Dept of the near future

💈 Martin Wolf: How should governments prepare for a future with significant labour displacement? This may require a more generous state providing public services, including subsidising work, but it might also mean a move towards techno-feudalism where the uber-wealthy access large amounts of servile cheap labour. (See also: Adair Turner’s excellent speech and supporting slides.)

💥 Joy Buolamwini echoes Sojourner Truth and asks: “AI, ain’t I a woman?”. STUNNING video exploring the ongoing algorithmic fails towards black women from the major computer vision systems provided by Google, IBM, Amazon et al. It’s been three years since we first discussed this issue in EV and it is disappointing that it persists.

🔑 Mike Maples on the idea that  “Crypto-powered governance markets will solve the tragedy of the commons and drive future abundance”. THOUGHT PROVOKING

🎯 The American venture firm, Andreesen Horowitz has launched a dedicated “cryptofund”. The rationale behind this fund is worth reading. “Blockchain computers are new types of computers where the unique capability is trust between users, developers, and the platform itself [...] Trust is a new software primitive from which other components can be constructed.” I spent some time with crypto-developers in the past few weeks, and I’d concur about how transformative this technology can be and, ICOs aside, how much real computer science & engineering work needs to be done. Some liken blockchain to the Internet of 1994. I’d say it’s more like 1981, before SMTP was a thing, but that we’re just moving faster.

🏆 Extreme winners and losers are the staple feature of the tech industry, but it turns out to be a more common phenomenon than that, argues Morgan Housel. My comment: turns out the long-tailed/power-law distributions are much more common in nature and in emergent systems (like economies or cities) than the bell-curve we all studied at school. Realising this has significant implications on both how you invest and the kind of policies societies need (particularly around taxation).

💯 Mariana Mazzucato: Private data should become a public good. “We should ask how the value of these [dominant tech firms] has been created… and who benefits from it? Let’s not forget that a large part of the technology and necessary data was created by all of us, and should belong to all of us.”

📺  Farhad Manjoo: With Americans spending 11 hours a day looking at the screen, we’ve reached “peak screen”. Now tech firms are thinking about new modalities, such as voice, which are necessarily less immersive and distracting. (See also, Apple’s plan to launch new high-end over the ear headphones.)

Dept of artificial intelligence and work

I read Martin Wolf’s wonderful essay in the same week of two relevant, but distinct, announcements from Babylon Health and OpenAI. I just want to connect the dots between these.

First, Babylon: the company announced that their AI-based chatbot had performed better than the typical British GP (a GP is a generalist physician rather than a specialist) on the qualifying exams run by the Royal College of General Practitioners. Babylon’s bot scored 81% on a test where humans averaged 72%, although there are some methodology issues. You can read a news story here, and the research paper, which I’ve skimmed, here.

The Royal College of General Practitioners responds with two key points: one silly, the other less so.

The first was a sense of outrage along-the-lines that a GP will never be replaced by a machine. The outrage was misplaced. The technology is helpful because, frankly, it can assist GPs to make better and faster diagnoses (as such technologies are currently helping others in the medical profession). It could also reduce triage times for patients in some circumstances: doctors are scarce and expensive. (Babylon, to their credit, has been delivering physician services via their AI system in Rwanda where there is a severe paucity of human doctors.)

The second observation was a more considered one: that Babylon’s services were more likely to appeal to the young, healthy, educated and technology-savvy, allowing Babylon to cherry pick low-cost patients, leaving the traditional GPs with more complex, older patients. This is a real concern, if only because older patients often have multiple co-morbidities and are vulnerable in many ways other than their physical health. The nature of health funding in the UK depends, in some ways, on pooling patients of different risks. In other words, that unequal access to technology ends up benefiting the young (and generally more healthy) at the cost of those who aren’t well served by the technology in its present state.

Exponential View has repeatedly flagged the risks of unequal access to technology because these technologies are, whatever you think of them, literally the interface to the resources we need to live in the societies of today and tomorrow.

Elsewhere, OpenAI, a research group, announced that it had developed bots that could beat human teams in a collaborative game called Dota. It’s a pretty huge step. Dota is a complex game with delayed rewards and apparently requiring a good deal of strategic planning and intuition. Each character in Dota has different skills and capabilities, leading to a good deal of uncertainty in the game. Open AI Five was a team of bots that won 2 out of 3 games against an amateur team.

As Stephen Merity says:

The bots learnt when to fail to help the team, to waive local award for global award and had no sense of hero complex […] We as humans aren't smart enough to see through that fog of complexity and complex interaction - but the systems we write might be. They might help us achieve the objectives which we've been lossily and haphazardly walking towards for hundreds of years

You can read more technical details in this decent breakdown.

A couple of observations: the bots are trained using self-play. They had pretty significant computing resources, about 128,000 CPU cores and 256 GPUs (by comparison AlphaGo required 1,920 CPU and 280 GPUs in the match against Lee Sedol). And the machines can train themselves for 900 years of gameplay per earth day.

So it’s clear that these systems don’t learn as efficiently as humans do: power consumption is higher and the amount of training systems off the charts. But note: that the cost of compute is set to get much cheaper in the coming years with the influx of novel architectures to support machine learning. If it followed a Moore’s Law trajectory, the cost to execute something like this would be 100 times cheaper in a decade. I’ll put my neck out and say that the combination of algorithmic improvements (in how the systems learn), more optimised architecture and simple scale economics will result in improvements far better than 100-fold in ten years..

Let me connect the dots here. In two very different domains, we’re able to use (software) machines to tackle things that previously were the demesne of Homo sapiens. The progress is rapid—only last year, OpenAI’s Dota bot could only win less complex single player games, but there is a long way to go.

Equally, the real mid-term opportunity of technologies like Babylon’s is not to replace GPs, but ideally to enable them. Those technologies of enablement (the stethoscope, thermometer, probabilistic graphical model) allow them to do their job – which is delivering patient outcomes – better.

However, there is a real transition problem that we have to manage. This is illustrated by the risk of Babylon cherry-picking low-cost patients and leaving the expensive ones to the traditional physician. This may not matter to society when it comes to other goods, but it does when it comes to certain fundamental services.

That transition problem is a cousin of the one that Martin Wolf alludes to in his essay. Many versions of our future may be filled with sunny uplands; figuring out how to navigate that steep climb has to be a priority.

Elsewhere:

• The Trolley problem has become a favourite tool for discussing how we implement ethical decision-making in machines. Turns out humans behave differently when presented with a real-world trolley problem than a thought experiment. (Hint: we all become a tad more consequentialist.)

• Google opens Duplex AI to public testing.

• Online fingerprints, such as your operating system and device, can be a better predictor of creditworthiness than traditional credit scores.

• A treasure trove which I haven't had time to fully digest: EV reader Ian Hogarth, and Nathan Benaich published a comprehensive State of AI report.

The Exponential View podcast 📢

Thanks to the hundreds of you who recommended Exponential View to your friends and peers, the EV Podcast returns in a couple of weeks. Recommendations work.

If you didn’t catch-up with the podcast series, take a look at them at iTunes or SoundCloud.

Short morsels to appear smart at dinner parties

Old is gold. 50-year old founders are twice as likely to have a runaway success than a whippersnapper 20 years younger.

🇨🇳 Great reportage on the other frontier of US: Chinese tech rivalry, with the attempted theft of American chip designs.

Chinese retailers are orchestrating delivery-by-drone to reach rural customers.

🚴 Little vehicles – e-bikes, velomobiles, motorized skateboards and more – might significantly erode private and shared vehicles use in cities. **#exciting **

😮 Fortnite is the most revenue generating free game ever generating $318m revenues in May for optional extras.

The vortex of seediness: enter cryptocurrency influencers. $105k for a tweet?

The NSA is moving its data into the cloud.

Scientists create a water purifier that works using only sunlight, water and oxygen.

🔌  Oil giant buys the UK's largest electric charging network.

Mayans used chocolate as money. 😋

End note

This week’s Exponential View is shorter than usual. So please enjoy the outdoors this weekend.

Reality: I was in Iceland at the discussion on the Future of Software Development hosted by Blueyard Capital. It was a chance to meet some of the most exciting thinkers in this important domain. At the one end, we’re witnessing tremendous innovation in making it easier to develop and maintain software for mobile and cloud platforms. At the other end, we’re figuring out what the software stack & tools on distributed crypto-platforms should look like. And, at the most extreme, thinking through what types of problems we could solve using quantum computers and what programming them might even mean. It’s certainly an area to watch.

I took Friday off to go weekend camping with one of my kids. And between these and many other commitments, EV was on a back burner this week.

Have a great weekend,
Azeem  😎

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