Tag Archives: Siri

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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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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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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Superintelligence. Is it the last invention we will ever need to make?

I believe it is crucial that we move beyond preparation to adapt or react to the future but to actively engage in shaping it.

An excellent example of this kind of thinking is Nick Bostrom’s TED talk from 2015.

Bostrom is concerned about the day when machine intelligence exceeds human intelligence (the guess is somewhere between twenty and thirty years from now). He points out that, “Once there is super-intelligence, the fate of humanity may depend on what the super-intelligence does. Think about it: Machine intelligence is the last invention that humanity will ever need to make. Machines will then be better at inventing [designing] than we are, and they’ll be doing so on digital timescales.”

His concern is legitimate. How do we control something that is smarter than we are? Anticipating AI will require more strenuous design thinking than that which produces the next viral game, app, or service. But these applications are where the lion’s share of the money is going. When it comes to keeping us from being at best irrelevant or at worst an impediment to AI, Bostrom is guardedly optimistic about how we can approach it. He thinks we could, “[…]create an A.I. that uses its intelligence to learn what we value, and its motivation system is constructed in such a way that it is motivated to pursue our values or to perform actions that it predicts we would approve of.”

At the crux of his argument and mine: “Here is the worry: Making superintelligent A.I. is a really hard challenge. Making superintelligent A.I. that is safe involves some additional challenge on top of that. The risk is that if somebody figures out how to crack the first challenge without also having cracked the additional challenge of ensuring perfect safety.”

Beyond machine learning (which has many facets), there are a wide-ranging set of technologies, from genetic engineering to drone surveillance, to next-generation robotics, and even VR, that could be racing forward without someone thinking about this “additional challenge.”

This could be an excellent opportunity for designers. But, to do that, we will have to broaden our scope to engage with science, engineering, and politics. More on that in future blogs.

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Privacy or paranoia?


If you’ve been a follower of this blog for a while, then you know that I am something of a privacy wonk. I’ve written about it before (about a dozen times, such as), and I’ve even built a research project (that you can enact yourself) around it. A couple of things transpired this week to remind me that privacy is tenuous. (It could also be all the back episodes of Person of Interest that I’ve been watching lately or David Staley’s post last April about the Future of Privacy.) First, I received an email from a friend this week alerting me to a little presumption that my software is spying on me. I’m old enough to remember when you purchased software on as a set of CDs (or even disks). You loaded in on your computer, and it seemed to last for years before you needed to upgrade. Let’s face it, most of us use only a small subset of the features in our favorite applications. I remember using Photoshop 5 for quite awhile before upgrading and the same with the rest of what is now called the Adobe Creative Suite. I still use the primary features of Photoshop 5, Illustrator 10 and InDesign (ver. whatever), 90% of the time. In my opinion, the add-ons to those apps have just slowed things down, and of course, the expense has skyrocketed. Gone are the days when you could upgrade your software every couple of years. Now you have to subscribe at a clip of about $300 a year for the Adobe Creative Suite. Apparently, the old business model was not profitable enough. But then came the Adobe Creative Cloud. (Sound of an angelic chorus.) Now it takes my laptop about 8 minutes to load into the cloud and boot up my software. Plus, it stores stuff. I don’t need it to store stuff for me. I have backup drives and archive software to do that for me.

Back to the privacy discussion. My friend’s email alerted me to this little tidbit hidden inside the Creative Cloud Account Manager.

Learn elsewhere, please.
Learn elsewhere, please.

Under the Security and Privacy tab, there are a couple of options. The first is Desktop App Usage. Here, you can turn this on or off. If it’s on, one of the things it tracks is,

“Adobe feature usage information, such as menu options or buttons selected.”

That means it tracks your keystrokes. Possibly this only occurs when you are using that particular app, but uh-uh, no thanks. Switch that off. Next up is a more pernicious option; it’s called, Machine Learning. Hmm. We all know what that is and I’ver written about that before, too. Just do a search. Here, Adobe says,

“Adobe uses machine learning technologies, such as content analysis and pattern recognition, to improve our products and services. If you prefer that Adobe not analyze your files to improve our products and services, you can opt-out of machine learning at any time.”

Hey, Adobe, if you want to know how to improve your products and services, how about you ask me, or better yet, pay me to consult. A deeper dive into ‘machine learning’ tells me more. Here are a couple of quotes:

“Adobe uses machine learning technologies… For example, features such as Content-Aware Fill in Photoshop and facial recognition in Lightroom could be refined using machine learning.”

“For example, we may use pattern recognition on your photographs to identify all images of dogs and auto-tag them for you. If you select one of those photographs and indicate that it does not include a dog, we use that information to get better at identifying images of dogs.”

Facial recognition? Nope. Help me find dog pictures? Thanks, but I think I can find them myself.

I know how this works. The more data that the machine can feed on the better it becomes at learning. I would just rather Adobe get their data by searching it out for it themselves. I’m sure they’ll be okay. (Afterall there’s a few million people who never look at their account settings.) Also, keep in mind, it’s their machine not mine.

The last item on my privacy rant just validated my paranoia. I ran across this picture of Facebook billionaire Mark Zuckerberg hamming it up for his FB page.

zuckerberg copy


In the background, is his personal laptop. Upon closer inspection, we see that Zuck has a piece of duct tape covering his laptop cam and his dual microphones on the side. He knows.



Go get a piece of tape.

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Vision comes from looking to the future.


I was away last week, but I left off with a post about proving that some of the things that we current think of as sci-fi or fantasy are not only plausible, but some may even be on their way to reality. In the last post, I was providing the logical succession toward implantable technology or biohacking.

The latest is a robot toy from a company called Anki. Once again, WIRED provided the background on this product, and it is an excellent example of technological convergence which I have discussed many times before. Essentially, “technovergence” is when multiple cutting-edge technologies come together in unexpected and sometimes unpredictable ways. In this case, the toy brings together AI, machine learning, computer vision science, robotics, deep character development, facial recognition, and a few more. According to the video below,

“There have been very few applications where a robot has felt like a character that connects with humans around it. For that, you really need artificial intelligence and robotics. That’s been the missing key.”

According to David Pierce, with WIRED,

“Cozmo is a cheeky gamer; the little scamp tried to fake me into tapping my block when they didn’t match, and stormed off when I won. And it’s those little tics, the banging of its lift-like arm and spinning in circles and squawking in its Wall-E voice, that really makes you want to refer to the little guy as ‘he’ rather than ‘it.’”

What strikes me as especially interesting is that my students designed their own version of this last semester. (I’m pretty sure that they knew nothing about this particular toy.) The semester was a rigorous design fiction class that took a hard look at what was possible in the next five to ten years. For some, the class was something like hell, but the similarities and possibilities that my students put together for their robot are amazingly like Cozmo.

I think this is proof of more than what is possible; it’s evidence that vision comes from looking to the future.

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Future proof.


There is no such thing as future proof anything, of course, so I use the term to refer to evidence that a current idea is becoming more and more probable of something we will see in the future. The evidence I am talking about surfaced in a FastCo article this week about biohacking and the new frontier of digital implants. Biohacking has a loose definition and can reference using genetic material without regard to ethical procedures, to DIY biology, to pseudo-bioluminescent tattoos, to body modification for functional enhancement—see transhumanism. Last year, my students investigated this and determined that a society willing to accept internal implants was not a near-future scenario. Nevertheless, according to FastCo author Steven Melendez,

“a survey released by Visa last year that found that 25% of Australians are ‘at least slightly interested’ in paying for purchases through a chip implanted in their bodies.”

Melendez goes on to describe a wide variety of implants already in use for medical, artistic and personal efficiency and interviews Tim Shank, president of a futurist group called TwinCities+. Shank says,

“[For] people with Android phones, I can just tap their phone with my hand, right over the chip, and it will send that information to their phone..”

Amal Graafstra’s Hands [Photo: courtesy of Amal Graafstra] c/o WIRED
The popularity of body piercings and tattoos— also once considered as invasive procedures—has skyrocketed. Implantable technology, especially as it becomes more functionally relevant could follow a similar curve.

I saw this coming some years ago when writing The Lightstream Chronicles. The story, as many of you know, takes place in the far future where implantable technology is mundane and part of everyday life. People regulate their body chemistry access the Lightstream (the evolved Internet) and make “calls” using their fingertips embedded with Luminous Implants. These future implants talk directly to implants in the brain, and other systemic body centers to make adjustments or provide information.

An ad for Luminous Implants, and the "tap" numbers for local attractions.
An ad for Luminous Implants, and the “tap” numbers for local attractions.
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When the stakes are low, mistakes are beneficial. In more weighty pursuits, not so much.


I’m from the old school. I suppose, that sentence alone makes me seem like a codger. Let’s call it the eighties. Part of the art of problem solving was to work toward a solution and get it as tight as we possibly could before we committed to implementation. It was called the design process and today it’s called “design thinking.” So it was heresy to me when I found myself, some years ago now, in a high-tech corporation where this was a doctrine ignored. I recall a top-secret, new product meeting in which the owner and chief technology officer said, “We’re going to make some mistakes on this, so let’s hurry up and make them.” He was not speaking about iterative design, which is part and parcel of the design process, he was talking about going to market with the product and letting the users illuminate what we should fix. Of course, the product was safe and met all the legal standards, but it was far from polished. The idea was that mass consumer trial-by-fire would provide us with an exponentially higher data return than if we tested all the possible permutations in a lab at headquarters. He was, apparently, ahead of his time.

In a recent FastCo article on Facebook’s race to be the leader in AI, author Daniel Terdiman cites some of Mark Zuckerberg’s mantras: “‘Move fast and break things,’ or ‘Done is better than perfect.’” We can debate this philosophically or maybe even ethically, but it is clearly today’s standard procedure for new technologies, new science and the incessant race to be first. Here is a quote from that article:

“Artificial intelligence has become a vital part of scaling Facebook. It’s already being used to recognize the faces of your friends in photographs, and curate your newsfeed. DeepText, an engine for reading text that was unveiled last week, can understand “with near-human accuracy” the content in thousands of posts per second, in more than 20 different languages. Soon, the text will be translated into a dozen different languages, automatically. Facebook is working on recognizing your voice and identifying people inside of videos so that you can fast forward to the moment when your friend walks into view.”

The story goes on to say that Facebook, though it is pouring tons of money into AI, is behind the curve, having begun only three or so years ago. Aside from the fact that FB’s accomplishments seem fairly impressive (at least to me), people like Google and Microsoft are apparently way ahead. In the case of Microsoft, the effort began more than twenty years ago.

Today, the hurry up is accelerated by open sourcingWikipedia explains the benefits of open sourcing as:

“The open-source model, or collaborative development from multiple independent sources, generates an increasingly more diverse scope of design perspective than any one company is capable of developing and sustaining long term.”

The idea behind open sourcing is that the mistakes will happen even faster along with the advancements. It is becoming the de facto approach to breakthrough technologies. If fast is the primary, maybe even the only goal, it is a smart strategy. Or is it a touch short sighted? As we know, not everyone who can play with the code that a company has given them has that company’s best interests in mind. As for the best interests of society, I’m not sure those are even on the list.

To examine our motivations and the ripples that emanate from them, of course, is my mission with design fiction and speculative futures. Whether we like it or not, a by-product of technological development—aside from utopia—is human behavior. There are repercussions from the things we make and the systems that evolve from them. When your mantra is “Move fast and break things,” that’s what you’ll get. But there is certainly no time the move-fast loop to consider the repercussions of your actions, or the unexpected consequences. Consequences will appear all by themselves.

The technologists tell us that when we reach the holy grail of AI (whatever that is), we will be better people and solve the world’s most challenging problems. But in reality, it’s not that simple. With the nuances of AI, there are potential problems, or mistakes, that could be difficult to fix; new predicaments that humans might not be able to solve and AI may not be inclined to resolve on our behalf.

In the rush to make mistakes, how grave will they be? And, who is responsible?

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