How do you feel about taking financial advice from an AI chatbot?
ETHICS
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The Importance Of AI Policies For The AI Revolution
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The Importance Of AI Policies: Laying The Foundation For The AI Revolution
#AI #AIio #BigData #ML #NLU #Futureofwork @briansolis @Xbond49 @VitalikButerin @DataScienceCtrl @Benioff @johnnosta @IrmaRaste @Tiffani_Bova http://
ow.ly/JZqN30swTuh -
Natural Stupidity versus Artificial Intelligence Comparison
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Natural stupidity vs #ArtificialIntelligence Credits: @PawlowskiMario #FridayThoughts #AI #chatgpt3 #MachineLearning #ML #chatgpt #chatbot #innovation #tech #CES2023 #FutureofWork
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Understanding Model Hallucinations in AI Systems
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What is a model hallucination? Model hallucinations are when you ask a factual question, like "how many dogs are in the state of Illinois in the United States?", and the model generates something that appears to be a potentially valid answer but is entirely made up.
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Understanding AI Alignment: Post-Training Model Refinement
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What is alignment? Alignment is a process done after training and consists of taking a base model and making it more beneficial to a human. You can go through this alignment process to make the model more in tune with what humans want to see.
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RLHF: Aligning AI Models with Human Preferences
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What is RLHF? In some sense, RLHF is part of that alignment process, where you tune the model to look at a bunch of human preferences.
You can show a bunch of example outputs and then let users, actual humans, decide what output is better for them, and you tune the model. -
Optimal resource use aligned with values and goals
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Success is the optimal use of your resources considering your values, goals, and priorities.
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Meta Releases FACET: Fairness Benchmark for Vision Models
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We’re also releasing FACET, a comprehensive benchmark dataset to evaluate fairness of models across different vision tasks involving people. It consists of 32K images, labeled by expert annotators for demographic & physical attributes.
— AI at Meta (@AIatMeta) 31 août 2023
Explore FACET ➡️ https://t.co/XWXjlY9eV7 pic.twitter.com/a3OtCiNsaSWe’re also releasing FACET, a comprehensive benchmark dataset to evaluate fairness of models across different vision tasks involving people. It consists of 32K images, labeled by expert annotators for demographic & physical attributes. Explore FACET https://
bit.ly/3syYHPy -
DINOv2 License Expansion and FACET Benchmark for Vision Fairness
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Today we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models.
— AI at Meta (@AIatMeta) 31 août 2023
More details ➡️ https://t.co/fDHYNpGrta
🧵 pic.twitter.com/dOXDWOLKSYToday we’re announcing two new updates in our computer vision work — a new, expanded license for our DINOv2 model and the release of FACET, a comprehensive new benchmark dataset to help evaluate and improve fairness in vision models. More details https://
bit.ly/3L35E1U -
Balancing automation and workplace tension in AI era
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finding the right balance overtime & reducing the tension between the two is the challenge IMO