Tag Archives: AI

Autonomous Assumptions

I’m writing about a recent post from futurist Amy Webb. Amy is getting very political lately which is a real turn-off for me, but she still has her ear to the rail of the future, so I will try to be more tolerant. Amy carried a paragraph from an article entitled, “If you want to trust a robot, look at how it makes decisions” from The Conversation, an eclectic “academic rigor, journalistic flair” blog site. The author, Michael Fisher, a Professor of Computer Science, at the University of Liverpool, says,

“When we deal with another human, we can’t be sure what they will decide but we make assumptions based on what we think of them. We consider whether that person has lied to us in the past or has a record for making mistakes. But we can’t really be certain about any of our assumptions as the other person could still deceive us.

Our autonomous systems, on the other hand, are essentially controlled by software so if we can isolate the software that makes all the high-level decisions – those decisions that a human would have made – then we can analyse the detailed working of these programs. That’s not something you can or possibly ever could easily do with a human brain.”

Fisher thinks that might make autonomous systems more trustworthy than humans. He says that by software analysis we can be almost certain that the software that controls our systems will never make bad decisions.

There is a caveat.

“The environments in which such systems work are typically both complex and uncertain. So while accidents can still occur, we can at least be sure that the system always tries to avoid them… [and] we might well be able to prove that the robot never intentionally means to cause harm.”

That’s comforting. But OK, computers fly and land airplanes, they make big decisions about air traffic, they are driving cars with people in them, they control much of our power grid, and our missile defense, too. So why should we worry? It is a matter of definitions. We use terms when describing new technologies that clearly have different interpretations. How you define bad decisions? Fisher says,

“We are clearly moving on from technical questions towards philosophical and ethical questions about what behaviour we find acceptable and what ethical behaviour our robots should exhibit.”

If you have programmed an autonomous soldier to kill the enemy, is that ethical? Assuming that the Robocop can differentiate between good guys and bad guys, you have nevertheless opened the door to autonomous destruction. In the case of an autonomous soldier in the hands of a bad actor, you may be the enemy.

My point is this. It’s not necessarily the case that we understand how the software works and that it’s reliable, it may be more about who programmed the bot in the first place. In my graphic novel, The Lightstream Chronicles, there are no bad robots (I call them synths), but occasionally bad people get a hold of the good synths and make them do bad things. They call that twisting. It’s illegal, but of course, that doesn’t stop it. Criminals do it all the time.

You see, even in the future some things never change. In the words of Aldous Huxley,

“Technological progress has merely provided us with more efficient means for going backwards.”

 

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The right thing to do. Remember that idea?

I’ve been detecting some blowback recently regarding all the attention surrounding emerging AI, it’s near-term effect on jobs, and it’s long-term impact on humanity. Having an anticipatory mindset toward artificial intelligence is just the logical thing to do. As I have said before, designing a car without a braking system would be foolish. Anticipating the eventuality that you might need to slow down or stop the car is just good design. Nevertheless, there are a lot of people, important people in positions of power that think this is a lot of hooey. They must think that human ingenuity will address any unforeseen circumstances, that science is always benevolent, that stuff like AI is “a long way off,” that the benefits outweigh the downsides, and that all people are basically good. Disappointed I am that this includes our Treasury Secretary Steve Mnuchin. WIRED carried the story and so did my go-to futurist Amy Webb. In her newsletter Amy states,

“When asked about the future of artificial intelligence, automation and the workforce at an Axios event, this was Mnuchin’s reply: ‘It’s not even on our radar screen,’ he said, adding that significant workforce disruption due to AI is ‘50 to 100’ years away. ‘I’m not worried at all’”

Sigh! I don’t care what side of the aisle you’re on, that’s just plain naive. Turning a blind eye to potentially transformative technologies is also dangerous. Others are skeptical of any regulation (perhaps rightly so) that stifles innovation and progress. But safeguards and guidelines are not that. They are well-considered recommendations that are designed to protect while facilitating research and exploration. On the other side of the coin, they are also not laws, which means that if you don’t want to or don’t care to, you don’t have to follow them.

Nevertheless, I was pleased to see a relatively comprehensive set of AI principles that emerged from the Asilomar Conference that I blogged about a couple of weeks ago. The 2017 Asilomar conference organized by The Future of Life Institute,

“…brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI.”

The gathering generated the Asilomar AI Principles, a remarkable first step on the eve of an awesome technological power. None of these people, from the panel I highlighted in the last blog, are anxious for regulation, but at the same time, they are aware of the enormous potential for bad actors to undermine whatever beneficial aspects of the technology might surface. Despite my misgivings, an AGI is inevitable. Someone is going to build it, and someone else will find a way to misuse it.

There are plenty more technologies that pose questions. One is nanotechnology. Unlike AI, Hollywood doesn’t spend much time painting nanotechnological dystopias, perhaps that along with the fact that they’re invisible to the naked eye, lets the little critters slip under the radar. While researching a paper for another purpose, I decided to look into nanotechnology to see what kinds of safeguards and guidelines are in place to deal with that rapidly emerging technology. There are clearly best practices by reputable researchers, scientists, and R&D departments but it was especially disturbing to find out that none of these are mandates. Especially since there are thousands of consumer products that use nanotechnology including food, cosmetics, clothing, electronics, and more. A nanometer is very small. Nanotech concerns itself with creations that exist in the 100nm range and below, roughly 7,500 times smaller than a human hair. In the Moore’s Law race, nanothings are the next frontier in cramming data onto a computer chip, or implanting them into our brains or living cells. However, due to their size, nanoparticles can also be inhaled, absorbed into the skin, flushed into the water supply and leeched into the soil. We don’t know what happens if we aggregate a large number of nanoparticles or differing combinations of nanoparticles in our body. We don’t even know how to test for it. And, get ready. Currently, there are no regulations. That means manufacturers do not need to disclose it, and there are no laws to protect the people who work with it. Herein, we have a classic example of bad decisions in the present that make for worse futures. Imagine the opposite: Anticipation of what could go wrong and sound industry intervention at a scale that pre-empts government intervention or the dystopian scenarios that the naysayers claim are impossible.

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The genius panel has some serious concerns.

Occasionally in preparing this blog, there are troughs in the technology newsfeed. But not now, and maybe never again. So it is with technology that accelerates exponentially. This idea, by the way, is a concept of which I will no longer try to convince my readers. I’m going to stop referencing why Kurzweil’s theorem, that technology advances exponentially is no longer a theorem and just move forward with the assumption that you know that it is. If you don’t agree,  then scout backwards—probably six months of previous blogs—and you’ll be on the same page. From here on, technology advances exponentially! With that being said, we are also no longer at the base of the exponential curve. We are beginning a steep climb.

Last week I highlighted Kurzweil’s upgraded prediction on the Singularity (12 years). I agree, though now I think he may be underselling things. It could easily arrive before that.

Today’s blog comes from a hot tip from one of my students. At the beginning of each semester, I always turn my students on to the idea of GoogleAlerts. It works like this: You tell Google to send you anything and everything on whatever topic interests you. Then, anytime there is news online that fits your topic, you get an email with a list of links from Google. The emails can be inundating so choose your search wisely. At any rate, my student who drank the GoogleAlert kool-aid sent me a link to a panel discussion that took place in January of 2017. The panel convened at something called Beneficial AI 2017 in Asilomar, California. And what a panel it was. Get this: Bart Selman (Cornell), David Chalmers (NYU), Elon Musk (Tesla, SpaceX), Jaan Tallinn (CSER/FLI), Nick Bostrom (FHI), Ray Kurzweil (Google), Stuart Russell (Berkeley), Sam Harris, Demis Hassabis (DeepMind). Sam is a philosopher, author, neuroscientist and noted secularist. I’ve cited nearly all of these characters before in blogs or research papers, so to see them all on one panel was, for me, amazing.

L to R: Elon Musk, Stuart Russell , Bart Selman, Ray Kurzweil, David Chalmers, Nick Bostrom, Demis Hassabis, Sam Harris, Jaan Tallinn.

 

Why were they there? The Future of Life Institute (FLI) organized the BAI 2017 event:

“In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI.”

FLI works together with CSER. (The Centre for the Study of Existential Risk). I confess that I was not aware of either organization, but this is encouraging. For example, CSER’s mission is stated as

“[…]within the University of Cambridge dedicated to the study and mitigation of human extinction-level risks that may emerge from technological advances and human activity.”

FLI describes themselves thus:

“We are a charity and outreach organization working to ensure that tomorrow’s most powerful technologies are beneficial for humanity […] We are currently focusing on keeping artificial intelligence beneficial and we are also exploring ways of reducing risks from nuclear weapons and biotechnology.”

Both organizations are loaded with scientists and technologists including Steven Hawking, Bostrom, and Musk.

The panel of genius’ got off to a rocky start because there weren’t enough microphones to go around. Duh. But then things got interesting. The topic of safe AI or what these fellows refer to as AGI, Artificial General Intelligence, is a deep well fraught with promise and doom. The encouraging thing is that these organizations realize the potential for either, the discomforting thing is that they’re genuinely concerned.

As I have discussed before, this race to a superintelligence which Kurzweil moved up to 2029 a few weeks ago, is moving full speed ahead and it is climbing in a steep exponential incline. It is likely that we will be able to build it long before we have figured out how to keep it from destroying us. I’m on record as saying that even the notion of a superintelligence is an error in judgment. If what you want to do is cure disease, aging, and save the planet, why not stop short of full-tilt superintelligence. Surely you get a very, very, very intelligent AI to give you what you want and go no further. After hearing the panel discussion, however, I see this as naive. As Kurzweil stated in the discussion,

“…there really isn’t a foolproof technical solution to this… If you have an AI that is more intelligent than you and is out for your destruction, it’s out for the world’s destruction, and there is no other AI that is superior to it, that’s a bad situation. So that’s the specter […] Imagine that we’ve done our job perfectly, and we’ve created the most safe, beneficial AI possible, but we’ve let the political system become totalitarian and evil, either an evil world government or just a portion of the globe, that is that way, it’s not going to work out well. So part of the struggle is in the area of politics and policy to have the world reflect the values we want to achieve. Human AI is by definition at human levels and therefore is human. So the issue is, ‘How do we make humans ethical?’ is the same issue as, ‘How we make AIs that are at human level, ethical?’”

So there we have the problem of human nature, again. If we can’t fix ourselves if we can’t even agree on what’s broken, how can we build a benevolent god? Fortunately, brilliant minds are honestly concerned about this but that doesn’t mean they’re going to put on the brakes. It was stated in full agreement by the panel: a superintelligence is inevitable. If we don’t build it, someone else will.

It is also safe to assume that our super ethical AI won’t have the same ethics as someone else’s AI. Hence, Kurzweil’s specter. I could turn this into an essay, but I’ll stop here for now. What do you think?

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But nobody knows what better is.

South by Southwest, otherwise known as SXSW calls itself a film and music festival and interactive media conference. It’s held every spring in Austin, Texas. Other than maybe the Las Vegas Consumer Electronics Show or San Diego’s ComicCon, I can’t think of many conferences that generate as much buzz as SXSW. This year it is no different. I will have blog fodder for weeks. Though I can’t speak to the film or music side, I’m sure they were scintillating. Under the category of interactive, most of the buzz is about technology in general, as tech gurus and futurists are always in attendance along with celebs who align themselves to the future.

Once again at SXSW, Ray Kurzweil was on stage. In my blogs, Kurzweil is probably the one guy I quote the most throughout this blog. So here we go again. Two tech sites caught my eye they week, reporting on Kurzweil’s latest prediction that moves up the date of the Singularity from 2045 to 2029; that’s 12 years away. Since we are enmeshed in the world of exponentially accelerating technology, I have encouraged my students to start wrapping their heads around the idea of exponential growth. In our most recent project, it was a struggle just to embrace the idea of how in only seven years we could see transformational change. If Kurzweil is right about his latest prognostication, then 12 years could be a real stunner. In case you are visiting this blog for the first time, the Singularity to which Kurzweil refers is, acknowledged as the point at which computer intelligence exceeds that of human intelligence; it will know more, anticipate more, and analyze more than any human capability. Nick Bostrom calls it the last invention we will ever need to make. We’ve already seen this to some extent with IBM’s Watson beating the pants off a couple of Jeopardy masters and Google’s DeepMind handily beat a Go genius at a game that most thought to be too complex for a computer to handle. Some refer to this “computer” as a superintelligence, and warn that we better be designing the braking mechanism in tandem with the engine, or this smarter-than-us computer may outsmart us in unfortunate ways.

In an article in Scientific American, Northwestern University psychology professor Paul Weber says we are bombarded each day with about 2.5 exabytes of data and that the human brain can only store an estimated 2.5 petabytes (a million gigabytes). Of course, the bombardment will continue to increase. Another voice that emerges in this discussion is Rob High IBM’s vice president and chief technology officer. According to the futurism tech blog, High was part of a panel discussion at the American Institute of Aeronautics and Astronautics (AIAA) SciTech Conference 2017. High said,

“…we have a very desperate need for cognitive computing…The information being produced is far surpassing our ability to consume and make use of…”

On the surface, this seems like a compelling argument for faster, more pervasive computing. But since it is my mission to question otherwise compelling arguments, I want to ask whether we actually need to process 2.5 exabytes of information? It would appear that our existing technology has already turned on the firehose of data (Did we give it permission?) and now it’s up to us to find a way to drink from the firehose. To me, it sounds like we need a regulator, not a bigger gullet. I have observed that the traditional argument in favor of more, better, faster often comes wrapped in the package of help for humankind.

Rob High, again from the futurism article, says,

“‘If you’re a doctor and you’re trying to figure out the best way to treat your patient, you don’t have the time to go read the latest literature and apply that knowledge to that decision’ High explained. ‘In any scenario, we can’t possibly find and remember everything.’ This is all good news, according to High. We need AI systems that can assist us in what we do, particularly in processing all the information we are exposed to on a regular basis — data that’s bound to even grow exponentially in the next couple of years.’”

From another futurism article, Kurzweil uses a similar logic:

“We’re going to be able to meet the physical needs of all humans. We’re going to expand our minds and exemplify these artistic qualities that we value.”

The other rationale that almost always becomes coupled with expanding our minds is that we will be “better.” No one, however, defines what better is. You could be a better jerk. You could be a better rapist or terrorist or megalomaniac. What are we missing exactly, that we have to be smarter, or that Bach, or Mozart are suddenly inferior? Is our quality of life that impoverished? And for those who are impoverished, how does this help them? And what about making us smarter? Smarter at what?

But not all is lost. On a more positive note, futurism in a third article (they were busy this week), reports,

“The K&L Gates Endowment for Ethics and Computational Technologies seeks to introduce the thoughtful discussion on the use of AI in society. It is being established through funding worth $10 million from K&L Gates, one of the United States’ largest law firms, and the money will be used to hire new faculty chairs as well as support three new doctoral students.”

Though I’m not sure whether we can consider this a regulator, rather something to lessen the pain of swallowing.

Finally (for this week), back to Rob High,

“Smartphones are just the tip of the iceberg,” High said. “Human intelligence has its limitations and artificial intelligence is going to evolve in a lot of ways that won’t be similar to human intelligence. But, I think they will work best in the presence of humans.”

So, I’m more concerned with when artificial intelligence is not working at its best.

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Disruption. Part 2.

 

Last week I discussed the idea of technological disruption. Essentially, they are innovations that make fundamental changes in the way we work or live. In turn, these changes affect culture and behavior. Issues of design and culture are the stuff that interests me and my research: how easily and quickly our practices change as a result of the way we enfold technology. The advent of the railroad, mass produced automobiles, radio, then television, the Internet, and the smartphone all qualify as disruptions.

Today, technology advances more quickly. Technological development was never a linear idea, but because most of the tech advances of the last century were at the bottom of the exponential curve, we didn’t notice them. New technologies that are under development right now are going to being realized more quickly (especially the ones with big funding), and because of the idea of convergence, (the intermixing of unrelated technologies) their consequences will be less predictable.

One of my favorite futurists is Amy Webb whom I have written about before. In her most recent newsletter, Amy reminds us that the Internet was clunky and vague long before it was disruptive. She states,

“However, our modern Internet was being built without the benefit of some vital voices: journalists, ethicists, economists, philosophers, social scientists. These outside voices would have undoubtedly warned of the probable rise of botnets, Internet trolls and Twitter diplomacy––would the architects of our modern internet have done anything differently if they’d confronted those scenarios?”

Amy inadvertently left out the design profession, though I’m sure she will reconsider after we chat. Indeed, it is the design profession that is a key contributor to transformative tech and design thinkers, along with the ethicists and economists can help to visualize and reframe future visions.

Amy thinks that voice will be the next transformation will be our voice,

“From here forward, you can be expected to talk to machines for the rest of your life.”

Amy is referring to technologies like Alexa, Siri, Google, Cortana, and something coming soon called Bixby. The voices of these technologies are, of course, only the window dressing for artificial intelligence. But she astutely points out that,

“…we also know from our existing research that humans have a few bad habits. We continue to encode bias into our algorithms. And we like to talk smack to our machines. These machines are being trained not just to listen to us, but to learn from what we’re telling them.”

Such a merger might just be the mix of any technology (name one) with human nature or the human condition: AI meets Mike who lives across the hall. AI becoming acquainted with Mike may have been inevitable, but the fact that Mike happens to be a jerk was less predictable and so the outcome less so. The most significant disruptions of the future are going to come from the convergence of seemingly unrelated technologies. Sometimes innovation depends on convergence, like building an artificial human that will have to master a lot of different functions. Other times, convergence is accidental or at least unplanned. The engineers over at Boston Dynamics who are building those intimidating walking robots are focused a narrower set of criteria than someone creating an artificial human. Perhaps power and agility are their primary concern. Then, in another lab, there are technologists working on voice stress analysis, and in another setting, researchers are looking to create an AI that can choose your wardrobe. Somewhere else we are working on facial recognition or Augmented Reality or Virtual Reality or bio-engineering, medical procedures, autonomous vehicles or autonomous weapons. So it’s a lot like Harry meets Sally, you’re not sure what you’re going to get or how it’s going to work.

Digital visionary Kevin Kelly thinks that AI will be at the core of the next industrial revolution. Place the prefix “smart” in front of anything, and you have a new application for AI: a smart car, a smart house, a smart pump. These seem like universally useful additions, so far. But now let’s add the same prefix to the jobs you and I do, like a doctor, lawyer, judge, designer, teacher, or policeman. (Here’s a possible use for that ominous walking robot.) And what happens when AI writes better code than coders and decides to rewrite itself?

Hopefully, you’re getting the picture. All of this underscores Amy Webb’s earlier concerns. The ‘journalists, ethicists, economists, philosophers, social scientists’ and designers are rarely in the labs where the future is taking place. Should we be doing something fundamentally differently in our plans for innovative futures?

Side note: Convergence can happen in a lot of ways. The parent corporation of Boston Dynamics is X. I’ll use Wikipedia’s definition of X: “X, an American semi-secret research-and-development facility founded by Google in January 2010 as Google X, operates as a subsidiary of Alphabet Inc.”

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Paying attention.

I want to make a Tshirt. On the front, it will say, “7 years is a long time.” On the back, it will say, “Pay attention!”

What am I talking about? I’ll start with some background. This semester, I am teaching a collaborative studio with designers from visual communications, interior design, and industrial design. Our topic is Humane Technologies, and we are examining the effects of an Augmented Reality (AR) system that could be ubiquitous in 7 years. The process began with an immersive scan of the available information and emerging advances in AR, VR, IoT, human augmentation (HA) and, of course, AI. In my opinion, these are a just a few of the most transformative technologies currently attracting the heaviest investment across the globe. And where the money goes there goes the most rapid advancement.

A conversation starter.

One of the biggest challenges for the collaborative studio class (myself included) is to think seven years out. Although we read Kurzweil’s Law of Accelerating Returns, our natural tendency is to think linearly, not exponentially. One of my favorite Kurzweil illustrations is this:

“Exponentials are quite seductive because they start out sub-linear. We sequenced one ten-thousandth of the human genome in 1990 and two ten-thousandths in 1991. Halfway through the genome project, 7 ½ years into it, we had sequenced 1 percent. People said, “This is a failure. Seven years, 1 percent. It’s going to take 700 years, just like we said.” Seven years later it was done, because 1 percent is only seven doublings from 100 percent — and it had been doubling every year. We don’t think in these exponential terms. And that exponential growth has continued since the end of the genome project. These technologies are now thousands of times more powerful than they were 13 years ago, when the genome project was completed.”1

So when I hear a policymaker, say, “We’re a long way from that,” I cringe. We’re not a long way away from that. The iPhone was introduced on June 29, 2007, not quite ten years ago. The ripple-effects from that little technological marvel are hard to catalog. With the smartphone, we have transformed everything from social and behavioral issues to privacy and safety. As my students examine the next possible phase of our thirst for the latest and greatest, AR (and it’s potential for smartphone-like ubiquity), I want them to ask the questions that relate to supporting systems, along with the social and ethical repercussions of these transformations. At the end of it all, I hope that they will walk away with an appreciation for paying attention to what we make and why. For example, why would we make a machine that would take away our job? Why would we build a superintelligence? More often than not, I fear the answer is because we can.

Our focus on the technologies mentioned above is just a start. There are more than these, and we shouldn’t forget things like precise genetic engineering techniques such as CRISPR/Cas9 Gene Editing, neuromorphic technologies such as microprocessors configured like brains, the digital genome that could be the key to disease eradication, machine learning, and robotics.

Though they may sound innocuous by themselves, they each have gigantic implications for disruptions to society. The wild card in all of these is how they converge with each other and the results that no one anticipated. One such mutation would be when autonomous weapons systems (AI + robotics + machine learning) converge with an aggregation of social media activity to predict, isolate and eliminate a viral uprising.

From recent articles and research by the Department of Defense, this is no longer theoretical; we are actively pursuing it. I’ll talk more about that next week. Until then, pay attention.

 

1. http://www.bizjournals.com/sanjose/news/2016/09/06/exclusivegoogle-singularity-visionary-ray.htm
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Now I know that Kurzweil is right.

 

In a previous blog entitled “Why Kurzweil is probably right,” I made this statement,

“Convergence is the way technology leaps forward. Supporting technologies enable formerly impossible things to become suddenly possible.”

That blog was talking about how we are developing AI systems at a rapid pace. I quoted a WIRED magazine article by David Pierce that was previewing consumer AIs already in the marketplace and some of the advancements on the way. Pierce said that a personal agent is,

“…only fully useful when it’s everywhere when it can get to know you in multiple contexts—learning your habits, your likes and dislikes, your routine and schedule. The way to get there is to have your AI colonize as many apps and devices as possible.”

Then, I made my usual cautionary comment about how such technologies will change us. And they will. So, if you follow this blog, you know that I throw cold water onto technological promises as a matter of course. I do this because I believe that someone has to.

Right now I’m preparing my collaborative design studio course. We’re going to be focusing on AR and VR, but since convergence is an undeniable influence on our techno-social future, we will have to keep AI, human augmentation, the Internet of Things, and a host of other emerging technologies on the desktop as well. In researching the background for this class, I read three articles from Peter Diamandis for the Singularity Hub website. I’ve written about Peter before, as well. He’s brilliant. He’s also a cheerleader for the Singularity. So that being said, these articles, one on the Internet of Everything (IoE/IoT), Artificial Intelligence (AI), and another on Augmented and Virtual Reality (AR/VR), are full of promises. Most of what we thought of as science fiction, even a couple of years ago are now happening with such speed that Diamandis and his cohorts believe they are imminent in only three years. And by that I mean commonplace.

If that isn’t enough for us to sit up and take notice, then I am reminded of an article from the Silicon Valley Business Journal, another interview with Ray Kurzweil. Kurzweil, of course, has pretty much convinced us all by now that the Law of Accelerating Returns is no longer hyperbole. If anyone thought that it was only hype, sheer observation should have brought them to their senses. In this article,
Kurzweil gives this excellent illustration of how exponential growth actually plays out—no longer as a theory but—as demonstrable practice.

“Exponentials are quite seductive because they start out sub-linear. We sequenced one ten-thousandth of the human genome in 1990 and two ten-thousandths in 1991. Halfway through the genome project, 7 ½ years into it, we had sequenced 1 percent. People said, “This is a failure. Seven years, 1 percent. It’s going to take 700 years, just like we said.” Seven years later it was done because 1 percent is only seven doublings from 100 percent — and it had been doubling every year. We don’t think in these exponential terms. And that exponential growth has continued since the end of the genome project. These technologies are now thousands of times more powerful than they were 13 years ago when the genome project was completed.”

When you combine that with the nearly exponential chaos of hundreds of other converging technologies, indeed the changes to our world and behavior are coming at us like a bullet-train. Ask any Indy car driver, when things are happening that fast, you have to be paying attention.
But when the input is like a firehose and the motivations are unknown, how on earth do we do that?

Personally, I see this as a calling for design thinkers worldwide. Those in the profession, schooled in the ways of design thinking have been espousing our essential worth to realm of wicked problems for some time now. Well, problems don’t get more wicked than this.

Maybe we can design an AI that could keep us from doing stupid things with technologies that we can make but cannot yet comprehend the impact of.

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Big-Data Algorithms. Don’t worry. Be happy.

 

It’s easier for us to let the data decide for us. At least that is the idea behind global digital design agency Huge. Aaron Shapiro is the CEO. He says, “The next big breakthrough in design and technology will be the creation of products, services, and experiences that eliminate the needless choices from our lives and make ones on our behalf, freeing us up for the ones we really care about: Anticipatory design.”

Buckminster Fuller wrote about Anticipatory Design Science, but this is not that. Trust me. Shapiro’s version is about allowing big data, by way of artificial intelligence and neural networks, to become so familiar with us and our preferences that it anticipates what we need to do next. In this vision, I don’t have to decide what to wear, or eat, or how to get to work, or when to buy groceries, or gasoline, what color trousers go with my shoes and also when it’s time to buy new shoes. No decisions will be necessary. Interestingly, Shapiro sees this as a good thing. The idea comes from a flurry of activity about something called decision fatigue. What is that? In a nutshell, it says that our decision-making capacity is a reservoir that gradually gets depleted the more decisions we make, possibly as a result of body chemistry. After a long string of decisions, according to the theory, we are more likely to make a bad decision or none at all. Things like willpower disintegrate along with our decision-making.

Among the many articles in the last few months on this topic was FastCompany, who wrote that,

“Anticipatory design is fundamentally different: decisions are made and executed on behalf of the user. The goal is not to help the user make a decision, but to create an ecosystem where a decision is never made—it happens automatically and without user input. The design goal becomes one where we eliminate as many steps as possible and find ways to use data, prior behaviors and business logic to have things happen automatically, or as close to automatic as we can get.”

Supposedly this frees “us up for the ones we really care about.”
My questions are, who decides which questions are important? And once we are freed from making decisions, will we even know that we have missed on that we really care about?

Google Now is a digital assistant that not only responds to a user’s requests and questions, but predicts wants and needs based on search history. Pulling flight information from emails, meeting times from calendars and providing recommendations of where to eat and what to do based on past preferences and current location, the user simply has to open the app for their information to compile.”

It’s easy to forget that AI as we currently know it goes under the name of Facebook or Google or Apple or Amazon. We tend to think of AI as some ghostly future figure or a bank of servers, or an autonomous robot. It reminds me a bit of my previous post about Nick Bostrom and the development of SuperIntelligence. Perhaps it is a bit like an episode of Person of Interest. As we think about designing systems that think for us and decide what is best for us, it might be a good idea to think about what it might be like to no longer think—as long as we still can.

 

 

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Election lessons. Beware who you ignore.

It was election week here in America, but unless you’ve been living under a rock for the last eight months, you already know that. Not unlike the Brexit vote from earlier this year, a lot of people were genuinely surprised by the outcome. Perhaps most surprising to me is that the people who seem to be the most surprised are the people who claimed to know—for certain—that the outcome would be otherwise. Why do you suppose that is? There is a lot of finger-pointing and head-scratching going on but from what I’ve seen so far none of these so-called experts has a clue why they were wrong.

Most of them are blaming polls for their miscalculations. And it’s starting to look like their error came not in who they polled but who they thought irrelevant and ignored. Many in the media are in denial that their efforts to shape the election may have even fueled the fire for the underdog. What has become of American Journalism is shameful. Wikileaks proves that ninety percent of the media was kissing up to the left, with pre-approved interviews, stories and marching orders to “shape the narrative.” I don’t care who you were voting for, that kind of collusion is a disgrace for democracy. Call it Pravda. But I don’t want to turn this blog into a political commentary, but it was amusing to watch them all wearing the stupid hat on Wednesday morning. What I do want to talk about, however, is how we look at data to reach a conclusion.

In a morning-after article from the LinkedIn network, futurist Peter Diamandis posted the topic, “Here’s what election campaign marketing will look like in 2020.” It was less about the election and more about future tech with an occasional reference to the election and campaign processes. He has five predictions. First is, the news flash that “Social media will have continued to explode. [and that] The single most important factor influencing your voting decision is your social network.” Diamandis says that “162 million people log onto Facebook at least once a month.” I agree with the first part of his statement but what about the people the other 50% and those that don’t share their opinions on politics. A lot of pollsters are looking at the huge disparity in projections vs. actuals in the 2016 election. They are acknowledging that a lot of people simply weren’t forthcoming in pre-election polling. Those planning to vote Trump, for example, knew that Trump was a polarizing figure and they weren’t going to get into it with their friends on social media or even a stranger taking a poll. Then, I’m willing to bet that a lot of voters who put the election over the top are in the fifty percent that isn’t on social media. Just look at the demographics for social media.

Peter Diamandis is a brilliant guy, and I’m not here to pick on him. Many of his predictions are quite conceivable. Mostly he’s talking about an increase in data mining, and AI is getting better at learning from it, with a laser focus on the individual. If you add this together with programmable avatars, facial recognition improvements and the Internet of Things, the future means that we are all going to be tracked with increasing levels of detail. And though our face is probably not something we can keep secret, if it all creeps you out, remember that much of this is based on what we choose to share. Fortunately, it will take a little bit longer than 2020 for all of these new technologies to read our minds—so until then we still hold the cards. As long as you don’t share our most private thoughts on social media or with pollsters, you’ll keep them guessing.

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Yes, you too can be replaced.

Over the past weeks, I have begun to look at the design profession and design education in new ways. It is hard to argue with the idea that all design is future-based. Everything we design is destined for some point beyond now where the thing or space, the communication, or the service will exist. If it already existed, we wouldn’t need to design it. So design is all about the future. For most of the 20th century and the last 16 years, the lion’s share of our work as designers has focused primarily on very near-term, very narrow solutions: A better tool, a more efficient space, a more useful user interface or satisfying experience. In fact, the tighter the constraints, the narrower the problem statement and greater the opportunity to apply design thinking to resolve it in an elegant and hopefully aesthetically or emotionally pleasing way. Such challenges are especially gratifying for the seasoned professional as they have developed almost an intuitive eye toward framing these dilemmas from which novel and efficient solutions result. Hence, over the course of years or even decades, the designer amasses a sort of micro scale, big data assemblage of prior experiences that help him or her reframe problems and construct—alone or with a team—satisfactory methodologies and practices to solve them.

Coincidentally, this process of gaining experience is exactly the idea behind machine learning and artificial intelligence. But, since computers can amass knowledge from analyzing millions of experiences and judgments it is theoretically possible that an artificial intelligence could gain this “intuitive eye” to a degree far surpassing the capacity of an individual him-or-her designer.

That is the idea behind a brash (and annoyingly self-conscious) article from the American Institute of Graphic Arts (AIGA) entitled Automation Threatens To Make Graphic Designers Obsolete. Titles like this are a hook. Of course. Designers, deep down assume that they can never be replaced. They believe this because inherent to the core of artificial intelligence there is a lack of understanding, empathy or emotional verve, so far. We saw this earlier in 2016 when an AI chatbot went Nazi because a bunch of social media hooligans realized that Tay (the name of the Microsoft chatbot) was in learn mode. If you told “her” Nazi’s were cool, she believed you. It was proof, again, that junk in is junk out.

The AIGA author Rob Peart pointed to AutoDesk’s Dreamcatcher software that is capable of rapid prototyping surprisingly creative albeit roughly detailed prototypes. Peart features a quote from an Executive Creative Director for techno-ad-agency Sapient Nitro. “A designer’s role will evolve to that of directing, selecting, and fine tuning, rather than making. The craft will be in having vision and skill in selecting initial machine-made concepts and pushing them further, rather than making from scratch. Designers will become conductors, rather than musicians.”

I like the way we always position new technology in the best possible light. “You’re not going to lose your job. Your job is just going to change.” But tell that to the people who used to write commercial music, for example. The Internet has become a vast clearing house for every possible genre of music. It’s all available for a pittance of what it would have taken a musician to write, arrange and produce a custom piece of music. It’s called stock. There are stock photographs, stock logos, stock book templates, stock music, stock house plans, and the list goes on. All of these have caused a significant disruption to old methods of commerce, and some would say that these stock versions of everything lack the kind of polish and ingenuity that used to distinguish artistic endeavors. The artist’s who’s jobs they have obliterated refer to the work with a four-letter word.

Now, I confess I have used stock photography, and stock music, but I have also used a lot of custom photography and custom music as well. Still, I can’t imagine crossing the line to a stock logo or stock publication design. Perish the thought! Why? Because they look like four-letter-words; homogenized, templates, and the world does not need more blah. It’s likely that we also introduced these new forms of stock commerce in the best possible light, as great democratizing innovations that would enable everyone to afford music, or art or design. That anyone can make, create or borrow the things that professionals used to do.

As artificial intelligence becomes better at composing music, writing blogs and creating iterative designs (which it already does and will continue to improve), we should perhaps prepare for the day when we are no longer musicians or composers but rather simply listeners and watchers.

But let’s put that in the best possible light: Think of how much time we’ll have to think deep thoughts.

 

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