This month the Oracle Developer Community Podcast looks beyond chatbots to explore artificial intelligence -- its current capabilities, staggering potential, and the challenges along the way.
One of the most surprising comments to emerge from this discussion reveals how a character from a 50 year-old feature film factors into one of the most pressing AI challenges.
According to podcast panelist Phil Gordon, CEO and founder of Chatbox.com, the HAL 9000 computer at the center of Stanley Kubrick’s 1968 science fiction classic “2001: A Space Odyssey” is very much on the minds of those now rushing to deploy AI-based solutions. “They have unrealistic expectations of how well AI is going to work and how much it’s going to solve out of the box.” (And apparently they're willing to overlook HAL's abysmal safety record.)
It's easy to see how an AI capable of carrying on a conversation while managing and maintaining all the systems on a complex interplanetary spaceship would be an attractive idea for those who would like to apply similar technology to keeping a modern business on course. But the reality of today’s AI is a bit more modest (if less likely to refuse to open the pod bay doors).
In the podcast, Lyudmil Pelov, a cloud solutions architect with Oracle’s A-Team, explains that unrealistic expectations about AI have been fed by recent articles that portray AI as far more human-like than is currently possible.
“Most people don't understand what's behind the scenes,” says Lyudmil. “They cannot understand that the reality of the technology is very different. We have these algorithms that can beat humans at Go, but that doesn't necessarily mean we can find the cure for the next disease.” Those leaps forward are possible. “From a practical perspective, however, someone has to apply those algorithms,” Lyudmil says.
For podcast panelist Brendan Tierney, an Oracle ACE Director and principal consultant with Oralytics, accessing relevant information from within the organization poses another AI challenge. “When it comes to customer expectations, there's an idea that it's a magic solution, that it will automatically find and discover and save lots of money automatically. That's not necessarily true.” But behind that magic is a lot of science.
“The general term associated with this is, ‘data science,’” Brendan explains. “The science to it is that there is a certain amount of experimental work that needs to be done. We need to find out what works best with your data. If you're using a particular technique or algorithm or whatever, it might work for one company, but it might not work best for you. You've got to get your head around the idea that we are in a process of discovery and learning and we need to work out what's best for your data in your organization and processes.”
For panelist Joris Schellekens, software engineer at iText, a key issue is that of retractability. “If the AI predicts something or if your system makes some kind of decision, where does that come from? Why does it decide to do that? This is important to be able to explain expectations correctly, but also in case of failure—why does it fail and why does it decide to do this instead of the correct thing?”
Of course, these issues are only a sampling of what is discussed by the experienced developers in this podcast. So plug in and gain insight that just might help you navigate your own AI odyssey.
CEO/founder of Chatbox.com
Oracle A-Team Cloud Architect, Mobile, Cloud and Bot Technologies, Oracle
Software Engineer, iText
Consultant, Architect, Author, Oralytics
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