The Fifth Elephant Winter Edition videos are available to watch here
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.
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.
Forecasting Architecture, Metrics and Learnings @ Samsung Ads
Samsung Ads is an intuitive audience platform that delivers meaningful experiences reaching the right audience across screens, formats and devices. With more than 900M Mobiles and 150M Smart TVs, and the largest first party data set powered by ACR, we help marketers reach targets and enhance experiences that span digital landscapes. The business has grown 10x since 2015. Our foundation is based on Samsung’s strength as a manufacturer in two key connected consumer device spaces: Mobiles & Smart TVs ...from which we derive two critical components that power our Samsung Ads businesses today: Data and Ad Impressions. We combine these assets to create powerful Ad offerings that drive reach, performance, and return on Ad spend for the world’s leading marketers.
Samsung Ads needs to sell to advertisers in advance to show advertisements. The Ad opportunities depend on user behaviour – users turning the TV on and going to specific screens. Having an automated way to forecast availability opportunities is critical to:
• Ensure we do not over-commit to advertisers (monetary implications + hurts reputation)
• Ensure we do not under-commit (opportunities are wasted, potential revenue wasted)
• Ensure users are able to self service the forecast process (tap into a larger market segment that wants self service
Our goal is to predict how many impression events will be received for a specific campaign over the duration of the campaign. The campaigns are setup to target opportunities based on various criteria including Location, Time of day, TV Model, Type of ad opportunity and User identifiers.
Forecasting is a complex problem that typically involves a single time series, and to predict for one step ahead. We have many different time series patterns, and multi-step forecasts with a long forecast horizon (over 90 days). This requires the use of sequence to sequence models and modern techniques such as transformer architectures. We are building this solution using the state of the art Temporal Fusion Transformer models. We will go over the different type of Ads such as Roadblocks, Audience Take overs, Rotationals and Video Ads and the factors affecting forecasting.
We will go over the key challenges faced in coming up with a working model architecture, such as erroneous ground truth data, data availability & quality issues and data understanding gaps, and our approaches to deal with these challenges. We will go over the use of data sketches and an OLAP DB like druid to get past data and use that and other features as inputs to a TFT model. For modeling external competition, we will explain how we estimate price dependence of win rates using survival models. We will also introduce the evaluation framework built to evaluate the forecasting accuracy at three different phases - during development, pre-release and post release. Earlier, the analysis was done manually which had many challenges like lack of consistency, delays, lack of historical data etc. which were solved with the evaluation framework.