Today Hank explores artificial intelligence, including weak AI and strong AI, and the various ways that thinkers have tried to define strong AI including the Turing Test, and John Searle's response to the Turing Test, the Chinese Room. Hank also tries to figure out one of the more personally daunting questions yet: is his brother John a robot? Get your own Crash Course Philosophy mug from DFTBA: http://store.dftba.com/products/crash... The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list... -- All other images and video either public domain or via VideoBlocks, or Wikimedia Commons, licensed under Creative Commons BY 4.0: https://creativecommons.org/licenses/... -- Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Crash Course Philosophy is sponsored by Squarespace.

There is much debate among scholars and practitioners about what data science is, and what it isn't. Does it deal only with big data? Is data science really that new? How is it different from statistics and analytics? One way to consider data science is as an evolutionary step in interdisciplinary fields like business analysis that incorporate computer science, modeling, statistics, analytics, and mathematics.

I've been working on a tutorial series for creating self-driving cars in Grand Theft Auto 5 for a bit now. The most recent creation is the result of day or so worth of collecting training data, and about 4 days of actual training of the model. It's currently a 30-layer convolutional neural network, it works purely on a frame-by-frame basis with no preprocessing other than an image resize and grayscale. It makes actions based on the current frame's pixel data, with no memory of what it's been doing. I plan to eventually incorporate some form of memory with something like recurrent layers, but...baby steps at a time!

It's a few years old, but still acts as a great technical introduction to the topic.

How To Create a Website in a Weekend! (Project-Centered Course) State University of New York via Coursera (3 ratings) 8th May, 2017

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN).

It covers self-study tutorials and end-to-end projects on topics like: Loading data, visualization, modeling, algorithm tuning, and much more...

With Artificial Intelligence (AI) now being seen as an essential tool in various sectors, it is important for our generation to incorporate innovative dynamic learning needs into our global education system. But when cutting-edge sectors evolve at lightning pace, it is not always possible for traditional sectors to change at the same speed. My company, Gravity4, has been intensively exploring Deep Learning in our development lab to advance our understanding with the ad technology platforms. Recently, I did a mentorship series with the youth in a struggling education system. The fascination of all great things possible, through the cutting edge revolution of AI, bought much energy in the room.

About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Raising capital is like playing a game of chess with a grand master. Every conversation is a strategic move to position your startup as an awesome investment. To win the game and get a'yes,' you have to tell investors exactly what they need to hear. Here's the dirty secret: how you describe your business makes a lot of difference. There are ways to make a bad idea sound far better than it really is.