Tag Archives: Microsoft

Future proof.

 

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

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

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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Artificial intelligence isn’t really intelligence—yet. I hate to say I told you so.

 

Last week, we discovered that there is a new side to AI. And I don’t mean to gloat, but I saw this potential pitfall as fairly obvious. It is interesting that the real world event that triggered all the talk occurred within days of episode 159 of The Lightstream Chronicles. In my story, Keiji-T, a synthetic police investigator virtually indistinguishable from a human, questions the conclusions of an Artificial Intelligence engine called HAPP-E. The High Accuracy Perpetrator Profiling Engine is designed to assimilate all of the minutiae surrounding a criminal act and spit out a description of the perpetrator. In today’s society, profiling is a human endeavor and is especially useful in identifying difficult-to-catch offenders. Though the procedure is relatively new in the 21st century and goes by many different names, the American Psychological Association says,

“…these tactics share a common goal: to help investigators examine evidence from crime scenes and victim and witness reports to develop an offender description. The description can include psychological variables such as personality traits, psychopathologies and behavior patterns, as well as demographic variables such as age, race or geographic location. Investigators might use profiling to narrow down a field of suspects or figure out how to interrogate a suspect already in custody.”

This type of data is perfect for feeding into an AI, which uses neural networks and predictive algorithms to draw conclusions and recommend decisions. Of course, AI can do it in seconds whereas an FBI unit may take days, months, or even years. The way AI works, as I have reported many times before, is based on tremendous amounts of data. “With the advent of big data, the information going in only amplifies the veracity of the recommendations coming out.” In this way, machines can learn which is the whole idea behind autonomous vehicles making split-second decisions about what to do next based on billions of possibilities and only one right answer.

In my sci-fi episode mentioned above, Detective Guren describes a perpetrator produced by the AI known as HAPP-E . Keiji-T, forever the devil’s advocate, counters with this comment, “Data is just data. Someone who knows how a probability engine works could have adopted the characteristics necessary to produce this deduction.” In other words, if you know what the engine is trying to do, theoretically you could ‘teach’ the AI using false data to produce a false deduction.

Episode 159. It seems fairly obvious.
Episode 159. It seems fairly obvious.

I published Episode 159 on March 18, 2016. Then an interesting thing happened in the tech world. A few days later Microsoft launched an AI chatbot called Tay (a millennial nickname for Taylor) designed to have conversations with — millennials. The idea was that Tay would become as successful as their Chinese version named XiaoIce, which has been around for four years and engages millions of young Chinese in discussions of millennial angst with a chatbot. Tay used three platforms: Twitter, Kik and GroupMe.

Then something went wrong. In less than 24 hours, Tay went from tweeting that “humans are super cool” to full-blown Nazi. Soon after Tay launched, the super-sketchy enclaves of 4chan and 8chan decided to get malicious and manipulate the Tay engine feeding it racist and sexist invective. If you feed an AI enough garbage, it will ‘learn’ that garbage is the norm and begin to repeat it. Before Tay’s first day was over, Microsoft took it down, removed the offensive tweets and apologized.

Taygoeswild
Crazy talk.

Apparently, Microsoft, though it had considered that such a thing was possible, but decided not to use filters (conversations to avoid or canned answers to volatile subjects). Experts in the chatbot field were quick to criticize: “‘You absolutely do NOT let an algorithm mindlessly devour a whole bunch of data that you haven’t vetted even a little bit.’ In other words, Microsoft should have known better than to let Tay loose on the raw uncensored torrent of what Twitter could direct her way.”

The tech site, Arstechnica also probed the question of “…why Tay turned nasty when XiaoIce didn’t?” The assessment thus far is that China’s highly restrictive measures keep social media “ideologically appropriate”, and under control. The censors will close your account for bad behavior.

So, what did we learn from this? AI, at least as it exists today, has no understanding. It has no morals and or ethical behavior unless you give it some. Then next questions are: Who decides what is moral and ethical? Will it be the people (we saw what happened with that) or some other financial or political power? Maybe the problem is with the premise itself. What do you think?

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