Deep Learning, NLP, NMT, AI, ML
Figure 1: Performance assessment methods change across the development lifecycle for complex AI systems in ways that differ from general purpose AI. The emphasis shifts from rapid technical innovation that requires easy-to-calculate aggregate performance
Read moreAs AI becomes more deeply integrated into every aspect of our lives, it is essential that AI systems perform appropriately for their intended use. We know AI models can never be perfect, so how do we decide when AI performance is ‘good enough’ for use in a real life application? Is level of accuracy a sufficient gauge? What else matters? These are questions Microsoft Research tackles every day as part of our mission to follow a responsible, human-centered approach to […]
Read moreToday, we are experiencing waves of breakthroughs in computing that are transforming just about every aspect of our lives. Artificial intelligence is changing the way we develop and create. Human language technologies are revolutionizing the workflows of healthcare professionals. Deep learning is accelerating our ability to understand and predict natural phenomena, from atomic to galactic scales. Meanwhile, the foundations of cloud computing are undergoing a reinvention from the atoms up. Realizing the benefits of these new breakthroughs demands that we […]
Read moreOnline trust has come a long way since the time of centralized databases, where information was concentrated in one location and the security and validation of that information relied on a core set of people and systems. While convenient, this model of centralized management and oversight had
Read moreDespite increasingly widespread use of machine learning (ML) in all aspects of our lives, a broad class of scenarios still rely
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tensor([[[0.0702, 0.4700, 0.8626, 0.5981, 0.2569], [0.5060, 0.4742, 0.7492, 0.3597, 0.8754], [0.7581, 0.8954, 0.3205, 0.7038, 0.3167], [0.1799, 0.9890, 0.9624, 0.2595, 0.6935]], [[0.0338, 0.9996, 0.2049, 0.0127, 0.1300], [0.6330, 0.4086, 0.6630, 0.0960, 0.5759], [0.1510, 0.3219, 0.5052, 0.5823, 0.8093], [0.1546, 0.9815, 0.6459, 0.1247, 0.1438]]])
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tensor([[[0.5540, 0.2965, 0.7656, 0.0864, 0.4719], [0.1797, 0.8056, 0.5731, 0.3665, 0.7524], [0.7091, 0.5366, 0.6332, 0.8776, 0.0760], [0.1010, 0.5353, 0.9416, 0.2458, 0.8791]], [[0.8621, 0.0030, 0.5289, 0.6757, 0.2340], [0.5413, 0.6108, 0.5478, 0.5954, 0.1434], [0.6445, 0.6780, 0.4488, 0.3309, 0.0727], [0.2652, 0.6528, 0.6589, 0.6072, 0.5976]]])
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Introduction This tutorial will show you how to create Tensors with random values in PyTorch by using torch.rand() and torch.rand_like() functions. In
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Introduction In this tutorial, we will teach how to use torch.randn() and torch.randn_like() to create PyTorch tensors with random values. Initializing tensors with random values is quite common in
Read moreWhat would it take to get humanoid, bipedal robots to dance like Mick Jagger? Indeed, for something more mundane, what does it take to get them to simply stand still? Sit down? Walk? Move in myriads of other ways many people take for granted? Bipedalism provides unparalleled versatility in an environment designed for and by humans. By mixing and matching a wide range of basic motor skills, from walking to jumping to balancing on one foot, people routinely dance, play […]
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