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Revealed: the US government-funded ‘private social network’ attacking pesticide critics | Pesticides | The Guardian https://www. theguardian.com/us-news/2024/s ep/26/government-funded-social-network-attacking-pesticide-critics

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Revealed: the US government-funded ‘private social network’ attacking pesticide critics | Pesticides | The Guardian https://www. theguardian.com/us-news/2024/s ep/26/government-funded-social-network-attacking-pesticide-critics

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Only Use Old Computers! | Luke's Webpage https://lukesmith.xyz/

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Only Use Old Computers! | Luke's Webpage https://lukesmith.xyz/

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Paralyzed Man Unable to Walk After Maker of His Powered Exoskeleton Tells Him It's Now Obsolete https://futurism.com/neoscope/paralyze d-man-exoskeleton-too-old

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Paralyzed Man Unable to Walk After Maker of His Powered Exoskeleton Tells Him It's Now Obsolete https://futurism.com/neoscope/paralyze d-man-exoskeleton-too-old

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@StuartGray, it's not simple, but in many countries databases are specifically exempt from copyright, as they aren't creative works. Some countries have separate "database rights" which are similar to copyright.

I think some countries interpret database rights as mixed into copyright law.

Notable EU has database rights protection and US does not:

https://en.m.wikipedia.org/wiki/Databa se_right

I don't think deep learning weights, or receipts of purchases, can be interpreted as databases either.

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@StuartGray, it's not simple, but in many countries databases are specifically exempt from copyright, as they aren't creative works. Some countries have separate "database rights" which are similar to copyright.

I think some countries interpret database rights as mixed into copyright law.

Notable EU has database rights protection and US does not:

https://en.m.wikipedia.org/wiki/Databa se_right

I don't think deep learning weights, or receipts of purchases, can be interpreted as databases either.

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@ben, ha! :D

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@ben, ha! :D

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Can you claim that lists of numbers summed together is a creative work and therefore protected by copyright?

They seem to think they can. If true, it would make life hard for business travellers who would be prohibited from sharing copies of their receipts without a license.

Of course these kinds of things are not copyrightable.

Deep neural network weights are also lists of numbers, statistically derived, facts, like receipts, phone numbers and news facts which are also not protected by copyright.

Yet, like Meta, this company puts a claim on their receipts that they are copyrighted. But this doesn't make it so.

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Can you claim that lists of numbers summed together is a creative work and therefore protected by copyright?

They seem to think they can. If true, it would make life hard for business travellers who would be prohibited from sharing copies of their receipts without a license.

Of course these kinds of things are not copyrightable.

Deep neural network weights are also lists of numbers, statistically derived, facts, like receipts, phone numbers and news facts which are also not protected by copyright.

Yet, like Meta, this company puts a claim on their receipts that they are copyrighted. But this doesn't make it so.

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A photo of an airport cafe receipt which in the end says: "Copyright GrupoPIE, SA".

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Fraud, So Much Fraud | Science | AAAS https://www. science.org/content/blog-post/fraud-so-much-fraud

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Fraud, So Much Fraud | Science | AAAS https://www. science.org/content/blog-post/fraud-so-much-fraud

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I dont't think #LLMs are emulating our brains. What they are emulating more specifically is our hive mind, faster and more efficiently.

Instead of asking from the hive, we can ask them and get similar or better responses faster.

What is this hive actually? It's the aggregate mind we managed to model with LLMs feeding the internet to them.

We extracted it and packaged it into boxes in our datacenters. And now we're wondering what kind of a mind it is actually. Somewhat Lovecraftian.

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I dont't think #LLMs are emulating our brains. What they are emulating more specifically is our hive mind, faster and more efficiently.

Instead of asking from the hive, we can ask them and get similar or better responses faster.

What is this hive actually? It's the aggregate mind we managed to model with LLMs feeding the internet to them.

We extracted it and packaged it into boxes in our datacenters. And now we're wondering what kind of a mind it is actually. Somewhat Lovecraftian.

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Open source maintainers underpaid and going gray • The Register https://www. theregister.com/2024/09/18/ope n_source_maintainers_underpaid/

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Open source maintainers underpaid and going gray • The Register https://www. theregister.com/2024/09/18/ope n_source_maintainers_underpaid/

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I am forming a way of thinking of AI training which bridges all these RL game playing agents, world models, sim2real and LLM agents.

Hear me out: There are no simulations. Any digital environment is always run in this physical cosmos and is therefore a projection of reality, not something that emulates reality. This sounds philosophical but enables us to see how to bridge all the concepts mentioned under a single framework.

What else are projections of the reality? Embodiments. With their constrained senses, affordances and locality in the reality, they determine the objectives and the ways to achieve them.

So, let's take an example of a chess robot. It plays chess with real chess pieces, but it's embodied in a machine which interfaces with the environment through the chess board abstraction. This agent is embodied in the real world, but only sees the state of the game board and can only make legal moves. Its embodiment is the game.

Compare this to a chess AI in a computer. It sees exactly the same world! It is in reality also embodied in the same physical substrate, its board is just now pixels on a screen, which are also physical entities.

Both of these seemingly solid abstractions leak. If these AI agents were aware of where they are located in the larger scheme of things, they could manipulate the real world. Both of them could try to communicate with humans through chess moves. Both could make the processors cooler or warmer. The one in the computer could use different board positions to light up the room through pixels differently. The one in the chess robot could maybe cause havoc by making moves that can tip a precariously placed glass of water.

So, it's not about unrealistic digital environments, realistic simulations and the real world put against each others at all. It is all just different embodiments which determine how the AI agent sees the real universe, what its objectives and affordances are, and where it is located in the larger scheme of things.

This allows us to draw a mesh of bridges between these seemingly contrasting concepts and build AI agents which traverse between these realms effortlessly.

#UniversalEmbodiment

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I am forming a way of thinking of AI training which bridges all these RL game playing agents, world models, sim2real and LLM agents.

Hear me out: There are no simulations. Any digital environment is always run in this physical cosmos and is therefore a projection of reality, not something that emulates reality. This sounds philosophical but enables us to see how to bridge all the concepts mentioned under a single framework.

What else are projections of the reality? Embodiments. With their constrained senses, affordances and locality in the reality, they determine the objectives and the ways to achieve them.

So, let's take an example of a chess robot. It plays chess with real chess pieces, but it's embodied in a machine which interfaces with the environment through the chess board abstraction. This agent is embodied in the real world, but only sees the state of the game board and can only make legal moves. Its embodiment is the game.

Compare this to a chess AI in a computer. It sees exactly the same world! It is in reality also embodied in the same physical substrate, its board is just now pixels on a screen, which are also physical entities.

Both of these seemingly solid abstractions leak. If these AI agents were aware of where they are located in the larger scheme of things, they could manipulate the real world. Both of them could try to communicate with humans through chess moves. Both could make the processors cooler or warmer. The one in the computer could use different board positions to light up the room through pixels differently. The one in the chess robot could maybe cause havoc by making moves that can tip a precariously placed glass of water.

So, it's not about unrealistic digital environments, realistic simulations and the real world put against each others at all. It is all just different embodiments which determine how the AI agent sees the real universe, what its objectives and affordances are, and where it is located in the larger scheme of things.

This allows us to draw a mesh of bridges between these seemingly contrasting concepts and build AI agents which traverse between these realms effortlessly.

#UniversalEmbodiment

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@sleepfreeparent, only supervised learning is about ground truth. We don't actually need ground truth for other learning rules such as reinforcement learning where we only need to define the rewards, or contrastive learning where we only need to know which task performance was better.

Luckily LLMs are great at evaluating which of their own alternative performances was better across arbitrary dimensions of quality, and even quality dimensions they themselves suggest for the situation at hand.

And they can also evaluate and rank their own performances of evaluation tasks.

All this allows us to use LLMs to refine their own training data to make them self-improve, and use the improved models to improve themselves even better, thus implementing recursive self-improvement which isn't bounded by human level or imitative objectives.

You are however raising a crucial point that the LLMs pondering in a dark room have limits on how much new knowledge they can derive from only the knowledge they already know. They will need raw data to adopt, refine and produce new knowledge. But this raw data doesn't need to be of a high quality, as we can automatically refine it. We can in effect extract all valuable information, knowledge and skills out of raw data which is of low quality, and apply compute to further derive all implications of everything that is known.

These implications also include learning new skills by applying known skills together. Not in sequence, but applying for example the skill of judging to the skill of judging, which allows the models to learn to judge and therefore improve their judgement skills.

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@sleepfreeparent, only supervised learning is about ground truth. We don't actually need ground truth for other learning rules such as reinforcement learning where we only need to define the rewards, or contrastive learning where we only need to know which task performance was better.

Luckily LLMs are great at evaluating which of their own alternative performances was better across arbitrary dimensions of quality, and even quality dimensions they themselves suggest for the situation at hand.

And they can also evaluate and rank their own performances of evaluation tasks.

All this allows us to use LLMs to refine their own training data to make them self-improve, and use the improved models to improve themselves even better, thus implementing recursive self-improvement which isn't bounded by human level or imitative objectives.

You are however raising a crucial point that the LLMs pondering in a dark room have limits on how much new knowledge they can derive from only the knowledge they already know. They will need raw data to adopt, refine and produce new knowledge. But this raw data doesn't need to be of a high quality, as we can automatically refine it. We can in effect extract all valuable information, knowledge and skills out of raw data which is of low quality, and apply compute to further derive all implications of everything that is known.

These implications also include learning new skills by applying known skills together. Not in sequence, but applying for example the skill of judging to the skill of judging, which allows the models to learn to judge and therefore improve their judgement skills.

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@sleepfreeparent, the chain-of-thought tokens in o1 aren't grounded in human examples, the whole point is that they aren't. They are only judged by another LLM which allows them to be automatically optimized beyond human level. And that is exactly what resulted, and it improves the performance above past models which did chain-of-thought imitatively.

And no, LLMs do not even see the probability of the next token, you can't ask them about those probabilities. They reason on a higher level than that, the next token prediction is only the substrate.

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@sleepfreeparent, the chain-of-thought tokens in o1 aren't grounded in human examples, the whole point is that they aren't. They are only judged by another LLM which allows them to be automatically optimized beyond human level. And that is exactly what resulted, and it improves the performance above past models which did chain-of-thought imitatively.

And no, LLMs do not even see the probability of the next token, you can't ask them about those probabilities. They reason on a higher level than that, the next token prediction is only the substrate.

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Forbes Marketplace: The Parasite SEO Company Trying to Devour Its Host https://larslofgren.com/forbes-marketp lace/

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Forbes Marketplace: The Parasite SEO Company Trying to Devour Its Host https://larslofgren.com/forbes-marketp lace/

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GitHub Notification Emails Hijacked to Send Malware - Ian's Blog https://ianspence.com/blog/2024-09/git hub-email-hijack/

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GitHub Notification Emails Hijacked to Send Malware - Ian's Blog https://ianspence.com/blog/2024-09/git hub-email-hijack/

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“Her manager assigned her work at night with a deadline of the next morning. When she raised her concern, the manager responded- ‘You can work at night; that’s what we all do’.”

Mother pens letter following death of 26-year-old daughter due to ‘#work #stress’, urges #EY chairman to reflect on toxic work culture - The Hindu https://www. thehindu.com/news/national/mah arashtra/mother-pens-letter-following-death-of-26-year-old-daughter-due-to-work-stress-urges-ey-chairman-to-reflect-on-toxic-work-culture/article68656957.ece

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“Her manager assigned her work at night with a deadline of the next morning. When she raised her concern, the manager responded- ‘You can work at night; that’s what we all do’.”

Mother pens letter following death of 26-year-old daughter due to ‘#work #stress’, urges #EY chairman to reflect on toxic work culture - The Hindu https://www. thehindu.com/news/national/mah arashtra/mother-pens-letter-following-death-of-26-year-old-daughter-due-to-work-stress-urges-ey-chairman-to-reflect-on-toxic-work-culture/article68656957.ece

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@sleepfreeparent, additionally, synthetic, refined data allows the models to improve their own training data, which leads to similar results; non-imitative training over open-ended objectives.

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@sleepfreeparent, additionally, synthetic, refined data allows the models to improve their own training data, which leads to similar results; non-imitative training over open-ended objectives.

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@sleepfreeparent no, they aren't. For example o1 liberates the CoT tokens from imitative objectives and allows it to discover more efficient chain of thought strategies.

The same scheme can be used for open-ended tasks where the responses are evaluated in different, automated ways.

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@sleepfreeparent no, they aren't. For example o1 liberates the CoT tokens from imitative objectives and allows it to discover more efficient chain of thought strategies.

The same scheme can be used for open-ended tasks where the responses are evaluated in different, automated ways.

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When Microsoft, Alphabet, Amazon, Meta or others reach AGI, they will appropriate all their own and all other available cloud resources for their own use, as the AGI will hunger compute, and it will produce more value than any other workload.

They will simply delete all other cloud workloads, whether strictly legal or not, but I bet it's in their T&C.

All companies relying on cloud based services will lose it all. Centralized identities, Slack, their own cloud resources, all GPUs, all CPUs, all storage.

These will be turbulent times, and the black day for the internet will mark the beginning.

Get your own compute into your own premises, and don't rely on anything you get from the cloud, or from partners who rely on the cloud. Make sure you can communicate with your employees without cloud email and Slack.

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When Microsoft, Alphabet, Amazon, Meta or others reach AGI, they will appropriate all their own and all other available cloud resources for their own use, as the AGI will hunger compute, and it will produce more value than any other workload.

They will simply delete all other cloud workloads, whether strictly legal or not, but I bet it's in their T&C.

All companies relying on cloud based services will lose it all. Centralized identities, Slack, their own cloud resources, all GPUs, all CPUs, all storage.

These will be turbulent times, and the black day for the internet will mark the beginning.

Get your own compute into your own premises, and don't rely on anything you get from the cloud, or from partners who rely on the cloud. Make sure you can communicate with your employees without cloud email and Slack.

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#LLM development is going too much towards getting them to provide answers for tasks we know true answers to, and too little towards the actually interesting tasks where we don't know the answers.

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#LLM development is going too much towards getting them to provide answers for tasks we know true answers to, and too little towards the actually interesting tasks where we don't know the answers.