This meme has been all over social media lately, producing appreciative chuckles across the internet as the hype around deep learning begins to subside. The sentiment that machine learning is really nothing to get excited about, or that it's just a redressing of age-old statistical techniques, is growing increasingly ubiquitous; the trouble is it isn't true. I get it -- it's not fashionable to be part of the overly enthusiastic, hype-drunk crowd of deep learning evangelists. ML experts who in 2013 preached deep learning from the rooftops now use the term only with a hint of chagrin, preferring instead to downplay the power of modern neural networks lest they be associated with the scores of people that still seem to think that import keras is the leap for every hurdle, and that they, in knowing it, have some tremendous advantage over their competition. While it's true that deep learning has outlived its usefulness as a buzzword, as Yann LeCun put it, this overcorrection of attitudes has yielded an unhealthy skepticism about the progress, future, and usefulness of artificial intelligence. This is most clearly seen by the influx of discussion about a looming AI winter, in which AI research is prophesied to stall for many years as it has in decades past.

There are many reasons why this shift has happened so quickly. Obviously, storage costs continue to fall, the proliferation of data and data sources continues to sky-rocket and computing continues to become more powerful. Just as important, public cloud providers continue to improve, and add to, the impressive machine learning and deep learning capabilities, available to the masses. When you combine all of the technological improvements with the growing corporate investment in this space, it becomes clear why AI is expected to be the defining technology of our future. The number of AI use cases, from enhancing the client experience in call centers (improved language processing and speech recognition) to predictive maintenance (fixing equipment before failures) is resulting in another powerful wave of business improvement driven by technology.

There are many reasons why this shift has happened so quickly. Obviously, storage costs continue to fall, the proliferation of data and data sources continues to sky-rocket and computing continues to become more powerful. Just as important, public cloud providers continue to improve, and add to, the impressive machine learning and deep learning capabilities, available to the masses. When you combine all of the technological improvements with the growing corporate investment in this space, it becomes clear why AI is expected to be the defining technology of our future. The number of AI use cases, from enhancing the client experience in call centers (improved language processing and speech recognition) to predictive maintenance (fixing equipment before failures) is resulting in another powerful wave of business improvement driven by technology.

AI has moved a step closer to achieving human-like thought, after a new project developed machines capable of abstract thought to pass parts of an IQ test. Experts from DeepMind, which is owned by Google parent company Alphabet, put machine learning systems through their paces with IQ tests, which are designed to measure a number of reasoning skills. The puzzles in the test involve a series of seemingly random shapes, which participants need to study to determine the rules of that dictate the pattern. Once they have worked out the rules of the puzzle, they should be able to accurately pick the next shape in the sequence. DeepMind researchers hope that developing AI which is capable of thinking outside the box could lead to machines dreaming-up novel solutions to problems that humans may not ever have considered.

Standard human IQ tests often require test-takers to interpret perceptually simple visual scenes by applying principles that they have learned through everyday experience. For example, human test-takers may have already learned about'progressions' (the notion that some attribute can increase) by watching plants or buildings grow, by studying addition in a mathematics class, or by tracking a bank balance as interest accrues. They can then apply this notion in the puzzles to infer that the number of shapes, their sizes, or even the intensity of their colour will increase along a sequence. We do not yet have the means to expose machine learning agents to a similar stream of'everyday experiences', meaning we cannot easily measure their ability to transfer knowledge from the real world to visual reasoning tests. Nonetheless, we can create an experimental set-up that still puts human visual reasoning tests to good use.

Business magnate Elon Musk enters the Heavenly Bodies: Fashion & The Catholic Imagination Costume Institute Gala at The Metropolitan Museum on May 07, 2018 in New York City. Google DeepMind CEO Demis Hassabis and Tesla CEO Elon Musk were invited to sign a 350-year-old book in London last Friday. The Royal Society, which aims to promote excellence in science, is the world's oldest independent scientific academy. The Charter Book dates back to 1663 and contains the signature of every Royal Society fellow and member. Over the years, the book has been signed by scientists such as Isaac Newton, Charles Darwin, Alan Turing, David Attenborough, and Tim Berners-Lee.

Machine Learning as the name signifies allows machines to learn with huge volumes of data that an algorithm can process to make predictions. Essentially, machine learning eliminates the need to continuously code or analyze data themselves to solve a solution or present a logic. In other words, this form of AI enables a computer's ability to learn and teach itself to evolve as it is fed new data. And since machine learning deploys an iterative approach to glean from data, this learning process is automated and the models are run until a robust pattern is found. ML software constitutes of two main elements -- statistical analysis and predictive analysis which is used to spot patterns and uncover hidden insights from previous computations without being programmed.

If you want to put machine learning to work in your organisation, you should really consider securing a place at one of the four all-day workshops we're running as part of MCubed before our early bird ticket offer expires in two weeks time. Oliver Zeigermann returns to take you through the basics of machine learning, before diving into neural networks and deep learning and working up to convolutional neural networks, all using TensorFlow and sklearn. To learn how to build basic models and crucially get them into production in the real world, join Terry McCann for his workshop on "From model to production using the cloud, Containers and Devops". As well as using Python to develop models, this highly interactive session will show how to exploit common technologies such as Azure, Docker and Kubernetes. For a holistic, soup to nuts introduction to machine learning, join Prof Mark White and Kate Kilgour.

Most people find it a pain to receive parcels between wide time slots like 8am and 5pm, so when delivery startup Paack offered a service where everyone could narrow that window down to one hour, with no extra charge, it had a challenge on its hands. The startup's routing engine worked but needed to be more efficient. Enter Prowler.io, a Cambridge, UK-based machine-learning startup that bills itself as a decision-making platform for any company with complex problems to solve. Paack's investor at Balderton in London introduced it to Prowler in February 2018 and within months, its delivery vans and trucks were being coordinated by an intelligent, digital simulation. With the beta test over, Paack's CEO Fernando Benito sees a potential benefit to his bottom line. Some of his startup's deliveries are now 15% more efficient, he tells Forbes.

Deep learning is an amazing field that help us create great solutions and solve hard problems in the data science world. I've talk about Deep Learning in the past, and how it can help you in your workflow specifically with computer vision and NLP problems. And one of the things you should learn when entering in a new computational field are good tools. Tools allow us to solve our problems without spending hours coding simple or complex things from the beginning. There are several tools for deep learning right now, in the coding space and also in the visual space.