By Jim Sinur
(Aragon Research) – I think we have all realized that 2017 is the year of Artificial Intelligence (AI). We’re out of the 30+ year AI winter – finally. When AI first appeared on the big stage in the mid to late 80s, it over promised and under delivered, for the most part. And while there were spotty successes, AI was too hard and expensive to leverage.
Today, while AI has emerged from its winter and has been the star of many successful technologies and use cases, myths still abound. This blog dispels the top seven myths of AI today.
Myth #1: AI is a general intelligence that mimics humans
While this has always been the dream, the reality of successful AI use today revolves around specialty problems that involve specific knowledge, rules, and constraints. This is a great way to focus around beneficial application of AI to special problems that require fast learning and adaptation.
Myth #2: About half of all our jobs will disappear soon because of AI
While it is true that certain jobs will be displaced, there will be other jobs created. The same anxiety was expressed about computers. Yes, robots and AI will take some jobs away, but the majority of AI applications will help people be better at what they do in a more efficient way.
Myth #3: AI is data, math, patterns and iteration only
While a number of early AI problems used this formulaic approach, today we have natural language processing, knowledge, voice, image and video/vision approaches to AI. There have been a growing number of approaches emerging over time that will cross leverage and converge over a long period of time.
Myth #4: AI is only for the super intelligent technology elite
At one time, AI was programmed by only the expensive and elite types. Today however, machine learning, model-driven, and simple knowledge representation can be used as a starting point for iterative learning. Today we all can use chatbots.
Myth #5: AI is only for difficult or expensive problems
AI is easily accessible today and has been embedded into a number of digital business platforms. The overhead for creating such a solution is much less expensive. You can even find libraries of cognitive components to leverage (COGs) as a developer.
Myth #6: Algorithms are more important than data
Yes we love our algorithms, but data has embedded knowledge and can be learned from to create rules and processing optimizations. Mining data can reveal much about a problem domain. In fact, a large number of robotic programming approaches start with data.
Myth #7: Machines are greater than humans
Yes machines are available 24/7, are extremely accurate, and are faster than humans and don’t complain, but humans can handle emotions, are creative, and can handle unexpected situations naturally. We need a balance of each helping the other.
Bottom Line
In 2017, AI is better and will be stickier, but the problems are greatly exaggerated. After a long winter’s night, AI is here to stay.