The Columbia Center of AI Technology in collaboration with Amazon was founded in 2020 with a mission to better society through the development and adoption of advanced AI technology contributing to a more secure, connected, creative, sustainable, healthy, and equitable humanity.
To further this mission, the Center awards funding to 5 projects each year which demonstrate the most innovative, forward-thinking research in AI technology. The Annual CAIT Research Showcase displays the work and progress of these awarded projects and demonstrates how they will influence the field of AI.
Prior to the Showcase, graduate students and postdocs are invited to join the Amazon Science team to learn about the Graduate Research Internship program and the new Amazon Postdoctoral Science Program.
Following the Showcase, in-person attendees are invited to participate in a mixer event to explore collaboration possibilities, network, and connect with each other.
Registration is required for the Showcase and Mixer.
RSVP is not required for the Grad Student & Post Doc Opportunities Info Session but is encouraged.
On June 15th from 1:00-1:45 pm ET, graduate students and postdocs are invited to join the Amazon Science team to learn about the Graduate Research Internship program and the new Amazon Postdoctoral Science Program.
Hear from the programs team as well as current scientists about these opportunities for students to gain industry experience. This session is open to all current graduate students, postdoctoral researchers, and faculty that would like to learn more about these programs. Following this session, we encourage you to attend the CAIT Research Showcase and Mixer. The Graduate Research and the Postdoctoral Science teams will be at the mixer to network and answer additional questions.
This proposal aims to develop fast optimization techniques for fundamental problems in machine learning. In a wide variety of domains, such as computer vision, recommender systems, and immunology, objectives we care to optimize exhibit a natural diminishing returns property called submodularity. Off-the-shelf tools have been developed to exploit the common structure of these problems and have been used to optimize complex objectives. However, the main obstacle to the widespread use of these optimization techniques is that they are inherently sequential and too slow for problems on large data sets. Consequently, the existing toolbox for submodular optimization is not adequate to solve large scale optimization problems in ML.
This proposal considers developing novel parallel optimization techniques for problems whose current state-of-the-art algorithms are inherently sequential and hence cannot be parallelized. In a recent line of work, we developed algorithms that achieve an exponential speedup in parallel running time for problems that satisfy a diminishing returns property. These algorithms use new techniques that have shown promising results for problems such as movie recommendation and maximizing influence in social networks. They also open exciting possibilities for further speedups as well as for applications in computer vision and public health, where important challenges remain.
Much research has been done in the past 15 years on creating empathetic responses in text, facial expression and gesture in conversational systems. However almost none has been done to identify the speech features that can create an empathetic sounding voice. Empathy is the ability to understand another’s feelings as if we were having those feelings ourselves and Compassionate Empathy includes the ability to take action to mitigate any problems. This type of category has been found to be especially useful in dialogue systems, avatars, and robots, since empathetic behavior can encourage users to like a speaker more, to believe the speaker is more intelligent, to actually take the speaker’s advice, to trust and like it more, and to want to speak with the speaker longer and more often. We propose to identify acoustic/prosodic as well as lexical features which produce empathetic speech by collecting the first corpus of empathetic podcasts and videos, crowdsourcing their labels for empathy, building machine learning models to identify empathetic speech and the speech and language features as well as the visual features which can be used to generate it.
Fueled by the insatiable customer desire for faster delivery, e-tailers have begun deploying "forward" distribution centers close to city centers, which have very limited space. Our proposal is to develop scalable optimization algorithms that allow e-tailers to systematically determine the SKU variety and inventory that should be placed in these precious spaces. Our model accounts for demand that depends endogenously on our SKU selection, inventory pooling effects, and the interplay between different categories of SKU's. Our model is designed to yield insights about: the relationship between demand variability and SKU fragmentation; sorting rules for selecting a few SKU's within a given category; and the marginal value of capacity to different categories.
We propose novel methods for leveraging human assistance in Reinforcement Learning (RL). The sparse reward problem has been one of the biggest challenges in RL, often leading to inefficient exploration and learning. While real-time immediate feedback from a human could resolve this issue, it is often impractical for complex tasks that require a large number of training steps. To address this problem, we aim to develop new confidence measures, which the agent computes during both training and deployment. In this paradigm, a Deep RL policy will train autonomously, but stop and request assistance when the confidence in the ultimate success of the task is too low to continue. We aim to show that expert assistance can speed up learning and/or increase performance, while minimizing the number of calls for assistance made to the expert.
Full-precision deep learning models are often too large or costly to deploy on edge devices such as Amazon Echo, Ring, and Fire devices. To accommodate to the limited hardware resources, models are often quantized, com-pressed, or pruned.While such techniques often have a negligible impact on top-line accuracy, the adapted models exhibit subtle differences in output compared to the full-precision model from which they are derived.
We propose a new attack termed Adversarial Deviation Attack, or ADA, that exploits the differences in model quantization, compression and pruning, by adding adversarial noise to input data that maximizes the output difference between the original and the edge model. It will construct malicious inputs that will trick the edge model but will be virtually undetectable by the original model. Such an attack is particularly dangerous: even after extensive robust training on the original model, quantization, compression or pruning will always introduce subtle differences, providing ample vulnerabilities for the attackers. Moreover, data scientists may not even be able to notice such attacks because the original model typically serves as the authoritative model version, used for validation, debugging and retraining. We will also investigate how new or existing defenses can fend off ADA attacks, greatly improving the security of edge devices.
