Nir’s take on the future of machine learning—where it’s heading and where it should be heading—can be seen as contrary to the current, prevailing wisdom.
Now you can see what we’ve been working on for the past few months.
Are you heading to NeurIPS in Vancouver this December 8-14th? So are we! And we would love to meet you. Stop by booth #9 and say hello. Who is Neural Magic? Coming out of MIT, Neural Magic is a pure software engine that delivers GPU-class performance when running neural networks on commodity CPUs. Our software… Read More Neural Magic at NeurIPS 2019
We are proud to announce the first version of the Neural Magic Inference Engine, offering GPU-class performance on commodity CPUs.
The seed investment is led by Comcast Ventures, and including NEA, Andreessen Horowitz, Pillar VC and Amdocs
A Union Square Ventures post from last fall called out the fact that throughout the course of technology history, apps beget the infrastructure that supports them. The concept of an “app” is loosely defined as something that directly touches the end-user (examples included light bulbs, planes, email, etc.) In the case of web applications, this… Read More What is the Infrastructure Phase of Machine Learning?
The practice of training and putting machine learning systems into production involves a lot of trial and error. However, there are some recurring mistakes many data scientists make that can be avoided with the proper awareness and preparation. Here are a few that we’ve seen relatively frequently, and some tips on how to prevent these… Read More Data Scientists Make These 3 Common Mistakes
The field of machine learning has progressed quickly in the last decade, and there’s a big reason why: both academics and industry groups are working on the problem, often in competition with one another. Much ink has been spilled over the fierce rivalry for talent between corporate and academic research labs, but I’d argue that… Read More Why Machine Learning Needs Academia and Industry to Survive
Convolutional neural networks (CNNs) are a type of neural network most often used for image recognition and classification. CNNs excel at these tasks because they are designed to automatically learn how to recognize spatial hierarchies in an image. Once these algorithms are trained, they can ‘infer’ the next best prediction for the task at hand. … Read More Using CNNs for Inference
Let’s take a brief look at the history of GPUs before machine learning, and their current status in machine learning applications.