Parliament adds that the data must be processed in a manner allowing the data subject to exercise his/her rights and protects the integrity of the data
* Council adds that the data must be processed in a manner that ensures the security of the data processed under the responsibility and liability of the data controller
Imagine sitting in a bolted‐to‐the‐floor chair in a small room at a heavily scarred table with a single, bright spotlight overhead and a detective leaning in asking, “So how did your system screw this up so badly and how are you going to fix it? Show me the decision‐making process!”
This is a murky area at the moment, and one that is being reviewed and pursued. Machine learning systems will have to come with tools that allow a decision to be explored and explained.
ARE WE THERE YET?
Most of this sounds a little over‐the‐horizon and science‐fiction‐ish, and it is. But it's only just over the horizon. (Quick – check the publication date at the front of this book!) The capabilities have been in the lab for a while now. Examples are in the field. AI and machine learning are being used in advertising, marketing, and customer service, and they don't seem to be slowing down.
But there are some projections that this is all coming at an alarming rate.12
According to researcher Gartner, AI bots will power 85 % of all customer service interactions by the year 2020. Given Facebook and other messaging platforms have already seen significant adoption of customer service bots on their chat apps, this shouldn't necessarily come as a huge surprise. Since this use of AI can help reduce wait times for many types of interactions, this trend sounds like a win for businesses and customers alike.
The White House says it's time to get ready. In a report called “Preparing for the Future of Artificial Intelligence” (October 2016),13 the Executive Office of the President National Science and Technology Council Committee on Technology said:
The current wave of progress and enthusiasm for AI began around 2010, driven by three factors that built upon each other: the availability of big data from sources including e‐commerce, businesses, social media, science, and government; which provided raw material for dramatically improved Machine Learning approaches and algorithms; which in turn relied on the capabilities of more powerful computers. During this period, the pace of improvement surprised AI experts. For example, on a popular image recognition challenge14 that has a 5 percent human error rate according to one error measure, the best AI result improved from a 26 percent error rate in 2011 to 3.5 percent in 2015.
Simultaneously, industry has been increasing its investment in AI. In 2016, Google Chief Executive Officer (CEO) Sundar Pichai said, “Machine Learning [a subfield of AI] is a core, transformative way by which we're rethinking how we're doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we're in early days, but you will see us – in a systematic way – apply Machine Learning in all these areas.” This view of AI broadly impacting how software is created and delivered was widely shared by CEOs in the technology industry, including Ginni Rometty of IBM, who has said that her organization is betting the company on AI.
The commercial growth in AI is surprising to those of little faith and not at all surprising to true believers. IDC Research “predicts that spending on AI software for marketing and related function businesses will grow at an exceptionally fast cumulative average growth rate (CAGR) of 54 percent worldwide, from around $360 million in 2016 to over $2 billion in 2020, due to the attractiveness of this technology to both sell‐side suppliers and buy‐side end‐user customers.”15
Best to be prepared for the “ketchup effect,” as Mattias Östmar called it: “First nothing, then nothing, then a drip and then all of a sudden – splash!”
You might call it hype, crystal‐balling, or wishful thinking, but the best minds of our time are taking it very seriously. The White House's primary recommendation from the above report is to “examine whether and how (private and public institutions) can responsibly leverage AI and Machine Learning in ways that will benefit society.”
Can you responsibly leverage AI and machine learning in ways that will benefit society? What happens if you don't? What could possibly go wrong?
AI‐POCALYPSE
Cyberdyne will become the largest supplier of military computer systems. All stealth bombers are upgraded with Cyberdyne computers, becoming fully unmanned. Afterwards, they fly with a perfect operational record. The Skynet Funding Bill is passed. The system goes online August 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self‐aware at 2:14 a.m. Eastern time, August 29th. In a panic, they try to pull the plug.
At the end of 2014, Professor Stephen Hawking rattled the data science world when he warned, “The development of full artificial intelligence could spell the end of the human race… It would take off on its own, and re‐design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.”16
In August 2014, Elon Musk took to Twitter to express his misgivings:
“Worth reading Superintelligence by Bostrom. We need to be super careful with AI. Potentially more dangerous than nukes,” (Figure 1.2) and “Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable.”
Figure 1.2 Elon Musk expresses his disquiet on Twitter.
In a clip from the movie Lo and Behold, by German filmmaker Werner Herzog, Musk says:
I think that the biggest risk is not that the AI will develop a will of its own, but rather that it will follow the will of people that establish its utility function. If it is not well thought out – even if its intent is benign – it could have quite a bad outcome. If you were a hedge fund or private equity fund and you said, “Well, all I want my AI to do is maximize the value of my portfolio,” then the AI could decide, well, the best way to do that is to short consumer stocks, go long defense stocks, and start a war. That would obviously be quite bad.
While Hawking is thinking big, Musk raises the quintessential Paperclip Maximizer Problem and the Intentional Consequences Problem.
The AI that Ate the Earth
Say you build an AI system with a goal of maximizing the number of paperclips it has. The threat is that it learns how to find paperclips, buy paperclips (requiring it to learn how to make money), and then work out how to manufacture paperclips. It would realize that it needs to be smarter, and so increases its own intelligence in order to make it even smarter, in service of making paperclips.
What is the problem? A hyper‐intelligent agent could figure out how to use nanotech and quantum physics to alter all atoms on Earth into paperclips.
Whoops, somebody seems to have forgotten to include the Three Laws of Robotics from Isaac Asimov's 1950 book, I Robot:
1. A robot
“9 Artificial Intelligence Stats that Will Blow You Away,” http://www.foxbusiness.com/markets/2016/12/10/artificial‐intelligence‐stats‐that‐will‐blow‐away.html.
“Preparing for the Future of Artificial Intelligence,” https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf.
“Machine Learning Will Revolutionize Market Segmentation Practices,” January 2017, http://www.idgconnect.com/view_abstract/41712/machine‐learning‐will‐revolutionize‐market‐segmentation‐practices.
