MIT’S new AI coding paper reported and explained

MIT’S new AI coding paper reported and explained


A team of MIT researchers is making it easier
for novices to get their feet wet with artificial intelligence, while also helping experts advance
the field. In a paper presented at the Programming Language
Design and Implementation conference this week, the researchers describe a novel probabilistic-programming
system named “Gen.” Users write models and algorithms from multiple fields where
AI techniques are applied — such as computer vision, robotics, and statistics — without
having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated
models and inference algorithms — used for prediction tasks — that were previously
infeasible. In their paper, for instance, the researchers
demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision
inference task that has applications in autonomous systems, human-machine interactions, and augmented
reality. Behind the scenes, this program includes components
that perform graphics rendering, deep-learning, and types of probability simulations. The combination of these diverse techniques
leads to better accuracy and speed on this task than earlier systems developed by some
of the researchers. Due to its simplicity — and, in some use
cases, automation — the researchers say Gen can be used easily by anyone, from novices
to experts. “One motivation of this work is to make
automated AI more accessible to people with less expertise in computer science or math,”
says first author Marco Cusumano-Towner, a PhD student in the Department of Electrical
Engineering and Computer Science. “We also want to increase productivity,
which means making it easier for experts to rapidly iterate and prototype their AI systems.” The researchers also demonstrated Gen’s
ability to simplify data analytics by using another Gen program that automatically generates
sophisticated statistical models typically used by experts to analyze, interpret, and
predict underlying patterns in data. That builds on the researchers’ previous
work that let users write a few lines of code to uncover insights into financial trends,
air travel, voting patterns, and the spread of disease, among other trends. This is different from earlier systems, which
required a lot of hand coding for accurate predictions. “Gen is the first system that’s flexible,
automated, and efficient enough to cover those very different types of examples in computer
vision and data science and give state of-the-art performance,” says Vikash K. Mansinghka
’05, MEng ’09, PhD ’09, a researcher in the Department of Brain and Cognitive Sciences
who runs the Probabilistic Computing Project. Joining Cusumano-Towner and Mansinghka on
the paper are Feras Saad ’15, SM ’16, and Alexander K. Lew, both CSAIL graduate students
and members of the Probabilistic Computing Project. Best of all worlds In 2015, Google released TensorFlow, an open-source
library of application programming interfaces (APIs) that helps beginners and experts automatically
generate machine-learning systems without doing much math. Now widely used, the platform is helping democratize
some aspects of AI. But, although it’s automated and efficient,
it’s narrowly focused on deep-learning models which are both costly and limited compared
to the broader promise of AI in general. But there are plenty of other AI techniques
available today, such as statistical and probabilistic models, and simulation engines. Some other probabilistic programming systems
are flexible enough to cover several kinds of AI techniques, but they run inefficiently. The researchers sought to combine the best
of all worlds — automation, flexibility, and speed — into one. “If we do that, maybe we can help democratize
this much broader collection of modeling and inference algorithms, like TensorFlow did
for deep learning,” Mansinghka says. In probabilistic AI, inference algorithms
perform operations on data and continuously readjust probabilities based on new data to
make predictions. Doing so eventually produces a model that
describes how to make predictions on new data. Building off concepts used in their earlier
probabilistic-programming system, Church, the researchers incorporate several custom
modeling languages into Julia, a general-purpose programming language that was also developed
at MIT. Each modeling language is optimized for a
different type of AI modeling approach, making it more all-purpose. Gen also provides high-level infrastructure
for inference tasks, using diverse approaches such as optimization, variational inference,
certain probabilistic methods, and deep learning. On top of that, the researchers added some
tweaks to make the implementations run efficiently. Beyond the lab External users are already finding ways to
leverage Gen for their AI research. For example, Intel is collaborating with MIT
to use Gen for 3-D pose estimation from its depth-sense cameras used in robotics and augmented-reality
systems. MIT Lincoln Laboratory is also collaborating
on applications for Gen in aerial robotics for humanitarian relief and disaster response. Gen is beginning to be used on ambitious AI
projects under the MIT Quest for Intelligence. For example, Gen is central to an MIT-IBM
Watson AI Lab project, along with the U.S. Department of Defense’s Defense Advanced
Research Projects Agency’s ongoing Machine Common Sense project, which aims to model
human common sense at the level of an 18-month-old child. Mansinghka is one of the principal investigators
on this project. “With Gen, for the first time, it is easy
for a researcher to integrate a bunch of different AI techniques.

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