Garrison Cottrell - US grants

The long term objective of this research is to understand the possible neural mechanisms underlying the learning of simple cognitive procedures such as arithmetic through neural network modeling of the process. The specific goals are: (1) To implement and test a neural network model of addition. (2) To analyze how the network has solved the task and evaluate it with respect to the human data. (3) To lesion the network and analyze its behavior with reference to human data. (4) To determine the optimal method of retraining the network after damage. Neural network of addition have been done before, but only for single digit addition. The difference here is the attempt to model the sequential process of addition in a parallel network. The network will be trained in this task via the back propagation algorithm. We will use recurrent networks that record their processing history in their state so that the network can use this information to keep track of where it is in the procedure. Once the network has learned the task, standard techniques will be applied to analyze how the network has solved the task. Also at this stage, random lesions may be introduced into the network, which allow comparison of its behavior with that observed in humans. Finally, the network will then be retrained on the task to determine the optimal method of retraining. This will have obvious implications for the treatment of brain damaged patients with acalculia. Thus exploring the behavior of networks trained to do these tasks, we hope to learn better ways of training children and retraining lesion patients.

This project is concerned with techniques for active selection of training examples for neural network learning, while simultaneously growing the network to fit the data. The approach uses a statistical sampling criterion, Integrated Mean Squared Error, to derive a "greedy" selection criterion which picks the next training example that maximizes the decrement in this measure. This selection criterion is usable for a wide class estimators. A practical realization of this schemes for multi- layer neural networks is demonstrated. //

Central pattern generators drive rhythmic behaviors such as walking, chewing and flying. While they have been intensively studied, the underlying mechanisms are as yet not well understood. The long term objective of this search is to elucidate the neural mechanisms underlying the behavior of central pattern generators, in particular, the lobster stomatogastric ganglion (STG). Our approach is twofold: We will complement experimental investigation with modeling. Currently, there are a myriad of cell physiology parameters that could be incorporated into a model of circuit behavior (Selverston, 1988). By building simplifying models of the gastric mill portion of the STG, our goal is to determine which physiological parameters am necessary to account for the behavior, and which are peripheral to that explanation. My approach is to start with a minimal set, try to account for normal oscillatory behavior, and add properties as they appear necessary for accounting for more behavior. The modeling work also raises experimental questions which need to be answered to inform, constrain and validate the model. The modeling goals are: 1) Extend our current model with further constraints: We now can incorporate gap junctions, delay, and intrinsic currents in the model. We will use actual data to train the model instead of artificially produced sine wave data to further constrain it. 2) Explore the ability of the model to predict cell properties where not every cell is known. This is known in other fields as a systems identification task. If successful this could be extremely useful technique for identifying circuit components that are inaccessible experimentally. 3) Investigate hypotheses regarding constraints concerning functional constraints on the form of circuit. 4) Investigate the relationship between weights and phase relationships. 5) Investigate analytical methods such as bifurcation theory and models of relaxation oscillators to understand the mathematical aspects of our model. These modeling experiments require more data than is currently available. following biological experiments as planned: 1) Obtain better synaptic strength and electrotonic coupling estimates under normal and neuromodulated conditions. 2) Obtain a better estimate of the input/output function and intrinsic currents of a single neuron under different conditions. 3) Determine the effect of perturbations caused by cell kills and hyperpolarization on the output of the gastric mill. 4) Determine the changes m non-spiking oscillations following exposure to putative neuromodulators.

9221276 Cottrell Learning Semantic Representations for Information Retrieval This is the first year funding of a three-year continuing award. The objective of this project is to develop methods to automatically represent text-based documents from a large collection in a way which facilitates semantically precise retrieval. A critical problem in representing documents is that words in the documents are not accurate descriptors of document content. This is in part due to the polysemy of natural language: A single concept can be described in many different ways. Most current approaches fail to account for this, as they determine semantic relevance using co-occurrence of words in documents. The approach is to index documents so that they are representationally similar when they are semantically related, not just when they coincidentally share terms. Multidimensional Scaling (MDS) and Neural Network theory are foundations of the work. This approach is demonstrated to be similar to the best current technique for statistical semantic analysis of documents: Latent Semantic Indexing (LSI). The work suggests a generalization of LSI, a linear and metric technique, to non-linear and non-metric techniques. This work is expected to provide a well-founded theoretical framework for document indexing based on MDS, to advance the use of neural network techniques in document indexing, and to help in the quantitative evaluation of current document retrieval methods. ***

DESCRIPTION (provided by applicant): There has been a great deal of progress in understanding how complex visual objects, in particular, human faces, are processed by the cortex. At the same time, sophisticated neural network models have been developed that do many of the same tasks required by these cortical areas. The aim of this proposal is to extend these simplifying models toward an understanding of the extent to which facial expression processing is "universal," versus the extent to which it is experientially mediated. In particular, we focus upon elucidating the following issues through modeling: (1) Understanding cultural variation in expression recognition: contrasting the influence of other race effects versus cultural display rules; (2) Understanding how we become face "experts." Why does the same region of the Fusiform Gyrus get recruited for faces as well as other visual tasks we may be expert in? (3) Understanding the dynamics of facial expertise: How are eye movements planned for efficient feature extraction? In each case, we have developed or will develop a neurocomputational model of the process. Cultural and other-race experience will be modeled by the composition of the internal representations and the training signals of our model. Facial expertise is modeled as a combination of different task requirements (varying the level of categorization required) and length of training. Eye movement modeling will be based on novel and traditional methods for extracting the informative locations on the face for each task. We will develop a theoretical criterion for saccade targets the face based upon mutual information between feature values and the categories required for the task. We also will be performing behavioral experiments to test the predictions of our model. The project will shed light on the way faces are represented and processed by the brain, and should give insights into the problems underlying deficits in face processing such as prosopagnosia.

Consider creating (a) a computer system to help physicians make a diagnosis using all of a patient's medical data and images along with millions of case histories; (b) intelligent buildings and cars that are aware of their occupants activities; (c) personal digital assistants that watch and learn your habits -- not only gathering information from the web but recalling where you had left your keys; or (d) a computer tutor that watches a child as she performs a science experiment. Each of these scenarios requires machines that can see and learn, and while there have been tremendous advances in computer vision and computational learning, current computer vision and learning systems for many applications (such as face recognition) are still inferior to the visual and learning capabilities of a toddler. Meanwhile, great strides in understanding visual recognition and learning in humans have been made with psychophysical and neurophysiological experiments. The intellectual merit of this proposal is its focus on creating novel interactions between the four areas of: computer and human vision, and human and machine learning. We believe these areas are intimately intertwined, and that the synergy of their simultaneous study will lead to breakthroughs in all four domains.

Our goal in this IGERT is therefore to train a new generation of scientists and engineers who are as versed in the mathematical and physical foundations of computer vision and computational learning as they are in the biological and psychological basis of natural vision and learning. On the one hand, students will be trained to propose a computational model for some aspect of biological vision and then design experiments (fMRI, single cell recordings, psychophysics) to validate this model. On the other hand, they will be ready to expand the frontiers of learning theory and embed the resulting techniques in real-world machine vision applications. The broader impact of this program will be the development of a generation of scholars who will bring new tools to bear upon fundamental problems in human and computer vision, and human and machine learning.

We will develop a new curriculum that introduces new cross-disciplinary courses to complement the current offerings. In addition, students accepted to the program will go through a two-week boot camp, before classes start, where they will receive intensive training in machine learning and vision using MatLab, perceptual psychophysics, and brain imaging. We will balance on-campus training with summer internships in industry.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries. In this sixth year of the program, awards are being made to institutions for programs that collectively span the areas of science and engineering supported by NSF

The faculty and researchers in the Temporal Dynamics of Learning Center (UCSD) are engaged in REU Site with the overall aim of providing undergraduate students a research experience that leads to publishable work in a new interdisciplinary area studying the role of time and timing in learning, at multiple time and spatial scales, from the scale of synapses operating at the millisecond timescale up to the scale of teachers and students interacting over months. This project allows the team to give REU students access to all of the facilities and activities of the Center, including a state-of-the-art motion capture/brain dynamics facility, regular meetings of research networks composed of highly interdisciplinary and collaborative faculty, postdocs, graduate students and undergraduate researchers from more than seventeen institutions in the US, Canada, and Australia, and a yearly All Hands Meeting where they present their results. In addition to the training REU students receive in the individual laboratories, extensive professional development opportunities are provided through workshops, an undergraduate research conference, panel discussions, and GRE preparation courses.

Intellectual Merit

The intellectual merit of this proposal is the advancement of a new science of the Temporal Dynamics of Learning through undergraduate research experiences in highly productive laboratories, and training in collaborative, rather than competitive, research. The research field is inherently interdisciplinary, combining cognitive science, psychology and computer science.

Broader Impacts

A significant number of under-represented minorities are recruited from local community colleges (as it is a school-year program), resulting in the training of a diverse group of future scientists advancing the science of learning from multiple perspectives. The PI-team taps into established working relationships with these institutions in order to ensure an adequate applicant pool from the honors program, and use recommendations by their professors so that they can enroll the best students in the program.

The site is co-funded by the Department of Defense in partnership with the NSF REU program.

It is commonly accepted that there is a crisis in education in the US. There are too many struggling learners, too many students who cannot read or do basic arithmetic, let alone advanced mathematics. What is not commonly accepted is what to do about this crisis. The researchers at the Temporal Dynamics of Learning Center (TDLC) believe that part of the current crisis in education is the lack of scientific understanding of how the brain learns, and the lack of translation of this scientific understanding to the classroom. An essential, yet understudied, component of learning that could have a strong impact on education is the role of time and timing in learning. TDLC brought together an interdisciplinary team of over 40 investigators from 16 different research institutions in order to focus research energy on this goal. TDLC's purpose is to achieve an integrated understanding of the role of time and timing in learning, across multiple time and spatial scales, brain systems, and social systems, to 1) create a new science of the temporal dynamics of learning; 2) to use this understanding to transform educational practice; and 3) to create a new collaborative research structure, the network of research networks, to transform the practice of science.

Why study timing? Timing is critical for learning at every level, from learning the precise temporal patterns of speech sounds, to learning when to give feedback in the classroom, to the optimal frequency and timing of studying new material. Moreover, a decade of neuroscience research demonstrates that the intrinsic temporal dynamics of the brain itself also reinforce and constrain learning. For example, work at TDLC has shown that measurements of the brain waves of a toddler-the temporal dynamics of thought - can predict how well that child will perform at language tasks years later. This provides the possibility that early intervention could overcome these difficulties, demonstrating the usefulness of studying temporal dynamics. A research program of this size and scope is clearly only possible through the Center Funding model, in order to provide resources at the scale necessary to coordinate the large team of researchers. The work is organized by dividing the personnel into four research networks, where researchers from multiple disciplines are interested in common questions, and who synchronize their research around experiments that can be carried out in humans, animals, and computational models, allowing unprecedented convergence of techniques on a single question.

ABSTRACT
This international venture aims to coordinate the US Science of Learning Center scientists with those from the Australian Science of Learning Centre through an exciting two day workshop and extended visits to the Australian laboratories for the purpose of planning collaborations and exchanging information. The two day workshop will bring together scientists, policy makers, and government officials for the purpose of introducing the scientific goals and progress from the Science of Learning communities and engaging in a rigorous day of scientific talks and discussions aimed towards gaining interdisciplinary perspectives on the role of attention in learning and formulating research topics designed to move this science into the educational setting. Finally, US trainees and scientists will spend extended time furthering their discussions and plans with Australian scientists through laboratory visits, meetings over data, and extended information exchange on topics relevant to their specific lines of work. Hence the intellectual merit of this activity rests in the exchange of ideas between the US scientists and the Australian scientists, focused on particular issues in the Science of Learning. Cyberinfrastructure will be developed to support the ongoing exchange of information and sharing of data from this workshop. The Australian Science of Learning Centre has graciously offered to incorporate the US community in their existing symposium on Attention and Learning, in addition to providing all facilities and organizational services. The broader impacts of this workshop will be in developing synergies between the scientists in the two countries as well as within the Science of Learning Centers itself. The workshop will serve as a foundation for developing an International Science of Learning Community. Gaining cross-cultural perspectives will enrich our science and be excellent an excellent experience for our trainees. A major challenge that faces every nation is how to provide effective and high quality education. This workshop will contribute to solving this problem and is likely to have an impact on the future of the global workforce and the global nature of science in general. The grass-root efforts of international collaborations can be transformational both to the education of the centers' trainees and to the future quality of the education this country provides children. These grass-root efforts are essential in this era of global science.

This proposal requests funds to support the annual Inter-Science of Learning Centers (i-SLC) Conference, organized by the graduate students and postdoctoral fellows of the 6 Science of Learning Centers (SLCs). This event has served as a vital venue for exchanging research findings across the SLCs, sharing resources and stimulating career development opportunities for Center trainees. This highly productive activity has resulted in cross-center collaborations, including cross-center student exchanges for additional training in other laboratories.

This year, the conference will be hosted by the NSF-funded Temporal Dynamics of Learning Center (TDLC) in San Diego, CA on April 21-23, 2012. To capitalize on the host-center's primary research focus on the temporal dynamics of learning and how time and timing influences learning, the iSLC 2012 will have the general theme of "Time, Mind, and Education Interwined". Requested funds will be used to pay for conference facilities at the University of California San Diego (UCSD) and to cover participants' costs of attending, including travel and per diem

Earlier iSLC conferences experiment in various ways to maximize the effectiveness with which the meeting meets its objectives, and each evolution of this event reflects deliberate efforts to improve the meeting based on feedback from prior meetings. Individual students and postdoctoral fellows attending iSLC will benefit from visiting UCSD, learning from experiences of their peers, and by being exposed to new and alternative methodologies and paradigms. Most importantly, they will be provided with opportunities to participate actively in the cross-center, and inter-disciplinary network that will provide support for them in their careers. Since most graduate students and postdoctoral fellows typically attend field-specific conferences with little opportunity to interact with peers outside of their disciplinary niches, this annual event is addressing the need to provide the infrastructure for a national, and even international, interdisciplinary network of young scholars who are likely to continue to cooperate and collaborate throughout their future careers. This year, the trainees are proactively implementing several strategies to broaden the participation of underrepresented minorities in science, by leveraging on-going efforts at their respective centers.

Contrary to some depictions in popular media, humans are still far better than any computer program at understanding the visual world around them. If we understood how the visual system does this, perhaps better artificial vision systems could be built. The goal of this project is to understand how the brain represents the visual world and why. Following a mantra famously credited to Richard Feynman -- What I cannot create, I do not understand -- this project's approach is to create a computer system that learns from natural input (images, videos), assuming that the visual system operates with the goal of efficiently representing the world. These representations will then be compared to measurements of visual neurons. The long term goal is to understand the functional roles of the early visual processing layers in the human visual pathway.

The model is based on the efficient coding hypothesis, in which the early visual pathway serves to capture the statistical structure of its visual inputs by efficiently coding visual information in its outputs. Most computational models following this hypothesis have focused on modeling only one or two visual layers. In this project, Cottrell's group proposes a hierarchical information processing model, which concurs with the efficient coding hypothesis, yet provides the most complete description so far of the early visual processing layers. In this model, the visual inputs are first compressed to reduce noise using Sparse Principal Components Analysis (SPCA), then the data dimensions are expanded to capture the statistical structure of the visual inputs using overcomplete Sparse Coding. A nonlinear activation function then formats the outputs of this layer for the next layer up, and the whole process is repeated. Preliminary work shows that the resulting hierarchical model can learn visual features exhibiting the receptive field properties of neurons in the early visual pathway, including retinal ganglion cells, LGN, V1 simple and complex cells, and V2 cells.

Abstract:

This proposal requests funds to support the eighth annual Inter-Science of Learning Centers (i-SLC) Conference, organized by the graduate students and postdoctoral fellows of the 6 Science of Learning Centers (SLCs). This event has served as a vital venue for exchanging research findings across the SLCs, sharing resources and stimulating career development opportunities for Center trainees. This highly productive activity has resulted in cross-center collaborations, including cross-center student exchanges for additional training in other laboratories.

This year, the conference will be hosted by the NSF-funded Temporal Dynamics of Learning Center (TDLC) in San Diego, CA on May 31-June 2, 2015. The theme is "Research and Innovation: the intersection with Education ". Requested funds will be used to pay for conference facilities at the University of California San Diego (UCSD) and to cover participants' costs of attending, including travel and per diem.

The 2015 Conference will build upon the previous Conferences to facilitate intra-center and cross-Center collaborative through oral/signed presentations in symposia and methodology workshops, alumni panels, alumni presentations and the creation of an onllne research network which connects past, present and future SLC trainees.

Broader Impacts

The i-SLC has been instrumental in catalyzing a large and robust network of graduate students and postdoctoral fellows who will provide a firm foundation on which the future of Science of Learning will build upon, as it evolves into a full-bodied, interdisciplinary research field.

Mapping the Secondary Metabolomes of Marine Cyanobacteria Bacteria are extraordinarily prolific sources of structurally unique and biologically active natural products that derive from a diversity of fascinating biochemical pathways. However, the complete structure elucidation of natural products is often the most time consuming and costly endeavor in natural product drug discovery programs. Compounding this, advancements in genome sequencing have accelerated the identification of unique modular biosynthetic gene clusters in prokaryotes and revealed a wealth of new compounds yet to be isolated and biologically and chemically characterized. Resultantly, there is an urgent and continuing need in this field to connect biosynthetic gene clusters to their respective MS fragmentation signatures in the MS2 molecular networks. The capacity to make such connections will accelerate new compound discovery as well as create associations between gene cluster and biosynthetic pathway, and aid in fast and accurate structure elucidations. Combined with this informatics approach, this proposed continuation project explores innovative methods by which to solve complex molecular structures by enhanced MS and NMR experiments, as well as the development of new algorithms by which to accelerate their analysis. Thus, the overarching goal of this grant is to develop efficient methods that facilitate automated structural classification, structural feature discovery and ultimately efficient structure elucidation of natural products (or any small molecule) and to build an infrastructure that interacts with data input from the community. We will achieve this with the following four specific aims: Aim 1. Integration of MS2 molecular networking with gene cluster networking to rapidly and efficiently locate natural products that have unique molecular architectures; Aim 2. To develop a suite of high sensitivity pulse sequences for natural product structure elucidation; Aim 3. To develop NMR based molecular networking strategies using Deep Convolutional Neural Networks (DCNNs) to facilitate the categorization and structure elucidation of organic compounds; Aim 4. To integrate NMR molecular networking and MS2-based molecular networking as an efficient structure characterization and elucidation strategy. By achieving these aims we will develop an innovative workflow for finding new compounds and for determining their structures, both quickly and accurately. The connection between gene cluster and molecule will shed light on stereochemistry and potential halogenations and methylations. This information can then be used in combination with more efficient NMR and MS methods to accurately determine structures. These tools will be widely shared, such as through the Global Natural Products Social (GNPS) Molecular Network, to enhance the overall capacity of the natural products and organic chemistry communities to solve complex molecular structures.

Summary The overarching goal for this proposed renewal application will be to further advance tools that are in development and to effectively integrate several types of analytical data with biological assay data and genomic information. This will create a powerful set of tools for faster and even more accurate identification of new molecules, dereplication of known ones, and to directly infer biological activities from spectroscopic information. In the current period of support, we have made substantial progress in developing highly useful tools for automatic annotations and identifications of organic molecules, specifically focused on natural products. The Global Natural Products Social (GNPS) Molecular Networking analysis and knowledge dissemination ecosystem has processed almost 160,000 jobs in nearly 160 countries worldwide, has 4-6,000 new job submissions per month and is accessed over 200,000 times a month (majority accessions are for reference library access, inspection of public data and previous jobs that the community shares as hyperlinks in papers), and has become a mainstream tool for the annotation of organic molecules deriving from diverse sources, especially in metabolomics workflows. The public website for Small Molecule Accurate Recognition Technology (SMART), a deep learning model for providing candidate structures based on 1H-13C HSQC NMR data, went live in December 2019 and already has over 3000 jobs in 50 countries. All tools developed in this proposal will become part of this analysis ecosystem. The four laboratories contributing to this proposed research activity have created an open and integrated team that is continuing to creatively innovate new informatic tools to enhance small molecule structure annotations and inference of their chemical and biological properties. We have four specific aims: 1) To complete the development and evaluation of a set of new and innovative tools for natural products analysis, and deploy these as freely available resources for the worldwide community. 2) To refine the structural characterization of molecules through leveraging repository scale mass spectral information along with NMR data and genomic inputs. 3) To create a new SMART-based tool that integrates mass spectrometry and HSQC NMR data as the input for a new deep learning system with the goal of achieving more accurate predictions of structure. 4) To use deep learning to enhance SMART with bioactivity data so as to enable SMART to predict activities of molecules based on spectroscopic features. The data will also augment the GNPS database with biological assay binding data. An additional consequence of these goals will be the further digitization of natural products analytical data so that they can be used in the computational tools planned herein, as well as other tools in the future. Completion of these four specific aims will create new integrated tools for the precise identification of new natural product structures, and enable inference of their structural relatedness to other classes of organic molecules and their biological properties. Thus, these new informatic tools will have the potential to greatly enhance the small molecule drug discovery process.        

The goal of this project is to understand how we see by building computer models that "see the way we do." It is obvious that we learn to talk; it is less obvious that we learn to see. Babies have roughly 20/400 vision, which means they are legally blind, and the world initially looks very blurry to them. They must learn to distinguish people (especially their mother and family) as well as toys, food, and other objects over months and years of development. How is it that we come to be able to see so well that we can play ball, read a book, and thread a needle? One way to understand how this happens is to build computational models that mimic the way the brain works. Artificial Intelligence has blossomed in recent years with the advent of deep neural networks, which are a very simplified model of the brain. They are capable of recognizing faces and objects, and are enabling the creation of self-driving cars. However, there are fundamental differences between these computer vision models and our own visual system that make them less robust. This project will add more features of the human visual system to these models. For example, we have a foveated retina, which enables high fidelity vision only within a small spot of the visual field, about the size of your thumbnail at arm's length. As a result, we move our eyes about 3 times a second in order to bring the world into focus. This project will build a computational model that has a foveated retina, "moves its eyes," and takes data from brain recordings into account.<br/><br/>Recent models of the visual system have been benchmarked against cortical recordings (CORnet, BrainScore), but appear to be reaching a plateau. To move beyond this, the next generation of models will have to come closer to the brain in both anatomy and physiology. This project will incorporate radical changes to convolutional networks as well as novel data from the primate visual system. Missing from most models of the visual system are: 1) biologically realistic lateral and feedback connections, including distinct pools of excitatory (E) and inhibitory (I) neurons with the full set of lateral interactions (E->E, E->I, I->E, I->I), and purely excitatory feedback connections; 2) the log-polar mapping from retina to V1, separating central from peripheral representations and adding rotation and scale invariance; and 3) saccades, adding dynamics to the representations. Missing from most neurophysiological recordings are 1) recordings from IT during free viewing of objects (saccading); 2) pharmacological suppression of central and peripheral V1 while recording from IT in order to measure their contributions to representations; and 3) simultaneous recording from multiple areas of IT providing crucial data on their interactions. This project will incorporate all of these advances in order to build biologically realistic vision systems.<br/><br/>A companion project is being funded by the National Institute of Information and Communications Technology, Japan (NICT).<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Artificial Intelligence (AI) is having an increasing impact on everyday life, from smart speakers and digital assistants to medical discoveries and judicial sentencing. Therefore, the ethical and social implications of AI technologies make it imperative that all citizens understand both the positive and negative impacts on the future of society. This new RET site at the University of California San Diego (UCSD) will provide research opportunities for high school teachers to deepen their understanding of the field of AI while developing materials to use in their classrooms. Teachers will be primarily recruited from high schools in districts serving students who are underrepresented in STEM and from low socio-economic backgrounds. The six-week summer program will include a two-week boot camp to prepare teachers to participate in an intensive AI research project across a range of applications. During the academic year, teachers will continue to engage with research faculty through monthly dinner seminars where they will exchange ideas and discuss the latest updates in AI research. <br/><br/>The intellectual focus of this RET Site from UCSD is Interdisciplinary Artificial Intelligence, with a focus on the applications of Deep Learning. The high school computer science and math teachers, mostly from the Computer Science Teachers Association San Diego Chapter (CSTA SD), which is headquartered at UCSD, will participate in a two-week summer “boot camp”, followed by four weeks of intensive research with AI faculty and graduate students from Computer Science and Engineering, Cognitive Science, and Psychology. Additional objectives are to improve the ability of UCSD faculty to communicate ideas to the public through collaborating with and learning from teacher participants. The team also strives to use ideas from participants that can be incorporated into teaching UCSD AI systems. Finally, the main goal of the site is that teachers understand the ethical implications and current challenges of AI to promote awareness, discussion, and excitement among their students.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.