A data-sharing deal between DeepMind and London's Royal Free Hospital Trust was struck on an "inappropriate legal basis," a top UK government advisor has said. In April 2016, NewScientist revealed that the company had received 1.6 million patient records to develop an app called "Streams." While there are strict rules regarding patient data and confidentiality, common law states that consent is "implied" if the information is being used for "direct care." Google's AI division used this line of thinking to justify the deal, however Dame Fiona Caldicott, the UK's National Data Guardian, disagrees because the app was still in testing at the time. If the app was in development, that means it wasn't yet ready for medical use.

We can see that on average this model configuration achieved a test RMSE of about 92 monthly shampoo sales with a standard deviation of 5. A box and whisker plot is also created from the distribution of test RMSE results and saved to a file. The plot provides a clear depiction of the spread of the results, highlighting the middle 50% of values (the box) and the median (green line).

Google's use of Brits' medical records to train an AI and treat people was legally "inappropriate," says Dame Fiona Caldicott, the National Data Guardian at the UK's Department of Health. In April 2016 it was revealed the web giant had signed a deal with the Royal Free Hospital in London to build an artificially intelligent application called Streams, which would analyze patients' records and identify those who had acute kidney damage. As part of the agreement, the hospital handed over 1.6 million sets of NHS medical files to DeepMind, Google's highly secretive machine-learning nerve center. However, not every patient was aware that their data was being given to Google to train the Streams AI model. And the software was supposed to be used only as a trial – an experiment with software-driven diagnosis – yet it was ultimately used to detect kidney injuries in people and alert clinicians that they needed treatment. Dame Caldicott has told the hospital's medical director Professor Stephen Powis that he overstepped the mark: it's one thing to create and test an application, it's another thing entirely to use in-development code to treat people.

Without a doubt one of the most exciting potential uses for AI (Artificial Intelligence) and in particular deep learning is in healthcare. Traditionally, diagnosis of killer illnesses such as cancer and heart disease have relied on examinations of x-rays and scans to spot early warning signs of developing problems. Image recognition is of course one of the tasks at which deep learning excels – from Facebook's facial recognition to Google's image search, practical examples of it in use are becoming more common by the day. Although being able to tag pictures of our friends without typing their name, or find amusing images of cats when we want them, may seem trivial use cases, the same technology is quickly advancing to a point where more far-reaching implications are being realized. In China, lung cancer is the leading cause of death, claiming over 600,000 lives each year, largely due to high levels of air pollution.

Research into artificial intelligence is going gangbusters, and the frenetic pace won't let up for about five years -- after which the industry will concentrate around a handful of core technologies and leaders, the head of Intel's new AI division predicts. Intel is keen to be among them. In March, it formed an Artificial Intelligence Products Group headed by Naveen Rao. He previously was CEO of Nervana Systems, a deep-learning startup Intel acquired in 2016. Rao sees the industry moving at breakneck speed. "You go three weeks without reading a paper and you're behind.

How does this lead to innovation? What does this have to do with Deep Learning? Intuition like consciousness is something that we are all aware of its existence but likely have not investigated in enough detail to have a grounded understanding of its nature. In fact, I would say that there's more research on the nature of consciousness than research on intuition. I've written earlier about a few research groups that have explored consciousness with respect to an artificial general intelligence, however I don't think has been equivalently the same effort with the study of intuition.

We propose a novel approach to combine state-of-the-art time series data processing methods, such as symbolic aggregate approximation (SAX), with very recently developed deep neural network architectures, such as deep recurrent neural networks (DRNN), for time series data modeling and prediction. Time series data appear extensively in various scientific domains and industrial applications, yet the challenges in accurate modeling and prediction from such data remain open. Deep recurrent neural networks (DRNN) have been proposed as promising approaches to sequence prediction. We extend this research to the new challenge of the time series prediction space, building a system that effectively combines recurrent neural networks (RNN) with time series specific preprocessing techniques. Our experiments show comparisons of model performance with various data preprocessing techniques. We demonstrate that preprocessed inputs can steer us towards simpler (and therefore more computationally efficient) architectures of neural networks (when compared to original inputs).

During the past decade, there was an exponential growth in the number of websites available. Automatic website categorization systems can help to manage these immense amounts of content, making search tasks and recommendation easier. However, most websites have a significant proportion of visual content that conventional, text-based web mining systems can not handle. In this paper, we present a novel hybrid CBR framework designed to perform image-based website categorization. Our system incorporates state-of-the-art deep learning techniques which help attain high accuracy rates. In addition, the system was designed with the goal of minimizing computational costs.

Character-level deep learning for text classification tasks enables models to be trained without any prior knowledge of the data or language; however, an optimal neural network design for different text domains is not known and may vary. In this paper, we expand on current efforts to train neural networks from character-level data by conducting an experimental investigation on neural network design for text classification of short text documents. We trained and evaluated four networks, two consisting of convolutional layers followed by dense layers and two consisting of convolutional layers followed by a LSTM layer. Our experimental results show tweets need network architectures compatible with their short length. Networks found effective for other sentiment classification tasks may not produce an effective classifier in this domain, if their architecture is ill-suited for short instances.

Interaction between humans and machines, like social robots, requires real time recognition of human actions. Most approaches to this problem wait for the end of the gesture to perform classification. In this paper we present a deep learning approach to online gesture recognition that allows for an estimation of the current gesture since its beginning. Our approach is to modify the existing Temporal Deep Belief Network (TDBN) architecture. The result is a Discriminative Temporal Deep Belief Network (DTDBN) which we apply to the online classification of motion capture streams. We optimize and evaluate our model in comparison with related work.