Tag Archives: study

#434781 What Would It Mean for AI to Become ...

Posted on March 26, 2019 by Android

As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”

We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?

Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?

In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.

Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.

“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.

Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.

“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.

Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.

“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.

This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.

Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.

But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.

The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?

Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.

“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”

Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?

This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”

She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).

“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.

Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”

Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.

“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”

Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.

As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.

Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.

Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.

Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”

This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.

“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”

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Posted in Human Robots

#434767 7 Non-Obvious Trends Shaping the Future

Posted on March 21, 2019 by Android

When you think of trends that might be shaping the future, the first things that come to mind probably have something to do with technology: Robots taking over jobs. Artificial intelligence advancing and proliferating. 5G making everything faster, connected cities making everything easier, data making everything more targeted.

Technology is undoubtedly changing the way we live, and will continue to do so—probably at an accelerating rate—in the near and far future. But there are other trends impacting the course of our lives and societies, too. They’re less obvious, and some have nothing to do with technology.

For the past nine years, entrepreneur and author Rohit Bhargava has read hundreds of articles across all types of publications, tagged and categorized them by topic, funneled frequent topics into broader trends, analyzed those trends, narrowed them down to the most significant ones, and published a book about them as part of his ‘Non-Obvious’ series. He defines a trend as “a unique curated observation of the accelerating present.”

In an encore session at South by Southwest last week (his initial talk couldn’t fit hundreds of people who wanted to attend, so a re-do was scheduled), Bhargava shared details of his creative process, why it’s hard to think non-obviously, the most important trends of this year, and how to make sure they don’t get the best of you.

Thinking Differently
“Non-obvious thinking is seeing the world in a way other people don’t see it,” Bhargava said. “The secret is curating your ideas.” Curation collects ideas and presents them in a meaningful way; museum curators, for example, decide which works of art to include in an exhibit and how to present them.

For his own curation process, Bhargava uses what he calls the haystack method. Rather than searching for a needle in a haystack, he gathers ‘hay’ (ideas and stories) then uses them to locate and define a ‘needle’ (a trend). “If you spend enough time gathering information, you can put the needle into the middle of the haystack,” he said.

A big part of gathering information is looking for it in places you wouldn’t normally think to look. In his case, that means that on top of reading what everyone else reads—the New York Times, the Washington Post, the Economist—he also buys publications like Modern Farmer, Teen Vogue, and Ink magazine. “It’s like stepping into someone else’s world who’s not like me,” he said. “That’s impossible to do online because everything is personalized.”

Three common barriers make non-obvious thinking hard.

The first is unquestioned assumptions, which are facts or habits we think will never change. When James Dyson first invented the bagless vacuum, he wanted to sell the license to it, but no one believed people would want to spend more money up front on a vacuum then not have to buy bags. The success of Dyson’s business today shows how mistaken that assumption—that people wouldn’t adapt to a product that, at the end of the day, was far more sensible—turned out to be. “Making the wrong basic assumptions can doom you,” Bhargava said.

The second barrier to thinking differently is constant disruption. “Everything is changing as industries blend together,” Bhargava said. “The speed of change makes everyone want everything, all the time, and people expect the impossible.” We’ve come to expect every alternative to be presented to us in every moment, but in many cases this doesn’t serve us well; we’re surrounded by noise and have trouble discerning what’s valuable and authentic.

This ties into the third barrier, which Bhargava calls the believability crisis. “Constant sensationalism makes people skeptical about everything,” he said. With the advent of fake news and technology like deepfakes, we’re in a post-truth, post-fact era, and are in a constant battle to discern what’s real from what’s not.

2019 Trends
Bhargava’s efforts to see past these barriers and curate information yielded 15 trends he believes are currently shaping the future. He shared seven of them, along with thoughts on how to stay ahead of the curve.

Retro Trust
We tend to trust things we have a history with. “People like nostalgic experiences,” Bhargava said. With tech moving as fast as it is, old things are quickly getting replaced by shinier, newer, often more complex things. But not everyone’s jumping on board—and some who’ve been on board are choosing to jump off in favor of what worked for them in the past.

“We’re turning back to vinyl records and film cameras, deliberately downgrading to phones that only text and call,” Bhargava said. In a period of too much change too fast, people are craving familiarity and dependability. To capitalize on that sentiment, entrepreneurs should seek out opportunities for collaboration—how can you build a product that’s new, but feels reliable and familiar?

Muddled Masculinity
Women have increasingly taken on more leadership roles, advanced in the workplace, now own more homes than men, and have higher college graduation rates. That’s all great for us ladies—but not so great for men or, perhaps more generally, for the concept of masculinity.

“Female empowerment is causing confusion about what it means to be a man today,” Bhargava said. “Men don’t know what to do—should they say something? Would that make them an asshole? Should they keep quiet? Would that make them an asshole?”

By encouraging the non-conforming, we can help take some weight off the traditional gender roles, and their corresponding divisions and pressures.

Innovation Envy
Innovation has become an over-used word, to the point that it’s thrown onto ideas and actions that aren’t really innovative at all. “We innovate by looking at someone else and doing the same,” Bhargava said. If an employee brings a radical idea to someone in a leadership role, in many companies the leadership will say they need a case study before implementing the radical idea—but if it’s already been done, it’s not innovative. “With most innovation what ends up happening is not spectacular failure, but irrelevance,” Bhargava said.

He suggests that rather than being on the defensive, companies should play offense with innovation, and when it doesn’t work “fail as if no one’s watching” (often, no one will be).

Artificial Influence
Thanks to social media and other technologies, there are a growing number of fabricated things that, despite not being real, influence how we think. “15 percent of all Twitter accounts may be fake, and there are 60 million fake Facebook accounts,” Bhargava said. There are virtual influencers and even virtual performers.

“Don’t hide the artificial ingredients,” Bhargava advised. “Some people are going to pretend it’s all real. We have to be ethical.” The creators of fabrications meant to influence the way people think, or the products they buy, or the decisions they make, should make it crystal-clear that there aren’t living, breathing people behind the avatars.

Enterprise Empathy
Another reaction to the fast pace of change these days—and the fast pace of life, for that matter—is that empathy is regaining value and even becoming a driver of innovation. Companies are searching for ways to give people a sense of reassurance. The Tesco grocery brand in the UK has a “relaxed lane” for those who don’t want to feel rushed as they check out. Starbucks opened a “signing store” in Washington DC, and most of its regular customers have learned some sign language.

“Use empathy as a principle to help yourself stand out,” Bhargava said. Besides being a good business strategy, “made with empathy” will ideally promote, well, more empathy, a quality there’s often a shortage of.

Robot Renaissance
From automating factory jobs to flipping burgers to cleaning our floors, robots have firmly taken their place in our day-to-day lives—and they’re not going away anytime soon. “There are more situations with robots than ever before,” Bhargava said. “They’re exploring underwater. They’re concierges at hotels.”

The robot revolution feels intimidating. But Bhargava suggests embracing robots with more curiosity than concern. While they may replace some tasks we don’t want replaced, they’ll also be hugely helpful in multiple contexts, from elderly care to dangerous manual tasks.

Back-storytelling
Similar to retro trust and enterprise empathy, organizations have started to tell their brand’s story to gain customer loyalty. “Stories give us meaning, and meaning is what we need in order to be able to put the pieces together,” Bhargava said. “Stories give us a way of understanding the world.”

Finding the story behind your business, brand, or even yourself, and sharing it openly, can help you connect with people, be they customers, coworkers, or friends.

Tech’s Ripple Effects
While it may not overtly sound like it, most of the trends Bhargava identified for 2019 are tied to technology, and are in fact a sort of backlash against it. Tech has made us question who to trust, how to innovate, what’s real and what’s fake, how to make the best decisions, and even what it is that makes us human.

By being aware of these trends, sharing them, and having conversations about them, we’ll help shape the way tech continues to be built, and thus the way it impacts us down the road.

Image Credit: Rohit Bhargava by Brian Smale Continue reading →

Posted in Human Robots

#434701 3 Practical Solutions to Offset ...

Posted on March 11, 2019 by Android

In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.

The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.

As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?

Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.

First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.

Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.

Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.

At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.

Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.

Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.

Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.

Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.

Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.

Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.

Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).

The evolving social contract (U.S. examples)
– 1842 | Right to strike
– 1924 | Abolish child labor
– 1935 | Right to unionize
– 1938 | 40-hour work week
– 1962, 1974 | Trade adjustment assistance
– 1964 | Pay discrimination prohibited
– 1970 | Health and safety laws
– 21st century | AI and automation adjustment assistance?

Figure 2. Labor laws have historically adjusted as technology and society progressed

Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.

A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.

Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.

We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.

However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.

Note: The full paper this article is based on is available here.

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Posted in Human Robots

#434648 The Pediatric AI That Outperformed ...

Posted on February 20, 2019 by Android

Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.

Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.

The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.

The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.

Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.

Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.

Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.

When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.

Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.

That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.

Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.

Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.

In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.

Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.

In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.

Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.

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Posted in Human Robots

#434623 The Great Myth of the AI Skills Gap

Posted on February 13, 2019 by Android

One of the most contentious debates in technology is around the question of automation and jobs. At issue is whether advances in automation, specifically with regards to artificial intelligence and robotics, will spell trouble for today’s workers. This debate is played out in the media daily, and passions run deep on both sides of the issue. In the past, however, automation has created jobs and increased real wages.

A widespread concern with the current scenario is that the workers most likely to be displaced by technology lack the skills needed to do the new jobs that same technology will create.

Let’s look at this concern in detail. Those who fear automation will hurt workers start by pointing out that there is a wide range of jobs, from low-pay, low-skill to high-pay, high-skill ones. This can be represented as follows:

They then point out that technology primarily creates high-paying jobs, like geneticists, as shown in the diagram below.

Meanwhile, technology destroys low-wage, low-skill jobs like those in fast food restaurants, as shown below:

Then, those who are worried about this dynamic often pose the question, “Do you really think a fast-food worker is going to become a geneticist?”

They worry that we are about to face a huge amount of systemic permanent unemployment, as the unskilled displaced workers are ill-equipped to do the jobs of tomorrow.

It is important to note that both sides of the debate are in agreement at this point. Unquestionably, technology destroys low-skilled, low-paying jobs while creating high-skilled, high-paying ones.

So, is that the end of the story? As a society are we destined to bifurcate into two groups, those who have training and earn high salaries in the new jobs, and those with less training who see their jobs vanishing to machines? Is this latter group forever locked out of economic plenty because they lack training?

No.

The question, “Can a fast food worker become a geneticist?” is where the error comes in. Fast food workers don’t become geneticists. What happens is that a college biology professor becomes a geneticist. Then a high-school biology teacher gets the college job. Then the substitute teacher gets hired on full-time to fill the high school teaching job. All the way down.

The question is not whether those in the lowest-skilled jobs can do the high-skilled work. Instead the question is, “Can everyone do a job just a little harder than the job they have today?” If so, and I believe very deeply that this is the case, then every time technology creates a new job “at the top,” everyone gets a promotion.

This isn’t just an academic theory—it’s 200 years of economic history in the west. For 200 years, with the exception of the Great Depression, unemployment in the US has been between 2 percent and 13 percent. Always. Europe’s range is a bit wider, but not much.

If I took 200 years of unemployment rates and graphed them, and asked you to find where the assembly line took over manufacturing, or where steam power rapidly replaced animal power, or the lightning-fast adoption of electricity by industry, you wouldn’t be able to find those spots. They aren’t even blips in the unemployment record.

You don’t even have to look back as far as the assembly line to see this happening. It has happened non-stop for 200 years. Every fifty years, we lose about half of all jobs, and this has been pretty steady since 1800.

How is it that for 200 years we have lost half of all jobs every half century, but never has this process caused unemployment? Not only has it not caused unemployment, but during that time, we have had full employment against the backdrop of rising wages.

How can wages rise while half of all jobs are constantly being destroyed? Simple. Because new technology always increases worker productivity. It creates new jobs, like web designer and programmer, while destroying low-wage backbreaking work. When this happens, everyone along the way gets a better job.

Our current situation isn’t any different than the past. The nature of technology has always been to create high-skilled jobs and increase worker productivity. This is good news for everyone.

People often ask me what their children should study to make sure they have a job in the future. I usually say it doesn’t really matter. If I knew everything I know now and went back to the mid 1980s, what could I have taken in high school to make me better prepared for today? There is only one class, and it wasn’t computer science. It was typing. Who would have guessed?

The great skill is to be able to learn new things, and luckily, we all have that. In fact, that is our singular ability as a species. What I do in my day-to-day job consists largely of skills I have learned as the years have passed. In my experience, if you ask people at all job levels,“Would you like a little more challenging job to make a little more money?” almost everyone says yes.

That’s all it has taken for us to collectively get here today, and that’s all we need going forward.

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