Nuit Blanche: 03/01/2021

Wednesday, March 24, 2021

Computing with Light: How LightOn intends to unlock Transformative AI

I gave a talk at #mathia2021 conference on March 9th, 2021 where I drew a parallel between the scaling laws that enabled industrialization in the 1920's and the new scaling laws in AI of the 2020's. AI is at its infancy and it needs to have guiding principles (as embedded in these empirical laws) and it also needs to develop new hardware. I showed how, in this context, LightOn can help unlock Transformative AI. Enjoy!

All these other presentations by Yann LeCun, Kathryn Hess, Michael Jordan, Emmanuel Candès and others can be found in this collection of videos on Vimeo. Let me note that Michael made a similar argument as mine where we think of current stage of AI at its infancy in terms of industrialization.

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Monday, March 08, 2021

Unveiling LightOn Appliance

Today is a big day at LightOn as we unveil a hardware product, the Appliance, the world's first commercially available photonic co-processor for AI and HPC

If interested pre-ordering information is here: http://lighton.ai/lighton-appliance

We have had a few of these optical processing units in our own LightOn Cloud for the past two years and just retired one after more than 800 days working full time.

Here is the press release:

The future is now!

Leasing starts at 1900€/month or about US$2250/month

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Thursday, March 04, 2021

Video: LightOn unlocks Transformative AI

In the coming days, we'll be making another announcement but I wanted to first share a video we did recently. At LightOn, we don't build photonic computing hardware because it's fancy or cool (even though, it is cool) but because computing hardware is hitting the limits. I know what some say about Moore's law not being dead but the recent focus on Transformers and their attendant scaling laws makes it obvious that in order for more people to have access to these models, we need a new computing paradigm. Indeed not everyone can afford to spend a billion dollars in training these models. As Azeem was recently pointing out in one of his newsletters, this is how bad things will become:

The amazing thing is that we can start to compare the cost of training single AI models with the cost of building the physical fabs that make chips. TSMC’s state-of-the-art 3nm fab will run to around $20bn when it is completed in two years. A fab like this may be competitive for 5-7 years, which means it’ll need to churn out $7-8m worth of chips every day before it pays back.

And so at LightOn, we think that a combination of algorithms and (cool) hardware as the only pathway forward for computing large-scale AI. The video is right here, enjoy!

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