Welcome to the first in a series of blog posts that is designed to get you quickly up to speed with deep learning; from first principles, all the way to discussions of some of the intricate details, with the purposes of achieving respectable performance on two established machine learning benchmarks: MNIST (classification of handwritten digits) and CIFAR-10 (classification of small images across 10 distinct classes--airplane, automobile, bird, cat, deer, dog, frog, horse, ship & truck). The accelerated growth of deep learning has lead to the development of several very convenient frameworks, which allow us to rapidly construct and prototype our models, as well as offering a no-hassle access to established benchmarks such as the aforementioned two. The particular environment we will be using is Keras, which I've found to be the most convenient and intuitive for essential use, but still expressive enough to allow detailed model tinkering when it is necessary. By the end of this part of the tutoral, you should be capable of understanding and producing a simple multilayer perceptron (MLP) deep learning model in Keras, achieving a respectable level of accuracy on MNIST. The next tutorial in the series will explore techniques for handling larger image classification tasks (such as CIFAR-10).

Fintech start-up Clinc says it has created "the world's first" open source platform for building artificial intelligence (AI) and deep learning-based personal assistants. Clinc calls it "the financial genie" or "Finie". It is pitched to banks, financial institutions and personal finance managers. The solution was developed by a group of professors at the University of Michigan, Jason Mars, Lingjia Tang and Michael Laurenzano. They set up Clinc in 2015, with Mars as CEO, Tang as chief scientific officer, and Laurenzano as CTO.

This is the last part of our special series on Artificial Intelligence (AI). If you are catching it for the first time you can check out our early posts here, here and here where we build on the bigger idea behind AI. In this series, I have seized moments to highlight just how quickly AI is unfolding. The interesting bit is that the developments silently running in the background, like computer processes, for the most part, are away from everyday headlines but it's still a big deal. From my point of view, judging by the number of AI startups that have come up and are being acquired, we are in the middle of an AI gold rush right now which some have characterized as the AI spring . This hurry is also the reason behind key backers and AI enthusiasts such as Elon Musk, the CEO of Tesla, have been a sounding alarm.

M&C Saatchi partnered with outdoor advertising experts Clear Channel and Posterscope to evolve a traditional channel: outdoor advertisements. The companies worked together to test a unique algorithm using a Microsoft Kinect camera, which would help them gauge consumer sentiment when looking at a bus shelter ad. The agency's began by asking lofty questions such as "how can we create the most attention grabbing advert?" and "can an ad write itself?", and drilled it down to a meaningful execution. The resulting ad is a structure that evolves similarly to how an organism survives in nature: it evolves and "mutates" based on how much attention and engagement it receives. In short, a series of ads are pitted against each other and the most attention-grabbing one will not only appear most often but it will also inform the weaker performing ads by tweaking their layout and language.

"AI isn't going to replace marketers and marketing teams, it's going to make them more efficient. It isn't going to replace human intelligence, it's going to add to it through uncovering new insights." In his recent post, Erik Devaney discusses how we as human marketers need to ditch the aggrandized, fearful mentality we've had towards AI and find ways to work together if we hope to advance. Rather than replace marketers and marketing teams, he argues, AI is going to make us more efficient. I want to elaborate on Erik's point about how AI should be seen as an opportunity to improve our marketing efforts rather than as a threat to replace human marketers.

On a scale from 1 to 5, WaveNet's quality of voice outstrips Google's current best parametric and concatenative systems. Google's UK artificial intelligence lab, DeepMind, has developed a deep neural network that produces more human-like speech than Google's previous text-to-speech (TTS) systems. DeepMind has published a new paper describing WaveNet, a convolutional neural network it says has closed the gap between machine-generated and human speech by 50 percent in both US English and Mandarin Chinese. Not only this, but the network can also seamlessly switch between different voices and generate realistic music fragments. The researchers note that today's best TTS systems, generally considered to be powered by Google, are built on "speech fragments" recorded from a single speaker.

Enterprises today rely on data as the foundation of business success, whether the goal is to better understand customers, build new or better products and services, or manage cost and risk. Data is now the prime raw material for creating value; across all industries, it's the norm to hold vast stores of data. An issue that remains unresolved, however, is how well and how efficiently data can be applied. Firms are still wrestling with the challenge of making big data work for them, in use cases ranging from enterprise analytics, customer 360, and product personalization to revenue assurance and fraud detection. All the data in the world has no value unless it's accessible and actionable.

When John Guttag keynotes the HIMSS and Healthcare IT News Big Data and Healthcare Analytics Forum in Boston on October 24, the MIT professor will describe the unique challenges of applying machine learning to healthcare – as well as the huge potential for efficiencies and quality improvements as these data techniques become more widespread across the industry. Guttag, who heads the Data Driven Inference Group at the MIT's Computer Science and Artificial Intelligence Laboratory, and his MIT students are currently working closely with Mass General on integrating machine learning into clinical workflows, specifically with the aim of reducing healthcare-associated infections. "I want to actually see things change in the system, not just write papers saying things could change," Guttag said. "The goal here is to have something good happen. I hope a year from now I'm able to say, 'Guess what, we've lowered the rate of nosocomial infections at MGH – and more importantly put together a description of how we've done it that is exportable to other organizations.'"

There are two main approaches to machine learning – supervised and unsupervised – and each has specific applications in the context of healthcare. And even though their impact has not yet sent shockwaves through the industry, the potential of each is enormous, according to John Guttag, head of the Data Driven Inference Group at MIT's Computer Science and Artificial Intelligence Laboratory. At its basic level, machine learning involves looking at data, and from that data finding information that is not readily visible. Example: Applying machine learning to data about patients infected with Zika or another virus and using what we can learn about what happens to those people to inform care decisions regarding the best ways to treat people who get infected in the future. "Typically we use machine learning to build inference tools, where we find patterns in existing data that allow us – when presented with new data – to infer something interesting about that data," said Guttag.

What will the future look like, and how will be it be shaped by new and emerging technologies? Well, according to new research from Nesta, 60% of people in the UK believe new technological innovations will improve their future wellbeing. In the run up to Nesta's FutureFest event, which will take place on 17th and 18th September in London's Tabacco Dock, the innovation foundation commissioned market research agency, ComRes, to explore how consumers in the UK feel about emerging technologies, such as augmented reality, driverless cars and artificial intelligence, and whether they believe the future innovations that will be developed within the next 20 years will be beneficial to their lives. Many of the responses were upbeat: almost half of respondents (49%) said DNA sequencing and editing represents a future opportunity for the healthcare sector, while 68% think future technologies will help to improve food production. Around a third (36%) of those surveyed in London said they would be willing to be microchipped in order to secure access to their work devices and homes, while 28% were open to eating meals in pill form and 27% were up for dating a partner they were paired up with through pheromone or DNA-matching.