Artificial Intelligence, or AI, is the most promising and overhyped technology of our times. AI techniques such as deep learning have allowed computers to match or even beat world experts at games like chess and Go, and even board-certified doctors at diagnosing diabetic retinopathy and skin cancer.
While AI is great for recognizing patterns in puzzles and pictures, it is much harder for AI to change the behavior of people, in all their fascinating and frustrating complexity. In an earlier post on the Keystone Habit, I introduced the concept of using goals and habit change loops for personal development. Now let’s explore how to combine these loops for product development, in order to design a system to help someone change.
Here, I argue that human intelligence is better applied to helping people form goals, while AI is better applied to helping people form habits. This is not to say that an effective fully-automated AI system could not be built. But if you already have human and software resources, they can be synergistically combined to create an “Augmented Intelligence” system for behavior change.
The Sepah Behavior Change Model:
While there are a lot of behavior change models out there, I created a new model to integrate two important concepts—the goal formation loop on the left, and the habit formation loop on the right—into a unified loop that allows for continual improvement. Let’s walk through an example of how this works:
Plan the Goal:
All behavior begins with intention. Let’s say my intention is to exercise more often. While I may want to start working out every day, I decide to work with a personal trainer who takes into account my current routine to help me set a S.M.A.R.T. goal, that is Specific, Measurable, Agreed Upon, Realistic, and Time-Bound. For example, since he knows my gym is conveniently located two blocks from my work, and I’m more motivated to exercise in the morning because I work long hours, we mutually set a goal of going to the gym next to my work at 7:30AM, to lift weights for 30 minutes on Monday, Wednesday, and Friday, and to check-in at week’s end.
Theoretically, an AI-based system could also ask me a series of questions about my routine and preferences, then creates a reasonably-tailored S.M.A.R.T. goal for me. But these systems often run into problems adjusting goals over time, as we’ll see later in the loop, which is why humans are better suited to plan and revise goals.
Act on the Goal:
Next comes an attempt to act on the goal. I’m motivated on Monday morning, and I successfully go to the gym for 30 minutes before work. Great! Action is the centerpiece of the model for good reason, since goals are merely dreams without action. It’s worth noting that inaction, such as procrastination, is counterintuitively an action itself (what the Taoists call ‘Wu Wei’, the action of inaction). So whether you do or you don’t, you’re still acting. The difference is direct action takes you towards your goals and values, while avoidance takes you away from them.
Reward the Habit:
Here we cross into the habit loop: actions only become habits when they are repeatedly rewarded over time, even if the rewards are occasional. These can be intrinsic rewards (e.g. the mood boost from the workout) or extrinsic rewards (e.g. my personal trainer telling me I did a good job). While social reinforcement is an incredibly powerful motivator—it fueled the rise of phenomena like Crossfit—AI can reward habits equally well.
Gamification, which is the application of behavioral principles to game mechanics, is the best example of AI reinforcement. It successfully gets people to play video games for hours on end using tokens such as points and badges to designate accomplishment and skill development. This would seemingly pale in comparison to the sense of satisfaction that comes from a real person giving you a heartfelt high-five after a workout. But the high-five after every single workout can become repetitive, while gamified systems use variable schedules, quantities, and types of rewards (like how slot machines randomly dole out different jackpots) to prevent habituation and continually provide the addictive dopamine hit that powerful rewards bring.
AI is also superior to human reinforcement on an economic basis. While a personal trainer is motivational, they are usually not as cost-effective as an AI system emailing me a coupon for free protein shake at my gym when I achieve 3 workouts a week, or notifying me that I am among the top 20 most active gym-goers via an automated text message. Applications like Pact, DietBet, and Stikk are examples of companies that blend social and behavioral economic rewards to get people to go to the gym more often.
Remind the Habit:
Habits not only need to be rewarded, but they need a reminder (also known as a cue or trigger) to initiate the behavior regularly. Since I am out of town for work on Tuesday, I call the hotel front desk and set a wake-up call on Wednesday morning to remind me to get up for my scheduled workout. While that is an effective reminder, it is not a good use of human resources, which is why most hotels have automated wake-up call systems (or most people just use their smart phone’s alarm).
Though it seems obvious, Omada ran a randomized controlled trial to validate this hypothesis. We found an automated email performs just as well as a human coach in reminding a participant to weigh-in on our connected scale. Given that reminders are quick and easy, outsourcing this to AI is an effective move.
Act on Your Goal (Again):
For an automatic habit to form, the action must be repeated. My alarm goes off on Friday, but I am so sleep-deprived and tired that I decide to hit the snooze button. I text my trainer to cancel our session last minute, and fail to go for the third scheduled workout that week. Thus, I unfortunately fall short of my initial goal.
Reflect on the Goal:
Here we cross back into the goal loop by reflecting on the week and how well I’ve executed against my goal. This step is missing or glossed over in most versions of habit loops that you’ll see in books, but is critical if you want to continually iterate and improve upon your goals. In a previous article, I argued that reflection is the Keystone Habit when it comes to personal development, and articulated a concrete system to achieve your goals. Without reflection, we are doomed to repeat mistakes, and as I always tell my patients:
“a mistake repeated more than once is a decision.”
My trainer calls me over the weekend to reflect on our first week together. I share that I used my initial motivation to successfully go to the gym on Monday. I then used an alarm to remind me to go again on Wednesday, and was motivated by remembering the sense of accomplishment I felt after the first workout.
However, by the time Friday rolled around, we identified I was sore from starting a new exercise routine and the accumulated sleep deprivation from a long work week hindered my physical recovery and sapped my motivation. Here we see the power of human intelligence. While there were many factors that contributed to my skipping the gym on Friday, a quick conversation with my trainer identified the two major ones: muscle soreness and lowered motivation from sleep deprivation. An AI system has a much harder time identifying and isolating variables, especially psychological ones.
Plan the Goal (Again):
Based on the information gathered, my trainer and I work together to plan a revised goal for next week. He recommends I go to the gym next Monday and Wednesday at 7:30AM, but knowing I’m likely to be sleep-deprived and less motivated on Friday, recommends I go at 6PM just to warm up and stretch (thus reinforcing the habit of going regularly, while making the workout easy enough for my level of motivation). If that doesn’t work, we will switch the third weekly workout to Saturdays when I’m feeling more recovered and motivated.
Human intelligence shines at iterating on goals. Good trainers and therapists accurately identify individual strengths and barriers and leverage them to creatively optimize goals towards success. People’s physical and psychological states also vary tremendously on a day-to-day basis. Humans are better at picking up on these through our intuitive ability to read body language and facial expressions. Once a new goal is planned, the behavior change loop is executed repeatedly until the action becomes an automatic habit.
The Future of Augmented Intelligence
While AI has tremendous promise (and I advise AI startups in Silicon Valley because I believe they will be transformative), it’s currently better suited for specific intelligence, rather than general intelligence. When it comes to behavior change, AI is a clinically effective and cost-effective tool for habit formation by automating reminders for behaviors and providing variable rewards. But when it comes to goal formation, human intelligence (particularly that of an effective manager, trainer, or psychologist) is currently better leveraged to help people with goal planning and reflection to continually improve.
As a result, I believe the immediate future holds tremendous promise for hybrid systems, what I call “Augmented Intelligence,” which best combines human and artificial intelligence to change human behavior. Rather than replacing human coaching altogether, AI can support coaches by automating the easier tasks of reminding and rewarding habits. Thus freeing time to focus on what’s difficult and meaningful: helping others find and achieve their dreams.
by KENYA FOY This approach to indoor gardening literally turns the concept of plant-growing inside out. Touted as an “innovative solution to growing plants indoors,” the TerraPlanter works in the exact opposite way of traditional planters in that it holds water on the inside and diffuses the liquid to the outside, allowing the plants to grow on the exterior of the container. Think of it as a fancier Chia Pet.
On what it took to execute the fresh hydroponic design, TerraPlanter creators explain, “We wanted to design a piece that was aesthetically beautiful but it also needed to serve a function; it needed to be a good home for plants. For this we needed to produce a complex shape for the surface of the terraplanter to meet the needs of the plants.”
This resulted in a porous ceramic surface that holds seeds and water for the plant roots while allowing the roots to easily intertwine themselves with the planter’s surface. This design allows the plants to slowly absorb the water, which means they don’t need to be watered as frequently as with a standard planter, making the TerraPlanter an ideal choice for new or inexperienced plant parents.
It’s made of a ceramic material and terracotta in color, so it goes with any type of decor. The TerraPlanter is also reusable and makes a perfect home for various plants, including the Venus Flytrap, moss, chia, grass, begonias, silver lace ferns, and more.
TerraPlanter is still available on Kickstarter starting at $59 for a single unit and $99 for two. They are expected to ship in October 2020.
Virtually all human achievements have been made by groups of people, not lone individuals. As we incorporate smart technologies further into traditionally human processes, an even more powerful form of collaboration is emerging.
The ongoing, and sometimes loud, debate about how many and what kinds of jobs smart machines will leave for humans to do in the future is missing a salient point: Just as the automation of human work in the past allowed people and machines to do many things that couldn’t be done before, groups of people and computers working together will be able to do many things in the future that neither can do alone now.
To think about how this will happen, it’s useful to contemplate an obvious but not widely appreciated fact. Virtually all human achievements — from developing written language to making a turkey sandwich — require the work of groups of people, not just lone individuals. Even the breakthroughs of individual geniuses like Albert Einstein aren’t conjured out of thin air; they are erected on vast amounts of prior work by others.
The human groups that accomplish all these things can be described assuperminds. I define a supermind as a group of individuals acting together in ways that seem intelligent.
Superminds take many forms. They include the hierarchies in most businesses and other organizations; the markets that help create and exchange many kinds of goods and services; the communities that use norms and reputations to guide behavior in many professional, social, and geographical groups; and the democracies that are common in governments and some other organizations.
All superminds have a kind of collective intelligence, an ability to do things that the individuals in the groups couldn’t have done alone. What’s new is that machines can increasingly participate in the intellectual, as well as the physical, activities of these groups. That means we will be able to combine people and machines to create superminds that are smarter than any groups or individuals our planet has ever known.
To do that, we need to understand how people and computers can work together more effectively on tasks that require intelligence. And for that, we need to define intelligence.
What Is Intelligence?
The concept of intelligence is notoriously slippery, and different people have defined it in different ways. For our purposes, let’s say that intelligence involves the ability to achieve goals. And since we don’t always know what goals an individual or group is trying to achieve, let’s say that whether an entity “seems” intelligent depends on what goals an observer attributes to it.
Based on these assumptions, we can define two kinds of intelligence. The first is specialized intelligence, which is the ability to achieve specific goals effectively in a given environment. This means that an intelligent entity will do whatever is most likely to help it achieve its goals, based on everything it knows. Stated even more simply, specialized intelligence is “effectiveness” at achieving specific goals. In this sense, then, specialized collective intelligence is “group effectiveness,” and a supermind is an effective group.
The second kind of intelligence is more broadly useful and often more interesting. It is general intelligence, which is the ability to achieve a wide range of different goals effectively in different environments. This means that an intelligent actor needs not only to be good at a specific kind of task but also to be good at learning how to do a wide range of tasks. In short, this definition of intelligence means roughly the same thing as “versatility” or “adaptability.” In this sense, then, general collective intelligence means “group versatility” or “group adaptability,” and a supermind is a versatile or adaptable group.
What Kind of Intelligence Do Computers Have?
The distinction between specialized intelligence and general intelligence helps clarify the difference between the abilities of today’s computers and human abilities. Some artificially intelligent computers are far smarter than people in terms of certain kinds of specialized intelligence. But one of the most important things most people don’t realize about AI today is that it is all very specialized.1
Google’s search engine is great at retrieving news articles about baseball games, for example, but it can’t write an article about your son’s Little League game. IBM’s Watson beats humans at Jeopardy!, but the program that played Jeopardy! can’t play tic-tac-toe, much less chess.2 Teslas can (sort of) drive themselves, but they can’t pick up a box from a warehouse shelf.
Of course, there are computer systems that can do these other things. But the point is that they are all different, specialized programs, not a single general AI that can figure out what to do in each specific situation. Humans, with their general intelligence, must write programs that contain rules for solving different specific problems, and humans must decide which programs to run in a given situation.
In fact, none of today’s computers are anywhere close to having the level of general intelligence of any normal human 5-year-old. No single computer today can converse sensibly about the vast number of topics an ordinary 5-year-old can, not to mention the fact that the child can also walk, pick up weirdly shaped objects, and recognize when people are happy, sad, or angry.
How soon, if ever, will this change? Progress in the field of artificial intelligence has been notoriously difficult to predict ever since its early days in the 1950s. When researchers Stuart Armstrong and Kaj Sotala analyzed 95 predictions made between 1950 and 2012 about when general AI would be achieved, they found a strong tendency for both experts and nonexperts to predict that it would be achieved between 15 and 25 years in the future — regardless of when the predictions were made.3 In other words, general AI has seemed about 20 years away for the last 60 years.
More recent surveys and interviews tend to be consistent with this long-term pattern: People still predict that general AI will be here in about 15 to 25 years.4 So while we certainly don’t know for sure, there is good reason to be skeptical of confident predictions that general AI will appear in the next couple of decades. My own view is that, barring some major societal disasters, it is very likely that general AI will appearsomeday, but probably not until quite a few decades in the future.
All uses of computers will need to involve humans in some way until then. In many cases today, people are doing parts of a task that machines can’t do. But even when a computer can do a complete task by itself, people are always involved in developing the software and usually modifying it over time. They also decide when to use different programs in different situations and what to do when things go wrong.
How Can People and Computers Work Together?
One of the most intriguing possibilities for how people and computers can work together comes from an analogy with how the human brain is structured. There are many different parts of the brain that specialize in different kinds of processing, and these parts somehow work together to produce the overall behavior we call intelligence. For instance, one part of the brain is heavily involved in producing language, another in understanding language, and still another in processing visual information. Marvin Minsky, one of the fathers of AI, called this architecture a “society of mind.”5
Minsky was primarily interested in how human brains worked and how artificial intelligence programs might be developed, but his analogy also suggests a surprisingly important idea for how superminds consisting of both people and computers might work: Long before we have general AI, we can create more and more collectively intelligent systems by building societies of mind that include both humans and machines, each doing part of the overall task.
In other words, instead of having computers try to solve a whole problem by themselves, we can create cyber-human systems where multiple people and machines work together on the same problem. In some cases, the people may not even know — or care — whether they are interacting with another human or a machine. People can supply the general intelligence and other skills that machines don’t have. The machines can supply the knowledge and other capabilities that people don’t have. And, together, these systems can act more intelligently than any person, group, or computer has done before.
How is this different from current thinking about AI? Many people today assume that computers will eventually do most things by themselves and that we should put “humans in the loop” in situations where people are still needed.6 But it’s probably more useful to realize that most things now are done by groups of people, and we should put computers into these groups in situations where that is helpful. In other words, we should move away from thinking about putting humans in the loop to putting computers in the group.
What Roles Will Computers Play Relative to Humans?
If you want to use computers as part of human groups in your business or other organization, what roles should computers play in those groups? Thinking about the roles that people and machines play today, there are four obvious possibilities. People have the most control when machines act only as tools; and machines have successively more control as their roles expand to assistants, peers, and, finally, managers.
Tools
A physical tool, like a hammer or a lawn mower, provides some capability that a human doesn’t have alone — but the human user is directly in control at all times, guiding its actions and monitoring its progress. Information tools are similar. When you use a spreadsheet, the program is doing what you tell it to do, which often increases your specialized intelligence for a task like financial analysis.
But many of the most important uses of automated tools in the future won’t be to increase individual users’ specialized intelligence, but to increase a group’s collective intelligence by helping people communicate more effectively with one another. Even today, computers are largely used as tools to enhance human communication. With email, mobile applications, the web in general, and sites such as Facebook, Google, Wikipedia, Netflix, YouTube, and Twitter, we’ve created the most massively connected groups the world has ever known. In all these cases, computers are not doing much “intelligent” processing; they are primarily transferring information created by humans to other humans.
While we often overestimate the potential of AI, I think we often underestimate the potential power of this kind of hyperconnectivity among the 7 billion or so amazingly powerful information processors called human brains that are already on our planet.
Assistants
A human assistant can work without direct attention and often takes initiative in trying to achieve the general goals someone else has specified. Automated assistants are similar, but the boundary between tools and assistants is not always a sharp one. Text-message platforms, for instance, are mostly tools, but they sometimes take the initiative and autocorrect your spelling (occasionally with hilarious results).
Another example of an automated assistant is the software used by the online clothing retailer Stitch Fix Inc., based in San Francisco, California, to help its human stylists recommend items to customers.7 Stitch Fix customers fill out detailed questionnaires about their style, size, and price preferences, which are digested by machine-learning algorithms that select promising items of clothing.
The algorithmic assistant in this partnership is able to take into account far more information than human stylists can. For instance, jeans are often notoriously hard to fit, but the algorithms are able to select for each customer a variety of jeans that other customers with similar measurements decided to keep.
And it is the stylists who make the final selection of five items to send to the customer in each shipment. The human stylists are able to take into account information the Stitch Fix assistant hasn’t yet learned to deal with — such as whether the customer wants an outfit for a baby shower or a business meeting. And, of course, they can relate to customers in a more personal way than the assistant does. Together, the combination of people and computers provides better service than either could alone.
Peers
Some of the most intriguing uses of computers involve roles in which they operate as human peers more than assistants or tools, even in cases where there isn’t much actual artificial intelligence being used. For example, if you are a stock trader, you may already be transacting with an automated program trading system without knowing it.
And if your job is dealing with claims for Lemonade Insurance Agency LLC, based in New York City, you already have an automated peer named AI Jim.8 AI Jim is a chatbot, and Lemonade’s customers file claims by exchanging text messages with it. If the claim meets certain parameters, AI Jim pays it automatically and almost instantly. If not, AI Jim refers the claim to one of its human peers, who completes the job.
Managers
Human managers delegate tasks, give directions, evaluate work, and coordinate others’ efforts. Machines can do all these things, too, and when they do, they are performing as automated managers. Even though some people find the idea of a machine as a manager threatening, we already live with mechanical managers every day: A traffic light directs drivers; an automated call router delivers work to call center employees. Most people don’t find either situation threatening or problematic.
It’s likely that there will be many more examples of machines playing the role of managers in the future. For instance, the CrowdForge system crowdsources complex tasks such as writing documents. In one experiment, the system used online workers (recruited via the Amazon Mechanical Turk online marketplace) to write encyclopedia articles.9 For each article, the system first asked an online worker to come up with an outline for the article. Then it asked other workers to find relevant facts for each section in the outline. Next it asked still other workers to write coherent paragraphs using those facts. Finally, it assembled the paragraphs into a complete article. Interestingly, independent readers judged the articles written in this manner to be better than articles written by a single person.
How Can Computers Help Superminds Be Smarter?
If you want to design a supermind (like a company or a team) that can act intelligently, it needs to have some or all of the five cognitive processes that intelligent entities have — whether they are individuals or groups. Your supermind will need to create possibilities for action, decide which actions to take, sense the external world, remember the past, and learn from experience. (See “The Basic Cognitive Processes Needed by Any Intelligent Entity.”)
