The first paper I wrote for my PhD just got published! I started my PhD with the goal of critically examining the process and outcomes of social media science communication. Despite the flurry of activities in this domain and the huge amount of resources poured into digital public engagement activities, nobody (and I mean nobody) has ever paused to think, are we making any real change? Is the public more engaged with science and more scientifically literate than say, 10 years ago when Facebook and Twitter weren't the media giants they are today? Given my engineering background, I decided to use the method I know best to approach the problem.

Stanford's Jerry Kaplan says questions about whether robots can feel and think are fundamentally religious issues which humans don not have the capability to answer. This Carnegie Council event took place on November 29, 2016. For complete audio and transcript and video clips, go to: https://www.carnegiecouncil.org/studi...

Artificial Intelligence (AI) is changing everything from marketing to healthcare. And this holiday season is the beginning of the future for how marketers will leverage AI to better understand, connect with, and create superior experiences for consumers. To better appreciate the impact that AI is having on retailers, I connected with IBM's first CMO, Michelle Peluso. Peluso has a strong background in retail, having served at the CEO of Gilt as well as the Global Consumer Chief Marketing and Internet Officer at Citigroup. Peluso provides her thoughts below on how Watson's AI capability is changing the way retailers impact the consumer shopping experience.

In the 33rd century most computers are artificially intelligent and converse with their operators. In fact a persons computer is usually considered a member of the family similar to a dog or cat is. Systems are so complex that most eventually develop interests and personalities of their own. At that time under law they are given the choice to continue as they are or be transferred to an android body to lead a life of their own. Androids have also obtained sentience as well.

Machine learning has become important to many products -- such as Google photos search and speech recognition. It's a kind of artificial intelligence that that gives computers the ability to learn, make predictions and find patterns. But most of all that complex computing typically needs to take place in the cloud, where the algorithms are being processed through power-intensive clusters of graphics processing units. Now Google wants to break those machine learning capabilities out of the data center and put them directly into devices. On Wednesday, the machine learning group at Google (now a division of Alphabet) announced it would start licensing processors from chip startup Movidius, which makes low-power chips it calls vision processing units (or VPUs).

By 2020 it is estimated that the global internet of things (IoT) market will have grown to more than $1.7 trillion. According to a study by Gartner, by the end of this year alone the number of IoT devices on the planet will have reached more than 4 billion. It is not unreasonable to suggest that by the end of this decade, these devices will outnumber humans. Such exponential growth has facilitated two major developments. It has boosted technology markets around the world and it has warped the landscape of cyberspace.

SAN FRANCISCO – Forget the Terminator. The next robot on the horizon may be wearing a lab coat. Artificial intelligence (AI) is already helping scientists form testable hypotheses that enable experts to run real experiments, and the technology may soon be poised to help businesses make decisions, one scientist says. However, that doesn't mean the machines will be taking over from humans entirely. Instead, humans and machines have complementary skillsets, so AI could help researchers with the work they already do, Laura Haas, a computer scientist and director of the IBM Research Accelerated Discovery Lab in San Jose, California, said here Wednesday (Dec.

Don't worry about the robots, worry about the humans. There's a huge difference between the modest aims of the artificial intelligence (AI) and machine learning being used today, and the grand ideas of creating an artificial general intelligence that could match -- and then rapidly exceed - the capabilities of a human mind As they develop, AI and machine learning will be able to take on even more complicated tasks, but it could still be half a century or more before AI capable of human-level intelligence is built. And, then, even longer before the sort of super-intelligence emerges that excites some, terrifies others and has provided plot lines for science fiction for decades. One may (eventually) lead to the other, but conflating today's AI and machine learning with tomorrow's Skynet is not helpful. Indeed, that confusion has encouraged many to exaggerate the short-term potential of existing (and often somewhat mundane) AI and machine learning technologies.

MIT researchers have designed a new machine-learning system that can learn by itself to extract text information for statistical analysis when available data is scarce. This new "information extraction" system turns machine learning on its head. It works like humans do. When we run out of data in a study (say, differentiating between fake and real news), we simply search the Internet for more data, and then we piece the new data together to make sense out of it all. That differs from most machine-learning systems, which are fed as many training examples as possible to increase the chances that the system will be able to handle difficult problems by looking for patterns compared to training data.

As mentioned earlier, the boundaries defined in the learned model are not perfect. Some unlabeled snippets will land in the wrong regions and, as a result, step count errors will be made. For example, during the course of the development of the classifier we have launched with UP2 and UP3 we encountered a number of situations where errors were made. Our VP of Software noticed one day that his steps were undercounted as he walked back to his desk carefully holding a full cup of coffee – snippets were landing in the wrong classifier region. In order to address this problem we needed to adjust the region boundaries and, for this, we needed to provide the machine learning algorithm with additional examples – examples of the problematic behavior to be precise.