Intelligence is a much-debated term, with varying connotations to distinct disciplines. Humans have an innate intelligence that is capable of achieving complex, integrative goals through multiple faculties. These faculties involve learning and creativity, deal with ambiguity and uncertainty, critical thinking, strategy and planning, scenario analysis, and more. Humans have an evolutionary mind that is capable of drawing inferences and insights.
Creating machines, bots, or capabilities imbued with human-like intelligence has fascinated humans for a long time and has been the subject of active technical effort since John McCarthy coined the term ‘Artificial Intelligence’ (AI). Interest in AI has waxed and waned, with unrealized hype leading to a long AI winter. However, recent advances, such as Hinton’s backpropagation based deep neural networks for ImageNet that match human accuracy for image recognition, have revived hope and optimism for the advent of ‘Artificial General Intelligence’ (AGI).
AGI is about emulating or even exceeding, human levels of intelligence. At the moment, it is more of a pipe dream in the realm of sci-fi movies like Terminator. Silicon Valley leaders and scientists like Elon Musk, Bill Gates, and Stephen Hawking have predicted a dystopian, even Frankensteinian, world with recursively- improving technological singularity potentially turning against the humans.
Strong Vs. Weak AI
Weak or narrow AI is categorized as mimicking a specific human ability to perform a well-defined task. Humans seem to have become pretty good at aspects of narrow AI lately, such as natural language processing (NLP), image recognition, machine translation, and detecting fraudulent credit card transactions. In the words of Andrew Ng, any task that takes a few minutes of human cognition can be automated with supervised machine learning and the help of labeled data. Recent advances in machine and deep learning have upped the ante on weak AI. For example, DeepMind’s AlphaFold can solve the intractable problem of predicting a protein’s folding structure from its amino acid sequence, thereby circumventing years of laborious work. This goes far beyond narrow AI into the gray zone.
Strong AI, or artificial general intelligence, can solve present-day ‘AI-hard’ problems that require a complex interplay of human cognitive abilities. For example, understanding the nuances of language is hard, but humans are slowly making strides. Some human skills are multifactorial, such as driving that requires image recognition, fine motor skills, or estimation with a high degree of situational awareness. A point has been reached where a self-driving car with level five autonomy can emulate that with simultaneous localization and mapping (SLAM) while being vulnerable to getting tricked at the same time.
Leading voices have articulated several benchmarks for having accomplished AGI, such as:
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Turing test: If a human and machine are indistinguishable most of the time while conversing with another human. With OpenAI’s GPT-n series, that is probably not far away.
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A bot or computational system successfully passes grad school.
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An AGI bot becomes a productive member of society, possibly paying taxes while performing a complex job.
Emulating the Human Brain
Unraveling the human brain is as enigmatic as solving the mysteries of the cosmos. With approximately 100 billion neurons interconnected through a quadrillion synapses, leading to 100 trillion synaptic updates per second (SUPS), the human brain is inordinately complex to simulate. Other than the interconnectedness of the brain, its evolutionary neurophysiology at the molecular and cellular level requires a level of chemical, physical, and biological understanding that leaves one confounded. How the three-pound mass of mostly fat, protein and water, with neurons firing in a chemical soup, allows cognitive abilities is quite hard to fathom.
All the advances in artificial neural networks, IoT sensing, 5G bandwidth, real-time big data, GPUs or TPUs, and storage put together get nowhere close to creating a computational system that has characteristics of sentience, self-awareness, sapience, and consciousness. Some even argue that there can be no human-like intelligence and consciousness without the accompanying embodiment.
Challenging as that may be, the advances in narrow AI are quickly adding up, with a bottom-up approach, to an impressive array of well-defined and compartmentalized human abilities. While AGI is the holy grail, the key point is that such pursuits are enabling scientific and technological advances that are the sweet spot of enabling human-in-the-loop technologies that augment humans instead of replacing them. Progress will likely stay in the augmentation zone for the next couple of decades, as Ray Kurzweil’s prediction of AGI comes true by 2045. Others argue that humans may not accomplish AGI in this century at all. But there is little disagreement over the fact that AI is likely to create US$15 trillion of economic value by 2030, with US$6 trillion being attributed to deeplearning alone. Individuals, societies, and businesses have to brace for that impact.
How Can Businesses Prepare and Respond to General AI
China is leading the AI frontier, as much as due to its lack of regulatory and ethical oversight as to its dogged commitment to winning the AI supremacy race. The US is not far behind whereas other nations occupy different positions on the leaderboard. Expertise in AI is likely to shake up the global economic and geopolitical order in the future world. While individuals grapple with the widespread displacement of world labor markets, enterprises need to sense and respond as well to ensure they thrive in a world replete with AI.
Here are some steps they can take to ensure they are not sidelined in a world of sustained disruption and mere transient advantages:
#1 Create a vision of yourself in the future world of AGI. Make small bets to preserve strategic options in aspects of your business potentially exposed to general AI.
#2 Make big, bold moves on narrow AI for quick wins. This will instill confidence and purpose to respond to general AI as it comes of age. Embrace AI augmentation as opposed to resisting it.
#3 Put your digital maturity on the front burner and prioritize digital transformation initiatives. Be a digital leader, not a laggard.
#4 Data maturity is a precursor to digital maturity. Invest in advantaged data with internal data or external data from partnerships, acquisitions, or ecosystem orchestration. AI is contingent upon data and algorithmic advances.
#5 Democratize technology by expanding it beyond the traditional IT organization of the company.
#6 Embrace a digital culture with rapid test-and-learn abilities. Don’t ostracize failure as long as you pivot fast and fail cheaply.
#7 Institutionalize innovation incubation. Also, explore open innovation models by partnering with other businesses and institutions.
#8 Orchestrate between exploitation and exploration strategies – the former for the here and now and the latter for the future.
#9 Deploy a forward-thinking governance framework that can orchestrate across near, mid-, and long-term growth.
#10 Deploy your workforce in fluid, agile, self-organizing teams that can ‘flow to the work’.