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In recent years AI has grown significantly and become a substantial area of business investment. What has changed, and how does this affect you?
For a long time, artificial intelligence was pure science fiction, relegated to books, television and movies—and you don’t need us to tell you that we are well past that point today. In the last few years we have seen extremely rapid advancement in a series of technologies that have come together to unlock a wave of AI investment. According to Accenture, 85% of executives plan to invest extensively in AI in the next three years, and in the same time period, Forrester estimates that businesses using AI will “steal” $1.2 trillion from companies that don’t.
Whether they have implemented AI into their business plans or not, most organizations are now spending considerable time and money thinking about it. Why is AI becoming so accessible today? There are five major reasons driving this change.
1. The Internet of Things
More machines are instrumented than ever before, as sensors have proliferated across devices from connected cars to industrial machinery. The result is an explosion of data that is being collected from myriad sources, providing businesses with the raw material needed for powerful analytics.
2. Data Lakes
The deluge of data would be of limited impact if it were locked into silos and hard to access. Organizations, aided by new technologies, have become more adept at restructuring data into data lakes, providing a single place where data from across the company can be effectively analyzed together.
3. Computational Infrastructure
With so much data being collected and subsequently analyzed all at once, an enormous amount of computing power is required to conduct an analysis. Fortunately, computational infrastructure has never been more powerful or readily available, including the ability to run workloads in parallel.
4. Machine Learning Advances
Machine learning has advanced tremendously quickly, and learning algorithms have become increasingly powerful and capable. They are now well suited to solving a variety of complex problems, from predicting the durability of production machinery to anticipating or even preventing recalls, not to mention identifying images and winning at Go.
These trends have made it possible to incorporate AI into a line of business and produce impressive results—but it’s often only feasible for the digital giants. That’s because implementing AI at scale still requires expensive data scientist or analytics resources to generate and deploy accurate models. However, what if we could automate this last step, using cognitive capabilities to create and improve our models? It’s the fifth reason that moves AI from possible to truly accessible.
5. Meta-Learning Automates Machine Learning
With meta-learning, the process of creating and tuning your models is automated, resulting in increased accuracy and faster results. Importantly, it also greatly reduces the need to invest in expensive and hard to find data scientists, allowing you to run leaner and produce even stronger results. This growing approach is key to enabling organizations across a wide spectrum of markets to implement AI solutions that can produce powerful results, and to making sure you get your share of the $1.2 trillion Forrester estimates is up for grabs.
This is part of the cognitive-first future that is coming soon for many industries, but for industrial IoT, for example, this future is already here. DataRPM has pioneered meta-learning in the field of cognitive predictive maintenance (CPdM) for industrial IoT. Read about how they have operationalized the insights derived by data, and you can learn more about their platform right here.
We believe that CPdM is just the tip of the iceberg for meta-learning. Imagine the potential of this technology when it is applied to healthcare, hospitality, government and virtually all other industries that are grappling with the unprecedented explosion of data and how to best make sense of it all.
Mark Troester is the Vice President of Strategy at Progress. He guides the strategic go-to-market efforts for the Progress cognitive-first strategy. Mark has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect. Before these positions, Mark worked as a developer and developer manager for start-ups and enterprises alike.
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