By day, Pat Scannell is a professional technologist, who has spent a 25 year career commercializing disruptive technologies into mass market adoption, and he has done this in domains ranging from Internet, Mobile, IoT and Defense. By night, he researches and writes about the cumulative effects of technology in humans, specifically the effects on how we think, now and in the future. In this article, Pat summarizes the results of his findings and invites those interested in discussing the issues in-depth to join the dialog. "For every dollar and every minute we invest in improving AI, we would be wise to invest a dollar and a minute in exploring and developing human consciousness" – Yuval Harari As CES wraps up, it's clear that the Artificial Intelligence hype is peaking, from self-driving cars, business decision software, and even the AI powered cat litter box. Our tech and our industries are going to become disrupted by the ever-accelerating technology around us, but what if our thoughts will be, too?

Deployed for healthcare, environmental and even bartending duties around the world, robots have just been enlisted to sort through waste flowing through a Florida recycling plant, where they perform faster and safer than humanly possible. The 14 high-speed, precision robots installed this summer at Single Stream Recyclers (SSR) in Sarasota are guided by an artificial intelligence platform that applies computer vision and machine learning to direct the robots' rapid-fire movements. "The average human can pick 30-40 items per minute. The robots can pick 80 items per minute," said John Hansen, co-owner of SSR, which processes materials from numerous Southwest Florida communities at its nearly 100,000-square-foot recovery facility. Developed by Denver-based AMP Robotics, the robots identify and sort plastics, cartons, paper, cardboard, metals and other materials streaming through their cube-like housing.

If somebody hacked communications to grid-connected devices and interrupted a demand response (DR) event, peak demand might not be cut, capacity prices could spike and that somebody could make a lot of money. Because of the fast-rising number of grid-connected devices in DR programs like smart thermostats and water heaters and the even faster-rising number of smart phones and other Internet technologies through which customers communicate with DR programs, market manipulations like that are possible, cybersecurity experts from the Electric Power Research Institute (EPRI) told the Demand Response World Forum October 17. It is one of many potential intrusions of communications between utilities and customers with grid connected devices and distributed energy resources (DER), they said. To counter these threats, data analytics experts are using the laws of physics and unprecedented masses of data to find cybersecurity breaches. And their work is leading to machine learning (ML) and artificial intelligence (AI) algorithms which, though only just beginning to find actual deployment, are expected to soon advance the ability to identify patterns to the intrusions and raise the level of protection for critical power systems.

Self-driving trucks may have once seemed like a futuristic vision. But in recent years, they've begun taking to the road -- and their implications for the labor market, and long-haul truck drivers in particular, could be enormous. In the above excerpt from the new FRONTLINE documentary In the Age of AI, meet the young CEO of a self-driving truck company whose vehicles are already delivering freight from California to Arizona; an independent trucker and his wife whose livelihood could be threatened by the new tech; and a sociologist and author who's been studying the forces reshaping the trucking industry. Among them: the rise in automation, some forms of which are powered by AI -- including self-driving trucks. "The trucking industry is $740 billion a year … in many of these operations, labor's a third of that cost," Steve Viscelli, author of The Big Rig, tells FRONTLINE in the above excerpt from the film.

A few steps remain for China's plans for a military expansion into Taiwan with the incorporation of an AI Digital Brain that can connect to the 5G network to power drones, machines, robotics, surveillance systems, and total control of the Taiwanese people. Through IP theft, forced tech transfers, espionage, social engineering, open-source sharing, collaborations, investments, and mergers, China's government has put their tentacles into every country, and every domain and sphere that incorporates trade, and human existence. China's moves against Taiwan and the West links Chip Makers (TSMC) for AI guided Weapons that interconnect Huawei, Baidu, Megvii Face, Sensetime, and numerous tech companies. These tech companies interlink with social engineering of U.S democratic politicians, the penetration of KMT by Chinese and Taiwanese implants, with the laying of the massive groundwork to control Taiwan with AI automated drones, robotics & smart cities on the 5G network. This interconnection includes soft power initiative's at a global level to social engineer reporters, media, politicians, corporate tech leaders and the Chinese citizens to imprint a mental impression that China's 70 year track record of concentration camps, one party system, torture, rape, and Orwellian surveillance, will not transfer over to worldwide Chinese subjugation of humanity. The social engineering also makes the implication, "Chinese government stands by its contracts and words", yet they are breaking their agreement by invading Hong Kong and embedding soldiers within the Hong Kong populous, including Hong Kong police.

The relationship between Apache Kafka and machine learning (ML) is an interesting one that I've written about quite a bit in How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning. This blog post addresses a specific part of building a machine learning infrastructure: the deployment of an analytic model in a Kafka application for real-time predictions. Model training and model deployment can be two separate processes. However, you can also use many of the same steps for integration and data preprocessing because you often need to perform the same integration, filter, enrichment, and aggregation of data for model training and model inference. We will discuss and compare two different options for model deployment: model servers with remote procedure calls (RPCs), and natively embedding models into Kafka client applications.

Economic disruption is a reality which has been a gradual development over the last several decades. Artificial intelligence (AI) has simply accelerated this process. Virtually every industry has been impacted by AI and certainly data science is no exception. Yet, we may also inquire how does machine learning fit within this overall discussion. The explosion of literature on these topics over the last several years is a testament to the popularity of both topics.

A small group of mujahidin is trekking through the mountains. They carry their Kalashnikov rifles on their shoulders, but they are not especially worried. The nearest enemy unit is several hours away. So high in the mountains, they would see them coming from a long distance. There are other dangers, though.

I've encountered a lot of artificial intelligences, both the ones I've trained for my blog AI Weirdness, and the ones I've written about for my book on artificial intelligence, You Look Like a Thing and I Love You: How AI Works and Why it's Making the World a Weirder Place. I focus on the machine learning algorithms that exist today, the ones that sort spam, tag photos, and drive cars. We call them AI, but they're as different from the AI of science fiction as a toaster is from a person. In the book, I spend a lot of time explaining why today's AIs, with their tiny worm brains, don't understand their tasks or the human world. They won't be taking over from people, but they also won't be saving us by questioning bad orders.

Election officials and social media firms already flummoxed by hackers, trolls and bots are bracing for a potentially more potent weapon of disinformation as the 2020 election approaches -- doctored videos, known as "deep fakes," that can be nearly impossible to detect as inauthentic. In tech company board rooms, university labs and Pentagon briefings, technologists on the front lines of cybersecurity have sounded alarms over the threat, which they say has increased markedly as the technology to make convincing fakes has become increasingly available. On Tuesday, leaders in artificial intelligence plan to unveil a tool to push back -- it includes scanning software that UC Berkeley has been developing in partnership with the U.S. military, which the industry will start providing to journalists and political operatives. The goal is to give the media and campaigns a chance to screen possible fake videos before they could throw an election into chaos. The software is among the first significant efforts to arm reporters and campaigns with tools to combat deep fakes.