As promising as Deep Learning is, and despite its initial success stories, it might actually slow the development of true AI.
If you’ve been keeping tabs on our recent AI coverage, you are probably aware that AI is the umbrella under which exists many concepts and methods of development. In the end, these different but not exclusive approaches seek to bestow machines with more human-like reasoning and intelligence.Deep Learning is an obstacle to the development of true AI.Click To Tweet
Deep Learning is one type of reasoning that AI platforms adopt. Deep Learning, as a representation of that concept, is receiving significant research efforts, investment, and even media buzz.
Deep Learning, the “True” Path to Human-Like Intelligence?
Since the dawn of computing technology, developers created programs and algorithms by writing code that machines translate into precise instructions.
Computers, however powerful and versatile they might be, are often times incapable of carrying out tasks that humans perform effortlessly. Complex problems that take real-time experience into account can’t yet be reduced into code lines, hence the need for more novel approaches.
Deep Learning proposes a different way of solving problems.
Instead of code-writing the way a program solves a problem, the program “learns” to solve it on its own.
That’s the broad concept behind Deep Learning, which relies on multi-layered neural networks, where each layer starts where the last layer has left off to solve the problem.
It should be noted that neural networks are separate but linked nodes that simultaneously run calculations, yet they only resemble our own neural system.
Some experts argue that Deep Learning isn’t the true path to create human-like intelligence for machines, and that it might be hindering “true AI” progress.
All Deep Learning systems currently in use are “supervised”–meaning they need pre-determined data that they will, basically, classify–which take up huge resources that otherwise would be directed to the development of AI with real potential, referred to as “unsupervised AI”.
In a not too dissimilar way than the human brain, unsupervised AI would recognize new patterns, label them on its own and classify them without human prior input. This is the “true AI” MIT scientists were referring to as “unsupervised”.
Big Corporations Make do With “Supervised” Deep Learning
Deep neural networks have been around since the 1960s, but their development has really only taken off in recent years when two conditions have come together: Big Data and computing power.
Deep Learning systems must have at their disposal large amounts of data, and sufficient computing power to continuously update themselves, learn from their experience and keep improving.
These applications and many others represent for Google, Facebook, Apple, Microsoft, Amazon and the likes the stakes of deep learning.
Big corporations are already cashing on the constant flow of data, dedicating their colossal resources to supervised Deep Learning.
Besides some initiatives with timid progress (like Google’s Artificial Brain), no “unsupervised AI” big project has gotten off the ground.
Per MIT Technology Review, Quoc Le (one of Google’s Brain research scientists) has identified “unsupervised learning” as the biggest challenge to developing true AI that can learn without the need for labeled data.