Artificial intelligence (AI) bots are going to replace our jobs. AI machines will inevitably conspire to kill us all. These are exaggerated versions of three fears commonly associated artificial intelligence (AI). Even the late Stephen Hawking spoke about a potential future in which humans could be superseded by advanced forms of artificial intelligence. But these concerns are not so present in the mind of Nathan Myhrvold, the former chief technology officer at Microsoft who once worked in Hawking's theoretical physics group at the University of Cambridge.

Travel executives love to say that their employees are at the heart of their brands and they are on the front lines of the guest and visitor experience. Employees will toast to that notion, but hope they won't have to tussle with a robot or virtual assistant to prove their worth: Tourism Jobs Numbers Increase Despite Modest Introduction of Artificial Intelligence Tools are finally emerging that empower event professionals with the insights they need to better understand how attendees operate at events and the value created for sponsors. It's about time, and these tools will only become more useful over time as event planners learn more about what makes their gatherings successful: The Era of Personalization in Event Tech Is Finally Here Do locals spend more money on at-home activities than travelers spend on in-trip activities? The answer isn't as pressing as the fact that most of the world's activities are still up for grabs by booking platforms: Skift Forum Europe Preview: What Travelers Want From Activities The phrase "cash is king" is fast becoming outdated as Chinese travelers popularize mobile payment methods like Alipay and Weixin Pay: Alipay Steps Up Push to Cater to Chinese Travelers in the U.S. Cruise lines had a lot of big plans for China a few years ago, but as the market has cooled, those plans are changing. SkySea Cruises shows that winning Chinese travelers isn't as easy as taking an old ship and tailoring it to local preferences: Royal Caribbean Ends Its Joint Venture With Ctrip in China Meeting and event planners know they need to do a better job tailoring events to their attendees and providing their sponsors with a stronger idea of how successful their partnership has been.

Camment is an engagement tool for streaming platforms utilising video messages and artificial intelligence. Camment technology works in your video platform. Camment technology is implemented to your mobile and connected TV applications and web streaming platforms. Implementation is super simple and fast. Your company, content and your business model are unique.

Today I am back at the Data Fest Data Summit 2018, for the second day. I'm here with my EDINA colleagues James Reid and Adam Rusbridge and we are keen to meet people interested in working with us, so do say hello if you are here too! I'm liveblogging the presentations so do keep an eye here for my notes, updated throughout the event. As usual these are genuinely live notes, so please let me know if you have any questions, comments, updates, additions or corrections and I'll update them accordingly. We've just opened with a video on Ecometrica and their Data Lab supported work on calculating water footprints. I'd like to start by thanking our sponsors, who make this possible. And also I wanted to ask you about your highlights from yesterday. These include Eddie Copeland from Nesta's talk, discussion of small data, etc. Data science has a huge impact for the business world, but also for societal good. I wanted to talk about the 5 i's of data science for social good: So, the number one, is the Interest. The data can attrat people to engage with a problem. Everything we do is digital now. And all this information is useful for something. No matter what your passion, you can follow this as a data scientist. I wanted to give an example here… My background is astrophysics and I love teaching people about the world, but my day job has always been other things. About 20 years ago I was working in data science at NASA and we saw an astronomical – and I mean it, we were NASA – growth in data. And we weren't sure what to do with it, and a colleague told me about data mining. It seemed interesting but I just wasn't getting what the deal was. We had a lunch talk from a professor at Stanford, and she came in and filled the board with equations… She was talking about the work they were doing at IBM in New York. And then she said "and now I'm going to tell you about our summer school" – where they take kids from inner city kids who aren't interested in school, and teach them data science. Deafening silence from the audience… And she said "yes, we teach the staff data mining in the context of what means most for these students, what matters most. And she explained: street basketball. So IBM was working on a software called IBM Advanced Calc specifically predicting basketball strategy. And the kids loved basketball enough that they really wanted to work in math and science… And I loved that, but what she said next changed my life. My PhD research was on colliding galaxy. It was so exciting… I loved teaching and I was so impressed with what she had done.

IBM is hoping to create a new voice assistant for the business world -- and before Amazon can create one first. At its Think conference in Las Vegas, IBM on Tuesday took the wraps off Watson Assistant, a virtual helper it's been developing with its customers for over a year. Don't expect to start saying, "Watson, what's the weather?" or "Watson, play They Might be Giants" to an IBM-branded speaker anytime soon. Instead, Watson Assistant will function as the behind-the-scenes brains for a variety of new digital helpers made by a variety of businesses for your home and in stores, airports, hotels and cars. For example, Watson Assistant is already in use at Munich Airport to power a robot that can tell you directions and gate information.

Earth's oceans are having a rough go of it these days. On top of being the repository for millions of tons of plastic waste, global warming is affecting the oceans and upsetting marine ecosystems in potentially irreversible ways. Coral bleaching, for example, occurs when warming water temperatures or other stress factors cause coral to cast off the algae that live on them. The coral goes from lush and colorful to white and bare, and sometimes dies off altogether. This has a ripple effect on the surrounding ecosystem.

Ongoing developments in neural network models are continually advancing the state-of-the-art in terms of system accuracy. However, the predicted labels should not be regarded as the only core output; also important is a well calibrated estimate of the prediction uncertainty. Such estimates and their calibration is critical in relation to robust handling of out of distribution events not observed in training data. Despite their obvious aforementioned advantage in relation to accuracy, contemporary neural networks can, generally, be regarded as poorly calibrated and as such do not produce reliable output probability estimates. Further, while post-processing calibration solutions can be found in the relevant literature, these tend to be for systems performing classification. In this regard, we herein present a method for acquiring calibrated predictions intervals for neural network regressors by posing the regression task as a multi-class classification problem and applying one of three proposed calibration methods on the classifiers' output. Testing our method on two exemplar tasks - speaker age prediction and signal-to-noise ratio estimation - indicates both the suitability of the classification-based regression models and that post-processing by our proposed empirical calibration or temperature scaling methods yields well calibrated prediction intervals. The code for computing calibrated predicted intervals is publicly available.

We present a neural model for representing snippets of code as continuous distributed vectors. The main idea is to represent code as a collection of paths in its abstract syntax tree, and aggregate these paths, in a smart and scalable way, into a single fixed-length \emph{code vector}, which can be used to predict semantic properties of the snippet. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of $14$M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. Comparing previous techniques over the same data set, our approach obtains a relative improvement of over $75\%$, being the first to successfully predict method names based on a large, cross-project, corpus.

Processing data collected by a network of agents often boils down to solving an optimization problem. The distributed nature of these problems calls for methods that are, themselves, distributed. While most collaborative learning problems require agents to reach a common (or consensus) model, there are situations in which the consensus solution may not be optimal. For instance, agents may want to reach a compromise between agreeing with their neighbors and minimizing a personal loss function. We present DJAM, a Jacobi-like distributed algorithm for learning personalized models. This method is implementation-friendly: it has no hyperparameters that need tuning, it is asynchronous, and its updates only require single-neighbor interactions. We prove that DJAM converges with probability one to the solution, provided that the personal loss functions are strongly convex and have Lipschitz gradient. We then give evidence that DJAM is on par with state-of-the-art methods: our method reaches a solution with error similar to the error of a carefully tuned ADMM in about the same number of single-neighbor interactions.

Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along with a lot of visibility. The question arises how reliable are our evaluation methodologies to compare approaches? One common methodology to identify the state-of-the-art is to partition data into a train, a development and a test set. Researchers can train and tune their approach on some part of the dataset and then select the model that worked best on the development set for a final evaluation on unseen test data. Test scores from different approaches are compared, and performance differences are tested for statistical significance. In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach. Instead, there is a high risk that the difference is due to chance. For example for the CoNLL 2003 NER dataset we observed in up to 26% of the cases type I errors (false positives) with a threshold of p < 0.05, i.e., falsely concluding a statistically significant difference between two identical approaches. We prove that this evaluation setup is unsuitable to compare learning approaches. We formalize alternative evaluation setups based on score distributions.