I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty Take some time to learn about the human brain! This is my favorite intro to neuroscience course: https://www.mcb80x.org/

As long as the bias is not too strong, you are better off with a robust, outlier-insensitive estimate, than with an unbiased one. It would be interesting to do some analysis, to figure out the impact that a 10% bias has on your yield metric (measured as correctness of predictions, or revenue). The impact might be much smaller than 10%. Your model might be a bad model. It is better to reduce the variance generated by your model, rather than picking up a kosher (perfect) statistical estimate.

Artificial intelligence is moving from science fiction to practical reality fast. AI -- technology that teaches machines to learn so they can perform cognitive tasks and interact with people -- is suddenly accessible to many companies. Costs associated with the advanced computing and data-storage hardware behind AI are plummeting. A growing number of vendors also offer AI tools such as robotic processing automation that can be configured without the help of a rocket scientist. So this is clearly an area more banks will need to pay attention to going forward.

This is the second installment in a three-part series covering AI2 and machine learning. Be sure to read Part 1 for an introduction to AI2. AI2 is an "analyst-in-the-loop" system, meaning that it exploits the expertise of a security analyst to improve itself. A "human-in-the-loop" system is used to generate more supervised examples for the machine learning stage to use in an iterative training algorithm. This is exactly what AI2 does, allowing feedback to make the machine gradually smarter in the security domain.

The pace of technological change continues its inexorable march forward, with the pace steadily clicking faster. After decades of attending CES and riding the ebbs and flows it seems to me that this year finally shows a convergence of many waves of development. Artificial intelligence, voice recognition, virtual reality and internet of things are converging in ways that portend major changes in the way we will interact 24/7. From the seemingly arcane (a chess board that allows you to play anyone in the world, or against the best computers) to autonomous cars to the HAL-like ubiquity of Alexa, we are yet again in a brave new world. Consciously or otherwise, often in slippery slope fashion, we have individually opened the kimono to our personal world in exchange for seeming convenience.

It's a beautiful introduction ... Try not to drool too much! Read "A Few Useful Things to Know about Machine Learning" by Prof. Pedro Domingos. It's densely packed with valuable information, but not opaque. The author understands that there's a lot of "black art" and folk wisdom, and they invite you in. Take your time with this one.

Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation. One of the most impressive achievements this year was AlphaGo beating the best Go player in the world. With the victory, Go joins checkers, chess, othello, and Jeopardy as games machines have defeated human at. While beating someone at a board game might not seem useful on the surface, this is a huge deal.

Artificial intelligence (AI) interfaces and chatbots could be revolutionary for financial institutions – but only if they strike the right balance between human and machine interaction, argues Jeremy Pounder, futures director at Mindshare. AI is changing the banking industry as we know it. Already, banks are using AI within everyday payments, money management and digital self-service. For instance, voice recognition technology is being used by the likes of Barclays as a form of secure ID for telephone banking customers, while challenger institution Atom Bank allows its customers to log on via a facial recognition system. AI's advanced natural language processing and machine learning means it can generalise large data sets and detect and extrapolate patterns in order to create new solutions and actions.

The book definitely provides the ability to do data analysis from scratch. However, I took that to mean it would provide me with the basic knowledge to do data science as its done in production. What you want find hardly anywhere in this book is a single discussion of how to use pandas, scikit-learn, numpy, matplotlib, or any of the other tools used by today's data scientists. It does provide you with the basics of how the analysis is done and the math behind a bunch of machine learning models. So, while I think this book had value, it was very different from what I was expecting. I think the target audience for this book is more an academic looking to apply their knowledge to a data science realm, and not so much a programmer wanting to learn how to actually implement machine learning or data analysis in code.

Recent digital tech advancements have produced prototype artificial neurons and light-based neural networks, but we're still discovering ways our brain actually works. Researchers at MIT have built a computational model that could illustrate how inhibitory neurons work efficiently to block others from firing. The team's model, as described in their paper, uses theoretical computer science applied to a "winner-take-all" operation. It starts with a number of input neurons and finding an efficient configuration of inhibitory ones that will produce a signal from a single output neuron. Abstracting real neural networks in this way could help the field of computational analysis assist neuroscience.