The implementation of Microsoft's biomedical text-generation model #BioGPT is going viral on Github
V/ @alphasignalai https://
buff.ly/3RSTqe7
#AI #MachineLearning #DeepLearning @kirkdborne @Khulood_Almani @AkwyZ @Victoryabro @Fabriziobustama @MaiaGabunia @amalmerzouk
MACHINE LEARNING
-
Microsoft’s BioGPT biomedical text-generation model goes viral
By
–
-
BioGPT-Large Achieves 81% Accuracy in Biomedical Language Model
By
–
This is a domain-specific generative language model pre-trained on large-scale biomedical literature. BioGPT-Large outperformed previous existing models with 81% accuracy and achieved the “human parity”. Checkout the repo: http://
github.com/microsoft/BioG
PT
… -
AI Today Podcast: Pattern Recognition Definition and Importance
By
–
In this @Cognilytica #AIToday #podcast AI Glossary Series episode 'Pattern Recognition' hosts @rschmelzer & @kath0134 define the term #PatternRecognition, explain how it relates to #AI, and why it’s important to know about. Full episode: https://
aidatatoday.com/ai-today-podca
st-ai-glossary-series-pattern-recognition/?utm_source=dlvr.it&utm_medium=twitter
…
#data #ML #tech -
Computability Constraints Redefine Online Learning Theory Characterization
By
–
Congrats to @UWCheritonCS colleagues Niki Hasrati (
@niki_hasrati
) & Shai Ben-David (
@shaibendavid5
) on best paper at #ALT2023. Main result shows when an online learner must be computable, it's no longer characterized by Littlestone dimension. Check it out: https://
arxiv.org/abs/2302.04357 -
Theory of Mind Experiments on LLMs Show Promising Results
By
–
These theory of mind experiments on LLMs are surprising and quite fascinating (also BLOOM is pretty strong) https://
t.co/iZ2llu4vxm -
GPT’s remarkable ability to generalize with invented words
By
–
Aún recuerdo del paper original de GPT-3 lo impresionante que me resultaban los experimentos donde el modelo entendía y operaba con palabras inventadas, demostrando así su capacidad de generalizar su conocimiento. Ahora ya estamos aquí, y no deja de ser igualmente impresionante!
-
Aspect-based Sentiment Analysis: Modeling Approaches and Techniques
By
–
Aspect-based sentiment is stuff like “service: good, food: bad”. Sometimes you can model it as multiple label vectors on the text, other times it’s more like spancat or relation extraction
-
Data Engineering vs Data Science: Infrastructure and Analysis
By
–
Data Engineering deals with the practical aspects of collecting, storing, & processing large amounts of data, while Data Science involves using that data to build models and make data-driven decisions. Simply put, DE focuses on the infrastructure while DS focuses on the analysis.
-
Data Engineering: The Essential Foundation for Data Science
By
–
All paths to Data Science leads through Data Engineering.
— Shubham Saboo (@Saboo_Shubham_) 11 février 2023
Credits: @christianbdata pic.twitter.com/BgtB2T3SAIAll paths to Data Science leads through Data Engineering. Credits: @christianbdata
-
Skepticism about prompt vs human-written policy in ChatGPT RLHF
By
–
Thought about this more: It doesn’t make sense it’s the prompt, because inference costs. Still feels human-written — but why would you RLHF into reciting all policy verbatim? ChatGPT falls for the same trick and the text it recites is just parameters.