I've just run my second briefing on intelligent assistance. Much happened in the few months between sessions. This time, the second half of the day was dominated with stories about bots and their use cases on messaging platforms. It also included the amazing things now possible via automated voice which Amazon's Alexa Challenge exemplifies. Meanwhile IBM's Watson continues its conquest of carbon life forms with the trashing of an extraordinarily talented Go grand master.

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.

Artificial intelligence… Two words which together conjure up so much wonder and awe in the imagination of programmers, sci-fi fans and perhaps just about anyone with an interest in the fate of the world! Thanks to man's best friend the dog R2-D2, the evil Skynet, the fantastical 2001: A Space Odyssey, post-apocalyptical androids dreaming of electric sheep, and maybe also Gary Numan, everyone is pretty well familiar with the concept of artificial intelligence (AI). Yep, books, the big screen, comics, er… mashed potato advertisements – AI is in all of them in a big way. It also features heavily in the marketing materials of recently-appearing and exceptionally-ambitious cybersecurity companies. In fact, there's probably only one place today where you can't find it.

If you ever wondered how Google's self-driving car can tell drivers apart from cyclists and other users of the road, the company's latest report on the project should shed a bit of light on the topic. It turns out that (as with many of the company's products) machine learning algorithms figure heavily into the car's detection technology. By "seeing" many examples of bicycles with its cameras and sensors, the car's computer has effectively been taught what bicycles look like from every angle. "Our software learns from the thousands of variations it has seen -- from multicoloured frames, big wheels, bikes with car seats, tandem bikes, conference bikes, and unicycles," Google said in its report, published Tuesday. Haven't heard of some of the bicycle types mentioned on that list?

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer beha... read more

Google DeepMind and the NHS are developing a machine learning system with Moorfields Eye Hospital that can recognize sight-threatening conditions from just a digital scan of the eye. Mustafa Suleyman, Deepmind's co-founder, says this is the company's first foray into a purely medical research. In this new collaboration with Moorfields, an algorithm will be trained using one million anonymized eye scans to train to identify early signs of degenerative eye conditions such as wet age-related macular degeneration and diabetic retinopathy. "If you have diabetes you're 25 times more likely to go blind. If we can detect this, and get in there as early as possible, then 98% of the most severe visual loss might be prevented," says Suleyman.

Increasingly becoming a necessity in Ecology and Earth Science research, handling complex data can be a tough nut when traditional statistical methods are applied. As its first publication, the new technologically-advanced Open Access journal One Ecosystem features a review paper describing the benefits of using machine learning technologies when working with highly-dimensional and non-linear data. Natural sciences, such as Ecology and Earth science, focus on the complex interactions between biotic and abiotic systems in order to infer understand these systems and make predictions. Traditional statistical methods can impose unrealistic assumptions that result in unsound conclusions as the era of'big data' meets ecology and earth science. Machine-learning-based methods, capable of inferring missing data and handling complex interactions, are more apt for handling complex scientific data.

There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been shown to be particularly effective when stacked into a deep configuration, achieving state-of-the-art results on a diverse array of problems from language translation to automatic captioning of images and videos. In this post you will get a crash course in recurrent neural networks for deep learning, acquiring just enough understanding to start using LSTM networks in Python with Keras.

The makers of a new trend-forecasting platform claim they predicted the Rainbow Bagel before it was a thing. Santa Monica, California-based Tilofy is currently in a private, invitation-only beta of its new platform, which it says is the first automated trend forecaster. Unlike services that, say, spot current trends in social media, Tilofy utilizes machine learning, artificial intelligence and machine vision of imagery to forecast trends weeks or months before they become mainstream. "There's a bagel store in Brooklyn that was doing something interesting, tapping into the LGBT community" by creating a rainbow-colored bagel, CEO and founder Ali Khoshgozaran told me. He recalled that Tilofy predicted the future mainstream popularity of the Rainbow Bagel in November of last year. In February, The Wall Street Journal wrote an article about it, and now The Bagel Store is restructuring around its hit product.

A major study into the use of mobile phone data as a tool for predicting clinical decisions, came to a scathing conclusion, characterising the practice as "voodoo machine learning". The widespread use of smartphones to collect healthcare data has been thrown into doubt by a couple of recent studies. In some fields, such as the management of chronic diseases and mental health monitoring smartphones have enabled a greater level of patient self-control, and personalised clinical intervention. Success in one area, however, does not imply success in all – especially as smartphone health apps are often rolled out before evidence of their effectiveness has been rigorously analysed. A major study into the use of mobile phone data as a tool for predicting clinical decisions, released in June, came to a scathing conclusion, characterising the practice as "voodoo machine learning".