This is a Call for Proposals to discuss papers.
This group seeks to curate sessions and discussions around the papers related to the domain of Artificial Intelligence, Machine Learning, Deep Learning, and Large Language Models - be it the research, applications, and surveys around landscapes relevant to these.
Those interested can propose a session under the submissions tab above by highlighting the paper of their choice with a small gist on why they want to discuss this paper keeping in mind the below guidelines.
Topics under which we seek papers to discuss
- Artificial Intelligence, Robotics, Reinforcement learning research and applications
- Machine Learning and Deep Learning research and applications.
- Large language models, Multimodal models, and Large Visual Models
- Advances in Hardware and Infrastructure to handle data science operations and workloads
- Best practices to be taken into consideration around:
-- implementation, training
-- data augmentation
-- inference deployments and
-- applications wrt safety, ethics, security, etc.
Selection process - what criteria should we use for selecting papers and finalizing the sessions?
- The paper being taken up for the session must be highly cited/reviewed. It should be one of the popular and key papers in the domain of AI/ML/DL and LLMs.
- The presenter must prepare the slides that simplify the paper into easily understandable essence and topics to focus on.
- Code notebooks to show how the concepts in the paper can be applied are useful and encouraged most of the time.
- Review of the slides and checks on understanding of the paper and the relevant material will happen before confirmation.
- Once this is done, a discussant from the relevant domain will be matched with the presenter to anchor the discussion and session.
About the curators
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Bharat Shetty Barkur has worked across different organizations such as IBM India Software Labs, Aruba Networks, Fybr, Concerto HealthAI, and Airtel Labs. He has worked on products and platforms across diverse verticals such as retail, IoT, chat and voice bots, ed-tech, and healthcare leveraging AI, Machine Learning, NLP, and software engineering. His interests lie in AI, NLP research, and accessibility.
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Simrat Hanspal, Technical Evangelist (CEO’s office) and AI Engineer at Hasura, has over a decade of experience as an NLP practitioner. She has worked with multiple startups like Mad Street Den, Fi Money, Nirvana Insurance, and large organizations like Amazon and VMware. She will anchor and lead the discussion.
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Sachin Dharashivkar is the founder of AthenaAgent, a company that creates AI-powered cybersecurity solutions. Before this, he worked as a Reinforcement Learning engineer. In these roles, he developed agents for doing high-volume equity trading at JPMorgan and for playing video games at Unity.
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Sidharth Ramachandran works at a large European media company and has been applying text-to-image techniques as part of building data products for a streaming platform. He is also a part-time instructor and has co-authored a book published by O’Reilly.
About The Fifth Elephant
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Contact
For inquiries, leave a comment or call The Fifth Elephant at +91-7676332020.
HA
Harini Anand
PWL MAY 2024: Med-PaLM M: A generalist biomedical AI system that flexibly encodes and integrates multimodal biomedical data
Submitted Feb 29, 2024
About the Paper:
- Medicine is inherently multimodal and clinicians interpret data from a wide range of modalities when providing care, including clinical notes, laboratory tests, vital signs and observations, medical images, and genomics.
- Since issues like clinician burnout and lack of access to quality healthcare were rising, the emergence of foundation models proved to be a turning point in addressing and solving these global problems.
- Med-PaLM M is a large multimodal generative model that interprets biomedical data spanning clinical language, medical imaging, genomics, and more performing competently on a diverse array of tasks - all with the same set of model weights.
- It is built by fine-tuning and aligning PaLM-E - an embodied multimodal language model developed by the researchers at Google AI using MultiMedBench, a newly curated open source biomedical benchmark containing 14 diverse tasks such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling.
- The future of AI in medicine and bio is incredibly exciting! This paper dives deep into why that is the case, so do join in :)
Link to the paper: https://arxiv.org/pdf/2307.14334.pdf
Key Takeaways:
- How LLMs help scale up world-class healthcare on a global scale.
- Understand the importance of AI in clinical workflows and how it exponentiates efficiency and utility with its multimodality.
- Demonstrate the importance of biomedical fine-tuning and alignment.
- Learn about zero-shot generalization to novel medical concepts and tasks, positive transfer learning across tasks, and emergent zero-shot medical reasoning.
- Build intuition for developing large-scale generalist biomedical AI with language as a common grounding across tasks which shows the possibility of combinatorial generalization and positive task transfer.
About the Speaker:
Harini Anand is a CSE Undergrad with a keen interest in Computational Cognition. She is working at Niramai Health Analytix, a deep tech startup focusing on Breast Cancer, and is using ML frameworks as a research intern at the Indian Institute of Technology Hyderabad, to predict gene regulatory networks. She has previously worked on building cognitive tools for reducing the onset of Dementia as a student entrepreneur.
She leads the largest technical community on campus and also has imparted knowledge to peers through workshops on Machine Learning, NLP, and Data Science. She’s been admitted to top summer schools at Oxford University, London, and Massachusetts Institute of Technology which specialize in the applications of AI in Healthcare. She is a strong advocate for representation in STEM and is recognized as a High Impact APAC Ambassador for the Women In Data Science Community, an initiative by Stanford University. She is also a Harvard WE Tech Fellow.
To know more:
https://www.linkedin.com/in/harini-anand-2002/
https://twitter.com/anscombes4tet
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