LOS ANGELES, May 31 (Xinhua) -- "The ultimate power, in my opinion, is love, not AI," a top artificial intelligence (AI) expert and chief scientist of AI/ML (Google Cloud) Fei-Fei Li told Xinhua in a recent exclusive interview. In the Chinese language, the abbreviation of artificial intelligence, or AI, is pronounced the same as the character for "love." From the introduction of a second generation of its TensorFlow Processing Unit (TPU) to TensorFlow Research Cloud, almost all of Google's latest big announcements touched on AI in one way or another. Last year, the technology giant recruited Li, one of the most eminent women working on AI, to lead machine learning research for Google's cloud computing division. "Goal is to democratize AI for everyone!"

Beyond membership in the Pantheon of Famous Brits, Winston Churchill and George Orwell would seem to have little in the way of common ground. Orwell was a journalist and novelist. Churchill had money and pedigree; the young Orwell lived on the street and raised his own vegetables during World War II. Churchill's political leanings were conservative; Orwell flirted with communism until he witnessed the betrayal of his Republican comrades by Soviet agents in the Spanish Civil War. In "Churchill & Orwell: The Fight for Freedom," Thomas E. Ricks gets beyond these differences and finds the iron core of both men.

The New York Times published a provocative article on Friday, asking in the headline "Is China outsmarting America in A.I.?". But depending on how you define "outsmarting," and the context in which the question is asked, the answer might not even matter much. The answer might matter very much in terms of geopolitics and national security. Just like with supercomputing, quantum computing and other areas of deep computer science research, having better capabilities in artificial intelligence can arguably lead to an edge in areas like military, energy and climate science that can shift the world-power balance. But if we're talking about consumer or enterprise AI, then comparing China and the United States is kind of like comparing apples and oranges.

What do we call the collection of technologies that make up what we used to call "artificial intelligence?" This conundrum reminds me of a Raymond Carver short story (and book) called What We Talk About When We Talk About Love. I was prompted to discuss this issue by a conversation with Jeremy Achin, the young CEO of DataRobot. We were preparing for a collaborative presentation at the Open Data Science Conference in Boston a few weeks ago, and I told him I could present on "The Cognitive Company." Achin, who doesn't mince words, wrinkled up his nose and said he really didn't like the use of the word "cognitive".

What does the creation of new artificial intelligence products look like today, and what do experts in this field foresee realistically happening in the near future? One thing's for sure, the way we work and function in life will change as a result of growth in this field. Listen and find out more. Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast.

One of the big trends we've seen over the last five years is automation. At the same time, we're also seeing more intelligence built into tools we already have, like phones and computers. Where do you see this process in five years? In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things. You will have more intelligent systems in the physical world, too -- not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world.

During an interview with Walt Mossberg tonight at the Code Conference, Android creator and now Essential Products CEO Andy Rubin showed off his latest creation, the Essential Phone. When it was announced this morning and reservations opened the company didn't say much about when to expect shipments, but in response to a question from Mossberg, Rubin said that he "wouldn't take reservations if it wasn't going to ship in, you know, 30 days or so." He also mentioned that the company's Home assistant would be able to support AI helpers from other companies like Google, Amazon or Apple (if they choose to make them available on its Ambient OS). Essential has its own AI assistant that runs on both the PH-1 phone and Home, but as Rubin put it, "All these people that have ecosystem envy...they have a thing. Like SmartThings as an example which is Samsung's thing...but consumers don't want just Samsung's stuff in their house."

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data.

Facebooks' announcement last month that it's building a brain-computer interface (BMI) that will let users type with just their minds without invasive implants is exciting as it is concerning. The prospect of being able to type with just your mind at a speed of 100 words per minute – 5 times faster than the typing speed on a phone – which is Facebook's goal, is fascinating. You can get more done with lesser effort and in shorter time, right? But how effectively will users' privacy be protected when this happens and how transparent will Facebook be about it? Like in George Orwell's 1984, is this about governments or corporations wanting to read people's mind to detect thought crime?

The recently proposed DeGroot-Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that each individual exponentially forgets its initial social power. Specifically, individual social power is dependent only on the dynamic network topology, and initial (or perceived) social power is forgotten as a result of sequential opinion discussion. Last, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results.