A lot of people do, and for powerful reasons – we are facing enormous challenges unprecedented in human history, from climate change and nuclear war to engineered pandemics and malicious artificial intelligence. A 2017 survey showed that nearly four in 10 Americans think that climate change alone has a good chance of triggering humanity's extinction. But we seem largely blind to the many profound reasons for hope – and it's not entirely our fault. Humans are wired with a "negativity bias" that triggers a stronger emotional response to bad news than good news – evident in the journalism maxim "if it bleeds, it leads". This loss-aversion behavior served a purpose in our evolutionary past, when information and resources were scarce, but in the age of endless information access, it can lead to pessimism, anxiety and a distorted vision of what humanity is capable of.

"I want to go to Mars, and he is going to take us," she says. "Space is the final frontier. It's in our DNA to find the final frontier--to keep going until we get to the edge." Though Veerasingam is lightyears from Mars, she is currently on the edge of Santa Monica Boulevard and North Orange Drive, in the heart of Hollywood, for the opening of the new Tesla Diner, modeled in the likeness of the same kind of retro-futuristic space station she one day dreams of inhabiting on the red planet. An actress who lives in Toluca Lace, Veerasingam wanted to see Musk's latest window into the future up close.

The actions look something like those of an amusement park claw game, except they are executed rapidly and smoothly, just like the countless movements that workers undertake to pick and pack millions of online orders each day in warehouses across the world. Top news and in-depth analysis on the world of logistics, from supply chain to transport and technology. But the robotic device, known as Sparrow, is outfitted with suction cups and artificial intelligence software rather than the eyes and hands of human workers. It is the latest attempt by Amazon. Warehouse workers pick items up, sort them and put them down millions of times a day. But Amazon is trying to get Sparrow to do something that robots have long struggled with--picking up a variety of objects as easily as humans can, as well as identifying them by characteristics such as color, shape and size.

In 1950, when Alan Turing conceived "The Imitation Game" as a test of computer behavior, it was unimaginable that humans of the future would spend most hours of their day glued to a screen, inhabiting the world of machines more than the world of people. That is the Copernican Shift in AI. "I propose to consider the question, 'Can machines think?'" Buried in the controversy this summer about Google's LaMDA language model, which an engineer claimed was sentient, is a hint about a big change that's come over artificial intelligence since Alan Turing defined the idea of the "Turing Test" in an essay in 1950. Turing, a British mathematician who laid the groundwork for computing, offered what he called the "Imitation Game." Two entities, one a person, one a digital computer, are asked questions by a third entity, a human interrogator.

Production is often viewed as the final frontier in the machine learning process. By now, your data scientists trained a model on your data, the machine learning and software engineers incorporated that model into an application, the DevOps team configured the automation that containerizes the application for use by the rest of the organization, and the IT department set up infrastructure to host your model's application. At this point, most program managers flip the proverbial switch, allow users to rely on the solution, and move on to the next thing. That's also the wrong thing to do. This blog in the ModelOps blog series covers the model production step in the ModelOps pipeline, or AI in production, and the active management required to successfully field a machine learning model.

From writer-director Neil Burger ("Divergent") comes another young adult science-fiction tale, this one of a cruise ship in deep space full of restless teenagers under the supervision of a single adult. Some of the young people find out that the adult is keeping them drugged and docile and forcing them to reproduce artificially. Is that a recipe for YA trouble or what? Just when you thought you could not watch one more film of this kind, here is "Voyagers," a title that sounds enough like "Passengers" (2016) to put you off you spaceship-grown peas and carrots. The story is set in 2063 when Earth is ravaged, and scientists have searched for another planet to colonize.

When space exploration became the hottest topic nearly half a century ago, many declared it to be mankind's final frontier. Today, however, the tides have completely shifted. Man's final frontier appears to be something else. And if you would just closely look at the latest in digital technology, it would be easily made clear that AI is now that final frontier. We've seen a lot of movies and TV shows that are depicting robotic creatures in a creative and compelling way that we could somehow visualize a digital apocalypse so clearly in our heads.

Data threats never rest, nor should the protection of your sensitive information. That's the driving principle behind confidential computing, which seeks to plug a potentially crippling hole in data security. Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names -- multiparty computing, federated learning and privacy-preserving analytics, among them -- and confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance. Data exists in three states: in transit when it is moving through the network; at rest when stored; and in use as it's being processed.

One important data type which includes time series, digital signals and any sequential observations is still mainly processed with rather standard mathematical and algorithmic routines. In this talk, we will review, what are the main sources of time series in the world, what are the "basic" algorithms and how exactly they might be improved and replaced with different neural network architectures. Apart from the models' details, we will also study the typical tasks that have to be solved while working with time series: classification, prediction, anomaly detection, simulation and others and exactly deep learning can be leveraged to solve them on the state-of-the-art level. Some previous experience with time series/signal processing is useful for getting the most out of this session, but not required. Alex Honchar is developing production-ready AI solutions for small and medium businesses for the last 5 years, giving public speeches in Europe and blogging about ML and AI recent advances.

Watch CQ Roll Call's senior writers Gopal Ratnam and Kate Ackley dispel some common misconceptions about artificial intelligence. They also help explain some yet-to-be mitigated risks with this still developing technology, and how you use Hollywood's favorite dystopian theme every day. Get breaking news alerts and more from Roll Call on your iPhone.