Tag Archives: machine learning

An AI as President?


Back on May 19th, before I went on holiday, I promised to comment on an article that appeared that week advocating that we would better off with artificial intelligence (AI) as President of the United States. Joshua Davis authored the piece: Hear me out: Let’s Elect
An AI As President, for the business section of WIRED  online. Let’s start out with a few quotes.

“An artificially intelligent president could be trained to
maximize happiness for the most people without infringing on civil liberties.”

“Within a decade, tens of thousands of people will entrust their daily commute—and their safety—to an algorithm, and they’ll do it happily…The increase in human productivity and happiness will be enormous.”

Let’s start with the word happiness. What is that anyway? I’ve seen it around in several discourses about the future, that somehow we have to start focusing on human happiness above all things, but what makes me happy and what makes you happy may very well be different things. Then there is the frightening idea that it is the job of government to make us happy! There are a lot of folks out there that the government should give us a guaranteed income, pay for our healthcare, and now, apparently, it should also make us happy. If you haven’t noticed from my previous blogs, I am not a progressive. If you believe that government should undertake the happy challenge, you had better hope that their idea of happiness coincides with your own. Gerd Leonhard, a futurist whose work I respect, says that there are two types of happiness: first is hedonic (pleasure) which tends to be temporary, and the other is a eudaimonic happiness which he defines as human flourishing.1 I prefer the latter as it is likely to be more meaningful. Meaning is rather crucial to well-being and purpose in life. I believe that we should be responsible for our happiness. God help us if we leave it up to a machine.

This brings me to my next issue with this insane idea. Davis suggests that by simply not driving, there will be an enormous increase in human productivity and happiness. According to the website overflow data,

“Of the 139,786,639 working individuals in the US, 7,000,722, or about 5.01%, use public transit to get to work according to the 2013 American Communities Survey.”

Are those 7 million working individuals who don’t drive happier and more productive? The survey should have asked, but I’m betting the answer is no. Davis also assumes that everyone will be able to afford an autonomous vehicle. Maybe providing every American with an autonomous vehicle is also the job of the government.

Where I agree with Davis is that we will probably abdicate our daily commute to an algorithm and do it happily. Maybe this is the most disturbing part of his argument. As I am fond of saying, we are sponges for technology, and we often adopt new technology without so much as a thought toward the broader ramifications of what it means to our humanity.

There are sober people out there advocating that we must start to abdicate our decision-making to algorithms because we have too many decisions to make. They are concerned that the current state of affairs is simply too painful for humankind. If you dig into the rationale that these experts are using, many of them are motivated by commerce. Already Google and Facebook and the algorithms of a dozen different apps are telling you what you should buy, where you should eat, who you should “friend” and, in some cases, what you should think. They give you news (real or fake), and they tell you this is what will make you happy. Is it working? Agendas are everywhere, but very few of them have you in the center.

As part of his rationale, Davis cites the proven ability for AI to beat the world’s Go champions over and over and over again, and that it can find melanomas better than board-certified dermatologists.

“It won’t be long before an AI is sophisticated enough to
implement a core set of beliefs in ways that reflect changes in the world. In other words, the time is coming when AIs will have better judgment than most politicians.”

That seems like grounds to elect one as President, right? In fact, it is just another way for us to take our eye off the ball, to subordinate our autonomy to more powerful forces in the belief that technology will save us and make us happier.

Back to my previous point, that’s what is so frightening. It is precisely the kind of argument that people buy into. What if the new AI President decides that we will all be happier if we’re sedated, and then using executive powers makes it law? Forget checks and balances, since who else in government could win an argument against an all-knowing AI? How much power will the new AI President give to other algorithms, bots, and machines?

If we are willing to give up the process of purposeful work to make a living wage in exchange for a guaranteed income, to subordinate our decision-making to have “less to think about,” to abandon reality for a “good enough” simulation, and believe that this new AI will be free of the special interests who think they control it, then get ready for the future.

1. Leonhard, Gerd. Technology vs. Humanity: The Coming Clash between Man and Machine. p112, United Kingdom: Fast Future, 2016. Print.

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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.

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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.

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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