Tag Archives: E. Scott Denison

Watching and listening.

 

Pay no attention to Alexa, she’s an AI.

There was a flurry of reports from dozens of news sources (including CNN) last week that an Amazon Echo, (Alexa), called the police during a New Mexico incident of domestic violence. The alleged call began a SWAT standoff, and the victim’s boyfriend was eventually arrested. Interesting story, but after a fact-check, that could not be what happened. Several sources including the New York Times and WIRED debunked the story with details on how Alexa calling 911 is technologically impossible, at least for now. And although the Bernalillo, New Mexico County Sheriff’s Department swears to it, according to WIRED,

“Someone called the police that day. It just wasn’t Alexa..”

Even Amazon agrees from a spokesperson email,

“The receiving end would also need to have an Echo device or the Alexa app connected to Wi-Fi or mobile data, and they would need to have Alexa calling/messaging set up,”1

So it didn’t happen, but most agree, while it may be technologically impossible today, it probably won’t be for very long. The provocative side of the WIRED article proposed this thought:

“The Bernalillo County incident almost certainly had nothing to do with Alexa. But it presents an opportunity to think about issues and abilities that will become real sooner than you might think.”

On the upside, some see benefits from the ability of Alexa to intervene in a domestic dispute that could turn lethal, but they fear something called “false positives.” Could an off handed comment prompt Alexa to make a call to the police? And if it did would you feel as though Alexa had overstepped her bounds?

Others see the potential in suicide prevention. Alexa could calm you down or make suggestions for ways to move beyond the urge to die.

But as we contemplate opening this door, we need to acknowledge that we’re letting these devices listen to us 24/7 and giving them the permission to make decisions on our behalf whether we want them to or not. The WIRED article also included a comment from Evan Selinger of RIT (whom I’ve quoted before).

“Cyberservants will exhibit mission creep over time. They’ll take on more and more functions. And they’ll habituate us to become increasingly comfortable with always-on environments listening to our intimate spaces.”

These technologies start out as warm and fuzzy (see the video below) but as they become part of our lives, they can change us and not always for the good. This idea is something I contemplated a couple of years ago with my Ubiquitous Surveillance future. In this case, the invasion was not as a listening device but with a camera (already part of Amazon’s Echo Look). You can check that out and do your own provocation by visiting the link.

I’m glad that there are people like Susan Liautaud (who I wrote about last week) and Evan Selinger who are thinking about the effects of technology on society, but I still fear most of us take the stance of Dan Reidenberg, who is also quoted in the WIRED piece.

“‘I don’t think we can avoid this. This is where it is going to go. It is really about us adapting to that,” he says.’”

 

Nonsense! That’s like getting in the car with a drunk driver and then doing your best to adapt. Nobody is putting a gun to your head to get into the car. There are decisions to be made here, and they don’t have to be made after the technology has created seemingly insurmountable problems or intrusions in our lives. The companies that make them should be having these discussions now, and we should be invited to share our opinions.

What do you think?

 

  1. http://wccftech.com/alexa-echo-calling-911/
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Ethical tech.

Though I tinge most of my blogs with ethical questions, the last time I brought up this topic specifically on this was back in 2015. I guess I am ready to give it another go. Ethics is a tough topic. If we deal with this purely superficially, ethics would seem natural, like common sense, or the right thing to do. But if that’s the case, why do so many people do the wrong thing? Things get even more complicated if we move into institutionally complex issues like banking, or governing, technology, genetics, health care or national defense, just to name a few.

The last time I wrote about this, I highlighted Michael Sandel Professor of Philosophy and Government at Harvard’s Law School, where he teaches a wildly popular course called “Justice.” Then, I was glad to see that the big questions were still being addressed in in places like Harvard. Some of his questions then, which came from a FastCo article, were:

“Is it right to take from the rich and give to the poor? Is it right to legislate personal safety? Can torture ever be justified? Should we try to live forever? Buy our way to the head of the line? Create perfect children?”

These are undoubtedly important and prescient questions to ask, especially as we are beginning to confront technologies that make things which were formerly inconceivable or plain impossible, not only possible but likely.

So I was pleased to see last month, an op-ed piece in WIRED by Susan Liautaud founder of The Ethics Incubator. Susan is about as closely aligned to my tech concerns as anyone I have read. And she brings solid thinking to the issues.

“Technology is approaching the man-machine and man-animal
boundaries. And with this, society may be leaping into humanity defining innovation without the equivalent of a constitutional convention to decide who should have the authority to decide whether, when, and how these innovations are released into society. What are the ethical ramifications? What checks and balances might be important?”

Her comments are right in line with my research and co-research into Humane Technologies. Liataud continues:

“Increasingly, the people and companies with the technological or scientific ability to create new products or innovations are de facto making policy decisions that affect human safety and society. But these decisions are often based on the creator’s intent for the product, and they don’t always take into account its potential risks and unforeseen uses. What if gene-editing is diverted for terrorist ends? What if human-pig chimeras mate? What if citizens prefer to see birds rather than flying cars when they look out a window? (Apparently, this is a real risk. Uber plans to offer flight-hailing apps by 2020.) What if Echo Look leads to mental health issues for teenagers? Who bears responsibility for the consequences?”

For me, the answer to that last question is all of us. We should not rely on business and industry to make these decisions, nor expect our government to do it. We have to become involved in these issues at the public level.

Michael Sandel believes that the public is hungry for these issues, but we tend to shy away from them. They can be confrontational and divisive, and no one wants to make waves or be politically incorrect. That’s a mistake.

An image from the future. A student design fiction project that examined ubiquitous AR.

So while the last thing I want is a politician or CEO making these decisions, these two constituencies could do the responsible thing and create forums for these discussions so that the public can weigh in on them. To do anything less, borders on arrogance.

Ultimately we will have to demand this level of thought, beginning with ourselves. This responsibility should start with anticipatory methodologies that examine the social, cultural and behavioral ramifications, and unintended consequences of what we create.

But we should not fight this alone. Corporations and governments concerned with appearing sensitive and proactive toward the environment and social justice need to add a new pillar to their edifice as responsible global citizens: humane technology.

 

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Power sharing?

Just to keep you up to speed, everything is on schedule or ahead of schedule.

In the race toward a superintelligence or ubiquitous AI. If you read this blog or you are paying attention at any level, then you know the fundamentals of AI. But for those of you who don’t here are the basics. Artificial Intelligence comes from processing and analyzing data. Big data. Then programmers feed a gazillion linked-up computers (CPUs) with algorithms that can sort this data and make predictions. This process is what is at work when the Google search engine makes suggestions concerning what you are about to key into the search field. These are called predictive algorithms. If you want to look at pictures of cats, then someone has to task the CPUs with learning what a cat looks like as opposed to a hamster, then scour the Internet for pictures of cats and deliver them to your search. The process of teaching the machine what a cat looks like is called machine learning. There is also an algorithm that watches your online behavior. That’s why, after checking out sunglasses online, you start to see a plethora of ads for sunglasses on just about every page you visit. Similar algorithms can predict where you will drive to today, and when you are likely to return home. There is AI that knows your exercise habits and a ton of other physiological data about you, especially when you’re sharing your Fitbit or other wearable data with the Cloud. Insurance companies extremely interested in this data, so that it can give discounts to “healthy” people and penalize the not so healthy. Someday they might also monitor other “behaviors” that they deem to be not in your best interests (or theirs). Someday, especially if we have a “single-payer” health care system (aka government healthcare), this data may be required before you are insured. Before we go too far into the dark side (which is vast and deep), AI can also search all the cells in your body and identify which ones are dangerous, and target them for elimination. AI can analyze a whole host of things that humans could overlook. It can put together predictions that could save your life.

Googles chips stacked up and ready to go. Photo from WIRED.

Now, with all that AI background behind us, this past week something called Google I/O went down. WIRED calls it Google’s annual State-of-the-Union address. There, Sundar Pichai unveiled something called TPU 2.0 or Cloud TPU. This is something of a breakthrough, because, in the past, the AI process that I just described, even though lighting fast and almost transparent, required all those CPUs, a ton of space (server farms), and gobs of electricity. Now, Google (and others) are packing this processing into chips. These are proprietary to Google. According to WIRED,

“This new processor is a unique creation designed to both train and execute deep neural networks—machine learning systems behind the rapid evolution of everything from image and speech recognition to automated translation to robotics…

…says Chris Nicholson, the CEO, and founder of a deep learning startup called Skymind. “Google is trying to do something better than Amazon—and I hope it really is better. That will mean the whole market will start moving faster.”

Funny, I was just thinking that the market is not moving fast enough. I can hardly wait until we have a Skymind.

“Along those lines, Google has already said that it will offer free access to researchers willing to share their research with the world at large. That’s good for the world’s AI researchers. And it’s good for Google.”

Is it good for us?

Note:
This sets up another discussion (in 3 weeks) about a rather absurd opinion piece in WIRED about why we should have an AI as President. These things start out as absurd, but sometimes don’t stay that way.

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Humanity is not always pretty.

The Merriam-Webster online dictionary among several options gives this definition for human: “[…]representative of or susceptible to the sympathies and frailties of human nature human kindness a human weakness.”

Then there is humanity which can either confer either the collective of humans, or “[…]the fact or condition of being human; human nature,” or benevolence as in compassion and understanding. For the latter, it seems that we are the eternal optimists when it comes to describing ourselves. Hence, we often refer to the humanity of man as one of our most redeeming traits. At the same time, if we query human nature we can get, “[…]ordinary human behavior, esp considered as less than perfect.” This is a diplomatic way of acknowledging that flaws are a characteristic of our nature. When we talk about our humanity, we presumptively leave out our propensity for greed, pride, and the other deadly sins. We like to think of ourselves as basically good.

If we are honest with ourselves, however, we know this is not always the case and if we push the issue we would have to acknowledge that this not even the case most of the time. Humanity is primarily driven by the kinds of things we don’t like to see in others but rarely see in ourselves. But this is supposed to be a blog about design and tech, isn’t it? So I should get to the point.

A recent article on the blog site QUARTZ, Sarah Kessler’s article, “Algorithms are failing Facebook. Can humanity save it?” poses an interesting question and one that I’ve raised in the past. We like to think that technology will resolve all of our basic human failings—somehow. Recognizing this, back in 1968 Stewart Brand introduced the first Whole Earth Catalog with,

“We are as gods and might as well get good at it.”

After almost 50 years it seems justified to ask whether we’ve made any improvements whatsoever. The question is pertinent in light of Kessler’s article on the advent of Facebook Live. In this particular FB feature, you stream whatever video you want, and it goes out to the whole world instantly. Of course, we need this, right? And we need this now, right? Of course we do.

Like most of these wiz bang technologies they are designed to attract millennials with, “Wow! Cool.” But it is not a simple task. How would a company like Facebook police the potentially billions of feeds coming into the system? The answer is (as is becoming more the case) AI. Artificial Intelligence. Algorithms will recognize and determine what is and is not acceptable to go streaming out to the world. And apparently, Zuck and company were pretty confident that they could pull this off.

Let’s get this thing online. [Photo: http://wersm.com]

Maybe not. Kessler notes that,

“According to a Wall Street Journal tally, more than 50 acts of violence, including murders, suicides, and sexual assault, have been broadcast over Facebook Live since the feature launched 13 months ago.”

Both articles tell how Facebook’s Mark Zuckerberg put a team on “lockdown” to rush the feature to market. What was the hurry, one might ask? And Kessler does ask.

“Let’s make sure there’s a humanitarian angle. Millennials like that.” [Photo: http://variety.com]

After these 13 months of spurious events, the tipping point came with a particularly heinous act that ended up circulating on FB Live for nearly 24 hours. It involved a 20-year-old Thai man named Wuttisan Wongtalay, who filmed himself flinging his 11-month-old daughter off the side of a building with a noose around her neck. Then, off-camera, he killed himself.

“In a status update on his personal Facebook profile, CEO Mark Zuckerberg, himself the father of a young girl, pledged that the company would, among other things, add 3,000 people to the team that reviews Facebook content for violations of the company’s policies.”

Note that the answer is not to remove the feature until things could be sorted out or to admit that the algorithms are not ready for prime time. The somewhat surprising answer is more humans.

Kessler, quoting the Wall Street Journal article states,

“Facebook, in a civic mindset, could have put a plan in place for monitoring Facebook Live for violence, or waited to launch Facebook Live until the company was confident it could quickly respond to abuse. It could have hired the additional 3,000 human content reviewers in advance.

But Facebook ‘didn’t grasp the gravity of the medium,’ an anonymous source familiar with Facebook’s Live’s development told the Wall Street Journal.”

Algorithms are code that helps machines learn. They look at a lot of data, say pictures of guns, and then they learn to identify what a gun is. They are not particularly good at context. They don’t know, for example, whether your video is, “Hey, look at my new gun?” or “Die, scumbag.”

So in addition to algorithms, Zuck has decided that he will put 3,000 humans on the case. Nevertheless, writes Kessler,

“[…]they can’t solve Facebook’s problems on their own. Facebook’s active users comprise about a quarter of the world’s population and outnumber the combined populations of the US and China. Adding another 3,000 workers to the mix to monitor content simply isn’t going to make a meaningful difference. As Zuckerberg put it during a phone call with investors, “No matter how many people we have on the team, we’ll never be able to look at everything.”[Emphasis mine.]

So, I go back to my original question: We need this, right?

There are two things going on here. First is the matter of Facebook not grasping the gravity of the medium (which I see as inexcusable), and the second is how the whole thing came around full circle. Algorithms are supposed to replace humans. Instead we added 3,000 more jobs. Unfortunately, that wasn’t the plan. But it could have been.

Algorithms are undoubtedly here to stay, but not necessarily for every application and humans are still better at interpreting human intent than machines are. All of this underscores my position from previous blogs, that most companies when the issue is whether they get to or stay on top, will not police themselves. They’ll wait until it breaks and then fix it, or try to. The problem is that as algorithms get increasingly more complicated fixing them gets just as tricky.

People are working on this so that designers can see what went wrong, but the technology is not there yet.

And it is not just so that we can determine the difference between porn and breastfeeding. Algorithms are starting to make a lot of high stakes decisions, like autonomous vehicles, autonomous drones, or autonomous (fill in the blank). Until the people who are literally racing each other to be the first step back and ask the tougher questions, these types of unanticipated consequences will be commonplace, especially when the prudent actions like stop and assess are rarely considered. No one wants to stop and assess.

Kessler says it well,

“The combination may be fleeting—the technology will catch up eventually—but it’s also quite fitting given that so many of the problems Facebook is now confronting, from revenge porn to fake news to Facebook Live murders, are themselves the result of humanity mixing with algorithms.” [Emphasis mine.]

We can’t get past that humanity thing.

 

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Augmented evidence. It’s a logical trajectory.

A few weeks ago I gushed about how my students killed it at a recent guerrilla future enactment on a ubiquitous Augmented Reality (AR) future. Shortly after that, Mark Zuckerberg announced the Facebook AR platform. The AR uses the camera on your smartphone, and according to a recent WIRED article, transforms your smartphone into an AR engine.

Unfortunately, as we all know, (and so does Zuck), the smartphone isn’t currently much of an engine. AR requires a lot of processing, and so does the AI that allows it to recognize the real world so it can layer additional information on top of it. That’s why Facebook (and others), are building their own neural network chips so that the platform doesn’t have to run to the Cloud to access the processing required for Artificial Intelligence (AI). That will inevitably happen which will make the smartphone experience more seamless, but that’s just part the challenge for Facebook.

If you add to that the idea that we become even more dependent on looking at our phones while we are walking or worse, driving, (think Pokemon GO), then this latest announcement is, at best, foreshadowing.

As the WIRED article continues, tech writer Brian Barrett talked to Blair MacIntyre, from Georgia Tech who says,

“The phone has generally sucked for AR because holding it up and looking through it is tiring, awkward, inconvenient, and socially unacceptable,” says MacIntyre. Adding more of it doesn’t solve those issues. It exacerbates them. (The exception might be the social acceptability part; as MacIntyre notes, selfies were awkward until they weren’t.)”

That last part is an especially interesting point. I’ll have to come back to that in another post.

My students did considerable research on exactly this kind of early infancy that technologies undergo on their road to ubiquity. In another WIRED article, even Zuckerberg admitted,

“We all know where we want this to get eventually,” said Zuckerberg in his keynote. “We want glasses, or eventually contact lenses, that look and feel normal, but that let us overlay all kinds of information and digital objects on top of the real world.”

So there you have it. Glasses are the end game, but as my students agreed, contact lenses not so much. Think about it. If you didn’t have to stick a contact lens in your eyeball, you wouldn’t and the idea that they could become ubiquitous (even if you solved the problem of computing inside a wafer thin lens and the myriad of problems with heat, and in-eye-time), they are much farther away, if ever.

Student design team from Ohio State’s Collaborative Studio.

This is why I find my student’s solution so much more elegant and a far more logical trajectory. According to Barrett,

“The optimistic timeline for that sort of tech, though, stretches out to five or 10 years. In the meantime, then, an imperfect solution takes the stage.”

My students locked it down to seven years.

Finally, Zuckerberg made this statement:

“Augmented reality is going to help us mix the digital and physical in all new ways,” said Zuckerberg at F8. “And that’s going to make our physical reality better.”

Except that Zuck’s version of better and mine or yours may not be the same. Exactly what is wrong with reality anyway?

If you want to see the full-blown presentation of what my students produced, you can view it at aughumana.net.

Note: Currently the AugHumana experience is superior on Google Chrome.  If you are a Safari or Firefox purest, you may have to wait for the page to load (up to 2 minutes). We’re working on this. So, just use Chrome this time. We hope to have it fixed soon.

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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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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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