Hi friends 👋,

Happy Friday and welcome back to our 37th Weekly Dose of Optimism.

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(1) Quantum Computers, explained with MKBHD

From Huge If True

We’re big fans of Cleo Abram’s Youtube series “Huge If True” in which she explains important, often complicated topics in simple language and with some dope video editing. The combo of the the two means that even guys like us can start to understand topics as complex as quantum computing.

In the WDoO #31, we covered that a team of Google researchers had achieved the second of six milestones on the way towards producing a “useful quantum computer.” Quantum computing may be closer than we think, and the ways in which it could transform the world are hard to imagine because these are not simply bigger, faster computers…they’re something different. They are 158 million times faster than the most powerful supercomputers we have today.

Cleo was joined by big-time tech YouTuber, Marques Brownlee, to take on the topic. If you have 18 minutes to spare this weekend and want to get smarter on what will be a very relevant topic in the future, we recommend watching!

(2) Checks and balances: Machine learning and zero-knowledge proofs

Elena Burger for a16z

The question isn’t “will AI be tremendously valuable,” the question is “how do we build these systems in such a way that anyone interacting with them will be able to reap its economic benefits and, if they so desire, ensure that their data is used in a way that honors their right to privacy.”

Packy here. Something that I feel very strongly about but have only started to put into words is the idea that AI and crypto were made for each other, different technologies that came to be at right about the same time through a mix of riding similar waves and that everything happens for a reason je ne sais quoi.

Venkatesh Rao captured the idea best in one of my favorite essays of the past few months, The Rise of Mediocre Computing:

It feels like AI and crypto are mathematical evil twins of sorts; that each is somehow deeply incomplete without the other. The mild culture-warring between the two tribes is in fact a symptom of deeper kinships.

The hints are subtle and all over the place. I’ll take an inventory in a future post, but here’s one as a sample: AIs can be used to generate “deep fakes” while cryptographic techniques can be used to reliably authenticate things against such fakery. Flipping it around, crypto is a target-rich environment for scammers and hackers, and machine learning can be used to audit crypto code for vulnerabilities. I am convinced there is something deeper going on here. This reeks of real yin-yangery that extends to the roots of computing somehow. It’s not just me hallucinating patterns where there are none.

Unifying AI and crypto at a foundational level smells like a problem on par with unifying relativity and quantum mechanics in physics.

Elena’s essay focuses on one of the more immediately compelling intersections of the two domains: ML and ZKPs. She adds more meat to the bone than VGR or I did.

The question, she writes, isn’t whether AI will be valuable — of course it will be — but how to best distribute that value and allow people to control their own data. The answer Elena proposes isn’t to halt progress, but to “push for models that are open-source, and in cases where model providers want their weights or data to be private, to secure them with privacy-preserving zero-knowledge proofs that are on-chain and fully auditable.”

The whole piece is worth reading — Elena goes in-depth on five ways ZKPs can be applied to ML and on shortcomings that remain to be overcome — but there’s one point I want double-highlight:

High demand for blockchain compute has incentivized zero-knowledge proof research.

As we’ve covered here before, ZKPs are going to be useful beyond crypto, but research on them has only accelerated so quickly because of crypto. It’s pretty incredible how these things line up.

(3) Artificial intelligence has advanced despite having few resources dedicated to its development – now investments have increased substantially

Max Roser for Our World in Data

The developments in the past happened despite the fact that funding and brainpower dedicated to AI were quite limited. As these charts have shown, this has changed. Across a range of metrics, the resources dedicated to AI development have increased substantially.

The fact that the field advanced with relatively small resources, and now has much larger resources at its disposal – leading to rapid advances in the last few years – is one reason why I expect AI technology to continue to develop rapidly and to exert a larger and larger influence on our world. 

ChatGPT and other Transformer-based models have captured the world’s attention, but AI advancements may just be heating up. Why? The corporate investment in AI has gone exponential in the last three years, with no signs of slowing down.

In the three years prior to the publication of “Attention Is All You Need” — the 2017 research paper from Google that introduced Transformers — about $75B was invested in AI globally. In 2021 alone, that number more than doubled to $160B. And with the recent GPT-hype, I’d be willing to bet 2023 will be closer to $300B. That’s a >10x increase in annual investment over the last decade.

If we think things are crazy now, imagine what $300B+ of annual AI R&D and a useful quantum computer will yield…

Back in the present day, you can’t check Twitter without getting hit with at least 2-3 very cool AI products/advancements each day, spun up by individuals without funding. Here are two that caught our eye:

(Twitter has seemingly restricted embedded Tweets in Substack — thanks Elon!)

(4) UK develops genetic early warning system for future pandemics

Robin McKie for The Guardian

The ultimate aim of the project – the Respiratory Virus and Microbiome Initiative – is to create a system that would deploy DNA sequencing technology to identify all viral, bacterial and fungal species in a single sample collected from a nose swab from a patient.

Remember back in 2020/2021 when new variants of Covid were popping up in specific geographies and we were able to pinpoint the severity and the infectiousness of those variants? That information, and our ability to access it quickly, was the result of genomic sequencing technology and it played a vital role in containing the threat of the pandemic.

Now that same technology is being targeted at other viruses including influenza, RSV, and other coronaviruses to help us better surveil, track, and ultimately fight the viruses. The technology, which was developed in the UK and only accessible to a few other developed countries, is now going global.

We don’t know when or where the next pandemic will start, but this time, thanks to a surveillance network of more affordable and advanced technologies, we may be more prepared to take it on.

(5) Rex Woodbury on Optimism

From Rex Woodbury

Index Partner and author of the Substack Digital Native Rex Woodbury dropped a worthwhile 🧵 (link to tweet) on how capitalism and technology are drivers of positive social impact at scale. He gives reason to be optimistic on topics ranging from climate, biotech, work, and even dating. Life has certainly gotten better over the past 200 years, and that has almost certainly be the result of accelerating technological advancements.

There’s a lot of pessimism out there, but we agree with Rex that the good outweighs the bad and we thank him for the healthy dose of optimismporn.

And now, to those of you who celebrate:

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That’s all for this week. We’ll be back in your inbox on Monday.

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