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#432549 Your Next Pilot Could Be Drone Software

Would you get on a plane that didn’t have a human pilot in the cockpit? Half of air travelers surveyed in 2017 said they would not, even if the ticket was cheaper. Modern pilots do such a good job that almost any air accident is big news, such as the Southwest engine disintegration on April 17.

But stories of pilot drunkenness, rants, fights and distraction, however rare, are reminders that pilots are only human. Not every plane can be flown by a disaster-averting pilot, like Southwest Capt. Tammie Jo Shults or Capt. Chesley “Sully” Sullenberger. But software could change that, equipping every plane with an extremely experienced guidance system that is always learning more.

In fact, on many flights, autopilot systems already control the plane for basically all of the flight. And software handles the most harrowing landings—when there is no visibility and the pilot can’t see anything to even know where he or she is. But human pilots are still on hand as backups.

A new generation of software pilots, developed for self-flying vehicles, or drones, will soon have logged more flying hours than all humans have—ever. By combining their enormous amounts of flight data and experience, drone-control software applications are poised to quickly become the world’s most experienced pilots.

Drones That Fly Themselves
Drones come in many forms, from tiny quad-rotor copter toys to missile-firing winged planes, or even 7-ton aircraft that can stay aloft for 34 hours at a stretch.

When drones were first introduced, they were flown remotely by human operators. However, this merely substitutes a pilot on the ground for one aloft. And it requires significant communications bandwidth between the drone and control center, to carry real-time video from the drone and to transmit the operator’s commands.

Many newer drones no longer need pilots; some drones for hobbyists and photographers can now fly themselves along human-defined routes, leaving the human free to sightsee—or control the camera to get the best view.

University researchers, businesses, and military agencies are now testing larger and more capable drones that will operate autonomously. Swarms of drones can fly without needing tens or hundreds of humans to control them. And they can perform coordinated maneuvers that human controllers could never handle.

Could humans control these 1,218 drones all together?

Whether flying in swarms or alone, the software that controls these drones is rapidly gaining flight experience.

Importance of Pilot Experience
Experience is the main qualification for pilots. Even a person who wants to fly a small plane for personal and noncommercial use needs 40 hours of flying instruction before getting a private pilot’s license. Commercial airline pilots must have at least 1,000 hours before even serving as a co-pilot.

On-the-ground training and in-flight experience prepare pilots for unusual and emergency scenarios, ideally to help save lives in situations like the “Miracle on the Hudson.” But many pilots are less experienced than “Sully” Sullenberger, who saved his planeload of people with quick and creative thinking. With software, though, every plane can have on board a pilot with as much experience—if not more. A popular software pilot system, in use in many aircraft at once, could gain more flight time each day than a single human might accumulate in a year.

As someone who studies technology policy as well as the use of artificial intelligence for drones, cars, robots, and other uses, I don’t lightly suggest handing over the controls for those additional tasks. But giving software pilots more control would maximize computers’ advantages over humans in training, testing, and reliability.

Training and Testing Software Pilots
Unlike people, computers will follow sets of instructions in software the same way every time. That lets developers create instructions, test reactions, and refine aircraft responses. Testing could make it far less likely, for example, that a computer would mistake the planet Venus for an oncoming jet and throw the plane into a steep dive to avoid it.

The most significant advantage is scale: Rather than teaching thousands of individual pilots new skills, updating thousands of aircraft would require only downloading updated software.

These systems would also need to be thoroughly tested—in both real-life situations and in simulations—to handle a wide range of aviation situations and to withstand cyberattacks. But once they’re working well, software pilots are not susceptible to distraction, disorientation, fatigue, or other human impairments that can create problems or cause errors even in common situations.

Rapid Response and Adaptation
Already, aircraft regulators are concerned that human pilots are forgetting how to fly on their own and may have trouble taking over from an autopilot in an emergency.

In the “Miracle on the Hudson” event, for example, a key factor in what happened was how long it took for the human pilots to figure out what had happened—that the plane had flown through a flock of birds, which had damaged both engines—and how to respond. Rather than the approximately one minute it took the humans, a computer could have assessed the situation in seconds, potentially saving enough time that the plane could have landed on a runway instead of a river.

Aircraft damage can pose another particularly difficult challenge for human pilots: It can change what effects the controls have on its flight. In cases where damage renders a plane uncontrollable, the result is often tragedy. A sufficiently advanced automated system could make minute changes to the aircraft’s steering and use its sensors to quickly evaluate the effects of those movements—essentially learning how to fly all over again with a damaged plane.

Boosting Public Confidence
The biggest barrier to fully automated flight is psychological, not technical. Many people may not want to trust their lives to computer systems. But they might come around when reassured that the software pilot has tens, hundreds, or thousands more hours of flight experience than any human pilot.

Other autonomous technologies, too, are progressing despite public concerns. Regulators and lawmakers are allowing self-driving cars on the roads in many states. But more than half of Americans don’t want to ride in one, largely because they don’t trust the technology. And only 17 percent of travelers around the world are willing to board a plane without a pilot. However, as more people experience self-driving cars on the road and have drones deliver them packages, it is likely that software pilots will gain in acceptance.

The airline industry will certainly be pushing people to trust the new systems: Automating pilots could save tens of billions of dollars a year. And the current pilot shortage means software pilots may be the key to having any airline service to smaller destinations.

Both Boeing and Airbus have made significant investments in automated flight technology, which would remove or reduce the need for human pilots. Boeing has actually bought a drone manufacturer and is looking to add software pilot capabilities to the next generation of its passenger aircraft. (Other tests have tried to retrofit existing aircraft with robotic pilots.)

One way to help regular passengers become comfortable with software pilots—while also helping to both train and test the systems—could be to introduce them as co-pilots working alongside human pilots. Planes would be operated by software from gate to gate, with the pilots instructed to touch the controls only if the system fails. Eventually pilots could be removed from the aircraft altogether, just like they eventually were from the driverless trains that we routinely ride in airports around the world.

This article was originally published on The Conversation. Read the original article.

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#432519 Robot Cities: Three Urban Prototypes for ...

Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.

Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.

Good city governance enables and maintains smooth flow of things, data, and people. These include public services, traffic, and delivery services. Long queues in hospitals and banks imply poor management. Traffic congestion demonstrates that roads and traffic systems are inadequate. Goods that we increasingly order online don’t arrive fast enough. And the WiFi often fails our 24/7 digital needs. In sum, urban life, characterized by environmental pollution, speedy life, traffic congestion, connectivity and increased consumption, needs robotic solutions—or so we are led to believe.

Is this what the future holds? Image Credit: Photobank gallery / Shutterstock.com
In the past five years, national governments have started to see automation as the key to (better) urban futures. Many cities are becoming test beds for national and local governments for experimenting with robots in social spaces, where robots have both practical purpose (to facilitate everyday life) and a very symbolic role (to demonstrate good city governance). Whether through autonomous cars, automated pharmacists, service robots in local stores, or autonomous drones delivering Amazon parcels, cities are being automated at a steady pace.

Many large cities (Seoul, Tokyo, Shenzhen, Singapore, Dubai, London, San Francisco) serve as test beds for autonomous vehicle trials in a competitive race to develop “self-driving” cars. Automated ports and warehouses are also increasingly automated and robotized. Testing of delivery robots and drones is gathering pace beyond the warehouse gates. Automated control systems are monitoring, regulating and optimizing traffic flows. Automated vertical farms are innovating production of food in “non-agricultural” urban areas around the world. New mobile health technologies carry promise of healthcare “beyond the hospital.” Social robots in many guises—from police officers to restaurant waiters—are appearing in urban public and commercial spaces.

Vertical indoor farm. Image Credit: Aisyaqilumaranas / Shutterstock.com
As these examples show, urban automation is taking place in fits and starts, ignoring some areas and racing ahead in others. But as yet, no one seems to be taking account of all of these various and interconnected developments. So, how are we to forecast our cities of the future? Only a broad view allows us to do this. To give a sense, here are three examples: Tokyo, Dubai, and Singapore.

Tokyo
Currently preparing to host the Olympics 2020, Japan’s government also plans to use the event to showcase many new robotic technologies. Tokyo is therefore becoming an urban living lab. The institution in charge is the Robot Revolution Realization Council, established in 2014 by the government of Japan.

Tokyo: city of the future. Image Credit: ESB Professional / Shutterstock.com
The main objectives of Japan’s robotization are economic reinvigoration, cultural branding, and international demonstration. In line with this, the Olympics will be used to introduce and influence global technology trajectories. In the government’s vision for the Olympics, robot taxis transport tourists across the city, smart wheelchairs greet Paralympians at the airport, ubiquitous service robots greet customers in 20-plus languages, and interactively augmented foreigners speak with the local population in Japanese.

Tokyo shows us what the process of state-controlled creation of a robotic city looks like.

Singapore
Singapore, on the other hand, is a “smart city.” Its government is experimenting with robots with a different objective: as physical extensions of existing systems to improve management and control of the city.

In Singapore, the techno-futuristic national narrative sees robots and automated systems as a “natural” extension of the existing smart urban ecosystem. This vision is unfolding through autonomous delivery robots (the Singapore Post’s delivery drone trials in partnership with AirBus helicopters) and driverless bus shuttles from Easymile, EZ10.

Meanwhile, Singapore hotels are employing state-subsidized service robots to clean rooms and deliver linen and supplies, and robots for early childhood education have been piloted to understand how robots can be used in pre-schools in the future. Health and social care is one of the fastest growing industries for robots and automation in Singapore and globally.

Dubai
Dubai is another emerging prototype of a state-controlled smart city. But rather than seeing robotization simply as a way to improve the running of systems, Dubai is intensively robotizing public services with the aim of creating the “happiest city on Earth.” Urban robot experimentation in Dubai reveals that authoritarian state regimes are finding innovative ways to use robots in public services, transportation, policing, and surveillance.

National governments are in competition to position themselves on the global politico-economic landscape through robotics, and they are also striving to position themselves as regional leaders. This was the thinking behind the city’s September 2017 test flight of a flying taxi developed by the German drone firm Volocopter—staged to “lead the Arab world in innovation.” Dubai’s objective is to automate 25% of its transport system by 2030.

It is currently also experimenting with Barcelona-based PAL Robotics’ humanoid police officer and Singapore-based vehicle OUTSAW. If the experiments are successful, the government has announced it will robotize 25% of the police force by 2030.

While imaginary robots are fueling our imagination more than ever—from Ghost in the Shell to Blade Runner 2049—real-world robots make us rethink our urban lives.

These three urban robotic living labs—Tokyo, Singapore, Dubai—help us gauge what kind of future is being created, and by whom. From hyper-robotized Tokyo to smartest Singapore and happy, crime-free Dubai, these three comparisons show that, no matter what the context, robots are perceived as a means to achieve global futures based on a specific national imagination. Just like the films, they demonstrate the role of the state in envisioning and creating that future.

This article was originally published on The Conversation. Read the original article.

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#432487 Can We Make a Musical Turing Test?

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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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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#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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