Over the weekend, a senior researcher at Google spoke about how he and his team are trying to build a computer that can be creative. Douglas Eck, part of Google Brain, a deep-learning research project explained at Moogfest, a four-day music and technology festival in the US that he and his team are using TensorFlow, an open-source library for machine intelligence to investigate whether AI systems can be taught to create original pieces of music, art or even video. Our biggest ever edition of TNW Conference is fast approaching! The inspiration behind the project, Eck explained, was to create a computer system that could create entirely new pieces of music on a regular basis. While an impressive engineering feat, it completely misses the point of what creativity is, and its importance in helping us interpret, challenge and add meaning to our existence.

Andrew Lo, director of the Laboratory for Financial Engineering at the Massachusetts Institute of Technology, has announced that, "The capability is here," suggesting that, "The biggest hurdle is the cultural barrier. You've got a lot of central bankers who are not as open to technology." The use of computers to do economic modeling is not new, and is an important tool in answering specific questions, but predictions made using computers are often less accurate than that of their flesh and blood counterparts. Lo suggests that artificial intelligence, or AI, will bridge the gap, using the process whereby technology learns to do tasks for which it hasn't been programmed. Called'machine learning,'the concept is already being applied to perform complex tasks, including classifying DNA sequences, detecting credit-card fraud, information retrieval, marketing, online advertising and stock market analysis.

Adidas, the German maker of sportswear and equipment, has announced it will start marketing its first series of shoes manufactured by robots in Germany from 2017. More than 20 years after Adidas ceased production activities in Germany and moved them to Asia, chief executive Herbert Hainer unveiled to the press the group's new prototype "Speedfactory" in Ansbach, southern Germany. The 4,600-square-metre plant is still being built but Adidas opened it to the press, pledging to automate shoe production – which is currently done mostly by hand in Asia – and enable the shoes to be made more quickly and closer to its sales outlets. The factory will deliver a first test set of around 500 pairs of shoes from the third quarter of 2016. Large-scale production will begin in 2017 and Adidas was planning a second "Speed Factory" in the United States in the same year, said Hainer.

I just got out of a meeting at SRI International, where representatives from both SRI and international banking group BBVA showed off something they've been working on for the past couple of years. Currently, SRI is best known as the research institute where Siri was developed before spinning out into a separate company and eventually being acquired by Apple, where it powers the Siri feature on the iPhone. SRI and BBVA have been collaborating on a new project, Lola, which they're pitching as a successor of sorts to Siri. Bill Mark, SRI's VP of Information and Computer Sciences, calls it "the next generation personal assistant". In this case, that personal assistant technology is being applied to a specific industry -- banking.

Robin Hanson thinks the robot takeover, when it comes, will be in the form of emulations. In his new book, The Age of Em, the economist explains: you take the best and brightest 200 human beings on the planet, you scan their brains and you get robots that to all intents and purposes are indivisible from the humans on which they are based, except a thousand times faster and better. Related: The Guardian view on artificial intelligence: look out, it's ahead of you Editorial For some reason, conversationally, Hanson repeatedly calls these 200 human prototypes "the billionaires", even though having a billion in any currency would be strong evidence against your being the brightest, since you have no sense of how much is enough. But that's just a natural difference of opinion between an economist and a mediocre person who is now afraid of the future. These Ems, being superior at everything and having no material needs that couldn't be satisfied virtually, will undercut humans in the labour market, and render us totally unnecessary.

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computation (both stochastic gradients and parallelization can be used). Even so, conjugate gradient is not without its own issues: the conditioning of kernel matrices is often such that conjugate gradients will have poor convergence in practice. Preconditioning is a common approach to alleviating this issue. Here we propose preconditioned conjugate gradients for kernel machines, and develop a broad range of preconditioners particularly useful for kernel matrices. We describe a scalable approach to both solving kernel machines and learning their hyperparameters. We show this approach is exact in the limit of iterations and outperforms state-of-the-art approximations for a given computational budget.

IMAGE: Insilico Medicine will present their results in applying deep learning to biomarker development and cosmetics applications at the INNOCOS World Beauty Innovation Summit in Vienna 9-10th of June. Insilico Medicine to present their results in applying deep learning to biomarker development and cosmetics applications at INNOCOS World Beauty Innovation Summit in Vienna 9-10th of June. INNOCOS is one of the largest annual events in the beauty industry bringing together top experts from many areas of research, R&D heads of the cosmetics conglomerates, innovation and strategy professionals and digital media experts. In addition to heading Insilico Medicine, Inc, a big data analytics company focused on applying advanced signaling pathway activation analysis and deep learning methods to biomarker and drug discovery in cancer and age-related diseases, Alex Zhavoronkov, PhD is the co-founder and principal scientist of Youth Laboratories, a company focusing on applying machine learning methods to evaluating the condition of human skin and general health status using multimodal inputs. The company developed an app called RYNKL, a mobile app for evaluating the effectiveness of various anti-aging interventions by analyzing "wrinkleness" and other parameters.

Consistently predicting the next disruptive company is the holy grail if you are interested in start-ups. We've been testing out some Deep Learning techniques on our data to help make such predictions, and have had some interesting early results we thought we'd share. Word2vec is a Deep Learning technique first described by Tomas Mikolov and his team at Google in 2013, and in basic terms it allows a model to be built for a particular dataset (or corpus) in which words are represented as vectors. One of the most interesting outcomes of this approach is that we can gain insights about text by analysing word vectors arithmetically. In a classic example of the power of Word2Vec (trained on a large dataset), the vector of Queen is found to be almost equal to King Woman – Man.

In a first, a team of physicists is using artificial intelligence (AI) to run a complex experiment to create an extremely cold gas trapped in a laser beam known as a Bose-Einstein condensate -- thus replicating the experiment that won the 2001 Nobel Prize. Bose-Einstein condensates are some of the coldest places in the universe -- far colder than outer space and typically less than a billionth of a degree above absolute zero. They can be used for mineral exploration or navigation systems as they are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth's magnetic field or gravity. Indian physicist Satyendra Nath Bose, along with German-born theoretical physicist Albert Einstein, founded the basis for Bose-Einstein statistics. It describes the statistical distribution of identical particles with integer spin, now called subatomic particle or the "God particle" Boson.

Like any proud father, Gary Marcus is only too happy to talk about the latest achievements of his two-year-old son. More unusually, he believes that the way his toddler learns and reasons may hold the key to making machines much more intelligent. Sitting in the boardroom of a bustling Manhattan startup incubator, Marcus, a 45-year-old professor of psychology at New York University and the founder of a new company called Geometric Intelligence, describes an example of his boy's ingenuity. From the backseat of the car, his son had seen a sign showing the number 11, and because he knew that other double-digit numbers had names like "thirty-three" and "seventy-seven," he asked his father if the number on the sign was "onety-one." "He had inferred that there is a rule about how you put your numbers together," Marcus explains with a smile.