🤖⚡️ World's First All-Optical AI Processor, End-to-End Machine Learning, and Apple Intelligence

A Newsletter for Computing Geeks, Entrepreneurs, and STEM Graduates

This week, we announced our collaboration with Intel Ignite, Intel’s global startup program for early-stage deep tech companies—learn more here. We also released a database of all the computing startups we have interviewed previously–more on this below.

Michael Kissner: Developing the World’s First All-Optical Processor For High-Performance Computing and AI at Akhetonics*

Making transistors increasingly smaller so they could fit on a silicon chip triggered a microprocessor revolution in the 1970s and formed the basis for our modern digital society. 

Fifty years later, we can see history being made by Akhetonics, assembling optical transistors into logic circuits and processing information all-optically—reaching orders of magnitude greater efficiency, bandwidth, and speed than electronic processors.

Since our last interview in the summer of 2022, Akhetonics has gone through the Intel Ignite program and made significant progress in making optical, general-purpose processors a reality. We had the pleasure of talking again with Michael Kissner, co-founder and CEO of Akhetonics, about how they’re making optical computing a reality, what challenges remain, and how the Intel Ignite program accelerated their journey:

*Sponsored post—we greatly appreciate the support from Intel Ignite

Future of Computing News

🧠 Cerebras’s wafer-scale chips excel at molecular dynamics and AI inference: Giant Chips Give Supercomputers a Run for Their Money (Spectrum IEEE)

Learn more about AI-based materials discovery from our previous interview with Materials Nexus: Shaping the Future of Machine Learning for Net-Zero Materials

Funding News

Learn more about their tech from our interview with Black Semiconductor: Shaping the Future of Optical Chip Interconnects

🤖 Reasonance: Shaping the Future of End-to-End Machine Learning Workflows

Over the past five years, machine learning has evolved from a curiosity to a transformative force in various industries.

However, many companies face significant challenges in harnessing the power of machine learning. Whether developing their own models or leveraging external ones, organizations often grapple with data ownership, security, and compliance issues.

Reasonance was founded by Todor Kostov, Konstantin Tsenkov, and Manuel Lang in 2020 to help customers implement enterprise-grade machine learning and analytics solutions and they have built the MLOps platform ATLAS, helping organizations streamline their machine learning projects.

Learn more about the future of end-to-end machine learning workflows from our interview with the co-founder and CEO, Todor Kostov:

Check Out Our Exclusive Airtable of all Interviewed Startups:

👉 Country & City: where they are based
👉 Tech Tags: what computing technologies they're working on
👉 Insight: key insight from my interview with them

You can also filter and download the Airtable, and I'll update it as I'm interviewing more startups – just click on the screenshot below :-)

(and please don’t share the link, but tell people instead to sign up for the newsletter, and they’ll get access too: https://www.future-of-computing.com/database/)

Join Our New Discord Community

… and connect with fellow computing geeks:

Good Reads

In this work, we show that MatMul operations can be completely eliminated from LLMs while maintaining strong performance at billion-parameter scales. Our experiments show that our proposed MatMul-free models achieve performance on-par with state-of-the-art Transformers that require far more memory during inference at a scale up to at least 2.7B parameters.”

“This video covers the whole process: First we build the GPT-2 network, then we optimize its training to be really fast, then we set up the training run following the GPT-2 and GPT-3 paper and their hyperparameters, then we hit run, and come back the next morning to see our results, and enjoy some amusing model generations.”

“I started to wonder if the whole process can be represented in a spreadsheet since all the calculations are fairly simple. I'm a visual thinker, I couldn't think of a better way to do it. Then with some trial and errors, I wrote the full inference pipeline of the nanoGPT architecture into a single spreadsheet. Forget python, it turned out that spreadsheet is all you need.”

“But digital infrastructure ultimately requires physical infrastructure. All that software requires some sort of computer to run it. The more computing that is needed, the more physical infrastructure is required.”

“Apple’s orientation towards prioritizing users over developers aligns nicely with its brand promise of privacy and security: Apple would prefer to deliver new features in an integrated fashion as a matter of course; making AI not just compelling but societally acceptable may require exactly that, which means that Apple is arriving on the AI scene just in time.”

“Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity.”

“Agnostiq is operating at the forefront of AI infrastructure with their flagship product, Covalent. Covalent is a cloud-agnostic accelerated computing platform, allowing startups and enterprises to build any AI or HPC applications in a simple, scalable, and cost-effective way.”

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