Top 5 Predictions for the Future of Deep LearningComputers can crunch through mountains of data. Deep learning, a specialized subset of artificial intelligence (AI), allows human beings to reap the benefits.
A computer's neural network is fundamentally concerned with connecting the computer's "cells." This allows it to learn from its surroundings and make predictions.
The world's top minds are pouring their attention and resources into improving deep learning. Here's what coming up on the horizon.
Future of Deep LearningBefore we get started, let's explain exactly what a neural network is. It's comprised of artificial neurons called units.
There are three types of units. Hidden units, input units, and output units. Each has its own function and use.
All of these units are connected. That's what gives the network its power.
Many of the predictions about the future of deep learning revolve around new forms of neural networks.
1. The Rise of AIIt seems clear that AI development is becoming more valuable. Deep learning is allowing voice assistants like Amazon's Alexa and Apple's Siri to integrate with our lives. This trend is only going to continue.
Consumers will see AI become a big part of their lives. Everything from organizing your schedule to driving around will be easier with AI.
2. Capsule NetworkA capsule network is a type of deep neural network. They're starting to become popular because they provide a higher level of accuracy and reduce errors.
3. Increased Corporate UseCorporations can receive a huge benefit from deep learning. They can gain valuable insights about their customers and their buying patterns.
Should the next sale be 50 percent off on Saturday or 10 percent off every day? AI can help business owners model those types of questions.
The possibilities are endless.
4. Deep Reinforcement LearningDeep Reinforcement Learning (DRL) is an advanced form of deep learning. It refers to a neural network that uses actions and rewards to communicate with its environment. Game theorists have used DRL to win games like Go and Atari.
DRL is also popular because it takes a lot less data to train. It's not a development, but there is renewed interest.
5. The Black BoxDecisions are less valuable if you don't know how they were made. One of the problems with deep learning is that researchers have had trouble presenting a "black box", or explaining how the network reached its decision.
That's going to change in the future. Each step in the decision-making process will be closely audited.
The Current StateDeep learning is wrapped up in science fiction. Many people have fantastical expectations. Avoiding the bubble is one of the things that technology enthusiasts have to do to survive.
Investors will continue to give money to deep learning and AI projects. The danger comes projects are over-hyped.
Living a life where robots do everything for you seems unlikely, at least in the near future. However, that doesn't mean that AI doesn't accomplish amazing things every day. It just means that the technology is still growing.
Deep learning is expected to grow more precise as time passes. Keep reading our blog to stay current.
Get in touch