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.
- 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.
- 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.
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.
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.
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.
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.
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Language Models are Few Shot Learners
Language Models are few short learners is an important paper in the space of GenerativeAI and Natural Language Processing. It introduced GPT-3 and showed the capability of large language models to generalize as task-agnostic learners.
The paper sowed the seeds for building NLP applications by prompting large language models with zero-shot, one-shot, and few-shot learning prompts. This was a huge advancement from task-specific modeling and also closer to how the human brain works by applying past learning to new data.
GPT-3 used the similar but scaled-up(100x) model architecture as GPT-2 except for the use of Sparse Attention (introduced in the Sparse Transformer paper).
The paper talks in great detail about the result and impact.
In this session, I will provide a condensed and simplified understanding of the key points and takeaways from this long paper.
Simrat has a career spanning over a decade in the AI ML space, specializing in Natural Language Processing.
Currently spearheading AI product strategy at Hasura and has led AI teams at renowned organizations such as VMware, FI Money, and Nirvana Insurance in the past.