Surprise! Your usual host, the lovely Nick O’Connor, is out today. Well, he’s preparing for a sit-down interview with Eoin Treacy (from Trigger Point Trader and Frontier Tech Investor). Eoin’s flown over from Trump-ravaged America and has a hot new trade he wants everyone to know about.
Nick can fill you in tomorrow. Meanwhile, today’s letter will focus on the accelerating pace of change in the tech sector. Frankly it’s giving me the creeps: robots that are teaching themselves how to walk and then “evolving” the capacity for self-replication. But heebie-jeebies aside,
The robot that evolves itself
Remember that there’s a future out there. And in that future, robots are designing, manufacturing and improving themselves. And now remember that the future is now. It’s called “self-evolution” and here’s how Matthew Griffin describes it:
Experts at the University of Oslo, Norway have discovered a new way for robots to design, evolve and manufacture themselves, without input from humans, using a form of artificial evolution called “Generative design,” and 3D printers – although admittedly the team, for now at least, still has to assemble the final product, robot, when it’s printed.
You can watch a video as the robot tests different ways to move. It looks awkward and again, somewhat creepy yet amusing. But don’t let the awkwardness fool you about what’s going on. What’s going on is really important and could change the world in radical ways. Why?
A “creative machine” is one that can evolve itself. In the natural world, evolution is “dumb”. That is, it is trial and error over huge swathes of time. Mutations are “selected” based on their usefulness to promote the survival of a species. But the mutations themselves are random.
In “generative design”, the evolution is guided by an intelligence. That intelligence learns much faster than you and I. So while its first steps appear awkward and amusing, you shouldn’t forget that it has the capacity to learn at an exponential rate. The baby steps you see now could quickly become giant strides.
But what does that mean?
The issue is whether machine intelligence will accelerate the process of innovation itself. If a learning machine/robot can predict outcomes more quickly (and test them) then a lot of things will change. Here’s the key idea from a recent article called “The Simple Economics of Machine Intelligence”:
Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will centre around a drop in the cost of prediction.
The first effect of machine intelligence will be to lower the cost of goods and services that rely on prediction. This matters because prediction is an input to a host of activities including transportation, agriculture, healthcare, energy manufacturing, and retail.
When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.
The cost of predictions
It may not be obvious what the value of a “prediction technology” is (it wasn’t to me). And therefore, it’s hard to say what industries could benefit (or be destroyed) by lowering the cost of prediction. The two economic implications follow: that the lower cost of prediction will increase its use; and that the value of things that complement prediction will rise. But how about a little more detail from the authors:
As machine intelligence lowers the cost of prediction, we will begin to use it as an input for things for which we never previously did. As a historical example, consider semiconductors, an area of technological advance that caused a significant drop in the cost of a different input: arithmetic. With semiconductors we could calculate cheaply, so activities for which arithmetic was a key input, such as data analysis and accounting, became much cheaper.
However, we also started using the newly cheap arithmetic to solve problems that were not historically arithmetic problems. An example is photography. We shifted from a film-oriented, chemistry-based approach to a digital-oriented, arithmetic-based approach. Other new applications for cheap arithmetic include communications, music, and drug discovery.
The same goes for machine intelligence and prediction. As the cost of prediction falls, not only will activities that were historically prediction-oriented become cheaper — like inventory management and demand forecasting — but we will also use prediction to tackle other problems for which prediction was not historically an input.
Healthcare and transportation
Two concrete areas where this will have implications are transportation and healthcare. Connected machines with intelligence will be better at finding the least-crowded, most efficient route between point A and point B. Whether the cargo is a human being or crate of crustaceans doesn’t matter. Getting things from one place to another should become much more efficient.
This is why I’m convinced that in ten years, 90% of the traffic in central London will be self-driving cars and lorries. The other 10% will be people on bikes because they choose to get around that way. Transportation of good, services and people will become commodified. Owning a car will be unnecessary and rare – which is bad news for the big carmakers.
And health? Improved diagnostics (better prediction) coupled with personalised genetic medicine will destroy the bureaucratic and centralised nature of the healthcare system we have. Costs will go down. People will be healthier and live longer (which has its own set of demographic problems).
None of this, by the way, improves human judgement. That is a quality that I think is not perfectible. And that’s the interesting situation you find yourself in as technology advances more rapidly. Machines are getting smarter and better. Human beings appear to be getting dumber and more violent.
Until next time!
Publisher, Southbank Investment Research