There is a long-standing, red-hot debate in educational circles about the most effective way to teach kids. Others advocate for inquiry-based learning--where students drive their own learning (pdf) through discovery and exploration, working with peers and developing their own ideas--arguing it results in deeper, and more meaningful learning. The two are sometimes pitted against each other as "sage on a stage" (teacher directed) vs. "guide on the side" (student-led, or inquiry based). Both cite ample evidence to prove the superiority of their method (see here for teacher-directed, and here for inquiry-based). McKinsey applied machine learning to the world's largest student database to try and come up with a more scientific answer.

About 4,000 people listened to Cuban as he kicked off his shoes--literally--and explained how AI will change the game for companies, educators, and future developments. He's also keeping his eyes peeled for smaller companies in machine learning and AI, and already has at least three companies in his investment portfolio. "[Software writing] skill sets won't be nearly as valuable as being able to take a liberal arts education … and applying those [skills] in assisting and developing networks." But in order for the country to advance to that future, AI and robotics need to become core competencies in the U.S., and not just in the business world, Cuban said.

My mother's passion for mathematics has always been to the fore, and she has had great pleasure in teaching maths to students, during her career as a teacher, and discusses its incredible impact on any number of walks of life. This has influenced my own interest in putting statistics, data analytics and insight at the centre of business, and I am always curious to understand trends and rhythms to anticipate improvements. Data analysis and targeting technology has delivered productivity gains and heightened customer relevance during the last 20 years. Amazon has used predictive modelling since its launch in 1998. Tesco achieved a significant competitive advantage with the establishment of Club Card and the 50% Share in Dunnhumby in order to mine its data to segment and target customers.

You will find here nine interesting topics that you won't learn in college classes. Most have interesting applications in business and elsewhere. They are not especially difficult, and I explain them in simple English. Yet they are not part of the traditional statistical curriculum, and even many experienced data scientists with a PhD degree have not heard about some of these concepts. This is a well known model, used as a base stochastic process to model the logarithm of stock prices, yet it has interesting properties (depending on dimension) that few people know about.

It's an amazing time to get into Machine Learning. There are tools and resources available to help anyone with some coding skills and a problem to solve to do interesting work. I've been following along with Practical Deep Learning For Coders and the Reinforcement Learning Course by David Silver. Machine Learning without a PhD is an exellent intro to some of deep learning techinques. These along with all the papers linked from Hacker News and Two Minute Papers have inspired me to give some ideas a try.

The methodology of community detection can be divided into two principles: imposing a network model on a given graph, or optimizing a designed objective function. The former provides guarantees on theoretical detectability but falls short when the graph is inconsistent with the underlying model. The latter is model-free but fails to provide quality assurance for the detected communities. In this paper, we propose a novel unified framework to combine the advantages of these two principles. The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs). SGC-GEN incorporates the predictability on correct community detection with a measure of community fitness to GCMs. It resembles the formulation of supervised learning problems by enabling various community detection loss functions and model mismatch metrics. We further establish a theoretical condition for correct community detection using the normalized graph Laplacian matrix under a GCM, which provides a novel data-driven loss function for SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN, and show that the computational complexity of SGC-GEN is comparable to the baseline methods. Our experiments on 18 real-world datasets demonstrate that SGC-GEN possesses superior and robust performance compared to 6 baseline methods under 7 representative clustering metrics.

Part 3. Here's a look at industry specific companies that utilise various forms of artificial intelligence to solve some really interesting and particular problems for different markets. Basket -- e-commerce shopping cart chatbot Choice.ai AltSchool -- a platform made to improve learning capabailities Content Technologies (CTI) -- research and development company Coursera -- online courses from top universities Gradescope -- streamlines the tedious parts of grading Hugh -- helps library users find any book quickly Ivy.ai -- customer service chatbot for higher education Knewton -- personalised learning for high and primary schools Volley -- makes training and development more engaging and effective AlphaSense -- highly intelligent search functionality Alta5 -- scriptable trading automation for your online brokerage account Analytic.ai Atomwise -- for novel small molecule discovery Babylon -- online doctor consultations using AI BuddiHealth -- helps improve process, payment systems and costs with RCM Behold.ai Imagia -- helps detect changes in cancer early Kuznech -- computer vision products range Lunit Inc. -- a range of medical imaging software Zebra Medical Vision -- medical imaging to help physicians and practitioners Cape Analytics -- identify property attributes at scale for underwriting Underwrite.ai

This experiment lets anyone explore how machine learning works, in a fun, hands-on way. You can teach a machine to using your camera, live in the browser – no coding required. You train a neural network locally on your device, without sending any images to a server. That's how it responds so quickly to you. Here are some links to things people have done so far: Make your hand say moo.

Byron Reese: This is Voices in AI, brought to you by Gigaom. Today, our guest is Oren Etzioni. He's a professor of computer science who founded and ran University of Washington's Turing Center. And since 2013, he's been the CEO of the Allen Institute for Artificial Intelligence. The Institute investigates problems in data mining, natural language processing, and the semantic web. And if all of that weren't enough to keep a person busy, he's also a venture partner at the Madrona Venture Group. Business Insider called him, quote: "The most successful entrepreneur you've never heard of." Welcome to the show, Oren. Oren Etzioni: Thank you, and thanks for the kind introduction. I think the key emphasis there would be, "you've never heard of." Well, I've heard of you, and I've followed your work and the Allen Institute's as well. And let's start, if that's Okay, let's start there. So if you would just start off by telling us a bit about the Allen Institute, and then I would love to go through the four projects that you feature prominently on the website. And just talk about each one; they're all really interesting. The Allen Institute for AI is really Paul Allen's brainchild. He's had a passion for AI for decades, and he's founded a series of institutes--scientific institutes--in Seattle, which were modeled after the Allen Institute for Brain Science, which has been very successful running since 2003. We were launched as a nonprofit on January 1, 2014, and it's a great honor to serve as CEO. Our mission is AI for the common good, and as you mentioned, we have four projects that I'm really excited about.

Summary: If you are mid-career and thinking about switching into data science here are some things to think about in planning your journey. We get lots of inquiries from readers asking for career advice and many of these identify as mid-career looking to switch into data science. If you're in this group you face some of the same challenges beginners do but also some that are unique to your circumstance. Here are some thoughts and observations that may be valuable. When folks self-identify as mid-career they usually cite 10 or 20 years experience. By my way of thinking that makes you most likely 30 or 40 years old.