Is artificial intelligence (AI) making the working world better or worse? That was the question explored last week at an interactive symposium hosted by The A.I. Age, a Seattle Times reporting project. AI is seen in workplaces, such as in writing technology used to craft job postings, autonomous floor scrubbers in retail stores and food and service robots in hotels. Yet the impacts of AI on the future of work remains unknown. Experts, including University of Washington public-policy lecturer Akhtar Badshah, co-executive director of the nonprofit United for Respect Andrea Dehlendorf and UW technology law professor Ryan Calo shared their views on the topic during a panel discussion Wednesday evening in downtown Seattle.

We're in the age of artificial intelligence (AI) and machine learning (ML), and most industry leaders believe the two concepts--working in tandem--will be what creates massive growth for companies in every vertical. It's actually what's known as the Fourth Industrial Revolution, and we probably haven't even hit the thick of it yet. But with new change comes unexpected problems, and one such problem with the rise of AI and ML is ignorance--people don't know which is which, or they use the two terms interchangeably. We at Botkeeper know the difference, and that's why we've put together a free ebook to help explain the two concepts and their differences. The content of this ebook was originally presented by Botkeeper CEO Enrico Palmerino to the Maryland Association of CPAs (MACPA), and it includes an introduction by MACPA Chief Communications Officer Bill Sheridan.

GlobalData has identified ten key technology trends that will impact the IoMT theme over the next 12 to 24 months. The Internet of Things (IoT) in the defence industry, also known as the Internet of Military Things (IoMT) or Internet of Battlefield Things (IoBT), is in its early stages. GlobalData forecasts that leading companies in C4ISR, cybersecurity, autonomy, and other related fields, such as Northrop Grumman, Boeing, Lockheed Martin, Thales, BAE Systems, L3 Harris Technologies, Leonardo DRS, and Airbus, will be part of the IoMT revolution. Below are some of the key technology trends impacting the IoMT theme over the next 12 to 24 months, as identified by GlobalData. AI is a key element for the optimal use of IoMT, as it allows for more efficient analysis of the vast amounts of data that flow at a high rate from an increasingly large number of edge devices.

At its annual Ignite event in Orlando, Fla., Microsoft today announced that Azure FarmBeats, a project that until now was mostly a research effort, will be available as a public preview and in the Azure Marketplace, starting today. FarmBeats is Microsoft's project that combines IoT sensors, data analysis and machine learning. "The goal of FarmBeats is to augment farmers' knowledge and intuition about their own farm with data and data-driven insights," Microsoft explained in today's announcement. The idea behind FarmBeats is to take in data from a wide variety of sources, including sensors, satellites, drones and weather stations, and then turn that into actionable intelligence for farmers, using AI and machine learning. In addition, FarmBeats also wants to be somewhat of a platform for developers who can then build their own applications on top of this data that the platform aggregates and evaluates.

America has been glued to their TV screens since the MLB playoffs began on October 1. As the field has whittled down to just four teams, odds makers are eager to figure out which team has the edge. Researchers at DataRobot thought it would be a fun exercise to pull all of the MLB data from the last few decades and have their AI figure out who will win the 2019 World Series. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic Premium) At the start of the playoffs, the AI predicted the Los Angeles Dodgers were most likely to win the pennant, followed closely by the Houston Astros. In the American League, DataRobot's AI said the Houston Astros had a 40% probability of winning the American League, followed by the New York Yankees at 25% and Minnesota Twins at 18%.

Have you ever witnessed two people talking past each other? They seem to be discussing the same topic using the same language, but you begin to wonder if they are actually talking about two different things. The public debate about the use of artificial intelligence in the Department of Defense is beginning to feel that way to me. Some technologists have called for DoD AI ethics, but in the next breath they call for an end to programs that have never been demonstrated to be unethical. I recently completed a study examining ethics across all scientific disciplines, and my team identified 10 ethical principles that span disciplines and international borders.

During Aragon Research's research community meeting this week, Jim and I were discussing what it will take to manage your workforce in the future; a hybrid mixture of humans and digital labor. The discussion got us thinking about what it means to manage technology versus what it takes to manage people, and how this will change as organizations introduce AI-enabled technologies. In this blog, we explore the differences and similarities between managing humans and technology to understand how management will change as we introduce technologies that can learn, recognize patterns, and change/respond. Digital labor is a term that applies to the automation of tasks that are performed by computer applications. Our future workforce will be a hybrid combination of humans and AI-enabled technologies (i.e., bots, assistants, robotics, etc.), supported by traditional non-AI technology.

What we've learned from the early days of CX technology is that forcing customers to conform to rigid mechanical processes (language choices, service versus support options) doesn't provide a customer-centric approach. It didn't take long for people to figure out pressing "O" to get a live operator--or shouting "agent" in frustration--was almost always the best choice. Thankfully, AI, powered by Machine Learning and evaluated and fine-tuned by humans, has come a long way in the last decade. AI-powered virtual assistants are now able to ask-- and respond to--open-ended questions like, "What can I do for you today?" Marketers fulfill a dual function within their organizations. But because of the unique insights that they gain into how their customers think, feel, act, behave, and buy, they're also customer advocates.

Think back to school and science class: You probably conducted an experiment where you placed an alarm clock (set to go off in 5 minutes) under a glass jar and the teacher pumped all the air out of the jar. When the alarm went off, you couldn't hear it, right? Without air, sound does not travel – nature abhors a vacuum. This is also true of artificial intelligence - it cannot survive in a vacuum and needs a rich ecosystem of data where it can thrive. This can only be achieved by integrating "trustworthy AI" systems with the rest of an organization's IT landscape.

Not many scientists get solicited for photo ops, but for Daphne Koller it's a regular occurrence. "It happens at pretty much any event that has tech people," Koller says when asked about one recent snapshot. It's not like I feel like this is something I deserve." Selfie requests are just one sign of Koller's stardom, earned from more than 20 years bridging computer science, biology and education. She chalked up a string of accolades along the way: getting a master's degree from Jerusalem's Hebrew University at 18; becoming a Stanford University professor focused on machine learning at 26; winning, nearly a decade later, a Mac Arthur "genius grant" for research that combined artificial intelligence and genomics; and cofounding $1 billion (valuation) Coursera, an early platform to let people around the world take university classes for free. The next act for this 51-year-old innovator: Insitro, a firm in South San Francisco that aims to find new drugs by sorting through masses of data. If it succeeds, it will have overturned how drugs get discovered. Lab biologists typically focus on a few specific proteins as drug targets. If those fail, data scientists make suggestions for others to try. Insitro, on the other hand, wants to collect much more data before the biologists go off on their hunt. It will leverage advances in bioengineering (such as Crispr gene editing) and in software that enables computers to see things that escape humans. Koller describes her aha moment this way: "Machine learning is now doing amazing things if you give it enough data.