Tuesday, February 28, 2017

Top Seven Myths of AI

I think we have all realized that 2017 is the year of AI or at least we are out of the 30+ year AI winter. When AI first appeared on the big stage in the mid to late 80s, it over promised and under delivered for the most part. While there were spotty successes, AI was too hard and expensive to leverage. This time there are some great differences the successful use of AI, but myths still abound. Here are my top seven myths of AI today.

AI is a general intelligence that mimics humans:

While this has always been the dream, the reality of successful use today revolves around specialty problems that involves specific knowledge, rule and constraints. This is a great way to focus around beneficial application of AI to special problems that require fast learning and adaptation.

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. They said the same thing 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.

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 we a growing number of approaches emerging over time that will cross leverage and converge over a long period of time.

AI is only for the supper 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 chat bots.

AI is only for difficult or expensive problems:

AI is easily accessible today and embedded in a number of digital business platforms. The overhead for creating a solution is much less expensive. You can even find libraries of cognitive components to leverage (COGs) as a developer.

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 optimization's. Mining data can teach us much about a problem domain. In fact a large number of robotic programming approaches start with data.

Machines are greater than humans:
Yes machines are available 24 by 7, extremely accurate, faster than humans and don't complain, but humans can handle emotions, are creative and handle unexpected situations naturally. we need a balance of each helping the other. 
Net; Net:
AI is better this time and will be stickier, but the problems are greatly exaggerated. AI is here to stay after a long winters night

Additional Reading:

Top Seven Uses of AI 

Tuesday, February 7, 2017

AI To Manage Agility

If you watched the Superbowl, you saw an example of agility in action with the swarming lighted drones creating backgrounds in the sky behind Lady Gaga. By giving the drones goals of flying into the right position to participate in creating an image with the right color, images were created in the sky. Not only were they wondrous as paintings in the sky with each drone being a dynamic pixel in a dynamic piece of art, they were used for ads for sponsors. Imagine if organizations would paint a customer experience dynamically or respond to a competitor dynamically or intercept a market trend? It's going to take AI to do this not a bunch of hand / human controlled responses or computer programming alone. 

Dealing With Goals:

As organizations move from fixed process and applications to more dynamic processes, they will change to be goal driven. This means that all resources will swarm to the established goals and adjust when the goals are changed. AI combined with analytics will help noodle out what are the best goals initially. 

Dealing With Constraints:

Besides goals these dynamic processes and applications need boundaries to stay helpful and sometimes even legal. This is particularly true when there are multiple  constraints. AI and analytics can be helpful in establishing these boundaries / constraints initially. 

Dynamically Setting the Goals and Constraints

Add dynamism to the mix and now you have a new set of problems that AI is quite adept at today. AI can learn from situations collected over time, select the right set of prediction algorithms and project the potential outcomes. All of this can be done within scenarios and policies that have been selected for in force or alternative scenarios

Bottom Line: 

Goals not only conflict and compete, they are shifting. As this shifting takes on a new speed of change, Cognitive AI can create the right balance for emerging situations. As processes and swarming agents become more goal directed than flow directed, organizations can act on shifting goals that leverage constraints. Cognitive AI can play a big role in setting these goals and constraints.

Additional Reading:

Uses of AI Today 
Leveraging AI 4 Events
AI & IoT
AI & Strategy
AI & Decisions