As an industry reliant on patient records and beset by outdated technology, health care is widely thought to be a prime target for an artificial intelligence revolution. Many believe the technology will provide a host of benefits to clinical practitioners, speeding up the overall experience and diagnosing illnesses early on to identify potential treatment. Just two days ago, DeepMind, an AI (artificial intelligence) firm owned by Google, said it had lent its technology to London's Moorfields Eye Hospital for groundbreaking research into detecting eye diseases. It was used to scan and identify more than 50 ophthalmological conditions. DeepMind's machine-learning technology made correct diagnoses 94 percent of the time, Moorfields said. The development indicated that AI can analyze health problems with as much accuracy as a doctor.

Today, sales and marketing teams are faced with new challenges: consumers, although constantly connected, are becoming harder and harder to reach as messages get lost in a plethora of media and notifications. The superficial personalization of automated messages is no longer enough to stand out in consumer inboxes. In our recent Expert View, Elena Ndrepepa, Frank Niemann and I discuss how artificial intelligence is being integrated into client acquisition processes. Artificial intelligence (AI) is defined by PAC as the combined use of algorithms, knowledge bases (big data sets) and neural networks/deep learning techniques to mimic and complement human abilities in a variety of domains. For marketers and sales teams, AI applied to client acquisition is becoming increasingly popular.

The AI revolution is taking place right now. In contrast to what the scary headlines and stories suggest, the revolution is not about robots or computers taking over humanity. The real revolution does have and will have a continuing impact on all facets of society, but in a more subtle way. This blog will keep you informed about the developments in AI by emphasising the actual practical implications for society and business rather than stating futuristic claims about what may happen. Let us start with the concept of artificial intelligence, AI, for short.

Human features as objects of study have been widely used in various machine learning applications -- be it face detection, video surveillance or even the development of autonomous cars. In fact, the task of ascertaining human features becomes the major work of ML systems in these applications. However, in the case of video surveillance, capturing human figures at an aerial level, especially from a moving equipment, becomes very challenging. Due to factors such as video equipment alignment or lighting, the accuracy of detection in the system takes a hit significantly. In order to resolve these issues, researchers are now exploring deep learning (DL) in video surveillance.

You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machines to learn like humans? And, an even scarier notion for some, why would we want machines to exhibit human-like behavior? Here, we look at 10 examples of how deep learning is used in practice that will help you visualize the potential. Both machine and deep learning are subsets of artificial intelligence, but deep learning represents the next evolution of machine learning.

Traditionally, deep learning systems are implemented on a computer to learn data representation and abstraction and perform tasks, on par with – or better than – the performance of humans. However the team led by Dr. Aydogan Ozcan, the Chancellor's Professor of electrical and computer engineering at UCLA, didn't use a traditional computer set-up, instead choosing to forgo all those energy-hungry electrons in favor of light waves. The result was its all-optical Diffractive Deep Neural Network (D2NN) architecture.

Marc Andreessen made the famous comment -- "Software is eating the world" almost 7 years ago. With the increase in the structured and unstructured data, coupled with the power of cloud computing, one can relate to the comment made by Marc. Although the use of analytics and cloud has been on the rise across many sectors, the financial services industry has seen this trend of disruptive software more than any sector in the last decade. This has led to a rise in new products and offerings in the Fintech sector. Goldman Sachs made a prediction that Fintech could displace $4.7 trillion in revenue for financial service firms.

This picture show the facilities of the Google data center in Changhua, central Taiwan, on December 11, 2013. US search engine giant Google announced that it has decided to double its investment in Taiwan to $600 million while opening its first data centre in Asia cashing in on the robust demands. Google is trusting an artificial intelligence (AI) system developed by DeepMind to stop its data centres around the world from overheating. The AI system -- able to reduce the amount of energy Google used to cool its data centres by 40% -- has been giving cooling recommendations to Google's data centre operators since 2016. But now Google is allowing the data centre operators to take a back seat, giving the AI an unprecedented level of autonomy in the process.

Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints. Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple "A.I. winters"? And why is Silicon Valley buzzing about artificial intelligence again?

This talk will cover how to accelerate deep learning with GPUs. GPUs have an architecture that is well-adapted to speeding up the massive parallel array calculations at the heart of deep learning. Today, manufacturers like NVIDIA are releasing GPUs with deep learning-specific features to further speed up model training and improve the throughput of deployed models. Installing and deploying GPU accelerated code can be challenging, so Anaconda has curated popular deep learning frameworks and packed them with GPU acceleration in the Anaconda Distribution. There they can be combined with Python packages like Pandas, Dask, and Jupyter to power data science experiments and production deployments.