A number of definitions of artificial intelligence (AI) have surfaced over the last few decades, but IBM offers the following definition, “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.”
There are two types of AI – weak and strong. Weak AI is AI trained and focused to perform specific tasks. This form of AI drives most of the AI that surrounds us today. Strong AI is a theoretical form of AI where a machine would have intelligence equivalent to humans. It has a self-aware consciousness that can solve problems, learn and plan for the future.
Then there is deep learning vs. machine learning. These two terms tend to be used interchangeably despite there being a difference. Deep learning is actually a sub-field of machine learning. It’s comprised of neural networks. The difference between the two is how the algorithm learns. Deep learning automates most of the process, eliminating some of the manual human intervention requires and enabling the use of larger data sets.
All of the world’s tech giants (from Alibaba to Amazon) are in a race to become the world’s leaders in artificial intelligence. These companies are trailblazers and embrace AI to provide next-level products and services.
For example, Waymo, Google’s self-driving technology division, began as a project. Its autonomous vehicles are currently shuttling riders around California in self-driving taxis. Today, the company can’t charge a fare and a human driver still sits behind the wheel during the pilot program. Another AI innovation from Google is Google Duplex. Using natural language processing, an AI voice interface can make phone calls and schedule appointments on your behalf.
Apple uses AI and machine learning in products like the iPhone, where it enables the FaceID feature, or in products like the AirPods, Apple Watch, or HomePod smart speakers, where it enables the smart assistant Siri. Apple is also growing its service offering and is using AI to recommend songs on Apple Music, help you find your photo in the iCloud, or navigate to your next meeting using Maps.
IBM has been at the forefront of artificial intelligence for years. The latest AI accomplishment for IBM is Project Debater. This AI is a cognitive computing engine that competed against two professional debaters and formulated human-like arguments.
But of course there is skepticism when it comes to AI and high-level decision making. Higher-level, more strategic decisions that shape the direction of a business represent the last great frontier for AI in enterprises. And, to date, there is no shortage of skepticism among decision makers when it comes to strategic AI.
When faced with identical AI outputs, many businesspeople still make their own decisions, a recent study concludes. In a survey of executives published in MIT Sloan Management Review, many felt the “human filter makes all the difference in organizations’ AI-based decisions.”
The researchers presented participants with what was purportedly AI-generated recommendation of a new technology that would enable them to pursue potential new business opportunities, and asked them how much they trusted the AI recommendation. Many didn’t put full faith in the output, and still went with their own choices.
Can different decision-making archetypes within an organization feed into this skepticism?
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