Previously, I had started this conversation with the saying "I am not a Geek, but I need a job too…". Here is why: Technological anxiety (oh yes, it is a thing). I don't want to be a victim of the inevitable wave of "robots taking over our jobs" which is a simplistic explanation for the impact of advancements in technology in the workplace. The idea that half of today's jobs may vanish has changed my view of my children's future. Quincy Larson, Teacher at FreeCodeCamp (an open-source community that helps you learn to code, build pro bono projects for nonprofits, and get a job as a developer) has not stopped in his attempt to get more people coding.

Following Alphabet Inc (NASDAQ: GOOG) (NASDAQ: GOOGL)'s first-quarter results, Loup Ventures' Gene Munster said the results reiterate the company's artificial intelligence, virtual reality and augmented reality ambitions. Munster noted Google CEO Sundar Pichai emphasized on the earnings call his "AI-first" mantra. The analyst noted increased machine learning investment was highlighted in at least two segments of Google's ad model, namely smart bidding and universal app campaigns. Loup Ventures explained that smart bidding helps machines predict in real time how an ad should perform in front of a particular target and adjusts advertiser bids to maximize ROI. In universal app campaigns, machine learning is being used to best promote developer apps across Google properties including Search, YouTube and the Display Network, the firm added.

Adoption of artificial intelligence (AI) is on the increase, but its critics are still fearful that the technology could replace human workers and even do much of the work of HR. Ji-A Min from software company Ideal highlights 10 HR tech leaders who believe AI will support, rather than replace, HR. You can't escape it: everywhere you turn, AI is taking over. AI is a machine's ability to mimic human capabilities such as learning, problem solving, and perception. For HR, this is the application of artificial intelligence to the function in order to streamline or automate some part of the workflow.

Good day, everyone, and welcome to today's Nidec's Conference Call hosted by Mitsubishi UFJ Morgan Stanley Securities. Today's call is being recorded. At this time, I would like to pass this conference to Mr. Abe at Mitsubishi UFJ Morgan Stanley Securities for opening remarks. Mr. Abe, please go ahead, sir. Ladies and gentlemen, thank you very much for joining this conference call. This is Abe, General Manager, Institutional Sales Department of Mitsubishi UFJ Securities. Before the meeting starts, please make sure all materials have been distributed. If not, please download the files on Nidec's Homepage right now. Now, may I introduce Mr. Akira Sato, Executive Vice President and Chief Financial Officer, who will be speaking to you shortly. First, Mr. Sato will make a presentation. After his presentation, we will move to a Q&A session. Mr. Sato will now discuss Nidec's fourth quarter fiscal year 2016 results, future outlook and management strategy. Mr. Sato, please go ahead. Thank you very much, Mr. Abe. My name is Akira Sato, Chief Financial Officer of Nidec, and I will be your main speaker for today.

We have discussed Tensorflow before on this blog when we talked about some common libraries used by data science professionals. It doesn't hurt to talk about it again though! The fact is, if you are in the world of machine learning, you have probably heard, tried, or implemented some form of deep learning algorithm. Are they necessary, not all the time. Are they cool when done right, yes.

Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on. In this article, we propose a solution to solve this problem based on a novel deep learning model dubbed Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machine (DFFW-CRBM). Our method improves state-of-the-art deep learning techniques for high dimensional time-series modeling by introducing a novel tensor factorization capable of driving forth order Boltzmann machines to considerably lower energy levels, at no computational costs. DFFW-CRBMs are capable of accurately estimating, recognizing, and performing near-future prediction of three-dimensional trajectories from their 2D projections while requiring limited amount of labeled data. We evaluate our method on both simulated and real-world data, showing its effectiveness in predicting and classifying complex ball trajectories and human activities.

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with respect to an output variable in an agnostic (supervised) learning setting. We also suggest an efficient version of our approach which has the same asymptotic performance. Moreover, our theory also applies when we have more than one such change point. Independent post analysis of a change point identified by our method for a large retailer revealed that it corresponded in time with certain unflattering news stories about a brand that resulted in the change in customer behavior. We also applied our method to data from an advanced manufacturing plant identifying the time instant from which downstream features became relevant. To the best of our knowledge this is the first work that formally studies change point detection in a distribution independent agnostic setting, where the change point is based on the changing relationship between input and output.

There is a large literature explaining why AdaBoost is a successful classifier. The literature on AdaBoost focuses on classifier margins and boosting's interpretation as the optimization of an exponential likelihood function. These existing explanations, however, have been pointed out to be incomplete. A random forest is another popular ensemble method for which there is substantially less explanation in the literature. We introduce a novel perspective on AdaBoost and random forests that proposes that the two algorithms work for similar reasons. While both classifiers achieve similar predictive accuracy, random forests cannot be conceived as a direct optimization procedure. Rather, random forests is a self-averaging, interpolating algorithm which creates what we denote as a "spikey-smooth" classifier, and we view AdaBoost in the same light. We conjecture that both AdaBoost and random forests succeed because of this mechanism. We provide a number of examples and some theoretical justification to support this explanation. In the process, we question the conventional wisdom that suggests that boosting algorithms for classification require regularization or early stopping and should be limited to low complexity classes of learners, such as decision stumps. We conclude that boosting should be used like random forests: with large decision trees and without direct regularization or early stopping.

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill's sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training methodcalled "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.