deep learning

Your Guide to AI and Machine Learning at re:Invent 2018 Amazon Web Services


As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)--with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You'll hear success stories about machine learning on AWS firsthand from customers and partners such as Sony, Moody's, NFL, Intuit, 21st Century Fox, Toyota, and more. This year's re:Invent also includes the AI Summit, where thought leaders in the academic community will share their perspectives on the future of AI. Here are a few highlights of this year's lineup from the re:Invent session catalog to help you plan your event agenda.

The Best Resources I Used to Teach Myself Machine Learning


The field of machine learning is becoming more and more mainstream every year. With this growth come many libraries and tools to abstract away some of the most difficult concepts to implement for people starting out. Most people will say you need a higher level degree in ML to work in the industry. If you love working with data and practical math, then I would say this is not true. I did not graduate college with a Machine Learning or data degree yet I am working with ML right now at a startup.

Making Sense of Artificial Intelligence, Machine Learning, and Deep Learning


There seems to be much confusion among the ranks of the untrained when it comes to an understanding of some basic IT concepts. This is especially true when looking at Artificial Intelligence (AI) and the misuse of nomenclature such as Machine Learning (ML), and Deep Learning (DL). In this article, I will try to brush away the mists of confusion, and present the case for each specific data related technologies. First of all, you need to understand that all three are related and sit within a specific hierarchy. To properly understand the differences between these three technologies, let's first look at how they stack together in terms of hierarchy.

Review: Artificial Intelligence in 2018 – Towards Data Science


Artificial Intelligence is not a buzzword anymore. As of 2018, it is a well-developed branch of Big Data analytics with multiple applications and active projects. Here is a brief review of the topic. AI is the umbrella term for various approaches to big data analysis, like machine learning models and deep learning networks. We have recently demystified the terms of AI, ML and DL and the differences between them, so feel free to check this up.

Google ponders the shortcomings of machine learning


Critics of the current mode of artificial intelligence technology have grown louder in the last couple of years, and this week, Google, one of the biggest commercial beneficiaries of the current vogue, offered a response, if, perhaps, not an answer, to the critics. In a paper published by the Google Brain and the Deep Mind units of Google, researchers address shortcomings of the field and offer some techniques they hope will bring machine learning farther along the path to what would be "artificial general intelligence," something more like human reasoning. The research acknowledges that current "deep learning" approaches to AI have failed to achieve the ability to even approach human cognitive skills. Without dumping all that's been achieved with things such as "convolutional neural networks," or CNNs, the shining success of machine learning, they propose ways to impart broader reasoning skills. The paper, "Relational inductive biases, deep learning, and graph networks," posted on the arXiv pre-print service, is authored by Peter W. Battaglia of Google's DeepMind unit, along with colleagues from Google Brain, MIT, and the University of Edinburgh.

Understanding deep learning (part 3): Applications of deep neural networks


This video has been produced by the Tesseract Academy (, a company whose mission is to educate decision makers in deep technical topics such as data science, AI, and blockchain.

The Building Blocks of Reinforcement Learning: Deep Open Sources TRFL


Deep reinforcement learning(DRL) has been categorized many times as the future of artificial intelligence(AI). Some of the most important AI breakthroughs of the last few years such as DeepMind's AlphaGo or OpenAI's Dota Five have been based on DRL applications. Despite its importance, the implementation of DRL models remains an incredibly challenging exercise and, for the most part, we have very little ideas about the pieces that make an efficient DRL solution. Earlier this week, DeepMind open sourced TRFL(pronounced truffle, of course), a framework that compiles a series of useful building blocks of DRL models. Most of the current wave of DRL methods have had their origin in the academic environments and they haven't been tested in real world implementations.

Video Friday: TALOS Humanoid Robot, and More

IEEE Spectrum Robotics Channel

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. With all the hype about SpotMini recently, it's a good time to take a look back at another quadruped that Boston Dynamics helped develop. This system is the first of its kind that can automatically keep a cluttered room neat and tidy at a practical level, something that has been difficult to achieve using conventional robot system.