Kent Oliver Bhupathi, MEcon

This is going to enable stunning levels of survey fraud. AI can now literally just look at the computer screen and take the survey.

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News from Meta's FAIR AI team! They're publicly releasing five new models: 1️⃣ Meta Chameleon: An open-source multimodal model that can process and generate both text and images. 2️⃣ Multi-Token Prediction: A new approach to build better and faster LLMs by using multi-token prediction. 3️⃣ JASCO: Enhances control over generated music outputs by accepting various inputs, such as chords or beats. 4️⃣ AudioSeal: The first audio watermarking technique designed specifically for localized detection of AI-generated speech. 5️⃣ PRISM Dataset: Provides insights into dialogue and preference diversity, fostering inclusive AI development and broader participation. #AI #GenAI #Innovation #TechNews #Meta #FAIR

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More great AI tips from Joel Anderson below. If you are attending ESOMAR in Athens and listening to all the talks in the uses of synthetic data, I encourage you to follow Joel and read his post or check out our Dig Insights events page and watch the “Between Two Joel’s” webinars. Useful, practical, and unbiased.

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Humans outperformed GenAI in Strategic thinking Here's everything about this new paper... When released, Powerful LLMs sparked a theory. People wondered if they were better than humans at even important tasks. This paper gives you a valid result on that Theory, 📌 This paper assesses the creative and strategic skills of ChatGPT and Bard versus humans. ↳ For testing, 4,000+ participants: including more than 3,000+ humans, 2 AI chatbots were used. ↳ All Human participants and LLMs were given two tasks: 🎨 Creativity Test:  ↳ Participants need to create creative answers to open-ended questions like:  ↳ "Describe a town, city, or society in the future." ↳ "If you had the talent to invent things just by thinking of them, what would you create?" ♟️ Strategic Test: ↳ For this test, Participants were given a game to play Rock-Paper Scissors. ↳ Both humans and AI had to adapt their strategies based on the opponent's moves. ↳ They were provided with a Balanced and Unbalanced method for this test. - Balanced Treatment: The opponent randomly plays rock, paper, or scissors, creating a fair, predictable game. - Unbalanced Treatment: The opponent has limited moves (rock, paper) to make the game unpredictable. ↳ They were checking the Frequency of suboptimal ’rock’ choices in the last 12 rounds. ↳ Though competitors were clear, they created an interesting division for competition. They divided the contenders into HumanBaseline, HumanPlusAI, Bard, and ChatGPT4. So, what were the results: 📌 Let's learn from the numbers: In creativity tasks, ↳ ChatGPT generated 8 of the top 1% of best-rated answers.  ↳ While humans using ChatGPT produced 3%. ↳ And Humans only generated 6%. ↳ The results even show that the GPT was able to come up with better answers than even Bard, which was an older model. In Strategic tasks, ↳ The Balanced treatment showed no significant difference in earnings between Humans and ChatGPT-4. ↳ In the Unbalanced treatment, human players scored more points than ChatGPT-4 (p < 0.001). 💭Personal Thoughts: ↳ I think ChatGPT-4 was able to perform better than Humans because of a lack of Cognitive Bias. ↳ People tend to write or state things from what they've been inspired from, this makes each output different. ↳ Humans can learn to think longer and learn from past Experiences. This makes us excellent strategic thinkers ↳ Since, this experiment was done before 01 release, i think that's why few stats for GenAI are lower. ↳ Otherwise, with the current pace; I think we will soon see GenAI producing Human level Outputs. What do you think about this comparison ? Let me know in the comments below 👇 Every day I cover the latest happening in the AI Industry, Make sure to follow to not miss out! If you liked this post, Please share ♻️ it with your network! #genai #humanvsai #ai #llm #chatgpt #bard

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"The findings suggest that LLMs might be better at learning by example and discovering patterns in data than at following explicit instructions. This has important implications for the use of LLMs in real-world scenarios. While on the surface, LLMs might show impressive abilities to follow logical instructions, there is a great chance that they are just following patterns they observed during their training, which means their performance will degrade as soon as the examples they see deviate from their training distribution.  On the other hand, SolverLearner provides a framework that ensures the model learns the correct rules that map the inputs to the outputs. However, SolverLearner is only applicable in settings where a verification mechanism such as a code interpreter is available. "

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