Machine learning is complex, but nonetheless has pushed its way to professional and maker communities alike. Nvidia has lead much of this with their TK1 and TX1 modules; now, with the new release of the Jetson TX2, the AI capabilities we have access to have just doubled. The new hardware, announced last night at a press event in San Francisco, retains the same form factor as the TX1 -- roughly the size of a credit card, it's meant as a drop-in replacement. It replaces the TX1's Mawell GPU with a Pascal unit, doubles the TX1's storage and memory, and increases its video encoding and decoding specs. With it, the company states that it can get either twice the performance of the TX1 (handling object detection and tracking from two 4K cameras simultaneously), or get double the efficiency running the same configuration as a TX1.

"Let us consider an augmented architect at work. He sits at a working station that has a visual display screen some three feet on a side, this is his working surface, controlled by a computer with which he can communicate by means of small keyboards and various other devices." This vision of the future architect was imagined by engineer and inventor Douglas Engelbart during his research into emerging computer systems at Stanford in 1962. At the dawn of personal computing he imagined the creative mind overlapping symbiotically with the intelligent machine to co-create designs. This dual mode of production, he envisaged, would hold the potential to generate new realities which could not be realized by either entity operating alone.

Stephen Hawking worries that humanity sometimes seems like a bunch of schoolyard bullies ... in a schoolyard littered with loaded machine guns and land mines. "Since civilization began, aggression has been useful inasmuch as it has definite survival advantages," the world's most famous cosmologist told the UK Times. "Now, however, technology has advanced at such a pace that this aggression may destroy us all by nuclear or biological war." Hawking says logic and reason must act as safeties on the triggers of such apocalyptic weapons. Besides catastrophic war, he also worries about climate change, mass extinctions and unchecked artificial intelligence, as he's mentioned before.

Can a planet be alive? Lynn Margulis, a giant of late 20th-century biology, who had an incandescent intellect that veered toward the unorthodox, thought so. She and chemist James Lovelock together theorized that life must be a planet-altering phenomenon and the distinction between the "living" and "nonliving" parts of Earth is not as clear-cut as we think. Many members of the scientific community derided their theory, called the Gaia hypothesis, as pseudoscience, and questioned their scientific integrity. But now Margulis and Lovelock may have their revenge. Recent scientific discoveries are giving us reason to take this hypothesis more seriously. At its core is an insight about the relationship between planets and life that has changed our understanding of both, and is shaping how we look for life on other worlds.

At this moment in history, there are more satellites photographing Earth from orbit than just about anyone knows what to do with. Planet, Inc., has more than 150 orbiting cameras, each the size of a shoebox. And more startups are planning to launch their own. What should we do with all that imagery? How can we search it and process it?

When coming across the term "text search", one usually thinks of a large body of text, which is indexed in a way that makes it possible to quickly look up one or more search terms when they are entered by a user. This is a classic problem for computer scientists, to which many solutions exist. What if what's available for indexing beforehand is a group of search phrases, and only at runtime is a large body of text presented for searching? These questions are what this trie data structure tutorial seeks to address. A real world application for this scenario is matching a number of medical theses against a list of medical conditions and finding out which theses discuss which conditions.

Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get quotes people said, etc. Stanford CoreNLP's goal is to make it very easy to apply a bunch of linguistic analysis tools to a piece of text. A tool pipeline can be run on a piece of plain text with just two lines of code. CoreNLP is designed to be highly flexible and extensible.

At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable. The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video. Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos. That means you can search for "dog" or "flower," for example. Besides extracting metadata, the API allows you to tag scene changes in a video.

According to recent reports, 70% of Facebook Messenger chatbots are failing to fulfil user requests. Consequently, the social network is set to scale back its AI efforts, instead focusing on a more simplistic system to ensure success. But will this spell the end of the current chatbot trend? And what does it mean for brands that have already invested? Here's a bit more on the story, as well as few examples of the latest brand bots to appear.

Customer Engagement approaches, and the technology used to enable them, have evolved immensely over the last 25 years. Two distinct eras define this period, as well as a major technological shift to real-time systems with AI feedback loops. The BI Guesswork Era During the advent of the Business Intelligence (BI), Marketing Technology and Campaign Management era (circa 1990), marketers had limited predictive powers. In many cases, when it came to what individuals really needed, they resorted to guesswork. They channeled their energy to perfect efficiencies in targeting and automation.