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This is a contributed article by The Future Society, edited by Emerj and authored by Samuel Curtis, Sacha Alanoca, Nicolas Miailhe, Yolanda Lannquist, Adriana Bora. To inquire about contributed articles from outside experts, contact [email protected].

This is a contributed article by Ian Wilson, Founder at Strategy 4 AI - learn more about Ian's online AI strategy courses here. Ian is also the former Head of AI for HSBC, one of the largest financial institutions in the world. To inquire about contributed articles from outside experts, contact [email protected].
As the use of AI to support business operations moves through its maturity cycle we have passed a number of key milestones along the way. However, following a pattern reminiscent of previous emerging technology introductions, organizations initially hired experts only to balk when they used blasphemous words like "infrastructure," "industrialize" or "strategy" rather than soothing words like "use case," "quick win" or "easy ROI." 
Unfortunately, instead of sacrificing their misaligned expectations, many businesses sacrificed their experts... 
Fast forward a couple of years and many of those businesses, having attempted to cut corners and seeing mainly failure, now have a visceral understanding of what their experts were advising and are looking, with more experienced eyes, at how to move forward from this point.
However, many businesses are still making avoidable mistaken assumptions when it comes to the use of AI Capabilities to support business objectives. I like to call the most egregious of these assumptions The 4 Horsemen of the AI-Pocolypse:

This article was written by Sergii Gorpynich, co-Founder and CTO at Star, co-written by Perry Simpson, Managing Director of Star, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. Learn more about reaching our AI-focused executive audience on our Emerj advertising page.

This article was written by Sergii Gorpynich, co-Founder and CTO at Star, co-written by Perry Simpson, Managing Director of Star, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. Learn more about reaching our AI-focused executive audience on our Emerj advertising page.

This article was written and contributed by Thomas A. Campbell, Ph.D. and Jon Fetzer of FutureGrasp, and was edited and published by the Emerj team.

In 2018, James Kobielus wrote an article on the AI market’s shift to workload-optimized hardware platforms, in which he proposed:
Workload-optimized hardware/software platforms will find a clear niche for on-premises deployment in enterprises’ AI development shops. Before long, no enterprise data lake will be complete without pre-optimized platforms for one or more of the core AI workloads: data ingest and preparation, data modeling and training, and data deployment and operationalization.
We are seeing Kobielus’ words come true. In the past year, nearly 100 companies have announced some sort of AI-optimized IP, chip, or system optimized, primarily for inferencing workloads but also for training. Hyperscalers like Facebook, Amazon, and Google are increasingly talking publicly about "full-stack" optimization of AI, from silicon, through algorithms, up to the application layer.

The following brand partnership article was written by BIS Research analyst Rahul Papney, and edited by Raghav Bharadwaj of Emerj. For information about our advertising and publishing arrangements with brands, please visit our partnerships page.

This article was written by Sudhir Jha (Senior Vice President & Global Head of Product Management and Strategy), and was edited and created in partnership with Infosys. For more information about content and promotional partnerships with Emerj, visit the Emerj Partnerships page.

Emerj's own Daniel Faggella will be speaking at BootstrapLaps Applied Artificial Intelligence Conference on April 12th, 2018. Leading up to the #AAI18 event, we've partnered with BootstrapLabs to spark a conversation around AI in industry by putting together an article about Dan's recent conversation with  Nicolai Wadstrom, Founder and CEO of BootstrapLabs.

When we search on Google or Amazon, we’re reminded of the improved capabilities of artificial intelligence over the last half decade.
What we often don’t realize is the role that human beings have in tagging and manually working through near-infinite reams of data to develop genuinely relevant search.
Sure, data scientists and ML specialists must construct a search system - but for the time being - much of the “human-like” results that we get in the world of social media, search engines, and Commerce - comes from - well, humans.
This week on AI in Industry we interview Vito Vishnepolsky of ClickWorker. Clickworker is a large and well-rated microtasking marketplace. Clickworker cloud technology platform caters data management and web research services as well as AI algorithms training. The firm claims to have over one million workers on its global platform as of September 2017.
Vito’s perspective is valuable because he has a finger on the pulse of crowdsourced demand, handing business development for various crowdsourced AI support services - both for tech giants and startups.