Vasudev Gupta @thevasudevgupta
trying to learn what AI learns | getting stuff done @unboxai_ | its all about investing thevasudevgupta.github.io India Joined December 2018-
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AI training cost estimates from the Stanford 2024 AI Index Report: Original transformer model - $930 GPT-3 - $4.3M GPT-4 - $78.4M Gemini Ultra - $191.4M
most of the people who claimed to implement 10x performance boost are either just enabling basic things supported by frameworks OR choosing the wrong baseline for comparison; and they pitch as if they did something super big and new.
🧠: “Let’s but this (text)book! Nice and now… instead of reading it… let’s buy another one!” 💡 All of the dopamine is generated only at the point of resolving to read something. After that there is no juice left 😅
many great deep learning advances are *so* obvious in hindsight that it’s hard to tell what was big deal all about
Programming is thinking + syntax. Maybe 90% thinking and 10% syntax. While you can automate away syntax, you can only ever outsource thinking to someone/something that can think. Attempts to outsource thinking to a syntax generator do not end well.
One of x computers is not willing to cooperate to learn.
are you utilising your model parameters to solve most important tasks or is it stuck is solving something which you don’t care because of how your data looks
flexibility and specialization are two opposite ends somehow. You push for one and you loose other. scaled up systems requires high specialization at cost of flexibility.
everything becomes so boring without enough incentive
current sota approaches on training ai are great but not sure how they are enough to achieve agi. i think agi can be achieved only when model is capable of rejecting to learn during training stage. current systems are basically forced to learn from whatever is there in data.
5 sleepless nights are worth it once your baby model starts learning something cool
If your words and actions don’t match => words are big lies
You value something only after you no longer have it.
feels like training on 8 nodes is small thing these days
Undervaluing oneself is self-harm at its worst.
merve @mervenoyann
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These are all true simultaneously: 1. Scaling up deep learning will keep paying off (unlock more applications, or higher performance on existing ones). 2. Scaling up deep learning isn't the path to AGI. 3. We aren't particularly close to AGI, and LLMs did not represent a step…
How to be as "smart" as Auto-Regressive LLMs: - memorize lots of problem statements together with recipes on how to solve them. - to solve a new problem, retrieve the recipe whose problem statement superficially matches the new problem. - apply the recipe blindly and declare…
There’s an art to distilling these to the absolute minimal necessary text. The human brain can’t comprehend how stupid these things are without practice.
@TheZachMueller @huggingface It’s such an awesome library, and an even better codebase to read. Thanks for your hard work :)
It's a great week for open source AI! Data is among the highest impact work to push the field forward. Bravo to 🤗
Llama3 was trained on 15 trillion tokens of public data. But where can you find such datasets and recipes?? Here comes the first release of 🍷Fineweb. A high quality large scale filtered web dataset out-performing all current datasets of its scale. We trained 200+ ablation…
The whole problem with the world is that fools and fanatics are always so certain of themselves, and wiser people so full of doubts. - Bertrand Russell
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@AzamHussai70792 I am not sure if emergent behavior exists really? The evidence for emergence seems spotty and possibly just explained by high dimensional interpolation? Not sure fully though
Most of the neural network parameters is allocated to model what the neural network doesn’t know So if you show it noise it will spend even more parameters trying to reveal the signal, I think
Programming with copilot is like having a gps that kind of works but is also constantly trying to drive you off a cliff
From what I’ve seen AI model training startup : the first two rounds of funding are easy the subsequent ones are hard AI model tooling startup : the first two rounds of funding are hard the subsequent ones are easy(-ier)
@ClementDelangue I think they are more likely saying they couldnt license the data they paid for, for general use
BIG NEWS 🚨 Apple has reportedly refused to help ED in unlocking Arvind Kejriwal's iPhone. Apple has claimed that the data can only be accessed with the password set by the owner of the device. Kejriwal is saying that he has forgotten the password ⚡ ED had contacted Apple and…
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
It’s not how much compute you have, it’s how you use it
The idea that you can get rich without doing a lot of work is so false that you can use its falsity as a heuristic. You can make more by deliberately chasing hard problems.
Good engineering matters. It's not a nice-to-have. Poor engineering means leaving your GPUs underutilized and spending 2-3x more on model training.
@HanchungLee You just have to multiply by a mask on the loss. Would be relatively easy to do.