Tag Archives: machine learning

Disruption. Part 1

 

We often associate the term disruption with a snag in our phone, internet or other infrastructure service, but there is a larger sense of the expression. Technological disruption refers the to phenomenon that occurs when innovation, “…significantly alters the way that businesses operate. A disruptive technology may force companies to alter the way that they approach their business, risk losing market share or risk becoming irrelevant.”1

Some track the idea as far back as Karl Marx who influenced economist Joseph Schumpeter to coin the term “creative destruction” in 1942.2 Schumpeter described that as the “process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” But it was, “Clayton M. Christensen, a Harvard Business School professor, that described it’s current framework. “…a disruptive technology is a new emerging technology that unexpectedly displaces an established one.”3

OK, so much for the history lesson. How does this affect us? Historical examples of technological disruption go back to the railroads, and the mass produced automobile, technologies that changed the world. Today we can point to the Internet as possibly this century’s most transformative technology to date. However, we can’t ignore the smartphone, barely ten years old which has brought together a host of converging technologies substantially eliminating the need for the calculator, the dictaphone, land lines, the GPS box that you used to put on your dashboard, still and video cameras, and possibly your privacy. With the proliferation of apps within the smartphone platform, there are hundreds if not thousands of other “services” that now do work that we had previously done by other means. But hold on to your hat. Technological disruption is just getting started. For the next round, we will see an increasingly pervasive Internet of Things (IoT), advanced robotics, exponential growth in Artificial Intelligence (AI) and machine learning, ubiquitous Augmented Reality (AR), Virtual Reality (VR), Blockchain systems, precise genetic engineering, and advanced renewable energy systems. Some of these such as Blockchain Systems will have potentially cataclysmic effects on business. Widespread adoption of blockchain systems that enable digital money would eliminate the need for banks, credit card companies, and currency of all forms. How’s that for disruptive? Other innovations will just continue to transform us and our behaviors. Over the next few weeks, I will discuss some of these potential disruptions and their unique characteristics.

Do you have any you would like to add?

1 http://www.investopedia.com/terms/d/disruptive-technology.asp#ixzz4ZKwSDIbm

2 http://www.investopedia.com/terms/c/creativedestruction.asp

3 http://www.intelligenthq.com/technology/12-disruptive-technologies/

See also: Disruptive technologies: Catching the wave, Journal of Product Innovation Management, Volume 13, Issue 1, 1996, Pages 75-76, ISSN 0737-6782, http://dx.doi.org/10.1016/0737-6782(96)81091-5.
(http://www.sciencedirect.com/science/article/pii/0737678296810915)

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The end of code.

 

This week WIRED Magazine released their June issue announcing the end of code. That would mean that the ability to write code, as is so cherished in the job world right now, is on the way out. They attribute this tectonic shift to Artificial Intelligence, machine learning, neural networks and the like. In the future (which is taking place now) we won’t have to write code to tell computers what to do, we will just have to teach them. I have been over this before through a number of previous writings. An example: Facebook uses a form of machine learning by collecting data from millions of pictures that are posted on the social network. When someone loads a group photo and identifies the people in the shot, Facebook’s AI remembers it by logging the prime coordinates on a human face and attributing them to that name (aka facial recognition). If the same coordinates show up again in another post, Facebook identifies it as you. People load the data (on a massive scale), and the machine learns. By naming the person or persons in the photo, you have taught the machine.

The WIRED article makes some interesting connections about the evolution of our thinking concerning the mind, about learning, and how we have taken a circular route in our reasoning. In essence, the mind was once considered a black box; there was no way to figure it out, but you could condition responses, a la Pavlov’s Dog. That logic changes with cognitive science which is the idea that the brain is more like a computer. The computing analogy caught on, and researchers began to see the whole idea of thought, memory, and thinking as stuff you could code, or hack, just like a computer. Indeed, it is this reasoning that has led to the notion that DNA is, in fact, codable, hence splicing through Crispr. If it’s all just code, we can make anything. That was the thinking. Now there is machine learning and neural networks. You still code, but only to set up the structure by which the “thing” learns, but after that, it’s on its own. The result is fractal and not always predictable. You can’t go back in and hack the way it is learning because it has started to generate a private math—and we can’t make sense of it. In other words, it is a black box. We have, in effect, stymied ourselves.

There is an upside. To train a computer you used to have to learn how to code. Now you just teach it by showing or giving it repetitive information, something anyone can do, though, at this point, some do it better than others.

Always the troubleshooter, I wonder what happens when we—mystified at a “conclusion” or decision arrived at by the machine—can’t figure out how to make it stop arriving at that conclusion. You can do the math.

Do we just turn it off?

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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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