A casual conversation over the holidays with a family member raises some provocative questions about the nature of Artificial Intelligence and Big Data Analytics, topics of more than passing interest to Achmad Chadran, Content Strategist and Managing Editor of the online publication TechBeacon.
A simple question
This Christmas, my brother-in-law, Ross, and I set about to make up for lost time, I while mincing onions, he while cradling a glass of bourbon.
“Are there any examples of AI development,” he asked, unconsciously swirling his drink, “where the technology actually does something?”
I stopped to read his face.
“I mean something with tangible or material benefit?” he continued.
I work at Micro Focus. My company does a lot with AI and Big Data. Was this going to be the first family brawl of the season?
The public face of AI
“I can think of a handful of AI applications my company develops,” I said matter-of-factly, “all with outcomes that are completely tangible.” Thus kicked off my data-intensive holiday season.
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A quick web search — parenthetically, perhaps the most highly visible practical application of AI — readily yields press articles about applications that mimic human capabilities. These often include Turing Test-type “Can You Spot the Bot” challenges, while noting the vast investments that IT behemoths have publicly pledged to advance AI R&D.
Equally pervasive today are articles that pose ethical and moral questions about which uses for AI technology are acceptable. The AI poster children of the moment include DALL-E and ChatGPT, whose capabilities have spurred a mix of wonder, fear, and loathing, along with the requisite debates about the boundaries of their ethical use. It’s clear that skepticism about the power of AI has fallen out of fashion. To the contrary, the public discourse on AI tends to skew toward its potential to – on one hand – reduce the tedium of human labor, and – on the other– to put humans who do that work out of business.
Expanding, not mimicking, human ability
Yet examples of AI and Big Data applications that expand on our abilities – instead of merely emulating them at superhero scale or speed – abound.
While the clickbait appeal of alarmist cyborg onslaught stories is undeniable, the stories I prefer are those that presage a more symbiotic rapport between us humans and our machines. According to one source, the AI market reached US$120 billion in 2022, so there’s clearly more going on than robotic art or robotic journalism.
Sure enough, examples of this innovation abound, once one gets beyond the news headlines. AI technologies allow people to extract insight quickly from data stores otherwise too vast to process. This has proven vital for such processes as insurance underwriting and telecommunications network optimization. The ability for AI applications to learn and improve continuously has made the technology indispensable for such processes as fraud detection, speech recognition, medical diagnosis.
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AI-driven innovation at Micro Focus
My conversation with Ross continued well into the weekend. Here are some examples I felt compelled to share with him:
For starters, there’s IDOL Unstructured Data Analytics, an advanced search, knowledge discovery, and analytics platform. Radiotelevisión Española (RTVE), the state-owned corporation that manages the Spanish public radio and television service, uses IDOL to manage video, image, audio and text data compiled over more than 50 years. Searches that used to take hours or even days to run now take seconds. Relevant assets can be quickly downloaded and edited into new items for use in news or other programs.
Another example of AI that improves the quality of human work is Operations Bridge, an enterprise event and performance management software platform. Mobile comms giant Vodafone uses Operations Bridge to provide a consolidated, end-to-end view of its environment, detecting incidents and automatically triggering tickets whenever an intervention is required. With Operations Bridge, Vodafone now enjoys automatic resolution of issues that used to take hours to resolve.
My third example was Vertica, a unified analytics platform with a massively scalable architecture and a broad set of analytical functions spanning event and time series, pattern matching, geospatial and end-to-end in-database machine learning. One Vertica customer, Anew Design Automation, creates Electronic Design Automation (EDA) solutions for Long LifeCycle (LLC) electronic products in markets such as aerospace, automotive, defense, industry automation, smart infrastructure, medical, power and energy and telecom. Anew Design Automation uses Vertica to simplify and accelerate data preparation and ML model iteration.
While I was prepared to mention the recognition that Micro Focus has garnered among industry analysts (among them, Bloor Research and Research in Action) for its advancement of AI and Big Data R&D, by this time Ross conceded my point and talk turned to extended family gossip.
The conversation continues
Suffice to say that not only is there plenty of AI R&D driven by a vision to augment human potential instead of to replace it. Not surprisingly, the innovators behind these programs are asking the very same questions surrounding its ethical applications. Indeed, the significance of the topic demands not only that we ask these questions, but that we reflect on them, and revisit them over time and across new developments and iterations of these technologies. You can delve deeper into the conversation by listening to Quality Engineering: A Podcast Series from industry leaders Micro Focus and Capgemini. The series, which explores innovations in the quality engineering domain, tackles the question of whether AI has the potential to replace quality assurance professionals. Listen to Episode 1 of this series, “Human vs. AI in Quality Engineering,” now, by clicking here.
