of food and that a girl is bigger than a cookie. Allen Institute researchers estimate they need about a million human-sourced pieces of common sense to train their AIs.
The Cyc project, the world's longest-running AI project, takes a different approach. Since 1984, Doug Lenat and his team have hand coded more than 25 million pieces of commonsense knowledge in machine-usable form. Cyc knows things like “Every tree is a plant” and “Every plant dies eventually.” From those pieces of information, it can deduce that every tree will die. Cycorp Company, Cyc's current developers, claims that half of the top 15 companies in the world use Cyc under license. Cyc is used in financial services, healthcare, energy, customer experience, the military, and intelligence.
As they mature, commonsense knowledge systems may help future AIs to answer more complex questions and assist humans in more meaningful ways.
Causal AIs
Causal AIs understand cause and effect, while deep learning systems work by finding correlations inside data. To reason, deep learning AIs find complex associations within data and assess the probabilities of association. Reasoning by association has proven adequate for today's simple AI solutions, but correlation does not imply causation. To create an AI with human-level intelligence, researchers will need far more capable machines. Some AI researchers, most notably Dr. Judea Pearl, believe that the best path forward for AI development is to design AIs that understand cause and effect. This would allow AIs to reason based on an understanding of causation. A deep learning AI associates events (A and B happen together) while a causal AI understands that one event caused the another (A caused B), and not the other way around (B caused A). The sophisticated machines needed to solve big problems like climate change will need to understand all of the causational relationships involved in highly complex systems. Causal AI will rely on the commonsense knowledge mentioned in the previous section to give it the vital context it needs for sound reasoning.
Neuromorphic Computers
Neuromorphic computers are inspired by the way brains work. Today's neural network designs are based on an understanding of neuroscience from the 1960s. Half a century later, we finally have the computing horsepower needed to implement these archaic machine models. The next time you ask Google Assistant or Alexa to play some Beatles music, remember that the foundation of the AI you're using was designed at the time when the Stones and The Beatles were first vying to be top of the charts. Neuromorphic computers, also referred to as cognitive computers, are based on a much more recent understanding of how the brain operates. Nodes are hyperconnected, their connections can change over time (in the same way that the human brain exhibits plasticity), and there is no separation between memory and processing functions.
Major research projects—the Human Brain Project in the European Union and the BRAIN (Brain Research through Advanced Innovative Neurotechnologies) initiative in the United States—seek to advance our understanding of the human brain, mapping and understanding brain function. These efforts, and others like them, push the boundaries of our understanding and offer new frameworks for the design of future neuromorphic computers. New computer chips, inspired by neuromorphic insights, may accelerate AI functions, reduced power consumption for AI tasks, and enable exciting new capabilities.
Whether it's capsule networks, neuromorphic computing, or common sense and causal AI, there are plenty of avenues of research that should fuel future advances in AI in the coming decades.
Narrow, General, and Super Intelligence
All of today's AI is considered to be “narrow” AI. The holy grail of AI research is the development of “general” and “super” AIs. Let's quickly review these three categories.
Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence (ANI), also known as weak AI or vertical AI, refers to any AI that solves problems or performs tasks with a level of intelligence equivalent to, or higher than, a human, but only within a narrowly defined domain. Every AI available today, and every AI described in this book, is an example of narrow AI. Narrow AI is only good at the task it was designed for and useless at others. A chess-playing AI can't filter the spam from your email, and your spam filter can't play chess.
Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI), also known as strong AI, describes AIs with the equivalent intelligence of a human being in any area of human expertise that you might imagine. An AGI can perform any intellectual task that a human can. Researchers continue to chip away at the problem of creating an AGI but currently have no clear plan for how such a feat might be achieved. AGIs aren't created by bolting together enough ANIs. It doesn't work that way. Never say never, but AGI, if we achieve it, is likely several decades in the future.
Artificial Super Intelligence (ASI)
Artificial Super Intelligence (ASI) is where things get really exciting, or really scary, depending on your viewpoint. ASI defines an intelligence that surpasses the intellectual ability, knowledge, creativity, wisdom, and social skills of the very best human brains in almost every field. An ASI could be just 1% smarter or a million times smarter than the smartest human. In theory, an ASI machine that we build could design future, more powerful machines that operate in a way we aren't able to comprehend. Once ASI is achieved, rapid, runaway advances could follow. If that idea makes you uncomfortable, you're not alone. Exemplars of the high-tech and scientific world—including Elon Musk, Bill Gates, and Stephen Hawking—have all spoken out on the dangers of artificial super intelligence. It's hard not to think of the movie Terminator when you think about the professed dangers of AI becoming self-aware and designing better versions of itself. How engineers develop narrow AIs today may inform the design of super AIs in the future. This is why research and engineering focused on transparent and unbiased AIs is so vital.
Strategies to Get You Started
Artificial intelligence holds huge potential and will drive successive waves of innovation throughout every industrial sector. As your organization starts to build out a comprehensive, multiyear AI strategy, here are a few places to start your thinking.
Make Predictions about Customers, Operations, and Trends
Use AI's predictive capabilities to forecast demand, streamline operations, target marketing efforts, predict trends, and influence the design of future products. Energy companies use AI to forecast demand for electricity. Fashion designers use AI to suggest fall colors for a couple of years from now. Equipment makers use AI to predict when their machines will fail so they can schedule preventative maintenance. What could AI predict for your business?
See More, Understand More, Make Better Decisions
Future super sensors may improve the safety of autonomous vehicles, become “the next CT-scan” in medicine, or give us all a sixth and seventh sense. Super sensors will smooth business operations, revolutionize human interfaces, and transform the products and services of the future.
Every company should lead a strategic discussion around the possibilities super sensors present. Determine how your company can use super sensors to get eyes on your business, gain insight on operations in real time, and use those insights to make high-quality, data-driven business decisions. Inspired by Google Soli technology, consider how you could build super sensors into your products to
