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This article was written in partnership and collaboration with the IEEE. For more information about content partnerships visit the Emerj Partnerships page.

Artificial intelligence companies continue to find ways of developing technology that will manage laborious tasks in different industries for better speed and accuracy. In the legal profession, AI has already found its way into supporting lawyers and clients alike.

Thanks to the relative ease with which local governments can now gather real time data, combined with the capabilities of artificial Intelligence, cities are realizing interesting new ways to run more efficiently and effectively.

Genomics is a branch of molecular biology focused on studying all aspects of a genome, or the complete set of genes within a particular organism. Today, machine learning is playing an integral role in the evolution of the field of genomics.

Despite the massive venture investments going into healthcare AI applications, there's little evidence of hospitals using machine learning in real-world applications. We decided that this topic is worth covering in depth since any changes to the healthcare system directly impact business leaders in multiple facets such as employee insurance coverage or hospital administration policies.

Historically, space has been an industry only governments and heavyweight airspace corporations (such as Boeing) could handle. Uses of AI in space technology might be expected to be even more expensive, but the last decade of innovation has made space more accessible - and thanks to AI - data from space is becoming much more useful for businesses and governments.

High costs and technical limitations kept the uses of drones relatively limited until recently. After significant excitement starting around 2012, the FAA's 2016 adoption of regulations - combined with the drop in price - has made drones an economically viable option for a broad range of commercial functions.

Episode Summary: In some ways, investors in AI have to do a lot of what we do at Emerj, which is sort through marketing fluff and determine what's actually working and what's more of a pipe dream, as well as what's coming up in the next five years that seems inevitable and what's more likely to flop. In this episode we're joined by Li Jiang, a venture capitalist with GSV Capital whom I was connected with through BootstrapLabs. This week, Jiang speaks about the current areas of AI that he sees driving business process automations, as well as what technologies he believes will make a long-term impact in terms of automation. His insights on where AI automations are generating cost savings and increased efficiency, as well as what roles might be completely replaced or significantly augmented by AI, are useful nuggets for companies who are thinking through some of their own business processes and are eager to identify low-hanging fruit.

Episode Summary: Not all knowledge work can be crunched by a program, but there are some hard-to-automate business processes that a select few entities are making an attempt to automate now. Boston-based Rage Frameworks, Inc. is one such company, and in this episode we speak with Senior Vice President (SVP) Joy Dasgupta about specific applications of automation technologies applied to white collar environments. Rage Frameworks has developed intelligent machines that have been able to take over process that, prior to the emergence of AI and automation technologies, would have required thousands of people to accomplish. These developments are a microcosm of what is to come, and the process is not without its ethical considerations (as discussed in a previous interview with Yoshua Bengio). But Dasgupta's insights provide a concrete glimpse into how these processes are being automated in the knowledge workplace today and what that might mean or look like decades from now.

It's easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for "machine learning" since 2012 - but most technology executives often have trouble identifying where their business might actually apply machine learning (ML) to business problems.