BEGIN:VCALENDAR VERSION:2.0 PRODID:-//142.1.176.180//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://engsci.utoronto.ca X-WR-TIMEZONE:America/Toronto BEGIN:VTIMEZONE TZID:America/Toronto X-LIC-LOCATION:America/Toronto BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-99134@engsci.utoronto.ca DTSTAMP:20240330T021258Z CATEGORIES;LANGUAGE=en-CA:Alumni Events CONTACT:events@engineering.utoronto.ca\; https://alumni.engineering.utoront o.ca/events/ DESCRIPTION: \n\nU of T Engineering alumni\, join us for this monthly serie s.\nArtificial Intelligence (AI) is now a part of the standard physical sc ientist’s tool kit\, and it is regularly used to discover exciting new mat erials and processes. But AI is famously fickle\, susceptible to data set bias and imbalance\, subject to information leakage during training\, and reliant on humans to evaluate its performance.\nProfessor Jason Hattrick-S impers (MSE) discusses best practices for the implementation of AI techniq ues in the field of materials science\, the challenges and successes of hi s research\, and why he believes that robots can help us learn to better t rust AI.\nRead the abstracts and register for this free and exclusive even t.\nTickets: https://alumni.engineering.utoronto.ca/event/skule-lunch-lear n-apr13-2022/. DTSTART;TZID=America/Toronto:20220413T120000 DTEND;TZID=America/Toronto:20220413T130000 LOCATION:Online event SEQUENCE:0 SUMMARY:Skule Lunch & Learn presents: An Experimentalist’s View on Trusting AI and Its BFF (Data) URL:https://engsci.utoronto.ca/event/skule-lunch-learn-presents-bold-innova tions-engineering-research-highlights-2/ X-COST-TYPE:external X-WP-IMAGES-URL:thumbnail\;https://engsci.utoronto.ca/wp-content/uploads/20 21/09/lunch_learn_2022-150x150.png\;150\;150\;1\,medium\;https://engsci.ut oronto.ca/wp-content/uploads/2021/09/lunch_learn_2022-300x115.png\;300\;11 5\;1\,large\;https://engsci.utoronto.ca/wp-content/uploads/2021/09/lunch_l earn_2022-1024x394.png\;1024\;394\;1\,full\;https://engsci.utoronto.ca/wp- content/uploads/2021/09/lunch_learn_2022.png\;1280\;492\; X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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Artificial Intelligence (AI) is now a part of the standard physic al scientist’s tool kit\, and it is regularly used to discover exciting ne w materials and processes. But AI is famously fickle\, susceptible to data set bias and imbalance\, subject to information leakage during training\, and reliant on humans to evaluate its performance.
\nProfessor
Read the abstracts and register for this free and exclusive event.
\nTickets: https://alumni.engineering.utoronto.ca/event/skule-lunch-learn-apr13-202 2/.
X-TAGS;LANGUAGE=en-CA:alumni\,artificial intelligence\,materials science\,M SE\,Professor Jason Hattrick-Simpers\,research\,Skule Lunch &\; Learn X-TICKETS-URL:https://alumni.engineering.utoronto.ca/event/skule-lunch-lear n-apr13-2022/ END:VEVENT END:VCALENDAR