AUGUST 2008: Border agents recovered this 2-foot-high drone that was seen swooping over the border fence in southern California. Drug cartels are using unmanned drones to carry drugs across the southern border, challenging the U.S. technological ability to stop the advance. Brandon Judd, an agent and president of the National Border Patrol Council, warned that the border patrol does not have the technology to contain drones. "The number is just astronomical," Judd told The Washington Times. At least 13 drones believed to be carrying drugs were spotted in November alone, agents said, according to the Times.

"Ray Kurzweil and the Singularity" is a brilliant animation short by Lew Keilar and was a 2012 Happy Endings FilmFest finalist. This is a whiteboard animation explaining, in three minutes, the Technological Singularity concept made famous by Ray Kurzweil and other futurists. Technological change is happening at an exponential rate, leading to a date in a future we can barely glimpse. Ray Kurzweil, innovator & inventor, adviser to US Presidents and CEOs is one of the visionaries articulating this extraordinary development in human evolution.

The Food and Drug Administration has approved Arterys' web-based imaging analytics platform, which marries cloud-based supercomputing and artificial intelligence, for clinical use. The FDA approval allows the Arterys product, called MICA, to supply clinical guidance to clinicians treating cardiac patients. The company is awaiting FDA clearance that would allow the platform to be used by oncology teams treating patients with lung and liver conditions. MICA is web-based and runs on a scalable distributed graphics processing architecture, enabling the product to apply various AI algorithms to image studies of specific cases. This addresses one of the major challenges of applying a variety of algorithms that use artificial intelligence to imaging studies--intelligently calling upon the right algorithm to provide guidance to clinicians while they are investigating a case or making treatment decisions.

This article is part of Inc42's Startup Watchlist annual series where we list the top startups to watch for 2018 from industries like AI, IoT, Blockchain etc. Explore all the stories from'Startup Watchlist' series here. Once used as a tool for an application, Internet of Things (IoT) has become one of the widest ecosystems today. Currently at the centre stage of industries like energy management, healthcare, logistics, fintech, manufacturing and agritech, IoT, in convergence with AI, has the potential to disrupt all these verticals. Previously dictated by big players like IBM, Google, Intel, Cisco, Ericsson, Apple and Amazon, the IoT space has now become a startup ecosystem enabler across the world. While it was the Internet that drove the emergence of ecommerce startups in the early 2000s, IoT has been facilitating the growth of this decade's tech startups. What lightning does to mushrooms, IoT has done to startups!

An Ottawa-based firm has been tapped by the federal government for a three-month pilot project designed to look for warning signs for suicide before tragedy strikes. Advanced Symbolics Inc., is an artificial intelligence service company set to examine suicide hot spots across the country to better understand precursors to suicide. The pilot, expected to start by the beginning of February, will examine all parts of the country including Indigenous communities, said chief scientist Kenton White, though he stressed the goal is not to focus on any particular group. "What we would like to try and understand is what are the signals … that would allow us to forecast where the next hot spots are so that we can help the government of Canada to provide the resources that are … going to be needed to help prevent suicide before the tragedies happen," White said. There were a number of high profile "hot spots" in 2017, White added, noting northern communities and places like Cape Breton were hit particularly hard by spikes in suicide.

Many leading AI researchers think that in a matter of decades, artificial intelligence will be able to do not merely some of our jobs, but all of our jobs, forever transforming life on Earth. The reason that many dismiss this as science fiction is that we've traditionally thought of intelligence as something mysterious that can only exist in biological organisms, especially humans. But such carbon chauvinism is unscientific. From my perspective as a physicist and AI researcher, intelligence is simply a certain kind of information-processing performed by elementary particles moving around, and there is no law of physics that says one can't build machines more intelligent than us in all ways. This suggests that we've only seen the tip of the intelligence iceberg and that there is an amazing potential to unlock the full intelligence that is latent in nature and use it to help humanity flourish -- or flounder. If we get it right, the upside is huge: Since everything we love about civilization is the product of intelligence, amplifying our own intelligence with AI has the potential to solve tomorrow's thorniest problems.

Malware recognition modules decide if an object is a threat, based on the data they have collected on it. This data may be collected at different phases: – Pre-execution phase data is anything you can tell about a file without executing it. This may include executable file format descriptions, code descriptions, binary data statistics, text strings and information extracted via code emulation and other similar data. In the early epochs of the cyber era, the number of malware threats was relatively low, and simple handcrafted pre-execution rules were often enough to detect threats. But a decade ago, the tremendous growth of the malware stream did not allow anti-malware solutions to rely solely on the expensive manual creation of detection rules. It was natural for anti-malware companies to start augmenting their malware detection and classification with machine learning, a computer science area that has shown great success in image recognition, searching and decision- making. Machine Learning Methods for Malware Detection In this article, we summarize our decade's worth of experience with implementing machine learning into protecting our customers from cyberthreats. In other words, a machine learning algorithm discovers and formalizes the principles that underlie the data it sees. With this knowledge, the algorithm can reason the properties of previously unseen samples. In malware detection, a previously unseen sample could be a new file. Its hidden property could be malware or benign. A mathematically formalized set of principles underlying data properties is called the model. Machine learning has a broad variety of approaches that it takes to a solution rather than a single method. These approaches have different capacities and different tasks that they suit best. Unsupervised learning One machine learning approach is unsupervised learning. In this setting, we are given only a data set without the right answers for the task. The goal is to discover the structure of the data or the law of data generation. One important example is clustering. Clustering is a task that includes splitting a data set into groups of similar objects. Another task is representation learning – this includes building an informative feature set for objects based on their low- level description (for example, an autoencoder model). Large unlabeled datasets are available to cybersecurity vendors and the cost of their manual labeling by experts is high – this makes unsupervised learning valuable for threat detection. Clustering can help with optimizing efforts for the manual labeling of new samples. With informative embedding, we can decrease the number of labeled objects needed for the usage of the next machine learning approach in our pipeline: supervised learning.

As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." And it can be vastly consequential when people are divided into Us and Them, ingroup and outgroup, "the people" (i.e., our kind) and the Others. The core of Us/Them-ing is emotional and automatic. Humans universally make Us/Them dichotomies along lines of race, ethnicity, gender, language group, religion, age, socioeconomic status, and so on. We do so with remarkable speed and neurobiological efficiency; have complex taxonomies and classifications of ways in which we denigrate Thems; do so with a versatility that ranges from the minutest of microaggression to bloodbaths of savagery; and regularly decide what is inferior about Them based on pure emotion, followed by primitive rationalizations that we mistake for rationality. But crucially, there is room for optimism. Much of that is grounded in something definedly human, which is that we all carry multiple Us/Them divisions in our heads. A Them in one case can be an Us in another, and it can only take an instant for that identity to flip. Thus, there is hope that, with science's help, clannishness and xenophobia can lessen, perhaps even so much so that Hollywood-extra chimps and gorillas can break bread together.

When it comes to artificial intelligence and jobs, the prognostications are grim. The conventional wisdom is that A.I. might soon put millions of people out of work -- that it stands poised to do to clerical and white collar workers over the next two decades what mechanization did to factory workers over the past two. And that is to say nothing of the truckers and taxi drivers who will find themselves unemployed or underemployed as self-driving cars take over our roads. But it's time we start thinking about A.I.'s potential benefits for society as well as its drawbacks. The big-data and A.I. revolutions could also help fight poverty and promote economic stability.

Inc said on Tuesday it shipped over 5 billion items worldwide via its subscription based Prime service in 2017 while adding more new members than ever before. The e-commerce giant, which revealed its Prime shipment numbers for the first time, did not give comparable full-year shipment number for 2016. Amazon claimed that its Fire TV Stick and voice controlled smart device Echo Dot were the best-selling products among U.S. Prime members from any manufacturer in any category across all of its product offerings. The e-commerce giant, which revealed its Prime shipment numbers for the first time, did not give comparable full-year shipment number for 2016. Customers can receive free two-day shipping on most items by paying for a $99 annual'Prime' membership.