One of my favorite places to learn data science is an under-the-radar educational website, DataCamp. DataCamp doesn't get nearly the attention that some of the larger, more well-funded online coding schools get, but, I often find myself on one of their tutorials whenever I'm learning something new related to statistics or machine learning. Over the past few months, I've dedicated at least a few hours a week to learning the underpinnings of automation and, where I find something interesting, to blog about my experience. Unlike almost every other school or tutorial I've encountered, DataCamp has a delightfully distinct and powerful approach to education: every single piece of instruction is paired with a simple example and interactive tutorial. There are no long lectures; there are no complicated diagrams.

I truly believe that artificial intelligence (AI) will shape our future and will bring tremendous impact and applications in industries such as health and agriculture. One of the things that I aim to achieve with dair.ai is to discuss interesting open-source AI technologies that help to address important problems such as medical diagnosis and personalized learning. One of the tools that have caught my attention this week is MedicalTorch (developed by Christian S. Perone), which is an open-source medical imaging analysis tool built on top of PyTorch. It contains a set of loaders, pre-processors and utility functions to efficiently and easily analyze medical images such as those acquired from magnetic resonance imaging (MRI) scans. In this post, I will summarize some of the functionalities offered by the medicaltorch library and how it can be used to conduct medical imaging analysis. Specifically, this will be a tutorial on how to perform spinal cord gray matter segmentation using a technique based on convolutional neural networks (CNNs).

Prime Minister Narendra Modi will attend Monday the fourth edition of the NITI Lecture Series focussed on'leveraging artificial intelligence for inclusive growth', according to an official statement. Modi will attend the lecture series in which the key note address will be delivered by Jensen Huang, president and co-Founder of US-based technology firm NVIDIA Corporation, the Niti Aayog said Sunday. The government think tank said the 2018 theme for the lecture series'AI for All: Leveraging Artificial Intelligence for Inclusive Growth' is part of the National Strategy for Artificial Intelligence aimed at evolving a robust ecosystem in India for AI research and adoption. Union ministers, policy makers, experts from different walks of life along with Niti Aayog vice chairman, CEO, members and other senior officials will also be present on the occasion. The Union Budget 2018 had mandated the Niti Aayog to come up with a national programme on employing artificial intelligence towards national development and since the Aayog has published a National Strategy for artificial intelligence (AI).

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.

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Many teams try to start an applied AI project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that's like raising a puppy in a New York City apartment for a few years, then being surprised that it can't herd sheep for you. Instead, the first step is for the owner -- that's you! -- to form a clear vision of what you want from your dog (or ML/AI system) and how you'll know you've trained it successfully. My previous article discussed the why, now it's time to dive into how to do this first step for ML/AI, with all its gory little sub-steps. This reference guide is densely-packed and long, so feel free to stick to large fonts and headings for a two-minute crash course. Cast of characters: decision-maker, ethicist, ML/AI engineer, analyst, qualitative expert, economist, psychologist, reliability engineer, AI researcher, domain expert, UX specialist, statistician, AI control theorist. The tasks we're about to tackle are the responsibility of the project's responsible adult. That's whoever calls the shots.

Earlier this week, Google announced that it was piloting a machine learning intensive for college students. Today, its broader Machine Learning Crash Course is adding a new training module on fairness when building AI. As adoption of machine learning continues, ethics and fairness are very important considerations. While AI can have the "potential to be fairer and more inclusive at a broader scale than decision-making processes based on ad hoc rules or human judgments," there might be underlying biases present in the data used to train these models. Other issues involve insuring that AI is fair in all situations, while more broadly there is "no standard definition of fairness."

Why is there so much buzz around it in the enterprise technology space right now? What's changed with the way that we can access and use data that's made technology like machine learning and robotics possible? In the enterprise space, market firm Tractica says that the revenue from the AI software market worldwide will grow to nearly $60 billion by 2025. Gartner analysts believe that by 2020, AI will be pervasive in almost every new product and service. Business leaders must be armed with the tools to take advantage.

Another year has flown by and stores everywhere are yet again full of school supplies, one more useful (or eccentric) than others. The back-to-school season is a stressful season, for students, parents and teachers alike. But stress is a part of life and back-to-school stress is supposed to be worth it: educated kids will turn into smart adults who'll ensure everyone's future. We've only taken just a few steps into the 21st Century, after all. With smartphones in our hands, virtual assistants in our homes and various ed-tech tools in our classrooms, we all rely on technology to make our lives easier during this stressful period.

In my undergraduate studies, I majored in philosophy with a focus on ethics, spending countless hours grappling with the notion of fairness: both how to define it and how to effect it in society. Little did I know then how critical these studies would be to my current work on the machine learning education team where I support efforts related to the responsible development and use of AI. As ML practitioners build, evaluate, and deploy machine learning models, they should keep fairness considerations (such as how different demographics of people will be affected by a model's predictions) in the forefront of their minds. Additionally, they should proactively develop strategies to identify and ameliorate the effects of algorithmic bias. To help practitioners achieve these goals, Google's engineering education and ML fairness teams developed a 60-minute self-study training module on fairness, which is now available publicly as part of our popular Machine Learning Crash Course (MLCC).