Tag Archives: nanotechnology

Corporate Sci-Fi.

Note: Also published on LinkedIn

 

Why your company needs to play in the future.

As a professor of design and a design fiction researcher, I write academic papers and blog weekly about the future. I teach about the future of design, and I create future scenarios, sometimes with my students, that provoke us to look at what we are doing, what we are making, why we are making it and the ramifications that are inevitable. Primarily I try to focus both designers and decision makers on the steps they can take today to keep from being blindsided tomorrow. Futurists seem to be all the rage these days telling us to prepare for the Singularity, autonomous everything, or that robots will take our jobs. Recently, Jennifer Doudna, co-inventor of the gene editing technique called CrisprCas9 has been making the rounds and sounding the alarm that technology is moving so fast that we aren’t going to be able to contain a host of unforeseen (and foreseen) circumstances inside Pandora’s box. This concern should be prevalent, however, beyond just the bioengineering fields and extend into virtually anywhere that technology is racing forward fueled by venture capital and the desperate need to stay on top of whatever space in which we are playing. There is a lot at stake. Technology has already redefined privacy, behavioral wellness, personal autonomy, healthcare, labor, and maybe even our humanness, just to name a few.

Several recent articles have highlighted the changing world of design and how the pressure is on designers to make user adoption more like user addiction to ensure the success of a product or app. The world of behavioral economics is becoming a new arena in which we are using algorithms to manipulate users. Some designers are passing the buck to the clients or corporations that employ them for the questionable ethics of addictive products; others feel compelled to step aside and work on less lucrative projects or apply their skills to social causes. Most really care and want to help. But designers are uniquely positioned and trained to tackle these wicked problems—if we would collaborate with them.

Beyond the companies that might be deliberately trying to manipulate us, are those that unknowingly, or at least unintentionally, transform our behaviors in ways that are potentially harmful. Traditionally, we seek to hold someone responsible when a product or service is faulty, the physician for malpractice, the designer or manufacturer when a toy causes injury, a garment falls apart, or an appliance self-destructs. But as we move toward systemic designs that are less physical and more emotional, behavioral, or biological, design faults may not be so easy to identify and their repercussions noticeable only after serious issues have arisen. In fact, we launch many of the apps and operating systems used today with admitted errors and bugs. Designers rely on real-life testing to identify problems, issue patches, revisions, and versions.

In the realm of nanotechnology, while scientists and thought leaders have proposed guidelines and best-practices, research and development teams in labs around the world race forward without regulation creating molecule-sized structures, machines, and substances with no idea whether they are safe or what might be long-term effects of exposure to these elements. In biotechnology, while folks like Jennifer Doudna appeal to a morally ethical cadre of researchers to tread carefully in the realm of genetic engineering (especially when it comes to inheritable gene manipulation) we do not universally share those morals and ethics. Recent headlines attest to the fact that some scientists are bent on moving forward regardless of the implications.

Some technologies such as our smartphones have become equally invasive technology, yet they are now considered mundane. In just ten years since the introduction of the iPhone, we have transformed behaviors, upended our modes of communication, redefined privacy, distracted our attentions, distorted reality and manipulated a predicted 2.3 billion users as of 2017. [1] It is worth contemplating that this disruption is not from a faulty product, but rather one that can only be considered wildly successful.

There are a plethora of additional technologies that are poised to refine our worlds yet again including artificial intelligence, ubiquitous surveillance, human augmentation, robotics, virtual, augmented and mixed reality and the pervasive Internet of Things. Many of these technologies make their way into our experiences through the promise of better living, medical breakthroughs, or a safer and more secure life. But too often we ignore the potential downsides, the unintended consequences, or the systemic ripple-effects that these technologies spawn. Why?

In many cases, we do not want to stand in the way of progress. In others, we believe that the benefits outweigh the disadvantages, yet this is the same thinking that has spawned some of our most complex and daunting systems, from nuclear weapons to air travel and the internal combustion engine. Each of these began with the best of intentions and, in many ways were as successful and initially beneficial as they could be. At the same time, they advanced and proliferated far more rapidly than we were prepared to accommodate. Dirty bombs are a reality we did not expect. The alluring efficiency with which we can fly from one city to another has nevertheless spawned a gnarly network of air traffic, baggage logistics, and anti-terrorism measures that are arguably more elaborate than getting an aircraft off the ground. Traffic, freeways, infrastructure, safety, and the drain on natural resources are complexities never imagined with the revolution of personal transportation. We didn’t see the entailments of success.

This is not always true. There have often been scientists and thought leaders who were waving the yellow flag of caution. I have written about how, “back in 1975, scientists and researchers got together at Asilomar because they saw the handwriting on the wall. They drew up a set of resolutions to make sure that one day the promise of Bioengineering (still a glimmer in their eyes) would not get out of hand.”[2] Indeed, researchers like Jennifer Doudna continue to carry the banner. A similar conference took place earlier this year to alert us to the potential dangers of technology and earlier this year another to put forth recommendations and guidelines to ensure that when machines are smarter than we are they carry on in a beneficent role. Too often, however, it is the scientists and visionaries who attend these conferences. [3] Noticeably absent, though not always, is corporate leadership.

Nevertheless, in this country, there remains no safeguarding regulation for nanotech, nor bioengineering, nor AI research. It is a free-for-all, and all of which could have massive disruption not only to our lifestyles but also our culture, our behavior, and our humanness. Who is responsible?

For nearly 40 years there has been an environmental movement that has spread globally. Good stewardship is a good idea. But it wasn’t until most corporations saw a way for it to make economic sense that they began to focus on it and then promote it as their contribution to society, their responsibility, and their civic duty. As well intentioned as they may be (and many are) much more are not paying attention to the effect of their technological achievements on our human condition.

We design most technologies with a combination of perceived user need and commercial potential. In many cases, these are coupled with more altruistic motivations such as a “do no harm” commitment to the environment and fair labor practices. As we move toward the capability to change ourselves in fundamental ways, are we also giving significant thought to the behaviors that we will engender by such innovations, or the resulting implications for society, culture, and the interconnectedness of everything?

Enter Humane Technology

Ultimately we will have to demand this level of thought, beginning with ourselves. But we should not fight this alone. Corporations 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.

Humane technology considers the socio-behavioral ramifications of products and services: digital dependencies, and addictions, job loss, genetic repercussions, the human impact from nanotechnologies, AI, and the Internet of Things.

To whom do we turn when a 14-year-old becomes addicted to her smartphone or obsessed with her social media popularity? We could condemn the parents for lack of supervision, but many of them are equally distracted. Who is responsible for the misuse of a drone to vandalize property or fire a gun or the anticipated 1 billion drones flying around by 2030? [4] Who will answer for the repercussions of artificial intelligence that spouts hate speech? Where will the buck stop when genetic profiling becomes a requirement for getting insured or getting a job?

While the backlash against these types of unintended consequences or unforeseen circumstances are not yet widespread and citizens have not taken to the streets in mass protests, behavioral and social changes like these may be imminent as a result of dozens of transformational technologies currently under development in labs and R&D departments across the globe. Who is looking at the unforeseen or the unintended? Who is paying attention and who is turning a blind eye?

It was possible to have anticipated texting and driving. It is possible to anticipate a host of horrific side effects from nanotechnology to both humans and the environment. It’s possible to tag the ever-present bad actor to any number of new technologies. It is possible to identify when the race to master artificial intelligence may be coming at the expense of making it safe or drawing the line. In fact, it is a marketing opportunity for corporate interests to take the lead and the leverage their efforts to preempt adverse side effects as a distinctive advantage.

Emphasizing humane technology is an automatic benefit for an ethical company, and for those more concerned with profit than ethics, (just between you and me) it offers the opportunity for a better brand image and (at least) the appearance of social concern. Whatever the motivation, we are looking at a future where we are either prepared for what happens next, or we are caught napping.

This responsibility should start with anticipatory methodologies that examine the social, cultural and behavioral ramifications, and unintended consequences of what we create. Designers and those trained in design research are excellent collaborators. My brand of design fiction is intended to take us into the future in an immersive and visceral way to provoke the necessary discussion and debate that anticipate the storm should there be one, but promising utopia is rarely the tinder to fuel a provocation. Design fiction embraces the art critical thinking and thought problems as a means of anticipating conflict and complexity before these become problems to be solved.

Ultimately we have to depart from the idea that technology will be the magic pill to solve the ills of humanity, design fiction, and other anticipatory methodologies can help to acknowledge our humanness and our propensity to foul things up. If we do not self-regulate, regulation will inevitably follow, probably spurred on by some unspeakable tragedy. There is an opportunity, now for the corporation to step up to the future with a responsible, thoughtful compassion for our humanity.

 

 

1. https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/

2. http://theenvisionist.com/2017/08/04/now-2/

3. http://theenvisionist.com/2017/03/24/genius-panel-concerned/

4. http://www.abc.net.au/news/2017-08-31/world-of-drones-congress-brisbane-futurist-thomas-frey/8859008

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What did one AI say to the other AI?

I’e asked this question before, but this is an entirely new answer.

We may  never know.

Based on a plethora of recent media on artificial intelligence (AI), not only are there a lot of people working on it, but many of those on the leading edge are also concerned with what they don’t understand in the midst of their ominous new creation.

Amazon, DeepMind/Google, Facebook, IBM, and Microsoft teamed up to form the Partnership on AI.(1) Their charter talks about sharing information and being responsible. It includes all of the proper buzz words for a technosocial contract.

“…transparency, security and privacy, values and ethics, collaboration between people and AI systems, interoperability of systems, and of the trustworthiness, reliability, containment, safety, and robustness of the technology.”(2)

They are not alone in this concern, as the EU(3) is also working on AI guidelines and a set of rules on robotics.

Some of what makes them all a bit nervous is the way AI learns, the complexity of neural networks and inability to go back and see how the AI arrived at its conclusion. In other words, how do we know that its recommendation is the right one? Adding to that list is the discovery that AIs working together can create their own languages; languages we don’t speak or understand. In one case, at Facebook, researchers saw this happening and stopped it.

For me, it’s a little disconcerting that Facebook, a social media app is one of those corporations leading the charge and leading the research into AI. That’s a broad topic for another blog, but their underlying objective is to market to you. That’s how they make their money.

To be fair, that is at least part of the motivation for Amazon, DeepMind/Google, IBM, and Microsoft, as well. The better they know you, the more stuff they can sell you. Of course, there are also enormous benefits to medical research as well. Such advantages are almost always what these companies talk about first. AI will save your life, cure cancer and prevent crime.

So, it is somewhat encouraging to see that these companies on the forefront of AI breakthroughs are also acutely aware of how AI could go terribly wrong. Hence we see wording from the Partnership on AI, like

“…Seek out, support, celebrate, and highlight aspirational efforts in AI for socially benevolent applications.”

The key word here is benevolent. But the clear objective is to keep the dialog positive, and

“Create and support opportunities for AI researchers and key stakeholders, including people in technology, law, policy, government, civil liberties, and the greater public, to communicate directly and openly with each other about relevant issues to AI and its influences on people and society.”(2)

I’m reading between the lines, but it seems like the issue of how AI will influence people and society is more of an obligatory statement intended to demonstrate compassionate concern. It’s coming from the people who see huge commercial benefit from the public being at ease with the coming onslaught of AI intrusion.

In their long list of goals, the “influences” on society don’t seem to be a priority. For example, should they discover that particular AI has a detrimental effect on people, that their civil liberties are less secure, would they stop? Probably not.

At the rate that these companies are racing toward AI superiority, the unintended consequences for our society are not a high priority. While these groups are making sure that AI does not decide to kill us, I wonder if they are also looking at how the AI will change us and are those changes a good thing?

(1) https://www.fastcodesign.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it

(2) https://www.partnershiponai.org/#

(3) http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML%2BCOMPARL%2BPE-582.443%2B01%2BDOC%2BPDF%2BV0//EN

Other pertinent links:

https://www.fastcompany.com/3064368/we-dont-always-know-what-ai-is-thinking-and-that-can-be-scary

https://www.fastcodesign.com/90133138/googles-next-design-project-artificial-intelligence

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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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Heady stuff.

Last week we talked about how some researchers and scientists on the cutting edge are devising guidelines to attempt to ensure that potentially transformative technologies (like AI) remain safe and beneficial, rather than becoming a threat to humanity. And then, there were industries (like nanotech) that have already blown past any attempt at a meaningful review and now exist in thousands of consumer products, and nobody knows if their safe and the companies who produce them don’t even have to tell us they are part of the composition.

This week I’m going to talk about why I look askance at transformative technologies. Maybe it is because I am a writer at heart. Fiction, specifically science fiction, has captured my attention since childhood. It is my genre of choice. Now that nearly all of the science-based science fiction is no longer fiction, our tendency is to think that the only thing left to do is react or adapt. I can understand this since you can’t isolate a single technology as a thing, you can’t identify precisely from where it started, or how it morphed into what it is. Technologies converge, and they become systems, and systems are dauntingly complex. As humans, we create things that become systems. Even in non-digital times, the railroad ushered in a vastly complex system so much so that we had to invent other things just to deal with it, like a clock. What good was a train if it wasn’t on time? And what good was your time if it wasn’t the same as my time?

Fast forward. Does the clock have any behavioral effect in your life?

My oft-quoted scholars at ASU, Allenby, and Sarewitz see things like trains as level one technologies. They spawn systems in the level two realm that are often far more intricate than figuring out how to get this train contraption to run on rails across the United States.

So the nature of convergence and the resulting complexity of systems is one reason for my wariness of transformative tech.Especially now, that we are building things and we don’t understand how they work. We are inventing things that don’t need us to teach them, and that means that we can’t be sure what they are learning or how. If we can barely understand the complexity of the system that has grown up around the airline industry (which we at one time inherently grasped), how are we going to understand systems that spring up around these inventions that, at the core, we know what they do, but don’t know how?

The second reason is human nature. Your basic web dictionary defines the sociology of human nature as: “[…]the character of human conduct, generally regarded as produced by living in primary groups.” Appreciating things like love and compassion, music and art, consciousness, thought, languages and memory are characteristics of human nature. So are evil and vice, violence and hatred, the quest for power and greed. The latter have a tendency to undermine our inventions for good. Sometimes they are our downfall.

With history as our teacher, if we go blindly forward paying little attention to reason one, the complexity of systems, or reason two, the potential for bad actors, or both, that does not bode well.

I’ve been rambling a bit, so I have to wrap this up. I’ve taken a long way around to say that if you are among those who look at all this tech, and the unimaginable scope of the systems we have created and that the only thing left to do is react or adapt, that this is not the case.

While I can see the dark cloud behind every silver lining, it enables me to bring an umbrella on occasion.

Paying attention to the seemingly benign and insisting on a meaningful review of that which we don’t fully understand is the first step. It may seem as though it will be easier to adapt, but I don’t think so.

I guess that’s the reason behind this blog, behind my graphic novel, and my ongoing research and activism through design fiction. If you’re not paying attention, then I’ll remind you.

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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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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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