This book was designed around major ideas and methods that are directly relevant to machine learning algorithms. There are a lot of things you could learn about probability, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials. I designed the tutorials to focus on how to get things done with probability. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you less than one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

This white paper is focused on ways in which AI can accelerate your organizations performance and will introduce you to a new category of deep-learning AI: The Corporate Brain.

Osaro, a San Francisco startup developing AI-based solutions for industrial robots, today announced that it's closed a $16 million series B funding round led by King River Capital (KRC), with participation from Alpha Intelligence Capital, Founders Fund, Fenox Venture Capital, GiTV Fund, and existing and strategic investors. It brings the startup's total raised to $29.3 million coming after a $10 million series A in April 2017, which cofounder and CEO Derik Pridmore said will bolster Osaro's hiring, international deployment, and R&D efforts. Alongside the funding round, Osaro revealed that Applied Digital Access, Mahi Networks, and Calix vereran Kevin Pope has joined as VP of engineering. "A key element of our competitive advantage is Osaro's … deep learning algorithms," said Pridmore, an MIT computer science and electrical engineering graduate who cofounded Osaro in 2015 with a team hailing from UC Berkeley, Stanford, and the University of Massachusetts. "These algorithms generalize picking tasks with minimal training data and no SKU registration for quick, scalable solutions. In addition, as a software company, we support a wide array of commodity hardware and robotic arms which lets our customers select options that best fit their needs."

As the leading cloud provider, Amazon Web Services offers numerous tools for a variety of applications. The sheer number of offerings can be overwhelming and it may not be so clear which tools are worth using. The following outlines a number of advanced tools that may be relevant to data scientists and explains how they can be useful. This is by no means an exhaustive list but should provide some insights as to the basic, essential tools available. There are a number of scalable storage options for data science needs, including data lake and data warehousing services. Data scientists typically require storage options that go beyond the capabilities of Amazon Simple Storage Service (S3).

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities.

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities.

Cardiovascular diseases are the most common cause of death in the world. More than a third of deaths in Finland are caused by cardiovascular diseases. The current objective is to create an assessment, based on health data, of each person's risk of illness before they consult a doctor. Andrea Ganna, Group Leader from Institute for Molecular Medicine Finland FIMM at the University of Helsinki and instructor from Harvard Medical School, wants to establish a nationwide, personalised risk assessment as foundation for planning public health interventions. The assessment is based on the health, demographic and genetic information of the citizens.

The AI lab gets to throw Microsoft's supercomputing and cloud computing muscle at its bid to build artificial general intelligence (AGI). The news: Microsoft says OpenAI will help it jointly develop and train new AI technologies for its Azure cloud computing service. It will also work with it to develop new supercomputing hardware to try to achieve AGI--machines with the capacity to learn tasks the way human beings do. That's a holy grail of AI that still remains (and may always remain) out of reach. OpenAI's founders, which include Elon Musk and other tech leaders, reckon AGI could help solve longstanding challenges in areas that range from climate change to health care.

"Bias" in AI is often treated as a dirty word. But to Dr. Andreas Tolias at the Baylor College of Medicine in Houston, Texas, bias may also be the solution to smarter, more human-like AI. I'm not talking about societal biases--racial or gender, for example--that are passed onto our machine creations. Rather, it's a type of "beneficial" bias present in the structure of a neural network and how it learns. Similar to genetic rules that help initialize our brains well before birth, "inductive bias" may help narrow down the infinite ways artificial minds develop; for example, guiding them down a "developmental" path that eventually makes them more flexible.

Neural networks have helped scientists monitor the Sun's extreme ultraviolet outbursts after an instrument on NASA's Solar Dynamic Observatory suffered an electrical malfunction, making it difficult for scientists to monitor a portion of extreme ultraviolet energy (EUV) being spewed by our star. EUV rays ejected from solar flares are particularly worrisome. The surge of highly energetic particles bombarding Earth can cause radio communication blackouts, knock satellites out of place, and disturb GPS signals. Space agencies around the world keep a close eye on the Sun's activity in an attempt to study and predict these outbursts. NASA's SDO is just one of the many spacecrafts currently orbiting our planet's star.