Google just killed the Captcha, perhaps the most obstructive thing on the entire internet. For years, Captcha served as the primary way of telling humans and robots apart on the internet. It made sure that the person looking to access a website was actually a human being – ensuring that robots couldn't be used to send spam or flood a website with requests, for instance. But over time, robots have gradually become too clever for the often simple tests – which early on required people to transcribe hard-to-read text. With that, the technologies have become more complex, too.

Intel is no longer satisfied just partnering with other companies to create self-driving cars: it wants to own the whole stack. The chip maker just announced it intends to purchase Jerusalem-based Mobileye for $15.3 billion. The two companies were already working together on various projects. The pair announced a partnership with BMW in July 2016 with the aim of putting an autonomous car on the streets by 2021. Then in November, the two companies partnered with auto parts maker Delphi to create the Automated Driving Group, which will create a self-driving car system that can be sold to automakers.

Technology is most successful when it impacts society in a positive way, and whilst Artificial Intelligence is still a sensitive subject for some, another benevolent application for AI has been found in fighting crime. AI powered platforms are brilliant at recognising patterns, which also means that they can pick out anomalies. That's why AI is such a valuable tool for detecting fraud, for instance. It can flag up suspicious behaviours amongst datasets that are so huge, human administrators wouldn't know where to start. The latest use of the technology to combat crime isn't on the streets, or even in the courts – it's a prison that is pioneering this new high-tech approach to security.

The two words that can send goosebumps to every college basketball fan in the country. It's the month where every fan will fill out a bracket, each thinking that they picked the right 12 over 5 seed upset or that they were the only one to pick the Cinderella team that makes it to the Elite 8. The month where people will spend hours watching regular season games, pouring over stats and expert analysis, trying to carefully predict the most likely winner, only to find their pick lose in the first round (Thanks Michigan State). The month where your younger sister ends up having a better bracket than you because she picked the teams with the "cooler" mascots (Sad, but true story). March Madness is the sports phenomenon that incites anxiety, regret, elation, and every other possible emotion in the spectrum. And it's about to start in 4 days.

Personally, I am working with Vamsi on some awesome content for an upcoming DZone item. I'm also preparing for Oracle Code New York, where I will be doing a talk on NiFi, Deep Learning, Machine Learning, NLTK, streaming, IoT, and Java microservices. Not sure how I will cram that into 45 minutes at 5 p.m. with a hands-on demonstration -- I think I will need compression. If you are at that event in two weeks, come say hi! Mention the secret password, "DZone," and get a free sticker. Check out the source for yourself in Using Python With Keras Intro of Deep Learning 4J (on top of Hortonworks HDP).

Historically, some banking activities such as trading have been relying heavily on analytics and cutting edge algorithmic tools. The coming of age of powerful data analytics solutions combined with the development of intelligent algorithms have created new opportunities for financial institutions. In his session at 20th Cloud Expo, Sebastien Meunier, Head of Digital for North America at Chappuis Halder & Co., will discuss how these tools can be leveraged to develop a lasting competitive advantage in priority areas: customer analytics, financial crime prevention, regulatory compliance and risk management. Speaker Bio Sebastien Meunier is an expert in Innovation in Finance, with 14 years of experience in managing business and technology transformations in Financial Services, and among the top 10 Fintech influencers on social media. He is the Head of Digital for North America at the consulting firm Chappuis Halder & Co.

Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, will discuss the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.

One of the big problems in Artificial Intelligence is the gigantic amount of GPUs (or computers) needed to train large networks. The training time of neural networks grows quadratically (think squared) in function of their size. This is due to how the network is trained. For each example, the entire network is modified, even though some parts might not even activate while processing this particular example. However, the memory of a network is directly dependent on the size of the network.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.