For many people with depression, taking the first step and visiting a doctor can be daunting. But a new app could help to combat the condition without the need to even leave the house. Researchers have created a chatbot that has been shown to decrease symptoms of depression and anxiety two times faster than traditional therapy. To assess how effective the chatbot is, the researchers tested Woebot on students suffering from anxiety and depression. In a randomised study, 70 students were asked to either engage with Woebot, or a self-help eBook over a two-week period.

The days of looking at food on Instagram in ignorant bliss are coming to an end. According to a new study from MIT's Computer Science and Artificial Intelligence Laboratory, a deep-learning AI algorithm called "Pic2Recipe" is able to retrieve the likely ingredients of a meal based on just a picture. Researchers gathered 1,029,720 recipes and 887,706 meal images from popular cooking websites such as All Recipes and Food.com and manually removed duplicate images as well as unwanted characters such as exclamation points or question marks. This culminated in a robust database of common meals and their ingredients. When the Pic2Recipe AI was asked to view an image of a meal, it was able to use the database to identify the correct ingredients 65 percent of the time.

Five years from now, company leaders will be looking back and wishing they developed an artificial intelligence strategy sooner, according to one of the veterans of the field. Andrew Ng, the cofounder of Coursera and the former machine learning chief at Chinese tech powerhouse Baidu, said that he thinks Fortune 500 businesses will find the rise of AI similar to the rise of the internet. Some top CEOs bemoan how their businesses were late to the party when it came to competing on the internet, and Ng said that the same thing will be true when it comes to AI. In his view, businesses are best off hiring a leader with deep knowledge of the field who can help build up an organization's knowledge and capabilities in a centralized way. That chief AI officer, as he described it, would be charged with helping to bring expertise in the field to the rest of the a company.

Are you finding it difficult to retain information? If you feel that you suffer from poor levels of retention power or a bad memory then you could resort to a simple solution -- just grab a drink after you have learned or studied something new. A new study published in the journal Scientific Reports on Monday claimed that alcohol can boost your memory and ability to retain information. Read: Marijuana And Memory: Cannabis Could Help Elderly, Alzheimer's Patients A group of scientists from the University of Exeter in the U.K. conducted a study to examine how alcohol consumption can affect the brain and its abilities. In the study, they took 88 social drinkers and split them into two groups.

That's because the insurance industry is one of the least innovative areas for customer experience, meaning that customers typically come away from their interactions disappointed and dissatisfied. However, things are definitely changing, and artificial intelligence is playing a large role. The fast-growing technology has the potential to disrupt the entire industry and greatly improve the insurance customer experience. The insurance agency is notorious for its outdated processes. Filing a claim often looks the same today as it did decades ago because the industry isn't consistently leveraging new technologies that are available to them.

Humans are formed from matter that flew billions of miles from another galaxy, according to a new study. Much of the stuff around us and spread throughout our Milky Way is made up of "extragalactic matter", according to the research. The study used computer models to find out how the matter around us came to be acquired by our galaxy. It found that supernova explosions throw out huge amounts of matter from galaxies, spreading it throughout the universe as it is carried on powerful galactic winds. From the International Space Station, Expedition 42 Flight Engineer Terry W. Virts took this photograph of the Gulf of Mexico and U.S. Gulf Coast at sunset This image of an area on the surface of Mars, approximately 1.5 by 3 kilometers in size, shows frosted gullies on a south-facing slope within a crater.

We usually associate alcohol with memory loss, but a small new study suggests it may actually have the opposite effect. The research found that individuals were better able to remember information if they drank alcohol after learning. Of course, this does not offset the dangerous health consequences of abusing alcohol, but a celebratory drink after class may not be such a bad idea. The study found that individuals who drank alcohol after completing a word-learning task were able to remember more of what they learned than those who did not drink alcohol. The researchers suggest the reason for this may be related to alcohol's effect on the hippocampus, the area of the brain that consolidates new information into memories.

We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main assets are the implementation of several ensemble learning algorithms, a flexible and generic interface to compare and blend any existing machine learning algorithm available in Python libraries (as long as a \texttt{predict} method is given), and visualisation tools such as Voronoi tessellations. \texttt{pycobra} is fully \texttt{scikit-learn} compatible and is released under the MIT open-source license. \texttt{pycobra} can be downloaded from the Python Package Index (PyPi) and Machine Learning Open Source Software (MLOSS). The current version (along with Jupyter notebooks, extensive documentation, and continuous integration tests) is available at \href{https://github.com/bhargavvader/pycobra}{https://github.com/bhargavvader/pycobra}.

Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.