How The Future Of Deep Learning Could Resemble The Human Brain Posted on : Nov 11 - 2020

Over the last several years, deep learning — a subset of machine learning in which artificial neural networks imitate the inner workings of the human brain to process data, create patterns and inform decision-making — has been responsible for significant advancements in the field of artificial intelligence. Building on what is possible with the human brain, deep learning is now capable of unsupervised learning from data that is unstructured or unlabeled. This data, often referred to as big data, can be drawn from various sources such as social media, internet history and e-commerce platforms, among others.

These sources of data are so vast that it could take decades for humans to comprehend it and extract relevant information, but interpreting this data through deep learning allows models to detect objects, recognize speech, translate language and make decisions at remarkable speeds. Many companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adopting AI systems driven by deep learning to gain a competitive advantage through data and automation.

However, real-world deployments of deep learning remain very limited. While the technology is there to process the data, a recent project (download required) led by MIT researchers argues that the computational and storage demands required to do so are incredibly costly from an economic, environmental and technical perspective. These demands can increase exponentially with each incremental hardware advancement. Additionally, I've found that the storage space needed almost entirely restricts deep learning to the cloud, which creates latency, bandwidth and connectivity challenges.

To overcome these barriers, we should shrink the computational and storage requirements of deep learning. While it is easier said than done, luckily, we have the framework in place with our own brain. Just as we looked to the human brain for inspiration in developing AI, we can look to the human brain as a model for increasing efficiency — specifically, by taking the early development phase of the brain and mirroring it for deep learning.

Mirroring The Intricacies Of The Human Brain In Early Childhood

To continue to drive AI advancement in the decades to come, we need to reimagine deep learning at its core. A promising approach is to mirror how the human brain develops, particularly in early childhood.

During infancy, the brain experiences synaptogenesis — an explosion of synapse formation as the brain begins to develop. In early childhood, we have the greatest number of synapses that we will have in our lifetime, with totals increasing until about two years old. Over time, our synapses begin to "train" — strengthening, weakening and evolving as the connections in our brains begin to sparsify. View More