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At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote Brains and Chains, an interesting conference in New York exploring the intersection of artificial intelligence and blockchain.
This is both an exciting and challenging topic, and the goal of my talk was to provide a broad introduction to kick things off, and frame the discussion for the rest of the day: discuss why the topic matters in the first place, and highlight the work of some interesting companies in the space.
Below is the presentation, with some added commentary when relevant. Scroll to the very bottom for a SlideShare widget, if youâd like to flip through the slides.
Iâm coming at this topic from the perspective of a VC investor. My firm FirstMark has been active in both AI and crypto/blockchain.
Itâs certainly easy to poke gentle fun at the topic. AI (machine learning) and blockchain are both experimental and much buzzed about. AI reached a peak of hype in 2016â2017, blockchain in 2017â2018. Each trend individually could very well end up disappointing, and explorations at the intersection of both could prove fruitless.
But if one looks at the history of computing, a massive new trend seems to appear every 10 or 15 yearsâââsilicon chips, the PC, the Internet, Web 2.0, etc.
We are probably on the tail end of the current trend, which has been propelled by three concurrent phenomenons: social, mobile and cloud.
Many of the giants we know today emerged from those.
But the magnitude of those trends wasnât always obvious, of course.
Cloud computing, for example, seems obvious now, but if you rewind back to 2008, it was controversial, and called âmarketing snake oilâ by some. All in all, it took well over a decade to become the massive industry it is today.
At first, new trends often seem both highly experimental and over-hyped, but over time they take hold, attracting ever more capital and talent, and gradually become the new paradigm.
As per Amaraâs law, the impact of new technology is often overestimated in the short run, and underestimated in the long run.
The timing seems ripe for a new paradigm in technology to emerge. What will define and propel the the next big wave of computing innovation?
Thereâs a rationale for making the argument that âAI, blockchain and the Internet of Thingsâ is the new âSocial, Mobile and Cloudâ. Those trends are still very much emerging, but their potential impact is massive.
What new giants will emerge from this paradigm?
Just like social, mobile and cloud have fed off each other, those three trends have very interesting areas of overlap. I have discussed at least one example of intersection between the Internet of Things and blockchain, but there is a number of others.
Today, Iâll focus on the intersection between AI and blockchain.
One really interesting starting point is to observe that AI and blockchain are philosophically opposed in many ways, as nicely expressed by Peter Thiel and Reid Hoffman in a recent conversation.
For example, AI is very much centralizedâââwithin a handful of companies, primarily Google, Apple, Facebook and Amazon (âGAFAâ) and the large Chinese Internet companies, Alibaba, Tencent and Baidu. While some of the AI research is open sourced via academic papers, those companies have been able to attract top AI talent around the world, and most importantly, they have access to unprecedented amounts of data to train their AI algorithms. Those datasets are a massive competitive advantage and are closed to the rest of the world.
The centralization of AI opens the door to all sorts of abuse. Reports of government surveillance in China abound, powered by computer vision and face recognition technologies.
But just over the last few months, stories started surfacing in the US that echoed some of the concerns about China, demonstrating that this is a global concern.
Beyond political issues, centralized platforms tend compete with the ecosystem that emerged around them. Read Chris Dixonâs brilliant essay âWhy Decentralization Mattersâ.
Blockchain emerged as a powerful response to political and organizational problems, rather than purely technical ones.
Since many of the issues we discussed are political and organizational in nature, can it be leveraged as a foil against the pitfalls of AI?
Could blockchain also help create better AI?
Pioneers in the field have been exploring various ideas, ranging from a decentralized way to create AI to networks of bots and fully autonomous organizations run by AI.
Weâll chat today about how blockchain can help AI, but it is worth noting that there is a number of ways AI can help blockchainâââanother interesting discussion for another day.
The first big idea is to create a decentralized marketplace that would help create better AI.
The high level idea is as follows: All of us (individuals, institutions) would be financially incentivized to provide our personal and professional data. Knowing it would be kept completely secure and private (through decentralization and secure computing), weâd feel more comfortable sharing sensitive data (spending, health information). Over time, the marketplace(s) would accumulate a lot more data, and higher quality data, than what GAFA has access to. On top of the data, machine learning experts would be incentivized to compete and highest performing models would get disproportionally rewarded.
To explore how to build such a decentralized marketplace, letâs chat about how to decentralize the three key building blocks of AI: data, models and computing power.
At this stage of the presentation, weâll start providing examples of companies doing exciting work at the intersection of AI and blockchain. The space is very vibrant and fast-moving, and this is not meant to be an exhaustive list of all great companies and projects, by any means.
Also worth noting: many companies in the space have ambitious plans to build a lot of pieces of the ecosystem, and many sound a little bit the same. Most of those projects are pre-launch, so it will take time for the dust to settle and see who actually does what in earnest.
Letâs start with data. One important point: if weâre going to use the blockchain to store massive amounts of data, it will need to become a much better database than it currently is.
Hereâs BigChainDB, out of Berlin, building a scalable blockchain database. The chart is interesting in that it shows that there is little overlap between what a distributed database offers and blockchain technology. As a result, building a true database-grade blockchain is a challenging project.
To help share data, another key infrastructure component is a protocol.
Ocean Protocol has been doing pioneering work in the field, and for anyone interested in digging in, itâs worth reading just about everything its founder Trent McConaghy has written on the general topic of AI and blockchain.
Computable Labs is also working on building a data market protocol, and this great piece by its CEO Roger Chen is well worth a read as well.
Sometimes you need to create your own data for purposes of AI trainingâââeither because you donât have access to the right data set, or because the use case you are training the AI is too new that the data simply does not exist.
Snips, out of Paris, is using crypto economics to incentivize a network of workers involved in synthetic data generation.
Letâs talk about the second building block of AI:Â models.
For a decentralized AI marketplace to work, you need to be able to guarantee that whatever data is provided by individuals and companies is processed in a completely private manner. Enter secure computing.
A good example is the OpenMined project, which includes a key focus on private machine learning, leveraging various secure computing techniques, including federated learning (championed by Google) and differential privacy (championed by Apple).
Letâs switch to the third building block of AI: computing power. A lot of the recent progress in AI has been facilitated by a massive ramp in computing power, that resulted both from better leveraging existing hardware, and also building new high performance hardware specifically for AI (Google TPUs, etc).
DeepBrain Chain is an interesting project that aims to share idle computing resources around the world. Its general philosophy is comparable to other projects such as Coronai, Hadron, Golem, or Hypernet, but DeepBrain Chain is more specifically focused on the type of computing power (and related hardware) necessary for the specific requirements of AI.
Putting it altogether, you can imagine a fully decentralized AI marketplace where people provide their data, developers compete to provide the best machine learning models, and the whole system works as a self-reinforcing network that attracts more and more participants and creates better and better AI.
The secret weapon here is really crypto-economics: the ability to create a mini-economy where participants accrue and exchange value through tokens. Because they incentivize people to participate in the network early, tokens help solve the cold start problem which has plagued so many network-building efforts in the past.
The chart showin the slide was taken form Fred Ehrsamâs excellent Medium post: Blockchain-based Machine Learning Marketplaces.
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