neural network


Arm Adds Muscle To Machine Learning, Embraces Bfloat16

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Arm Holdings has announced that the next revision of its ArmV8-A architecture will include support for bfloat16, a floating point format that is increasingly being used to accelerate machine learning applications. It joins Google, Intel, and a handful of startups, all of whom are etching bfloat16 into their respective silicon. Bfloat16, aka 16-bit "brain floating point, was invented by Google and first implemented in its third-generation Tensor Processing Unit (TPU). Intel thought highly enough of the format to incorporate bfloat16 in its future "Cooper Lake" Xeon SP processors, as well in its upcoming "Spring Crest" neural network processors. Wave Computing, Habana Labs, and Flex Logix followed suit with their custom AI processors.


Computers can't tell if you're happy when you smile

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Barrett has suggestions for how to do emotion recognition better. Don't use single photos, she says; study individuals in different situations over time. Gather a lot of context--like voice, posture, what's happening in the environment, physiological information such as what's going on with the nervous system--and figure out what a smile means on a specific person in a specific situation. Repeat, and see if you can find some patterns in people with similar characteristics like gender. "You don't have to measure everybody always, but you can measure a larger number of people that you sample across cultures," she says.


How to build a deep learning model in 15 minutes

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As Instacart has grown, we've learned a few things the hard way. We're open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers. To address these issues we're standardizing our machine learning in Lore. At Instacart, three of our teams are using Lore for all new machine learning development, and we are currently running a dozen Lore models in production. Skip to the Outline if you want the full tour.


Having mastered Space Invaders, chess, and Go, AI tackles video soccer

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Google leads the world in research on machine intelligence. Its DeepMind subsidiary, in particular, has an impressive list of achievements under its belt. DeepMind's neural networks have achieved superhuman performance in a wide range of games. These include Atari video games such as Pong, Breakout, and Space Invaders and more complex challenges such as the online multiplayer game Starcraft.


The Importance of Language in Human Cognition and Artificial General Intelligence

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I talk to myself a lot. I'd say that most people utilize internal speech to work their way through various problems. Language seems to be a crucial component to the problem solving process and how we describe the world to ourselves. How do we comprehend atomic scales and cosmic scales, and the relationship between them? Such things are in many ways incomprehensible.


Discrete Probability Distributions for Machine Learning

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The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.


Discrete Probability Distributions for Machine Learning

#artificialintelligence

The probability for a discrete random variable can be summarized with a discrete probability distribution. Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems, but also in evaluating the performance for binary classification models, such as the calculation of confidence intervals, and in the modeling of the distribution of words in text for natural language processing. Knowledge of discrete probability distributions is also required in the choice of activation functions in the output layer of deep learning neural networks for classification tasks and selecting an appropriate loss function. Discrete probability distributions play an important role in applied machine learning and there are a few distributions that a practitioner must know about. In this tutorial, you will discover discrete probability distributions used in machine learning.


Acoustic Noise Cancellation by Machine Learning

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In this post I describe how I built an active noise cancellation system by means of neural networks on my own. I've just got my first results which I am sharing, but the system looks like a ravel of scripts, binaries, wires, soundcard, microphone and headphones, so I am not going to publish any sources yet. During the last year I've been building an Acoustic Noise Cancellation system based on an Artificial Neural Network. I did it in my spare time, so that's why it took so long for a relatively small experiment. The system I've built is a proof-of-concept, it showed consistency of an idea of NN as a noise canceller.


Weights & Biases - Part I: Best Practices for Picking a Machine Learning Model

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The number of shiny models out there can be overwhelming, which means a lot of times people fallback on a few they trust the most, and use them on all new problems. This can lead to sub-optimal results. Today we're going to learn how to quickly and efficiently narrow down the space of available models to find those that are most likely to perform best on your problem type. We'll also see how we can keep track of our models' performances using Weights and Biases and compare them. Unlike Lord of the Rings, in machine learning there is no one ring (model) to rule them all.


AI Project Development – How Project Managers Should Prepare

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As a project manager, you've probably engaged in a number of IT projects throughout your career, spanning complex monolithic structures to SaaS web apps. However, with the advancement of artificial intelligence and machine learning, new projects with different requirements and problems are coming onto the horizon at a rapid speed. With the rise of these technologies, it is becoming less of a "nice to have" and instead essential for technical project managers to have a healthy relationship with these concepts. According to Gartner, by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove--generating $2.9 trillion in business value by 2021. Google's CEO goes so far as to say that "AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire." With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, technical PMs who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape. Before going deeper, it's important to have a solid understanding of what AI really is.