Viewing a single comment thread. View all comments

Ortus14 t1_j2mbg8x wrote

Reply to comment by Akimbo333 in Why can artificial intelligences currently only learn one type of thing? by ItsTimeToFinishThis

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.

4

Akimbo333 t1_j2mdvn6 wrote

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?

2

Ortus14 t1_j2mfz2n wrote

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.

2

airduster_9000 t1_j2mg9rc wrote

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.

1

Akimbo333 t1_j2mimns wrote

Wow that's cool! What is CLIP?

1

airduster_9000 t1_j2mrgtl wrote

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

​

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/

2

Akimbo333 t1_j2miqzn wrote

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

1

ItsTimeToFinishThis OP t1_j2mgc06 wrote

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

1

Ortus14 t1_j2nlbxk wrote

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.

2