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This article was originally written in 2017 by Lauren D'Ambra, former editor at Emerj.com.

Women in artificial intelligence (AI) and machine learning (ML), or the lack thereof, is not a new topic in media, just as gender equity and disparity in the workplace is not a new subject of research for academics and think tanks. But discussing these issues openly is no less important. While we address the potential reasons and implications of these issues toward the end of this article, our initial interest in this subject came from our desire to know the following:

Episode Summary: In the last few months, we've had a string of fantastic interviews with investors and have gained a cross-industry picture of what's important for start-ups and emerging trends in the AI and ML space. This week's interview is no exception. Ann Miura-Ko, co-founder and partner at Floodgate, starts with an explanation of the "self-driving enterprise" concept, her functioning idea about AI investing and the future of software in general. Her high-level insights embody an interesting emphasis on the dynamic of human-machine interactions and relationships cross industries, including the constant workflows and interactions of people using software and bolstering the predictive and prescriptive analytics capabilities of that software. While forward-thinking, Miura-Ko also paints a picture of how these synergistic relationships between humans and machines are happening with companies today.

The last few years have yielded a tremendous amount of attention at the intersection of AI and healthcare, from DeepMind's partnership with the UK's National Health Service to IBM's continued pushes into areas of genomics and drug discovery. From the perspective of healthcare executives, however, many important questions are left unanswered and rarely addressed in detail:
What difference are healthcare's machine learning innovations likely to make in the lives of patients?
What disruptions should healthcare executives prepare themselves for now?
How will the healthcare industry operate differently in 5 or 10 years into the future?
We surveyed over 50 executives of healthcare companies leveraging AI. We aimed to do the hard work of separating the companies actually applying AI from those who use it as a buzzword (over 15 of our initial survey responses were turned down due to lack of evidence of real AI in use), presenting important predictions and industry insights in clear and interactive charts and graphs.
The following research article is broken down into five sections:

Episode Summary: This week we interview Polaris Partners' Gary Swart, who gives his perspective on companies that are doing "AI right" i.e. laying strong foundations for using AI applications optimally. Swart provides valuable examples of how he's seen companies use AI as a tool to build more defensible and durable business models in an increasingly competitive landscape. Getting an investor's perspective in AI is always a good idea for companies looking to raise money, particularly when it comes to understanding the types of AI trends that excite VC's. Even more broadly, an investor's perspective can point to emerging factors in how AI is going to impact a particular industry, shining a light on industry developments and commonalities that matter for companies across industries who are leveraging an increasing number of AI tools and applications.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time.

Episode Summary: The upsurge of malware and sophisticated attacks continue to keep cybersecurity in the spotlight, but new developments in AI and deep learning offer more advanced solutions to combat security threats. This week, we catch up with Eli David, CTO of Deep Instinct—a company founded in Israel with US headquarters in San Francisco—that applies deep learning in malware defense and information security. David spoke with us about why and how the deep-learning approach to AI is relevant to the future of cybersecurity.

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle—from assembly to post-sale customer service interactions, but retail players need answers to important questions:

Episode Summary: One of the most clear insights from our recent consensus on machine learning in marketing was that companies who have more digital touch points along the path to conversion—and more conversion in general—have an advantage when applying AI and ML technologies. In this week's episode, Scopely Co-Founder Ankur Bulsara shines a light on this dynamic and describes how gaming companies are taking advantage of digital trails and applying machine learning technologies. We don't cover much gaming on the Emerj podcast, so this interview is a bit off the beaten path. Bulsara speaks about how dialed-in and instrumented the mobile gaming environment is and how data is used to leverage higher conversions over time, as well as how Scopely's systems are set in place to ensure success of their business model. We think his insights on how gaming companies leverage higher conversions with (and without) machine learning can serve as an analogy for companies in other industries that are considering how to set in place similar, optimal digital processes over time.

Episode Summary: In this episode we speak with Co-founder and CEO Alex Holub of  Vidora about how businesses, particularly in the digital and B2C spaces, can improve marketing results with AI. Holub discusses the resources needed—time, money, in-house or outside expertise, calibration, and data—in order to leverage AI in a realistic way. It's safe to say that today, some businesses are not yet set up to be leveraging AI, while others should be seriously considering taking the leap to using machine learning in their marketing processes. Holub draws some firm lines as to what kinds of businesses are primed to take advantage of AI, and what it takes to flip the switch and make AI a useful and inspired revenue driver in the marketing domain. Many of Holub's useful insights are echoed in our machine learning in marketing consensus from last month, also worth reading if you're interested in additional first-hand perspectives from executives using AI in the marketing space. Alex was introduced to us by our friends are BootstrapLabs.

In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade.