Artificial Intelligence: The Good, the Bad, and the Ugly

Stephen DeAngelis

July 17, 2014

There has been a lot of fear-mongering about artificial intelligence in the news lately and it has involved some famous people like Stephen Hawking (@Prof_S_Hawking) and Elon Musk (@elonmusk). Fear is often the desired reaction that directors of blockbuster movies and authors of some best-selling books hope to achieve. “High-functioning artificial intelligence is the stuff of science fiction,” writes Matthew Feeney, “the malicious HAL in 2001, the malevolent machines in Battlestar Galactica and The Matrix, the Butlerian Jihad in Frank Herbert’s Dune series. Charles Stross’ novel Accelerando describes the Matrioshka brain, an artificial mind that requires the energy of a star to function.” [“Here Comes Artificial Intelligence,” Reason, 13 November 2012] Fear is a powerful motivator but it can also be paralyzing. That’s the bad side of artificial intelligence. Although there are good reasons for exercising caution when it comes to creating systems that employ artificial intelligence (especially in the area of weapons design), we can’t allow fear to paralyze efforts to create peaceful and beneficial systems that utilize AI.

 

Ben Lorica (@bigdata) and Roger Magoulas (@rogerm) remind us, “As computing power catches up to scientific and engineering ambitions, and as our ability to learn directly from sensory signals — i.e., big data — increases, intelligent systems are having a real and widespread impact. Every Internet user benefits from these systems today — they sort our email, plan our journeys, answer our questions, and protect us from fraudsters. And, with the Internet of Things, these systems have already started to keep our houses and offices comfortable and well-lit, our data centers running more efficiently, our industrial processes humming, and even are driving our cars.” [“Welcome To Intelligence Matters,” Forbes, 14 May 2014] That’s the good side of artificial intelligence. They also remind us that the term “artificial intelligence” is a big tent under which crowds a number of different concepts. “Artificial intelligence and related labels (machine learning, strong or true AI, artificial general intelligence),” they write, “refer to a wide range of systems built with varied goals, technologies, and even philosophical foundations.” That’s the ugly side of AI.

 

My interest in AI (and the interest of the smart folks that work at my company, Enterra Solutions®) focuses on business uses such as those described above. Such use cases don’t pose the Terminator-like threat to humankind that seems to keep Hawking and Musk up at night. Lorica and Magoulas remind us, however, that “these systems don’t exist in a vacuum; in fact, some of the most fascinating aspects of machine intelligence arise from their deep interconnections with other technologies. The impact of big data and the Internet of Things will both be magnified once these massive information streams can be interpreted and acted upon by truly intelligent systems.” Eric Siegel, Ph.D., (@predictanalytic) founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, obviously believes in the predictive power of AI-based analytics. He indicates that predictive analytics are possible thanks to two capabilities: machine learning and natural language processing. “Machine learning,” he writes, “[is] the core to prediction – learning from data how to predict. That’s also known as predictive modeling. And the other is natural language processing or computational linguistics.” [“Becoming a Believer in Artificial Intelligence,” Big Think, 12 May 2013] I agree with Siegel that machine learning and natural language processing are key for AI-based predictive analytics, but these capabilities are also essential to other AI-based business solutions as well.

 

Lars Hård, the CTO and founder of Expertmaker, notes, “AI is working across many different industries.” [“The Disruptive Potential of Artificial Intelligence Applications,” Data Informed, 16 January 2014] He also point out that, like it or not, AI is already an integral part of your life experiences. He explains, “If you are an information technology professional, you are probably aware of at least a few systems that gather data to create more intelligent systems and informed business decisions. But you may not even realize that artificial intelligence (AI) is increasingly becoming a part of your everyday life – not only in your business, but also in your life as a consumer.” He specifically mentions one way that AI is going to affect the retail space: targeted marketing. He writes:

“Machine learning will begin to allow retailers to process this data and generate a deep knowledge of not only their products but their users, and even more importantly, their preferences and behaviors. Better recommendations can be built by classification of products and multiple recognition and data enhancement methods – laying the groundwork for retailers to establish meaningful relationships with their customers by recommending them truly relevant products.”

Beau Cronin (@beaucronin) insists that almost any difficult problem can be addressed using machine learning. He offers “a simple recipe for solving crazy-hard problems with machine intelligence.” [“Untapped Opportunities In AI,” Forbes, 5 June 2014] The recipe involves three steps. They are:

“First, collect huge amounts of training data — probably more than anyone thought sensible or even possible a decade ago. Second, massage and preprocess that data so the key relationships it contains are easily accessible (the jargon here is ‘feature engineering’). Finally, feed the result into ludicrously high-performance, parallelized implementations of pretty standard machine-learning methods like logistic regression, deep neural networks, and k-means clustering (don’t worry if those names don’t mean anything to you — the point is that they’re widely available in high-quality open source packages).”

Cronin believes this simple recipe has been so successful that it is the reason that artificial intelligence is once again a hot topic and area of intense research. He notes that researchers discovered “that simple models fed with very large datasets” can generate results that were never achieved by more “sophisticated theoretical approaches that were all the rage before the era of big data.” That also explains why there has been so much hype about big data over the last few years. For businesses, the advent of the big data era and the rise of AI systems have changed the business landscape dramatically. Companies now truly have a chance of breaking down corporate silos, aligning company strategies, and achieving end-to-end supply chain visibility. New applications allow manufacturers and retailers ways to understand and connect with consumers as never before. On the other hand, privacy advocates believe that some companies go to too far. That’s why, when people discuss artificial intelligence there is always a bit of good, a bit of bad, and bit of ugly involved. Lorica and Magoulas conclude:

“With two sides — marketing boosterism and the doomsayers — both vying for mindshare, it’s hard for journalists and other interested observers to interpret new developments. It’s also hard to evaluate claims of progress on their merits; the history of AI is full of approaches that showed early promise, only to turn out as blind alleys and false summits.”

There are still likely to be a lot of blind alleys and false summits in the search for artificial general intelligence (i.e., the pursuit of a truly sentient machine); but, in the business world, AI breakthroughs are more likely to lead to super highways than they are to blind alleys.