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The Smart, the Stupid, and the Catastrophically Scary
A long conversation with an anonymous veteran data scientist on AI, deep learning, FinTech, and the future.
datascience  proscons  blog  article  machinelearning  ai  ethics  future 
2 hours ago by wwwald
Lyrebird - Create a digital copy of voice
As pioneers of this technology, we believe that we have the responsibility to guide its launch to developers and the general public. We have worked hard to create principles that accurately reflect the values we espouse as technologists. We have sought the insights of machine learning researchers, our investors, ethics professors, and many others.

Our tech is still at its early stage but it will likely improve fast and become widespread in a few years - it is inevitable. Therefore the key question is more about how to introduce it to the world in the best possible manner so that the risk of misuse is avoided as much as possible. This is the approach that we consider the best:

First, we want to raise public awareness to make people realize that the technology exists by releasing audio samples from the digital voices of Donald Trump and Barack Obama.
Second, we want to ensure that your digital voice is yours. We are stewards of your voice, but you control its usage: no one can use it without your explicit consent.

Imagine that we had decided not to release this technology at all. Others would develop it and who knows if their intentions would be as sincere as ours: they could, for example, only sell the technology to a specific company or an ill-intentioned organization. By contrast, we are making the technology available to anyone and we are introducing it incrementally so that society can adapt to it, leverage its positive aspects for good, while preventing potentially negative applications.
Voice  Digital  Replicas  OnLine  WebApp  Beta  MachineLearning  AI  audio  generation 
11 hours ago by abetancort
When it comes to GIFs, size matters. Read our latest blog post on how uses models to del…
MachineLearning  from twitter_favs
15 hours ago by yolk
Live notes from 'Machine learning for enhancing cultural heritage collections' meetup, Monday Jan 8th - Google Docs
Earlier this month a group of cultural institutions in the UK got together to talk about machine learning for enhancing their collections. They published their notes in a Google Doc, which isn't super fleshed out or detailed but provides some interesting leads on other people thinking about these issues (see attendee list), a big-picture list of opportunities and challenges, and a list of tools and projects for further exploration. There are a lot of interesting ideas about how machine learning can make image-based and textual collections more accessible that could be really relevant to NARA's Catalog in the future. The doc also briefly refers to another big topic for discussion: how might the role and specific tasks of professional archivists and curators who catalog collections shift based on these new developments? In other words, how do we continue to be smart about focusing our professional resources on what humans can uniquely do that the machines cannot?
machinelearning  ai  collections  musetech 
17 hours ago by danamuses
Detectron – Facebook Research
Detectron is Facebook AI Research’s (FAIR) software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
machinelearning  facebook  opensource  ai  objectdetection  awesome  python 
19 hours ago by wjy
maps  geolocation  machinelearning 
21 hours ago by dalehumby

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