In the 1997 movie Gattica, Ethan Hawke displayed the brute-force determination of the human spirit in an hypothetical, transitional to full CRISPR, gene-editing future that 23 years later we are now in, where all parents who wanted their children to succeed were forced to make a hard choice. To edit, to give you children the'best' of your genes, or let mother nature randomly recombine them to produce an'uncertain' outcome. Ethan succeeded in all mental and physical tasks to become an astronaut in that fictional world populated by supposed physically perfect geniuses, but, setting aside exactly how the Gattica scientists determined the criteria for'the best genes', the reality we are facing in 2020 is an interesting one that has some of the features of this movie. While the Chinese geneticist He Jiankui is now missing, he may historically be the person credited with making a Gattica future real. Every parent with the financial means may decide to travel to countries with less strict gene control laws and decide to do this, either on in utero children or on themselves.

Christmas is just over a week away, which means the holiday shopping season is in full swing. Consumers are spending billions of dollars per day on gifts in anticipation of the big day. But the fraudsters are also out in force to steal a piece of the action. Luckily, AI and machine learning are getting better at identifying these grinches before they ruin things for the rest of us. The math is pretty simple: The bigger the holiday buying season, the bigger the pay day for fraudsters.

An autonomou truck from startup Plus.ai took three days to drive 2,800 miles, from Tulare, CA, to Quakertown, PA, to deliver 40,000 pounds of butter for Land O'Lakes. Startup Plus.ai has completed what is being called the first commercial freight cross-country trip by an autonomous truck, a 2,800-mile-run from Tulare, CA, to Quakertown, PA, that took three days to deliver 40,000 pounds of butter for Land O'Lakes. A human safety driver was aboard the autonomous 18-wheeler to take over if needed, accompanied by a safety engineer. The company said the trip was a smooth one with zero "disengagements," which occur when a self-driving system has to be suspended because of a problem. Ten to 15 companies in the U.S. are working on autonomous freight delivery, said Dan Ives of Wedbush Securities, who believes the trucking industry will be the first to adopt autonomous technology on a mass scale.

Artificial Intelligence offers great potential and, for some, risks for humans in the future. While still in its infancy it is being employed in some interesting ways. Here we explore some of the main AI trends predicted by experts in the field. If correct, 2020 should see some very exciting developments indeed. According to sources like Forbes, some of the next "big things" in technology include, but are not limited to: Further to the above, here are some more AI trends to look out for in 2020.

The question seems somewhat ironic if you think about it. Are the creations--intelligent systems--set to replace or displace their creators--programmers--in the future? It sounds like the plot of a sci-fi movie, doesn't it? The more important question, though, is: Can it be true? A recent future of employment study predicts that nearly 50% of the jobs in the U.S. are likely to be automated by 2030.

Wild animals are experts at staying out of sight, but a new partnership between Google and the conservation organization Wildlife Insights will try to help scientists capture and analyze pictures of them in their natural habitat. The program will use an artificial intelligence program to sort through photographs taken by small sensor driven camera installations placed in wilderness areas around the world. Google's AI and Cloud services will help researchers analyse and archive the enormous volume of images captured through the program as part of an effort to improve animal conservation strategies all around the world. The camera traps were originally developed in 1990 and in the intervening years have been placed everywhere from Mexico to Madagascar. To date, 4.553 million pictures have been taken from 8,209 camera deployments.

The Russian space agency Roscosmos is planning to install a nuclear-powered observatory on its future moon base to held spot deadly Earth-threatening asteroids. Establishing a permanent presence near the lunar south pole has been a priority for Roscosmos ever since NASA announced plans to return to the moon earlier this year. The base's telescopes will work in tandem with spacecraft placed in orbit around the Earth to help provide humanity with a space-rock early warning system. In addition, the lunar facility's permanent crew will be made up of robots -- with cosmonauts only visiting to handle more complicated tasks. The plans to establish an observatory on the future moon base were announced by Alexander Bloshenko, Roscosmos' Executive Director for Science and Long-Term Programs, Russian news outlets RT and TASS reported.

Are you ready to say goodbye to 2019? Tucked within that long list is the excitement of what 2020 will bring to the mobile world. Although 2019 wasn't exactly a banner year, it certainly set the stage for a lot of new technology trends to come. And thus, I pull out my 10 Ball of Prognostication and gaze deep into its shadowy realm to see what the upcoming 366 days--2020 is a leap year--have in store. If the Google Pixel 4 proved one thing, it's that Artificial Intelligence (AI) is not only here to stay, it's going to continue to lead the mobility charge.

Gay: As an astronomer, I have to admit, my day-to-day life is sitting at home writing software to help us better understand our universe. Then, as a communicator of science, it just makes me so excited to come out here and tell you about the kind of stuff I get to do. As an astronomer, I use data; images, spectra, photos but taken with cameras that are sometimes orbiting our world and other planets, moons, asteroids. For a lot of my career, everything I wanted to study, everything I wanted to learn, I could do with software, a database, and sometimes some really clumsy-linked lists because that was C in the 90s. Along the way though, I got curious about all these other areas of science that are different from mine. It was from the planetary-science community where I've somehow migrated over the years that I learned there are people - such as the folks who are today mapping out planet classic Pluto - that the way they do their analysis of the geological features on this world are to sit around round tables with a screen and a Wacom tablet. They draw by hand what they perceive to be the boundaries between different kinds of glaciers, different kinds of mountains, different features on this distant world. This is science by hand because humans and software don't know what to make of Pluto but the humans can at least guess. There's a lot of science that works this way. One of the most disturbing things I learned is there is a brilliant scientist Stuart Robbins who, as his PhD work at the University of Colorado, drew three million circles - again, with a Wacom tablet; go Wacom - three million circles on thousands and thousands of images of the surface of Mars. This ended up leading to a catalog of 600,000 craters. The reason he had to draw so many circles is he had to periodically remap regions to make sure that his bias hadn't changed over time. He had to map things at small scales, at big scales, at in-between scales, bridge across all of these, have overlapped between his image. Three million circles got him a PhD.

The Big Data paradigm has gained momentum last decade, because of its promise to deliver valuable insights to many real-world applications. With the advent of this emerging paradigm comes not only an increase in the volume of available data, but also the notion of its arrival velocity, that is, these real-world applications generate data in real-time at rates faster than those that can be handled by traditional systems. This situation leads us to assume that we have to deal with a potentially infinite and ever-growing datasets that may arrive continuously (stream learning) in batches of instances or instance by instance, in contrast to traditional systems where there is free access to all historical data. These traditional processing systems assume that data are at rest and simultaneously accessed. The models based on this traditional processing do not continuously integrate new information into already constructed models but, instead, regularly reconstruct new models from the scratch.