What AI needs to go mainstream
The future of artificial intelligence (AI) relies on advances in the underlying machine learning technology, and an effective ML architecture.
Through research with organizations such as Google, Arm, and leading universities that focus on data science, GigaOm reports on two important factors required to broaden the impact and deliver on the promise of AI. Neither factors pertain to how AI’s are built and trained, but rather to where they are deployed and used. They are:
- The decrease in cost and increase in power of high-performance chips that can do AI inference “at the edge.”
- The development of middleware that allows a broader range of intelligent AI applications to run seamlessly on a wider variety of chips.
Download the full report, “AI at the Edge,” to better understand how the right ML architecture will ensure that AI meets its potential in our pockets, our cars, our houses, and a hundred other places.