India considers the adoption of Universal Basic Income

A recent article published in The Economist reports that India is considering the adoption of a Universal Basic Income (UBI) scheme to replace a myriad of existing welfare systems.

Unlike the discussions that are taking place in other countries, this discussion about Universal Basic Income is not motivated by advances in technology and the fear of massive unemployment. The main aim of such a measure would be to replace many existing welfare mechanisms that are expensive, ineffective, and misused.

The scheme would provide every single citizen with a guaranteed basic income of 9 dollars a month ( hardly a vast sum ) and would cost between 6 and 7% of GDP. The 950 existing welfare schemes cost about 5% of GDP. Such a large scale experiment would, at least, contribute to make clear the advantages and disadvantages of UBI as a way to make sure every human being has a minimum wage, independent of any other considerations or the existence of jobs.

Photo by Amal Mongia, available at Multimedia Commons.

Will the fourth industrial revolution destroy or create jobs?

The impact of the fourth industrial revolution on jobs has been much discussed.

On one side, there are the traditional economists, who argue that technological advances have always created more and better jobs than the ones they destroyed. On the other side, the people that believe that with the arrival of artificial intelligence and robotics, there will simply not exist enough jobs that cannot be done by machines.

So, in this post, I try to present a balanced analysis on the subject, as deeply as allowed by the space and time available.

Many studies have addressed the question of which jobs are more likely to be destroyed by automation.  This study, by McKinsey, provides a very comprehensive analysis.

Recently, The Economist also published a fairly balanced analysis of the topic, already posted in this blog. In this analysis, The Economist makes a reference to a number of studies on the jobs that are at high risk but, in the end, it sides with the opinion that enough jobs will be created to replace the ones technology will destroy.

A number of books and articles have been written on the topic, including “Raising the Floor“, “The Wealth of Humans: Work, Power, and Status in the Twenty-first Century“, “The Second Machine Age“, and “No More Work“, some of them already reviewed in this blog.

In most cases, the authors of these books advocate the need for significant changes in the way society is organized, and on the types of social contracts that need to be drawn. Guaranteeing every one a universal basic income is a proposal that has become very popular, as a way to address the question of how humanity will live in a time when there are much less jobs to go around.

Further evidence that some deep change is in the cards is provided by data that shows that, with the begining of the XXI century, income is being moved away from jobs (and workers) towards capital (and large companies):

On the other side of the debate, there are many people who believe that humans will always be able to adapt and add value to society, regardless of what machines can or cannot do. David Autor, in his TED talk, makes a compelling point that many times before it was argued that “this time is different” and that it never was.

Other articles, including this one in the Washington Post, argue that the fears are overblown. The robots will not be coming in large numbers, to replace humans. Not in the near future, anyway.

Other economists, such as  Richard Freeman, in an article published in Harvard Magazine agree and also believe that the fears are unwarranted: “We should worry less about the potential displacement of human labor by robots than about how to share fairly across society the prosperity that the robots produce.”

His point is that the problem is not so much on the lack of jobs, but on the depression of wages. Jobs may still exist, but will not be well paid, and the existing imbalances in income distribution will only become worst.

Maybe, in the end, this opinion represents a balanced synthesis of the two competing views: jobs will still exist, for anyone who wants to take them, but there will be competition (from robots and intelligent agents) for them, pushing down the wages.

European Parliament committee approves proposal to give robots legal status and responsibilities

The committee on legal affairs of the European Parliament has drafted and approved a report that addresses many of the legal, social and financial consequences of the development of robots and artificial intelligence (AI).

The draft report addresses a large number of issues related with the advances of robotics, AI and related technologies, and proposes a number of european regulations to govern the utilization of robots and other advanced AI agents.

The report was approved with a 17-2 vote (and two abstentions) by the parliament’s legal affairs committee.

Among many other issues addressed, the report considers:

• The question of legal status: “whereas, ultimately, robots’ autonomy raises the question of their nature in the light of the existing legal categories – of whether they should be regarded as natural persons, legal persons, animals or objects – or whether a new category should be created”, advancing with the proposal of “creating a specific legal status for robots, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons with specific rights and obligations…”
• The impact of robotics and AI on employment and social security, and concludes that “consideration should be given to the possible need to introduce corporate reporting requirements on the extent and proportion of the contribution of robotics and AI to the economic results of a company for the purpose of taxation and social security contributions; takes the view that in the light of the possible effects on the labour market of robotics and AI a general basic income should be seriously considered, and invites all Member States to do so;”
• The need for a clear and unambiguous registration system for robots, recommending that “a system of registration of advanced robots should be introduced, and calls on the Commission to establish criteria for the classification of robots with a view to identifying the robots that would need to be registered;”

 

How to create a mind

Ray Kurzweil’s latest book, How to Create a Mind, published in 2012, is an interesting read and shows some welcome change on his views of science and technology. Unlike some of his previous (and influntial) books, including The Singularity is Near, The Age of Spiritual Machines and The Age of Intelligent Machines, the main point of this book is not that exponential technological development will bring in a technological singularity in a few decades.

True, that theme is still present, but takes second place to the main theme of the book, a concrete (although incomplete) proposal to build intelligent systems that are inspired in the architecture of the human neocortex.

Kurzweil main point in this book is to present a model of the human neocortex, what he calls The Pattern Recognition Theory of the Mind (PRTM). In this theory, the neocortex is simply a very powerful pattern recognition system, built out of about 300 million (his number, not mine) similar pattern recognizers. The input from each of these recognizers can come from either external inputs, through the senses, or from the older parts (evolutionary speaking) of the brain, or from the output of other pattern recognizers in the neocortex. Each recognizer is relatively simple, and can only recognize a simple pattern (say the word APPLE) but, through complex interconnections with other recognizers above and below, it makes possible all sorts of thinking and abstract reasoning.

Each pattern consists, in its essence, in a short sequence of symbols, and is connected, through bundles of axons, to the actual place in the cortex where these symbols are activated, by another pattern recognizer. In most cases, the memories these recognizers represent must be accessed in a specific order. He gives the example that very few persons can recite the alphabet backwards, or even their social security number, which is taken as evidence of the sequential nature of operation of these pattern recognizers.

The key point of the book is that the actual algorithms used to build and structure a neocortex may soon become well understood, and used to build intelligent machines, embodied with true strong Artificial Intelligence. How to Create a Mind falls somewhat short of the promise in the subtitle, The Secret of Human Thought Revealed, but still makes for some interesting reading.

Finland flirts with basic income

In an experimental trial started January 1st, 2017, Finland started to attribute a basic social income to 2000 unemployed persons. Unlike a standard unemployment income, this subsidy will still be paid even if the recipients find work.

Under this scheme, unemployed Finns, with ages in the 25 to 58 range will receive a guaranteed sum of €560, every month, independently of whether they have or find any other income. This value will replace other existing social benefits. A number of articles, including this one, in the Guardian, provide additional information about the scheme.

The move comes on the wake of a promise made by the centre-right government coalition elected in 2015, to run a basic income pilot project. The objective is to address concerns related with the disappearance of jobs caused by technological changes.

Other countries, cities and regions are running tentative experiments in basic income, including the Netherlands, Canada and the city of Livorno, in Italy. However, many concerns remain about whether this mechanism is the right mechanism to address the challenges brought in by the advances of technology.

Photo by Mikko Paananen, available at WikiMedia Commons.

Artificial Intelligence developments: the year in review

TechCrunch, a popular site dedicated to technology news, has published a list of the the top Artificial Intelligence news of 2016.

2016 seems indeed to have been the year Artificial Intelligence (AI) left the confinement of university labs to come into public view.

Several of the news selected by TechCrunch, were also covered in this blog.

In March a Go playing program, developed by Google’s DeepMind, AlphaGo, defeated 18-time world champion Lee Sedol (reference in the TechCrunch review).

Digital Art, where deep learning algorithms learn to paint in the style of a particular artist, was also the topic of one post (reference in the TechCrunch review).

In May, Digital Minds posted Moore’s law is dead, long live Moore´s law, describing how Google’s new chip can be used to run deep learning algorithms using Google’s TensorFlow (related article in the TechCrunch review).

TechCrunch has identified a number of other relevant developments that make for an interesting reading, including the Facebook-Amazon-Google-IBM-Microsoft mega partnership on AI, the Facebook strategy on AI and the news about the language invented by Google’s translation tool.

Will the AI wave gain momentum in 2017, as predicted by this article? I think the chances are good, but only the future will tell.

Human rights, animal rights, and robot rights.

An interesting article, in the New York Times, discusses the question of the rights of animals and robots.

The article describes the demise of Harambe, a gorilla from the Cincinnati Zoo, killed when a young boy fell in the moat that was part of the habitat that Harambe shared with two other gorillas. When they felt the boy was in danger, the zookeepers shot the gorilla, in an effort to save the life of the boy.

Angry protesters complained that shooting the gorilla was a violation of his rights. Discussing the rights of gorillas is particularly eerie because gorillas are so close to us. They are among our  nearest cousins, having separated from humans a mere  7 million years ago.

Today, these musings have another worrying side. We view ourselves as owners and sometimes protectors of the lower animals. Soon, however, robots may view ourselves in the same way we view gorillas, or other animals. Should robots be allowed to kill humans, if and when they endanger other humans or other animals? What are the moral questions at stake here? When is it legitimate to kill a human or, for that matter, an animal? And what about the rights of robots? It is ok to terminate them, as long as they are not sentient?

The key question is: how inhuman is it to kill an animal or to terminate a robot? Does it depend on the level of sentience the animal or the robot has? Most people believe lower animals are not conscious, but the jury is still out on that question. Are bats conscious? Do they experience some sort of consciousness when they fly in the dark, sensing the environment with ultrasounds? Can we ever get to know that is feels like to be a bat?

Thomas Nagel article ““What Is It Like to Be a Bat?”, discusses exactly this subjective aspect of consciousness, concluding that we can never fully understand what consciousness is, from the subjective point of view of the entity living the experience. Other philosophers, such as Daniel Dennett disagree, and believe that consciousness can, and will be, understood, since it is nothing more than an emerging phenomenon, a result of the operation of complex systems.

Illustration by Nishant Choksi, for the New York Times.

 

Black Mirror, a glimpse of the (near) future

 

If you didn’t yet watch any episodes of Black Mirror, a British series created by Charlie Brooker, go and fix that now. The 12 episodes of Black Mirror have been rated by The Wrap from “Good” to “Mind Blowing”, and they all cover the anticipated and non-anticipated consequences of new technologies.

According to the series creator, “each episode has a different cast, a different setting, even a different reality. But they’re all about the way we live now – and the way we might be living in 10 minutes’ time if we’re clumsy.” 

The series analyses, sometimes in excruciating ways, how new technologies, such as social networks, virtual reality, genetic engineering, and artificial intelligence, can lead to unexpected, if plausible, lifestyles, problems and challenges.

What if jobs are the problem, and not the solution?

In a fascinating article, worth reading in its entirety, James Livingston, author of No More Work: Why Full Employment Is a Bad Idea, asks a key question about the future of work: Why do we believe that every productive, adult, human being should have work, and get paid for it?

As he puts it: “For centuries – since, say, 1650 – we’ve believed that it builds character (punctuality, initiative, honesty, self-discipline, and so forth). We’ve also believed that the market in labour, where we go to find work, has been relatively efficient in allocating opportunities and incomes. And we’ve believed that, even if it sucks, a job gives meaning, purpose and structure to our everyday lives – at any rate, we’re pretty sure that it gets us out of bed, pays the bills, makes us feel responsible, and keeps us away from daytime TV. These beliefs are no longer plausible. In fact, they’ve become ridiculous, because there’s not enough work to go around..:”

His point is that, in the future, there will simply not be enough interesting, well-paid, jobs to create full employment. Of course, I am not forgetting the favorite argument of traditional economists, that technological revolutions have always created more valuable jobs than the ones they destroyed.

James Livingston has this to say about that argument: “But, wait, isn’t our present dilemma just a passing phase of the business cycle? What about the job market of the future? Haven’t the doomsayers, those damn Malthusians, always been proved wrong by rising productivity, new fields of enterprise, new economic opportunities? Well, yeah – until now, these times. The measurable trends of the past half-century, and the plausible projections for the next half-century, are just too empirically grounded to dismiss as dismal science or ideological hokum.”

It is time to face the truth: this time is different, the fourth industrial revolution will not create jobs enough to keep everyone employed, at least not with the full-time, well-paid jobs that we came to associate  with economically advanced societies. The fraction of GDP that is being paid in salaries shown an unmistakable tendency, since the beginning of the 21st century. Technology will only exacerbate this tendency, as more and more well-paid jobs are lost to machines.

Livingstone makes the point that we can, indeed, afford, a minimum guaranteed income for everyone (let’s just call it “entitlements”). In his words: “But are these transfer payments and ‘entitlements’ affordable, in either economic or moral terms? By continuing and enlarging them, do we subsidise sloth, or do we enrich a debate on the rudiments of the good life? … I know what you’re thinking – we can’t afford this! But yeah, we can, very easily. We raise the arbitrary lid on the Social Security contribution, which now stands at $127,200, and we raise taxes on corporate income, reversing the Reagan Revolution. These two steps solve a fake fiscal problem and create an economic surplus where we now can measure a moral deficit.”

Whether you want to call it a minimum guaranteed income, or just a overhaul of the social security system, it is time to face this truth, and to think of the mechanisms that should be put in place to address the social challenges. caused by technology.

Face it, jobs are the problem, not the solution!

 

IBM TrueNorth neuromorphic chip does deep learning

In a recent article, published in the Proceedings of the National Academy of Sciences, IBM researchers demonstrated that the TrueNorth chip, designed to perform neuromorphic computing, can be trained using deep learning algorithms.

The TrueNorth chip was designed to efficiently simulate the efficient modeling of spiking neural networks, a model for neurons that closely mimics the way biological neurons work. Spiking neural networks are based on the integrate and fire model, inspired on the fact that actual neurons integrate the incoming ion currents caused by synaptic firing and generate an output spike only when sufficient synaptic excitation has been accumulated. Spiking neural network models tend to be less efficient than more abstract models of neurons, which simply compute the real valued output directly from the values of the real valued inputs multiplied by the input weights.

As IEEE Spectrum explains: “Instead of firing every cycle, the neurons in spiking neural networks must gradually build up their potential before they fire. To achieve precision on deep-learning tasks, spiking neural networks typically have to go through multiple cycles to see how the results average out. That effectively slows down the overall computation on tasks such as image recognition or language processing.”

In the article just published, IBM researchers have adapted deep learning algorithms to run on their TrueNorth architecture, and have achieved comparable precision, with lower energy dissipation. This research raises the prospect that energy-efficient neuromorphic chips may be competitive in deep learning tasks.

Image from Wikimedia Commons