K1st World 2022 On Demand

Nov 16

Keynotes

Technical Sessions

Session 1: Physics-Inspired ML

Wed
11:10 am
LACC Sequoia
Jude Shavlik, PhD
(Emer.) Professor of Computer Science, University of Wisconsin-Madison

Listen to Dr. Shavlik's decades of research on creating machine learning algorithms that accept instruction beyond input-output pairs. Several algorithms and associated experimental results will be presented, along with shortcomings and challenges. The impact and role of the amazing recent large language models, such as OpenAI's GPT-3, which widely "read the web," and hence read concrete facts as well as general knowledge.

Wed
11:35 am
LACC Sequoia
Jeremy Coupe, PhD
Aerospace Engineer Lead, NASA

This talk will describe the ML Airport Surface Model and discuss how Dr. Coupe and his team developed prediction models that best support FO and ATC decision making with human-in-the-loop. We will show how we leverage a combination of human input and ML models to generate delay predictions which are used to recommend reroute opportunities to the FO. The talk will conclude with lessons learned throughout the deployment cycle.

Session 2: Human-Centric & Medical AI

No items found.

Keynote

Technical Sessions

Session 3: Industrial AI Applications

Wed
2:25 pm
LACC Sequoia
Roshan Nanu, PhD
Head of Product-Engineering, Aitomatic

The commercial fishing industry is extremely harmful to our world's oceans and under-sea ecosystems. In order to reduce the impact of industry on the environment, a more sustainable method of fishing, fixed-net fishing, exists, but hasn't been able to be profitable until now. AI enables fishermen to tell when the most profitable catch is within the net. However, the data needed to training this AI through traditional methods would take years to acquire. Instead, by incorporating generations of fisherman knowledge into the AI system, this system became possible now.

Wed
3:15 pm
LACC Sequoia
Seiya Miyazaki, PhD
Panasonic Cold Chain Solutions PM
R. Chiewvanichakorn
Panasonic Cold Chain Solutions DS

We introduced the combined system of machine learning models and diagnostic algorithms based on machine expertise to analyze possible causes of detected anomalies. The combined system effectively enhanced the precision of detecting failure events of cold chain equipment by 30% and reduced the daily workload of operation engineers for monitoring equipment condition by 89%.

Wed
3:40 pm
LACC Sequoia
Hiro Kutsumi
Panasonic Group Director, Digital & AI Technology Center
Takashi Kawamura
Panasonic Group Manager, Digital & AI Technology Center

Panasonic, leader in rechargeable batteries, automotives and avionic systems, industrial systems, is transforming its business through knowledge-driven AI. This is being achieved by leveraging their greatest asset, domain expert knowledge, and incorporating it systematically into the AI systems. This has resulted in higher accuracies at scale across multiple use cases such as Cold Chain Predictive Maintenance, Energy Optimization and Forecasting and overcome some of the practical challenges faced due to data-related issues and data-centric-ML that have plagued AI performance in the past. In this talk, Mr. Kutsumi and Mr. Kawamura will detail out the Panasonic way of building impactful knowledge-driven-AI solutions at scale.

Session 4: Ethical & Secure AI

Wed
3:40 pm
LACC Birch
Olaf Witkowski, PhD
Director of Research, Cross Labs

Investing early into complexity and intelligence sciences, we present a new model for AI research to investigate the integration of biological and artificial intelligence in the age of information. We designed new institutional structures and an international network of artificial life scientists, driving a new vision of the tech ecosystem through strong fundamentals rooted in academic research.

Keynote

Wed
4:45 pm
LACC Sequoia
Ovidiu Daescu, PhD
Head of Computer Science Department, University of Texas

In general, AI models target to match the human when making a choice, yet when the choices diverge, there is often no secondary method to verify a claim. The question then arises of what the right answer is, and how human and machine can learn from each other in deriving the right answer. This talk will address that question in the context of medical applications.

Nov 17

Keynotes

Thu
9:00 am
Stanford Faculty Club, Redwood
Jeannette Wing, PhD
EVP for Research and Professor of Computer Science, Columbia University

Calls for trustworthy AI are widespread from governments, industry, academia, and civil society. Purely data-driven approaches to AI development face well-known barriers to trustworthiness, so one might hope that a knowledge-first approach could overcome these challenges. In this talk, Dr. Wing will explore the nature of trustworthiness — what do we want from a trustworthy AI? — and the strengths and weaknesses of different approaches to building AI with respect to producing that trustworthiness. I will argue that all current approaches to building trustworthy AI face significant challenges, but integration of different approaches holds enormous promise.

Technical Sessions

Session 5: Applied Knowledge-First AI

Thu
11:35 am
Stanford Faculty Club, Redwood
Jerry Deng
General Manager of BI, Panasonic

As an increasing amount of internal and external business data become available, it is no longer sufficient to maintain an “analytic” competitive advantage by traditional data analysis. Meaningful utilization of modern AI/ML techniques, coupled with business-focused implementation, is the key recipe to build competitive advantage through data. In this session, Mr. Deng will share an ML implementation example with emphasis on the internal business user experience.

Technical Sessions

Session 6: Applied Knowledge-First AI 2

Thu
1:30 pm
Stanford Faculty Club, Redwood
Pardhu Gunnam
CEO & Co-Founder, Metaphor Data

The manufacturing and IoT industries are undergoing a rapid digital transformation by adopting state-of-the-art data infrastructures. However, many companies struggle to realize value from their heavy investments and find their people and processes are completely isolated from the technologies. A new approach to data governance that combines data with domain knowledge is needed in order for these companies to fully achieve the promise of data and machine learning. In this talk, Mr. Gunnam will cover a real-world use-case of a manufacturing firm's knowledge-first data governance using Metaphor—a modern metadata platform created by veterans from LinkedIn, Facebook, Google, and Amazon.

Thu
1:55 pm
Stanford Faculty Club, Redwood
Doan Ha, DrPH
Director of Operations, Koidra

There is a growing concern that humans are being replaced by automation, artificial intelligence, and other technology breakthroughs. Technology innovation is being viewed often as an onslaught on the current workforce. Will AI and automation replace our farmers? AI and humans can co-exist in a symbiotic way. Learn how AI collaboration with farmers not only yields positive business results but also elevates growers' well-being.

Keynote

Thu
2:30 pm
Stanford Faculty Club, Redwood
Rajamani Sambasivam, PhD
Chief Data Science Officer, Petronas

In the world of AI, there are two schools of thought: Data-centric AI, and Knowledge/Human-centric AI. With the latter, in addition to data, human intelligence, expertise, scientific knowledge and real-life learning need to be explicitly embedded in AI algorithms, for the models to become more accurate and derived insights to add further value to the human intellect. The first approach is widely followed and easily understood. With the advent of decision science, the second approach is gaining gradual traction and its real potential has just started to be realized by the AI world.

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