Gene Munster, a managing partner at Loup Ventures, a venture capital firm in Minneapolis, said it was harder than ever for new challengers because the top incumbents were so effective at “incremental evolution,” like Apple’s building subscription offerings to go with its hardware or Google’s branching out into cloud computing. The big tech companies skillfully move into new markets with lower prices and more money for marketing than their new competitors. In time, they take over.
You can try the service by creating a free account, as well. If there are too many people connected to the service, you may have to wait to launch a game. You’re also limited to one-hour sessions and less powerful hardware.
Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.
Guided by brain-like ‘spiking’ computational frameworks, neuromorphic computing—brain-inspired computing for machine intelligence—promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm–hardware codesign.