Seeing is believing? A global scramble to tackle deepfakes https://
japantimes.co.jp/news/2023/02/0
2/world/global-scramble-tackle-deepfakes/
… #fake #deepfake #faceswap #syntheticmedia #deepfakevideos #AI #cybersec #infosec #ML #NLP #Algorithms #Security #AIEthics #EthicalAI #OpenAI #100DaysOfCode #OpenSource #bot #facialrecognition #rt
MACHINE LEARNING
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Global Effort Against Deepfakes and Synthetic Media
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Deepfakes Becoming Cottage Industry: Security Implications
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Deepfakes Are Becoming a Cottage Industry! https://
spectrum.ieee.org/deepfake-cotta
ge-industry
… #fake #deepfake #faceswap #syntheticmedia #deepfakevideos #AI #cybersec #infosec #ML #DL #NLP #Algorithms #Security #AIEthics #EthicalAI #OpenAI #100DaysOfCode #OpenSource #bot #facialrecognition #rt -
Twitter as Academic Hub for Deep Learning Research Insights
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Aquí le doy toda la razón a Elon Musk. Twitter es el mejor lugar para leer entre líneas al mundo académico del Deep Learning.
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Abacus AI’s Core Research Areas in AI and Machine Learning
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At @abacusai we are doing active foundational research in the following areas – Neural architecture search
– Code gen LLMs
– Pre-trained forecasting
– NNs for tabular data
– LLMs for Enterprise AI -
ML Conference Publishing Standards and Workshop Paper Policies
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Interesting point. From that perspective, then ML conferences seem like the weird ones: because they wouldn't allow workshop papers from ICCV/CVPR becuse they're "published." But since they're <= 4 pages, then they can be submitted to ICCV/CVPR? Incompatible systems.
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Instruction tuning changes sculpting metaphor to RLHF data manifold
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Instruction tuning makes this less essential a mental model than it used to be with pre-trained LLMs. You’re still “sculpting,” but you’re usually sculpting the data manifold RLHF-evaluated generations.
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k-shot learning constrains distribution over next tokens
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It models a distribution over next tokens, yes — in a k-shot, each added example (ideally) constrains the distribution better. You’re appending to a search query in possible-text space that returns a more specific subset of texts.
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TPU Dynamic Sizes Constraints and GPU Attention Efficiency Trade-offs
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On TPUs you can't use dynamic sizes in a loop, so you use mask and static sizes, and therefore the first step of the loop is as costly as the last one. On GPUs you could have a faster first step, but the cost of attention reduction is relatively low for large models.
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Programming vs Machine Learning: Converting Processes into Value
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Programming is a way to convert an abstract description of a process into value. Machine learning is a way to convert inputs and outputs of a process you can’t describe into value. Much harder to get an ML system off the ground, but the upside is correspondingly much higher too.
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Machine Learning Practice Projects to Build Your Skills
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Sample projects you could build to practice machine learning: 1. Detect movement in a video
2. Stock trading bot
3. Track a person in a video
4. Speech emotion recognition
5. License plate recognition
6. Identify the language used in a text 1 of 2