Over the past few years, and after decades as little more than a mathematical curiosity, useful industrial applications of AI have become commonplace. AI is now recognised as one of the primary drivers of computing development. In 2018, Canadians Yoshua Bengio, Geoffrey Hinton and Yann LeCun – the'godfathers of AI' – received the Turing Award, computing's highest honour, for their foundational work on deep learning. The International Data Corporation forecasts that worldwide revenues for the AI market will grow to nearly $330 billion in 2021, and will exceed $550 billion by 2024 (IDC Semiannual AI Tracker, January 2021). Driven and enabled by the extraordinary growth of data globally, this surge in the AI industry has also spurred a flood of AI-related patenting.

LexisNexis may contact you in your professional capacity with information about our other products, services and events that we believe may be of interest. You can manage your communication preferences via our Preference Centre or via the unsubscribe link provided within our communications. You can learn more about how we handle your personal data and your rights by reviewing our Privacy Policy.

This project may be Apple's fallback to building its own car. From 2014 to 2019, roughly, Apple's Project Titan was a ground-up autonomous, electrified vehicle project. Apple found out that building a car is enormously complex, there are regulatory hurdles to clear far tougher than for phones or PCs, and you can't build a world-class auto factory in a couple of years. Apple also found out that not everyone wants to run a contract factory for Apple, including BMW and Daimler, and if there was an agreement, divorce court would have followed closely. Too many egos and everyone would want the final say.

From redefining talent management by evaluating job candidates on innate and emerging strengths by removing conscious and unconscious biases from hiring decisions as eightfold.ai International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021, attaining a 48% Compound Annual Growth Rate (CAGR). Please see the latest roundup of machine learning forecasts and market estimates, 2018 for more market data on machine learnings' exponential growth. The National Bureau of Economic Research distributed a study last month from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe.

From redefining talent management by evaluating job candidates on innate and emerging strengths by removing conscious and unconscious biases from hiring decisions as eightfold.ai International Data Corporation (IDC) forecasts that spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021, attaining a 48% Compound Annual Growth Rate (CAGR). Please see the latest roundup of machine learning forecasts and market estimates, 2018 for more market data on machine learnings' exponential growth. The National Bureau of Economic Research published a study last month from the Stanford Institute For Economic Policy Research titled Some Facts On High Tech Patenting. The study finds that patenting in machine learning has seen exponential growth since 2010 and Microsoft had the greatest number of patents in the 2000 to 2015 timeframe.

The memristor (short for "memory resistor") is poised to revolutionize computing. A memristor is a passive two-terminal electrical component that can function as a circuit element relating charge and magnetic flux linkage. When the current is stopped, the device retains the last resistance that it had, and when the flow of charge starts again, the circuit resistance matches what it was when it was last active. In a nutshell, memristors are resistive devices with an inherent memory. It is this characteristic that makes the memristor an ideal electronic component for neuromorphic computer architectures such as physical neural networks (as opposed to strictly software based neural networks), synaptic devices and AI (Artificial Intelligence) chips.