Last month I was reading about Autoencoders for Collaborative filtering. Using Autoencoders for Collaborative filtering is a fairly recent idea and it proven to be very effective beating all state-of-art SVD based methods. Actually the idea is pretty simple, instead of using linear function for Matrix Factorization (dot product of the two latent factor matrices, use some complex non-linear function of the two matrices so as to capture more complicated dependencies. Autoencoders are a way to do so. Matrix Factorization based CF minimizes the following objective: Note that in the objective function only known/observed user-rating pairs (set O) are considered.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. One valuable skill that is becoming popular in data science is machine learning. Machine learning is a method of data analysis that automates model building that allows computers to find hidden insights without being explicitly programmed to find a particular insight. Machine learning can be applied to data to help businesses quickly find clusters of similar objects (e.g., identify segments of customers) and to predict outcomes (e.g., identify customers who are at-risk of churning). While machine learning is a hot skill to possess, a recent study by Evans Data Corp. found that about a third of developers (36%) who are working on Big Data projects employ elements of machine learning.

Telefónica Open Future_, Telefónica's startup accelerator that helps the best entrepreneurs grow and build successful businesses, and PAPIs.io Artificial Intelligence (AI) has a track-record of improving the way we make decisions. So why not use it to decide which startups to invest in, and take advantage of all the startup data that is available? The AI Startup Battle, powered by PreSeries (a joint venture between BigML and Telefónica Open Future_), is a unique experience you don't want to miss, where you'll witness real-world and high-stakes AI. As an early stage startup, you will enjoy a great opportunity to secure seed investment, and get press coverage in one of the technology capitals of the world.

The system is called Recognition and will be running for three months in London up to 27 November 2016, both online and in a small exhibition at the Tate. A nice touch is that it will also take in feedback from matching selections made by viewers themselves at the art museum on Millbank. Created by Fabrica, it's the winner of the IK Prize 2016 for digital innovation, awarded by Tate Britain, in partnership with Microsoft. You can see it in action above, in the match of LS Lowry's Industrial Landscape (from 1955) with a recent construction image of Changi Airport in Singapore… Apparently, Recognition employs multiple artificial intelligence technologies. For example, there is natural language processing to interpret image captions and text, analysing context and subject matter.

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.