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.

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. Here are some links to things people have done so far: Make your hand say moo. And stay tuned, we'll add more examples here soon. Use the record button and share it on social media with #teachablemachine so we can check it out.)

Hot-air balloon pilot Richard Varney typically spends his weekends transporting tourists around central Massachusetts in a huge, multicolored balloon. But on a recent Sunday, Varney drove to a local community college and learned to fly a different type of aerial vehicle. "I want to try something new," he said as he watched an instructor demonstrate how to steer a $2,000 drone equipped with a camera. "This could help me launch a side business taking aerial photos of local towns." At least 15 community colleges across the country now have courses that teach people how to pilot drones, according to research conducted by MIT Technology Review.

Google's AlphaGo defeated Chinese Go player Ke Jie (left) this year to become the world champion. Google's AlphaGo may have unseated Ke Jie as the Go world champion this year, but the artificial intelligence behind AlphaGo is actually no smarter than a 6-year-old child. A study published Saturday showed Google's artificial intelligence technology scored best out of 50 systems that Chinese researchers tested against an AI scale they created, CNBC reported Monday. With a IQ score of 47.28, Google's AI was almost twice as smart as Apple virtual assistant Siri, which scored 23.94. AI systems have developed so quickly that they've been able to act as assistants, take exams and even outperform us at strategy games.