During the Intel AI Summit earlier this month where the company demonstrated its initial processors for artificial intelligence training and inference workloads, Naveen Rao, corporate vice president and general manager of the Artificial Intelligence Products Group at Intel, spoke about the rapid pace of evolution in the AI space that also includes machine learning and deep learning. The Next Platform did an in-depth look at the technical details Rao shared about the products. But as noted in the story, Rao explained that the complexity of neural network models – when talking about the number of parameters – is growing ten-fold every year, a rate that is unlike any other technology trend we have ever seen. For Intel and the myriad other tech vendors getting making inroads into the space, AI and components like machine learning and deep learning already is a big business and promises to get bigger. Intel's AI products are expected to generate more than $3.5 billion in revenue for the chip maker this year, according to Rao.

Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation "AI is not magic." Winner of the 2018 Turing Award (with the other "fathers of the deep learning revolution," Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the "amazing progress in AI" while stressing the importance of understanding its current limitations and recognizing that "we are still very far from human-level AI in many ways." Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this "learning component," and deep learning develops intuitive knowledge "by acquiring that knowledge from data, from interacting with the environment, from learning. That's why current AI is working so much better than the old AI."

This is the web version of Data Sheet, Fortune's daily newsletter on the top tech news. Sign up here to get it delivered to your inbox. The number of transistors packed onto a modern chip inside your phone or PC runs into the billions but it's still sometimes amazing to comprehend the computing power you can easily hold in the palm of your hand. When I met Intel vice presidents Gadi Singer and Carey Kloss on Wednesday, they showed me a new circuit board the company has created for speeding up artificial intelligence apps. The board is the size of an SSD drive, made to plug into a standard PC or server.

Carey Kloss has been intimately involved with the rise of AI hardware over the last several years, most notably with his work building the first Nervana compute engine, which Intel captured and is rolling into two separate products: one chip for training, another for inference. He tells The Next Platform that the real trick is to keep pace with the increasing size and complexity of training models with balanced architectures. Given that the compute required for training is doubling almost quarterly, this is more important that ever from performance, efficiency, and scalability standpoints. Kloss and Intel think they have finally struck that golden grail of balance with the Spring Crest Deep Learning Accelerator or, more simply, the Intel Nervana NNP-T. The name may lack the poetic ring of say, a "Volta" but we do see this training chip from its current status (they just got their first silicon back) as competitive and filling in some gaps in terms of performance/efficiency and data movement potential.