Shuran Song and Carl Vondrick are among the awardees chosen for their artificial intelligence (AI) research. The program aims to use AI for societal good.
Research from the department was accepted to the 35th AAAI Conference on Artificial Intelligence. The conference promotes research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines.
Automated Symbolic Law Discovery: A Computer Vision Approach
Hengrui Xing Columbia University, Ansaf Salleb-Aouissi Columbia University, Nakul Verma Columbia University
One of the most exciting applications of modern artificial intelligence is to automatically discover scientific laws from experimental data. This is not a trivial problem as it involves searching for a complex mathematical relationship over a large set of explanatory variables and operators that can be combined in an infinite number of ways. Inspired by the incredible success of deep learning in computer vision, the authors tackle this problem by adapting various successful network architectures into the symbolic law discovery pipeline. The novelty of this new approach is in (1) encoding the input data as an image with super-resolution, (2) developing an appropriate deep network pipeline, and (3) predicting the importance of each mathematical operator from the relationship image. This allowed to prior the exponentially large search with the predicted importance of the symbolic operators, which can significantly accelerate the discovery process.
The model was then applied to a variety of plausible relationships—both simulated and from physics and mathematics domains—involving different dimensions and constituents. The authors show that their model is able to identify the underlying operators from data, achieving a high accuracy and AUC (91% and 0.96 on average resp.) for systems with as many as ten independent variables. Their method significantly outperforms the current state of the art in terms of data fitting (R^2), discovery rate (recovering the true relationship), and succinctness (output formula complexity). The discovered equations can be seen as first drafts of scientific laws that can be helpful to the scientists for (1) hypothesis building, and (2) understanding the complex underlying structure of the studied phenomena. This novel approach holds a real promise to help speed up the rate of scientific discovery.
Bounding Causal Effects on Continuous Outcome
Junzhe Zhang Columbia University, Elias Bareinboim Columbia University
One of the most common methods for policy learning used throughout the empirical sciences is the use of randomization of the treatment assignment. This method is considered the gold standard within many disciplines and can be traced back, at least, to Fisher (Fisher 1935) and Neyman (Neyman 1923). Whenever human subjects are at the center of the experiment, unfortunately, issues of non-compliance arise. Namely, subjects do not necessarily follow the experimental protocol and end up doing what they want. It is well-understood that under such conditions, unobserved confounding bias will emerge. For instance, subjects who did not comply with the treatment assignment may be precisely those who would have responded adversely to the treatment. Therefore, the actual causal effects of the treatment, when it is applied uniformly to the population, might be substantially less effective than the data reveals. Moreover, since one does not observe how subjects decide/respond to the realized treatment, the actual treatment effects are not uniquely computably from the collected data, called non-identifiable.
Robins (1989) and Manski (1990) derived the first informative bounds over the causal effects from studies with imperfect compliance under a set of non-parametric assumptions called instrumental variables (IV). In their seminal work, Balke and Pearl (1994a, 1997) improved earlier results by employing an algebraic method to derive analytic expressions of the causal bounds, which are provably optimal. However, this approach assumes the primary outcome to be discrete and finite. Solving such a program could be intractable when high-dimensional context variables are present.
This paper presents novel non-parametric methods to bound causal effects on the continuous outcome from studies with imperfect compliance. These methods could be generalized to settings with a high-dimensional context. Perhaps surprisingly, this paper introduced a latent data representation that could characterize all constraints on the observational and interventional distributions implied by IV assumptions, even when the primary outcome is continuous. Such representation allows one to reduce the original bounding problem to a series of linear programs. Solve these programs, therefore, leads to tight causal bounds.
Estimating Identifiable Causal Effects through Double Machine Learning
Yonghan Jung, Jin Tian, Elias Bareinboim Columbia University
Learning causal effects from observational data is a pervasive challenge found throughout the data-intensive sciences. General methods of determining the identifiability of causal effect from a combination of observational data and causal knowledge about the underlying system have been well-understood in theory. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a novel approach, named double/debiased machine learning (DML) (Chernozhukov et al. 2018), has been proposed to learn parameters leveraging modern machine learning techniques, which are both robust to model misspecification (‘doubly robust’) and slow convergence (‘debiased’). Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds.
This paper aims to bridge this gap by developing a general class of estimators for any identifiable causal functionals that exhibit robustness properties of DML estimators, which the authors called ‘DML-ID.’ In particular, they provide a complete procedure for deriving an essential ingredient of the DML estimator called an influence function (IF) and construct a general class of estimators based on the IF. This means that one can estimate any causal functional and enjoy two robustness properties, doubly robustness and debiasedness.
Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding
Long Chen Tencent AI Lab, Wenbo Ma Zhejiang University, Jun Xiao Zhejiang University, Hanwang Zhang Nanyang Technological University, Shih-Fu Chang Columbia University
The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposals.
In this paper, the researchers argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., expression-agnostic), hoping that the proposals contain all right instances in the expression (i.e., expression-aware). Due to this mismatch, current two-stage methods suffer from a severe performance drop between detected and ground-truth proposals.
The paper proposes Ref-NMS, which is the first method to yield expression-aware proposals at the first stage. Ref-NMS regards all nouns in the expression as critical objects, and introduces a lightweight module to predict a score for aligning each box with a critical object. These scores can guide the NMS operation to filter out the boxes irrelevant to the expression, increasing the recall of critical objects, resulting in a significantly improved grounding performance.
Since RefNMS is agnostic to the grounding step, it can be easily integrated into any state-of-the-art two-stage method. Extensive ablation studies on several backbones, benchmarks, and tasks consistently demonstrate the superiority of Ref-NMS. Codes are available at: https://github.com/ChopinSharp/ref-nms.
Professor Kathy McKeown talks with DeepLearning.AI’s Andrew Ng about how she started in artificial intelligence (AI), her research projects, how her understanding of AI has changed through the decades, and AI career advice for learners of NLP.
Almost 400,000 babies were born prematurely—before 37 weeks gestation—in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided.
IBM has selected assistant professor Baishakhi Ray for an IBM Faculty Award. The highly selective award is given to professors in leading universities worldwide to foster collaboration with IBM researchers. Ray will use the funds to continue research on artificial intelligence-driven program analysis to understand software robustness.
Although much research has been done, there are still countless vulnerabilities that make system robustness brittle. Hidden vulnerabilities are discovered all the time – either through a system hack or monitoring system’s functionalities. Ray is working to automatically detect system weaknesses using artificial intelligence (AI) with her project, “Improving code representation for enhanced deep learning to detect and remediate security vulnerabilities”.
One of the major challenges in AI-based security vulnerability detection is finding the best source code representation that can distinguish between vulnerable versus benign code. Such representation can further be used as an input in supervised learning settings for automatic vulnerability detection and fixes. Ray is tackling this problem by building new machine-learning models for source code and applying machine learning techniques such as code embeddings. This approach could open new ways of encoding source code into feature vectors.
“It will provide new ways to make systems secure,” said Ray, who joined the department in 2018. “The goal is to reduce the hours of manual effort spent in automatically detecting vulnerabilities and fixing them.”
A successful outcome of this project will produce a new technique to encode source code with associated trained models that will be able to detect and remediate a software vulnerability with increased accuracy.
They could have been at the beach enjoying the summer. Instead, high school students gathered from across New York City and New Jersey for the AI4All program hosted by the Columbia community. The students came to learn about artificial intelligence (AI) but this program had a special twist – computer science (CS) and social work concepts were combined for a deeper, more meaningful look at AI.
“We created a space for young people to think critically about the social implications of artificial intelligence for the communities that they live in,” said Desmond Patton, the program co-director and associate professor of the School of Social Work. “We wanted them to understand how things like race, power, privilege and oppression can be baked into algorithms and their adverse effects on communities.”
The program participants, composed of 9th, 10th and 11th graders, are from racial and ethnic groups underrepresented in AI: Black, Hispanic, and Asian. Girls as well as youth from lower-income backgrounds were particularly encouraged to apply. For three weeks the students attended lectures, went on field trips to visit local companies (LinkedIn and Samsung) involved in the program, and visited other AI4All programs, like at Princeton University. Their work culminated in a final project which they presented to their classmates, mentors, and industry professionals.
“I believe that it is important to bring more ethics to AI,” said Augustin Chaintreau, the program co-director and a CS assistant professor. He sees ethics integrated into technical concepts and taught at the same time. Instead of learning about the social consequences and fixing it after, to solve an issue. Shared Chaintreau, “It shouldn’t be thought about just in passing but as a central part of why this is a tool and its implications.”
An interdisciplinary approach to AI was even part of how the classes were structured. Technical CS concepts, such as machine learning and Python, were taught in the morning by professors and student volunteers. While in the afternoon, guest speakers came to talk about their perspective to the day’s lesson. So, on the same day, students learned about supervised and unsupervised learning, and in the afternoon, someone who was formerly incarcerated described how the criminal policing that survey people on social media had a role in making a case against them.
“We were learning college courses meant to be taught in a month but for us it was just a couple of weeks and that was really impressive,” said Genesis Lopez, who is part of the robotics team at her school. Lopez loves robotics but works more on the mechanical side. She goes back to the team knowing how to use Python and is confident she can step up and code if needed. Continued Lopez, “I learned a lot but my favorite part was the people, we became a family.”
Kai-Fu Lee (B.S. ’83) included in WIRED’s anniversary issue for his work that brings humanity to artificial intelligence.
Artificial intelligence (AI) has seeped into the daily lives of people in the developed world. From virtual assistants to recommendation engines, AI is in the news, our homes and offices. There is a lot of untapped potential in terms of AI usage, especially in humanitarian areas. The impact could have a multiplier effect in developing countries, where resources are limited. By leveraging the power of AI, businesses, nongovernmental organizations (NGOs) and governments can solve life-threatening problems and improve the livelihood of local communities in the developing world.