Signalcentral_banner_160517_v2.png

AI: Why Now? An Old Technology Grows Up Fast

Posted by Georges Smine on Fri, Mar 31, 2017
Find me on:


Blog_Ai4.jpgArtificial Intelligence (AI) is helping the enterprise create dynamic new applications and new ways to better serve customers, prevent and cure diseases, detect security threats, and more. We’re seeing how rapid advances in the field as a whole as well as the underlying technology are leading to more real-world opportunities that already are making a big impact. Speaking at a 2017 panel discussion with the The Wall Street Journal, AI luminary Andrew Ng observed, “Things may change in the future, but one rule of thumb today is that almost anything that a typical person can do with less than one second of mental thought we can either now or in the very near future automate with AI.” That’s a startling assessment for what we can expect.

These past few years have seen a remarkable resurgence in AI. While the term — and the science behind it — have been around for decades, the technology has only recently come into its own for the mainstream enterprise. We’re seeing a thriving ecosystem of AI-based methodologies, tools, and services entering the marketplace. The VC community, always thirsty for the latest and greatest technology, is pouring hefty investments into AI startups. For example, one big area of interest is distributed In-Memory databases, as well as new graph analytic approaches — both accelerated by GPUs — that allow enterprises to access and interact with data much faster than before. In terms of industry appeal, we’re seeing AI being adopted by companies doing business in healthcare, manufacturing, transportation, customer service, finance, and many others.

So why, after all these years, is AI coming to the forefront? We break it down into three themes that have converged to drive this renewed purpose. The first is the scale of computation and supporting technology. Recent technology advancements have led to the scale of computation required by deep-learning algorithms. The second is the massive heap of data, as we cross a major milestone in the volume of data currently being collected and used by enterprises. The third theme is how corporations have gone from being overwhelmed by their data to actually being hungry for more. AI is the answer to this insatiable appetite.

1. Scale of Computation

Over the past 20 years, computers’ ability to process information has become simultaneously more robust and more affordable, allowing us to do more with increasingly more information, in less time and for less money. The intersection of these factors lands us at a place that makes exploring artificial intelligence for business impact economically advantageous. That said, it’s not as though companies haven’t explored different entities of data science before. Neural networks, which are computationally expensive algorithms, were used in the 1980s and early 90s, diminished in the late 90s, and are now having a major resurgence. This is likely attributable to companies realizing the minimal gains were not worth the high cost at the time. But in the past decade, we’ve seen compute resources evolve to a point where performance is now sufficient to run large-scale neural networks, using methods known as deep learning, in a cost-effective manner (supercomputers are no longer needed). Now, with the adoption of GPUs (the graphics processing unit originally designed nearly 20 years ago for gaming), neural network developers can run deep learning algorithms using the same amount of computing power required to bring AI to life quickly. Adding GPUs to the mix was pivotal in the rise of AI, machine learning, and deep learning because they improved speed of computation exponentially.

Most enterprises today are still at the proof-of-concept stage with respect to AI, where IT decision makers are exploring potential problems domains for AI solutions. And with GPU power now available through cloud services, such as Amazon Web Services, Azure, and Google Cloud, the barrier for AI experimentation has been lowered.

2. Scale of Data

The modern enterprise has evolved to a point where the C-suite views data science as the Holy Grail for achieving a broad digital transformation of the business. Consequently, as we move into a new scale of data volume — the zettabyte, coupled with new application domains — enterprises are seeing more opportunity to gather game-changing insights than ever before. Essentially, the combination of data volume and access to technology has upped the ante for enterprises, and they need to utilize both in new, innovative ways to stay competitive.

3. Shift in Mindset — from Data Deluge to Data Appetite

Companies are no longer drowning in their data deluge but are now looking for new solutions to better take advantage of the data they have. Many enterprises are enabling themselves to overcome the data deluge by investing in highly scalable software like Hadoop and building data lake architectures. Having made the investments, companies now have an insane appetite for more insights, and they’ve shifted their approach from using data mining to answer traditional hypothesis-based questions to wondering what the data can reveal to help them address business blind spots and challenges. It’s now a foregone conclusion that AI, machine learning, and deep learning are able to deliver key insights to gain competitive advantage. Important problems are ripe for solutions by these technologies, including AI-powered healthcare at scale, AI-powered weather forecasting, AI-accelerated cyber defense, and AI-powered customer service, to name a few. So while data volume is creating a need for AI, AI is simultaneously creating an insatiable desire for even more data.

Humans Still Needed

In general, AI technologies are making headway now thanks to the steady proliferation of data, the advancements and affordability of computing technology, and the applicability of data science to business applications, inspiring enterprises to take their data science investments and capabilities to the next level.

One of the hurdles that organizations need to overcome is the lack of human resources. With an influx of data, technology, and capability, enterprises must find the skilled data scientists required to run their AI applications. Many enterprises will find solace in partnering with third parties or buying solutions that can automate much of the data science process. Solutions are evolving to maximize the productivity of the few scientists an enterprise has, to do the work of tens or even hundreds more, and to enable simplified self-service to a growing number of hybrid analysts, skilled in both business analysis and data science.

To learn more about how to incorporate an AI-capable analytics platform into your enterprise — without hiring more data scientists — download our latest whitepaper, “Transforming Data to Intelligence to Value at Scale.”

Download Now

georges.jpgGeorges Smine is VP of Product Marketing at Opera Solutions.

Topics: Big Data, Data Science, Machine Learning, Artificial Intelligence