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Domain: petewarden.com
Stories and comments across the archive that link to petewarden.com.
Stories · 4
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Will Compression Be Machine Learning's Killer App? (petewarden.com)
Pete Warden, an engineer and CTO of Jetpac, writes: When I talk to people about machine learning on phones and devices I often get asked "What's the killer application?". I have a lot of different answers, everything from voice interfaces to entirely new ways of using sensor data, but the one I'm most excited about in the near-team is compression. Despite being fairly well-known in the research community, this seems to surprise a lot of people, so I wanted to share some of my personal thoughts on why I see compression as so promising.
I was reminded of this whole area when I came across an OSDI paper on "Neural Adaptive Content-aware Internet Video Delivery". The summary is that by using neural networks they're able to improve a quality-of-experience metric by 43% if they keep the bandwidth the same, or alternatively reduce the bandwidth by 17% while preserving the perceived quality. There have also been other papers in a similar vein, such as this one on generative compression [PDF], or adaptive image compression. They all show impressive results, so why don't we hear more about compression as a machine learning application?
All of these approaches require comparatively large neural networks, and the amount of arithmetic needed scales with the number of pixels. This means large images or video with high frames-per-second can require more computing power than current phones and similar devices have available. Most CPUs can only practically handle tens of billions of arithmetic operations per second, and running ML compression on HD video could easily require ten times that. The good news is that there are hardware solutions, like the Edge TPU amongst others, that offer the promise of much more compute being available in the future. I'm hopeful that we'll be able to apply these resources to all sorts of compression problems, from video and image, to audio, and even more imaginative approaches. -
Will Compression Be Machine Learning's Killer App? (petewarden.com)
Pete Warden, an engineer and CTO of Jetpac, writes: When I talk to people about machine learning on phones and devices I often get asked "What's the killer application?". I have a lot of different answers, everything from voice interfaces to entirely new ways of using sensor data, but the one I'm most excited about in the near-team is compression. Despite being fairly well-known in the research community, this seems to surprise a lot of people, so I wanted to share some of my personal thoughts on why I see compression as so promising.
I was reminded of this whole area when I came across an OSDI paper on "Neural Adaptive Content-aware Internet Video Delivery". The summary is that by using neural networks they're able to improve a quality-of-experience metric by 43% if they keep the bandwidth the same, or alternatively reduce the bandwidth by 17% while preserving the perceived quality. There have also been other papers in a similar vein, such as this one on generative compression [PDF], or adaptive image compression. They all show impressive results, so why don't we hear more about compression as a machine learning application?
All of these approaches require comparatively large neural networks, and the amount of arithmetic needed scales with the number of pixels. This means large images or video with high frames-per-second can require more computing power than current phones and similar devices have available. Most CPUs can only practically handle tens of billions of arithmetic operations per second, and running ML compression on HD video could easily require ten times that. The good news is that there are hardware solutions, like the Edge TPU amongst others, that offer the promise of much more compute being available in the future. I'm hopeful that we'll be able to apply these resources to all sorts of compression problems, from video and image, to audio, and even more imaginative approaches. -
Deep Learning Is Eating Software (petewarden.com)
Pete Warden, engineer and CTO of Jetpac, shares his view on how deep learning is already starting to change some of the programming is done. From a blog post, shared by a reader last week: The pattern is that there's an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution. I can only point to examples within Alphabet that we've made public, like upgrading search ranking, data center energy usage, language translation, and solving Go, but these aren't rare exceptions internally. What I see is that almost any data processing system with non-trivial logic can be improved significantly by applying modern machine learning. This might sound less than dramatic when put in those terms, but it's a radical change in how we build software. Instead of writing and maintaining intricate, layered tangles of logic, the developer has to become a teacher, a curator of training data and an analyst of results. This is very, very different than the programming I was taught in school, but what gets me most excited is that it should be far more accessible than traditional coding, once the tooling catches up. The essence of the process is providing a lot of examples of inputs, and what you expect for the outputs. This doesn't require the same technical skills as traditional programming, but it does need a deep knowledge of the problem domain. That means motivated users of the software will be able to play much more of a direct role in building it than has ever been possible. In essence, the users are writing their own user stories and feeding them into the machinery to build what they want. -
Deep Learning Is Eating Software (petewarden.com)
Pete Warden, engineer and CTO of Jetpac, shares his view on how deep learning is already starting to change some of the programming is done. From a blog post, shared by a reader last week: The pattern is that there's an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution. I can only point to examples within Alphabet that we've made public, like upgrading search ranking, data center energy usage, language translation, and solving Go, but these aren't rare exceptions internally. What I see is that almost any data processing system with non-trivial logic can be improved significantly by applying modern machine learning. This might sound less than dramatic when put in those terms, but it's a radical change in how we build software. Instead of writing and maintaining intricate, layered tangles of logic, the developer has to become a teacher, a curator of training data and an analyst of results. This is very, very different than the programming I was taught in school, but what gets me most excited is that it should be far more accessible than traditional coding, once the tooling catches up. The essence of the process is providing a lot of examples of inputs, and what you expect for the outputs. This doesn't require the same technical skills as traditional programming, but it does need a deep knowledge of the problem domain. That means motivated users of the software will be able to play much more of a direct role in building it than has ever been possible. In essence, the users are writing their own user stories and feeding them into the machinery to build what they want.