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Swaroop Vattam

I am a technical staff member at Lincoln Lab at MIT. Previously, I was a research scientist at Georgia Tech and a NAS fellow at NRL. I got my PhD in Computer Science from Georgia Tech in 2012. My research is broadly focused on applications of machine reasoning and machine learning to natural language processing problems. I am currently investigating data-driven model discovery systems in an effort to tackle the problem of automated machine learning.



cs.AI updates on arXiv.org: Computer Science -- Artificial Intelligence (cs.AI) updates on the arXiv.org e-print archive
Building Jiminy Cricket: An Architecture for Moral Agreements Among Stakeholders. (arXiv:1812.04741v1 [cs.AI]):

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the challenge of how the moral values and views of all stakeholders can be integrated and reflected in the moral behaviour of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. We show how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.

Gradient Descent Happens in a Tiny Subspace. (arXiv:1812.04754v1 [cs.LG]):

We show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training. The subspace is spanned by a few top eigenvectors of the Hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of training. A simple argument then suggests that gradient descent may happen mostly in this subspace. We give an example of this effect in a solvable model of classification, and we comment on possible implications for optimization and learning.

Designing Artificial Cognitive Architectures: Brain Inspired or Biologically Inspired?. (arXiv:1812.04769v1 [cs.AI]):

Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or technological undertaking. On the other hand, modern biological science posits that intelligence can be distinguished not only in human brains. Intelligence today is considered as a fundamental property of each and every living being. Therefore, lower simplified forms of natural intelligence are more suitable for investigation and further replication in artificial cognitive architectures.

Linking Artificial Intelligence Principles. (arXiv:1812.04814v1 [cs.AI]):

Artificial Intelligence principles define social and ethical considerations to develop future AI. They come from research institutes, government organizations and industries. All versions of AI principles are with different considerations covering different perspectives and making different emphasis. None of them can be considered as complete and can cover the rest AI principle proposals. Here we introduce LAIP, an effort and platform for linking and analyzing different Artificial Intelligence Principles. We want to explicitly establish the common topics and links among AI Principles proposed by different organizations and investigate on their uniqueness. Based on these efforts, for the long-term future of AI, instead of directly adopting any of the AI principles, we argue for the necessity of incorporating various AI Principles into a comprehensive framework and focusing on how they can interact and complete each other.

Towards Understanding Language through Perception in Situated Human-Robot Interaction: From Word Grounding to Grammar Induction. (arXiv:1812.04840v1 [cs.CL]):

Robots are widely collaborating with human users in diferent tasks that require high-level cognitive functions to make them able to discover the surrounding environment. A difcult challenge that we briefy highlight in this short paper is inferring the latent grammatical structure of language, which includes grounding parts of speech (e.g., verbs, nouns, adjectives, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) for phrases. This paves the way towards grounding phrases so as to make a robot able to understand human instructions appropriately during interaction.



Mathematical Moments from the American Mathematical Society: The American Mathematical Societys Mathematical Moments program promotes appreciation and understanding of the role mathematics plays in science, nature, technology, and human culture. Listen to researchers talk about how they use math: from presenting realistic animation to beating cancer.
Screening for Autism: Researcher: Jordan Hashemi, Duke University Moment: http://www.ams.org/samplings/mathmoments/mm142-autism.pdf Moment Title: Screening for Autism Description: Jordan Hashemi talks about an easy-to-use app to screen for autism. Podcast page: http://www.ams.org/samplings/mathmoments/mm142-autism-podcast Audio file: podcast-mom-autism.mp3
Unbunching Buses: Researchers: Vikash V. Gayah and S. Ilgin Guler, Pennsylvania State University Moment: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching.pdf Moment Title: Unbunching Buses Description: Gayah and Guler talk about mitigating the clustering of buses on a route. Podcast page: http://www.ams.org/samplings/mathmoments/mm141-bus-bunching-podcast Audio file: podcast-mom-bus-bunching.mp3
Winning the Race: Researcher: Christine Darden, NASA (retired) Moment: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures.pdf Moment Title: Winning the Race Description: Christine Darden on working at NASA. Podcast page: http://www.ams.org/publicoutreach/mathmoments/mm140-hidden-figures-podcast
Revolutionizing and Industry: Researchers: Christopher Brinton, Zoomi, Inc. and Princeton University, and Mung Chiang, Purdue University Moment: http://www.ams.org/samplings/mathmoments/mm139-netflix.pdf Description: Christopher Brinton and Mung Chiang talk about the Netflix Prize competition.
Going Into a Shell: Researcher: Derek Moulton, University of Oxford Moment: http://www.ams.org/samplings/mathmoments/mm138-shells.pdf Description: Derek Moulton explains the math behind the shapes of seashells.


AMS Feature Column: AMS Feature Column - RSS Feed
Upgrading Slums Using Topology:
Topology and Elementary Electric Circuit Theory, I:
Recognition:
Getting in Sync:
Reading the Bakhshali Manuscript:
Crochet Topology:
Mathematical Economics for Mathematics and Statistics Awareness Month:
Neural Nets and How They Learn:
Jakob Bernoulli's Zoo:
Regular-Faced Polyhedra: Remembering Norman Johnson:






Last modified 18 November 2018 at 5:47 pm by svattam