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#432568 Tech Optimists See a Golden ...

Technology evangelists dream about a future where we’re all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud.

Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there?

Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run. His rebuttal to critics was, “In the long run, we are all dead.” After all, if it takes 50 years of upheaval and economic chaos for things to return to normality, there has been an immense amount of human suffering first.

Similar problems arise with the transition to a world where AI is intimately involved in our lives. In the long term, automation of labor might benefit the human species immensely. But in the short term, it has all kinds of potential pitfalls, especially in exacerbating inequality within societies where AI takes on a larger role. A new report from the Institute for Public Policy Research has deep concerns about the future of work.

Uneven Distribution
While the report doesn’t foresee the same gloom and doom of mass unemployment that other commentators have considered, the concern is that the gains in productivity and economic benefits from AI will be unevenly distributed. In the UK, jobs that account for £290 billion worth of wages in today’s economy could potentially be automated with current technology. But these are disproportionately jobs held by people who are already suffering from social inequality.

Low-wage jobs are five times more likely to be automated than high-wage jobs. A greater proportion of jobs held by women are likely to be automated. The solution that’s often suggested is that people should simply “retrain”; but if no funding or assistance is provided, this burden is too much to bear. You can’t expect people to seamlessly transition from driving taxis to writing self-driving car software without help. As we have already seen, inequality is exacerbated when jobs that don’t require advanced education (even if they require a great deal of technical skill) are the first to go.

No Room for Beginners
Optimists say algorithms won’t replace humans, but will instead liberate us from the dull parts of our jobs. Lawyers used to have to spend hours trawling through case law to find legal precedents; now AI can identify the most relevant documents for them. Doctors no longer need to look through endless scans and perform diagnostic tests; machines can do this, leaving the decision-making to humans. This boosts productivity and provides invaluable tools for workers.

But there are issues with this rosy picture. If humans need to do less work, the economic incentive is for the boss to reduce their hours. Some of these “dull, routine” parts of the job were traditionally how people getting into the field learned the ropes: paralegals used to look through case law, but AI may render them obsolete. Even in the field of journalism, there’s now software that will rewrite press releases for publication, traditionally something close to an entry-level task. If there are no entry-level jobs, or if entry-level now requires years of training, the result is to exacerbate inequality and reduce social mobility.

Automating Our Biases
The adoption of algorithms into employment has already had negative impacts on equality. Cathy O’Neil, mathematics PhD from Harvard, raises these concerns in her excellent book Weapons of Math Destruction. She notes that algorithms designed by humans often encode the biases of that society, whether they’re racial or based on gender and sexuality.

Google’s search engine advertises more executive-level jobs to users it thinks are male. AI programs predict that black offenders are more likely to re-offend than white offenders; they receive correspondingly longer sentences. It needn’t necessarily be that bias has been actively programmed; perhaps the algorithms just learn from historical data, but this means they will perpetuate historical inequalities.

Take candidate-screening software HireVue, used by many major corporations to assess new employees. It analyzes “verbal and non-verbal cues” of candidates, comparing them to employees that historically did well. Either way, according to Cathy O’Neil, they are “using people’s fear and trust of mathematics to prevent them from asking questions.” With no transparency or understanding of how the algorithm generates its results, and no consensus over who’s responsible for the results, discrimination can occur automatically, on a massive scale.

Combine this with other demographic trends. In rich countries, people are living longer. An increasing burden will be placed on a shrinking tax base to support that elderly population. A recent study said that due to the accumulation of wealth in older generations, millennials stand to inherit more than any previous generation, but it won’t happen until they’re in their 60s. Meanwhile, those with savings and capital will benefit as the economy shifts: the stock market and GDP will grow, but wages and equality will fall, a situation that favors people who are already wealthy.

Even in the most dramatic AI scenarios, inequality is exacerbated. If someone develops a general intelligence that’s near-human or super-human, and they manage to control and monopolize it, they instantly become immensely wealthy and powerful. If the glorious technological future that Silicon Valley enthusiasts dream about is only going to serve to make the growing gaps wider and strengthen existing unfair power structures, is it something worth striving for?

What Makes a Utopia?
We urgently need to redefine our notion of progress. Philosophers worry about an AI that is misaligned—the things it seeks to maximize are not the things we want maximized. At the same time, we measure the development of our countries by GDP, not the quality of life of workers or the equality of opportunity in the society. Growing wealth with increased inequality is not progress.

Some people will take the position that there are always winners and losers in society, and that any attempt to redress the inequalities of our society will stifle economic growth and leave everyone worse off. Some will see this as an argument for a new economic model, based around universal basic income. Any moves towards this will need to take care that it’s affordable, sustainable, and doesn’t lead towards an entrenched two-tier society.

Walter Schiedel’s book The Great Leveller is a huge survey of inequality across all of human history, from the 21st century to prehistoric cave-dwellers. He argues that only revolutions, wars, and other catastrophes have historically reduced inequality: a perfect example is the Black Death in Europe, which (by reducing the population and therefore the labor supply that was available) increased wages and reduced inequality. Meanwhile, our solution to the financial crisis of 2007-8 may have only made the problem worse.

But in a world of nuclear weapons, of biowarfare, of cyberwarfare—a world of unprecedented, complex, distributed threats—the consequences of these “safety valves” could be worse than ever before. Inequality increases the risk of global catastrophe, and global catastrophes could scupper any progress towards the techno-utopia that the utopians dream of. And a society with entrenched inequality is no utopia at all.

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#432482 This Week’s Awesome Stories From ...

A Brain-Boosting Prosthesis Moves From Rats to Humans
Robbie Gonzalez | WIRED
“Today, their proof-of-concept prosthetic lives outside a patient’s head and connects to the brain via wires. But in the future, Hampson hopes, surgeons could implant a similar apparatus entirely within a person’s skull, like a neural pacemaker. It could augment all manner of brain functions—not just in victims of dementia and brain injury, but healthy individuals, as well.”

Here’s How the US Needs to Prepare for the Age of Artificial Intelligence
Will Knight | MIT Technology Review
“The Trump administration has abandoned this vision and has no intention of devising its own AI plan, say those working there. They say there is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes… That looks like a huge mistake. If it essentially ignores such a technological transformation, the US might never make the most of an opportunity to reboot its economy and kick-start both wage growth and job creation. Failure to plan could also cause the birthplace of AI to lose ground to international rivals.”

Underwater GPS Inspired by Shrimp Eyes
Jeremy Hsu | IEEE Spectrum
“A few years ago, U.S. and Australian researchers developed a special camera inspired by the eyes of mantis shrimp that can see the polarization patterns of light waves, which resemble those in a rope being waved up and down. That means the bio-inspired camera can detect how light polarization patterns change once the light enters the water and gets deflected or scattered.”

‘The Business of War’: Google Employees Protest Work for the Pentagon
Scott Shane and Daisuke Wakabayashi | The New York Times
“Thousands of Google employees, including dozens of senior engineers, have signed a letter protesting the company’s involvement in a Pentagon program that uses artificial intelligence to interpret video imagery and could be used to improve the targeting of drone strikes.

The letter, which is circulating inside Google and has garnered more than 3,100 signatures, reflects a culture clash between Silicon Valley and the federal government that is likely to intensify as cutting-edge artificial intelligence is increasingly employed for military purposes. ‘We believe that Google should not be in the business of war,’ says the letter, addressed to Sundar Pichai, the company’s chief executive. It asks that Google pull out of Project Maven, a Pentagon pilot program, and announce a policy that it will not ‘ever build warfare technology.’ (Read the text of the letter.)”

MIT’s New Headset Reads the ‘Words in Your Head’
Brian Heater | TechCrunch
“A team at MIT has been working on just such a device, though the hardware design, admittedly, doesn’t go too far toward removing that whole self-consciousness bit from the equation. AlterEgo is a headmounted—or, more properly, jaw-mounted—device that’s capable of reading neuromuscular signals through built-in electrodes. The hardware, as MIT puts it, is capable of reading ‘words in your head.’”

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#432467 Dungeons and Dragons, Not Chess and Go: ...

Everyone had died—not that you’d know it, from how they were laughing about their poor choices and bad rolls of the dice. As a social anthropologist, I study how people understand artificial intelligence (AI) and our efforts towards attaining it; I’m also a life-long fan of Dungeons and Dragons (D&D), the inventive fantasy roleplaying game. During a recent quest, when I was playing an elf ranger, the trainee paladin (or holy knight) acted according to his noble character, and announced our presence at the mouth of a dragon’s lair. The results were disastrous. But while success in D&D means “beating the bad guy,” the game is also a creative sandbox, where failure can count as collective triumph so long as you tell a great tale.

What does this have to do with AI? In computer science, games are frequently used as a benchmark for an algorithm’s “intelligence.” The late Robert Wilensky, a professor at the University of California, Berkeley and a leading figure in AI, offered one reason why this might be. Computer scientists “looked around at who the smartest people were, and they were themselves, of course,” he told the authors of Compulsive Technology: Computers as Culture (1985). “They were all essentially mathematicians by training, and mathematicians do two things—they prove theorems and play chess. And they said, hey, if it proves a theorem or plays chess, it must be smart.” No surprise that demonstrations of AI’s “smarts” have focused on the artificial player’s prowess.

Yet the games that get chosen—like Go, the main battlefield for Google DeepMind’s algorithms in recent years—tend to be tightly bounded, with set objectives and clear paths to victory or defeat. These experiences have none of the open-ended collaboration of D&D. Which got me thinking: do we need a new test for intelligence, where the goal is not simply about success, but storytelling? What would it mean for an AI to “pass” as human in a game of D&D? Instead of the Turing test, perhaps we need an elf ranger test?

Of course, this is just a playful thought experiment, but it does highlight the flaws in certain models of intelligence. First, it reveals how intelligence has to work across a variety of environments. D&D participants can inhabit many characters in many games, and the individual player can “switch” between roles (the fighter, the thief, the healer). Meanwhile, AI researchers know that it’s super difficult to get a well-trained algorithm to apply its insights in even slightly different domains—something that we humans manage surprisingly well.

Second, D&D reminds us that intelligence is embodied. In computer games, the bodily aspect of the experience might range from pressing buttons on a controller in order to move an icon or avatar (a ping-pong paddle; a spaceship; an anthropomorphic, eternally hungry, yellow sphere), to more recent and immersive experiences involving virtual-reality goggles and haptic gloves. Even without these add-ons, games can still produce biological responses associated with stress and fear (if you’ve ever played Alien: Isolation you’ll understand). In the original D&D, the players encounter the game while sitting around a table together, feeling the story and its impact. Recent research in cognitive science suggests that bodily interactions are crucial to how we grasp more abstract mental concepts. But we give minimal attention to the embodiment of artificial agents, and how that might affect the way they learn and process information.

Finally, intelligence is social. AI algorithms typically learn through multiple rounds of competition, in which successful strategies get reinforced with rewards. True, it appears that humans also evolved to learn through repetition, reward and reinforcement. But there’s an important collaborative dimension to human intelligence. In the 1930s, the psychologist Lev Vygotsky identified the interaction of an expert and a novice as an example of what became called “scaffolded” learning, where the teacher demonstrates and then supports the learner in acquiring a new skill. In unbounded games, this cooperation is channelled through narrative. Games of It among small children can evolve from win/lose into attacks by terrible monsters, before shifting again to more complex narratives that explain why the monsters are attacking, who is the hero, and what they can do and why—narratives that aren’t always logical or even internally compatible. An AI that could engage in social storytelling is doubtless on a surer, more multifunctional footing than one that plays chess; and there’s no guarantee that chess is even a step on the road to attaining intelligence of this sort.

In some ways, this failure to look at roleplaying as a technical hurdle for intelligence is strange. D&D was a key cultural touchstone for technologists in the 1980s and the inspiration for many early text-based computer games, as Katie Hafner and Matthew Lyon point out in Where Wizards Stay up Late: The Origins of the Internet (1996). Even today, AI researchers who play games in their free time often mention D&D specifically. So instead of beating adversaries in games, we might learn more about intelligence if we tried to teach artificial agents to play together as we do: as paladins and elf rangers.

This article was originally published at Aeon and has been republished under Creative Commons.

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#432456 This Planned Solar Farm in Saudi Arabia ...

Right now it only exists on paper, in the form of a memorandum of understanding. But if constructed, the newly-announced solar photovoltaic project in Saudi Arabia would break an astonishing array of records. It’s larger than any solar project currently planned by a factor of 100. When completed, nominally in 2030, it would have a capacity of an astonishing 200 gigawatts (GW). The project is backed by Softbank Group and Saudi Arabia’s new crown prince, Mohammed Bin Salman, and was announced in New York on March 27.

The Tengger Desert Solar Park in China, affectionately known as the “Great Wall of Solar,” is the world’s largest operating solar farm, with a capacity of 1.5 GW. Larger farms are under construction, including the Westlands Solar Park, which plans to finish with 2.7 GW of capacity. But even those that are only in the planning phases are dwarfed by the Saudi project; two early-stage solar parks will have capacity of 7.2 GW, and the plan involves them generating electricity as early as next year.

It makes more sense to compare to slightly larger projects, like nations, or even planets. Saudi Arabia’s current electricity generation capacity is 77 GW. This project would almost triple it. The current total solar photovoltaic generation capacity installed worldwide is 303 GW. In other words, this single solar farm would account for a similar installed capacity as the entire world’s capacity in 2015, and over a thousand times more than we had in 2000.

That’s exponential growth for you, folks.

Of course, practically doubling the world’s solar capacity doesn’t come cheap; the nominal estimate for the budget is around $200 billion (compared to $20 billion for around half a gigawatt of fusion, though, it may not seem so bad.) But the project would help solve a number of pressing problems for Saudi Arabia.

For a start, solar power works well in the desert. The irradiance is high, you have plenty of empty space, and peak demand is driven by air conditioning in the cities and so corresponds with peak supply. Even if oil companies might seem blasé about the global supply of oil running out, individual countries are aware that their own reserves won’t last forever, and they don’t want to miss the energy transition. The country’s Vision 2030 project aims to diversify its heavily oil-dependent economy by that year. If they can construct solar farms on this scale, alongside the $80 billion the government plans to spend on a fleet of nuclear reactors, it seems logical to export that power to other countries in the region, especially given the amount of energy storage that would be required otherwise.

We’ve already discussed a large-scale project to build solar panels in the desert then export the electricity: the DESERTEC initiative in the Sahara. Although DESERTEC planned a range of different demonstration plants on scales of around 500 MW, its ultimate ambition was to “provide 20 percent of Europe’s electricity by 2050.” It seems that this project is similar in scale to what they were planning. Weaning ourselves off fossil fuels is going to be incredibly difficult. Only large-scale nuclear, wind, or solar can really supply the world’s energy needs if consumption is anything like what it is today; in all likelihood, we’ll need a combination of all three.

To make a sizeable contribution to that effort, the renewable projects have to be truly epic in scale. The planned 2 GW solar park at Bulli Creek in Australia would cover 5 square kilometers, so it’s not unreasonable to suggest that, across many farms, this project could cover around 500 square kilometers—around the size of Chicago.

It will come as no surprise that Softbank is involved in this project. The founder, Masayoshi Son, is well-known for large-scale “visionary” investments. This is suggested by the name of his $100 billion VC fund, the Softbank Vision Fund, and the focus of its investments. It has invested millions of dollars in tech companies like Uber, IoT, NVIDIA and ARM, and startups across fields like VR, agritech, and AI.

Of course, Softbank is also the company that bought infamous robot-makers Boston Dynamics from Google when their not-at-all-sinister “Project Replicant” was sidelined. Softbank is famous in Japan in part due to their mascot, Pepper, which is probably the most widespread humanoid robot on the planet. Suffice it to say that Softbank is keen to be a part of any technological development, and they’re not afraid of projects that are truly vast in scope.

Since the Fukushima disaster in 2011 led Japan to turn away from nuclear power, Son has also been focused on green electricity, floating the idea of an Asia Super Grid. Similar to DESERTEC, it aims to get around the main issues with renewable energy (the land use and the intermittency of supply) with a vast super-grid that would connect Mongolia, India, Japan, China, Russia, and South Korea with high-voltage DC power cables. “Since this is such a grandiose project, many people told me it is crazy,” Son said. “They said it is impossible both economically and politically.” The first stage of the project, a demonstration wind farm of 50 megawatts in Mongolia, began operating in October of last year.

Given that Saudi Arabia put up $45 billion of the Vision Fund, it’s also not surprising to see the location of the project; Softbank reportedly had plans to invest $25 billion of the Vision Fund in Saudi Arabia, and $1 billion will be spent on the first solar farms there. Prince Mohammed Bin Salman, 32, who recently consolidated power, is looking to be seen on the global stage as a modernizer. He was effusive about the project. “It’s a huge step in human history,” he said. “It’s bold, risky, and we hope we succeed doing that.”

It is the risk that will keep renewable energy enthusiasts concerned.

Every visionary plan contains the potential for immense disappointment. As yet, the Asian Super Grid and the Saudi power plan are more or less at the conceptual stage. The fact that a memorandum of understanding exists between the Saudi government and Softbank is no guarantee that it will ever be built. Some analysts in the industry are a little skeptical.

“It’s an unprecedented construction effort; it’s an unprecedented financing effort,” said Benjamin Attia, a global solar analyst for Green Tech Media Research. “But there are so many questions, so few details, and a lot of headwinds, like grid instability, the availability of commercial debt, construction, and logistics challenges.”

We have already seen with the DESERTEC initiative that these vast-scale renewable energy projects can fail, despite immense enthusiasm. They are not easy to accomplish. But in a world without fossil fuels, they will be required. This project could be a flagship example for how to run a country on renewable energy—or another example of grand designs and good intentions. We’ll have to wait to find out which.

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#432311 Everyone Is Talking About AI—But Do ...

In 2017, artificial intelligence attracted $12 billion of VC investment. We are only beginning to discover the usefulness of AI applications. Amazon recently unveiled a brick-and-mortar grocery store that has successfully supplanted cashiers and checkout lines with computer vision, sensors, and deep learning. Between the investment, the press coverage, and the dramatic innovation, “AI” has become a hot buzzword. But does it even exist yet?

At the World Economic Forum Dr. Kai-Fu Lee, a Taiwanese venture capitalist and the founding president of Google China, remarked, “I think it’s tempting for every entrepreneur to package his or her company as an AI company, and it’s tempting for every VC to want to say ‘I’m an AI investor.’” He then observed that some of these AI bubbles could burst by the end of 2018, referring specifically to “the startups that made up a story that isn’t fulfillable, and fooled VCs into investing because they don’t know better.”

However, Dr. Lee firmly believes AI will continue to progress and will take many jobs away from workers. So, what is the difference between legitimate AI, with all of its pros and cons, and a made-up story?

If you parse through just a few stories that are allegedly about AI, you’ll quickly discover significant variation in how people define it, with a blurred line between emulated intelligence and machine learning applications.

I spoke to experts in the field of AI to try to find consensus, but the very question opens up more questions. For instance, when is it important to be accurate to a term’s original definition, and when does that commitment to accuracy amount to the splitting of hairs? It isn’t obvious, and hype is oftentimes the enemy of nuance. Additionally, there is now a vested interest in that hype—$12 billion, to be precise.

This conversation is also relevant because world-renowned thought leaders have been publicly debating the dangers posed by AI. Facebook CEO Mark Zuckerberg suggested that naysayers who attempt to “drum up these doomsday scenarios” are being negative and irresponsible. On Twitter, business magnate and OpenAI co-founder Elon Musk countered that Zuckerberg’s understanding of the subject is limited. In February, Elon Musk engaged again in a similar exchange with Harvard professor Steven Pinker. Musk tweeted that Pinker doesn’t understand the difference between functional/narrow AI and general AI.

Given the fears surrounding this technology, it’s important for the public to clearly understand the distinctions between different levels of AI so that they can realistically assess the potential threats and benefits.

As Smart As a Human?
Erik Cambria, an expert in the field of natural language processing, told me, “Nobody is doing AI today and everybody is saying that they do AI because it’s a cool and sexy buzzword. It was the same with ‘big data’ a few years ago.”

Cambria mentioned that AI, as a term, originally referenced the emulation of human intelligence. “And there is nothing today that is even barely as intelligent as the most stupid human being on Earth. So, in a strict sense, no one is doing AI yet, for the simple fact that we don’t know how the human brain works,” he said.

He added that the term “AI” is often used in reference to powerful tools for data classification. These tools are impressive, but they’re on a totally different spectrum than human cognition. Additionally, Cambria has noticed people claiming that neural networks are part of the new wave of AI. This is bizarre to him because that technology already existed fifty years ago.

However, technologists no longer need to perform the feature extraction by themselves. They also have access to greater computing power. All of these advancements are welcomed, but it is perhaps dishonest to suggest that machines have emulated the intricacies of our cognitive processes.

“Companies are just looking at tricks to create a behavior that looks like intelligence but that is not real intelligence, it’s just a mirror of intelligence. These are expert systems that are maybe very good in a specific domain, but very stupid in other domains,” he said.

This mimicry of intelligence has inspired the public imagination. Domain-specific systems have delivered value in a wide range of industries. But those benefits have not lifted the cloud of confusion.

Assisted, Augmented, or Autonomous
When it comes to matters of scientific integrity, the issue of accurate definitions isn’t a peripheral matter. In a 1974 commencement address at the California Institute of Technology, Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” In that same speech, Feynman also said, “You should not fool the layman when you’re talking as a scientist.” He opined that scientists should bend over backwards to show how they could be wrong. “If you’re representing yourself as a scientist, then you should explain to the layman what you’re doing—and if they don’t want to support you under those circumstances, then that’s their decision.”

In the case of AI, this might mean that professional scientists have an obligation to clearly state that they are developing extremely powerful, controversial, profitable, and even dangerous tools, which do not constitute intelligence in any familiar or comprehensive sense.

The term “AI” may have become overhyped and confused, but there are already some efforts underway to provide clarity. A recent PwC report drew a distinction between “assisted intelligence,” “augmented intelligence,” and “autonomous intelligence.” Assisted intelligence is demonstrated by the GPS navigation programs prevalent in cars today. Augmented intelligence “enables people and organizations to do things they couldn’t otherwise do.” And autonomous intelligence “establishes machines that act on their own,” such as autonomous vehicles.

Roman Yampolskiy is an AI safety researcher who wrote the book “Artificial Superintelligence: A Futuristic Approach.” I asked him whether the broad and differing meanings might present difficulties for legislators attempting to regulate AI.

Yampolskiy explained, “Intelligence (artificial or natural) comes on a continuum and so do potential problems with such technology. We typically refer to AI which one day will have the full spectrum of human capabilities as artificial general intelligence (AGI) to avoid some confusion. Beyond that point it becomes superintelligence. What we have today and what is frequently used in business is narrow AI. Regulating anything is hard, technology is no exception. The problem is not with terminology but with complexity of such systems even at the current level.”

When asked if people should fear AI systems, Dr. Yampolskiy commented, “Since capability comes on a continuum, so do problems associated with each level of capability.” He mentioned that accidents are already reported with AI-enabled products, and as the technology advances further, the impact could spread beyond privacy concerns or technological unemployment. These concerns about the real-world effects of AI will likely take precedence over dictionary-minded quibbles. However, the issue is also about honesty versus deception.

Is This Buzzword All Buzzed Out?
Finally, I directed my questions towards a company that is actively marketing an “AI Virtual Assistant.” Carl Landers, the CMO at Conversica, acknowledged that there are a multitude of explanations for what AI is and isn’t.

He said, “My definition of AI is technology innovation that helps solve a business problem. I’m really not interested in talking about the theoretical ‘can we get machines to think like humans?’ It’s a nice conversation, but I’m trying to solve a practical business problem.”

I asked him if AI is a buzzword that inspires publicity and attracts clients. According to Landers, this was certainly true three years ago, but those effects have already started to wane. Many companies now claim to have AI in their products, so it’s less of a differentiator. However, there is still a specific intention behind the word. Landers hopes to convey that previously impossible things are now possible. “There’s something new here that you haven’t seen before, that you haven’t heard of before,” he said.

According to Brian Decker, founder of Encom Lab, machine learning algorithms only work to satisfy their preexisting programming, not out of an interior drive for better understanding. Therefore, he views AI as an entirely semantic argument.

Decker stated, “A marketing exec will claim a photodiode controlled porch light has AI because it ‘knows when it is dark outside,’ while a good hardware engineer will point out that not one bit in a register in the entire history of computing has ever changed unless directed to do so according to the logic of preexisting programming.”

Although it’s important for everyone to be on the same page regarding specifics and underlying meaning, AI-powered products are already powering past these debates by creating immediate value for humans. And ultimately, humans care more about value than they do about semantic distinctions. In an interview with Quartz, Kai-Fu Lee revealed that algorithmic trading systems have already given him an 8X return over his private banking investments. “I don’t trade with humans anymore,” he said.

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