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Important announcement!
Today we launch a new Twitter-like space for the AI community - Sigmoid Social.
We hope to ensure the thriving AI Twitter community can live on by maintaining this Mastodon instance going forward.
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The Gradient
913 posts
The Gradient
@gradientpub
The Gradient cuts through the hype and the cynicism to provide accessible, sophisticated reporting on the latest AI research.
sigmoid.social/@thegradient
- The war between ML frameworks has raged on since the rebirth of deep learning. Who is winning? @cHHillee's data analysis shows clear trends: PyTorch is winning dramatically among researchers, while Tensorflow still dominates industry. #PyTorch #Tensorflow
- Overhyped claims about AI have contributed to past AI winters. @GaryMarcus fears that we could be headed down that same path again. Here's what we can do to stop it. #ai #hype
- Graph Neural Networks are one of the most important inventions in modern day machine learning. In our latest piece, Deepmind Oxford professor @mmbronstein gives his thoughts on the future of the field.
- ImageNet is a mainstay of modern computer vision research. Can we build such a resource for natural language? As @seb_ruder observes, “The time is ripe for practical transfer learning to make inroads into NLP.”
- "Deep learning has blown past its competition" but will it continue to scale towards increasingly difficult problems? @chenxi116 and Alan discuss what might lie ahead for visual deep learning.
- On episode 6 of The Gradient podcast, we are excited to have interviewed Deep Learning pioneer @ylecun!🎙️🔊 We touch on his start on #AI research and some exciting recent work on Self-Supervised learning for vision from @FacebookAI. Take a listen:
- Deep learning has made enormous strides in NLP, but state-of-the-art models are still spurious and brittle. @anmarasovic breaks down the problem and shares three ways we might solve it:
- Network graphs, like social media, are rich with useful insights. So why is this information often ignored when applying machine learning? "The answer, for the most part, is that it’s notoriously challenging to extract meaningful features from graphs."
- “As my watercolor teacher used to say: let the medium do it. My sketch simply provides the foundation, and then I let the network do its thing.” Helena Sarin, an artist who works with GANs, reflects on her creative process:
- Astounding progress in AI has led to speculation AI will cause explosive economic growth. @arjun_ramani3 and @zhengdongwang argue that such “transformative economic impact” from AI is much harder than at first glance.
