What is AI?
We can define Artificial Intelligence or AI as non-biological intelligence. Disturbingly among intelligence researchers there’s no agreement on what intelligence is so there’s no undisputed “correct” definition of intelligence. Instead there are many competing ones, including capacity for logic, understanding, planning, emotional knowledge, self-awareness, creativity, problem solving and learning.
Defining intelligence in a broad sense, not limited to the sorts of intelligence that exist so far, is important as the intelligence of future AI may be too complex for humans to understand.
Intelligence is the ability to accomplish complex goals.
By Tegmark’s definition, it makes no sense to quantify intelligence of humans, animals or machines by a single number such as IQ. What’s more intelligent: a computer programme that can only play chess or one that can only play scrabble? There is no sensible answer to this, since they’re good at different things that can’t be directly compared. We can, however, say that a third programme is more intelligent than both of the others if it’s at least as good as them at accomplishing all goals, and strictly better at at least one (winning at chess, say).
Humans vs. Machines
It’s natural for us to rate the difficulty of tasks relative to how hard it is for us humans to perform them. But this can give a misleading picture for how hard they are for machines. It feels much harder to multiply 314,159 by 271,828 than to recognise a friend in a photo, yet a computer would annihilate a human in arithmetic but only recently have computers been able to perform human-level image recognition.
The fact that low-level sensorimotor tasks (recognising your friends) seem easy despite requiring enormous computational resources is known as Moravec’s Paradox, and is because our brain makes such tasks feel easy by dedicating massive amounts of customised hardware to them. Hans Moravec had this to say in 1998:
Computers are universal machines, their potential extends uniformly over a boundless expanse of tasks. Human potentials, on the other hand, are strong in areas long important for survival, but weak in things far removed. Imagine a “landscape of human competence,” having lowlands with labels like “arithmetic” and “rote memorisation,” foothills like “theorem proving” and “chess playing,” and high mountain peaks labeled “locomotion,” “hand-eye coordination,” and “social-interaction.” Advancing computer performance is like water slowly flooding the landscape. A half century ago it began to drown the lowlands, driving out human calculators and record clerks, but leaving most of us dry. Now the flood has reached the foothills, and our outposts there are contemplating retreat. We feel safe on our peaks, but, at the present rate, those too will be submerged within another half century. I propose that we build Arks as that day nears, and adopt a seafaring life!
During the decades since Moravec wrote those passages, the sea level has kept rising relentlessly, and some of his foothills (including chess) have long since been submerged as the image below shows.
From glancing at the image above, you will already see some likely targets that AI will takeover in the near future. Areas such as speech recognition, investment, social interaction and driving are all undergoing rapid technological upgrades that will make it easier for AI to conquer these fields.
Speech recognition devices have proved popular in the forms of Amazon’s Alexa and Google Home. Siri has collected data on us for years and has vastly improved since it first arrived on the iPhone 4s. Social interaction has proved vastly popular online and continues to occupy us is many forms including Instagram, Facebook, Whatsapp and Slack. Today automated chatbots are readily available to use, eliminating the need for a human operator and some of the best make you question whether you are talking to a human or machine. As the sea level keeps rising, it may one day reach a tipping point, triggering dramatic change. This critical sea level is the one corresponding to machines being able to perform AI design. Before this tipping point is reached, the sea-level rise is caused by humans improving machines; afterward, the rise can be driven by machines, potentially much faster than humans could have done, rapidly submerging all land.
Creating a Digital Athens
If we can figure out how to grow our prosperity through automation without leaving people lacking income or purpose, then we have the potential to create a fantastic future with leisure and unprecedented opulence for everyone who wants it. Erik Brynjolfsson coined this optimistic job market vision “Digital Athens.” The reason that the ancient Athenians could enjoy democracy, art and games was mainly that the slaves had to do much of the work. But why not replace the slaves with AI-powered robots, creating a digital utopia that everyone could enjoy. Brynjolfsson’s AI-driven economy would not only eliminate stress and drudgery and produce an abundance of everything we want today, but it would also supply a bounty of new products and services that today’s consumers haven’t yet realised that they want.
We can get from where we are today to Brynjolfsson’s Digital Athens if everyone’s hourly salary keeps growing year by year, so that those who want more leisure can gradually work less while continuing to improve their standards of living. From World War II to the mid-1970s, the total size of the pie grew in such a way that almost everybody got a larger slice in the USA. The United Kingdom followed a similar trend too. But then, something changed. The gains over the past four decades went to the wealthiest, mostly to the top 1%, while the poorest 90% saw their incomes stagnate . In 2013, the combined wealth of the bottom half of the world’s population (over 3.6 billion people) is the same as that of the world’s eight richest people.
So what’s causing this inequality?
Technology is driving inequality in three different ways. First by replacing old jobs with ones that require more skills, technology has rewarded the educated: since the mid-1970s, salaries rose about 25% for those with graduate degrees while the average high-school dropout took a 30% pay cut.
Second an ever-larger share of corporate income has gone to those who own the companies as opposed to those who work there and as long as automation continues we should expect those who own machines to take a growing fraction of the pie. Additional digital copies of software, movies or e-books can be sold worldwide at essentially zero cost, without hiring additional employees. This allows most of the revenue to go to investors rather than workers, and helps explain why, even though the combined revenues of Detroit’s “Big 3” (GM, Ford and Chrysler) in 1990 were almost identical to those of Silicon Valley’s “Big 3” (Google, Apple, Facebook) in 2014, the latter had nine times fewer employees and were worth thirty times more on the stock market.
Third, the digital economy often benefits superstars over everyone else. Since most people are willing to pay little or nothing for the tenth best, there’s room in the market for only a modest number of superstars. As more people come online, competition will increase and you will either serve the world or nobody. If you want someone to watch your Youtube video, you are competing with millions of other videos for that person’s attention. On the freelancing website Upwork you can find Indian MBA students willing to work for £5.50 an hour.
Jobs that involve highly repetitive or structured actions in a predictable setting aren’t likely to last long before getting automated away. Computers and industrial robots took over the simplest such jobs long ago, and improving technology is in the process of eliminating many more, from telemarketers to warehouse workers, cashiers, train operators, bakers and line cooks. Drivers of trucks, buses, taxis and Uber/Lyft cars are likely to follow soon. There are many more professions (including paralegals, credit analysts, loan officers, bookkeepers and tax accountants) that, although they aren’t on the endangered list of full extinction, are getting most of their tasks automated and therefore demand many fewer humans. So how can you avoid being automated?
Go into professions that machines are currently bad at, and therefore seem unlikely to get automated in the near future. Ask yourself these three questions:
- Does it require interacting with people and using social intelligence?
- Does it involve creativity and coming up with clever solutions?
- Does it require working in an unpredictable environment?
The more of these questions you can answer with a yes, the better your career choice is likely to be. This means that relatively safe bets include becoming a teacher, nurse, doctor, dentist, scientist, entrepreneur, programmer, engineer, lawyer, social worker, artist, hairdresser or massage therapist. Emphasis on the word relatively because it is likely that many of these professions will radically change in the future. Let’s take law as an example.
Since the legal process can be abstractly viewed as a computation, inputting information about evidence and laws and outputting a decision, we could use Robojudges: AI systems that tirelessly apply the same high legal standards to every judgment without succumbing to human errors such as bias, fatigue or lack of the latest knowledge. Robojudges could in principle ensure that, for the first time in history, everyone becomes truly equal under the law: they could be programmed to all be identical and to treat everyone equally, transparently applying the law in a truly unbiased fashion. Robojudges would then mean lawyers would become more focused on managing relationships with their clients and clarifying outcomes of the trial and ongoing proceedings, rather than being focused on the quality of one’s own arguments and research.
But staying clear of automation is not the only career challenge. In this global digital age, aiming to become a professional writer, film-maker, actor, athlete, or fashion designer is risky for another reason: although people in these professions won’t get serious competition from machines anytime soon, they’ll get increasingly brutal competition from other humans around the globe according to the aforementioned superstar theory, and very few will succeed.
In many cases, it would be ill-considered to give career advice at the level of whole fields, especially given that I don’t know what certain jobs demand. There are many jobs that won’t get entirely eliminated, but will see many of their tasks automated. I think that in general, what you should be aiming for is maximising your technical skills alongside your people skills. For example, learning to programme so you can understand technology better while being able to present your research or work in engaging and easy to understand format. Having the combination of people and technical skills or ‘soft’ and ‘hard’ skills will help you to prosper in the future and keep you safe from automation for the short-term at least.
Most important of all will be the ability to deal with change, learn new things, and preserve your mental balance in unfamiliar situations. To keep up with the world of 2050, you will need to do more than merely invent new ideas and products, but above all, reinvent yourself again and again.
Yuval Noah Harari
A very interesting read and very informative of the book. Makes you think of AI in the future in a whole new way.
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Thank you! I’m glad that you enjoyed it 🙂
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The only job i cant imagine being automated is that of a politician. On a more serious note, i think most schools do their students a disservice by not making them aware of these ideas. Often, it’s just them trying to send students to university without considering how university might help them get skilled in an area that is unlikely to be automated.
My thought process for choosing a field is as follows:
1. Am i good at it? (Relative to others, because as you state, it is a competition)
2. Can i make a living from it? (No point in following something if you cannot get a good income)
University choices should be influenced by these two questions. If your degree does not enhance your already decent capabilities in a monetisable field, is it worth spending 3 to 4 years of your life on it? I cant say that i thought about it, but i wish i had.
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I absolutely love your insights!
Given how little people trust politicians, there is potential for an advanced AI to manage many of the things with dedicate to politicians. For example, the economy, healthcare, defence etc. So perhaps even politicians are at risk.
I do think that you are never going to be truly exceptional by following a path that many have walked before, and we can put blame on universities but given how archaic our education system is, I think we need to take greater responsibility in educating ourselves on matters such as AI.
The question you raise about being good at something is crucial because it doesn’t matter how passionate you are about something, if you are terrible you will not go very far. I think your second question can be answered by the first one as often by being exceptional at something, you can make money from it. Generally speaking, yes, there are certain careers and industries that on average pay higher but I don’t think choosing a career based on money is a wise decision.
Your last point is important because I feel we don’t consider the end result of completing a degree. I do believe that some degrees offer more valuable skills than others, especially in the context of the job market. But if you do a well-respect degree and lack direction or ambition, you are more likely to stagnate in your career compared to somebody who has ambition and a clear vision, yet has done a less-technical degree.
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You might find some of our infographics interesting. On very similar topics