In this article, we're going to talk about machine learning, the modern data lake, and what this means for you. But first, let's go back to the first Olympic games in modern times, held in Athens in April of 1896. This photograph is from the men's 100m final. There's only one runner in the 4-point stance, crouched down with hands on the ground, right behind the start line. That was Tom Burke, and he won--even though he was actually more of a distance runner.

This week in Vienna kicks off an incredibly important global discussion happening at Electrify Europe; energy, electricity, and the transformation of our entire power structure. Have you thought about how we will power 100 billion connected things, as well as, support all the electric vehicles set to disrupt the combustion engine automotive industry? In an electricity sector undergoing rapid change and transition, it's vital for us to wrap our minds around the implications on the industry as a whole. Most of us are keenly aware of the conversations happening around Artificial Intelligence, Machine Learning, Blockchain, etc,, but I find it interesting that what powers our future is energy and I'm not hearing much discussion at the global events I have been keynoting this year on what's going to keep the lights on. That's when I found, Electrify Europe, a conference dedicated to bringing together thousands of innovators and thought leaders to discuss how the latest technologies will affect us, and how we can all benefit from evolving our businesses to position them for success in the future.

Google plans to open an artificial-intelligence research center in Accra, Ghana, the latest in a string of investments the tech company has made in Africa. The research center will focus on using AI in areas such as healthcare, agriculture and education, Google said. "We're committed to collaborating with local universities and research centers, as well as working with policy makers on the potential uses of AI in Africa," t he company In a blog post on Wednesday. The new AI center in Ghana will open later this year and include machine learning researchers and engineers, Google said, thought it did provide details on the number of staff it will hire. Google CEO Sundar Pichai promised last year during a visit to Lagos that Google would continue raising its profile on the continent.

In Detroit: Become Human, the ultimate challenge is deciding what it means to be alive. The kinds of questions that this game forces you to ponder – what it means to be alive as a human, what it means to not be, and whether it's possible to switch between – are the kinds of questions that the greats of both both science fiction and science fact have been asking themselves for decades. But Detroit: Become Human makes them immediately real, lifting them out of the abstract and forcing you to confront what it actually means to be a person, in perhaps the most personal way ever. Starting the game, you're dropped into 2038, which is largely like our current world except is filled with androids that help out around the home. And you're dropped into the heads of three of those androids, all of whom are at different stages of figuring out their role in this new and computer-populated world: one who is tasked with hunting down other rogue androids, and two who are stuck in domestic servitude, each teetering on the brink of their own breakthrough.

The Space Invaders arcade video game, celebrating its 40th anniversary, is a classic piece of software credited as one of the earliest digital shooting games. As a game designer and teacher of games, I know how meaning is carried from designer to the mechanics of play. As a game studies researcher, I also know how games reveal myth, meaning and culture. An analysis of Pac-Man, for instance, shows how that game embodies many values of its day – including consumerism, drug use and gender politics. The message in Space Invaders is as basic as the graphics: when faced with conflict, players have no option except to blast it away.

Audiobooks are more emotionally engaging than TV and film – even if you don't realise it, according to a landmark new study. The new research from UCL suggests that having a book read to you causes physiological changes including an increased heart rate and heat spreading through your body. During the experiment, scientists had 103 participants of various ages listen to a range of different books, and compared their responses to how they felt when they watched the same scene in a film or TV adaptation. The study included emotional scenes from Game of Thrones and the Girl on the Train, for instance, both from the original book and their hugely popular adaptations. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identification from noisy spectra without the need for additional preprocessing. However, their application in practical spectroscopy is limited due to two shortcomings. The effectiveness of the classification using CNNs drops rapidly when only a small number of spectra per substance are available for training (which is a typical situation in real applications). Additionally, to accommodate new, previously unseen substance classes, the network must be retrained which is computationally intensive. Here we address these issues by reformulating a multi-class classification problem with a large number of classes, but a small number of samples per class, to a binary classification problem with sufficient data available for representation learning. Namely, we define the learning task as identifying pairs of inputs as belonging to the same or different classes. We achieve this using a Siamese convolutional neural network. A novel sampling strategy is proposed to address the imbalance problem in training the Siamese Network. The trained network can effectively classify samples of unseen substance classes using just a single reference sample (termed as one-shot learning in the machine learning community). Our results demonstrate better accuracy than other practical systems to date, while allowing effortless updates of the system's database with novel substance classes.

We show how the problem of estimating conditional Kendall's tau can be rewritten as a classification task. Conditional Kendall's tau is a conditional dependence parameter that is a characteristic of a given pair of random variables. The goal is to predict whether the pair is concordant (value of $1$) or discordant (value of $-1$) conditionally on some covariates. We prove the consistency and the asymptotic normality of a family of penalized approximate maximum likelihood estimators, including the equivalent of the logit and probit regressions in our framework. Then, we detail specific algorithms adapting usual machine learning techniques, including nearest neighbors, decision trees, random forests and neural networks, to the setting of the estimation of conditional Kendall's tau. A small simulation study compares their finite sample properties. Finally, we apply all these estimators to a dataset of European stock indices.

Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning.

The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which requires long-term memory to decode. Our data is a simple writing system, encoding characters 'X' and 'O' as their upper halves, which is possible due to symmetry of the two characters. The characters can be connected, as in some languages using cursive, such as Arabic (abjad). The string 'XOOXXO' may be encoded as '${\vee}{\wedge}\kern-1.5pt{\wedge}{\vee}\kern-1.5pt{\vee}{\wedge}$'. It follows that we may need to know arbitrarily long past to decode a current character, thus requiring long-term memory. Subsequently we constructed an RNN capable of decoding sequences encoded in this manner. Rather than by training, we constructed our RNN "by inspection", i.e. we guessed its weights. This involved a sequence of steps. We wrote a conventional program which decodes the sequences as the example above. Subsequently, we interpreted the program as a neural network (the only example of this kind known to us). Finally, we generalized this neural network to discover a new RNN architecture whose instance is our handcrafted RNN. It turns out to be a 3 layer network, where the middle layer is capable of performing simple logical inferences; thus the name "deductron". It is demonstrated that it is possible to train our network by simulated annealing. Also, known variants of stochastic gradient descent (SGD) methods are shown to work.