Digitization and automation are ever-growing topics in relation to the workplace. A famous Oxford study on the future of employment from 2013 estimated that up to 47% of American jobs may be automated by 2035; a brand new McKinsey study shows that current technologies could automate 45 percent of job activities; and the business mantra goes that if you can digitize, you should digitize to gain a competitive advantage. But how do we, as human beings, really feel about potentially working with or even for AIs, and what impact do we think they will have on our workplace? A recent study conducted in the US, UK and Denmark explores people's openness towards working with and for "unbiased computer programs"--defined as "a software robot that makes decisions or proposals for decisions based on data from HR, financial or market information. The software robot is unbiased, i.e. it is not affected by the personal, social and cultural bias that influence human decision making, but balances all input only based on the data."

It's a beautiful introduction ... Try not to drool too much! Read "A Few Useful Things to Know about Machine Learning" by Prof. Pedro Domingos. It's densely packed with valuable information, but not opaque. The author understands that there's a lot of "black art" and folk wisdom, and they invite you in. Take your time with this one.

NVIDIA (NASDAQ:NVDA) is primarily known as the company that revolutionized computer gaming. The debut of the Graphics Processing Unit (GPU) in 1999 provided gamers with faster, clearer, and more lifelike images. The GPU was designed to quickly perform complex mathematical calculations that were necessary to accelerate the creation of realistic graphics. It achieved this feat by performing many functions at the same time, known as parallel computing. This resulted in faster, smoother motion in game graphics and a revolution in modern gaming.

You've probably heard it before: "a bad craftsman blames his tools." But when is the last time you saw someone building a house with a hammer, a hand saw and some 2x4s? When you build a house, you need the right tools and materials to build a house. When you build a skills, there are a different set of tools and materials. The basic ingredients for learning are neurons and myelin. Each time you fire a set of neurons while learning, they get wrapped in another thin layer of myelin, which is like insulation on an electric cord.

SANTA CLARA, Calif., and Eindhoven, Netherlands, Jan. 3, 2017 – Intel has agreed to purchase a 15 percent ownership stake in HERE, a global provider of digital maps and location-based services, from HERE's current indirect shareholders: AUDI AG, BMW AG and Daimler AG. In conjunction with Intel's acquisition of a stake in HERE, the two companies also signed an agreement to collaborate on the research and development of a highly scalable proof-of-concept architecture that supports real-time updates of high definition (HD) maps for highly and fully automated driving. Additionally, the two companies plan to jointly explore strategic opportunities that result from enriching edge-computing devices with location data. "Cars are rapidly becoming some of the world's most intelligent, connected devices," said Brian Krzanich, Intel CEO. "We look forward to working with HERE and its automotive partners to deliver an important technology foundation for smart and connected cars of the future."

Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an'end-to-end' speech recognition system instead of hybrid settings. However, RNNs are computationally expensive and sometimes difficult to train. In this paper, inspired by the advantages of both CNNs and the CTC approach, we propose an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with CTC directly without recurrent connections. By evaluating the approach on the TIMIT phoneme recognition task, we show that the proposed model is not only computationally efficient, but also competitive with the existing baseline systems. Moreover, we argue that CNNs have the capability to model temporal correlations with appropriate context information.

ABSTRACT Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task. Index Terms-- Acoustic Event Detection, Acoustic Scene Classification, Convolutional Neural Networks, Deep Neural Networks, Video Classification 1. INTRODUCTION Image classification performance has improved greatly with the advent of large datasets such as ImageNet [6] using Convolutional Neural Network (CNN) architectures such as AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We are curious to see if similarly large datasets and CNNs can yield good performance on audio classification problems.

A major challenge in the training of recurrent neural networks is the so-called vanishing or exploding gradient problem. The use of a norm-preserving transition operator can address this issue, but parametrization is challenging. In this work we focus on unitary operators and describe a parametrization using the Lie algebra $\mathfrak{u}(n)$ associated with the Lie group $U(n)$ of $n \times n$ unitary matrices. The exponential map provides a correspondence between these spaces, and allows us to define a unitary matrix using $n^2$ real coefficients relative to a basis of the Lie algebra. The parametrization is closed under additive updates of these coefficients, and thus provides a simple space in which to do gradient descent. We demonstrate the effectiveness of this parametrization on the problem of learning arbitrary unitary operators, comparing to several baselines and outperforming a recently-proposed lower-dimensional parametrization. We additionally use our parametrization to generalize a recently-proposed unitary recurrent neural network to arbitrary unitary matrices, using it to solve standard long-memory tasks.

This generality was demostrated using various bechmark problems with utterly different attributes along with an example application in functional genomics. Furthermore, the current methodology can be applied to inverse problems involving characterization of materials, tomography and electrophysiology, design of effective metamaterials, etc. The methodology can be straightforwardly generalized to address data with multiple levels of fidelity [24, 39] and equations with variable coefficients and complex geometries. Non-Gaussian and input-dependent noise models (e.g., student-t, heteroscedastic, etc.) [3] can also be accommodated. Moreover, systems of linear integro-differential equations can be addressed using multi-output Gaussian process regressions [40, 23, 22]. These scenarios are all feasible because they do not affect the key observation that any linear transformation of a Gaussian process is still a Gaussian process. In its current form, despite its generality regarding linear equations, the proposed framework cannot deal with nonlinear equations. However, some specific nonlinear operators can be addressed with extensions of the current framework by transforming such equations into systems of linear equations [41, 42] - albeit in high dimensions. In the end, the proposed methodology in this work, being essentially a regression technology, is suitable for resolving such high-dimensional problems.

Jerome Feldman DRAFT 1/10/17 Introduction Science is a crowning glory of the human spirit and its applications remain our best hope for social progress. But there are limitations to current science and perhaps to any science. The general mind-body problem is known to be intractable and currently mysterious. This is one of many deep problems that are universally agreed to be beyond the current purview of Science, including quantum phenomena, etc. But all of these famous unsolved problems are either remote from everyday experience (entanglement, dark matter) or are hard to even define sharply (phenomenology, consciousness, etc.). In this note, we will consider some obvious computational problems in vision that arise every time that we open our eyes and yet are demonstrably incompatible with current theories of neural computation. The focus will be on two related phenomena, known as the neural binding problem and the illusion of a detailed stable visual world.