Artificial Intelligence is the ability of machines to seemingly think and act as humans do. Humans absorb data through our various senses, process data using our cognitive abilities, and then act. Machines also, in their own narrow way, absorb whatever information is made available to them and take relevant actions when prompted. Those actions may take the form of a conversational bot or a recommender engine. Over time, our decision-making sophistication has increased. We began making decisions relying solely on our judgment. We progressed to summarizing large swaths of data and then applying our judgment to that summary. And at present, we entrust AI with taking decisions across data and recommending actions. In narrow problems, machines have a greater ability than humans to process volumes of data and accurately identify the trends within. Was AI wrong about Nadal? Not really. It said that Nadal had a 4% chance of winning; at that snapshot in time, and based on all past data of similar matches, perhaps that was a fair assessment of his chances against Medvedev. Most humans would also have predicted a Medvedev win even if they hoped for a different outcome. I am sure that as the fifth set played out, the odds of Nadal winning rose steadily in his favor. So, the earlier prediction should not be considered wildly inaccurate just because Nadal ultimately won.

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Can AI measure the heart of a champion?

Waabi, a Toronto-based AI startup that came out of stealth last year, says it’s developed an advanced simulator that can train autonomous vehicles to handle nearly limitless types of driving conditions-in a virtual world-and do so faster and more thoroughly than self-driving rivals that prioritize road tests.

The Waabi World platform is more comprehensive than any used by competitors as it can more accurately mimic real-world scenarios and create the types of rare, challenging “edge cases” that occur on the road only rarely, company founder and CEO Raquel Urtasun tells *Forbes*. Learning in this elaborate virtual world is happening constantly, preparing the software to eventually drive a range of vehicles from robotaxis to semi-trucks.

It’s the “most scalable, high-fidelity, closed-loop simulator that ever existed and, we believe, the key to unlocking self-driving technology at scale,” says Urtasun, who is also professor of computer science at the University of Toronto and a past chief scientist for Uber’s autonomous vehicle team. “It’s an immersive and reactive environment that can automatically design tests for our self-driving brain, which we call the Waabi Driver, and also automatically assess the skills of the Waabi Driver. Ultimately, it can also teach the Waabi Driver to learn the skills of driving.”

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Toronto-based Waabi says its advanced new simulator can train autonomous vehicles to handle a nearly limitless number of road conditions–and do so faster than bigger self-driving rivals that rely more on road tests.