This Is the Secret Sauce Behind Effective AI and ML Technology
It's a solution making it possible for businesses that haven't yet adopted AI to get in the game.
It is virtually impossible to find a cutting-edge company today that doesn't claim to be built on artificial intelligence (AI) or use machine learning (ML) in some capacity. A lot of companies claim to be AI-driven platforms, but that can be misleading. The truth is, it is extraordinarily difficult to build an AI model that can be retrained, relearn and continuously updated, and most companies do not have that level of sophisticated technology. Even so, the uptick in use of AI is undeniable, and it's useful to understand that the development and increased adoption of AI haven't occurred overnight or in isolation.
The reason that AI is possible at all directly relates to the technology of neural networks, which experts are now realizing may hold the key to the future of AI.
Why is AI trending now?
There is nothing new about artificial-intelligence technology, which has in fact been around since the 1950s. Alan Turing's mathematical research resulted in work that led to the possibilities of building intelligent machines, then testing the intelligence of those machines. The early development of AI wasn't just limited by the lack of computational power, but also by the fact that there wasn't nearly as much data as there is today. But one additional component, often ignored, contributed to the rise of AI: neural networks.
Neural networks are modeled after brain functions. Experts in the field sought to untangle the complex processes of the human brain and developed systems around the input and output relationships that occur neurologically. Artificial neural networks (ANN) are a key tool for machine learning and the perfect way to create meaning from data-hungry AI systems.
What AI must do, if it is to do anything well, is turn unstructured, raw data into something businesses can use. ANNs are the backbone of AI models that run personalized news feeds, autocomplete our emails, power Google Search and run autonomous vehicles.
Related: When Should You Not Invest in AI?
Cutting edge AI and ML systems
For small and mid-size businesses to thrive in a world increasingly dependent on AI, they have no choice but to adopt new technology. There are thousands of companies today that don't use AI or haven't been using AI that are still good businesses, even enterprise-level businesses. If they could apply AI, they could get exponential gains, but the adaptation has to happen fast if they want to retain a competitive share of the market.
If AI is going to outlast the current hype cycle, it needs a sure foundation. The brightest minds in this space see that foundation being neural networks.
In the 80s and 90s, there was a winter of AI, where these technologies were dropped. Then, when the internet boomed, the world began producing data at an unprecedented pace. Computers blew up, and all types of machines used more and more powerful chips. The combination of powerful chips and the fact that there was so much data brought about the resurgence of AI and neural networks.
Big data and neural networks
Companies like Google started using some of the vast quantities of collected data to train large neural networks to understand patterns and data. A simple but groundbreaking illustration is when researchers took YouTube videos with cats and found a way for the models to recognize cats without ever identifying cats as such. Over time, the model simply learned how to recognize cats. Preliminary progress like that popularized neural networks.
In today's world, the AI models of any large company — Netflix, Google, Facebook, etc. — use neural-network techniques to extract intelligence from data. Outside of these companies, in late adopters, the use of neural networks is not as prevalent. This is largely due to a lack of talent and the misconception that only the largest data sets in the world are candidates for neural networks. In fact, more and more techniques have been developed by data scientists so that neural networks don't need so much data. Companies like Nike, for instance, can use the data they have and still extract quality intelligence from relatively smaller data sets.
AI for mid-sized companies
The Googles, Facebooks and YouTubes of the world clearly dominate market share for big data since their very function is data collection. So, where does this leave mid-sized companies? Scaling AI applications that leverage the best of neural-network technology is something being addressed by leaders in the industry.
Innovators are fashioning solutions that make AI accessible for smaller businesses. The idea is that a platform exists that makes it easier for companies of any size to create fully customizable deep and machine-learning systems. There are now world-class, cutting-edge ML infrastructures that can support businesses of any size in any industry, which is a truly democratic approach to developing this elite technology.
One keystone element is that a platform must use custom neural-network techniques to manage data, and the systems start performing right away. Many companies are limited because they don't have the capacity to develop true AI applications. When industry leaders commit to creating platforms that bridge this gap, the real competition in this space may begin.
AI isn't just about input: The ability to extrapolate action items from deep learning is the dream. The acceleration of AI models to react and continuously learn is made possible by neural networks, which means this technology is here to stay. Future use of AI in every industry — from robotics to automotives to churn to personalization — relies on neural networks. If done right, it may be possible for AI to be as intelligent as humans, and that level of capacity has infinite possibilities.
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