A colleague recently asked me if deep learning lives up to the hype. Is it really revolutionary or just the same old thing dressed up with a new name? As with most fads, the truth is somewhere in between. Let's start by dismissing the hype. I've heard deep learning referred to as the breakthrough that will lead to "Strong AI" (universal, general purpose AI) and seems to have gotten some rich (Elon Musk), smart (Stephen Hawking) guys (Steve Wozniak) all riled up about the end of humanity.

We are starting a series of articles explaining some of the more popular deep learning models and their architects. We'll start off with one called Neural Storyteller by Jamie Ryan Kiros. If you haven't seen it already, Neural Storyteller is a model that when given an image, can generate a romance story about the image. I had a train over the bridge and at the same time, it hurt so much . But it did n't take long for me to understand what he was saying .

Tech Insider recently published an article about the first artificially intelligent lawyer. ROSS – the name of the artificial intelligence (AI) – can search court rulings from 13 years ago, along with opinions about the case at hand and its significance to past court verdicts. ROSS also sifts through all of this information and provides the user with only highly relevant data. ROSS even operates in plain language, which is a huge step in artificial intelligence, as current systems search and process data in list form, which is not how court rulings and cases are documented. ROSS was recently'hired' at a bankruptcy firm, and several other firms have requested the AI lawyer as well.

The hype around artificial intelligence (AI) has reached a fever pitch in the past few months, as tech giants such as Google, Microsoft, Facebook and Apple have unveiled new AI technology that could bring it out of the realm of science fiction and into the mainstream. Google recently unveiled its Google Home platform as a competitor to Amazon's Echo AI, and blogged how its chip technology has pushed machine learning and intelligence "seven years into the future." Amazon's dedicated staff of 1,000 Alexa developers is fending off the big G by reportedly teaching its software to recognize your emotional state as it sells you stuff. And Facebook, IBM, and other tech giants are using AI to study your social media presence and search history to sell you goods and advertise products. Meanwhile, Apple announced at this week's WWDC that it's placing a smarter Siri on not just every iOS 10 device, but also macOS and allowing third-party developers access to the Siri SDK.

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

KnittBar is the world's most amazing versatile smart power bar. It will give you a new way to look at electricity and how to use your appliances and electronics in a better way; a smart way. Like building blocks, KnittBar's modular design enables you to build the perfect KnittBar that is just right for you. The locking mechanism makes sure that the modules are tightly connected. Don't forget to put on the End Cap:) More modules will be developed, the possibilities are endless!

We are in the midst of massive change--spurred by interconnected digital technologies that are fundamentally reshaping the global industrial landscape. Industry 4.0 meshes the physical and virtual worlds, combining digital capabilities like advanced robotics and artificial intelligence; sophisticated sensors; data capture and analytics; cloud computing; and 3D printing (for starters). Data analytics is at the very heart of Industry 4.0. Leading industrial companies are embracing Industry 4.0 to create tightly woven digital ecosystems of employees, customers, partners, and suppliers--empowering them to rapidly deliver customized products and services while increasing revenue and driving down costs. To remain competitive, every industrial company should review its data analytics skills and organizational structures, making sure it can capture, analyze and fully leverage the data at executives' fingertips to refine products and services and meet changing customer needs.

We are on the cusp of a major breakthrough in how organizations collect, analyze, and act on knowledge. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This is the third in a special series of commissioned essays MIT Sloan Management Review will publishing in Frontiers over the Spring and Summer of 2016. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Artificial Intelligence is about to transform management from an art into a combination of art and science.

In most of the specialization courses, we can enroll all courses in the same date or near by dates. However, for machine learning specialization, it is not the same case. This course -Machine Learning: Recommender Systems & Dimensionality Reduction - I have to wait until September. That is kind of crazy. It totally makes sense to force people to finish all course before the capstone project.