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Blog - Privacy - Lyrebird
While we're talking about AI generated voice, here's a true gem: Lyrebird's "ethics" statement that essentially says *don't abuse this, and be glad we published this and no one with worse intentions*. Or in their own words: "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."
newsletter  AI  voice  ethics 
32 minutes ago by thewavingcat
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
AI in medicine: Outperforming humans since the 1970s | EVOLVING ECONOMICS
A confusion matrix is a way to graph the errors and which directions they go.
ai  medicine  ruffian 
3 hours ago by Walpole
The End of Web Forms – UX Design Collective
To stay ahead of the game, UX designers should begin to ponder future scenarios filled with chatbots and conversational UIs. We will need to optimize the experience and figure out the use case scenarios. Are the interaction steps as smooth and efficient as possible? What is the tone of the chatbot? Are we reducing the time it takes to get things done or increasing it? With biometric ID verifications coming, what is that interaction going to look like? How can we serve our users better and deliver amazing experiences that make life easier?
ux  ai  forms 
8 hours ago by danamuses
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
Detectron - FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet
Facebook AI Research's 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.

At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, and Data Distillation: Towards Omni-Supervised Learning.
machine-learning  AI  opensource  Facebook  algorithms 
14 hours ago by liqweed
Forbes Welcome
RT : Great piece about in the enterprise by 's own via
AI  from twitter
16 hours ago by atxryan

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