Dr. Suchi Saria presents “Safe and Reliable AI: Overview and Novel Approach Approaches for Learning and Monitoring” at the AI Seminar (April 16, 2021).

The Artificial Intelligence (AI) Seminar is a weekly meeting at the University of Alberta where researchers interested in AI can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems, are explored.

Abstract: As the use of machine learning in safety-critical domains becomes widespread, the importance of proactively addressing sources of failure and evaluating model reliability has increased. Achieving this, however, can be difficult because the performance and reliability of ML models are vulnerable to being overly dependent on the “context” (i.e., artifacts specific to the training dataset) on which the model was trained. In this talk, we will overview work in this area over the last five years and describe in more detail two example state-of-the-art approaches tackling challenges in building safe and reliable AI. The first describes causally-inspired learning algorithms which allow model developers to specify potentially problematic changes in context and then learn models which are guaranteed to be stable to these shifts. The second tackles monitoring for safety: to be able to evaluate the stability of a model to changes in setting or population, typically requires applying the model to a large number of independent datasets. Since the cost of collecting such datasets is often prohibitive, we will describe a distributionally robust framework for evaluating this type of robustness using the fixed, available evaluation data.

This talk will be jointly presented by Prof. Suchi Saria and Adarsh Subbaswamy.

Bio: Dr. Suchi Saria is the John C. Malone Associate professor of computer science and statistics at the Whiting School of Engineering and of health policy at the Bloomberg School of Public Health. She is also the founding Research Director of the Malone Center for Engineering in Healthcare at Hopkins and the founder of Bayesian Health, which leverages state-of-the-art machine learning and behavior change expertise to unlock improved patient care outcomes at scale by providing real-time precise, patient-specific, and actionable insights at the point of care.

Recently, Dr. Saria won a grant from the FDA and is collaborating with them in the development of frameworks for the evaluation of safety and reliability of AI. She was named by IEEE Intelligent Systems as Artificial Intelligence’s “10 to Watch” (2015), MIT Technology Review’s ‘35 Innovators under 35’ (2017), World Economic Forum’s Young Global Leader (2018), DARPA Young Faculty Awardee (2016) and a Sloan Research Fellow (2018). She was invited to join the National Academy of Engineering’s Frontiers of Engineering (2017) and the National Academy of Medicine’s Emerging Leaders in Health and Medicine (2018). She has given over 250 invited talks and is on the editorial board of the Journal of Machine Learning Research.

Artificial Intelligence and Experience Series (AIEX):
“Do People Perceive Machines as Moral Agents?”
Bert F. Malle
Department of Cognitive, Linguistic, and Psychological Sciences Brown University

October 25, 2018

Dr. Anima Anandkumar, Professor at the California Institute of Technology, delivered a talk titled “Infusing Structure into Machine Learning Algorithms” on March 15, 2019 in Ann Arbor, Michigan as part of the Michigan Institute for Data Science(MIDAS) Seminar Series.

In this webinar the spekers first analysed the state-of-the-art in the use of artificial intelligence for judicial purposes. We answered the following questions:

1. Where and how is artificial intelligence being used for judicial purposes? Example of business cases.
2. Which legal issues are emerging as a consequence of its use.

Finally, they tried to imagine a world where artificial intelligence offers human the best of its capacities to serve the administration of Justice. How could that world be?

Speakers:
– María Jesús González-Espejo, ELTA’s vice president
– Arthur Dyevre, Professor at KU Leuven
– Dr. Paulius Astromskis, lawyer, entrepreneur and vice-dean for digitization at Vytautas Magnus university Faculty of Law (Kaunas, Lithuania)
– Maria Dymitruk, Researcher at the Faculty of Law, Administration and Economics, University of Wroclaw (Poland)

Time series is the fastest growing category of data out there! It’s a series of data points indexed in time order. Often, a time series is a sequence taken at successive equally spaced points in time. In this video, I’ll cover 8 different time series techniques that will help us predict the price of gold over a period of 3 years. We’ll compare the results of each technique, and even consider using a learning technique. From Holts Winter Method to Vector Auto Regression to Reinforcement Learning, we’ve got a lot to cover here. Enjoy!

Code for this video:
https://github.com/llSourcell/Time_Series_Prediction

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More learning resources:
https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks
https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f
https://towardsdatascience.com/bitcoin-price-prediction-using-time-series-forecasting-9f468f7174d3
https://www.datascience.com/blog/time-series-forecasting-machine-learning-differences
https://www.analyticsvidhya.com/blog/2018/02/time-series-forecasting-methods/
https://www.youtube.com/watch?v=hhJIztWR_vo

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A.I. has morphed from an academic niche to the leading differentiator in a wide range of industries, including manufacturing, health care, transportation and retail. Despite its name, there is nothing “artificial” about this technology — it is made by humans, intended to behave like humans and affects humans. Join the conversation with Fe-Fei Li, Co-Director of Stanford University’s Human-Centered AI Institute and the Stanford Vision and Learning Lab, as she discusses how AI is changing and what it means for companies and humans.

What You Will Learn
• How AI has changed and what it means for our future?
• Key principles of Human-Centered AI
• Impact of AI in key industries including healthcare, manufacturing and education

Dr. Fei-Fei Li is a Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford’s upcoming Human-Centered AI Institute. She served as the Director of Stanford’s AI Lab from 2013 to 2018. Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana- Champaign (2005-2006).

Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.

Website: https://deeplearning.mit.edu
Slides: http://bit.ly/2QEfbAm
References: http://bit.ly/deeplearn-sota-2020
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:00 – Introduction
0:33 – AI in the context of human history
5:47 – Deep learning celebrations, growth, and limitations
6:35 – Deep learning early key figures
9:29 – Limitations of deep learning
11:01 – Hopes for 2020: deep learning community and research
12:50 – Deep learning frameworks: TensorFlow and PyTorch
15:11 – Deep RL frameworks
16:13 – Hopes for 2020: deep learning and deep RL frameworks
17:53 – Natural language processing
19:42 – Megatron, XLNet, ALBERT
21:21 – Write with transformer examples
24:28 – GPT-2 release strategies report
26:25 – Multi-domain dialogue
27:13 – Commonsense reasoning
28:26 – Alexa prize and open-domain conversation
33:44 – Hopes for 2020: natural language processing
35:11 – Deep RL and self-play
35:30 – OpenAI Five and Dota 2
37:04 – DeepMind Quake III Arena
39:07 – DeepMind AlphaStar
41:09 – Pluribus: six-player no-limit Texas hold’em poker
43:13 – OpenAI Rubik’s Cube
44:49 – Hopes for 2020: Deep RL and self-play
45:52 – Science of deep learning
46:01 – Lottery ticket hypothesis
47:29 – Disentangled representations
48:34 – Deep double descent
49:30 – Hopes for 2020: science of deep learning
50:56 – Autonomous vehicles and AI-assisted driving
51:50 – Waymo
52:42 – Tesla Autopilot
57:03 – Open question for Level 2 and Level 4 approaches
59:55 – Hopes for 2020: autonomous vehicles and AI-assisted driving
1:01:43 – Government, politics, policy
1:03:03 – Recommendation systems and policy
1:05:36 – Hopes for 2020: Politics, policy and recommendation systems
1:06:50 – Courses, Tutorials, Books
1:10:05 – General hopes for 2020
1:11:19 – Recipe for progress in AI
1:14:15 – Q&A: what made you interested in AI
1:15:21 – Q&A: Will machines ever be able to think and feel?
1:18:20 – Q&A: Is RL a good candidate for achieving AGI?
1:21:31 – Q&A: Are autonomous vehicles responsive to sound?
1:22:43 – Q&A: What does the future with AGI look like?
1:25:50 – Q&A: Will AGI systems become our masters?

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In this broadcast, originally aired at SXSW 2019, leaders in the business of applying AI will discuss a wide array of business models that have been used to successfully generate crucial training data, how this data has been used, and what that means for your business. We’ll talk through the cost profile of building the data set, identify common technical challenges, and discuss the pros and cons of each approach.

Richard Socher is the Chief Scientist at Salesforce and one of the leading researchers in deep learning and natural language processing as an adjunct professor at Stanford University’s Department of Computer Science.

In this video he presents at our recent AirTree Speaker Series event in partnership with the Melbourne ML/AI community.

First up, Richard takes us through his team’s recent research on the Natural Language Decathlon: Multitask Learning as Question Answering (https://arxiv.org/abs/1806.08730), a new paradigm for natural language learning that casts all tasks as question answering over a context.

Then he sits down with John Henderson, one of the partners at AirTree Ventures, to talk about the global landscape for AI including recent big milestones, what’s hype vs reality, and what the opportunities are for start-ups in the space.

I’m pleased to announce our Wednesday, 6/20 event in the Blockchain in Healthcare Webinar Series, “AI Convergence, Concepts, and Controversies,” taking place 8-10PM EST. This lively discussion moderated by Heather Flannery will feature two thought leaders in the industry, David Houlding, Principle Healthcare Program Manager, Cloud + Enterprise Division, at Microsoft Corporation, and Jack Neil MD, CEO, at Zather and udifi, and CTO and Pediatric Anesthesiologist at Medstream.

We’ll introduce AI methods such as machine learning, deep learning, and robotic process automation and its convergence with blockchain and distributed ledger technology. The discussion will explore how blockchain can be used to more rapidly assemble the very large data sets needed to successfully train AIs, the role of smart contracts in this process, the mechanics of off-chain big data storage, the critical role auditable data provenance plays in manifesting ethical, transparent AI inferences and conclusions, and more.

This highly engaging two-hour live event will begin with 30 minutes of presentations from the guest speakers, followed by a one hour interview-format discussion, and close with 30 minutes of facilitated audience Q&A.

Jim Hendler discusses approaches to image recognition and methods for improving upon current iterations of AI.

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