Computer vision systems typically learn how to recognize an object by analyzing images of thousands of examples. But scientists at Disney Research have shown that computers also can learn to recognize objects they have never seen before, based in part on studying vocabulary. People, after all, can get an idea of what things might look like based on reading a book. Similarly, a computer that already has been taught to recognize certain objects - apples, for instance - can analyze word use to get hints about the existence of fruits such as pears and peaches, and how they might differ from apples, said Leonid Sigal, senior research scientist at Disney Research. The knowledge that other fruits exist also is helpful in teaching the computer about important characteristics of apples themselves, he added.

Google earlier this year revealed it's been using machine learning to teach a group of robotic arms to grasp household objects. Google says it wants to bring "precision" to the debate about safety and artificial intelligence (AI), which has often veered into discussions about smarter machines stealing jobs or even rising up and destroying humanity. Scientists from Google's deep-learning research unit, Google Brain, the Elon Musk-backed OpenAI, and Stanford and Berkeley universities, have teamed up to explore five safety problems that could arise as AI is applied to general systems for the home, office, and industry. "While possible AI safety risks have received a lot of public attention, most previous discussion has been very hypothetical and speculative. We believe it's essential to ground concerns in real machine-learning research, and to start developing practical approaches for engineering AI systems that operate safely and reliably," wrote Chris Olah, one of the Google Brain contributors to the paper.

We explore new hobbies, new passions, new adventures and new experiences. We crave knowledge, information and insight with the goal of improving our own condition. We do all of this with the ability to reason, understand and learn so that we can push ourselves to be better than we were yesterday. It's time, then, that we apply this same drive to how we embrace cognitive computing. Cognitive computing, like IBM's world-famous Watson, illuminates aspects of our world that were previously invisible, like patterns and insights in unstructured data.

I tell researchers on our team that if you can just build the thumb and forefinger into computational systems, you can build civilizations.

Remember when the world first really discovered who Katherine Webb was? It was during the national title game in 2013 between the Alabama Crimson Tide and Notre Dame Irish, with longtime commentator Brent Musberger basically drooling over h...

VentureBeat's Bots Channel tracks all the important and interesting news related to the exploding field of bots and messaging. And each week we select the top stories and present them in in our free weekly newsletter, BotBeat. We include news stories by VentureBeat staff, guest articles from leading figures in the bots community and a good number of posts from a wide variety of outlets. You can subscribe to our BotBeat newsletter to receive all this information in your inbox every Thursday. The story most likely to be seen in your social media stream this week may have been this one about a bot that lets you ghost undesirable dates.

There are two main debates when it comes to artificial intelligence, by which I mean self-aware thinking machines with a general intelligence equal to or greater than that of humans. The first is when / whether it's coming. The second is what it will mean for humanity when it gets here. The vast majority of smart people who know anything about the topic seem to agree that it is coming, albeit with an ETA ranging anywhere from the 2020s to a few thousand years from now. I'm not a smart enough to weigh in on that. But if we postulate that it's coming eventually, that heralds in the second and far more interesting question of what happens next.

Just a few weeks back, scientists at Google's artificial intelligence division DeepMind announced they were developing a "kill switch" to ensure that intelligent machines do not go all Terminator on us. Now, it seems, Google's AI-related concerns are a bit less dire and a bit more mundane. Right now, Google is not worried that an AI would suddenly become sentient and begin its nefarious plans to take over the world (à la Skynet). It is, however, worried whether the helpful house robot you bought would clean your house without, say, burning it down by sticking a wet mop in an electric socket. In order to address these worries -- or at least put them out there -- Google's computer scientists have now published a paper titled "Concrete Problems in AI Safety."

CIKM is a top-tier ACM conference in the areas of information retrieval, knowledge management and databases. Since 1992, it has successfully brought together leading researchers and developers from the three communities, with the purpose of identifying challenging problems facing the development of advanced knowledge and information systems, and shaping future research directions through the publication of high quality, applied and theoretical research findings. The infographic below describes India's participation at CIKM 2015. Knowledge Discovery and Data Mining (KDD), a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. It was held at Sydney, Australia in 2015.

Note: Before starting Part 2, be sure to read Part 1! When it comes to machine learning, ultimately the most important picture to have is the big picture. Whether it's logistic regression, random forests, Bayesian methods, support vector machines, or neural nets, everyone seems to have their favorite! Unfortunately these discussions tend to truncate the challenges of machine learning into a single problem, which is a particularly problematic misrepresentation for people who are just getting started with machine learning. Sure, picking a good model is important, but it's certainly not enough (and it's debatable whether a model can actually be'good' devoid of the context of the domain, the hypothesis, the shape of the data, and the intended application. In this post we'll discuss model selection in the context of the big picture, which I'll present in terms of the model selection triple, and we'll explore a set of visual tools for navigating the triple.