Shruthi Chari (@shruthichari) header
Shruthi Chari (@shruthichari) profile picture

ML Scientist - Bristol Myers Squibb | PhD - RPI | Applied Explainable AI. Pronouns: She/Her (Views are my own)

Woburn, MA
Joined June 2012
Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)Photo by Shruthi Chari (@shruthichari)
Happy to share that version 2.0 of our Explanation Ontology has been accepted as a journal paper to the Special Issue in XAI at the Semantic Web Journal (SWJ). Link to paper: semantic-web-journal.net/con… 1/6
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Happy to share that I am now a PhD candidate and I am looking forward to a defense next year on my dissertation research titled, "An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems" #phdlife @csatrpi
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A really nice article in the WSJ today about the various use cases of AI in hospitals across the US: wsj.com/articles/how-doctors…. It helps to both understand the trends of application of AI in healthcare and identify where we can improve. Thanks for sharing @dangruen.
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Many congratulations to my advisor, Prof. Deborah McGuinness as being elected as a AAAI Fellow 2023!
Excited to share that the @RealAAAI fellows have been announced for this year and I am a recipient "for significant contributions to the semantic web, knowledge representation and reasoning environments, and deployed AI applications. @RPIScience @csatrpi @rpi
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You can download our ontology and explore our resource from our website: tetherless-world.github.io/e…. Looking forward to feedback on using the EO or queries about applying it to your own use cases. 5/6
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Thanks to the efforts of all my collaborators and co-authors from @csatrpi and @IBMResearch - @oshaniws , @mo_ghalwash, @dangruen, Sola Shirai, @pchakrabt1, @jeriscience and @dlmcguinness. 6/6
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Shruthi Chari retweeted
.@Stanford’s PubMedGPT LLM passes US Medical Licensing Exam (MedQA-USMLE) with more than 50% correct answers. crfm.stanford.edu/2022/12/15…
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Nice thread on the shortcomings of LLMs and why we must build guards around them.
1/Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence - which reflects the data they’re trained on - makes them more likely to mislead.
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Never had a failed macOS upgrade before Ventura and it is painful to get your Mac to start again -.- Maybe finally a time where I appreciate more transparent options aka Linux.
I can't believe in 2022 we're still sending reporters out in Hurricanes like this
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Happy to see an application of language models (LMs) in a high-stakes setting like the government. I like how the post author says it is to 'show, not tell' in this thread. Working on LMs, I was frustrated with how little there is outside of large corpora like Wikipedia. 1/2
Today, I testified to the U.S. Senate Committee on Commerce, Science, & Transportation @commercedems. I used an @AnthropicAI language model to write the concluding part of my testimony. I believe this marks the first time a language model has 'testified' in the U.S. Senate.
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Hope such efforts bring to light the need to focus more on reporting results and releasing models more often outside of general-purpose domains. Also, hope that AI/ML papers start asking for more rigorous and illustrative analyses outside of just quantitative numbers. 2/2
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Shruthi Chari retweeted
Unpopular Opinion: Almost all academic papers do a decent job of *what* they found, many do pretty well to describe *how* they did it, very few do a stellar job of *why* they are doing it and *why* the community needs it. @PhDVoice @OpenAcademics #AcademicTwitter #research
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A common point raised by ML reviewers is that a method is too simple or is made of existing parts. But simplicity is a strength, not a weakness. People are much more likely to adopt simple methods, and simple ones are also typically more interpretable and intuitive. 1/2
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Very excited to introduce, OpenXAI - open-xai.github.io, an open-source framework we have been building for the past year to evaluate/benchmark the faithfulness, stability, fairness of post hoc explanation methods using easy-to-use API & just a few lines of code. [1/N] #XAI
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A comprehensive thread of XAI papers presented at this year’s XAI workshop at #IJCAI2022. Do give them a read if you are doing researching in the XAI space or are looking to improve explainability of your AI methods.
And the XAI2022 workshop @IJCAIconf is underway. Tobias Huber kicks off with a talk on alterfactual reasoning. A good turn out! #IJCAI2022Workshops
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