This week on The Digital Life, we discuss the pitfalls of predicting AI. AI predictions range from the measured and meaningful to highly unrealistic and downright hysterical. But how can you tell the difference? In this episode, we dig into some rules of thumb for thinking through the AI predictions we encounter, as laid out in the article “The Seven Deadly Sins of AI Predictions” by Rodney Brooks, a founder of Rethink Robotics. From better understanding the properties of narrow AI to asking “how will it be deployed?”, questioning supposed magical properties without limit, to admitting, in the long term, we just don’t know, we’ll explore the many factors that counter the breathless hysteria of AI predictions. Join us as we discuss.

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The Seven Deadly Sins of AI Predictions

This week on The Digital Life, we explore the difficulties of establishing an AI code of ethics, inspired by an article from MIT Technology Review, “Establishing an AI code of ethics will be harder than people think”.

There’s already ample evidence that artificial intelligence can exacerbate existing system bias if left unchecked. And, a set of design ethics guiding AI development may be far in the future, as the difficulty of defining an applicable rule set, and the subjective nature of ethics itself, makes the task extremely difficult. However, such arguments over AI ethics often emphasize top-down efforts rather than bottom-up — for instance, auditing AI decision-making from the initial data curation stage and throughout the process. In this view, AI design and development is not a purely technical practice, but instead incorporates cultural aspects, similar to teaching children right and wrong. Join us as we discuss.

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Establishing an AI code of ethics will be harder than people think

This week on The Digital Life, we discuss the intersection of artificial intelligence and science with special guest is Dany DeGrave, founder of Unconventional Innovation. AI and science are coming together in new and significant ways, including the use of cognitive and other innovative technologies in R&D — like NLP, machine learning, and advanced analytics. As bio-science companies rush to invest in AI, the implementation of scientific research, drug trials, and even personalized medicine is undergoing significant change. But with the potential to make erroneous decisions, and even be used for malicious purposes, it may be a long time before we fully trust AI to be used in such development.

This week on The Digital Life, we chat about automation and reinventing yourself in the job market. Yuval Noah Harari, bestselling author of books like Sapiens and Homo Deus had this to say about automation and the difficulties of reinventing your career on the Geek’s Guide to the Galaxy podcast:

“It’s questionable how many times a human being can reinvent himself or herself during your lifetime—and your lifetime is likely to be longer, and your working years are also likely to be longer. So would you be able to reinvent yourself four, five, six times during your life? The psychological stress is immense. So I would like to see a science fiction movie that explores the rather mundane issue of somebody having to reinvent themselves, then at the end of the movie—just as they settle down into this new job, after a difficult transition period—somebody comes and announces, ‘Oh sorry, your new job has just been automated, you have to start from square one and reinvent yourself again.’”

Have you ever had to reinvent yourself during your career, maybe due to automation? Personal characteristics like flexibility and creativity play a role as well as external factors like work availability and the market. Join us as we discuss.

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Why Science Fiction is the Most Important Genre

On The Digital Life this week we chat about human / machine hybrids — the combination of AI and people — into advanced teams that produce amazing creative output. From chess to photography, art to science, these so-called “Centaurs” may represent the future of work for many fields. Join us as we discuss.

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Art by algorithm