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Prof Sai Prasanth Krishnamoorthy (Honeywell) and Prof Ryan Rhodes, (RU Center for Cognitive Science) LOCATION:Hybrid - Registration Required DESCRIPTION:
In-Person Registration Link< /a>
& nbsp;
Sai received his PhD in Robotics from NYU Tandon School of Engineering and is currently at Honeywell Robotics as an Advanced Software Engineer. He is a big propone nt of advancing K-12 STEM education and encouraging younger generations.
Abst ract: Manufacturing facilities, distribution centers, and warehous es are increasingly favoring automation and usage of robotics & mechatr onics. This transition has accelerated significantly since the pandemic, pr imarily due to the shortage of workforce and nation wide shutdowns. Industr ial economy almost reached entirely halted due to this massive disruption. This incident is compelling industry to rapidly embrace strategy changes, i nfrastructure upgrades, and flexible policies to prevent future disruptions .
As a powerful automation tool and with its ever increasing populari ty, AI/ML (Artificial intelligence and Machine learning) in industrial auto mation is poised to achieve greater efficiency and independence from human workforce. However, synergy between human operators and autonomous agents i s needed to ensure optimal behavior. This talk investigates the role of hum an operators in advanced industrial settings and the importance of manual i nterventions to ensure accurate decision-making and safety. The talk also h ighlights several examples from state-of-the-art industrial robotics applic ations to show human-robot collaboration and coexistence.
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Ryan is a neurolinguist whose re search is at the intersection of theoretical linguistics and cognitive neur oscience. He received a PhD in linguistics from the University of Delaware, where he worked as lab manager for Arild Hestvik’s psycholinguistics ERP l ab, and an MA in linguistics from Fresno State, where he worked on the Chuk chansi Yokuts language documentation project. His current work combines ele ctrophysiological (EEG) and behavioral measures to investigate mental repre sentations of linguistic structure, auditory prediction, and rule learning.
Ab stract: A central question in linguistics is what sorts of computa tions humans make when we learn and process languages. By coupling artifici al grammar learning with the descriptive toolset of formal learning theory, we can make testable hypotheses about the core computations executed by th e faculty of language. For example, the subregular hypothesis claims that t he faculty of language contains a phonological learning module that is rest ricted to a very limited range of complexity—it can learn and compute only Strictly Local (SL) and Strictly Piecewise (SP) patterns (Heinz, 2010; Hein z & Idsardi, 2013). Using ERPs and a deviance detection AGL paradigm, w e investigate the learning strategies used by monolingual English speakers exposed to novel long-distance (SP) phonological patterns. I report two mai n findings here: (1) that L1 English learners have a bias toward an SL comp utational-representational strategy; and (2) that an implicit learning para digm leads to weak behavior evidence of learning but a robust error detecti on brain response; and conversely, that an explicit learning paradigm leads to much stronger behavioral evidence of learning but is an absent interpre table brain response.
X-ALT-DESC;FMTTYPE=text/html:In-Person Registration Link< /a>
Sai received his PhD in Robotics fro m NYU Tandon School of Engineering and is currently at Honeywell Robotics a s an Advanced Software Engineer. He is a big proponent of advancing K-12 ST EM education and encouraging younger generations.
Abstract: Manufact uring facilities, distribution centers, and warehouses are increasingly fav oring automation and usage of robotics & mechatronics. This transition has accelerated significantly since the pandemic, primarily due to the shor tage of workforce and nation wide shutdowns. Industrial economy almost reac hed entirely halted due to this massive disruption. This incident is compel ling industry to rapidly embrace strategy changes, infrastructure upgrades, and flexible policies to prevent future disruptions.
As a powerful a utomation tool and with its ever increasing popularity, AI/ML (Artificial i ntelligence and Machine learning) in industrial automation is poised to ach ieve greater efficiency and independence from human workforce. However, syn ergy between human operators and autonomous agents is needed to ensure opti mal behavior. This talk investigates the role of human operators in advance d industrial settings and the importance of manual interventions to ensure accurate decision-making and safety. The talk also highlights several examp les from state-of-the-art industrial robotics applications to show human-ro bot collaboration and coexistence.
* * * * * * * * * * * * * * * * *
Rya n is a neurolinguist whose research is at the intersection of theoretical l inguistics and cognitive neuroscience. He received a PhD in linguistics fro m the University of Delaware, where he worked as lab manager for Arild Hest vik’s psycholinguistics ERP lab, and an MA in linguistics from Fresno State , where he worked on the Chukchansi Yokuts language documentation project. His current work combines electrophysiological (EEG) and behavioral measure s to investigate mental representations of linguistic structure, auditory p rediction, and rule learning.
Abstract: A central question in linguis tics is what sorts of computations humans make when we learn and process la nguages. By coupling artificial grammar learning with the descriptive tools et of formal learning theory, we can make testable hypotheses about the cor e computations executed by the faculty of language. For example, the subreg ular hypothesis claims that the faculty of language contains a phonological learning module that is restricted to a very limited range of complexity—i t can learn and compute only Strictly Local (SL) and Strictly Piecewise (SP ) patterns (Heinz, 2010; Heinz & Idsardi, 2013). Using ERPs and a devia nce detection AGL paradigm, we investigate the learning strategies used by monolingual English speakers exposed to novel long-distance (SP) phonologic al patterns. I report two main findings here: (1) that L1 English learners have a bias toward an SL computational-representational strategy; and (2) t hat an implicit learning paradigm leads to weak behavior evidence of learni ng but a robust error detection brain response; and conversely, that an exp licit learning paradigm leads to much stronger behavioral evidence of learn ing but is an absent interpretable brain response.
CONTACT:Prof. Sai Prasanth Krishnamoorthy and Prof. Ryan Rhodes DTSTAMP:20240329T140211 DTSTART;TZID=America/New_York:20220412T130000 DTEND;TZID=America/New_York:20220412T135000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR