Two stories this past week caught my eye. The first is Nvidia's revelation of the new, AI-focused Tesla P100 computer chip. Introduced at April's annual GPU Technology Conference, the P100 is the largest computer chip in history in terms of the number of transistors, "the product of around 2.5 billion worth of research and development at the hands of thousands of computer engineers." Nvidia CEO Jen-Hsun Huang said that the chip was designed and dedicated "to accelerating AI; dedicated to accelerating deep learning." But the revolutionary potential of the P100 is dependent on AI engineers coming up with new algorithms that can leverage the full range chip's capabilities.

During the recent Consumer Goods Forum global summit here in Cape Town, I had the opportunity to briefly chat with Vivienne about some of the issues confronting the digital disruption of this industry sector. [The original transcript has been edited for clarity and space.] Named one of 10 Women to Watch in Tech in 2013 by Inc. Magazine, Vivienne Ming is a theoretical neuroscientist, technologist and entrepreneur. She co-founded Socos, where machine learning and cognitive neuroscience combine to maximize students' life outcomes. Vivienne is a visiting scholar at UC Berkeley's Redwood Center for Theoretical Neuroscience, where she pursues her research in neuroprosthetics. In her free time, Vivienne has developed a predictive model of diabetes to better manage the glucose levels of her diabetic son and systems to predict manic episodes in bipolar suffers. She sits on the boards of StartOut, The Palm Center, Emozia, and the Bay Area Rainbow Daycamp, and is an advisor to Credit Suisse, Cornerstone Capital, and BayesImpact. Dr. Ming also speaks frequently on issues of LGBT inclusion and gender in technology. Every once in a while I have the opportunity to discuss wide-ranging topics with an intellect that stimulates, is passionate and really cares about the bigger picture. Those opportunities are more rare than one would think. Although set in a somewhat unexpected venue (the elite innards of consumer capitalism) her observations on the inescapable disruption that the new wave of modern technologies are prescient and thoughtful. Ed: In a continent where there is a large focus on putting people to work, how do you see the challenges and disruptions resulting from AI, robotics, IoT, VR and other technologies playing out? These technologies, as did other disruptive technologies before them, tend to replace human workers with machine processes. Vivienne: There is almost no domain in which artificial intelligence (AI), machine learning and automation will not have a profound and positive impact. Medicine, farming, transportation, etc. will all benefit. There will be a huge impact on human potential, and human work will change. I think this is inevitable, that we are well on the way to this AI-enabled future.

Last month Salesforce released details on some recent acquisitions in machine learning, reporting spending of almost 33 million on the AI startup MetaMind, and an additional 41.6 million for two other companies, including the intelligence systems startup PredictionIO. This all furthers the Salesforce commitment to bolster their AI capabilities. Current predictions suggest that the global AI market in general will grow to over 5 billion by 2020, driven in part by the rising adoption of predictive marketing intelligence and natural language processing technologies across all business sectors. SugarCRM recently announced that they are developing a new intelligence service with a Siri-like agent called Candace. According to their literature, Candace will be able to listen in on business meetings and use natural language processing to analyze the conversations.

Decades of research and speculative fiction have led to today's computerized assistants such as Apple's Siri. Advances in artificial intelligence (AI) have given the world computers that can beat people at chess and "Jeopardy!," as well as drive cars and manage calendars. But despite the progress, engineers are still years away from developing machines that are self-aware. Some believe the resulting technological singularity will eradicate poverty and disease, while others warn it could endanger human survival.

HBO's mysterious Westworld sent fans into a tweeting frenzy last week after the first sustained peek at the long-delayed sci-fi Western, which upgrades Michael Crichton's 1973 androids-run-amuck thriller for the new millennium, debuted on HBO. Totally reengineered by executive producers Jonathan Nolan (Person of Interest) and Lisa Joy (Pushing Daisies), Westworld tackles the promise and the threat of artificial intelligence (hey, even Stephen Hawking and Bill Gates say they're truly worried about it) in a lawless R-rated play-scape where a theme park's guests' darkest desires run wild. Only this time, you'll find yourself sympathizing with the sentient bots who are slave-laboring under the creepily apathetic gaze of Dr. Robert Ford (Anthony Hopkins). The resulting future-shock series resembles a mash-up of Blade Runner, Ex Machina, Black Mirror, and Crichton's own Jurassic Park; but its creators initially struggled to get their prime-time machine operational. The series was ordered two years ago, with a scheduled 2015 debut, then was delayed amid casting changes, story-retooling, and a production pause. Below, we were able to sneak a few questions to Nolan and Joy over the firewall of secrecy surrounding the drama, which debuts in October.

In this Big Data world, a major goal for businesses is to maximize the value of all their customer data. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don't connect those pieces across those different data silos, you're only seeing parts of the entire customer puzzle. The integration of these disparate customer data silos helps your analytics team to identify the interrelationships among the different pieces of customer information, including their purchasing behavior, values, interests, attitudes about your brand, interactions with your brand and more. Integrating information/facts about your customers allows you to gain an understanding about how all the variables work together (i.e., are related to each other), driving deeper customer insight about why customers churn, recommend you and buy more from you.

Manufacturers often have to deal with up to 800 hours of downtime annually. On average an automotive manufacturer's TDC is 22,000 per minute; that is 1.3M per month! With the advance of predictive analytics, TDC can easily be reduced however only 14% of the manufacturing industry is taking advantage of its big data, according to a recent survey from MESA. Predictive maintenance is realized through the application of sophisticated machine learning techniques to equipment condition data collected in real-time or near real-time. It is now the new standard for reducing cost, risk and lost production in manufacturing facilities.

Israeli machine-learning radiology firm Zebra Medical Vision said today it raised 12 million to support the development of imaging algorithms being designed for automatic reading and diagnosis of medical imaging data. The round was led by InterMountain Healthcare, and joined by existing investors, Zebra Medical Vision said. As part of its investment, InterMountain Healthcare plans to work with Zebra to accelerate its development. "We are privileged that 1 of the top healthcare systems in the U.S. has placed such confidence in our team and our platform. In an environment where computing power and machine learning frameworks are becoming a commodity, the ability to quickly and efficiently curate large quantities of data from a world class integrated healthcare provider can make the difference between simplistic tools and insights that can truly add clinical value and positively impact patient care," CEO Elad Benjamin said in a prepared statement.

Imagine it's five minutes before a meeting. Your smartwatch, without prompting, sends you key points. While in the meeting, you take notes. Those notes are instantaneously absorbed by the system, then collated with relevant prior meetings, files and communications, in order to better prepare you for the next meeting. Born of the innovations of Big Data and possessed of a new net intelligence layer, self-learning software will have huge impacts on productivity across all departments of an enterprise.

Is AI ready for primetime? In a recent interview with Charlie Rose, he stated that machine learning showed great promise for cybersecurity, but that the necessary technology was probably five years out. If machine learning is currently so successful in other areas of society, why isn't it ready for cybersecurity? Machine learning is a subset of Artificial Intelligence, a field of computer science that started in 1958 when Marvin Minsky founded the Artificial Intelligence lab. Everyone, including DARPA, was pouring money into it.