Dance Diffusion is now on Replicate. It's a diffusion model from @harmonai_org that generates music:
AI
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Fashion-MNIST: 28×28 Single Channel Image Dataset Overview
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fashion-MNIST images are 28×28 single channel.
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AI NFT Games: Combining Artificial Intelligence with Digital Assets
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"AI NFT Game" An AI NFT Game uses NFTs and AI. The use of #NFTs in the game allows players to own and trade unique digital assets, adding an element of scarcity and value to the game. AINFTGame.Eth | AGIGame.Eth | AGIGames.Eth #AINFTGame #AGIGame #AGIGames #NFTCommunity
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AI Today Podcast: Autonomous Systems and Levels of Autonomy Glossary
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In this @Cognilytica #AIToday AI Glossary Series #podcast episode 'Autonomous Systems' hosts @rschmelzer & @kath0134 define Autonomous Systems, as well as related terms including Levels of #Autonomy, and #autonomousvehicle. Full episode: https://
cognilytica.com/2022/12/14/ai-
today-podcast-ai-glossary-series-autonomous-systems/?utm_source=dlvr.it&utm_medium=twitter
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IBM Research Explores Deep Learning Methods to Reduce AI Bias
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#AI bias is more than just unfair – it can amplify social inequalities and create distrust in technology. Using #deeplearning, @IBMResearch are exploring ways to reduce this bias in large pre-trained AI models: https://
ibm.co/3BxXObP -
5G Innovation Enables Fleet Automation and Sustainability
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Meet @kornehed Founder @einrideofficial #Forbes 30 Under 30 & one of the @Ericsson #5GTrailblazers ! See #5G #innovation in #fleets enabling #automation + #Sustainability by #design ! https://
bit.ly/3SWW9mq #SupplyChain #EricssonAmbassador #womenintech #logistics #AI -
ImageWoof vs Fashion-MNIST: Neural Network Architecture Considerations
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ImageWoof is much harder, and uses proper RGB images of a useful size! fashion-MNIST is interesting though exactly because they're not normal photographic images. So you gotta think about that carefully when creating your architecture.
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Privacy in AI: Beyond Training and Model Usage
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And finally, privacy is… hard! While a lot of work focuses on training and using models privately, this is a narrow view of privacy, which encapsulates much more. 14/n
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Private ML Benchmarks and Privacy-Focused Evaluation Standards
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The second is to re-focus towards benchmarks that are more appropriate for private ML. We now understand that public data can help for private CIFAR-10 and ImageNet classification, which is great. But maybe we should move towards settings where privacy is more important. 13/n
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Privacy-Respecting Public Pre-Training Datasets for AI Models
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So where do we go from here? We conclude with a number of suggestions for the field. The first ones focuses on making sure we have public pre-training sets which are truly privacy-respecting. Can we make such a dataset/model with comparable utility to what people use now? 12/n