What’s taken artificial intelligence (AI) so long? We invented AI capabilities like first-order logical reasoning, natural-language processing, speech/voice/vision recognition, neural networks, machine-learning algorithms, and expert systems more than 30 years ago, but aside from a few marginal applications in business systems, AI hasn’t made much of a difference. The business doesn’t understand how or why it could make a difference; it thinks we can program anything, which is almost true. But there’s one thing we fail at programming: our own brain — we simply don’t know how it works.
What’s changed now? While some AI research still tries to simulate our brain or certain regions of it — and is frankly unlikely to deliver concrete results anytime soon — most of it now leverages a less human, but more effective, approach revolving around machine learning and smart integration with other AI capabilities.
What is machine learning? Simply put, sophisticated software algorithms that learn to do something on their own by repeated training using big data. In fact, big data is what’s making the difference in machine learning, along with great improvements in many of the above AI disciplines (see the AI market overview that I coauthored with Mike Gualtieri and Michele Goetz on why AI is better and consumable today). As a result, AI is undergoing a renaissance, developing new “cognitive” capabilities to help in our daily lives.
The numbers in the new AI field are staggering: more than 2,300 startups (a comprehensive list can be found here) have been founded; venture capitalists are investing billions of dollars; and major vendors like Amazon, Google, IBM, Microsoft, SAS, and Yahoo are acquiring and investing in R&D and new products, and manufacturers like Audi, BMW, and Mercedes are investing in smarter applications to improve driving experience and safety.
What can you do with this if you are a bank, hospital, retail store, market research firm, insurance company, car manufacturer, airline, or any other business? At least five things:
- Leverage just-in-time expert assistance. Comes in many forms. As personal assistance, as business assistance to improve work productivity as well as expert business advises in finance, healthcare, retail, marketing and other.
- Add intuitive communication to your business applications. Intuitive communication capabilities capture the true meaning of a text by applying sentiment analysis (looking at the words and semantics used) and mapping facial expressions, gestures, and voice intonations to emotional states.
- Engage customers bypredicting needs. Leverage software agents that gather information (and answers) as quickly as possible to help predict customer needs on the fly before the customer makes contact or while a customer is talking to an operator.
- Produce intelligent narratives. AI capabilities in this category allow firms to look at comprehensive, complex, and curated data, mine it, and automatically generate an intelligent and intuitive story. Such solutions produce executive summaries in natural language rather than unnatural (albeit fancy) dashboards, tables, and graphics.
- Accessibility for the impaired.Capabilities that can assist and offer independence to the sight-, hearing-, or mobility-impaired, including helping those with visual impairments recognize text and products and allowing the hearing-impaired to listen and speak via unconventional media such as bone conduction earpieces.
This is only a starting point; the space has already begun to expand and we need only unleash our creativity to build more intelligent business applications. Our most recent report will tell you more about these five business capabilities, product categories and key market players, and what first steps enterprises should take to leverage AI.
Your comments on where you would like us to take this are very welcome. I’d like to explore how we can build applications more intelligently, not just applications that are more intelligent; this is my call to action for application development leaders!