Well ChatGPT can learn math and history and coding

I think that is the wrong take. It's like asking what would happen if we teach language to the cerebellum (the part of your brain that coordinates movement). Our brain has plenty of specialist structures to deal with specific functions e.g the visual cortex, the amygdala for memory etc.

What I think is significant is that the same technology (artificial neural networks) lets us do so much (in different implementations) suggesting we are on the right track towards AGI.

There is an ai that can perform over 600 tasks, most of them at or exceeding human level.

And humor, poetry, debate, and story telling

Deep mind has also combined a LLM with a vision system to create an Ai that's better at both tasks, including tasks combining vision and language.

Oh cool! So that means that it can look at created videos like Tik Tok, Instagram, YouTube, and video lectures on Khan academy. It sounds like this thing will be the smartest thing ever! Though I'm assuming that it can interpret the sound though lol! It would also be able to help humanoid robots to be able to sing and dance!!! Oh yeah, and what is the name of that new new Deepmind AI that allows the LLM to see?

It's called Flamingo. It can't do all of that yet but it can solve problems that combine text and images.

https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model

If I remember Correctly, Open Ai also has the goal of combining vision and LLM systems on their path of creating more and more general Ais.

?

People assume that GPT4 might be multi-modal- and be able to handle more than juts text. Since its OpenAI - combining GPT, CLIP and Dall-E at some point seems given.

If so, why then is the creation of an agi treated as a mystery?

Gato

You can't teach language to AlphaGo.

AlphaX is an architecture that is made to quickly traverse a huge space of possibilities. That's why it's good at games like chess and Go, where the AI has to think ahead of what the game state could be N moves down the line, each move exponentially increasing the amount of game states. Same for AlphaFold and protein folding.

GPT is a transformer, which gets an input vector, possibly, but not necessarily representing language, and produces an output vector. Through self attention it is able to weigh certain parts of the vector on it's own, similar to how humans weigh certain words in a sentence differently.

StableDiffusion is a Denoising Diffusion Model, a model that takes a 2D tensor as input (possibly representing an Image) and producing a 2D tensor as output. It's used to learn to reverse some noise algorithm that has been applied to the dataset.

You see, each of these architectures have a very specific form of input and output, and their structure enables them to perform a certain task very well. You can't "teach" ChatGPT to produce an image, because it doesn't have a way to process image data.

Wow that's cool! What is CLIP?

Oh so it can only do images. Now that's disappointing! But still cool though!

You know why the AI programmer limited his robot's intelligence to just folding towels? Because he was worried about a robot apocalypse. I mean, can you imagine? Robots taking over the world? Doing our jobs for us? It's almost too good to be true. But then again, I guess if a robot could fold towels better than me, it might make sense to let them take over. I mean, I'm pretty bad at it. But then again, I'm pretty bad at a lot of things. Like, I'm terrible at math. Like, really bad. I mean, I'm not even good at basic math. I'm at the level where I can't even do fractions. Like, I don't even know what a numerator is. But hey, at least I can tell a joke, right? Or maybe not. Maybe I'm just terrible at that too. I guess we'll never know. But hey, at least the robot is good at folding towels.

I use it to generate colloquial language, my favorite quote it generated was “Locked up tighter than a pair of plumber overalls” and “Creepier than barney at a cake party”

>AlphaX is an architecture that is made to quickly traverse a huge space of possibilities.

So, brute force.

Lol good ones!

Lol good one!

CLIP is the eyes that let it see images - not just read text and symbols.

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GPT = Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. Given an initial text as prompt, it will produce text that continues the prompt.

CHATGPT = Special trained version of GPT3.5 for chat.

DALL-E = DALL-E (stylized as DALL·E) and DALL-E 2 are deep learning models developed to generate digital images from natural language descriptions, called "prompts".

CLIP = CLIP does the opposite of DALL-E: it creates a text-description for a given image. Read more here: https://openai.com/blog/clip/

Cool thanks for the info!!!

AI can currently learn in three ways unsupervised, supervised with labeled data, or reinforced: it knows it has done well if it wins a game or achieved other objectives such as capturing a pawn. But AI is basically software and hardware configured by humans. Someone programmed the machines to interpret data in a certain way. You can tell them to interpret a list of numbers as the representation of a text or an image. Actually you are not telling them anything. If you write code it gets compiled or interpreted to lower level assembly code or instructions for a virtual machine. Which in turn is converted to machine language. All computers understand are very basic instructions, depending on the specifics of the hardware.

You can say that the human brain is just a soup of neurons, fluids, and neurotransmitters. But we clearly don't have machine or assembly language equivalents. The brain is much too complex with who knows how many layers of abstraction. It was clearly not designed by teams of engineers. Maybe this architecture is why brains are more flexible than current AI.

Click Bait articles and the desire for humans to feel special. Reading books and papers by those in the field and those who dedicate their lives to studying it, will give you a clearer perspective.

It's predicated on a semantic labeling mistake. The mistake being, labeling intelligences as either being "narrow" or "general", when in reality all intelligences fall on a spectrum in how broad the problem domains they can solve are. Humans are not general problem solvers but lie somewhere on this spectrum. The same goes for all other animal species and synthetic intelligences.

As compute costs predictable diminish over time do to a compounding effect of multiple exponential curves interacting with each other such as decreasing solar costs (energy costs), decreasing Ai hardware costs (advancing more rapidly than gaming hardware now), exponential increase in available compute (each super computer built is capable of exponentially more compute than the last), and decreasing software implementation costs (improvement in Ai software libraries and ease of use), the computation space for Ai's increases at an exponential rate.

As this computation space increases there is room for intelligences capable of a wider and wider range of problems. We already have algorithms for the full range of spaces, including an algorithm for perfect general intelligence (far more general than humans) that would require extremely high levels of compute. These algorithms are being improved and refined but they already exist, and the things we are doing now are refined implementations of decades old algorithms now that the compute space is available.

What the general public often misses is that, that compute space is growing exponentially (sometimes they miss this by hyper focusing on only one contributing factor such as the slow down of mores law missing the greater picture), and that Ai researchers have already effectively replicated human vision which accounts for roughly 20% of our compute space. When available compute increases by more than a thousand fold a decade, it's easy to see humans are about to be dwarfed by the cognitive capacity of our creations.