At Netflix, we observe network and device conditions as well as aspects of the user experience (e.g., video quality) we were able to deliver for every session, allowing us to leverage statistical modeling and machine learning in this space.
Video quality adaptation duringplaybackMovies and shows are often encoded at different video qualities to support different network and device capabilities.
Adaptive streaming algorithms are responsible for adapting which video quality is streamed throughout playback based on the current network and device conditions (see here for an example of our colleagues research in this area).
Predictive cachingAnother area in which statistical models can improve the streaming experience is by predicting what a user will play in order to cache (part of) it on the device before the user hits play, enabling the video to start faster and/or at a higher quality.
The aforementioned problems are a sampling of the technical challenges where we believe statistical modeling and machine learning methods can improve the state of the art: – there is sufficient data (over 117M members worldwide)the data is high-dimensional and it is difficult to hand-craft the minimal set of informative variables…
[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: video quality, network quality, device, device reliability team, video quality adaptation[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]https://twitter.com/julsimon/status/978879094380204032[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]Using Machine Learning to Improve Streaming Quality at Netflix[/vc_column_text][/vc_column][/vc_row][vc_row el_id=”How-artificial-intelligence-and-data-add-value-to-businesses”][vc_column width=”1/2″][vc_separator][vc_column_text]
In this video, recorded at the Aspen Ideas Festival in June, Andrew Ng, cofounder of Coursera, AI Fund, and Landing.AI, discusses the difference between an AI-enabled business versus a true AI company, and how businesses can organize, hire, and make use of AI to add value.
Almost all the economic value created by AI is through one type of technology, which learns inputs, outputs, or maybe A-to-B mappings, such as you might input an email, telling you its spam or not.
Thanks to the recent rise of AI, especially supervised learning, machine learning, the set of things we know how to automate is much bigger.
I think AI will bring about a transformation of a lot of companies and even the rise of new types of companies.
For a lot of the companies, the best hope might be to try to hire one strong AI leader and then build a centralized AI organization that you can then matrix into your various business units.
[/vc_column_text][vc_column_text el_class=”topfeed-tags”]Tags: AI, centralized AI organization, AI talent, true AI company, AI Fund[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_separator][vc_column_text el_class=”topfeed-tweet”]https://twitter.com/floriansemle/status/978871632805711872[/vc_column_text][vc_column_text el_class=”topfeed-embedly”]How artificial intelligence and data add value to businesses | McKinsey & Company[/vc_column_text][/vc_column][/vc_row]