The Future of Artificial Intelligence

Whether or not AI will be taking over the world is something for futurists and ethicists to debate. In the meantime there are some fantastic applications of AI that are very interesting and powerful which will provide some better means for us to manage our daily lives at work and at play. Keep in mind that all of these are being done today in various levels of experimentation with varying degrees of success. Some of those technologies include:

• You stock portfolio automatically modifies your market position and executes "smart" trades for you.
• Your car does your driving for you.
• Robots handle your housecleaning, yard work and cooking.
• Your groceries are automatically ordered based on preferences and patterns in purchasing.
• Your bills are automatically managed.
• AI tools that enable 3rd Generation "smart" search engines that allow you to get your information with more precision to help manage the ever-growing web.
• Competitive intelligence is managed by smart AI agents the peruse the web to look for relevant information (new releases, prices, marketing strategies, etc.)
• Drug researchers can utilize the intelligence for intensive bio-computational modeling in relation to the enormous amount of data from the human genome project to help find cures in ways never thought of.

These are just a few of many AI applications that currently are or will be available to all of us in the near future.

What are some of the AI tools used to accomplish this smart automation?

I have described these tools in a very simple non-technical fashion (I hope!).

Neural Network(s) - A Neural Network is an AI tool modeled by the way the human brain learns hence the name "neural". Neural networks are designed to recognize patterns in data and predict an output from a given set of information. A classic example is the use of neural nets to predict what stocks are considered to be undervalued given all of the information about the stock at that moment in time (P/E Ratio, Historical Fluctuations, Sector Performance, etc). Neural networks need to be trained on the data before it can predict or learn.

Mobile Agent System(s) - A mobile agent is generally characterized by a piece of software that can go from one computer to another computer (or network) and perform some type of useful execution on the user’s behalf. You can set up agents to peruse the Internet to look for something useful. For instance, a shopping agent could be set up to find a certain line of clothing in a certain size with instructions to make a purchase for the individual once a certain price criteria was met. In the B2B marketplace, agents will be programmed to negotiate price with other agents to execute many transactions between suppliers and customers.

Genetic Algorithms - A model of machine learning, which derives its behavior from a metaphor of some of the mechanisms of evolution in nature. This is done by modeling the way DNA morphs and adapts to varied environmental conditions within a certain population. The individuals in the population then go through a process of simulated "evolution".

Written by Chris Moy, CEO of SpinningLogic, LLC, a company that specializes in customizing AI applications for business and industry. Questions or comments can be sent to chrism@spinninglogic.com