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.
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 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.
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.
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.
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 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.
- 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 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 firstname.lastname@example.org.
How Differential Privacy Changed The World
and how engineering it into your pipelines can lead you to comply with legal requirements and meet consumer wants and needs.
We finally have a legal framework in India, The Digital Personal Data Protection Act 2023, which presents GDPR like requirements for Data Governance and Personally Identifiable Information Protection.
Our agenda with this talk is to look into Differential Privacy, a game changing approach to robust and mathematically rigorous data privacy preservation. We pitch it as a practical solution, and at the same time, look into associated risks and ways to cope, with the privacy-utility being the basis of the tradeoff.
As a case study, we look at how Wikipedia used differential privacy to release aggregate statistics in a privacy preserved manner.
We will discuss how with the help of libraries like PyDP, OpenDP or PyTorch Opacus and Tensorflow Privacy, one can work towards incorporating sturdy privacy practices into datasets and pipelines to serve this need. as asked for by consumers, as well as comply with legal requirements that in the coming couple of years, even startups will be asked to in a stringent manner.