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Hilary Mason: The Future of AI and Machine Learning | Keynote Address | ODSC East 2019
Sign up for our free livestream: https://odsc.com/boston/livestream/
In this video, watch this special keynote talk from Hilary Mason about "The Present and Future of Artificial Intelligence and Machine Learning" during the Open Data Science Conference in Boston 2019. Hilary is a data scientist at Accel Partners, as well as the founder of technology at her startup, Fast Forward Labs.
Do You Like This Video? Share Your Thoughts in Comments Below
Visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops: https://odsc.com/
Sign up for the newsletter to stay up to date with the latest trends in data science: https://opendatascience.com/newsletter/
Follow Us Online!
Facebook: https://www.facebook.com/OPENDATASCI/
Instagram: https://www.ins...
published: 04 Mar 2020
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Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, GM for Machine Learning, Cloudera
Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, General Manager for Machine Learning, Cloudera
About Hilary Mason
Hilary Mason is the General Manager for Machine Learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company, which was acquired by Cloudera in 2017. Hilary is the Data Scientist in Residence at Accel Partners and is on the board of the Anita Borg Institute. Previously, she co-founded HackNY.org, a non-profit that helps engineering students find opportunities in New York's creative technical economy, served on Mayer Bloomberg's Technology Advisory Council and was the Chief Scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.
published: 15 Oct 2018
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Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
This talk introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on sem...
published: 06 Sep 2016
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Seattle Neighborhood Greenways - 1.16.2013 Washington Institute of Transportation Engineers luncheon
PowerPoint deck can be found at: http://ugreenways.org/media/WA%20ITE%20Brownbag%20Final.pptx
Presentation by Cathy Tuttle and Eli Goldberg of Seattle Neighborhood Greenways to the Washington Institute of Transportation Engineers introducing neighborhood greenways and the opportunity for Washington's communities.
Visit us at: seattlegreenways.org
published: 28 Jan 2013
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SunoikisisDC Spring 2017: Session 7
SunoikisisDC Spring 2017: Session 7
Prosopography (Rada Varga and Gabriel Bodard)
Class Outline: https://github.com/SunoikisisDC/SunoikisisDC-2016-2017/wiki/Prosopography
published: 09 Mar 2017
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What Happens If The Sitting Prime Minister Dies? (Line Of Succession)
TL:DW - line of succession is 0 people long
http://reddit.com/r/toycat - Subreddit community!
Want to know what I use for my recording/gaming setup? https://www.amazon.com/shop/ibxtoycat
Check out my main channel at http://youtube.com/ibxtoycat
Also on twitter @ibxtoycat
published: 07 Apr 2020
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Fairfax County, Virginia
This article is about the county. For the city with the same name, see Fairfax, Virginia. For other uses, see Fairfax (disambiguation).
Fairfax County, officially the County of Fairfax, is a county in the Commonwealth of Virginia. As of the 2010 census, the population was 1,081,726, in 2013, the population was estimated to be 1,116,897, making it the most populous jurisdiction in the Commonwealth of Virginia, with 13.6% of Virginia's population. The county is also the most populous jurisdiction in the Washington Metropolitan Area, with 19.8% of the MSA population, as well as the larger Baltimore–Washington Metropolitan Area, with 13.1% of the CSA population. The county seat is Fairfax.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Com...
published: 18 Nov 2014
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PolyLM: Learning about Polysemy through Language Modeling por Alan Ansell
Abstract: To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondl...
published: 17 Mar 2021
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Predicting Water Resource & Hazard Risks - UCAR Congressional Briefing 2016
Water resources are critical to human survival. Water also drives many sectors of the U.S. economy. Water challenges range from growing resource demand, to flooding and drought events, to changes in how the environment and built structures store this vital resource.
This briefing to congressional staff and agency representatives outlines how U.S. academic and research organizations; federal, state, and local agencies; and private industry are working together to address society's needs for better water prediction tools.
Recording conditions for this briefing were not optimal; we regret the poor quality of the audio.
______________
PANEL
- Transforming NOAA water prediction for a water-prepared nation
EDWARD CLARK, Director, Geo-Intelligence Division, National Oceanic & Atmospheric Ad...
published: 31 Oct 2016
-
Identifying ESIP
Recording of session held at ESIP Winter Meeting in Bethesda, MD in January 2020. Learn more at https://sched.co/Xrhx.
published: 24 Jan 2020
38:51
Hilary Mason: The Future of AI and Machine Learning | Keynote Address | ODSC East 2019
Sign up for our free livestream: https://odsc.com/boston/livestream/
In this video, watch this special keynote talk from Hilary Mason about "The Present and Fu...
Sign up for our free livestream: https://odsc.com/boston/livestream/
In this video, watch this special keynote talk from Hilary Mason about "The Present and Future of Artificial Intelligence and Machine Learning" during the Open Data Science Conference in Boston 2019. Hilary is a data scientist at Accel Partners, as well as the founder of technology at her startup, Fast Forward Labs.
Do You Like This Video? Share Your Thoughts in Comments Below
Visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops: https://odsc.com/
Sign up for the newsletter to stay up to date with the latest trends in data science: https://opendatascience.com/newsletter/
Follow Us Online!
Facebook: https://www.facebook.com/OPENDATASCI/
Instagram: https://www.instagram.com/odsc/
Blog: https://opendatascience.com/
Linkedin: https://www.linkedin.com/company/open-data-science/
Learning Videos: https://learnai.odsc.com
#ODSC #AI #MachineLearning
https://wn.com/Hilary_Mason_The_Future_Of_Ai_And_Machine_Learning_|_Keynote_Address_|_Odsc_East_2019
Sign up for our free livestream: https://odsc.com/boston/livestream/
In this video, watch this special keynote talk from Hilary Mason about "The Present and Future of Artificial Intelligence and Machine Learning" during the Open Data Science Conference in Boston 2019. Hilary is a data scientist at Accel Partners, as well as the founder of technology at her startup, Fast Forward Labs.
Do You Like This Video? Share Your Thoughts in Comments Below
Visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops: https://odsc.com/
Sign up for the newsletter to stay up to date with the latest trends in data science: https://opendatascience.com/newsletter/
Follow Us Online!
Facebook: https://www.facebook.com/OPENDATASCI/
Instagram: https://www.instagram.com/odsc/
Blog: https://opendatascience.com/
Linkedin: https://www.linkedin.com/company/open-data-science/
Learning Videos: https://learnai.odsc.com
#ODSC #AI #MachineLearning
- published: 04 Mar 2020
- views: 7278
25:36
Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, GM for Machine Learning, Cloudera
Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, General Manager for Machine Learning, Cloudera
About Hilary Mason
Hilary Mason is the Genera...
Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, General Manager for Machine Learning, Cloudera
About Hilary Mason
Hilary Mason is the General Manager for Machine Learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company, which was acquired by Cloudera in 2017. Hilary is the Data Scientist in Residence at Accel Partners and is on the board of the Anita Borg Institute. Previously, she co-founded HackNY.org, a non-profit that helps engineering students find opportunities in New York's creative technical economy, served on Mayer Bloomberg's Technology Advisory Council and was the Chief Scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.
https://wn.com/Keynote_Ai_In_The_Real_World_Today_And_Tomorrow_Hilary_Mason,_Gm_For_Machine_Learning,_Cloudera
Keynote: AI in the Real World: Today and Tomorrow - Hilary Mason, General Manager for Machine Learning, Cloudera
About Hilary Mason
Hilary Mason is the General Manager for Machine Learning at Cloudera. Previously, she founded Fast Forward Labs, an applied machine learning research and advisory company, which was acquired by Cloudera in 2017. Hilary is the Data Scientist in Residence at Accel Partners and is on the board of the Anita Borg Institute. Previously, she co-founded HackNY.org, a non-profit that helps engineering students find opportunities in New York's creative technical economy, served on Mayer Bloomberg's Technology Advisory Council and was the Chief Scientist at Bitly. Hilary can be reached on Twitter @hmason and on LinkedIn.
- published: 15 Oct 2018
- views: 978
1:10:41
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
This talk introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including in...
This talk introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47 (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus.
https://wn.com/Human_Level_Performance_On_Word_Analogy_Questions_By_Latent_Relational_Analysis
This talk introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47 (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus.
- published: 06 Sep 2016
- views: 142
1:12:55
Seattle Neighborhood Greenways - 1.16.2013 Washington Institute of Transportation Engineers luncheon
PowerPoint deck can be found at: http://ugreenways.org/media/WA%20ITE%20Brownbag%20Final.pptx
Presentation by Cathy Tuttle and Eli Goldberg of Seattle Neighbor...
PowerPoint deck can be found at: http://ugreenways.org/media/WA%20ITE%20Brownbag%20Final.pptx
Presentation by Cathy Tuttle and Eli Goldberg of Seattle Neighborhood Greenways to the Washington Institute of Transportation Engineers introducing neighborhood greenways and the opportunity for Washington's communities.
Visit us at: seattlegreenways.org
https://wn.com/Seattle_Neighborhood_Greenways_1.16.2013_Washington_Institute_Of_Transportation_Engineers_Luncheon
PowerPoint deck can be found at: http://ugreenways.org/media/WA%20ITE%20Brownbag%20Final.pptx
Presentation by Cathy Tuttle and Eli Goldberg of Seattle Neighborhood Greenways to the Washington Institute of Transportation Engineers introducing neighborhood greenways and the opportunity for Washington's communities.
Visit us at: seattlegreenways.org
- published: 28 Jan 2013
- views: 893
1:22:24
SunoikisisDC Spring 2017: Session 7
SunoikisisDC Spring 2017: Session 7
Prosopography (Rada Varga and Gabriel Bodard)
Class Outline: https://github.com/SunoikisisDC/SunoikisisDC-2016-2017/wiki/Pr...
SunoikisisDC Spring 2017: Session 7
Prosopography (Rada Varga and Gabriel Bodard)
Class Outline: https://github.com/SunoikisisDC/SunoikisisDC-2016-2017/wiki/Prosopography
https://wn.com/Sunoikisisdc_Spring_2017_Session_7
SunoikisisDC Spring 2017: Session 7
Prosopography (Rada Varga and Gabriel Bodard)
Class Outline: https://github.com/SunoikisisDC/SunoikisisDC-2016-2017/wiki/Prosopography
- published: 09 Mar 2017
- views: 120
9:51
What Happens If The Sitting Prime Minister Dies? (Line Of Succession)
TL:DW - line of succession is 0 people long
http://reddit.com/r/toycat - Subreddit community!
Want to know what I use for my recording/gaming setup? https://w...
TL:DW - line of succession is 0 people long
http://reddit.com/r/toycat - Subreddit community!
Want to know what I use for my recording/gaming setup? https://www.amazon.com/shop/ibxtoycat
Check out my main channel at http://youtube.com/ibxtoycat
Also on twitter @ibxtoycat
https://wn.com/What_Happens_If_The_Sitting_Prime_Minister_Dies_(Line_Of_Succession)
TL:DW - line of succession is 0 people long
http://reddit.com/r/toycat - Subreddit community!
Want to know what I use for my recording/gaming setup? https://www.amazon.com/shop/ibxtoycat
Check out my main channel at http://youtube.com/ibxtoycat
Also on twitter @ibxtoycat
- published: 07 Apr 2020
- views: 57622
33:51
Fairfax County, Virginia
This article is about the county. For the city with the same name, see Fairfax, Virginia. For other uses, see Fairfax (disambiguation).
Fairfax County, official...
This article is about the county. For the city with the same name, see Fairfax, Virginia. For other uses, see Fairfax (disambiguation).
Fairfax County, officially the County of Fairfax, is a county in the Commonwealth of Virginia. As of the 2010 census, the population was 1,081,726, in 2013, the population was estimated to be 1,116,897, making it the most populous jurisdiction in the Commonwealth of Virginia, with 13.6% of Virginia's population. The county is also the most populous jurisdiction in the Washington Metropolitan Area, with 19.8% of the MSA population, as well as the larger Baltimore–Washington Metropolitan Area, with 13.1% of the CSA population. The county seat is Fairfax.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
https://wn.com/Fairfax_County,_Virginia
This article is about the county. For the city with the same name, see Fairfax, Virginia. For other uses, see Fairfax (disambiguation).
Fairfax County, officially the County of Fairfax, is a county in the Commonwealth of Virginia. As of the 2010 census, the population was 1,081,726, in 2013, the population was estimated to be 1,116,897, making it the most populous jurisdiction in the Commonwealth of Virginia, with 13.6% of Virginia's population. The county is also the most populous jurisdiction in the Washington Metropolitan Area, with 19.8% of the MSA population, as well as the larger Baltimore–Washington Metropolitan Area, with 13.1% of the CSA population. The county seat is Fairfax.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
- published: 18 Nov 2014
- views: 59
41:59
PolyLM: Learning about Polysemy through Language Modeling por Alan Ansell
Abstract: To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one t...
Abstract: To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others. We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, achieving state-of-the-art performance on the SemEval-2010 and 2013 datasets.
Short bio: Alan Ansell is a PhD student in NLP at the University of Cambridge's Language Technology Lab. He was previously a Masters student at the University of Waikato under the supervision of Bernhard Pfahringer and Felipe Bravo-Márquez.
https://wn.com/Polylm_Learning_About_Polysemy_Through_Language_Modeling_Por_Alan_Ansell
Abstract: To avoid the “meaning conflation deficiency” of word embeddings, a number of models have aimed to embed individual word senses. These methods at one time performed well on tasks such as word sense induction (WSI), but they have since been overtaken by task-specific techniques which exploit contextualized embeddings. However, sense embeddings and contextualization need not be mutually exclusive. We introduce PolyLM, a method which formulates the task of learning sense embeddings as a language modeling problem, allowing contextualization techniques to be applied. PolyLM is based on two underlying assumptions about word senses: firstly, that the probability of a word occurring in a given context is equal to the sum of the probabilities of its individual senses occurring; and secondly, that for a given occurrence of a word, one of its senses tends to be much more plausible in the context than the others. We evaluate PolyLM on WSI, showing that it performs considerably better than previous sense embedding techniques, achieving state-of-the-art performance on the SemEval-2010 and 2013 datasets.
Short bio: Alan Ansell is a PhD student in NLP at the University of Cambridge's Language Technology Lab. He was previously a Masters student at the University of Waikato under the supervision of Bernhard Pfahringer and Felipe Bravo-Márquez.
- published: 17 Mar 2021
- views: 228
1:02:11
Predicting Water Resource & Hazard Risks - UCAR Congressional Briefing 2016
Water resources are critical to human survival. Water also drives many sectors of the U.S. economy. Water challenges range from growing resource demand, to floo...
Water resources are critical to human survival. Water also drives many sectors of the U.S. economy. Water challenges range from growing resource demand, to flooding and drought events, to changes in how the environment and built structures store this vital resource.
This briefing to congressional staff and agency representatives outlines how U.S. academic and research organizations; federal, state, and local agencies; and private industry are working together to address society's needs for better water prediction tools.
Recording conditions for this briefing were not optimal; we regret the poor quality of the audio.
______________
PANEL
- Transforming NOAA water prediction for a water-prepared nation
EDWARD CLARK, Director, Geo-Intelligence Division, National Oceanic & Atmospheric Administration (NOAA)
- The value of an operational water forecasting model - WRF-Hydro
DAVID GOCHIS, Scientist, National Center for Atmospheric Research (NCAR), Boulder, Colorado
- Water for ecosystems and society: The mutual benefits of the National Water Model and watershed ecohydrology research
RYAN EMANUEL, Associate Professor, Department of Forestry and Environmental Resources, North Carolina State University
- The National Water Model: The first comprehensive framework for predicting streamflow
RICHARD HOOPER, Executive Director, The Consortium for the Advancement of Hydrologic Science, Inc. (CUAHSI)
- Using National Water Model results to benefit industry
JOHN McHENRY, Chief Scientist, Baron Advanced Meteorological Systems, Huntsville,
Alabama
______________
Hosted by UCAR in the Senate Visitor Center, Washington, DC, September 13, 2016.
More UCAR Congressional Briefings: http://president.ucar.edu/government-relations/ucar-briefings/stories
UCAR Government Relations:
http://president.ucar.edu/government-relations
https://wn.com/Predicting_Water_Resource_Hazard_Risks_Ucar_Congressional_Briefing_2016
Water resources are critical to human survival. Water also drives many sectors of the U.S. economy. Water challenges range from growing resource demand, to flooding and drought events, to changes in how the environment and built structures store this vital resource.
This briefing to congressional staff and agency representatives outlines how U.S. academic and research organizations; federal, state, and local agencies; and private industry are working together to address society's needs for better water prediction tools.
Recording conditions for this briefing were not optimal; we regret the poor quality of the audio.
______________
PANEL
- Transforming NOAA water prediction for a water-prepared nation
EDWARD CLARK, Director, Geo-Intelligence Division, National Oceanic & Atmospheric Administration (NOAA)
- The value of an operational water forecasting model - WRF-Hydro
DAVID GOCHIS, Scientist, National Center for Atmospheric Research (NCAR), Boulder, Colorado
- Water for ecosystems and society: The mutual benefits of the National Water Model and watershed ecohydrology research
RYAN EMANUEL, Associate Professor, Department of Forestry and Environmental Resources, North Carolina State University
- The National Water Model: The first comprehensive framework for predicting streamflow
RICHARD HOOPER, Executive Director, The Consortium for the Advancement of Hydrologic Science, Inc. (CUAHSI)
- Using National Water Model results to benefit industry
JOHN McHENRY, Chief Scientist, Baron Advanced Meteorological Systems, Huntsville,
Alabama
______________
Hosted by UCAR in the Senate Visitor Center, Washington, DC, September 13, 2016.
More UCAR Congressional Briefings: http://president.ucar.edu/government-relations/ucar-briefings/stories
UCAR Government Relations:
http://president.ucar.edu/government-relations
- published: 31 Oct 2016
- views: 706
1:12:23
Identifying ESIP
Recording of session held at ESIP Winter Meeting in Bethesda, MD in January 2020. Learn more at https://sched.co/Xrhx.
Recording of session held at ESIP Winter Meeting in Bethesda, MD in January 2020. Learn more at https://sched.co/Xrhx.
https://wn.com/Identifying_Esip
Recording of session held at ESIP Winter Meeting in Bethesda, MD in January 2020. Learn more at https://sched.co/Xrhx.
- published: 24 Jan 2020
- views: 23