The Future of AI Innovation is Exciting!

After rapid growth over the past few years, artificial intelligence has become one of the BIGGEST priorities of enterprises.

Well, what has made it so hot? With AI, we can design systems that learn and adapt to all the new data we collect. Just a few years ago, AI seemed to be impossible. But now, it’s quickly becoming necessary and expected.

As a result, major tech corporations, sprawling startups, and research institutes have been working diligently to develop AI infrastructure that augments human intelligence for advantages over their competitors.

The massive spurt of AI advancement is largely due to three cohesive factors:

  • Powerful Graphics Processing Units (GPUs)
  • The emergence of big data for complex computations.
  • The development of deep learning. Deep learning is an AI computation model that has been around for decades.

It’s always important to enlighten ourselves about the future of AI despite only scratching the surface of what these three factors can accomplish when combined. In the future, old variables will be enhanced into new variables to accomplish MUCH more.

So let’s explore the accelerators of AI innovation.

Behold, Deep Reasoning!

Just like how deep learning is crucial for AI innovation today, it’ll also be in the future. We’re realizing the power of deep learning, whether it be for natural language processing (NLP), computer vision, or speech recognition. This won’t end soon.

But deep learning has struggled with allowing smart machines the ability to reason, which can drive success in many AI applications.

Deep learning is great for perception and classification problems rather than real reasoning problems. It has only been successful when using A LOT of labelled data. Now we should put equal focus on solving reasoning problems as much as we do for those of perception and classification.

There are many uses of reasoning such as simple planning, basic common sense, dealing with varied circumstances, and making complex decisions in a specific profession or industry.

We need to build algorithms that understand the basic, natural idea that anything can change.

With a few examples in narrow applications such as autonomous vehicles and other technologies: Most professionals agree that we have a LONG way ahead of us in terms of teaching systems to deeply reason and making them efficient enough for scaling reasoning capabilities through a general approach rather than narrow.

Currently, we can map a natural language sentence to a logical form in some areas as a result of spending much effort in labelling text. Through the use of formalized reasoning mechanisms, we can work with these extracted formulas. The real challenge is to reduce the work needed to generalize these formulas to fit a variety of applications.

Some AI experts believe that we can use deep learning to solve the reasoning challenge within the next decade.

At the moment, we have the tools to develop a GAME-CHANGING solution for reasoning. One of the most important tools is large amounts of data, as it is strongly suggested that machine learning strategies use data as a resource required for automated reasoning. We must scale reasoning computations to a wide array of applications, similar to the surge that neural networks experienced as a result of GPUs.

Enhancing Deep Learning

Though deep learning will be used for a while, it’s subject to change in the coming waves of AI innovations. Technologists emphasize that to apply deep learning models that scale across more complex and diverse tasks, we must become more efficient at training deep learning models. We can achieve this level of efficiency using “small data” and more unsupervised learning.

The Emergence of Small Data

LOTS of data is needed to teach a task to neural networks of deep learning models. For example, you may need to feed tens of millions of images to a neural network for it to recognize an object. Training, testing, and refining AI systems is slow because it can be expensive and time-consuming to obtain relevant datasets that are sufficient.

But, at times, there may not even be enough accessible data to teach a deep learning model.

Engineers are working hard at finding ways to design systems that require less data to learn a task. They believe they can find a reasonable solution. Because of this, technologists are expecting small datasets to fuel future AI innovation rather than big data — quite the opposite!

Developing Unsupervised Learning

The deep learning models we have now need huge datasets that are labelled, so the algorithm knows what each piece of data represents. Supervised learning requires people to manually do the labelling. This is a tedious and expensive task that delays innovation and could introduce human bias to AI systems.

But even when data is labelled, deep learning algorithms require more human input to learn. Usually, you would have a subject matter expert giving all of their knowledge to the system. This can make the process very tiresome for the SME, despite resulting in a very accurate algorithm.

Unsupervised learning allows raw, unlabelled data to be used to train a model with little human effort. This is opposite to supervised learning, where the system learns from the patterns of the world.

But most AI experts believe deep learning’s best will be pure unsupervised learning, but they admit that we’re only starting to determine ways it can be used to train practical applications of AI. Upcoming AI advancements will likely be a result of deep learning models that combine supervised and unsupervised learning as semi-supervised learning.

Some machine learning engineers are exploring several learning methods with significant benefits such as requiring LESS labelled data, LESS data volume, and LESS human intervention. Unsupervised learning is best resembled by the “one-shot learning” method. This learning model follows the idea that most humans learn from one or two examples.

In practical applications, the deep network of machines should ideally be developed in a largely unsupervised manner. Then, you’d have a small labelling task on the back end.

The other methods also help speed and scale deep learning applications, but they require more supervision. These methods include:

  • Active learning: Labelled data is provided only if the system requests it. Rather than a human feeding the machine large amounts of data, the computer is initiating the labelling. Thus, active learning is a small step towards unsupervised learning.
  • Transfer learning: A trained model is applied to a completely different application, with minimal training and labelled data.
  • Reinforcement learning: A system takes actions in a certain environment as it is rewarded for desirable decisions and penalized for undesirable decisions.

AI Hardware & Efficient Algorithms

The power crunch that delays innovation and slows AI application performance can be mitigated by simplifying learning processes. GPUs are insufficient, despite speeding up the training and running of deep learning models which requires a great amount of computational power. Models can be tested and then trained in two to three weeks, so it isn’t ideal especially if the developers are trying to iterate fast. So a lot of research and experimentation has gone towards making model structures smaller and run faster.

To solve this, we can’t scale enough hardware. In the end, hardware won’t solve computational complexity, but a combination of hardware and model improvement will.

Technologists believe that GPUs will gain speed with algorithmic improvements while retaining their vital role as part of the “computational power” factor that determines the rate of AI innovation. But this factor isn’t alone. AI hardware such as neuromorphic chips or quantum computing systems are under development and could also be vital factors determining how fast AI systems can advance.

Ultimately, researchers do not want human thought patterns like reasoning and perception to be the limit of future AI systems. Instead, they want these systems to accomplish a completely different way of thinking. Though this may not happen in upcoming AI developments, it’s definitely in the plans of AI visionaries.

AI can allow us to invent many new types of thinking that don’t exist biologically and aren’t similar to human-like perception. So in the end, these AI systems won’t replace human thinking, but will rather augment it.

Key Takeaways:

  • Currently, AI is great at perception and classification problems. However, it is a LONG way from being successful in complex reasoning problems that can help in a variety of practical applications. One of the most important tools we can use for AI enhancement is large amounts of data. It’s strongly suggested that machine learning strategies use data as a resource required for automated reasoning.
  • Deep learning requires A LOT of computational power and human effort for labelling. Hardware like more powerful GPUs cannot solve this issue. Instead, we need to think of ways to make deep learning algorithms simpler, more efficient, and less reliant on human supervision (through unsupervised learning).

Thank you for reading! I hope this was helpful and enjoyable. Please let me know any questions you may have in the comments, and any feedback as well. Be sure to send me some claps, and follow me for more articles like this.

My goal in the future is to dive deeper into how we can leverage machine learning to revolutionize transportation and space exploration. Feel free to connect with me on LinkedIn, and reach out to me via my email at if you’d like to further discuss this article or just chat! To stay updated on my personal and professional growth, please also consider subscribing to my monthly newsletter!

17 y/o innovator passionate about using AI to revolutionize emerging fields like autonomous vehicles and space tech.

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