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Operations Practice An executive primer on artificial general While human-like artificial general intelligence may not be imminent, substantial advances may be possible in the coming years. Executives can prepare by recognizing the early signs of progress.

by Federico Berruti, Pieter Nel, and Rob Whiteman

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April 2020 Headlines sounding the alarms that artificial University (NYU), is much more direct: “It’s hard intelligence (AI) will lead humanity to a dystopian to explain to non-specialists that AGI is not a future seem to be everywhere. Prominent thought ‘thing’, and that most venues that have AGI in their leaders, from Silicon Valley figures to legendary name deal in highly speculative and theoretical scientists, have warned that should AI evolve into issues...” artificial general intelligence (AGI)—AI that is as capable of learning intellectual tasks as humans Still, many academics and researchers maintain are—civilization will be under serious threat. that there is at least a chance that human-level could be achieved in the Few seeing these warnings, stories, and images next decade. Richard Sutton, professor of could be blamed for believing that the arrival of AGI computer of the University of Alberta, is imminent. Little surprise, then, that so many media stated in a 2017 talk: “ human- stories and business presentations about machine level AI will be a profound scientific achievement learning are accompanied by unsettling illustrations (and economic boon) and may well happen by featuring humanoid robots. 2030 (25% chance), or by 2040 (50% chance)— or never (10% chance).” Many of the most respected researchers and academics see things differently, however. They What should executives take away from this argue that we are decades away from realizing AGI, debate? Even a small probability of achieving AGI and some even predict that we won’t see AGI in in the next decade justifies paying attention to this century. With so much uncertainty, why should developments in the field, given the potentially executives care about AGI today? The answer dramatic inflection point that AGI could bring is that, while the timing of AGI is uncertain, the about in society. As LeCun explains: “There is disruptive effects it could have on society cannot be a thin domain of research that, while having understated. ambitious goals of making progress towards human-level intelligence, is also sufficiently Much has already been written about the likely grounded in science and engineering impact of AI and the importance of carefully methodologies to bring real progress in managing the transition to a more automated world. technology. That’s the sweet spot.” The purpose of this article is to provide an AGI primer to help executives understand the path to machines For business leaders, it is critical to identify achieving human-level intelligence, indicators by those researchers who operate in this sweet which to measure progress, and actions the reader spot. In this executive’s guide to AGI, we aim to can take to begin preparations today. help readers make that assessment by reviewing the history of the field (see sidebar, “A brief history of AI”), the problems that must be solved How imminent is AGI? before researchers can claim they are close to In predicting that AGI won’t arrive until the year developing human-level artificial intelligence, 2300, Rodney Brooks, an MIT roboticist and and what executives should do given these co-founder of iRobot, doesn’t mince words: “It is insights. a fraught time understanding the true promise and dangers of AI. Most of what we read in the headlines… is, I believe, completely off the mark.” What capabilities would turn AI into AGI? Brooks is far from being a lone voice of dissent. To understand the complexity of achieving true Leading AI researchers such as and human-level intelligence, it is worthwhile to look Demis Hassabis have stated that AGI is nowhere at some the capabilities that true AGI will need to close to reality. In responding to one of Brooks’ . posts, Yann LeCun, a professor at the Courant Institute of Mathematical Sciences at New York Sensory perception. Whereas has

2 An executive primer on artificial general intelligence Sidebar

A brief history of AI

The term “artificial intelligence” was around , natural lan- knowledge of the world through rela- coined by John McCarthy in the research guage understanding, and neural nets tionships between these symbols. One proposal for a 1956 workshop at Dart- are, in many cases, several decades old. familiar example is the knowledge that mouth that would kick off humanity’s a German shepherd is a dog, which is a efforts on this topic. The AI topics that Within these AI research communities, mammal; all mammals are warm-blood- McCarthy outlined in the introduction there has been substantial success ed; therefore, a German shepherd included how to get a computer to use and advancement in what the field now should be warm-blooded. human language; how to arrange “neuron terms “narrow AI” applications. Narrow nets” (which had been invented in 1943) AI is the application of AI techniques to a The limitation is that humans still provide so that they can form concepts; how a specific and well-defined problem, such the ground truth by encoding our knowl- machine can improve itself (that is, learn as that help customers resolve edge of the world, rather than allowing or evolve); how a machine could form ab- issues with their phone bills, algorithms an AI system to observe and encode stractions by using its sensors to observe that spot fraud in credit-card transac- these relationships itself. Symbolic AI the world; and how to make computers tions, and natural-language-processing was the dominant paradigm of AI re- think creatively. In essence, McCarthy engines that quickly process thousands search from the mid-1950s until the late was describing in 1956 what we now of legal documents. Applying narrow AI 1980s. One ongoing effort to provide a call AGI. solutions in use cases across industries solution to common-sense reasoning can generate tremendous economic through symbolic AI is the Cyc Project, McCarthy was certainly not the first to benefits. Our colleagues’ research launched in 1984 to collect knowledge talk about machines and “intelligence.” shows that the potential value of ap- represented declaratively in the form ’s 1950 paper on “Comput- plying this sort of deep learning could of logical assertions, of which it had ing machinery and intelligence” intro- range from $3.5 trillion to $5.8 trillion collected 25 million by 2017. duced the “imitation game,” a test of a annually. machine’s ability to exhibit intelligent Neural networks (1954, 1969, 1986, behavior, and which became known as To differentiate themselves from re- 2012). Recent advances in speech the “Turing test.” Turing optimistically searchers solving narrow AI problems, recognition (now widely used in devices estimated that, in the year 2000, a com- a few research teams have claimed an such as smart speakers), and in com- puter with 128Mb of memory would have almost proprietary interest in producing puter vision (such as limited self-driving a 70 percent chance of fooling a person. human-level intelligence (or more) under abilities in cars) are all due to deep In his earlier 1948 paper on “Intelligent the name “artificial general intelligence.” neural networks. The artificial neuron as machinery,” he describes what we today Some have adopted the term “super- a computational model of the “nerve net” call computers, as well as a machine that intelligence” to describe AGI systems of the brain was proposed as early as fully imitates a person. He points out that by themselves could rapidly design 1943.¹ In the decades since then, it has that our ability to build adequate sensors even more capable systems, with those been through multiple highs and lows in and actuators might not be sufficient for systems further evolving to develop ca- its popularity as a tool for AI. some time and that our efforts are best pabilities that far exceed any possessed invested in the aspect of intelligence that by humans. MIT’s Marvin Minsky and Seymour relates to games and cryptography. The Papert put a damper on this research in clear aspiration, however, has always MIT roboticist Rodney Brooks describes their 1969 book “,” where been to achieve human-level intelligence. the four previous attempts at AGI, along they mathematically demonstrated that with their approximate start dates, in neural networks could only perform very The early work, like Turing suggested, discussions that provide important con- basic tasks. They also discussed the dif- revolved around subject areas that do text for understanding their progress. ficulty of training multi- networks. not require too much sensing and action, In 1986, however, Geoffrey Hinton and such as those of games and language Symbolic AI (1956). The key concept is some colleagues solved this problem . Research communities the use of symbols and the encoding of with the publication of the back-prop-

1 Warren McCulloch and Walter Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, volume 5, 1943.

An executive primer on artificial general intelligence 3 Sidebar cont.

agation algorithm. In the 1990’s, work vision to identify and navigate through Behavior-based (1985). by Yann LeCun made major advances in their environments or to understand Insects navigate well in the real world neural networks’ use in computer vision, the geometry of objects and maneuver with very few neurons. This observation and Jürgen Schmidhuber similarly them, such as moving around blocks of inspired researchers to investigate how advanced the application of recurrent various shapes and colors. Accordingly, a robot could resolve conflicts when neural networks as used in language although robots have been used in fac- partial observation and knowledge of processing. tories for decades, most rely on highly the physical world might provide con- controlled environments with thor- flicting commands to its actuators. A In 2012, Hinton and two of his students oughly scripted behaviors that they conflict-resolution mechanism needs to highlighted the power of deep learning perform repeatedly. As valuable as that be heuristic in . As Brooks states, when they obtained significant results is, it has not contributed significantly “Behavior-based systems work because in the ImageNet competition. With the to the advancement of AI itself. the demands of physics on a body em- power of graphics processing units bedded in the world force the ultimate (GPUs), Hinton’s back-prop algorithm One area where traditional robotics conflict resolution between behaviors, could be applied to neural networks with did have substantial impact is in the and the interactions.” substantially more layers, hence making way robots build maps of their environ- them “deeper” and introducing the ment using only partial knowledge and The behavior-based approach starts terms “deep neural networks” and “deep observations, through a process called out as reactive to how the world is learning.” This kicked off the past few “simultaneous localization and mapping” changing. As a result, robots applying years’ focus on deep learning, almost to (SLAM). SLAM algorithms became part this approach are more robust in the the exclusion of most other approaches. of the basis for self-driving cars and real world, which is why the majority of are used in consumer products, from deployed robots use it. Neural net- Traditional robotics (1968). During the vacuum-cleaning robots to quadcopter works by themselves aren’t capable first few decades of AI, researchers also drones. Today, this work has evolved of this level of adaptation. In each sought to build robots to advance re- into behavior-based robotics, com- recent successful deployment, neural search. Some robots were mobile, mov- monly referred to as haptic technology, networks are embedded as part of ing around on wheels, while others were which provides a semblance of human symbolic AI systems or behavior-based fixed, with articulated arms. Robots touch, or physicality, to AI. systems. used the earliest attempts at computer

enabled major advances in computer vision, AI Humans can also determine the spatial systems are far away from developing human-like characteristics of an environment from sound, sensory-perception capabilities. For example, even when listening to a monaural telephone systems trained through deep learning still have channel. We can understand the background poor color consistency: self-driving car systems noise and form a mental picture of where have been fooled by small pieces of black tape someone is when speaking to them on the or stickers on a red stop sign. For any human, the phone (on a sidewalk, with cars approaching in redness of the stop sign is still completely evident, the background). AI systems are not yet able to but the deep learning–based system gets replicate this distinctly human perception. fooled into thinking the stop sign is something else. Current computer vision systems are also Fine motor skills. Any human can easily retrieve largely incapable of extracting depth and three- a set of keys from a pocket. Very few of us would dimensional information from static images. let any of the robot manipulators or humanoid

4 An executive primer on artificial general intelligence hands we see do that task for us. Researchers Navigation. GPS, combined with capabilities such in the field are working on this problem. A recent as simultaneous localization and mapping (SLAM), demonstration showed how has made good progress in this field. Projecting could teach a robot hand to solve a Rubik’s cube. actions through imagined physical spaces, Although Claude Shannon built a robot to solve the however, is not far advanced when compared with cube decades ago, this demonstration illustrates the the current capabilities of video games. Years of dexterity involved in programming robot fingers on a work are still required to make robust systems single hand to manipulate a complex object. that can be used with no human priming. Current academic demonstrations have not come close to Natural language understanding. Humans record achieving this goal. and transmit skills and knowledge through books, articles, blog posts, and, more recently, how-to Creativity. Commenters fearing superintelligence videos. AI will need to be able to consume these theorize that once AI reaches human-level sources of information with full comprehension. intelligence, it will rapidly improve itself through Humans write with an implicit assumption of the a bootstrapping process to reach levels of reader’s general knowledge, and a vast amount of intelligence far exceeding those of any human. But information is assumed and unsaid. If AI lacks this in order to accomplish this self-improvement, AI basis of common-sense knowledge, it will not be systems will have to rewrite their own code. This able to operate in the real world. level of introspection will require an AI system to understand the vast amounts of code that humans NYU professors Gary Marcus and Ernest Davis cobbled together, and identify novel methods for describe this requirement in more detail in their improving it. Machines have demonstrated the book “Rebooting AI,” pointing out that this common- ability to draw pictures and compose music, but sense knowledge is important for even the most further advances are needed for human-level mundane tasks anyone would want AI systems to creativity. do. As Douglas Hofstadter notes, the fact that free machine-translation services have become fairly Social and emotional engagement. For robots accurate through deep learning does not mean that and AI to be successful in our world, humans must AI is close to genuine reading comprehension, as want to interact with them, and not fear them. The it has no understanding of context over multiple robot will need to understand humans, interpreting sentences—something which even toddlers handle facial expressions or changes in tone that reveal effortlessly. The various reports of AI passing underlying emotions. Certain limited applications entrance exams or doing well at eighth-grade are in use already, such as systems deployed in science tests are a few examples of how a narrow contact centers that can detect when customers AI solution can be easily confused for human-level sound angry or worried, and direct them to the right intelligence. queue for help. But given humans’ own difficulties interpreting emotions correctly, and the perception Problem solving. In any general-purpose challenges discussed above, AI that is capable of application, a robot (or an AI engine living in the empathy appears to be a distant prospect. cloud) will have to be able to diagnose problems, and then address them. A home robot would have to recognize that a light bulb is blown and either Four ways to measure progress replace the bulb or notify a repair person. To carry Instead of still trying to use the Turing Test, out these tasks, the robot either needs some aspect Brooks suggests four simple ways to measure our of the common sense described above, or the progress toward human-level intelligence that ability to run simulations to determine possibilities, are summarized here below. Similarly, numerous plausibility, and probabilities. Today, no known companies and research organizations are systems possess either such common sense, or exploring alternative frameworks to measure such a general-purpose simulation capability. progress based on granular human-equivalent

An executive primer on artificial general intelligence 5 capabilities, requirements to perform certain Very often in the literature, the concept of a robotic human tasks, or the combination of capabilities to elder-care robot is used as a conceptual test case. perform every human job. With the advances we’re seeing, it’s certainly plausible that a simplified and useful domestic The object-recognition capabilities of a robot that can offer some assistance to an elderly two-year-old person might be available within the next decade, In the first case, two-year-old children who are even if controlled by a remote human pilot at the only used to sitting on white chairs will realize that beginning. they can also sit on black chairs, three-legged brown stools, or even on rocks or stacks of books. What advances could hasten inflection The language-understanding capabilities of a points? four-year-old The reduction in storage costs over the last two Four-year-olds are typically able to converse decades brought about the concept of “big data.” and follow context and meaning over multiple The computing advances in GPUs uniquely enable exchanges with a decent understanding as to an algorithm to be applied to much larger neural the subtleties of language. We don’t need to networks. With these neural networks trained on start every sentence by first stating their names very large data sets, researchers accomplished all (unlike today’s “smart” speakers), and they can the recent advances brought about through deep understand when a conversation has ended, or learning. The combination of data, algorithms, and the participants have changed. Children can computing advances caused an inflection point. To understand singing, shouting, and whispering, look for the next AI inflection point, it is useful to and perform each of these activities. They even consider the landscape again using those three understand lying and humor. component parts.

The manual dexterity of a six-year-old Major algorithmic advances and new robotics Most six-year-olds ae able to dress themselves approaches. It may very well require completely and can likely even tie their own shoes. They can new approaches to move us toward the level of perform complex tasks requiring manual dexterity intelligence displayed by a dog or a two-year- using a variety of different materials, and can old human child. One example researchers are handle animals and even younger siblings. exploring is the concept of embodied cognition. Their hypothesis is that robots will need to learn The social understanding of an eight-year-old from their environment through a multitude of Eight-year-olds can hold their own beliefs, desires, senses, much like humans do in the early stages and intentions, explaining them to others and of life—and that they will have to experience the understanding when others explain theirs. They physical world through a body similar to that of can infer other people’s desires and intents from humans in order to cognitively develop in the same their actions and understand why they have those way as humans do. With the physical world already desires and intents. We don’t explain our desires designed around humans, there is merit in this and intents to children because we expect them to approach. It prevents us from having to redesign understand what they are observing. so many of our physical interfaces—everything from doorknobs to staircases and elevator buttons. Although the AI community is active in research to Certainly, as described in a previous section, if address all these aspects, we are likely decades we are going to bond with smart robots, we are away from achieving some of them. In more going to have to like them. And it is likely that such narrow applications, it seems plausible that object bonding is only going to happen if they look like us. recognition, language understanding, and manual dexterity can be mastered to a sufficient extent in The entire advance in deep learning is enabled the medium term to address specific use cases. by the algorithm, which allows

6 An executive primer on artificial general intelligence large and complex neural networks to learn from proposed not as a replacement for today’s devices, training data. Hinton, along with colleagues but for highly complex statistical problems that David Rumelhart and Ronald Williams, published current computing power cannot address. Moreover, “Learning representations by back-propagating the first real proof that quantum computers can errors” in 1986. It took another 26 years before handle these types of problems occurred only in late an increase in computing power and the growth 2019, and only for a purely mathematical exercise in “big data” enabled the use of that discovery with no real-world use at all. The hardware and at the scale seen today. Whereas a multitude of software to handle problems such as those required researchers have made improvements in the way for advancements in AI may not arrive until 2035 or backpropagation is used in deep learning, none later. Nonetheless, quantum computing remains one of these improvements has been transformative of the most likely possible inflection points and one in the same way. (Hinton’s more recent work on to keep close tabs on. “capsule networks” may very well be one such algorithmic advance which could, among other Substantial growth in data volume, and from new applications, overcome the limitations of today’s sources. The rollout of 5G mobile infrastructure neural networks in machine vision.) is one of the technology advances touted to bring about a significant increase in data due to Deep learning assumes a “blank slate” state, and the way the technology can enable growth in the that all “intelligence” can be learned from training internet of things (IoT). Research conducted by our data. Anyone who has ever observed a mammal colleagues has, nevertheless, noted roadblocks to being born would recognize that something like a 5G implementation, particularly in the economics fawn starts life with a level of built-in knowledge. for operators. Also, in a 2019 survey, operators It stands within 10 minutes, knows how to feed reported that they did not see IoT as a core objective almost immediately, and walks within hours. As for 5G, because the existing IoT capability was Marcus and Davis point out in Rebooting AI, “The likely sufficient for most use cases. As a result, real advance in AI, we believe, will start with an 5G appears unlikely by itself to serve as a major understanding of what kinds of knowledge and inflection point for increasing data volume and as representations should be built in prior to learning, a subsequent enabler of training data. Most of the in order to bootstrap the rest.” The recent success benefits may already have appeared. of deep learning may have drawn away research attention from the more fundamental cognitive New robotics approaches can yield new sources work required to make progress in AGI. of training data. By placing human-like robots with even basic functions among humans—and doing Major computing advancements. The application so at scale—large sets of data that mimics our own of GPUs to training deep neural networks was a senses can help close a training feedback loop that critical step-change that made the major advances enhances the state of the art. Advanced self-driving of the last several years possible. GPUs uniquely cars one such example: the data collected by cars enabled the complex calculations required by already on the market are acting as a training set for Hinton’s backpropagation algorithm to be applied future self-driving capability. Furthermore, much in parallel, thereby making it possible to train research is being done in human-robot interaction. hugely complex neural nets within a finite time. By finding initial use cases for human-like robots, Before any further exponential growth toward this research could greatly add to the training data AGI can be expected, a similar inflection point necessary to expand their capabilities. in computing infrastructure would need to be matched with unique algorithmic advances. What executives could do Quantum computing is often touted as one of the What are the next steps for executives? The best potential computing advances that could change way to counteract the hype about AGI is to take our society. But, as our colleagues recently noted tangible actions to monitor developments and in a research report, quantum computing is position your organization to respond appropriately

An executive primer on artificial general intelligence 7 to real progress in the field. The following checklist deploying platforms that require little or no offers categories of actions to consider. coding skills, and designing governance models that encourage rather than stifle — Stay closely informed about developments innovation. in AGI, especially with regard to the ways AGI could be advancing more rapidly than — Organize your workers for new economies of expected. To enable this, connect with start- scale and skill. The rigid organization structures ups and develop a framework for rating and and operating models of the past are poorly tracking progress of AGI developments that suited for a world where AI is advancing rapidly. are relevant to your business. Additionally, Embrace the power of humans to work in begin to consider the right governance, complex environments and self-organize. For conditions, and boundaries for success within example, institute flow-to-the-work models your business and communities. that allow people to move seamlessly between initiatives and groups. — Tailor environments to enable narrow-AI advances now—don’t wait for AGI to develop — Place small bets to preserve strategic options before acting. A number of steps can be taken in areas of your business that are most exposed today to adjust the landscape and increase to AGI developments. For example, consider uptake. These include simplifying processes, investments in technology firms pursuing structuring physical spaces, and converting ambitious AI research and development analog systems and unstructured data into projects in your industry. It’s impossible to digital systems and structured data. The know when (or if) your bets will pay off, but digital and automation programs of today targeted investments today can help you hedge can smooth the transition to AGI for your existential risks your business might face in the customers, employees, and stakeholders. future.

— Invest in combined human-machine — Explore open innovation models and interfaces or “human in the loop” technologies platforming with other companies, that augment human intelligence rather governments, and academic institutions. Such than replace it. This category includes arrangements are essential to test the art everything from analytics to improve human of the possible and the business nuances of decision making to cognitive agents that AGI development. It’s hard to keep up with work alongside call-center agents. Using the rapidly changing AGI landscape without technology to help people be more productive firsthand experience working alongside leading has been the engine of economic progress organizations. and will likely remain so for the foreseeable future.

— Democratize technology at your company, so AGI may not be ready this decade or even this progress is not bottlenecked by the capacity century—but some of the capabilities may start of your IT organization. This does not mean appearing in places you might not expect. The letting technology run wild. It means building benefits will accrue most to those who are technical capabilities outside of IT, selectively observant—and prepared.

Federico Berruti is a partner in McKinsey’s Toronto office, Pieter Nel is an alumnus of the New York office, and Rob Whiteman is a partner in the Chicago office.

Copyright © 2020 McKinsey & Company. All rights reserved.

8 An executive primer on artificial general intelligence