AI Articles and Analysis about Process automation

In 1895, the German physicist, Wilhelm Röntgen, showed his wife Anna an X-ray of her hand. “I have seen my death,” she said. Medical imaging broke paradigms when it first began more than 100 years ago, and deep learning medical applications that have evolved over the past few years seem poised to once again take us beyond our current reality and open up new possibilities in the field.

Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks.
We wanted to know - what work functions have the most potential for near-term automation?
In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks - on which business functions have the greatest potential for automation today, and in the coming five years ahead.

Since Facebook Messenger, WhatsApp, Kik, Slack, and a growing number of bot-creation platforms came online, developers have been churning out chatbots across industries, with Facebook's most recent bot count at over 33,000. At a CRM technologies conference in 2011, Gartner predicted that 85 percent of customer engagement would be fielded without human intervention. Though a seeming natural fit for retail and purchasing-related decisions, it doesn't appear that chatbot technology will play favorites in the coming few years, with uses cases being promoted in finance, human resources, and even legal services.

Episode Summary: Martin Ford started off as a software entrepreneur in Silicon Valley, but became better known for his speaking and writing on robotics' and automation's influence on the job market after writing his best-selling book, Rise of the Robots: Technology and the Threat of a Jobless Future. In this episode, Martin talks about why he believes 'white collar' jobs (as opposed to blue) are at a higher risk for automation, and gives his predictions on how automation and robotics will impact the job market over the next 5 to 10 years.

Episode Summary: In this episode, we speak with Dr. Matteo Berlucchi, the founder of Your.MD, which uses artificial intelligence to create one of the first personal health assistant platforms in 70+ countries. Berlucchi talks about the challenges in making an AI do what you want, specifically helping people self diagnose and seek proper treatment. He discusses the multiple approaches to AI that are blended together in order to yield optimal results, and touches on the sometimes stark differences between what AI can do in the lab versus the functional application for tens of thousands of people. If you're interested in the diverse applications of AI and the challenges in running a startup, Dr. Berlucchi's makes for an interesting episode.

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

Episode Summary: When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn’t fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML  to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.

Episode Summary: When we think about AI, we often think about optimizing some particular task. In most circumstances through computation there is an optimal chess move, or an optimal way to determine pattern in data, or solve a math problem, or route info through servers. Most of us are aware of these uses, but what about creative tasks? Can these also be optimized? If we want to give a computer information and tell it to create powerpoint slides, is there an optimal way to create such slides? Dr. Philippe Pasquier’s computational research is focused on artificial creativity. In this episode, we talk about how to define a very new field, train machines in this area, and also discuss trends and developments that might permit such technology to thrive in the next 10 years.