Barcelona Metropolis

The story goes back to 2012. That year saw the publication of some remarkable findings as regards deep learning, one of the specialties of AI. A group of Canadian researchers developed a multi-layered artificial neural network that could recognise objects in an image database at enormous speed. ImageNet is a database comprising 14 million pictures of objects in different categories in which each image is tagged. The neural network was trained with 150,000 of these images, corresponding to a thousand categories of objects. Once trained, it was presented with images that belonged to these thousand categories but were different from the training images, and it recognised them with an accuracy of 85%.

Beyond the gross exaggeration of the headline, the fact is that, in a short space of time, a giant leap had been made in the capacity of neural networks to generalise, make abstractions and capture the essence of the objects presented to them.

This is explained by the large amount of data in the ImageNet database and also that the algorithms were implemented in very powerful, high-performance machines with a high computational speed. An immense database and very powerful hardware explain why a two-decade-old algorithm achieved global prominence. The first to realise the economic possibilities of this breakthrough in AI were the Tech Giants (Facebook, Google, Baidu, etc.) and since then they have been deeply committed to deep learning. They were clear that they had the most valuable material – infinite data to train algorithms – and allocated significant resources to leading research work in this field. For example, Google immediately acquired DeepMind and turned a group of young entrepreneurs into billionaires. Today’s enthusiasm for AI has solid foundations.

The current interest in AI means that there are more resources for research, as it has become a priority area for research in the United States and the European Union. But we must try not to give false expectations and be cautious about AI’s capacity to solve complex social issues. The academic world is much less likely to have large amounts of data and access to huge computing power. We don’t have the resources or salaries that the Tech Giants offer to the brightest minds in universities.

However, the world of AI is very broad and there are still niches for the type of research that does not solely seek economic gain. Deep learning is one of the many branches of AI. There are many other areas of research that are of little interest to Google, Facebook, Baidu and other companies. Data constitute the fuel for deep learning, but there are also aspects with a knowledge focus. Therefore, we have a space in which to develop reasoning systems, to create hybrid knowledge-based and data-driven approaches, and to develop multi-agent systems that could prove useful in many areas.

No, it’s false. Today, deep neural network systems, which some equate to the functioning of the human brain, actually model a tiny aspect of information processing in a brain. Today we know that when the neuron is activated and emits a signal, it is not only electrical in nature. In fact, there is a lot of information that the brain processes that is chemical in nature. Neuroscience has shown that glial cells, which are part of the nervous system, play a key role in the processing of brain information. And that isn’t integrated in any artificial neural model. Therefore, to claim that artificial neural networks are similar to the human brain is inaccurate. In fact, to date, AI has managed to develop simple, inaccurate mathematical models of the biological neuron.

This is a highly exaggerated claim. Firstly, software has no purpose or intent. It does not learn by itself but rather is programmed. And this programming is very expensive – in terms of resources, qualified professionals, data collection, and definition of the specific system task – and it requires setting millions of parameters, and all of those tasks are performed by human programmers. Artificial intelligence is still a computer programme.

This is clearly not the case. Such a system needs examples to be trained; the decisions it must make (sorting an image, driving an autonomous vehicle) are learned from human-generated data. This is the case with a machine that performs medical diagnoses: doctors generate data about a disease that predetermines the results. The reality is that the data can be skewed. For example, this applies to the system implemented in the United States that provided advice on whether or not to grant parole to inmates based on their likelihood of recidivism. Behind it was an algorithm that was shown to discriminate against African Americans. Why? Because the training data in the system had biases that discriminated against this group. An African-American prisoner who disagreed with the decision appealed and requested he be given the arguments behind the denial of parole and they were unable to do so. The judge, to save himself work, didn’t go beyond the machine’s “advice”. The prisoner won the appeal and the whole system was called into question. I’m not against these systems to help make decisions, but first we need to analyse the possible biases, and determine what they are and whether they can be corrected. These systems cannot be hastily put on the market; they should previously go through some kind of VTI for algorithms.

It’s not quite like that. There are simple tasks that can be performed by a given machine that call for manual skills unattainable by a device. For example, some jobs performed by an electrician or many household chores (cleaning or putting on the laundry). These are tasks that seem simple but require a great deal of dexterity.

It can. And it can even imitate a certain journalistic style, as long as it has enough texts to build patterns. A machine can quickly analyse an extensive body of data. But we must be careful: more data does not mean more intelligence. The key lies in the quality of the data. Making good or bad decisions does not only depend on the quantity but also and mainly on the quality of the data. What inconsistent data, which incorporate noise, do is make decisions worse. Artificial intelligence can therefore replace routine work, whether manual or intellectual, more than simple intelligence. However, if we are faced with unforeseen, uncertain, undetermined situations that do not respond to patterns, machines have little to do. In contrast, humans do know how to respond, better or worse, to unforeseen situations.

Artificial intelligence is very horizontal. Take an agricultural entrepreneur who has to manage a great deal of information and uses drones to control the harvest or take market data into account, or even climate variables to make better decisions. Not even the art world escapes AI.

They can be, to some extent. Creativity is not a dual activity, but it has degrees. Obviously today’s machines cannot break rules and invent a new musical or pictorial style like the Cubists or the inventors of atonal music did. Instead, from the exhaustive analysis of a musical or pictorial style, deep learning systems can extract patterns from the style of a painter like Rembrandt, a composer like Bach, or any artist. Once the machine has captured the model, it is able to generate new paintings or compositions. This has already been done. Can this be called creativity? Many people argue that this isn’t creative, but they are really original and valuable works. I think that, at the very least, they undeniably have a certain artistic value.

First, caution. For example, not rolling out applications before determining the biases they have. Second, accountability. Systems cannot be black boxes and the processes established to justify decisions should be transparent. And, above all, not to forget the human being, that is, not to replace a machine with a person based solely on profitability criteria. In addition, it has been shown that better decisions arise from human-machine collaboration than from just a machine. We have seen this in the field of medicine. However, in other areas, it makes perfect sense to make a radical replacement. This is the case of the level 5 autonomous car which, when it is mainstream – that will still take time – will save thousands of lives on the road. In this case, the benefit for society as a whole is undisputed.

I don’t think so. Except in very basic matters, we cannot speak of a universal ethic across countries and cultures. If I had the solution to the problem of ethics in AI, it would be the cover of Time magazine. There are interesting proposals, such as the one made by Professor Stuart Russell from the University of California at Berkeley in his book Human Compatible, yet his proposal to ensure control of future machines, however intelligent they may be, touches on science fiction.

Finally, the responsibility lies with designers and programmers, as well as with users. This is where we need to focus and control. Moreover, more ethics content should be incorporated into science and engineering studies. In fact, ethics will be featured in the new artificial intelligence degrees Catalan universities are preparing. They will be technical degrees in which humanistic and ethical aspects will be fundamental. We’re late to do so, but we’re finally threading the needle.

Artificial intelligence has extraordinary potential to improve the well-being of our societies. In fact, many applications already help doctors make decisions. Whether we go this route or not will depend on political will and investing the necessary resources.

That would be the danger. In principle, information should be received from citizens’ mobile phones. We are told that there are systems that work on the basis of aggregate and anonymised information but, in fact, aggregate information about population groups is obtained from information on each individual mobile device and includes people’s location. The problem stems from the fact that this is done centrally and poses risks, such as the information receiver using it for surveillance or commercial purposes. The alternative would be to create a decentralised system that collects information in such a way that each individual manages their data. This system could monitor if any symptoms of the disease are presented and also, thanks to Bluetooth, if the person has been in close proximity to other people with symptoms. Finally, the person voluntarily decides to download their data and incorporate them into the system to monitor the spread of the epidemic.

Again, the potential exists but there needs to be political will and resources. Multi-agent systems and opinion aggregators could be designed based on arguments and counter-arguments. The AI system could be attached to a simulator that deploys different scenarios depending on the decisions made. It would be a way to make participation more appealing to citizens; participation could even be encouraged through rewards, such as tax rebates to people who actively participate.

We aren’t bad at all. A recent French study places Spain as the fourth European country in terms of the creation of new companies focused on AI. A large number of start-ups are emerging, especially in Catalonia. Hopefully many will be consolidated.