The killer app of chatbots is generating BS. The market is vast.
ETHICS
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AI as Alien Intelligence: Different from Human Inspiration
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“Artificial Intelligence is a very alien intelligence – just like birds inspired airplanes, but airplanes work very differently than birds.” — dynamite quote from @sama
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AI Technology: Powerful but Not Human Intelligence
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It has been “fed” thousands of documents up to 2021 so that you can ask about a given topic and be given a simple answer. Yes it can write in poetic form or write a one pager on climate change but it is #nothuman
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ChatGPT: Tool Not Threat to Humans, Like Thermomix for Cooks
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ChatGPT is not human rather it has been taught to write in grammatically correct and speak sounding human. It’s a threat to Wikipedia or automated telephone menus but not real people. A Thermomix is not a threat to a cook even when it nails a recipe every time @TVNaga01
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Public vs Proprietary Datasets in AI Model Training
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Thanks Alex. I like the papers that do this, but I also have some concern when this is done on a dataset that is proprietary and only Google has access (JFT). I would like to see a version that is pretrained on LAION. This still has privacy issues, but is at least all public.
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Human Touch in Customer Service vs ChatGPT Automation
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In 2014 a customer asked @Argos_Online on Twitter when he would be 'gettin da ps4 tings' referring to PlayStation 4 games consoles. #chatGPT would never reply like this. All Hail to human-generative speech
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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 -
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