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belacscole t1_j8bf6ol wrote

I wonder if this is the ultimate path to reaching general intelligence. After all, humans evolved by learning to master tools.

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big_gondola t1_j8biqv4 wrote

I might say we gain general intelligence by creating different models for different tasks and gain experience on when to call which. This has the when to call which, but not the creation of new models.

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diviludicrum t1_j8bxeji wrote

I still think u/belacscole is right - this is analogical to the rudimentary use of tools, which can be done by some higher primates and a small handful of other animals. Tool use requires a sufficient degree of critical thinking to recognise a problem exists and select the appropriate tool for solving it. If done with recursive feedback, this would lead to increasingly skilful tool selection and use over time, resulting in better detection and solution of problems over time. Of course, if a problem cannot possibly be solved with the tools available, no matter how refined their usage is, that problem would never be overcome this way - humans have faced these sorts of technocultural chokepoints repeatedly throughout our history. These problems require the development of new tools.

So the next step in furthering the process is abstraction, which takes intelligence from critical thinking to creative thinking. If a tool-capable AI can be trained on a dataset that links diverse problems with the models that solve those problems and the process that developed those models, such that it can attempt to create and then implement new tools to solve novel problems, then assess its own success (likely via supervised learning, at least at first), we may be able to equip it with the “tool for making tools”, such that it can solve the set of all AI-solvable problems (given enough time and resources).

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uristmcderp t1_j8db0gw wrote

The whole assessing its own success is the bottleneck for most interesting problems. You can't have a feedback loop unless it can accurately evaluate if it's doing better or worse. This isn't a trivial problem either, since humans aren't all that great at using absolute metrics to describe quality, once past a minimum threshold.

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ksatriamelayu t1_j8ebpx4 wrote

Do people use things like evolutionary fitness + changing environments to describe those quality? Seems dynamic environment might be the answer?

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Oat-is-the-Best t1_j8ef5x0 wrote

How do you calculate your fitness? That has the same problem of a model not being able to assess its own success

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LetterRip t1_j8dpgxc wrote

There are plenty of examples of tool use in nature that don't require intelligence. For instance ants,

https://link.springer.com/article/10.1007/s00040-022-00855-7

The tool use being demonstrated by toolformer can be purely statistical in nature, no need for intelligence.

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thecodethinker t1_j8dpuru wrote

It is purely statistical, isn’t it?

LLMs are statistical models after all.

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imaginethezmell t1_j8g4f64 wrote

there are apis for auto ml already

it can simply learn the task to use other ai to create models

its over

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robotix_dev t1_j8cekxc wrote

I’ve long thought this is the next stepping stone in the path the path to AGI. The next big step IMO is dynamic, online model augmentation to enable learning new concepts.

Both of those combined seem like a basic approximation of what goes on in our brain.

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yashdes t1_j8d8lf9 wrote

I've definitely wondered about this exact thing myself, especially when talking to chatgpt when it responds with insert x here, why couldn't that just be taken out and replaced with the appropriate API call

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pyepyepie t1_j8dgah3 wrote

Did it learn to master tools though? I see it more as a neuro-symbolic system (is it the correct term?). It happens a lot in production.

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Despacereal t1_j8d971u wrote

In a way yes. I think general intelligence (consciousness in most animals) developed evolutionarily to manage a wide variety of sensory inputs and tasks, and to bridge the gaps between them.

As we develop more individual areas of AI, we will naturally start to combine them to create more powerful programs, such as Toolformer combining the strengths of LLMs and other models. Once we have these connections between capabilities, it should be easier to develop new models that learn these connections more deeply and can do more things.

Some of the things that set us apart from other animals are our incredible language and reasoning capabilities which allow us to understand and interact with an increasingly complex world and augment our capabilities with tools. The perceived understanding that LLMs display using only patterns in text is insane. Combine that with the pace of developments in Chain of Thought reasoning, use of Tools, other areas handling visuals, sound, and motion, and multimodal AI, and the path to AGI is becoming clearer than the vision of a MrBeast™ cataracts patient.

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thedude0425 t1_j8hzozd wrote

Intelligence and physical traits evolved in humans through random mutation that eventually allowed humans to use tools.

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SnooStories4137 t1_j8lrsug wrote

Some reinforcement learning like algorithm seems like really interesting next step here. Observation = task (like qa or mask filling), actions = api call where the output updates the observation via concatenation as in the paper, environment is apis and database and python installation etc, state is network weights, reward is loss function before and after update to observation.

I feel like even if the only api is just generating text using itself to update the observation ('to help itself think') intuitively seems like it could help for some things. Rather than try to fill in the mask right away, it might recognize better to first 'think a little' to update its working memory (which is of course the observation here).

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