Throughout human evolution, multiple versions of humans co-existed. Could we be mid-upgrade now? At TEDxSummit, Juan Enriquez sweeps across time and space to bring us to the present moment -- and shows how technology is revealing evidence that suggests rapid evolution may be under way. Juan Enríquez Cabot (born 1959) is a Mexican-American academic, businessman, author, and speaker. He is currently the Managing Director of Excel Venture Management. На протяжении человеческой эволюции сосуществовали разные версии людей. Можем ли мы быть в середине обновления сейчас? На TEDxSummit Хуан Энрикес пронзает время и пространство, чтобы приблизить нас к настоящему моменту, и показывает, как технологии открывают доказательства, свидетельствующие о том, что может происходить быстрая эволюция.
Хуан Энрикес Кабот (род. 1959) - мексиканско-американский ученый, бизнесмен, писатель и докладчик. В настоящее время он является управляющим директором Excel Venture Management.
You don't have to be a fortune teller to identify AI as the key trend for the 2020s. But there is nuance regarding AI hardware and software that deserves to be highlighted. By George Anadiotisfor Big on Data
The last part of the 2010s has been all about AI, and the 2020s will not be any different. We will see AI widening its reach, and impacting every conceivable field. Having already seen the AI hype rise, however, we must also be prepared for a backlash. And it's very important to be aware of what "AI" actually means.
In essence, what we call AI today is an umbrella term for various pattern matching techniques. Machine learning and its various subdomains, such as deep learning, essentially boil down to pattern matching. We've seen several breakthroughs in the 2010s, but the seeds for most techniques and algorithms have been planted decades ago and remain essentially the same.
Still, we have seen the performance of AI systems in many domains going from being worse than human, to catching up and surpassing humans. How is that possible? The answer is twofold: Data and compute.
The digitization of nearly all aspects of human activity has led to an explosion in the volumes of data being generated. Algorithms now have much more data to work with, and that alone means they can perform much better. In parallel, however, progress was made in domains such as image recognition: adjustments in neural networks, brought about by vibrant communities, have boosted the accuracy of the algorithms. ImageNet is a good example of this.
Much of what's being sold as "AI" today is snake oil. It does not and cannot work. Recently, Arvind Narayanan, a Princeton Professor, made waves by calling this out.
The problem with the AI frenzy is the divide between the haves and the have nots is widening. And not just because of the resources and expertise the big players have. It's a self-reinforcing loop of sorts: Being data-driven, designing and producing data-driven products means these products not only can have an edge, but they also bring in more data as they operate
Data, however, is just one part of the AI equation. The other part is hardware. Without the tremendous progress in hardware, the 2010s have seen, AI would not be possible. Access to the compute power needed to process the massive amounts of data needed for machine learning used to be a privilege reserved for the select few.
Cerebras's "Wafer-Scale Engine" takes up almost all the area of a 12-inch silicon wafer, making it 57 times the size of Nvidia's largest graphics processing unit.
Cerebras Systems.
The big innovator, and winner, in the 2010s AI hardware was NVIDIA. The company that most people came to know as a maker of GPUs, specialized hardware typically used by gamers for fast graphics rendering, has reinvented itself as an AI superpower. The architecture of GPUs, it turns out, is very well suited to running AI workloads.
Up until the beginning of the 2010s, the world was mostly running on relational databases and spreadsheets. To a large extent, it still does. But if the 2010s brought the first traces of dissent in the monoculture of tabular data structures, the 2020s will bring the final nail in the coffin. The NoSQL wave of databases has largely succeeded in getting developers, administrators, CIOs, CTOs, and business people out of their comfort zone, and instilled the "best tool for the job" mindset.
Polyglot persistence, as is the lingo for using data models and data management interchangeably depending on the task at hand, is becoming the new normal. After relational, key-value, document, columnar, and time-series databases, the latest link in this evolutionary proliferation of data structures is graph. Graph databases and knowledge graphs have been making waves and being included in hype cycles for the last couple of years.
While it's understandable why many people tend to think of graph as a new technology, the truth is this technology is at least 20 years old. And it has been largely initiated by none other than Tim Berners Lee, who is also credited as the inventor of the web, in 2001 with the publication of his Semantic Web manifesto in the Scientific American. Lee also coined the term Giant Global Graph, to describe the next stage in the evolution of the web.
Having been into this technology since the early 2000s, it's exhilarating to see it getting steam with technical progress, funding, and use cases piling up to a snowball effect. It is also amusing to see graph-washing beginning to commence. In essence, progress in graph is happening along the trajectory of progress in machine learning.
It's not so much that there was a major breakthrough in the technology that made it feasible, but more about the right conditions that made it boom. Many of the concepts, formats, standards, and technology enabling graph databases and knowledge graphs to flourish today have been developed over more than 20 years. What has brought on the perfect graph storm is a combination of factors.
Google, NASA, and leading organizations from every domain are using knowledge graphs to manage and leverage vast amounts of data
Image: Google
Like AI, the data explosion has contributed to bringing graph in the fore. Now that Big is no longer a qualifier for Data, because we have mastered the art of storing lots of it, the question really is how to get value out of data. Leveraging connections in data is a prominent way of getting value out of data and graph is the best way of leveraging connections.
This is why graph databases excel in use cases that require finding connections in data, such as anti-fraud or master data management. This is why graph analytics, with algorithms such as centrality or PageRank that are based in accounting for nodes and edges, can offer valuable insights in connected datasets. As the terminology seems to still be in flux for many newcomers in this field, a short history lesson, and grounding in semantics, may be called for.
Google has played a key role in the rise of graphs, and knowledge graphs. As the web itself is a prime use case for graphs, PageRank was born. As crawling and categorizing content on the web is a very hard problem to solve without semantics and metadata, Google embraced them, and coined the term Knowledge Graph, in 2012. This, and the widespread adoption of schema.org that came with it, marked the beginning of the meteoric rise of graph technology and knowledge graphs.
Knowledge graphs can address key challenges such as data governance but ultimately, they can serve as the digital substrate to unify the philosophy of knowledge acquisition and organization with the practice of data management in the digital age. The NASAs and the Morgan Stanleys of the world are managing ontologies, and utilizing knowledge graphs.
Graphs and knowledge graphs cross-cut into AI, too. Much of the AI hardware and software for the 2020s utilizes graph data structures. A combination of bottom-up, pattern matching techniques with top-down, knowledge-based approaches is the most promising way for AI to continue to make progress.
Knowledge Graph is a technology that enables other technologies to accelerate their growth, and it also enables humans to take stock of their own knowledge. This is why the future is Knowledge Graph.
To infinity and beyond
Looking back, it becomes clear how far we have come in the relatively short span of the last decade. Counter-intuitive as this may seem, however, we are not certain this is a good thing. Somewhere along the way, technological progress left human ability to monitor, comprehend and digest technology in the dust. In the dawn of this new decade, we seem to be engrossed in the never-ending race for more: More data, more processing power, more technology.
The belief that more equals better seems to be firmly ingrained in most of us. And the signs of what's coming seem to tell the story of not just more, but immeasurably more. Quantum computing is progressing in leaps, promising to unlock compute power beyond our wildest imagination. DNA storage seems set to do the same for storage. More data and compute than we would know what to do with. To infinity and beyond. But what for, and for whom?
Is this technology making us happier, and bringing us closer, or is it alienating and distressing us? Where are all the huge productivity gains going? Who is in control, who gets to call the shots, and why?
For the time being, these are questions few people are prepared to tackle. But if the 2020s develop on the trajectory they are set on, more and more of us will have to face those questions head-on.
Graphene possesses a set of unique properties, making it a 'wonder' material.
Image: REUTERS/Akhtar Soomro
In brief
Researchers from several universities have teamed up to develop a radical kind of transistor. Instead of using silicon, the team used graphene to build a logic gate series that uses less power but could work 1,000 times faster than current ones.
Graphene at it again
The discovery of graphene in 2004 began a flurry of studies to isolate other two-dimensional materials. Graphene was found to be a wonder material, possessing a set of unique and remarkable properties. One of these is its ability to conduct electricity ten times better than copper, the most commonly used conductor in electronics. At room temperature, graphene is also capable of conducting electricity 250 times better than silicon, a rate faster than any other known substance.
These properties led a team of researchers from Northwestern University, The University of Texas at Dallas (UT Dallas), University of Illinois at Urbana-Champaign, and University of Central Florida (UCF) to consider developing a graphene-based transistor. In a study published in the journal Nature Communications, the team found that a graphene-based transistor could actually work better than silicon transistors used in today’s computers.
Image: Nature Communications
A quick explanation first: Transistors are key in today’s computer circuits, as these act as on and off switches that allow electronic signals and electrical power through. When put together, transistors form logic gates — the core of microprocessors, serving as input and output and acting either as 0s or 1s (so-called binary bits). These are what allow microprocessors to solve logic and computing problems.
“If you want to continue to push technology forward, we need faster computers to be able to run bigger and better simulations for climate science, for space exploration, for Wall Street,” co-author Ryan Gelfand, an assistant professor at UCF, said in a press release. “To get there, we can’t rely on silicon transistors anymore.”
Better logic gates
Microprocessors built using silicon transistors have been stuck at processing speeds mostly in the 3 to 4 gigahertz range since 2005. There’s a limit to the rate of signals and power these transistors can handle, largely due to the material’s resistance. The team of researchers, however, found a way through this limitation by using graphene instead of silicon.
The researchers first built a graphene ribbon by unzipping a carbon nanotube (a thin folded graphene sheet). They then applied a magnetic field to the graphene ribbon, which made them realize they could control the resistance of the flowing current through the ribbon. By using adjacent nanotubes to increase or decrease the current, the magnetic field could control the flow of current.
The team’s graphene transistor-based logic circuits improved the clock speed of microprocessors by a thousand times, and would require a hundredth of the power required by silicon-based computers. Plus, these circuits were also smaller than logic circuits that use silicon transistors. This could allow for smaller electronic devices that squeeze in more functionality, Gelfand explained. A similar study also explored graphene as a potential capacitor for quantum computers.
An all-carbon computing system still exists only on the drawing board, says co-author Joseph S. Friedman of UT Dallas, but Friedman and his collaborators in the NanoSpinCompute research laboratory are currently working on a prototype.
“The exceptional material properties of carbon materials permit Terahertz operation and two orders of magnitude decrease in power-delay product compared to cutting-edge microprocessors,” the researchers wrote. “We hope to inspire the fabrication of these cascaded logic circuits to stimulate a transformative generation of energy-efficient computing.”
Oliver CannHead of Media Content, World Economic Forum
What do lab-grown meat, a holographic museum guide and a supercharged version of Amazon’s Alexa have in common?
They’re all breakthrough technologies that are likely to shape our lives in the near future, according to a list published by the World Economic Forum. Selected by a panel of scientists and experts, each one has been identified as having the potential to be disruptive by altering deep-rooted practices or shaking up whole industries.
While we’ve all heard how technologies like artificial intelligence and quantum computing are set to transform our everyday lives, the coming change can seem like a nebulous concept that’s hard to define. In this list, experts seek to pinpoint the breakthroughs that will take effect within three to five years.
Here are the technologies generating the most excitement among experts this year:
Augmented reality
Overlaying information and animation on to real-world images is set to go mainstream. While the technology isn’t new - many of us have used a car display with guides to aid parking or played the game PokémonGo - it’s set to take a leap forward in terms of sophistication and everyday use. In the future, augmented reality will help surgeons visualize tissues beneath a patient’s skin in three dimensions and conjure up holographic-like guides to take you through a museum.
Personalised medicine
Advanced diagnostic tools are set to tailor your medicines to you, detecting and quantifying multiple signs of a disorder to decide how likely you are to contract a disease. Several advanced diagnostic tools are already in use for cancer. One helps women with certain types of breast cancer avoid chemotherapy. It can also be used to diagnose endometriosis, without the need for surgery, as well as brain disorders, like autism, Parkinson's and Alzheimer’s, that are currently diagnosed by an assessment of symptoms.
AI-led molecular design
The days of science relying on educated predictions - or guesses - to create new drugs and materials may become a thing of the past as artificial intelligence takes over. Instead of messy experiments, machine-learning algorithms will analyse all known past tests, discern patterns and predict what new molecules are likely to work. As well as speeding up the process and reducing chemical waste, it will help the pharmaceutical industry identify and develop new drugs at a rapid pace.
More capable digital helpers
If you’re becoming reliant on Siri and Alexa to switch on your music or give you the weather forecast, you’ll soon be able to access far more sophisticated digital aides. Powered by AI, the latest technology will mine the cloud and outline various arguments on topics that are important to you, without prior training. And it’s not difficult to think of all the ways this technology could help in the workplace: for example helping doctors find research relevant to a complicated medical case and then debating the merits of the different ways of treating it.
Implantable drug-making cells
For people who have to take medicine regularly, the idea of having a tiny drug factory implanted in the body is probably very appealing. At some point in your life you’ve probably needed to take a course of drugs and struggled to remember when to take them. Until now, implant use was limited because users also needed to take immune-suppressing drugs to prevent their bodies attacking the implant. Now the technology is sophisticated enough to work without being rejected by the immune system and could transform the treatment of long-term conditions, such as cardiovascular disease, tuberculosis, diabetes, cancer and chronic pain.
Gene drive
Changing genes knowingly can be controversial and often goes hand-in-hand with ethical questions. And while gene drives - natural or engineered genetic elements that spread through populations quickly - are no different, they offer enormous power to fight disease or eliminate species of pests such as mosquitoes that transmit malaria. Such efforts got a shot in the arm in recent years with the introduction of CRISPR gene-editing, which makes it easy to insert genetic material into specific spots on chromosomes.
Algorithms for quantum computers
Computers that use quantum mechanics to perform calculations can solve some problems far more efficiently than a conventional computer. While early use was held back by disruptions to their function, the latest research has improved that and a growing number of academics are developing programmes and quantum software. Once refined, powerful quantum computers could simulate nature and help design materials.
Plasmonic materials
Is this the technology that will make Harry Potter’s invisibility cloak a reality? While that is probably still a way off, plasmonic devices that manipulate electron clouds and light at the nanoscale are set to increase magnetic memory storage and the sensitivity of biological sensors. Several companies are developing new products, including a device that can distinguish viral from bacterial infections and a heat-assisted magnetic recording device. Light-activated nanoparticles are also being investigated for their ability to treat cancer without damaging healthy tissue.
Lab-grown meat
Would you eat a burger that you knew had been grown in a lab? Meat grown from cultured cells could cut the environmental costs of producing meat and eliminate the unethical treatment suffered by animals that are raised for food. Start-ups like Mosa Meat, Memphis Meats, SuperMeat and Finless Foods have already attracted millions in funding, even though the production costs remain very high and taste-test results have been mixed. With the technology improving all the time, duck, chicken, and beef produced without slaughter could be on its way to a kitchen near you sooner than you think.
Electroceuticals
Could we cut down our reliance on drugs to treat most health conditions? Some say yes, with electroceuticals offering the ability to treat ailments using electrical impulses. One approach, targeting the vagus nerve - the system that sends signals from the brain to most organs - is poised to transform care for many conditions, since it has the potential to regulate the immune system. This has been used to treat epilepsy and depression for more than a decade, and now looks set to aid sufferers of migraines, obesity and rheumatoid arthritis.
Read more about each of the top 10 emerging technologies of 2018 here.
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.”)
