Humanising Machines
/Socialising technology, and the industrialization of happiness.
In our present age we have reached a critical moment; Machine learning is on the verge of transforming our lives. However, our approach to this technology is still experimental; we are only beginning to make sense of what we are doing and the need for a moral compass is of great importance at a time when humanity is more divided than ever.
Many of the ethical problems of machine learning have already arisen in analogous forms throughout history and we will consider how, for example, we have developed trust and better social relations through innovative solutions at different times. History tells us that human beings tend not to foresee problems associated with our own development but, if we learn our lessons, then we take measures during the early stage of machine learning to minimize unintended and undesirable social consequences. It is possible to build incentives into machine learning that can help to improve trust in various transactions. Incentivizing desirable, ethical behaviors can also have a substantial impact on other manifestations of trust. As we shall observe, the new technologies may one day supersede the requirement for state-guided monopolies of force, and potentially create a fairer society. Machine learning could signify a new revolution for humanity; one of the heart and soul. If we can harness it to augment our ability to make good moral judgments and comprehend the complex chain of effects on society and the world at large then the potential benefits could be substantial.
Despite the rapid advances of machine intelligence, as a society, we are not prepared for the ethical and moral consequences of the new technologies. Part of the problem is that it is immensely challenging to respond to technological developments, particularly because they are developing at such a rapid pace. Indeed, the speed of this development means that the impact of machine learning can be unexpected and hard to predict. For example, most experts in the AI space did not expect the game of Go to be solvable problem by computers for at least another ten years. Thus there have been many significant developments that have caught some individuals off guard. Furthermore, advancements in machine intelligence present a misleading picture of human competence and control. In reality researchers in the area do not fully understand what they are doing and a lot of the progress is essentially based on ad hoc experimentation, If an experiment appears to work then it is immediately adopted.
To draw a historical analogy, humanity has reached the point where we are shifting from alchemy to chemistry. Alchemy would boil water to show how it was transformed into air, but they could not explain howwater changed to a gas or vapor, nor could they explain the white powdery earth left behind (the mineral residue from the water) after complete evaporation. In modern chemistry, humanity began to make sense of the phenomena through models, and we started to understand the scientific detail of cause and effect. We can observe a sort of transitional period, where people invented models, of how the world works on a chemical level. For instance, Phlogiston Theory was en vogue for nearly two decades; it essentially tried to explain why things burn. This was before Joseph Priestley discovered oxygen. We have reached a similar point in machine learning as we have a few of our own Phlogiston theories, such as the Manifold Hypothesis. But we do not really know how these things work, or why. We are now beginning to create a good model and a good objective understanding of how these processes work. In practice that means we have seen examples of researchers attempting to use a sigmoid function and then, due to the promising initial results, trying to probe a few layers deeper. Through a process of experimentation, we have found the application of big data can bring substantive and effective results. Although, in truth, many of our discoveries have been entirely accidental with almost no foundational theory, or even hypotheses to guide them. This experimentation without method creates a sense of uncertainty and unpredictability, which means that we might soon make an advancement in this space that would create orders of magnitude, more efficiency. Such a discovery could happen tomorrow, or it could take another twenty years.
In terms of the morality and ethics of machines, we face an immense challenge. Firstly, integrating these technologies into our society is a daunting task. These systems are little optimization genies; they can create all kinds of remarkable optimizations, or impressive generated content. However, that means that society might be vulnerable to fakery or counterfeiting. Optimization should not be seen as a panacea. Humanity needs to think more carefully about the consequences of these technologies as we are already starting to witness the effects of AI upon our society and culture. Machines are often optimized for engagement, and sometimes, the strongest form of engagement is to evoke outrage. If machines can get results by exploiting human weaknesses and provoking anger then there is a risk that they may be produced for this very purpose.
Over the last ten years, we have seen, across the globe, a very strong polarization of our culture. People are, more noticeably than ever, falling into distinctive ideological camps, which are increasingly entrenched, and distant from each other. In the past, there was a stronger consensus on morality and what was right and wrong. Individuals may have disagreed on many issues, but there was a sense that human beings were able to find common ground and ways of reaching agreement on the fundamental issues. However, today, people are increasingly starting to think of the other camp, or the other ideologies, as being fundamentally bad people. Consequently, we are starting to disengage with each other, which is damaging the fabric of our society, in a very profound and disconcerting way. We’re also starting to see our culture being damaged in other ways as well, due to the substantial amount of content that is uploaded to YouTube every minute.
It’s almost impossible to develop the army of human beings in the sufficient numbers required to moderate that kind of content. As a result, much of the content is processed and regulated by algorithms; unfortunately, a lot of those algorithmic decisions aren’t very good ones. A lot of content, which is in fact quite benign, ends up being flagged or demonetized, for very mysterious reasons, which are not explained to anyone. Entire channels of content can be deleted overnight, on a whim; with very little oversight, with very little opportunity for redress. There is minimum human intervention or reasoning involved to try to correct unjustified algorithmic decisions. This problem is likely to become more serious as more of these algorithms get used in our society in different ways. It may even be potentially dangerous because it can lead to detrimental outcomes, where people might be afraid to speak out. Not because fellow humans might misunderstand them, although this is also an increasingly prevalent factor in this ideologically entrenched world. For instance, a poorly constructed algorithm might select a few words in a paragraph and come to the conclusion that the person is trolling another person, or creating fake news, or something similarly negative. The ramifications of these weak decisions could be substantial: individuals might be mysteriously downvoted, or shadowbanned, and find themselves isolated, effectively talking to an empty room. As they expand, these flawed algorithmic decision systems have the potential to cause widespread frustration in our society. It may even engender mass paranoia as individuals start to think that there is some kind of conspiracy working against them even though they may not be able to confirm why they have been excluded from given groups and organizations. In short, an absence of quality control and careful consideration of the complex moral and ethical issues at hand may undermine the great potential of machine learning.
There are some major challenges to be overcome, but we now have the opportunity to make appropriate interventions before these potential problems impact in a significant way. We still have an opportunity to overcome them, but it is going to be a challenge. Thirty years ago, the entire world reached consensus on the need to cooperate on CFCs [chlorofluorocarbons]. Over a relatively short period of a few years, governments acknowledged the damage that we were inflicting on the ozone layer. They decided that action had to be taken and, through coordination, a complete ban on CFCs was introduced in 1996, which quickly made a difference. This remarkable achievement testifies to the fact that, when confronted with a global challenge, states are capable of acting rapidly and decisively to find mutually acceptable solutions. The historical example of CFCs should therefore provide us with grounds for optimism that we might find cooperative ethical and moral approaches to machine learning through agreed best practices and acceptable behaviors.
A cautious, pragmatic optimism can be regarded as an essential ingredient in daily life and in engineering. Only by adopting an optimistic outlook can we reach into an imagined better future and find a means of pulling it back into the present. If we succumb to pessimism and dystopian visions then we risk paralysis. This would amount to the sort of panic humans can experience when they find themselves in a bad situation, akin to drowning in quicksand slowly. In this sort of situation panicking is likely to lead to a highly negative outcome. Therefore, it is important that we remain cautiously optimistic and rationally seek the best way to move forward. It is also vital that the wider public are aware of the challenges, but also, aware of the many possibilities there are. Indeed, while there are many dangers, we must recognize the equally significant opportunities to harness machines that guide us, and that help us to be better human beings; that help us to have greater power and efficacy in the world, find more fulfilment, and find greater meaning in life.
There is a broader area of study called value alignment, or AI alignment; it’s about teaching machines how to understand human preferences, how to understand how humans tend to interact in mutually beneficial ways. In essence, AI alignment is about ensuring that machines are aligned on human goals, but also about how we begin to socialize machines, so that they know how to behave according to societal norms. There are some very interesting potential approaches and algorithms, adopting various forms of inverse reinforcement learning. Machines can observe how we interact and decipher the rules without being explicitly told; just by effectively watching how other people function. To a large extent, human beings learn socialization in similar ways. In an unfamiliar culture, individuals will wait for other people to start doing something, to discover how to greet someone, or which fork to use when eating. Humans sometimes learn in that way and there are great opportunities for machines to learn about us in similar ways.
One of the reasons why machine intelligence has taken off in recent years is because we have very rich datasets that are sets of experiences, about the world, for machines to learn from. We now have enormous amounts of data and there has been a huge increase in the amount of available data thanks to the Internet, and to new layers of information that machines can draw on. We have moved from a web of text and a few low-resolution pictures, to a world of video, to a world of location and health data, etc. And all of this can be used to train machines and get them to understand how our world works, and why it works in the way it does.
A few years ago, there was a particularly important dataset that was released, by a professor called Fei-Fei Li and her team. This dataset, ImageNet, was a corpus of information about objects, ranging from buses, or cows to teddy bears or tables. Now machines could begin to recognize objects in the world. The data itself was extremely useful for training things like convolutional neural networks; revolutionary new technologies for machine vision. But more than that, it was a benchmarking system because you could test one approach versus another, and you could test them in different situations. That led to a very rapid increase over just a few years in this space. It is now possible to achieve something similar when it comes to teaching machines about how to behave in socially acceptable ways. We can create a dataset of prosocial human behaviors, to teach machines about kindness, about congeniality, politeness, and manners. When we think of young children, often, we do not teach them right and wrong as such, rather we teach them to adhere to behavioral norms, such as remaining quiet in polite company. We teach them simple social graces before we teach them right and wrong. In many ways, good manners are the mother of morality, in a sense they constitute a moral foundational layer.
Therefore, based on that assumption, we are trying to teach machines basic social rules: for example, that it is not nice to stare at people or to be quiet in a church or museum, or if you see someone drop something that looks important, such as their wallet, alert them. These are the types of simple rules that we might ideally teach a well-raised, six-year-old child, to know and to understand. If this is achievable, then we can move on to a more complex level. The important thing will be that we will have some information that we can use to begin to benchmark these different approaches. Otherwise, it may take another twenty years to teach machines about human society and how to behave in ways that we would prefer.
While the abundance of ideas in the field is a positive sign, we cannot realize them in practice until we have the right quality and quantity of data. My nonprofit organization, EthicsNet, is creating a dataset of prosocial behaviors, which have been annotated, or labeled, by people from all across the world, every different culture and creed. The idea is to gauge as wide a spectrum of human values and morals as possible and to try to make sense of them, so that we can find the commonalities between different values. But we can also recreate the little nuances, or behavioral specificities that might be more suitable to particular cultures, or particular situations.
Human beings have been using different forms of encryption for a very long time. The ancient Sumerians had a form of token and crypto solution, 5,000 years ago. They would place these literal small tokens, that represented numbers or quantities, inside a clay ball (aBulla). This meant that you could keep your message secret, but you could also be sure that it had not been cracked open, for people to see, and the tokens would not get lost. Now, 5,000 years later, we are discovering a digital approach to solving a similar problem, so what appears to be novel is in many ways an age-old theme.
One of the greatest developments of the Early Renaissance was the invention of double-entry accounting, created in two different locations during the 10th and 12th centuries. Nevertheless, the idea did not reach fruition until a Franciscan friar, called Father Luca Pacioli, was inspired by this aesthetic that he saw as a divine mirror of the world. He thought that it would be a good idea to have a “mirror” of a book’s content, which meant that one book would contain an entry in one place, and there would be a corresponding entry in another book. Although this appears somewhat dull, the popularisation of the method of double-entry accounting actually enabled global trade in ways that were not possible before. If you had a ship at sea and you lost the books, then all those records were irrecoverable. But with duplicate records you could recreate the books, even if they had been lost. It made fraud a lot more difficult. This development enabled banking practices, and, eventually, the first banking cartels emerged, which, otherwise, would not have been possible. One interesting example is the Bank of the Knights Templar, where people could deposit money in one place and pick it up somewhere else; a little bit like a Traveler’s Cheque. None of this would have been possible if we did not have distributed ledgers.
Several centuries later, at the Battle of Vienna in 1683, the Ottomans invaded Vienna for the second time, and they were repulsed. They went home in defeat, but they left behind something remarkable. A miraculous substance, coffee. Some enterprising individuals took that coffee and opened the first coffeehouse in Vienna. And, to this day, Viennese coffee houses have a very long and deep tradition where people can come together and learn about the world by reading the available periodicals and magazines. In contrast to a different trend of inebriated people meeting in the local pub, people could have an enlightened conversation. Thus coffee, in many ways, helped to construct the Enlightenment because these were forums where people could share ideas in a safe place that was relatively private. The coffee house enabled new forms of coordination which were more sophisticated. From the first coffee houses, we saw the emergence of the first insurance companies, such as Lloyds of London. We also saw the emergence of the first joint stock companies, including the Dutch East India Company. The first stock exchange in Amsterdam grew out of a coffee house. This forum of communication, to some extent, enabled the Industrial Revolution. The Industrial Revolution wasn’t so much about steam. The Ancient Greeks had primitive steam engines; they might even have had an Industrial Revolution, from a technological perspective, but not from a social perspective. They did not yet have the social technologies required to increase the level of complexity in their society because they did not have trust-building mechanisms, the institutions, necessary to create trust. If you lose your ship, you do not necessarily lose your entire lifestyle: if you are insured, that builds trust which in turn builds security. In a joint stock company, those who run a company are obliged to provide shareholders with relevant performance information. The shareholders therefore have some level of security that company directors cannot simply take their money — they are bound by accountability and rules, which helps to build trust. Trust enables complexity, greater complexity enabled the Industrial Revolution.
Today, we have remarkable new technologies, built on triple- entry ledger systems. These triple-entry ledger technologies mean that we can build trust, we can use these as layers of trust-building mechanisms to augment our existing institutions. It is also possible to do this in a decentralized form where there is, in theory, no single point of failure, and no single point of control, or corruption, within that trust-building mechanism. This means we can effectively franchise trust to parts of the world that don’t have very good trust-building infrastructure. Not every country in the world has very efficient government, or a very trustworthy government, and so these technologies enable us to develop a firmer foundation for the social fabric in many parts of the world, where trust is not necessarily very strong.
This is very positive, not only for commerce, but also for human happiness. There is a very strong correlation between happiness and trust in society. Trust and happiness go hand in hand, even when you control for variables, such as GDP. Even if you are poor, if you believe that your neighbor generally has your best interest at heart, all things being equal, you will tend to be happy. You will be secure. Therefore, anything that we can use to build more trust in society will typically help to make people happier. But it also means that we can create new ways of organizing people, capital, and values in ways that enable a much greater level of complex societal function. If we are fortunate and approach this challenge in a careful manner, we might see something like another Industrial Revolution, built upon these kinds of technologies. Life before the Industrial Revolution was difficult, and then it significantly improved. If we look at human development and wellbeing on a long scale, basically nothing happened for millennia, and then a massive spike in wellbeing occurred. We are still extending the benefits of that breakthrough to the serve the needs of the entire world, and we have increasingly managed to accomplish this, as property rights and mostly-free markets have expanded.
However, there have also been certain negative consequences of economic development. Today, global GDP is over 80 trillion dollars, but we often fail to take into account the externalities that we’ve created. Externalities, in economic terms, are when one does something that affects an unrelated third party. Pollution is one example of an externality; although world GDP may be more than 80 trillion, there are quadrillions of unfunded externalities; which are not on the balance sheet. Entire species have been destroyed, populations enslaved. In short, there have been many unintended consequences, which have not been accounted for. To some extent, a significant portion of humanity has achieved all the trappings of a prosperous, comfortable society by not paying for these externalities. But it’s generally done ex post facto, after the fact. Historically, we have had a tendency to create our own problems through lack of foresight and then tried to correct them after inflicting the damage. However, as these machine ethics technologies get more sophisticated, we are able to intertwine them, with machine economics technologies, such as distributed ledger technology, and machine intelligence, to connect and integrate everything together, and understand how one area affects another. We will, in the 2020s and 2030s, be able to start accounting for externalities in society for the very first time; that means that we can include externalities in pricing mechanisms to make people pay for them at the point of purchase, not after the fact. And so that means that products or services that don’t create so many externalities in the world will, all things being equal, be a little bit cheaper. We can create economic incentives to be kinder to people and planet whilst still maintaining profit thus overcoming the traditional dichotomy between socialism and capitalism. We can still reap the benefits of free markets, if we follow careful accounting practices to consider externalities. That is what these distributed ledger technologies are going to enable, with machine ethics and machine intelligence; the confluence of the three together.
The potential of the emerging technologies is such that it is not inconceivable that they may even be able to supplant states’ monopoly of force in the future. We would have to consider whether or not this would be a desirable step forward as not all states could be trusted to use their means of coercion in a safe, responsible manner, even now, without the technology. States exist for a reason. If we look at the very first cities in the world, such as Çatalhöyük in modern day Turkey, these cities do not look like modern cities at all and are more akin to towns by comparison to contemporary scales and layout. They are more similar to a beehive in that they are built around little, square dwellings, all stacked on top of each other. There are no streets, no public buildings, no plazas, no temples, or palaces. All the buildings are identical. The archaeological record, tells us that people started to live in these kinds of conurbations for a while, and then they stopped for a period of about 800 years. They gave up living in this way, and they went back to living in very small villages, in little huts and more primitive dwellings. When we next see cities emerge, they are very different. In these next cities, such as Uruk and Babylon, boulevards, great temples, and workshops begin to take shape. We can also observe the development of specific quarters of the city given to certain industries, and also commercial areas. On a functional level, they are not too dissimilar from modern cities; at least in their general layout, and in terms, for instance, of the different divisions of labor. So what was the difference? And why did people abandon cities for a time? If we consider that these were really nomadic societies when individuals and groups moved from place to place, then it is easier to understand that property and personal possessions were not tied to a fixed location. Nomads had to take their property with them, and so these were very egalitarian societies, where no one person had much more than anyone else. Subsequently, these people started living together, and they started farming. Farming changed the direction of human development as it enabled people to turn one X of effort into ten X of output. As farming progressed, some individuals enjoyed greater success in production output than others. This allowed them to accumulate more possessions and accrue more wealth than others. These evolving inequalities engendered a growing tension in society, people started to resent one another, and it became necessary to find methods of protecting private property given the increasing risk of theft. This, in turn, necessitated the evolution of collective forms of coercion and the gradual evolution of the state. In its earliest forms, clans would protect themselves through collective, physical protection of their property and possessions. It was only the invention of that sort of centralization of power that enabled cities in their modern form. That is why the first cities failed; they had not yet developed this social technology.
10,000 years later, we still have the same technology, the same centralization of power, the same monopoly of force, and that is what generally governs the world. The state also enables order and has helped foster civilized society as we know it, so it can be a positive force. Nonetheless, the technologies we are now developing may enable us to move beyond monopolies of force and, paradoxically, return to a way of life that is, perhaps, a little bit more egalitarian again. Outcomes might potentially be less zero sum in character; where it’s less about winning and losing and more about tradeoffs. Generally speaking, trade can enable non- zero-sum outcomes. If I want your money more than you want those sausages then the best solution is a tradeoff. As we develop more sophisticated trading mechanisms, including machine economics technologies, we can begin to trade all kinds of goods. We can trade externalities, we can even pay people to behave in moral ways, or make certain value-based decisions. We can begin to incentivize all kinds of desirable behaviors, without needing to use the stick; we can use the carrot instead.
Yet, for the successful implementation of distributed ledger and blockchain technologies the question of trust is of central importance. In the current wild west environment, one of the most important aspects of trust in this space is actually knowing other people. Who are the advisors of your crypto company? Do you have some reliable individuals in organization? Are they actually involved in your company? These are the things that people want to verify, along with a close examination of your white paper. Most people lack the level of expertise required to really make sense of the mathematics. Even if they do have that expertise, they will have to vet a lot of code, which can be revised at any time. In fact, even in the crypto world, so much of the trust is merely built on personal reputation. Given that we are at an early stage, machine economics technologies are only really likely to achieve substantive results when they are married with machine intelligence and machine ethics. Such holistic integration will facilitate a new powerful form of societal complexity in the 2020s. The first Industrial Revolution was about augmenting muscle: the muscle of beasts of burden, the muscle of human beings, the mode of power. The second Industrial Revolution — the Informational Revolution, was about augmenting our cognition. It enabled us to perform a wide variety of complex information processing tasks and to remember things that our brains would not have the capacity for. That was why computers were initially developed. But we are now on the verge of another revolution, an augmentation of what might be described as the human heart and soul. Augmenting our ability to make good moral judgments; augmenting our ability to understand how an action that we take has an effect on others; giving us suggestions of more desirable ways of engaging. For example, we might want to think more carefully about everyday actions, such as sending an angry message to that person and perhaps reformulating that retort.
If we can develop technologies that encourage better behavior and which may be cheaper and kinder to the environment, then we can begin to map human values, and map who we are, deep in our core. They might help us to build relationships with people, that we otherwise might miss out on. In a social environment, when people gather together, the personalities are not exactly the same, but they do complement each other. The masculine and the feminine, the introvert and the extrovert, the people who have different skills and talents, and possibly even worldviews, but they share similar values. So individuals are similar in some ways, and yet, different. In your town, there may be a hundred potential close friends. But unless you have an opportunity to meet them, grab a little coffee with them, get to know them, you pass like ships in the night and never see each other, except to tip your hat to them. These technologies can help us to find those people that are most like us. As Timothy Leary said, “find the others”. Machines can help us to find the others in a world where people are increasingly feeling isolated. During the 1980s, statistically, many of us could count on three or four close friends. But today, people increasingly report having only one or no close friends. We live in a world of incredible abundance, resources, safety and opportunities. And yet, increasingly, people are feeling disconnected from each other, from themselves, from spirituality and nature.
By augmenting the human heart and soul we might be able to solve those higher problems in Maslow’s Hierarchy of Needs; to help us to find love and belonging, to build self-esteem, to get us towards self-actualization. There are very few truly self-actualized human beings on this planet, and that is lamentable because when a human being is truly self-actualized their horizons are limitless. So it will be possible to build, in the 2020s and beyond, a system that does not merely satisfy basic human needs, but supports the full realization of human excellence and the joy of being human. If such a system could reach an industrial scale, everyone on this planet would have the opportunity to be a self-actualized human being.
However, while the possibilities appear boundless, the technology is developing so rapidly that non-expert professionals, such as politicians, are often not aware of them and how they might be regulated. One of the challenges of regulation is that it is generally done in hindsight; a challenge appears, and political elites often respond to it after the fact. Unfortunately, it can be very difficult to keep up with both technological and social change. It can also be very difficult to regulate in a proactive way rather than a reactive way. That is one of the reasons why principles are so important because principles are the things that we decide in advance of a situation. So when that situation is upon us, we have an immediate heuristic of how to respond. We know what is acceptable and what is not acceptable and, if we have sound principles in advance of a dilemma, we are much less likely to accidentally make a poor decision. That is one of the main reasons why having good principles is very important.
Admittedly, we have to consider how effectively machines might interpret values; they might be very consistent even though we, as humans, may perhaps see grey areas, not only black and white. We might even engineer machines that on some levels, on some occasions, are more moral than the average human being. The psychologist, Lawrence Kohlberg, reckoned that there were about six different layers of moral understanding. It is not about the decision that you make, it is rather the reason why you make that decision. In the early years of life you learn about correct behavior and the possibility of punishment. Later humans learn about more advanced forms of desirable behavior, such as being loyal to your family, your friends, and your clan or recognizing when an act is against the law, or against religious doctrine. When considering the six levels, Kohlberg reckoned that most people get to about level four or so, before they pass on. Only a few people manage to get a little bit beyond that. Therefore, it may be the case that the benchmark of average human morality is not set that high. Most people are generally not aspiring to be angels; they are aspiring to protect their own interests. They are looking at what other people are doing and trying to be approximately as moral as they are. This is essentially a keeping-up-with-the-Joneses morality. Now, if there are machines involved, and the machines are helping to suggest potential solutions that might be a little bit more moral than many people find it easy to reason with on that level, then perhaps machines might add to this social cognition of morality. It is thus possible that machines might help to tweak and nudge us in a more desirable moral direction. Although, of course, given how algorithms can also take us in directions that can be very quietly oppressive, it remains to be seen how the technology will be used in the near future. People will readily rebel against a human tyrant, an oppressor that they can point at. But they don’t tend to rebel against repressive systems. They tend to passively accept that this is the way things work. That is why it is important for such technologies to be implemented in an ethical way, and not in a quietly tyrannical way.
Finally, the development of machine learning may depend, to some extent, on where the technological breakthroughs are made. Europe has a phenomenal advantage with these new technologies. Although progress might appear to be very rapid in China or Silicon Valley — they think fast in China, while they think big in Silicon Valley. But Europe is, in many ways, the cradle of civilization. There is a deep well of culture, intellect, and moral awareness in our wonderful continent. We have a remarkable artistic, architectural and cultural heritage and, as we begin to introduce machines to our culture, as we begin to teach these naive little agents, about society and how to socialize them, we can make a significant difference. Europe has a uniquely positioned opportunity, to be the leader in bringing culture and ethics into machines; given our long heritage of developing these kinds of technologies. While the USA tends to think in terms of scale, and China can produce prototypes at breakneck speed, Europe, tends to think deep. We tend to think more holistically, we tend to understand how things connect; how one variable might relate to another. We have a deep and profound understanding of history because Europe has been through many different positive and negative experiences. Consequently, Europeans have a slightly more cautious way of dealing with the world. However, caution and forethought are going to be essential ingredients, if we are going to do this right. Europe has a monumental opportunity to be the moral and cultural leader of this AI wave; especially in conjunction with machine economics, and machine ethics technologies.
To conclude, machine learning promises to transform social, economic and political life beyond recognition during the coming decades. History has taught us many lessons, but if we do not heed them, we run the risk of making the same mistakes over and over again. As the technology develops at a rapid rate it is vital that we start to get a better understanding of our experiments and develop rational and moral perspective. Machine learning can bring many benefits to humanity; however there is also potential for misuse. There is a tremendous need to infuse technology with the ability to make good moral judgments that can enrich our social fabric.
