How open source is helping to drive the AI industry

By using HPC as a foundation for your AI applications, you can help your organisation accommodate new and innovative uses for AI as they emerge

Artificial intelligence (AI) might seem futuristic but the truth is, AI isn’t a topic for tomorrow — it’s here today, happening all around us, and quickly growing into a force for change in business and society.

While we don’t as yet have flying cars or robotic butlers, AI is going to profoundly change the way people work as well the type of work we do.

The AI applications in use today are decidedly less glamorous than walking, talking androids — think software that compiles quarterly earnings reports (Associated Press) or a computer system that identifies and filters out deceptive online merchants (PayPal)— but the implications and results of these fledgling AI applications are exciting.

AI Is here to stay, so get used to it

The buzz around AI is growing with adoption across enterprises.

According to IDC in its Australian IT industry predictions for 2018 and beyond, by 2019, 50 percent of digital transformation initiatives will use artificial intelligence services; by 2021, 85 percent of commercial enterprise apps will use AI, over 65 percent of consumers will interact with customer support bots, and over 60 percent of new industrial robots will leverage AI.

It’s also clear that enterprises are earmarking a significant part of their budgets to AI. The Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies report by Tata Consulting found that there was a clear correlation between investments in AI and business impact with 84 percent of companies see the use of AI as “essential” to competitiveness, with a further 50 percent seeing the technology as “transformative.”

Building a better enterprise today

A small percentage of tech companies are putting AI dollars toward projects that will become reality in the near future. Apple, Microsoft and Tesla investments in autonomous car research, just to name a few. While these projects tend to get the most press, the majority of AI investments focus closely on bettering the enterprise today.

AI’s applications are about enhancing productivity, personalising customer interactions and automating rote tasks while building practical tools with valuable real-life applications.

The most common uses for AI today are predictive analytics, machine learning, natural language processing or generation, voice recognition and response, virtual assistants and chatbots, and diagnosis/recommendation engines. 

Companies large and small are finding all kinds of innovative ways to make these functions work for them. Using predictive analytics to forecast energy supplies and procurement to employing voice recognition technology to enable business customers to order supplies through a voice-activated automated system, AI has proven itself to be a technology that has yielded extremely efficient results.

Starting with the right foundation

As enterprises find more ways to put AI to work, the need for the right infrastructure grows. By its very nature, AI runs on data, and many applications need an ongoing supply of massive amounts of that data to identify patterns and learn. This requires a lot of computational power. To do this, many companies are implementing high-performance computing (HPC) infrastructures and putting parallel processing to work to speed up AI applications as they turn high volumes of data into business value.

HPC has several advantages. These include giving you the ability to customise your business processes, deliver faster results and ultimately saves you money as well as determining weak or slow spots in your organisation’s computing infrastructure and to applications more efficiently. 

It used to be that HPC was only used by government or academic research organisations, but that, too, is changing. The HPC systems of today are based on Linux clusters running on industry-standard x86 or AArch64 hardware — the same kind of Linux clusters that enterprises are likely already using for their big data and cloud architectures.

By using HPC as a foundation for your AI applications, you can help your organisation accommodate new and innovative uses for AI as they emerge.

Jeff Reser is global product and solutions marketing manager at SUSE.

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