Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models. No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success. "Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases," said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.

When you hear the word "AI," images that most likely come to mind are robots and Iron Man movies. In a way, you're not wrong, as AI does play a huge role in robotics, and well, Iron Man's suit is obviously AI-powered. But robotics and fictional movies aside, did you know that AI also has huge implications in business? AI can also help even non-tech businesses optimize their operations. The concept of artificial intelligence (AI) started in the late 1950s.

Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models. No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success. "Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases," said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.

Next week professional services firm Accenture will be launching a new tool to help its customers identify and fix unfair bias in AI algorithms. The idea is to catch discrimination before it gets baked into models and can cause human damage at scale. The "AI fairness tool", as it's being described, is one piece of a wider package the consultancy firm has recently started offering its customers around transparency and ethics for machine learning deployments -- while still pushing businesses to adopt and deploy AI. (So the intent, at least, can be summed up as: 'Move fast and don't break things'. "Most of last year was spent… understanding this realm of ethics and AI and really educating ourselves, and I feel that 2018 has really become the year of doing -- the year of moving beyond virtue signaling. And moving into actual creation and development," says Rumman Chowdhury, Accenture's responsible AI lead -- who joined the company when the role was created, in January 2017.

Artificial intelligence is not just a casual interest of the tech industry. "In ten years, every company will have to be an artificial intelligence company or they won't be competitive," Loup Ventures Managing Partner Doug Clinton wrote in a Thursday note. "Every company," he emphasized, noting disruptions span energy and transportation, food and finance, retail and health. Some firms are already anticipating AI standards of their industries and investing in relevant technologies and strategies. Their efforts, Clinton said, position them to profit.

While much has been written about information technology companies investing in artificial intelligence, Loup Ventures managing partner Doug Clinton notes that many non-tech companies are capitalizing on AI technology as well. Clinton has put together a portfolio of 17 publicly traded non-tech companies that are making investments in AI to improve their businesses. In a recent blog post, Clinton notes that he assembled the portfolio as a "fun exercise" and a way to draw attention to the sweeping nature of AI advancements. Loup Ventures is an early-stage venture capital firm. Clinton selected the companies from a range of industries including health care, retail, logistics, professional services, finance, transportation, energy, construction and food/agriculture.

The Forbes Technology Council recently wrote, "Machine learning isn't just for high-tech companies anymore. These days, any business can leverage machine learning for sales, marketing and to drive engagement. Here, six technology executives from Forbes Technology Council offer their thoughts on how even non-tech companies can start leveraging machine learning in the next few years." Erik Gustavson of Bitium suggests using machine learning "For Sales And Marketing Funnel Analysis. Companies will use machine learning for sales and marketing funnel analysis, since even non-tech companies generate a lot of data from sales and marketing. Instead of letting that data lie idle, it can be used to improve the routing and handling of customer interactions to improve the efficacy of sales and marketing teams."

For Intel, Microsoft, Hewlett-Packard, and the like, Computex in Taipei this week is the Oscars of the information technology world. There is the flurry of product hype, from virtual reality (VR) headsets to the internet of things (IoT) to artificial intelligence (AI) devices. One big topic that everyone has in mind is: machine learning. The idea of a thinking machine goes back at least as far as 1950, when British computer scientist Alan Turing famously wrote that if a machine was indistinguishable from a human during text-based conversations, then it was "thinking." Naturally, big tech giants such as Google, Facebook, and IBM, have all set up corporate labs to advance machine learning.