deep-learning

Stability.AI – Democratizing Access to Machine Learning

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Stability.AI, a UK-based startup famous (or notorious?) for releasing the Stable Diffusion image generation model, just raised $100m on a $1bn valuation. 

Their goal is to “Democratize AI.” They have done so by open-sourcing the Stable Diffusion text-to-image model and are working on releasing other models, including large language models. 

This approach is in stark contrast to the one taken by OpenAI, Facebook, Google, etc. These companies have gated access to ML models like GPT-3 via APIs or invite-only programs. The reasoning is that these models could be used to generate hateful text and images and are generally too dangerous to be released to the ignorant masses.

In a recent interview, Emad Mostaque, the CEO of Stability.Ai and a fascinating thinker, talks about the inevitability of generative and large language models leaking out to the wild. He wants to focus on giving people a framework for the ethical use of AI while giving them the tools to build and train models for their specific uses. 

Stability.Ai has struck a deal with Eros Interactive to get access to their massive library of Indian content. Can you imagine what could be trained using that data?

Congratulations to Stability.Ai. I am curious about what this more open (or perhaps reckless?) approach to ML will bring us.

Generated image of a Robot having a celebratory drink.
Image generated by Stable Diffusion – Prompt: “A happy robot drinking champagne at a cocktail party at night, oil painting, muted, candid, high resolution, trending on artstation”

AlphaTensor – Speeding up number crunching with Machine Learning

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For some, matrix multiplication may trigger memories of tedious high school algebra exercises. Last week, this humble mathematical operation was also the topic of a significant breakthrough in machine learning. 

Art generated by Stable Diffusion

Background – Matrix Multiplication

Matrix multiplication is the foundation on which many core computational operations are built. Graphic processing, machine learning, computer gaming, etc. – all rely on matrix multiplication. At any given point in time, there are millions of computers doing (probably) billions of matrix multiplication operations. 
Making this humble operation faster would result in significant computational and efficiency gains.

Why do we want faster matrix multiplication?

Multiplying two matrices involves doing a large number of multiplication and addition operations. 
For example, multiplying a 4X5 and a 5X5 matrix involves 100 multiplication operations using the traditional matrix multiplication method that has been around since the early nineteenth century. 
In 1969, a mathematician, Volker Strassen, came up with an ingenious method that reduced the number of operations required by about 10%. This was hailed as a groundbreaking discovery in the world of mathematics.

DeepMind Enters the Arena

This brings us to DeepMind’s paper last week, where they used the AlphaTensor deep learning model to discover a new algorithm for matrix multiplication that is faster by about 10 – 20% than the Strassen method. 
This is a *colossal deal*!
We are seeing a machine learning model find new algorithms to solve material, real-world problems. We have already seen DeepMind make groundbreaking discoveries in computational biology with AlphaFold. We now see applications of its Deep Learning models (based on playing games) to foundational aspects of the modern world. 
Exciting times are ahead!