Everything You Need to Know About the Future of Neural Networks

networks are arguably the technological development with the most potential currently on the horizon. Through neural networks, we could feasibly handle almost any computational or contemplative task automatically, and someday, with greater processing power than the human brain.

For now, neural networks are still in their infancy, but already, they’re an impressive technology responsible for tremendous breakthroughs in everything from speech recognition to medical diagnoses. The question is, where does it go from here?

How Neural Work Today

Let’s start by talking about how neural networks, or neural nets, work today in their current form. Neural nets are computer programs that are assembled from thousands to millions of units, each of which is designed to function like an artificial neuron. When being “trained,” a neural network is typically fed information, allowing it to recognize patterns like spotting familiar faces in photos or identifying the correct way to hit a tennis ball. With feedback, neural networks then work to modify the way they processed the problem, “learning” how to do better over long periods of time.

When done training, neural nets can solve a wide variety of different problems. They can notice deviations in historical patterns proactively, so you can receive alerts on new events relevant to your business, they can automatically recognize trigger points in a pattern (like picking a out of a photo or diagnosing a medical condition), and they can perform complex operations without supervision (like when playing a game).

Key Strengths of Neural Nets

There are several key strengths of neural nets that make them a favorite choice of AI developers:

  • Performance on problems with many variables. For a problem with a strict set of rules and requirements, and with constrained inputs, it’s easy for a machine to work out the answer. The go-to example here is a calculator; the rules of mathematics are never broken, and are relatively simple to follow. Input two variables (and two real numbers), and you can get their sum easily. But identifying speech patterns or diagnosing illnesses require far more variables; machines need to “understand” not only what they’re looking for, but how it’s differentiated from the noise, and how it might be influenced in different ways. Neural nets are ridiculously good at solving these big problems—sometimes even better than humans.
  • Feature engineering. Neural nets are also incredibly good at figuring out the correct features to ascribe to a problem, known as feature engineering. Let’s say you’re trying to teach an algorithm how to play (and win) a game of Go, the way Google did. Go is a game with practically limitless move possibilities and no clear way of determining whether a move is “good” or “bad” (especially in the early game). For the machine to learn effectively, it must be able to learn how to identify what makes a move more or less likely to get the machine closer to victory. Neural nets can do this; they can create new categories for consideration, and apply them to their work.
  • Applicability. Neural nets also have the power of flexibility. Once established, they can be applied to almost anything, whether it’s helping spot the issues interfering with their productivity or improving air traffic patterns for smoother flights. The core functionality of a neural net is to learn something efficiently, so if you have a system that can learn to recognize patterns, it could feasibly recognize patterns in almost any domain.
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Key Weaknesses of Neural Nets

That said, there are also some key weaknesses preventing neural nets from seeing full-range applications:

  • Data requirements. For starters, all neural nets must go through a “learning” period where they start to recognize patterns and refine themselves. While we’re able to “teach” machines more efficiently than ever before, there’s still a massive data requirement before those algorithms can start to be effective. Depending on the application, this could take 10,000 discrete sets of data or more. This could either substantially increase the time it takes to make a neural net effective, or limits the possible applications.
  • Expensiveness. Neural nets are also expensive and time-consuming to develop. The computational processes needed to handle all those variables and all those incoming sets of data demand CPU and GPU power beyond the scope of a normal system. This makes it a discouraging endeavor for some engineers, and drives up the price of a functional system, making it harder to use for your intended purposes.
  • Difficulty and blindness. As you might imagine, the realities of developing a neural net are much more in-depth and complicated than can be implied with a simple, overarching definition. It’s incredibly hard to learn how to develop a neural net, and many engineers who begin the journey eventually drop out of the running. On top of that, because of the intricacies of neural nets, we often don’t have transparency to see how our algorithms are coming to their conclusions; we can determine whether their findings are accurate, but we can’t see exactly how they came to those answers, which makes it even more mystifying—even to professionals.
  • Long-term potential. Neural nets have already been responsible for significant advancements in the realm of AI, but in terms of long-term potential, it may not have as much power as other possibilities, like kernel methods, or even classical AI. There’s a hard limit to how efficient or complicated neural nets can get, and that upper limit is discouraging to many researchers.
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What’s in Store for the Future?

With all those strengths fueling the of neural nets and all those weaknesses complicating things, what could the hold for this incredible technology?

  • Integration. The weaknesses of neural nets could easily be compensated if we could integrate them with a complementary technology, like symbolic functions. The hard part would be finding a way to have these systems work together to produce a common result—and engineers are already working on it.
  • Sheer complexity. Everything has the potential to be scaled up in terms of power and complexity. With technological advancements, we can make CPUs and GPUs cheaper and/or faster, enabling the production of bigger, more efficient algorithms. We can also design neural nets capable of processing more data, or processing data faster, so it may learn to recognize patterns with just 1,000 examples, instead of 10,000. Unfortunately, there may be an upper limit to how advanced we can get in these areas—but we haven’t reached that limit yet, so we’ll likely strive for it in the near future.
  • New applications. Rather than advancing vertically, in terms of faster processing power and more sheer complexity, neural nets could (and likely will) also expand horizontally, being applied to more diverse applications. Hundreds of industries could feasibly use neural nets to operate more efficiently, target new audiences, develop new products, or improve consumer safety—yet it’s criminally underutilized. Wider acceptance, wider availability, and more creativity from engineers and marketers have the potential to apply neural nets to more applications.
  • Obsolescence. Technological optimists have enjoyed professing the glorious future of neural nets, but they may not be the dominant form of AI or complex problem solving for much longer. Several years from now, the hard limits and key weaknesses of neural nets may stop them from being pursued. Instead, developers and consumers may gravitate toward some new approach—provided one becomes accessible enough, with enough potential to make it a worthy successor.

Regardless of what your business goals are, there’s a good chance neural nets will be able to help you achieve them—if not now, then in the very near future. Despite the shortage of developers, companies and engineers are working constantly to refine their neural net efforts, which means we’re in store for a “golden age” of neural networks (at least temporarily)

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