AST June 2018 Magazine Volume 24 | Page 13

Volume 24 about modeling a problem with re- June 2018 Edition spect to a set of similar examples. That is why AI inherently relies on huge data sets to ensure the best results. It is also why AI struggles to produce intelligent answers regarding tasks that have no statistical representation in the training data. Indeed, if the data AI is trained on is too small, not representative enough, or oth- erwise biased, then the outcome will be inherently flawed – in other words, inade- quate for modeling the real world. (Learn More. Artificial Intelligence experts gathered in Rome as part of the Allianz Global Explorer Program. Courtesy of Newsplex Now and YouTube.) In short, biased or otherwise substandard or limited data inputs will generate biased Realizing this vision, however, requires discarding the “black-box” view of AI – in which humans remain willful- or substandard outputs. ly ignorant of what goes on inside an AI system, simply Artificial intelligence, then, is not inherently intelligent. focusing on its inputs and outputs. Indeed, as AI pioneer Andrew Ng argues, deep What is needed instead is a more realistic, hands-on approach – viewing AI as a highly use- learning algorithms are essentially cartoons – no t models – of the human brain. ful technology, albeit with limitations, that can be applied responsibly when paired with human (Professor Andrew Ng is an adjunct professor at Stanford University where he led Stanford’s main MOOC platform and monitoring and accountability. When pondering how AI can be best developed for the benefit of humanity and how we can avoid the pitfalls which some are pre- dicting, it’s instructive to consider what AI actually is – something which often gets clouded by the technology’s cool but often misleading last name, “intelli- gence.” taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera.) Indeed, Artificial Intelligence is not intelligence at all, it is simply a way in which algorithms can model certain phe- nomena based on a large set of related phenomena. In short, it’s not about magic – it’s 11