AI automation is coming, and it's going to impact knowledge workers -- writers, artists, designers, scientists, managers, and entrepreneurs. However, even when AI automation completely replaces the need for a human -- such as in its conquest of strategy games like Chess and Poker -- the world doesn't end and the human element doesn't disappear. Largely better, in some ways worse, but life in fact goes on. Humans keep doing our thing. And the machines keep getting better, and better, and better.

Every week, there are news reports on how AI will impact jobs, but what about the impact of AI on indie authors? Can we surf the change instead of being drowned by it? Could it make our lives better as creative entrepreneurs? I recently did a mega-solo podcast on 9 Ways that Artificial Intelligence Will Disrupt Authors and the Publishing Industry, and in this interview, Orna Ross interviews me about some of the specifics that might impact indie authors, in particular. The interview is on the Alliance of Independent Authors AskAlli Podcast feed. The interview starts after the introduction around 12:25 mins. Links and notes here and you can subscribe below. Click here to watch the interview on YouTube. But today we are going to talk about something that sounds maybe mysterious, sounds maybe scary for a lot of authors. But that you have been investing a lot of time and attention and you're hugely interested in and you're actually making work for you. And that is artificial intelligence. And our theme for today is really how can indie authors harness the power of AI? Where are we with it? What can it do for us? And on all things AI, you are our guru so I'm going to be quizzing you, but first, before we start on what it can do for us, what is it? Explain to the people, what are we talking about when we're talking about artificial intelligence?

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine learning, AI, cognitive computing and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs.

In Japan's FANUC plant robots are producing robots and they outnumber people. Robots are beating humans in intelligence games, reading and writing pop songs. They are driving us to our chosen destinations and are on their way to become doctors, engineers, scientists, songwriters and painters. Never ever in history, the boundaries between fact and fiction were so thin. By 2030 AI will add an additional $15.7 trillion to the global economy.

We've all heard it before: "Win or go home." Whether in business or on the playing field, the pressure to win is intense. And in today's financial services industry, the winner can literally take all. As banks struggle to adapt in the throes of digital disruption, executives find themselves squeezed to use artificial intelligence (AI) or machine learning (ML) models to power their digital transformation initiatives forward. The industry's use of computational finance models to make decisions is nothing new.

Research is still being done on how to detect fake news without manual intervention. Detecting fake news by using stylometry-based provenance to track a text's writing style back to its first source has been accepted as one way to solve the challenge. Earlier, researchers from Harvard University and MIT-IBM Watson Lab had come up with an AI-powered tool to recognise AI-generated text. Known as the Giant Language Model Test Room (GLTR), the system works on finding out if a particular piece of writing was produced by a language model algorithm, aka computer or a human. With AI and natural language generation models being used to make fake news, GLTR can be used to differentiate machine-generated text from human-written text to a non-expert reader.

Humans can't keep pace with the rapid growth of new cyberthreats.

Workers place more trust in robots and AI than their managers according to the second annual AI at Work study conducted by Oracle and Future Workplace. To compile the study, the two firms surveyed 8,370 employees, managers and HR leaders across 10 countries to find that AI has changed the relationship between people and technology in the workplace and is reshaping the role HR teams and managers need to play when it comes to attracting, retaining and developing talent. In contrast to common fears that AI and robots will take workers jobs, the AI at Work study found that employees, managers and HR leaders across the globe are reporting increased adoption of AI in the workplace and many are welcoming the emerging technology with enthusiasm. AI is becoming more prominent in workplaces with 50 percent of workers currently using some form of AI at work compared to only 32 percent last year. Workers in China (77%) and India (78%) have adopted AI over two times more than those in France (32%) and Japan (29%).

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. In the Rev session, "Data Science at The New York Times", Chris Wiggins provided insights into how the Data Science group at The New York Times helped the newsroom and business be economically strong by developing and deploying ML solutions. Wiggins advised that data scientists ingest business problems, re-frame them as ML tasks, execute on the ML tasks, and then clearly and concisely communicate the results back to the organization. He advocated that an impactful ML solution does not end with Google Slides but becomes "a working API that is hosted or a GUI or some piece of working code that people can put to work". Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. The data science group, in particular, includes people from a "wide variety of intellectual trainings" including cognitive science, physics, finance, applied math, and more. Wiggins closed the session with indicating how he looks forward to hiring from even more diverse job applications. For more insights from this session, watch the video or read through the transcript. I have about 30 minutes with you. I'm going to try to tell you all about data science at the New York Times, and in case I run out of time my email address and my Twitter are here. If you don't remember anything else, just remember we're hiring.