGPT (Normalized GPT), designed to make machine learning models learn faster and better by changing how they handle information. Here's a simpler explanation of what the paper is about:

The Problem:

- Transformers are a powerful tool used in many AI tasks, especially in language models like GPT (used in chatbots).
- The problem with current Transformers is that they can train slowly and need a lot of computing power.

What’s New in nGPT:

The nGPT model changes the way Transformers work by forcing everything inside the model (embeddings, layers, and matrices) to live on something called a hypersphere. This just means that all the numbers in the model are controlled to fit within certain limits, which keeps things balanced and organized.

The Main Idea:

1. Normalizing Everything: In nGPT, every part of the model is "normalized," meaning it's kept at a constant size. This helps the model work more efficiently because it doesn't let things get out of control (like values growing too big or too small).

2. Faster Learning: Because everything is normalized, the model can figure out the patterns in data (like language) much faster. The researchers showed that nGPT can learn 4 to 20 times faster than the usual Transformer models!

3. Working on a Hypersphere: Imagine all the information the model processes as points on a globe (the hypersphere). As the model learns, it moves these points around the globe's surface in a controlled way to predict the next word in a sentence.

How It Works:

- Attention and MLP Blocks: These are the key parts of a Transformer that help it focus on different parts of the input (like focusing on specific words in a sentence). In nGPT, these blocks are carefully controlled using "scaling factors" to keep everything normalized.

- Eigen Learning Rates: These are special learning controls that adjust how much the model updates itself each time it learns from new data. This makes the learning process more flexible and efficient.

Results:

The researchers tested nGPT against the regular GPT models using a large collection of text data. They found that:
- nGPT learned much faster and required fewer training steps to achieve the same level of accuracy as the regular GPT models.
- Better organized embeddings: The model organized its internal data (like word representations) in a more balanced way, making it more stable and efficient.

Simple Takeaway:

This new model (nGPT) makes AI models like GPT faster and more efficient by keeping all the information inside it under control (normalized). It helps the model learn better by thinking of its data as moving on the surface of a sphere, which leads to quicker and smarter learning.

[firestar464]

Microsoft Open-Sources bitnet.cpp: A Super-Efficient 1-bit LLM Inference Framework that Runs Directly on CPUs

https://www.marktechpost.com/2024/10/18 ... y-on-cpus/