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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).

Your next professor could be a robot. Bina48 became the first robot to co-teach a university class when she helped lead a course at West Point, the U.S. Military academy, according to Axios. The humanoid AI taught two sessions of a philosophy course, with topics ranging from ethics, just war theory and use of artificial intelligence in society, which is pretty meta. Bina48 (pictured) became the first robot to co-teach a university class when she helped lead a course at West Point, the U.S. Military academy. William Barry, who has been using Bina48 to teach for several years, decided to put the robot in front of students in the classroom to see if she could'support a liberal education model.'

Evaluating a machine learning model responsibly requires doing more than just calculating loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias. This module looks at different types of human biases that can manifest in training data. It then provides strategies to identify them and evaluate their effects.

Apple will hold its next big event at the end of the month, it has announced. The launch – to be held in New York City on 30 October – is widely expected to see the unveiling of a new iPad Pro and fresh Macs. It comes just a few weeks after Apple launched its new iPhones. And it will come just days after the release of the iPhone XR, a cheaper handset that Apple delayed despite launching alongside the XS in September. Phil Schiller, Apple's senior vice president of worldwide marketing, speaks about the Apple iPhone XS and Apple iPhone XS Max Philip W. Schiller, Senior Vice President, Worldwide Marketing of Apple, speaks about the new Apple iPhone XR Phil Schiller, Apple's senior vice president of worldwide marketing, speaks about the new Apple iPhone XS, iPhone XS Max Philip W. Schiller, Senior Vice President, Worldwide Marketing of Apple, speaks about the new Apple iPhone XR Phil Schiller, Apple's senior vice president of worldwide marketing, speaks about the Apple iPhone XS and Apple iPhone XS Max Philip W. Schiller, Senior Vice President, Worldwide Marketing of Apple, speaks about the new Apple iPhone XR Phil Schiller, Apple's senior vice president of worldwide marketing, speaks about the new Apple iPhone XS, iPhone XS Max Philip W. Schiller, Senior Vice President, Worldwide Marketing of Apple, speaks about the new Apple iPhone XR The company is expected to release a new iPad Pro that will include the Face ID facial recognition technology found in the iPhone X.

In this Book we argue that the fruitful interaction of computer vision and belief calculus is capable of stimulating significant advances in both fields. From a methodological point of view, novel theoretical results concerning the geometric and algebraic properties of belief functions as mathematical objects are illustrated and discussed in Part II, with a focus on both a perspective 'geometric approach' to uncertainty and an algebraic solution to the issue of conflicting evidence. In Part III we show how these theoretical developments arise from important computer vision problems (such as articulated object tracking, data association and object pose estimation) to which, in turn, the evidential formalism is able to provide interesting new solutions. Finally, some initial steps towards a generalization of the notion of total probability to belief functions are taken, in the perspective of endowing the theory of evidence with a complete battery of estimation and inference tools to the benefit of all scientists and practitioners.