Crypto Market Commentary
9 December 2019
Doc's Daily Commentary
The 12/4 ReadySetLive session with Doc and Mav is listed below.
Mind Of Mav
A Vision Of Future Employement
If you remember, we closed off last week talking about the board game move that began the new AI era.
So, where are we going now?
Are developments in robotics and AI good for humanity or bad? This question has been at the root of a whole series of policy questions. Should we aim to stimulate and encourage developments in AI? If so, how? Or should we aim to restrict them or, at the least, slow them down? And again, if so, how? Is there a good case for a “robot tax”?
I think the AI visionary Kevin Kelly has the right idea. He has written: Many of the jobs that politicians are fighting to keep away from robots are jobs that no one wakes up in the morning really wanting to do.
However, much we try to imagine what new jobs may emerge, one thing we can be certain of is that we will surely miss many of the new jobs that will spring up. How could it be otherwise? We are about to enter a whole new world and our imagination can only get us so far.
This is the key reason why assessments of the impact of robots and AI on total employment tend to be unjustifiably pessimistic. It is an easy, although laborious, task to trawl through lists of job specifications, assessing which types of job, and how many people, are “at risk” of redundancy. There have been umpteen studies of this sort.
Doubtless there can be new studies, better or worse than their predecessors, and more or less apocalyptic.
But, as regards the impact on the macroeconomy, these exercises are largely beside the point. The really important questions concern what modifications to existing jobs will effectively transform them and what types of new job will emerge, and in what quantities.
Not for the first time in economics, it is on the aspects of the issue that are most important that we know least. When thinking about our policies for the uncertain future, we must beware of designing them to deal with those things that seem to be quantifiable, while ignoring or underplaying those that don’t. Without saying that we can lay claim to much detailed knowledge, the above discussion ought to have put paid to the idea that may have plagued you that the new world must necessarily involve mass unemployment.
In many sectors, including particularly transport, the scope for robots and AI to replace humans has been grossly exaggerated. In others – for example retail – there may well be significant job losses. And there will be many activities where the performance of routine mental tasks by humans are now replaced by AI. But in many other sectors, including healthcare and the leisure sector, the employment of humans can expand dramatically.
In some sectors, such as medicine and the law, robots and AI can enhance what professionals do. It is far from inevitable that this will lead to job losses in those sectors. Rather, as professionals’ productivity improves, it is likely that their output will rise also. Meanwhile, new jobs that barely exist today, will spring up and multiply.
Accordingly, I see no reason why the robot- and AI-infused economy of the future cannot be accompanied by full employment.
In short, in contrast to the prevailing pessimism that seems to surround this subject, I see the robot and AI revolution as being decidedly positive for humanity, just like the waves of economic progress since the Industrial Revolution. In one key characteristic, though, it will be positive because it is so different from most of the economic development that has happened since then. What this revolution will do is to release human beings from many of the dross jobs that have taxed their spirit and eroded their strength and enthusiasm, and in the process it will leave them free to be more truly human.
Over the last decade, however, a number of key developments have come together to power AI forward:
- Enormous growth in computer processing power.
- Rapid growth in available data.
- The development of improved technologies, including advances in text and image, including facial, as well as voice, recognition.
- The development of “deep learning”.
- The advent of algorithm-based decision-making.
So now AI seems close to its “James Watt moment.” Just as the steam engine was in existence for some time before Watt developed it and it came to transform production, so AI, which has been on the scene for some time, is about to stage a leap forward.
The AI literature is full of spectacular examples of exponential growth, often couched in easy-to-read and engaging terms, that bring out clearly the contrast between the apparently sedate pace of change early on and the dramatic transformation later. Consider the following example from Calum Chace:
“Imagine that you are in a football stadium … which has been sealed to make it waterproof. The referee places a single drop of water in the middle of the pitch. One minute later she places two drops of water there. Another minute later, four drops, and so on. How long do you think it would take to fill the stadium with water? The answer is 49 minutes. But what is really surprising – and disturbing – is that after 45 minutes, the stadium is just 7 percent full. The people in the back seats are looking down and pointing out to each other that something significant is happening. Four minutes later they have drowned.”
Exponential growth is at the root of what is known as “Moore’s Law,” which is generally taken to mean that the processing power of $1,000 worth of computer doubles every 18 months, or sometimes two years. Some analysts even suggest that there is exponential growth in the rate of exponential growth.
Federico Pistono says that, whereas computer speed (per unit cost) doubled every three years between 1910 and 1950, and every two years between 1950 and 1966, it is now doubling every year. He claims that: “According to the available evidence, we can infer that this trend will continue for the foreseeable future, or at least another 30 years.”
It isn’t only the speed and scope of advances in AI that impresses and appalls so many people and fills them with a sense of foreboding about the future. AI now threatens to replace people in activities that were recently thought to be exclusively human. It used to be thought that the game of chess would be beyond even the most capable computer. But in 1997 IBM’s Deep Blue defeated the world’s best player, Gary Kasparov. Deep Blue was able to evaluate between 100 and 200 million positions per second.
Kasparov said: “I had played a lot of computers but had never experienced anything like this. I could feel – I could smell – a new kind of intelligence across the table.”
In 2001 an IBM machine called Watson beat the best human players at the TV quiz game Jeopardy! In 2013 a DeepMind AI system taught itself to play Atari video games like Breakout and Pong , which involve hand–eye coordination. This was much more significant that it might have seemed. The AI system wasn’t taught how to play video games, but rather how to learn to play the games.
Kevin Kelly thinks that AI has now made a decided leap forward, but its significance is still not fully appreciated. He writes: “Once a computer manages to perform a task better than humans, the task is widely dismissed as simple. People then say that the next task is really hard – until that task is accomplished by the computer and so on. Indeed, once a machine is able to accomplish a particular thing, we often stop referring to it as AI. Tesler’s Theorem defines artificial intelligence as that which a machine cannot yet do.”
In this regard, it is the internet that has impelled AI to much greater capability and intelligence. A key feature behind human beings’ rise to dominion over the physical world was the development of exchange and specialization, which was a network effect. When the internet came along, connecting computers in a network, this transformed their capability.
And then we have coming along soon what the British entrepreneur Kevin Ashton has called “The Internet of Things.” IBM calls this the “Smarter Planet”; Cisco calls it the “Internet of Everything”; GE calls it “the Industrial Internet”; while the German government calls it “Industry 4.0.”
But all these terms refer to the same thing. The idea is simply that sensors, chips, and transmitters will be embedded in umpteen objects all around us.
The internet entrepreneur Marc Andreessen has said: “The end state is fairly obvious – every light, every doorknob will be connected to the internet.”
And, of course, all this connectivity with the material world will be in just the form that can readily be analyzed and reacted to by robots and AI. But all such connected things will also be able to communicate directly with humans by “speaking” to them. Ironically, some AI enthusiasts suggest that this may bring our attitudes to “things” closer to how people viewed them in pretechnological times, and how they are viewed in parts of the non-Western world today, that is to say, as possessing some sort of spirit and identity.
So, whatever you think about the scope for human advancement in the AI economy – what is happening in the AI world can surely not be dismissed lightly.
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