At Spark AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform's philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; and finally deploy, monitor and serve them seamlessly--all in an open manner with limited constraints. In this blog, we will focus on one of the factors: Minimal time to get started. In upcoming blogs, we will elaborate on the other factors, albeit we'll briefly mention them here. Let's consider the level of effort it takes to get started using MLflow in your favorite IDE.

Honor, a leading smartphone e-brand under the Huawei Group signed up to work with Central Saint Martins (CSM) students to explore the concept of colour and its emotional significance within the history of design, as well as the relationship between art, design and technological innovation. Collaborating to find beauty in Artificial Intelligence (AI), selected CSM students are to be awarded the Honor Art prize, which is an award given for a piece of final degree work that celebrates the innovative use of colour and technology in artistic practice. This year, the inaugural prize was awarded to MA Fine Art student Marco Pantaleoni for his series of works in 3D scanning, photography and painting. Receiving his award, Marco said: "I feel honoured to be awarded this prize. Technology is an essential part of my practice, so to be recognised by a technology brand like Honor, not only reinforces some of the concepts behind my work, it also really resonates with the way I create."

YOU'VE heard of it in movies or in passing conversations. Maybe your workplace uses it, or you're considering using it yourself. As technology continues to make ripples across the workplace, AI has become increasingly prevalent. Through AI, companies are able to analyse large amounts of data, which will allow them to better engage with customers. Today, AI is easily accessible.

The ability to learn from incrementally arriving data is essential for any life-long learning system. However, standard deep neural networks forget the knowledge about the old tasks, a phenomenon called catastrophic forgetting, when trained on incrementally arriving data. We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers. These biases cause the trained classifier to perform poorly. We propose an approach to remove these biases by distilling knowledge from the classifier of AC-GAN. Experiments on MNIST and CIFAR10 show that this method is comparable to current state of the art rehearsal based approaches. The code for this paper is available at https://bit.ly/incremental-learning

Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.

We present an algorithm to generate synthetic datasets of tunable difficulty on classification of Morse code symbols for supervised machine learning problems, in particular, neural networks. The datasets are spatially one-dimensional and have a small number of input features, leading to high density of input information content. This makes them particularly challenging when implementing network complexity reduction methods. We explore how network performance is affected by deliberately adding various forms of noise and expanding the feature set and dataset size. Finally, we establish several metrics to indicate the difficulty of a dataset, and evaluate their merits. The algorithm and datasets are open-source.

Due to the iterative nature of most nonnegative matrix factorization (\textsc{NMF}) algorithms, initialization is a key aspect as it significantly influences both the convergence and the final solution obtained. Many initialization schemes have been proposed for NMF, among which one of the most popular class of methods are based on the singular value decomposition (SVD). However, these SVD-based initializations do not satisfy a rather natural condition, namely that the error should decrease as the rank of factorization increases. In this paper, we propose a novel SVD-based \textsc{NMF} initialization to specifically address this shortcoming by taking into account the SVD factors that were discarded to obtain a nonnegative initialization. This method, referred to as nonnegative SVD with low-rank correction (NNSVD-LRC), allows us to significantly reduce the initial error at a negligible additional computational cost using the low-rank structure of the discarded SVD factors. NNSVD-LRC has two other advantages compared to previous SVD-based initializations: (1) it provably generates sparse initial factors, and (2) it is faster as it only requires to compute a truncated SVD of rank $\lceil r/2 + 1 \rceil$ where $r$ is the factorization rank of the sought NMF decomposition (as opposed to a rank-$r$ truncated SVD for other methods). We show on several standard dense and sparse data sets that our new method competes favorably with state-of-the-art SVD-based initializations for NMF.

This paper aims to instigate discussion, answering a call for papers from the ICML Machine Learning Debates workshop. While we stand by the points represented here, we do not purport to offer a full or balanced viewpoint or to discuss the overall quality of science in ML. In many aspects, such as reproducibility, the community has advanced standards far beyond what sufficed a decade ago. We note that these arguments are made by us, against us, by insiders offering a critical introspective look, not as sniping outsiders. The ills that we identify are not specific to any individual or institution. We ourselves have fallen into these patterns, and likely will again in the future. Exhibiting one of these patterns doesn't make a paper bad nor does it indict the paper's authors, however we believe that all papers could be made stronger by avoiding these patterns. While we provide concrete examples, our guiding principles are to (i) implicate ourselves, and (ii) to preferentially select from the work of better-established researchers and institutions that we admire, to avoid singling out junior students for whom inclusion in this discussion might have consequences and who lack the opportunity to reply symmetrically. We are grateful to belong to a community that provides sufficient intellectual freedom to allow us to express critical perspectives. In each subsection below, we (i) describe a trend; (ii) provide several examples (as well as positive examples that resist the trend); and (iii) explain the consequences. Pointing to weaknesses in individual papers can be a sensitive topic. To minimize this, we keep examples short and specific.

When talking about Artificial Intelligence, some people think about the destruction of the world and killer robots, but Artificial Intelligence is already playing a major role in our lives, non-destructively. We all are familiar with programs such as Siri and Google Now, which are improving our way of life. Similarly, you must have played chess against the computer, in which most people get beat atrociously. These programs are nothing but artificial intelligence, which is designed to assist us with a set of protocols. Similar programs are self-driving cars, or motion and reflex detecting video games, which evolve as time goes along.

Caltech scientists have recently developed an AI made out of DNA that can tackle a classic machine learning problem by precisely recognizing written by hand numbers. The work is a critical advance in showing the ability to program AI into engineered biomolecular circuits. Lulu Qian, assistant professor of bioengineering at Caltech said, "Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable. Similar to how electronic computers and smartphones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come." Scientists' goal behind this study is to program intelligent behaviors (the ability to compute, make choices, and more) with artificial neural networks made out of DNA.