There are plenty of toy robots on the market, but they tend to lack a real sense of personality. With scripted responses and set, repeated movements, they can be hard to love. A team of robotics engineers at Anki have attempted to create a truly loveable robot that relies on AI to give it some real personality. Cozmo is a cute little robot capable of recognizing and engaging with humans, thanks to a combination of robotics, artificial intelligence and computer vision. According to Anki, Cozmo possesses the kind of personality we usually associate with robots we see in the movies.

If these data sets aren't sufficiently broad, then companies can create AIs with biases. Speech recognition software with a data set that only contains people speaking in proper, stilted British English will have a hard time understanding the slang and diction of someone from an inner city in America. If everyone teaching computers to act like humans are men, then the machines will have a view of the world that's narrow by default and, through the curation of data sets, possibly biased.

When I updated Facebook Messenger Tuesday, I instantly had five new friends. Of course, with Facebook's rollout of its new chatbot-friendly Messenger platform, making new friends (with bots) won't be difficult. Yesterday, I decided to see just what my new friends were really capable of. Really, I was most interested in what the user experience these first-to-market bots offered. In summary, what I experienced reminded me that these are early days for chatbots.

We have constructed this page in order to sketch how AI is linked to various other disciplines, in both the sciences and humanities. We have done this not only in the hope of helping students and others who are just starting to study AI but also of facilitating further interactions between the AI community and other communities. The set of links is not exhaustive, nor is the explanation given for each individual link. In particular, we do not include all disciplines that do or could use AI tools and/or contribute tools to AI. We concentrate rather on disciplines where there is profound interaction in terms of research ideas.

Late last week my Twitter and Instagram were blowing up with photos of President Obama, U.S. Secretary of State John Kerry and entrepreneur/"Shark Tank" star Daymond John on the Stanford campus. Those three were among the 1,500 or so people who came to the university for the 2016 Global Entrepreneurship Summit, and there was clearly a lot of excitement. The global summit offered participants a wide range of workshops, panels, exhibitions and networking sessions, with one of the highlights being an in-depth panel discussion on the future -- and societal benefits -- of artificial intelligence. "In my own area of health and biomedicine, the opportunities [for AI] are ridiculously exciting," panel co-chair Russ Altman, MD, PhD, said during his opening remarks Thursday evening. He noted that the amount of biomedical data that electronic health records and "the little devices we wear" generate have become "far too big for us to interpret without intelligent assistance." From there, 15 experts from government and academia, including keynote government speaker Megan Smith, the U.S. chief technology officer in the White House Office of Science and Technology Policy, discussed issues surrounding the technology.

The world is quietly being reshaped by machine learning. We no longer need to teach computers how to perform complex tasks like image recognition or text translation: instead, we build systems that let them learn how to do it themselves. "It's not magic," says Greg Corrado, a senior research scientist at Google. The most powerful form of machine learning being used today, called "deep learning", builds a complex mathematical structure called a neural network based on vast quantities of data. Designed to be analogous to how a human brain works, neural networks themselves were first described in the 1930s.

Logistic regression, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. The implementation of logistic regression in scikit-learn can be accessed from class LogisticRegression. This implementation can fit a multiclass (one-vs-rest) logistic regression with optional L2 or L1 regularization.

How did you like that three-pack of tank tops you bought last time you were in?" Chief of PreCrime John Anderton is running from the law for a crime he has not committed yet. After a risky eye transplant in order to avoid the city-wide optical recognition system, he enters into a mall where his borrowed identity is immediately recognized and commercial adds and offers pop up on digital billboards based on his personal taste and past purchases. The reality is that we don't have to wait until 2054 for this legendary scene from the 2002 movie Minority Report to happen. Fortunately, we don't register people with optical recognition (yet!), but we can already track and identify individuals' position using the mobile phones we all carry -- and from that information make personalized offers. These are just a few examples of the many technologies available today.

CANNES: Planners need to embrace emerging data opportunities such as artificial intelligence (AI), but must also stick to tried and true research techniques, and consider the ever-present need to prove their value to clients, Warc's 2016 'Future of Strategy' debate has suggested. The panel session at the Cannes Lions International Festival of Creativity included Sandy Thompson, global planning director at Y&R, Chris Stephenson, APAC head of strategy at PHD Singapore, and Debbie Weinstein, managing director of brand solutions and innovation at Google. It was moderated by Adam Ferrier, the global CSO of Cummins&Partners. "There are a lot more analytics and statistics involved in strategy," said Weinstein. "In the future, it might be robots and AI, [though] right now it's a data scientist looking at the data."

Many Watson Developer Cloud services and other cognitive software systems provide a numerical score for how confident the system is in some result. For example, the IBM Watson Retrieve and Rank service provides a Ranker confidence score. The IBM Watson Natural Language Classifier also provides a confidence score as do Watson services for language identification, speech processing, entity and relation detection, etc. Some of these confidence scores may have some meaningful interpretation as a probability (e.g., a probability that some result is "correct" or "relevant"). You cannot assume that all results with a very high confidence score are very good and all results with a very low confidence score are very bad.