Seated against a deep red backdrop, gazing intently at hand-held mirrors, two eunuchs in sparkling saris inspect their appearance before Raksha Bandhan celebrations in the red light district of Mumbai. The photograph from the Reuters news agency is an arresting contemporary scene, but a new Tate Britain project is aiming to inspire deeper reflections with images from its own collection of paintings. Launching on Friday, Recognition is the winner of 2016's IK prize – an annual award, this year supported by Microsoft, for a project that embraces digital technology to explore and showcase Tate's collection of British art. This year, the challenge was to do it with artificial intelligence. The team behind the winning project, from the Italy-based communication research centre Fabrica, say their inspiration came from an intriguing conundrum: how can you apply rational thinking to a subject like art? Recognition matches stunning photographs from the 24/7 news cycle with centuries-old artworks, and presents them online.

Financial services group Manulife is collaborating with Indico Data Solutions, a Boston-based company that specializes in «Deep Learning». Manulife's Lab of Forward Thinking (LOFT) is collaborating with Indico Data Solutions, a Boston-based company that specializes in «Deep Learning». This collaboration is part of a strategic effort to leverage best-in-class products and accelerate business adoption of innovative technologies such as Artificial Intelligence (AI), blockchain and virtual reality. Manulife's LOFT will use indico's platform to develop an AI and Deep Learning tool to analyze unstructured financial data. Using Deep Learning, Manulife will be able to analyze data from news articles, analyst reports and other similar sources and present recommendations that could help investment researchers and portfolio managers make more informed decisions faster than ever before.

I have multivariate time series data from sensors. I want to feed the raw, unlabeled data into a deep learning model (Thinking of deep belief nets right now). I hope that in the output layer of the rbm, there will be features of the time series. With every additional rbm, i will learn features of higher level . Those features will be used to represent the time series.

This blog series is based on my upcoming talk on re-usability of Deep Learning Models at the Hadoop Strata World Conference in Singapore. This blog series will be in several parts – where I describe my experiences and go deep into the reasons behind my choices. Deep learning is an emerging field of research, which has its application across multiple domains. I try to show how transfer learning and fine tuning strategy leads to re-usability of the same Convolution Neural Network model in different disjoint domains. Application of this model across various different domains brings value to using this fine-tuned model.

This is a blog series in several parts -- where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. Part 2 of this blog series is based on my upcoming talk at The Data Science Conference, 2016. Here in Part 2, I describe Deep Convolutional Neural Networks (DCNNs) and how Transfer learning and Fine-tuning helps better the training process for domain specific images. Please feel free to email me at trivedianusua23@gmail.com if you have questions.

The mission of Google's DeepMind Technologies startup is to "solve intelligence." Now, researchers there have developed an artificial intelligence system that can mimic some of the brain's memory skills and even program like a human. The researchers developed a kind of neural network that can use external memory, allowing it to learn and perform tasks based on stored data. Neural networks are interconnected computational "neurons." While conventional neural networks have lacked readable and writeable memory, they have been used in machine learning and pattern-recognition applications such as computer vision and speech recognition.

The blending of home-based diagnostic platforms with medical care at home is arriving. The Tricorder XPRIZE competition is well underway, with several teams set to compete in the final stages. Leading contenders include CloudDx and Scanadu, a company started at our first Exponential Medicine program, have successfully leveraged crowdfunding to enable their clinical trials. Gale by 19Labs is a next generation "first aid kit meets home health center" (see the below video for a demo) exemplifying how integration of home diagnostics paired with menu-driven (and potentially AI-driven) assistance and optional telemedicine connectivity can provide increased access to home-based diagnosis, triage and management of minor bumps and scrapes and also more complex medical conditions. Interactive and engaging, from coaching on diet and nutrition to reminding you to take your medications or offering psychological support and follow up -- the chatbots are on their way.

The rate at which companies accumulate data continues to accelerate, and with it, so does the need to translate that data into meaningful, actionable insights to key decision makers and stakeholders. Where do our customers' interests lie today, tomorrow, and beyond? That demand has put increasing pressure on solution providers to deliver technology that addresses these questions, and Microsoft is responding to the call. Azure Machine Learning (Azure ML), one of Microsoft's cloud-based solutions, helps companies unlock key insights from its available data resources. Whereas traditional machine learning offerings focus on very rigid, tightly controlled processes and experiments, Azure ML's key benefit is its highly flexible machine learning model architecture.

Teacher's Day Special: Helping kids with special needs to shine

Boston-based indico data solutions, which specializes in deep learning and artificial intelligence, announced it will be partnering with Manulife - known to us in the States as John Hancock - and its Lab of Forward Thinking (LOFT). With indico's platform, LOFT will build an AI-enabled solution that will help guide investors. The tool will use artificial intelligence and deep learning to analyze unstructured financial data - in text and image form - from sources like news articles and analyst reports. From this analysis, it will then make recommendations for portfolio managers to make more informed investment decisions - and hopefully, in less time than they do now. "Indico will help us accelerate our use of Deep Learning to improve the decision-making capabilities of our analysts, portfolio managers and researchers," Greg Framke, chief information officer at Manulife, said in a statement.