Doing a small part to help close a gender gap
Few, if any, of my classmates shared my fascination with the Mark I Computer that was on display in our university’s Science Center. It is hard to blame them. Towering at 8 feet and filled with rotary switches, crystal diodes, and tangled wires, the Mark I resembles a prop from a science fiction movie rather than a computer the U.S. Navy once used. In its prime in the 1940s, the Mark I was one of the most powerful supercomputers on Earth; today, the smartphone we carry in our pockets would put it to shame. Yet, neither the smartphone nor the operating system that powers it would be possible without the early mainframes like the Mark I or the computer science pioneers who operated them. …
Breakthroughs in artificial intelligence complicate the belief that our intellect—even our creativity—will remain unrivaled
The belief in our distinctiveness is central to human self-understanding; however, time and again, what was seen as our distinguishing attribute has proven to be a mirage.
For millennia, humans have asserted themselves as superior to other living forms. From Mesopotamia to China, the mythologies of ancient civilizations traced humankind’s origin to divine creation. Religious doctrines have echoed and solidified this sentiment. In the Bible and the Qur’an, humans are portrayed as divinely ordained to have dominion over all creation. …
Introduction to Generative Adversarial Networks (GANs) and their potential
Teaching Computers Creativity
It started in a pub. Ian Goodfellow and his fellow doctoral students at the University of Montreal met to celebrate the completion of the 2014 academic year. Their conversation focused on the frontier of AI research: synthetic data generation; specifically, approaches that would allow a computer to produce realistic-looking images. Compared to everything machines have mastered, this problem may not seem worthy of the attention of some of the finest minds in AI research. However, they recognized the profound implications of computer’s shortcomings that lay hidden beneath the mountain of all it could accomplish.
Machine learning algorithms are effective at recognizing patterns in existing data and using that insight for tasks such as classification and prediction. When asked to generate new data, computers tend to struggle. An algorithm can defeat a chess grandmaster or classify whether a credit card transaction is likely to be fraudulent; in contrast, any attempt at making small talk with Amazon’s Alexa or Apple’s Siri is doomed. Indeed, humanity’s most basic and essential capacities — e.g., a convivial conversation or the crafting of an original creation — can leave even the most sophisticated supercomputers in digital spasms. …
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