One of the most-hyped – and imprecisely defined – technology trends of the moment is that of artificial intelligence (AI). In some ways, this situation is far from new. AI, sometime referred to as cognitive computing, has gone through cycles of ambitious promises and operational flops since at least the early 1980s. During this decades’ long span, what “AI” means – and how it gets used in the enterprise — has also shifted and evolved.
What’s new today: solutions that leverage AI capabilities are actually moving from research labs into commercially successful deployments. As noted in an earlier post, these solutions are building on advances in AI algorithms and modeling, computing power, and the massive amounts of digital data now available for informing AI-based “computing brains.”
Despite these early successes, the term “AI” itself has sometimes fallen into disfavor. Some business professionals prefer the less provocative term “cognitive technologies,” which attempts to keep claims of “intelligence” at arm’s length, while others prefer the subset term, machine learning. Others prefer to think of AI as a broad field of computational research, and cognitive technologies as discrete instantiations of that science.
However labeled, AI and cognitive technologies encompass many capabilities, and their applications are broad. Among the most powerful and widely embraced capabilities are pattern recognition, natural language understanding, speech-to-text conversion, language translation, and interactive chatbots.
One fundamental AI technology is machine learning, which some people use interchangeably with AI itself. In practice, it’s best to think of machine learning and its relative deep learning as a system’s ability to extrapolate beyond the bounds of human knowledge initially programmed into them.
Regardless of the exact definition, the use cases which employ AI capabilities are varied. Dozens of applications currently exist, including:
- Robots performing pick-and-pack processes in warehouses and distribution centers
- Customer service chatbots that handle routine inquiries or complaints, freeing human agents to tackle more complex, nuanced, or prioritized customer needs
- Systems analyzing images and videos to identify and categorize individual people, products, or discussion topics of interest
- Solutions that summarize large volumes of written text, such as medical research papers
- Sales-generation applications that serve up personalized messages and offers to customers reflecting their buying history and areas of interest
- Cybersecurity systems that sort through huge volumes of network traffic and events to identify – and, at times, automatically counter – emerging security threats
Despite the diversity of AI/cognitive applications, many share a common characteristic: they automate tasks that formerly required laborious manual involvement.
That said, because they can analyze volumes of data that would overwhelm humans’ capabilities, these systems are increasingly moving beyond simple automation tasks to deliver functions that were previously impractical or impossible.
For more information about how Pure Storage® and its partners can help your organization address the imposing data demands of AI and cognitive workloads, click here.