AI
We can push all that data through a model to train the weights and biases to get more and more results.
“ The magic of deep learning is you’ re adding more and more of these layers and you’ re trying to do more and more with the network. A lot of the same techniques that are traditional in machine learning still apply in deep learning.”
At its core, deep learning is what happens when you combine lots of data with immense compute power. As networks grow deeper, compute requirements scale accordingly. Randall quipped that if deep learning had been involved in The Terminator, there would have been no missed shots: every single action would be 100 % efficient and humanity would have been wiped out instantly.
Fortunately, real-world applications are far more humanitarian and practical. Today, deep learning powers tools used to locate missing
If deep learning had been involved in Terminator, every action would be 100 % efficient and humanity wiped out, according to Caylent’ s Randall Hunt children in high-traffic transportation hubs like airports and train stations, as well as the vision systems steering autonomous vehicles.
As Randall noted during his AWS presentation, these computer vision tools rely heavily on training at scale:“ The way these work is deep learning takes human-annotated data – where humans spend thousands and thousands of hours describing video – like labelling stop signs on the road, humans and trucks.”
This approach marks a dramatic shift from early software paradigms. Where engineers once relied on clever algorithmic shortcuts, deep neural networks ingest raw visual features directly. Services like Amazon Recognition showcase this evolution in real time, allowing software to process webcam feeds or static image captures to identify subtle emotional cues, objects and identities instantly.
Yet, as impressive as visual recognition is, the pace of innovation has accelerated even faster in natural language and generative models.
CREDIT: CLIVE MASON