Later in his book, Lee observes, "Significant as this jockeying between the world's two superpowers is (with respect to leadership in AI technologies), it pales in comparison to the problems of job losses and growing inequality -- domestically and between countries -- as AI will conjure. As deep learning washes over the global economy, it will indeed wipe out billions of jobs up and down the economic ladder: accountants, assembly line workers, warehouse operators, stock analysts, quality control inspectors, truckers, paralegals and even radiologists, just to name a few.

Microsoft today announced it has acquired Lobe, creator of a platform for building custom deep learning models using a visual interface that requires no code or technical understanding of AI. Lobe, a platform that can understand hand gestures, read handwriting, and hear music, will continue to develop as a standalone service, according to the company's website. Financial terms of the deal were not disclosed. People have only started to utilize the full potential of AI, Microsoft CTO Kevin Scott said today in a blog post announcing the acquisition. "This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we're committed to changing that," he said.

How long does it take you to install your complete GPU-enabled deep learning environment including RStudio or jupyter and all your packages? And do you have to do that on multiple systems? In this blog post series I'm going to show you how and why I manage my data science environment with GPU enabled docker containers. How are you managing your data science stack? I was never really satisfied in how I did it.

Today we are excited to strengthen our commitment to supporting PyTorch as a first-class framework on Azure, with exciting new capabilities in our Azure Machine Learning public preview refresh. In addition, our PyTorch support extends deeply across many of our AI Platform services and tooling, which we will highlight below. During the past two years since PyTorch's first release in October 2016, we've witnessed the rapid and organic adoption of the deep learning framework among academia, industry, and the AI community at large. While PyTorch's Python-first integration and imperative style have long made the framework a hit among researchers, the latest PyTorch 1.0 release brings the production-level readiness and scalability needed to make it a true end-to-end deep learning platform, from prototyping to production. Azure Machine Learning (Azure ML) service is a cloud-based service that enables data scientists to carry out end-to-end machine learning workflows, from data preparation and training to model management and deployment.

Machine learning and artificial intelligence (known as A.I.) both sound like futuristic terms for some dystopian future where robots take over the planet. There are lots of similarities and there is much overlap between different types of computer automated learning, inference, and autonomy, and each one comes with its own set of pros and cons. Sci-fi movies aside, there are lots of important differences between deep learning, machine learning, and artificial intelligence that highlight the different ways in which they work and the different applications they're best suited for. Here's what you need to know. This is the earliest and most broad term for computers acting on their own.

Healthcare is emerging as a prominent area for artificial intelligence research and applications. Image recognition is revolutionizing diagnostics. Pharma companies are experimenting with deep learning to design new drugs. In the private market, healthcare AI startups have raised $4.3B across 576 deals since 2013, topping all other industries in AI deal activity. AI in healthcare is currently geared towards improving patient outcomes, aligning the interests of various stakeholders, and reducing healthcare costs.

Habana Labs, a startup that came out of "stealth mode" this week, announced a custom chip that is said to enable much higher machine learning inference performance compared to GPUs. According to the startup, its Goya chip is designed from scratch for deep learning inference, unlike GPUs or other types of chips that have been repurposed for this task. The chip's die is composed of eight VLIW Tensor Processing Cores (TPCs), each having their own local memory, as well as access to shared memory. The external memory is accessed through a DDR4 interface. The Goya chip supports all the major machine learning software frameworks, including TensorFlow, MXNet, Caffe2, Microsoft Cognitive Toolkit, PyTorch and the Open Neural Network Exchange Format (ONNX).

In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-called pooling. Maximation and averaging over sliding windows (max/average pooling), and plain downsampling in the form of strided convolution are popular pooling methods. Since the pooling is a lossy procedure, a motivation of our work is to design a new pooling approach for less lossy in the dimensionality reduction. Inspired by the Fourier spectral pooling(FSP) proposed by Rippel et. al. [1], we present the Hartley transform based spectral pooling method in CNNs. Compared with FSP, the proposed spectral pooling avoids the use of complex arithmetic for frequency representation and reduces the computation. Spectral pooling preserves more structure features for network's discriminability than max and average pooling. We empirically show that Hartley spectral pooling gives rise to the convergence of training CNNs on MNIST and CIFAR-10 datasets.

Many distributed machine learning (ML) systems adopt the nonsynchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is challenging to directly monitor or control staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of stochastic gradient descent in non-convex optimization under staleness, matching the best-known convergence rate of O(1/ T). These works, however, point to seemingly contradictory conclusions on whether nonsynchronous execution outperforms synchronous counterparts in terms of absolute convergence, which is measured by the wall clock time to reach the desired model quality. For deep neural networks, Chilimbi et al. (2014); Dean et al. (2012) show that fully asynchronous systems achieve high scalability and model quality, but others argue that synchronous training converges faster (Chen et al., 2016; Cui et al., 2016). The disagreement goes beyond deep learning models: Ho et al. (2013); Zhang & Kwok (2014); Langford et al. (2009); Lian et al. (2015); Recht et al. (2011) empirically and theoretically show that many algorithms scale effectively under nonsynchronous settings, but McMahan & Streeter (2014); Mitliagkas et al. (2016); Hadjis et al. (2016) demonstrate significant penalties from asynchrony. The crux of the disagreement lies in the tradeoff between two factors contributing to the absolute convergence: statistical efficiency and system throughput. Nonsynchronous execution can improve system throughput due to lower synchronization overheads, which is well understood (Ho et al., 2013; Chen et al., 2016; Cui et al., 2014; Chilimbi et al., 2014).

Deep Learning has received significant attention due to its impressive performance in many state-of-the-art learning tasks. Unfortunately, while very powerful, Deep Learning is not well understood theoretically and in particular only recently results for the complexity of training deep neural networks have been obtained. In this work we show that large classes of deep neural networks with various architectures (e.g., DNNs, CNNs, Binary Neural Networks, and ResNets), activation functions (e.g., ReLUs and leaky ReLUs), and loss functions (e.g., Hinge loss, Euclidean loss, etc) can be trained to near optimality with desired target accuracy using linear programming in time that is exponential in the size of the architecture and polynomial in the size of the data set; this is the best one can hope for due to the NP-Hardness of the problem and in line with previous work. In particular, we obtain polynomial time algorithms for training for a given fixed network architecture. Our work applies more broadly to empirical risk minimization problems which allows us to generalize various previous results and obtain new complexity results for previously unstudied architectures in the proper learning setting.