The Fifth Elephant 2023 Winter
On the engineering and business implications of AI & ML
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Pinned update
Reminder about today's sessions - Papers Discussion and Book Club This update is for participants only
The Fifth Elephant Winter Edition videos are available to watch here
Event highlights:
Why Large Language Models are important in AI
In 2020, OpenAI released a Large Language Model (LLM) called GPT3 which has a billion parameters. With a minimal and intuitive user interface which was released to go with GPT3, it caught the imagination and attention of AI communities and researchers all over the world.
One by one, the domain use cases such as co-pilots for coding, creative AI, and other downstream tasks were shown to be fast-tracked by GenerativeAI models and LLMs. As such, there is a wide-ranging interest in large language models and applications around them for various domains and use cases in the AI space. Experiments which aim to find optimal hyperparameters, and those dealing with underfitting and overfitting models are being carried out regularly; more and more barriers are being broken down every day.
The Fifth Elephant 2023 Winter edition will cover topics on the research, engineering, and business aspects of AI, exploring the practical implementation and economic implications of these systems.
Themes
The winter edition of The Fifth Elephant will showcase talks, discussions and demos across generative and multimodal AI, and other classic AI/ML/DL applications on the below themes.
AI engineering Track
Share approaches and case studies covering the following use cases:
- Products and platforms using LLMs, GenerativeAI, ML, and Deep Learning techniques, and business formulation around AI engineering.
- Conversational AI and search, automatic speech recognition, healthcare, e-commerce, fintech, media and OTT, and other verticals.
- Multilingual needs in India in digital products/platforms - features discussions, models training, finetuning, RLHF, RAGs, quantization techniques, dataset curation and augmentations, challenges faced in pipelines, evaluation metrics, future roadmaps, applications such as multilingual voice bots using ASR/STT, text to speech for accessibility.
Data Science operations track
Share case studies and experiential talks on handling the operations for data science such as scaling challenges and fine-tuning challenges, and lessons learned, and best practices for incorporating ethics, safety, and bias.
Demo track
Show demos on features/products which leverage AI and LLM-based APIs and models. It can be from creative AI, generative AI space, and various verticals with relevant use cases.
The Fifth Elephant membership
The December edition will be held in-person. Attendance is open to The Fifth Elephant members only. Pick a membership to attend the in-person conference, and to support The Fifth Elephant’s community activities.
Who will benefit from participating in The Fifth Elephant community:
- AI/ML/Data Science Ops engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as health care, e-commerce, automobile, agri-tech and industrial verticals
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders and CTOs who are building products and platforms that leverage AI, ML and LLMs
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies who want to do tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
If you are interested in sponsoring The Fifth Elephant, email sales@hasgeek.com.
Contact information
Join The Fifth Elephant Telegram group on https://t.me/fifthel or WhatsApp group. Follow @fifthel on Twitter.
For inquiries, call The Fifth Elephant on +91-7676332020 or leave a comment here.
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Supported by
Sponsor
Microsoft for Startups Founders Hub is a digital ecosystem removing barriers to building a company with free access to technology, coaching, and support for founders in any stage of development. Let us accelerate your startup journey from idea-to-exit. Find out more here: https://startups.microsoft… more
dk
dhruvil karani
@althypothesis
Solving bias in recommender systems using negative sampling
Submitted May 12, 2023
Problem
Recommender systems suffer a major deficiency in their feedback loops.
When a user interacts with only a few out of many items on a website, we can only assume their interest in those specific items. Hence, the feedback is biased.
Regrettably, we don’t possess data on the items the user didn’t engage with, including those that weren’t presented. Our data only pertain to the positive class, and no explicit records of the negative class regarding binary classification exist. As a result, the model performs well for a small portion of items but not for the majority.
In addition, classical algorithms like matrix factorization do not directly support cold-start settings.
Implication
Every marketplace/social-media platform having millions of items/content pieces experience such a skew. Rarely a model can ensure consistent quality training for all items. In no time, this skew induces into the recommender system and hampers its performance.
Solution
The above two problems can be solved by producing negative examples using negative sampling.
Outline
In this talk, I wish to answer the following.
- Defining the problem with biased feedback. What happens if it is not carefully handled?
- How practical is this problem? What could be the potential business impact?
- What is negative sampling? Why should it work?
- What happens under the hood (accompanied by an example case study)?
- How to get the most out of this technique (tuning ideas)?
- How to measure the impact of negative sampling?
- Getting creative (Implementation from YouTube, Play Store, Meta, Airbnb)
Comments
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
Supported by
Sponsor
Microsoft for Startups Founders Hub is a digital ecosystem removing barriers to building a company with free access to technology, coaching, and support for founders in any stage of development. Let us accelerate your startup journey from idea-to-exit. Find out more here: https://startups.microsoft… more
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