With Erica, the company hopes to help consumers create better money habits, said Michelle Moore, Bank of America's head of digital banking. For example, Erica might send someone a predictive text: "Michelle, I found a great opportunity for you to reduce your debt and save you 300." Click the text to launch the app, and get Erica's advice. "Based on your typical monthly spending, you have an additional 150 you can be putting towards your cash rewards Visa. This can save you up to 300 per year." Though many banks have bots with some level of artificial intelligence, the customer experience has not always been great, said Daniel Latimore, senior vice president of Celent's banking practice.

While not well understood, neural networks, deep learning, and reinforcement learning are all machine learning. Each layer of a deep learning model lets the computer identify another level of abstraction of the same object. Reinforcement learning, takes ideas from game theory, and includes a mechanism to assist learning through rewards. Researchers refer to this challenge as the black box problem of machine learning.

Working with colleagues at Princeton University, a researcher from the University of Bath has shown that artificial intelligence (AI) can exhibit the same prejudice and bias as humans. Dr Joanna Bryson (pictured above) from the Computer Science department at Bath is spending a year at the Centre for Information Technology Policy (CITP) at Princeton, researching the ethics of computing and AI. "The term'AI' has negative associations yet we use AI every day. It is entrenched in almost everything we do with IT, from Google to Facebook to smartphones," she said. While artificial intelligence and machine learning are in a period of astounding growth, there are concerns that these technologies may be prone to the same prejudice and unfairness as many human institutions, say the researchers.

Within five years, the virtual assistants on smartphones -- such as Siri or Cortana -- should be able to talk to a refrigerator, a car or anything else that can be connected to the internet. A virtual assistant could ask a car to check its own oil and tell the owner if it's time for an oil change. It could figure out how to fix a broken dishwasher by contacting the virtual assistant for that dishwasher's manufacturer. It could tell the coffee pot to brew a morning cup of joe. That's the vision Andrea Sahli, senior implementation manager at noHold, presented Thursday during the Grace Hopper Celebration of Women in Computing.

As technology has developed, humans have been able to create machines that have the computing power to do incredible things. We can get from place to place using navigation applications such as Google Maps and Waze and track calorie consumption, sleeping habits and exercise efficiency with tools like the Fitbit. We can even start our cars remotely from our cell phones. All of these advancements in smartphone and Internet of Things technology have led tech advocates to the same question: What's the next big thing? The answer could be artificial intelligence, or AI.

RotM Artificial Intelligence can predict the outcomes of European Court of Human Rights trials to a high accuracy, according to research published today. The use of AI has is slowly seeping into many industries including the legal sector. AI can trawl through vast amounts of information at a faster rate than humans without slowing down, making it easier for lawyers to prepare for hearings. The paper, published in PeerJ Computer Science, shows that the new software has gone one step further. It can judge the final result of legal trials based on the information in human rights cases to 79 per cent accuracy.

During this on-demand webinar, Chief Scientist Kris Hammond, will present a quantitative method for identifying areas within an organization ripe for the application of Artificial Intelligence (AI) technologies, looking at the metrics of data, task, and volume. After viewing, you will have learned a scalable three-step process for assessing their organization's artificial intelligence opportunity. To learn more about AI and Natural Language Generation's application in the enterprise, be sure to check out our Solutions page.

An artificial intelligence system designed to predict the outcomes of cases at the European Court of Human Rights would side with the human judges 79 percent of the time. Researchers at University College London and the University of Sheffield in the U.K., and the University of Pennsylvania in the U.S., described the system in a paper published Monday by the Peer Journal of Computer Science. "We formulated a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights," wrote the paper's authors, Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preo?iuc-Pietro and Vasileios Lampos. The system examined public court documents relating to 584 cases of violations of articles 3 (prohibiting torture), 6 (right to a fair trial) and 8 (respect for private life) of the European Convention on Human Rights, which has been ratified by 47 European countries. The court documents have a distinctive structure, discussing first the procedure by which the case reached the court, the facts and circumstances of the case, relevant law, and the legal arguments applied.

A leading Antarctic researcher died on 22 October after his snowmobile plunged 30 metres into an unseen crevasse. Glaciologist Gordon Hamilton of the University of Maine Climate Change Institute in Orono was fatally injured as a result, according to a statement released yesterday by the National Science Foundation. At the time, Hamilton was working with a team from the US Antarctic Program (USAP) actively identifying and filling in newly formed crevasses along the McMurdo shear zone. This is a stretch of intensely crevassed Antarctic ice where the Ross and McMurdo ice shelves meet. Hamilton was using robots equipped with ground-penetrating radar to study the stability of the ice shelves.

The automatic classification of documents is an example of how Machine Learning (ML) and Natural Language Processing (NLP) can be leveraged to enable machines to better understand human language. By classifying text, we are aiming to assign one or more classes or categories to a document or piece of text, making it easier to manage and sort the documents. Manually categorizing and grouping text sources can be extremely laborious and time-consuming, especially for publishers, news sites, blogs or anyone who deals with a lot of content. Broadly speaking, there are two classes of ML techniques: supervised and unsupervised. In supervised methods, a model is created based on previous observations i.e. a training set.