On the second day of the COSM 2021 conference, speakers asked -- with appropriate skepticism -- whether we could ever produce true Artificial General Intelligence (AGI). But the final day of the conference hosted a conversation on the realistically achievable forms of AI and quantum computing that may pose existential threats to modern life. Robert J. Marks, Director of the Walter Bradley Center for Natural and Artificial Intelligence (which hosted COSM) -- also Distinguished Professor of Electrical and Computer Engineering at Baylor University -- spoke first. The title of his 2020 book, The Case for Killer Robots: Why America's Military Needs to Continue Development of Lethal AI, provides an unsubtle hint at his position. Marks thinks that AI will never be "will never be sentient. It will never understand what it is doing. And, currently, it has no common sense."

Last year, after breaking the Guinness World Record for the Key West to Cuba run, we wondered what was next for the #77 Lucas Oil SilverHook ocean racing powerboat? We found the answer in the 50th anniversary of the Trinidad & Tobago Great Race, one of the most grueling races in the world. The 115-mile endurance course starts in Trinidad's Port of Spain, where you head north and then east near the island before popping into the Atlantic Ocean for a 50-mile sprint to the finish in Store Bay, Tobago. Because of the logistical difficulties of racing on foreign shores, we were the first American entry in 29 years. We knew we would face stiff competition from Jumbie, Cat Killer, Mr. Solo and other local rivals that know the course well.

Federighi announced new APIs that help coders building apps for Apple devices do things like recognize faces or animals in photos, or parse the meaning of text. The reasoning goes that if you can make your phones, operating system, or cloud the best place to build smart new software that leverages AI, more users and revenue will follow. For example, Federighi boasted that Apple's new tools help developers run machine learning on data without it having to leave a person's device, giving performance and privacy benefits. A company that needs to run image recognition inside apps on both Apple and Android devices might prefer to use Google's cloud machine learning APIs instead, for example.

Apple's iMac updates and new HomePod speaker drew most of the attention at the company's World Wide Developers keynote. But tucked away in the middle were a short few minutes in which software chief Craig Federighi casually launched Apple into one of the tech industry's fiercest competitions– the contest to help developers build the next generation of AI-powered applications. Federighi had already spent some time boasting how Apple was using machine learning to do things like make Siri smarter, or help users rediscover old memories in the Moments photo app. Then he announced that Apple would also be gifting some AI superpowers to developers. "We want to make powerful machine learning easy for you to incorporate in your apps," he said.

Since the birth of the modern era of computing there has been an arms race between CPU microprocessor manufacturers that has pushed computer capabilities even higher, characterised by Moore's Law. This era's computer technology can be characterised as running sophisticated but essentially dumb applications. A new era is beginning that will drive microprocessor manufacturers to support intelligent applications, such as those based on newly emerged deep learning and other machine learning algorithms. Deep learning is the umbrella term for a set of techniques for architecting and training neural networks that in recent years has made huge leaps forward in accuracy. For example, deep learning neural networks are at the root of the most successful technologies for natural language understanding, image recognition, advanced game playing (such as Go), and others.