The consumer sentiment analysis of this one's pretty easy, but will they be compensated? When a person feels sufficiently wronged to lodge a complaint with the Consumer Financial Protection Bureau (CFPB), there's likely to be some negative sentiment involved. But is there a connection between the language they use and the likelihood they will be compensated by the offending company? At the upcoming Sentiment Analysis Symposium, I will discuss how machine learning and rule-based sentiment analysis can support each other in a complementary analysis, and produce actionable information from large amounts of free form text. In this case, machine learning and sentiment analysis could improve and evolve the CFPB's ability to assess consumer complaints.

Financial services incumbents along with early-stage startups and large tech companies are all competing in fintech. CB Insights CEO Anand Sanwal gave a 110-slide presentation at our fintech conference in New York. The full deck covered broad VC funding trends for fintech and the developments in large categories such as wealth management, blockchain, remittance tech, and insurance tech. On robo-advisors, Sanwal noted that market volatility has not necessarily been a boon to the category. "It's not all roses," he said. "There are headwinds … When market volatility picks up, people want to talk to an advisor." At the end Sanwal highlighted 9 trends we're watching that are generating excitement. The slides for those trends are included below. If you have questions, please use the hashtag. Use it on Twitter or on the mobile app, and I'll try to get to them. Please ask me easy questions only. If you like something about the presentation, please tweet it out. If you do not like something about the presentation, please just keep it to yourself. Let me jump in and we'll go through this pretty quickly, I think. So this is what we do, take lots of data and try to predict technology trends. You've seen this join the conversation piece. We have lots of great customers, many of whom are in the room.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.

As financial services organisations predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond, says Gideon Hyde from design consultancy Market Gravity. The co-founder shares his thoughts on the emerging technology and explains how businesses can embrace AI to enhance their offerings, meet consumer demand for speed, personalisation and convenience, and launch new products and services to stand out in the competitive marketplace. AI is already around us and used every day within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.

Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. Now, researchers in The Netherlands have coupled machine learning methods with a special MRI technique that measures the perfusion, or tissue absorption rate, of blood throughout the brain to detect early forms of dementia, such as mild cognitive impairment (MCI), according to a new study published online in the journal Radiology. "MRI can help with the diagnosis of Alzheimer's disease," said principal investigator Alle Meije Wink, Ph.D., from the VU University Medical Centre in Amsterdam. "However, the early diagnosis of Alzheimer's disease is problematic." Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease show up on imaging results.

Under Armour was founded on a simple idea: Make athletes better. To do that, it's turning human performance into a big data problem. The company is betting on the notion that the right hardware, the biggest dataset, a lot of machine learning, and powerful motivational tools can make everyone better, faster, and stronger. It's betting that technology doesn't exist solely to make us lazy, to bring everything to our door with the push of a button. The centerpiece of that bet is a 400 kit, announced today, called Healthbox, that provides a scale, an activity tracker wearable, and a chest strap for measuring your heart rate. The company also is updating Record, its mobile app, making it a 24/7 real-time barometer of your fitness and health. These tools, combined with three apps Under Armour has purchased in recent years, provide the most comprehensive ecosystem of fitness products yet made.

The classifiers can be represented as discrimination maps, where a red color indicates that the intensity at that location contributes to the likelihood of the images belonging to the more advanced stage, and a blue color to the likelihood of belonging to the less advanced stage. Weights are shown inside the mask that resulted in the highest accuracies for each classification: A: Alzheimer's disease (AD) vs. subjective cognitive decline (SCD); B: AD vs. mild cognitive impairment (MCI); C: MCI vs. SCD. Figure 2. Discrimination maps for classifying MCI subgroups, inside the masks that resulted in the highest accuracies. A: between patients with MCI that converted to AD (MCIc) and subjects with SCD; B: between MCIc and patients with MCI that remained stable (MCIs).

Google DeepMind, the British artificial intelligence research arm of Google, will collaborate with Britain's National Health Service (NHS) to tackle sight loss in humans. A retina-scanning system that will be able to detect the early signs of eye disease will be developed in partnership with with Moorfields Eye Hospital in London. About a million anonymized scans taken from patients who attended Moorfields between January 1, 2007 and February 29 this year will be used for analysis by Google's Artifical Intelligence (Al) computer. Scientists are hopeful that they will be able to recognize conditions such as macular degeneration, a gradual deterioration of the light-sensitive tissue lining at the back of the eye, and diabetic retinopathy, when high blood sugar levels damage the retina over time. Early treatment can be critical in both conditions.

Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.

Many people think that the healthcare sector will greatly benefit from the Fourth Industrial Revolution. Professor Klaus Schwab, founder and executive Chairman of the World Economic Forum, describes this revolution as "a fusion of technologies that is blurring the lines between the physical, digital and biological spheres." Many of the discussions around technology and healthcare have, in broad terms, focused on the internet of things, telemedicine, personalized medicine, and robotics. But how exactly are these technologies going to be transformative? As the Chair of the Global Agenda Council on Artificial Intelligence and Robotics, I've been part of many discussions on the impacts of these technologies, both good and bad.