Dear all,
Our next AI seminar on *"Quo Vadis? Predicting Future Trajectories of
Robots through Temporal Logics and Bayesian Inference" *by professor Sriram
Sankaranarayanan
is scheduled to be on February 16th, 1-2 PM PST. It will be followed by a
30 minute Q&A session by the graduate students.
Zoom Link:
https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U…
*Quo Vadis? Predicting Future Trajectories of Robots through Temporal
Logics and Bayesian Inference*
Sriram Sankaranarayanan
Associate Professor
Computer Science
University of Colorado, Boulder
*Abstract:*
Predicting the future states of robots through observation of its past and
current actions is a very interesting problem. It is a fundamental
"primitive" for applications such as runtime monitoring to prevent
impending failures such as collisions or entering forbidden regions in the
workspace. In this talk, we will present a few approaches to this problem
beginning with a simple learning-based extrapolation of the robot's past
positions to predict future trajectories, assuming a simple dynamical model
for the robot. Unfortunately, such an extrapolation will remain valid
only for relatively short time horizons. To improve upon this, we show how
the "intent" of the agent can be represented and reasoned with. To
represent intent, we use a restricted class of formulas from temporal
logics as hypothesized intents. By combining these temporal logic
representations through the machinery of Bayesian Inference, we show how we
can predict future trajectories of robots rapidly through a combination of
off-line pre-computations that enable cheaper real-time predictions. We
conclude by describing ongoing work that develops a hierarchical approach
wherein we separate "short-term" intents from "longer-term" intents that
can be represented by the full strength of temporal logic-based
specifications.
This presentation is based on a series of joint works with Hansol Yoon and
Chou Yi.
*Speaker Bio:*
Sriram Sankaranarayanan is an associate professor of Computer Science at
the University of Colorado, Boulder. His research interests include
automatic techniques for reasoning about the behavior of computer and
cyber-physical systems. Sriram obtained a PhD in 2005 from Stanford
University where he was advised by Zohar Manna and Henny Sipma.
Subsequently he worked as a research staff member at NEC research labs in
Princeton, NJ. He has been on the faculty at CU Boulder since 2009. Sriram
has been the recipient of awards including the President's Gold Medal from
IIT Kharagpur (2000), Siebel Scholarship (2005), the CAREER award from NSF
(2009), Dean's award for outstanding junior faculty (2012), outstanding
teaching (2014), and the Provost's faculty achievement award (2014).
*Please watch this space for future AI Seminars :*
* https://eecs.oregonstate.edu/ai-events
<https://eecs.oregonstate.edu/ai-events>*
Rajesh Mangannavar,
Graduate Student
Oregon State University
----
AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly
encouraged.
Hello everyone,
We'll be meeting Friday at 2 PM PST to discuss OpenAI's recent paper "Training
language models to follow instructions with human feedback
<https://cdn.openai.com/papers/Training_language_models_to_follow_instructio…>
".
Making language models bigger does not inherently make them better at
> following a user’s intent. For example, large language models can generate
> outputs that are untruthful, toxic, or simply not helpful to the user. In
> other words, these models are not aligned with their users. In this
> paper, we show an avenue for aligning language models with user intent on a
> wide range of tasks by fine-tuning with human feedback. Starting with a set
> of labeler-written prompts and prompts submitted through the OpenAI API, we
> collect a dataset of labeler demonstrations of the desired model behavior,
> which we use to fine-tune GPT-3 using supervised learning. We then collect
> a dataset of rankings of model outputs, which we use to further fine-tune
> this supervised model using reinforcement learning from human feedback
> (RLHF). We call the resulting models InstructGPT. In human evaluations on
> our prompt distribution, outputs from the 1.3B parameter InstructGPT model
> are preferred to outputs from the 175B GPT-3, despite having 100x fewer
> parameters. Moreover, InstructGPT models show improvements in truthfulness
> and reductions in toxic output generation while having minimal performance
> regressions on public NLP datasets. Even though InstructGPT still makes
> simple mistakes, our results show that fine-tuning with human feedback is a
> promising direction for aligning language models with human intent.
Anyone interested in language modeling, reinforcement learning, their
intersection, or language models that can actually follow instructions
should feel welcome to join!
Join Zoom Meeting
https://oregonstate.zoom.us/j/95843260079?pwd=TzZTN0xPaFZrazRGTElud0J1cnJLU…
Password: 961594
Phone Dial-In Information
+1 971 247 1195 US (Portland)
+1 253 215 8782 US (Tacoma)
+1 301 715 8592 US (Washington DC)
Meeting ID: 958 4326 0079
All the best,
Quintin
Dear all,
Our next AI seminar on *"Algorithmic Ethics for Autonomous Systems" *by
professor Houssam Abbas is scheduled to be on February 9th(Tomorrow), 1-2
PM PST. It will be followed by a 30 minute Q&A session by the graduate
students.
Zoom Link:
https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U…
*Algorithmic Ethics for Autonomous Systems*
Houssam Abbas
Assistant Professor
Electrical and Computer Engineering
Oregon State University
*Abstract:*
The creation of intelligent systems that are autonomous, update their own
objectives, and interact with humans in their daily lives, has long been a
driving force in systems engineering, robotics, and Artificial
Intelligence. Example systems include nursing robots in hospitals,
self-driving vehicles, and worker bots collaborating with humans. An
explicit ethical awareness in these systems is recognized as a necessary
condition for successful daily interaction with humans. However, to this
day, there are comparatively few algorithms, and even fewer tools, for
designing ethics-equipped Autonomous Intelligent Systems (AIS), especially
when integrated with a physical control loop. This research develops a
computational theory and formal design tools for ethics-equipped embodied
AIS.
This talk will describe some of my research in developing engineering tools
for automatic reasoning about ethical guidelines. Such guidelines take the
form of statements of Obligation (`The robot ought to care for the patient
in greater pain'), Permission (`The robot is permitted to offer a mask to a
contagious patient') and Prohibition (`The robot is forbidden from
factoring gender into care decisions'). We formalize such Obligations,
Permissions and Prohibitions in deontic logic, and develop model-checking
and learning algorithms for deontic properties of finite automata. I will
then describe the road ahead for the formal study of ethical obligations in
autonomous systems.
*Speaker Bio:*
Houssam Abbas is an Assistant Professor of Electrical Engineering and
Computer Science at Oregon State University. His research interests are in
the verification and control of cyber-physical systems and formal ethical
theories for autonomous agents, with particular emphasis on unpiloted
ground and aerial vehicles. He is a recipient of the NSF CAREER award. He
was a postdoctoral fellow at the University of Pennsylvania, and a design
automation engineer at Intel.
*Please watch this space for future AI Seminars :*
* https://eecs.oregonstate.edu/ai-events
<https://eecs.oregonstate.edu/ai-events>*
Rajesh Mangannavar,
Graduate Student
Oregon State University
----
AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly
encouraged.
Hello everyone,
We'll be discussing the paper On the Expressivity of Markov Reward
<https://arxiv.org/abs/2111.00876>.
Reward is the driving force for reinforcement-learning agents. This paper
> is dedicated to understanding the expressivity of reward as a way to
> capture tasks that we would want an agent to perform. We frame this study
> around three new abstract notions of "task" that might be desirable: (1) a
> set of acceptable behaviors, (2) a partial ordering over behaviors, or (3)
> a partial ordering over trajectories. Our main results prove that while
> reward can express many of these tasks, there exist instances of each task
> type that no Markov reward function can capture. We then provide a set of
> polynomial-time algorithms that construct a Markov reward function that
> allows an agent to optimize tasks of each of these three types, and
> correctly determine when no such reward function exists. We conclude with
> an empirical study that corroborates and illustrates our theoretical
> findings.
(Thank you to Professor Dietterich for bringing this paper to my attention)
We'll be sure to discuss the first post, and hopefully the other two if we
have time. Anyone interested in trying to better understand goal directed
behavior is welcome to join!
We're meeting at 2 PM PST on Friday like normal. Anyone interested in
reinforcement learning should feel welcome to attend!
Join Zoom Meeting
https://oregonstate.zoom.us/j/95843260079?pwd=TzZTN0xPaFZrazRGTElud0J1cnJLU…
Password: 961594
Phone Dial-In Information
+1 971 247 1195 US (Portland)
+1 253 215 8782 US (Tacoma)
+1 301 715 8592 US (Washington DC)
Meeting ID: 958 4326 0079
All the best,
Quintin
Dear all,
Our next AI seminar on *"The Familiarity Hypothesis: Explaining the
Behavior of Deep Open Set Methods" *by professor Tom Dietterich is scheduled
to be on February 2nd (Tomorrow), 1-2 PM PST. It will be followed by a 30
minute Q&A session by the graduate students.
Zoom Link:
https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U…
*The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set
Methods*
Tom Dietterich
Distinguished Professor Emeritus
Computer Science
Oregon State University
*Abstract:*
In many applications, object recognition systems encounter objects
belonging to categories unseen during training. Hence, the set of possible
categories is an open set. Detecting such “novel category'” objects is
usually formulated as an anomaly detection problem. Anomaly detection
algorithms for feature-vector data identify anomalies as outliers, but
outlier detection has not worked well in deep learning. Instead, methods
based on the computed logits of object recognition systems give
state-of-the-art performance. This talk proposes the Familiarity Hypothesis
that these methods succeed because they are detecting the absence of
familiar learned features. This talk will review evidence from the
literature and from our own experiments that supports this hypothesis. It
then experimentally tests a set of predicted consequences of this
hypothesis that provide additional support. The talk will conclude with a
discussion of whether familiarity detection is an inevitable consequence of
representation learning and concludes that we can go beyond familiarity
detection if we can learn to represent objects in terms of disentangled
attributes that support outlier detection.
*Speaker Bio:*
Tom Dietterich (AB Oberlin College 1977; MS University of Illinois 1979;
Ph.D. Stanford University 1984) is Distinguished Professor Emeritus in the
School of Electrical Engineering and Computer Science at Oregon State
University. He is one of the pioneers of the field of machine learning and
has authored more than 225 refereed publications and two books. His current
research topics include robust artificial intelligence, robust human-AI
systems, and applications in sustainability.
Dietterich has devoted many years of service to the research community. He
is a former president of the Association for the Advancement of Artificial
Intelligence and the founding president of the International Machine
Learning Society. Other major roles include executive editor of the Journal
Machine Learning, co-founder of the Journal for Machine Learning Research,
and program chair of AAAI 1990 and NIPS 2000. He currently serves as one of
the moderators for the cs.LG category on arXiv.
*Please watch this space for future AI Seminars :*
* https://eecs.oregonstate.edu/ai-events
<https://eecs.oregonstate.edu/ai-events>*
Rajesh Mangannavar,
Graduate Student
Oregon State University
----
AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly
encouraged.
