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And what happens when you apply this idea to other artworks across history? Read more here: https://pair-code.github.io/depth-maps-art-and-illusions/art_history_vis/blogpost/blogpost_1.html … And try it yourself with this interactive visualization: https://storage.googleapis.com/art_history_depth_data/demo/index.html …pic.twitter.com/n18PBMTIs1
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What does a neural net see when it looks at an Escher print? Explore illusions in an automatic depth perception system: https://pair-code.github.io/depth-maps-art-and-illusions/art_history_vis/blogpost/blogpost_2.html … From
@EllenJiang2,@emilyrreif and@_beenkimpic.twitter.com/fVKeE6YrI1
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Martin Wattenberg proslijedio/la je Tweet
What-If Tool 1.5 is now released, with full support for models that return feature-wise attributions! This accompanies the
@GCPcloud Explainable AI release. Let's dive in...#whatiftoolpic.twitter.com/Ag5GFzy6iN
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Martin Wattenberg proslijedio/la je Tweet
On Nov. 14th we'll be live at the
#PAIRSymposium to discuss transparency in AI &#participatoryML. Want to get involved?
Join the conversation by tweeting questions to Google speakers @viegasf@wattenberg@jessscon
Watch the livestream-> https://goo.gle/PAIRSymposium2019 …pic.twitter.com/u1KLceJemz
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Martin Wattenberg proslijedio/la je Tweet
Really excited to share that we launched a new version of
#TeachableMachine today! It's a fast easy way to train machine learning models on-device, in your browser. You can now save models for your projects, use sound/pose, and more. Try it out → https://teachablemachine.withgoogle.com pic.twitter.com/CTkLXHuWbdPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
UMAP is a real advance in visualizing high dimensional data. But how should we read it? And when should we use it? Take a look at this interactive essay by
@_coenen and@adamrpearce! https://pair-code.github.io/understanding-umap/ …pic.twitter.com/G6H9F0FThLHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Martin Wattenberg proslijedio/la je Tweet
Curious about AI and machine learning? Share what you want to know.
#participatoryML What do you most want to learn about machine learning?Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
“Time was up for Molly and Charlie. They had one chance, and they had to make it count.” What happens next in the story? Now what if I started, "Time was up for Deborah and Steven” instead? If you ask a neural network (GPT-2), the different answers tell a story of their own.https://twitter.com/BNW/status/1164213291411017728 …
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Martin Wattenberg proslijedio/la je Tweet
How are semantics and word senses represented in contextual word embeddings? Visualizations, blogpost, and paper by
@GreenBeanDou,@_coenen,@_beenkim,@adamrpearce,@viegasf and@wattenberg. https://pair-code.github.io/interpretability/context-atlas/blogpost/ …pic.twitter.com/o1lk2AdC4OHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
This was joint work with
@_coenen@emilyrreif@GreenBeanDou@_beenkim@adamrpearce and@viegasfPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The blog at https://pair-code.github.io/interpretability/bert-tree/ … explores the shape of these representations, with math and visualization. A mysterious “squared distance” effect they found turns out to be surprisingly natural!
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Some context: Recently
@johnhewtt and@chrmanning discovered certain neural networks (e.g., BERT) build internal geometric representations of syntax trees. It’s beautiful: the full tree is actually embedded in a high-dimensional vector space.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
How does a neural net represent language? See the visualizations and geometry in this PAIR team paper https://arxiv.org/abs/1906.02715 and blog post https://pair-code.github.io/interpretability/bert-tree/ …pic.twitter.com/ZkG81UBHcE
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One of many fascinating insights from the new baby name data that came out today. Read more at http://namerology.com !https://twitter.com/NamerologyTalk/status/1126857733200912385 …
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Designing with AI requires a big change in mindset—in my opinion, just as big as when graphical user interfaces were introduced. I'm delighted to announce the People + AI Guidebook, which lays out recommendations for designing human-first AI products.http://design.google/ai-guidebook
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What does it really mean to design for human/AI collaboration? How can doctors and ML work together effectively? If you're at CHI, go to this talk for some answers! (And if you're not, the paper's online.)https://twitter.com/Carryveggies/status/1125133135669284864 …
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Martin Wattenberg proslijedio/la je Tweet
3rd and last
#AI Award Winner@Netexplo 2019: TCAV by@_beenkim, Senior#research scientist at#Google Brain: ‘I created a tool for everyone to understand how#algorithms work’.#TCAV is Interpretable#MachineLearning for#ComputerVision and#DecisionMakingpic.twitter.com/c3NeS3wN6g
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I would recommend this essay to anyone starting to work with data, not just journalists! Data is rarely what it seems at first.https://twitter.com/kanarinka/status/1113734595646840833 …
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Bach to future! Compose your own music, with the help of the AI on today's http://google.com home page. Great collaboration with the Magenta, Doodle and TensorFlow.js teams. Congrats to all involved!
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Martin Wattenberg proslijedio/la je Tweet
The Periodic Table of Elements turns 150 years old this month and the history of its creation is astonishing. In celebration, here's a visualization our team made a few years ago: https://research.google.com/bigpicture/elements/ …pic.twitter.com/114yCPMQwX
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