With the endless promotion of artificial intelligence by analysts, media, and vendors, one can be forgiven for assuming AI is proliferating across and running enterprises far and wide. However, the reality is beyond simply automating narrow applications -- such as credit scoring, upselling recommendations, chatbots, or managing machine performance -- AI still has a limited range, and barely begun to achieve its full potential as a true augment to human intelligence and talent. That's the takeaway from recent panel discussion hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School. "I am not that excited about the narrow applications of AI," says Sadun. "I'm not excited about the dumb approach of automating one process and them claiming that you are on a different tier of technology."

AI needs to expand horizons. With the endless promotion of artificial intelligence by analysts, media, and vendors, one can be forgiven for assuming AI is proliferating across and running enterprises far and wide. However, the reality is beyond simply automating narrow applications -- such as credit scoring, upselling recommendations, chatbots, or managing machine performance -- AI still has a limited range, and barely begun to achieve its full potential as a true augment to human intelligence and talent. That's the takeaway from recent panel discussion hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School. "I am not that excited about the narrow applications of AI," says Sadun. "I'm not excited about the dumb approach of automating one process and them claiming that you are on a different tier of technology."

AI is becoming increasingly embedded in many of the things we interact with and use on a daily basis. AI is becoming increasingly more advanced over time, with some remarkable capabilities emerging over the past few years. There are many real-world, practical examples organizations and governments are using AI for their day-to-day activities. So, AI is a real thing, right? Well, on the other hand there's still substantial hype when it comes to AI.

One of the challenges with the pursuit of AI is the mismatch between the science fiction concept of artificial intelligence and the real-world, practical applications of AI. In movies and science fiction novels, AI systems are portrayed as super-intelligent machines that have cognitive capabilities equal to or greater than that of humans. However, the reality is that much of what organizations are implementing today for artificial intelligence are narrow applications of AI. This is in clear contrast to artificial general intelligence (AGI). The limit of our current AI abilities lets organizations implement specific cognitive abilities in narrow domains, such as image recognition, conversational systems, predictive analytics as well as pattern and anomaly detection.

AI is becoming increasingly embedded in many of the things we interact with and use on a daily basis. AI is becoming increasingly more advanced over time, with some remarkable capabilities emerging over the past few years. There are many real-world, practical examples organizations and governments are using AI for their day-to-day activities. So, AI is a real thing, right? Well, on the other hand there's still substantial hype when it comes to AI.

Fears surrounding AI and cybersecurity reflect very real risks. AI-powered malware isn't a threat we need to worry about right now, but attackers have become adept at manipulating AI systems to their own advantage, essentially turning them against users. Widespread manipulation of the algorithms used on social media is already causing problems in many parts of the world. And as sophisticated AI tools become freely available, it would be naive not to expect adversaries to take advantage of the technology. But for now, we suspect that threat actors are using AI in rather indirect ways, such as for data analysis or by using tools to produce fake content.

There are a few moments in the history of artificial intelligence (AI) that are considered major breakthroughs – events that showed the power of machine intelligence in matching or surpassing human performance. Two examples are Deep Blue versus Kasparov in 1997 and Watson versus Jennings in 2008. Most recently, AlphaGo versus Lee Sedol became another major victory, this time driven by a fast developing field known as "deep learning." Deep learning–a machine learning technique based on artificial neural networks–is growing in popularity due to a series of developments in the science and business of data mining. Prior to AlphaGo's victory over the currently best Go player Lee Sedol, computer programs that played Go had only been able to beat average players.

There are a few moments in the history of artificial intelligence (AI) that are considered major breakthroughs – events that showed the power of machine intelligence in matching or surpassing human performance. Two examples are Deep Blue versus Kasparov in 1997 and Watson versus Jennings in 2008. Most recently, AlphaGo versus Lee Sedol became another major victory, this time driven by a fast developing field known as "deep learning." Deep learning–a machine learning technique based on artificial neural networks–is growing in popularity due to a series of developments in the science and business of data mining. Prior to AlphaGo's victory over the currently best Go player Lee Sedol, computer programs that played Go had only been able to beat average players.