Artificial intelligence will humanize recommendation engines, improve the accuracy of logistics engines, and represent a monumental change in the friendliness of chatbot engines. Learning new languages (Duolingo), finding new dinner plans (Replika) and making photography exciting again (Prisma) is how our business partners will be introduced to the potential of artificial intelligence.
How we plan for AI
If I asked you how to build a house, you’d have a series of steps in mind. When asked how to validate a company’s technology security perimeter, other action steps come immediately to the forefront. And when booking a vacation to Brazil, a clear approach to get you on the beach fast rushes to the mind.
We’re of course not talking about building houses, creating security resilience, or booking vacations. We’re talking about how to introduce business leaders, scientists and medical professionals to the power of artificial intelligence. So where do we start? What’s our first step?
Three steps toward AI enlightenment
We start with a framework for all intelligence agents. Artificial intelligence can be separated into two categories: (1) thought processes and reasoning and (2) behavior. Whether you lean more toward the mathematics and engineering side (rationalist) or closer to the human-centered approach (behavior), the heart of AI is trying to understand how we think.
The first step: Decide which of the four categories of artificial intelligence the enterprise will explore.
- Thinking humanly: systems that think like humans
- Acting humanly: systems that act like humans
- Thinking rationally: systems that think rationally
- Acting rationally: systems that act rationally
The second step: determine the intent of our artificial intelligence initiative.
Thinking humanly (cognitive modeling) blends artificial intelligence with models—as in the case of neuro-physiological experiments. Actual experiments in the cognitive sciences depend on human or animal observations and investigations. Acting humanly (Turning Test) attempts to establish a line between non-intelligence and satisfactory intelligence. Thinking rationally captures “right thinking” in computer language. Coding logic is fraught with challenges, since informal knowledge doesn’t translate well to formal notation. Acting rationally is about acting. Agents perform acts, and “rational agents” can autonomously maneuver, adapt to change and evolve (learned intelligence).
Read the source article at CIO.com.