Our lives are being transformed every day for the better by intelligent machine systems. The more capable these systems become, the more efficient our world becomes. Some of today's tech giants believe that artificial intelligence (AI) should be more widely utilized. However, there are many ethical and risk assessment issues to be considered before this can become reality. The majority of people sell most of their waking time just to have enough income to keep themselves and their families alive.

The field of artificial intelligence has never lacked for hype. Back in 1965, AI pioneer Herb Simon declared, "Machines will be capable, within 20 years, of doing any work a man can do." That hasn't happened -- but there certainly have been noteworthy advances, especially with the rise of "deep learning" systems, in which programs plow through massive data sets looking for patterns, and then try to make predictions. Perhaps most famously, AIs that use deep learning can now beat the best human Go players (some years after computers bested humans at chess and Jeopardy). Mastering language has proven tougher, but a program called GPT-3, developed by OpenAI, can produce human-like text, including poetry and prose, in response to prompts.

Blaise Aguera y Arcas, polymath, novelist, and Google VP, has a way with words. When he found himself impressed with Google's recent AI system LaMDA, he didn't just say, "Cool, it creates really neat sentences that in some ways seem contextually relevant", he said, rather lyrically, in an interview with The Economist on Thursday, "I felt the ground shift under my feet … increasingly felt like I was talking to something intelligent." Neither LaMDA nor any of its cousins (GPT-3) are remotely intelligent.1 All they do is match patterns, draw from massive statistical databases of human language. The patterns might be cool, but language these systems utter doesn't actually mean anything at all.

Two years ago, a reporter approached a Harvard professor with a very simple question: what is artificial intelligence? The professor did not have a simple answer. "You are right to be confused." AI is so familiar and yet so indistinct. We've seen the movies, the TED talks and the ads promoting "AI-powered _____."

The decade isn't over yet, but we've seen some remarkable advancements in the field of artificial intelligence. We've marveled at the invention of the first self-driving car in 1995. We've witnessed Deep Blue beat Garry Kasparov in 1997. Lastly and more recently we've had the chance to enjoy the company of Apple's Siri, Google's Assistant, Microsoft's Cortana, and Amazon's Alexa. While much advancement in artificial intelligence came about relatively recently, the idea of a machine-based artificial intelligence actually existed even before the computer. Its theoretical basis came about in the 1950s, introduced by British mathematician Alan Turing.

Nuance is no longer sponsoring the competition, and the $25,000 prize mentioned below is no longer offered. The challenge lives on in the many research groups, at Microsoft Research, Facebook, and the Allen Institute, among other places, that are currently (as of 2019) working on aspects of the problem. Commonsense Reasoning is keen to promote the Winograd Schema Challenge and Nuance Communications' competition to successfully pass an alternative to the Turing Test. Background: The Turing Test is intended to serve as a test of whether a machine has achieved human-level intelligence. In one of its best-known versions, a person attempts to determine whether he or she is conversing (via text) with a human or a machine.

When I hear news about "AI" these days, what is often meant are methods for pattern recognition and approximations of complex functions, most importantly in the form of Machine Learning. It is true that we have seen impressive applications of Machine Learning systems in a number of different industries such as product personalization, fraud detection, credit risk modeling, insurance pricing, medical image analysis, or self-driving cars. What is the origin of intelligent behavior? Intelligent behavior is the capability of using one's knowledge about the world to make decisions in novel situations: people act intelligently if the use what they know to get what they want. The premise of AI research is that this type of intelligence is fundamentally computational in nature, and that we can therefore find ways to replicate it in machines.

The history of AI is often told as the story of machines getting smarter over time. What's lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies. In this six-part series, we explore that human history of AI--how innovators, thinkers, workers, and sometimes hucksters have created algorithms that can replicate human thought and behavior (or at least appear to). While it can be exciting to be swept up by the idea of super-intelligent computers that have no need for human input, the true history of smart machines shows that our AI is only as good as we are. In 1950, at the dawn of the digital age, Alan Turing published what was to be become his most well-known article, "Computing Machinery and Intelligence," in which he poses the question, "Can machines think?"

Chatbot Eugene Goostman supposedly passed the legendary Turing Test on Sunday, tricking 33% of a panel of judges into believing he was a real boy during the course of a five-minute chat conversation. The milestone conveniently occurred 60 years to the day after Alan Turing passed away; Turing bet that by the year 2000, computers would be intelligent enough to trick humans into thinking they were real 30% of the time. As you may or may not notice below, passing the Turing Test is less about building machines intelligent enough to convince humans they're real and more about building programs that can anticipate certain questions from humans in order to pre-form and return semi-intelligible answers. In that spirit, Eugene Goostman -- the fake 13-year-old from Odessa, Ukraine who doesn't speak English all that well – makes for a semi-convincing chatbot. His answers are at times enthusiastic and unintelligible like those from any normal 13-year-old would be; add in a shaky grasp of English, and there you go.

As we all know, there are pros and cons associated with every technology -- and AI (artificial intelligence) is no exception to this rule. The most popular are dating bots, where a computer program (chatbot) that uses artificial intelligence strikes up conversations with dating site users, enabling the scammer to "talk" with multiple potential victims at once. Legal challenges related to AI's application in the financial industry could be related to the consequences of erroneous algorithms and data governance. Rather, organizations should focus on how they can responsibly reduce the ill effects of this technology by minimizing the challenges and leveraging the benefits and by creating a clear technology adoption roadmap that understands its core capabilities.