1. Fascinating article in FT by a neurosurgeon, Henry Marsh, on brains and artificial intelligence. Consider the technical challenge,
It consists of some 85bn nerve cells, each of which is connected to many thousands of other nerve cells, with some 150tn connections... There are significant practical limitations on the extent to which we can experiment and explore our own brains. The resolution of the best MRI brain scanners, for instance, is about one cubic millimetre, and one cubic millimetre of cerebral cortex can contain up to 100,000 neurons and a billion synapses. The temporal resolution is a little less than one second, and much cerebral activity is measured in milliseconds, so what we see with MRI scans are, in effect, blurred snapshots... The energy consumption of a human brain is 20-30 watts — a dim lightbulb. An exascale computer, capable of a quintillion calculations per second, scaled up to the size of a human brain, would consume hundreds of megawatts. Computer engineers talk of the “von Neumann bottleneck”, a problem with classical computer design in which memory is stored separately from the Central Processing Unit, and one of the reasons why computers use so much energy.
And on the limitations of modern AI,
The remarkable progress in AI in recent years is largely based on “neural networks” and machine learning... Neural networks only resemble brain networks in a very loose way. They consist of layered assemblies whose output can feed back and modify their input in accordance with a pre-programmed target, so that they “learn”. They are engines of statistical association and classification. They neither explain nor do they understand. They have no internal model or theory of what is being analysed. The literature, however, abounds in anthropomorphisms — AI programmes are said to “see” and “think”. Google’s DeepMind programme AlphaGo “vanquished” champion Go player Lee Se-dol. This is all nonsense. It is easy to get carried away. The predictive texting on your smartphone prompts you simply by calculating the probability of the next word from mindless analysis of previous text. Google Translate has trawled the entire contents of the internet without understanding a single word... all current machine intelligences can only perform one task. This form of intelligence is reminiscent of the patients described in some of Oliver Sacks’ writings — people who can carry out remarkable feats of calculation but are utterly helpless in normal society... The Holy Grail for AI is “general intelligence” — a computer programme that could not only play games with simple rules but also perform other tasks, such as speech and face recognition, without being re-programmed. On present evidence it looks unlikely that neuronal networks and deep learning will ever be able to do this.
And what to expect in the future,
I find it strange that some people are so certain that the arrival of “superintelligent” machines is only a matter of time. Filled with a fervour reminiscent of the Second Coming and the Rapture, they talk of the Singularity, a time when AI will equal human intelligence. This belief — which is all it is — often comes with the hope that the human brain and all its contents can be re-written as computer code and that we will find the life everlasting as software programs. These ideas are not to be taken seriously, although they certainly sell books... There is no risk, however, in the foreseeable future, of superintelligent AIs replacing us, or treating us as we have so often treated, and continue to treat, animals.2. The graphic shows why China's economic growth has truly been extraordinary,
There have been two big drivers of the growth, especially in the recent times. One is private sector debt...
3. But, as John Mauldin writes pointing to an IMF study, this growth has come at a cost. In particular, the credit intensity of growth in recent years has been rocketing upwards.
The IMF study writes,
In 2007-08, about RMB 61⁄2 trillion of new credit was needed to raise nominal GDP by about RMB 5 trillion per year. In 2015-16, it took more than RMB 20 trillion in new credit for the same nominal GDP growth.
4. Upshot points to the latest research by David Autor which shows that while cities are the place to be for high skilled American workers, they may be becoming less so for low-skilled workers.
As to the reasons,
Mr. Autor attributes the declining urban wage premium in this chart to the disappearance of “middle-skill jobs” in production but also in clerical, administrative and sales work. Many of these jobs have gone overseas. Others have been automated out of existence. This kind of work, he argues, was historically clustered in cities (meaning the entire labor market around cities, within commuting zones). And because of that, workers with limited skills could find better opportunities by moving there. Now, the urban jobs available to people with no college education — as servers, cleaners, security guards, home health aides — are basically the same kind as those available in smaller towns and rural communities.5. Faced with low interest rates and low returns on conventional assets, pension funds are facing the brunt, with the gap between liabilities and assets rising and rising.
6. Tadit Kundu has a good graphical feature on the various Universal Basic Income option costs.
8. Bill Gates has a nice article which calls out the success of "financing and delivery" entities in global health care, like GAVI (children's vaccination), Global Fund (for HIV, TB, and Malaria), and Global Polio Eradication Initiative. Gates has spent $10 bn on the three entities which buy medicines and get them delivered to end-users. In terms of return to investment, Gates writes,
Suppose that our foundation hadn’t invested in Gavi, the Global Fund and GPEI and had instead put that $10 billion into the S&P 500, promising to give the balance to developing countries 18 years later. As of last week, those countries would have received about $12 billion, adjusted for inflation, or $17 billion if we factor in reinvested dividends. What if we had invested $10 billion in energy projects in the developing world? In that case, the return would have been $150 billion. What about infrastructure? $170 billion. By investing in global health institutions, however, we exceeded all of those returns: The $10 billion that we gave to help provide vaccines, drugs, bed nets and other supplies in developing countries created an estimated $200 billion in social and economic benefits... Institutions such as Gavi, the Global Fund and GPEI are the closest things that we have to surefire bets to alleviate suffering and save lives.
9. Finally, very good article by Chinmay Tumbe on internal migration in India, which is more than 100 m today.
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