Editor’s Note: The following blog is a special guest post by a recent graduate
of Berkeley BAIR’s AI4ALL summer program for high school students.

AI4ALL is a nonprofit dedicated to increasing diversity and inclusion in AI
education, research, development, and policy.

The idea for AI4ALL began in early 2015 with Prof. Olga Russakovsky, then
a Stanford University Ph.D. student, AI researcher Prof. Fei-Fei Li, and Rick
Sommer – Executive Director of Stanford Pre-Collegiate Studies. They founded
SAILORS as a summer outreach program for high school girls to learn about
human-centered AI, which later became AI4ALL. In 2016, Prof. Anca Dragan
started the Berkeley/BAIR AI4ALL camp, geared towards high school students from
underserved communities.

The case fatality rate quantifies how dangerous COVID-19 is, and how risk of death varies with strata
like geography, age, and race. Current estimates of the COVID-19 case fatality rate (CFR) are biased
for dozens of reasons, from under-testing of asymptomatic cases to government misreporting. We provide
a careful and comprehensive overview of these biases and show how statistical thinking and modeling can
combat such problems. Most importantly, data quality is key to unbiased CFR estimation. We show that a
relatively small dataset collected via careful contact tracing would enable simple and potentially more
accurate CFR estimation.

Despite recent advances in artificial intelligence (AI) research, human
children are still by far the best learners we know of, learning impressive
skills like language and high-level reasoning from very little data. Children’s
learning is supported by highly efficient, hypothesis-driven exploration: in
fact, they explore so well that many machine learning researchers have been
inspired to put videos like the one below in their talks to motivate research
into exploration methods. However, because applying results from studies in
developmental psychology can be difficult, this video is often the extent to
which such research actually connects with human cognition.

A time-lapse of a baby playing with toys. Source.

A remarkable characteristic of human intelligence is our ability to learn tasks
quickly. Most humans can learn reasonably complex skills like tool-use and
gameplay within just a few hours, and understand the basics after only a few
attempts. This suggests that data-efficient learning may be a meaningful part
of developing broader intelligence.

On the other hand, Deep Reinforcement Learning (RL) algorithms can achieve
superhuman performance on games like Atari, Starcraft, Dota, and Go, but
require large amounts of data to get there. Achieving superhuman performance on
Dota took over 10,000 human years of gameplay. Unlike simulation, skill
acquisition in the real-world is constrained to wall-clock time. In order to
see similar breakthroughs to AlphaGo in real-world settings, such as robotic
manipulation and autonomous vehicle navigation, RL algorithms need to be
data-efficient — they need to learn effective policies within a reasonable
amount of time.

To date, it has been commonly assumed that RL operating on coordinate state is
significantly more data-efficient than pixel-based RL. However, coordinate
state is just a human crafted representation of visual information. In
principle, if the environment is fully observable, we should also be able to
learn representations that capture the state.

In the last decade, one of the biggest drivers for success in machine learning has arguably been the rise of high-capacity models such as neural networks along with large datasets such as ImageNet to produce accurate models. While we have seen deep neural networks being applied to success in reinforcement learning (RL) in domains such as robotics, poker, board games, and team-based video games, a significant barrier to getting these methods working on real-world problems is the difficulty of large-scale online data collection. Not only is online data collection time-consuming and expensive, it can also be dangerous in safety-critical domains such as driving or healthcare. For example, it would be unreasonable to allow reinforcement learning agents to explore, make mistakes, and learn while controlling an autonomous vehicle or treating patients in a hospital. This makes learning from pre-collected experience enticing, and we are fortunate in that many of these domains, there already exist large datasets for applications such as self-driving cars, healthcare, or robotics. Therefore, the ability for RL algorithms to learn offline from these datasets (a setting referred to as offline or batch RL) has an enormous potential impact in shaping the way we build machine learning systems for the future.

The World is Continuously Varying

Imagine we want to train a self-driving car in New York so that we can take it
all the way to Seattle without tediously driving it for over 48 hours. We hope
our car can handle all kinds of environments on the trip and send us safely to
the destination. We know that road conditions and views can be very different.
It is intuitive to simply collect road data of this trip, let the car learn
from every possible condition, and hope it becomes the perfect self-driving car
for our New York to Seattle trip. It needs to understand the traffic and
skyscrapers in big cities like New York and Chicago, more unpredictable weather
in Seattle, mountains and forests in Montana, and all kinds of country views,
farmlands, animals, etc. However, how much data is enough? How many cities
should we collect data from? How many weather conditions should we consider? We
never know, and these questions never stop.

Figure 1: Domains boundaries are rarely clear. Therefore, it is hard to set up
definite domain descriptions for all possible domains.

Humans manipulate 2D deformable structures such as fabric on a daily basis,
from putting on clothes to making beds. Can robots learn to perform similar
tasks? Successful approaches can advance applications such as dressing
assistance for senior care, folding of laundry, fabric upholstery, bed-making,
manufacturing, and other tasks. Fabric manipulation is challenging, however,
because of the difficulty in modeling system states and dynamics, meaning that
when a robot manipulates fabric, it is hard to predict the fabric’s resulting
state or visual appearance.

In this blog post, we review four recent papers from two research labs (Pieter
Abbeel’s and Ken Goldberg’s) at Berkeley AI Research (BAIR) that
investigate the following hypothesis: is it possible to employ learning-based
approaches to the problem of fabric manipulation?

We demonstrate promising results in support of this hypothesis by using a
variety of learning-based methods with fabric simulators to train smoothing
(and even folding) policies in simulation. We then perform sim-to-real transfer
to deploy the policies on physical robots. Examples of the learned policies in
action are shown in the GIFs above.

We show that deep model-free methods trained from exploration or from
demonstrations work reasonably well for specific tasks like smoothing, but it
is unclear how well they generalize to related tasks such as folding. On the
other hand, we show that deep model-based methods have more potential for
generalization to a variety of tasks, provided that the learned models are
sufficiently accurate. In the rest of this post, we summarize the papers,
emphasizing the techniques and tradeoffs in each approach.

This post is cross-listed on the CMU ML blog.

The history of machine learning has largely been a story of increasing
abstraction. In the dawn of ML, researchers spent considerable effort
engineering features. As deep learning gained popularity, researchers then
shifted towards tuning the update rules and learning rates for their
optimizers. Recent research in meta-learning has climbed one level of
abstraction higher: many researchers now spend their days manually constructing
task distributions, from which they can automatically learn good optimizers.
What might be the next rung on this ladder? In this post we introduce theory
and algorithms for unsupervised meta-learning, where machine learning
algorithms themselves propose their own task distributions. Unsupervised
meta-learning further reduces the amount of human supervision required to solve
tasks, potentially inserting a new rung on this ladder of abstraction.