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Digests » 141
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A blog post on how two developers try to save elephants and rhinos by building an AI driven anti-poaching camera trap for national parks.
There has been no shortage of developments vying for a share of your attention over the last year or so. However, if you regularly follow the state of machine learning research you may recall a loud contender for a share of your mind in OpenAI’s GPT-3 and accompanying business strategy development from the group. GPT-3 is the latest and by far the largest in OpenAI’s general purpose transformer lineage working on models for natural language processing.
Machine Learning will become a standard tool in the toolbox of digital amp modeling. Deep Learning in particular is almost perfectly suited for the challenge of jointly optimizing an end-to-end non-linear system on unstructured data.
Designing a personalized ranking system for more than 2 billion people (all with different interests) and a plethora of content to select from presents significant, complex challenges. This is something we tackle every day with News Feed ranking. Without machine learning (ML), people’s News Feeds could be flooded with content they don’t find as relevant or interesting, including overly promotional content or content from acquaintances who post frequently, which can bury the content from the people they’re closest to.
Recently, we translated the predictive power of machine learning (ML) into $1.7 million a year in infrastructure cost savings by optimizing how Dropbox generates and caches document previews. Machine learning at Dropbox already powers commonly-used features such as search, file and folder suggestions, and OCR in document scanning. While not all our ML applications are directly visible to the user, they still drive business impact in other ways.