This week has brought another story from the artificial-intelligence-taking-over the-world files with Google’s new AlphaGo technology emerging victorious in the latest duel between robots and humans.
The impressive machine dispatched the reigning (living and breathing) Go champion 4-1 in the best-of-5 series.
The Go board game, which originated in China, requires complex strategic thinking with the number of possible outcomes dwarfing that in chess. AlphaGo’s win demonstrates the emergence of intuition with the abstract strategic thinking not mastered in previous artificial intelligence ventures.
AlphaGo’s systems include ‘deep learning’ methods, allowing the machine to run thousands of simulated scenarios to build its “experiences” to use when playing the game for real. The use of neural networks allows problem-solving without any prior programming.
So, has Google really nailed human intuition in AI?
AlphaGo’s “intuition” can be boiled down to an algorithm that is able to categorise and sample a huge number of real and simulated possibilities with varying degrees of available data, sort them and use probability to make a leap to a decision. Compare this with human intuition, which is little more than a guess with some post-hoc rationalisation, and you’ll agree this is a huge step forward for AI.
It’s a well-documented fear that these developments in AI threaten to create a “super intelligence” that overtakes the greatest human potential. But in a range of industries from healthcare to pure science, the extrapolation of this intelligence to analytics could help perform the most detailed root cause analysis on both actual and potential events. When dealing with matters as important as human life, however, the increased quality of decision-making cannot be a seen as a threat.
Demis Hassabis, who heads Google’s machine learning team said, “The methods we’ve used are general-purpose; our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis.”
In the meantime, we are likely to see more and more “Narrow” AI capabilities, either embedded in familiar applications or as new, standalone capabilities. Using new technology to solve specific problems or alleviate us from menial tasks illustrates that the proliferation of “AI” has already begun and it will only pick up pace. Rather than be weary that evolution will take away jobs or manual tasks, we should embrace the opportunity to develop new skills and knowledge.
One cool “Narrow AI” to check out is Amy: https://x.ai/
Amy is an AI-powered personal assistant for scheduling meetings. You interact with Amy as any other person and it will do all the email back and forth that comes with finding a time with your invitees to schedule a meeting. It will also manage cancellations and reschedules for you. While this is just a narrow menial task, I know it could save me a lot of time. And if you think about something like this being seamlessly integrated with Siri, you can expect lots of menial tasks to be taken care of for you in the near future.
Where do you see the potential of AI?
How else can businesses harness this technology?
