"Once the team were confident the computer could identify different signs in this way, they exposed it to around 10 hours of TV footage that was both signed and subtitled. They tasked the software with learning the signs for a mixture of 210 nouns and adjectives that appeared multiple times during the footage."
http://www.newscientist.com/article/dn17431-computer-learns-sign-language-by-watching-tv.html
Patrick Buehler:
http://patrick.buehler.googlepages.com/home
Andrew Zisserman:
http://www.robots.ox.ac.uk/~az/
Mark Everingham:
http://www.comp.leeds.ac.uk/me/
"We propose a framework based on multiple instance
learning which can learn a large number of British Sign
Language signs from TV broadcasts. We achieve very
promising results even under these weak and noisy conditions
by using a state-of-the-art upper-body tracker, descriptors
of the hands that properly model the case of touching
hands, and a plentiful supply of data. A similar method
could be applied to a variety of fields where weak supervision
is available, such as learning gestures and actions."
Learning Sign Language by Watching Tv
Friday, July 10, 2009
Social Security Numbers Can Be Predicted With Public Information
"Carnegie Mellon University researchers have shown that public information readily gleaned from governmental sources, commercial data bases, or online social networks can be used to routinely predict most — and sometimes all — of an individual's nine-digit Social Security number."
http://www.sciencedaily.com/releases/2009/07/090706171509.htm
More info from CMU: http://blogs.heinz.cmu.edu/ssnstudy/
Ralph Gross:
http://www.ri.cmu.edu/person.html?person_id=742
Alessandro Acquisti:
http://www.heinz.cmu.edu/~acquisti/
http://www.sciencedaily.com/releases/2009/07/090706171509.htm
More info from CMU: http://blogs.heinz.cmu.edu/ssnstudy/
Ralph Gross:
http://www.ri.cmu.edu/person.html?person_id=742
Alessandro Acquisti:
http://www.heinz.cmu.edu/~acquisti/
Wednesday, June 24, 2009
Machine Learning and Trading: Fina Technologies
Company applies machine learning to quantitative trading.
http://www.finatechnologies.com/about.html
CEO is Joshua Holden
"... trading expertise covers US Government Bonds and Options, US Agency Debt, FX spot and forwards, and US$ Derivatives (Swaps and Volatility). He has held desk-head positions at Goldman Sachs, Deutsche Bank, and most recently Countrywide Capital Markets. At every stop, he has focused on applying cutting-edge technology to the problems of price & model discovery, execution, and risk-management. Josh graduated MIT in 1993 with both a BS and MS in Electrical Engineering."
Investors include Reed Elsevier Ventures; spinoff from Gene Network Sciences,
http://www.finatechnologies.com/about.html
CEO is Joshua Holden
"... trading expertise covers US Government Bonds and Options, US Agency Debt, FX spot and forwards, and US$ Derivatives (Swaps and Volatility). He has held desk-head positions at Goldman Sachs, Deutsche Bank, and most recently Countrywide Capital Markets. At every stop, he has focused on applying cutting-edge technology to the problems of price & model discovery, execution, and risk-management. Josh graduated MIT in 1993 with both a BS and MS in Electrical Engineering."
Investors include Reed Elsevier Ventures; spinoff from Gene Network Sciences,
Thursday, June 18, 2009
Hedge Fund Startups
Hedge Fund Startups
The excel spreadsheet is available here: http://www.scribd.com/doc/16561645/Hedge-Fund-Startup-Investments
The excel spreadsheet is available here: http://www.scribd.com/doc/16561645/Hedge-Fund-Startup-Investments
Saturday, May 23, 2009
Robot Scientist; Ross King, Adam, & "Automation of Science"
Robot achieves scientific first http://www.ft.com/cms/s/0/f2b97d9a-1f96-11de-a7a5-00144feabdc0.html
"A laboratory robot called Adam has been hailed as the first machine in history to have discovered new scientific knowledge independently of its human creators.
Adam formed a hypothesis on the genetics of bakers’ yeast and carried out experiments to test its predictions, without intervention from its makers at Aberystwyth University.
The result was a series of “simple but useful” discoveries, confirmed by human scientists, about the gene coding for yeast enzymes. The research is published in the journal Science.
Professor Ross King, the chief creator of Adam, said robots would not supplant human researchers but make their work more productive and interesting."
The Paper, "Automation of Science":
"The Automation of Science
Ross D. King,1* Jem Rowland,1 Stephen G. Oliver,2 Michael Young,3 Wayne Aubrey,1 Emma Byrne,1 Maria Liakata,1 Magdalena Markham,1 Pinar Pir,2 Larisa N. Soldatova,1 Andrew Sparkes,1 Kenneth E. Whelan,1 Amanda Clare1
The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge."
http://www.sciencemag.org/cgi/content/full/324/5923/85
Robot Scientist Website: http://www.aber.ac.uk/compsci/Research/bio/robotsci/intro/
"The Robot Scientist is perhaps the first physical implementation of the task of Scientific Discovery in a microbiology laboratory. It represents the merging of increasingly automated and remotely controllable laboratory equipment and knowledge discovery techniques from Artificial Intelligence.
The robot in our lab
Automation of laboratory equipment (the "Robot" of Robot Scientist) has revolutionised laboratory practice by removing the "drudgery" of constructing many wet lab experiments by hand, allowing an increase in both the scope and scale of potential experiments. Most lab robots only require a simple description of the various chemical/ biological entities to be used in the experiments, along with their required volumes and where these entities are stored. Automation has also given rise to significantly increased productivity and a concomitant increase in the production of results and data requiring interpretation, giving rise to an "interpretation bottleneck" where the process of understanding the results is lagging behind the production of results.
The research fields of Computational Scientific Discovery and Bioinformatics have emerged in part as a response to this bottleneck. Both disciplines use computational approaches from Statistics and Machine Learning to provide an "automated understanding" of the experimental results.
It has become typical practice in Bioinformatics to separate the data collection or experimentation process and the understanding process, where large numbers of experiments are conducted and then specially designed data mining tools are used to identify correlations in the data that might represent hitherto undiscovered scientific knowledge.
This knowledge will initially correspond to the goals of the scientific task, but increasingly the internet repositories that are often constructed to store the data have become the focus of less directed scientific study, where "hidden" knowledge not originally anticipated by the goals of the scientific task may be found. However, this "scrapyard" approach is partly a result of overexperimentation where many unnecessary experiments were conducted along with the potentially informative ones.
PC and Sciclone
The Robot Scientist makes use of an iterative approach to experimentation, where knowledge aquired from a previous iteration is used to guide the next experimentation step. This is a process known as Active Learning, where the learner can plan its own agenda, i.e. decide how best to improve its knowledge base and how to go about acquiring this information. The Robot Scientist uses the laboratory robot to execute the experiment(s) selected as most informative; has a plate reader to analyse the experiments, generating data corresponding to the scientific observations; uses abductive logic programming to generate valid hypotheses that explain the observations; and uses these hypotheses to determine the next most informative experiment. At the beginning of any investigation, the Robot Scientist has not discovered any information, therefore all possible hypotheses are equally valid. As the directed discovery process continues, each new observation (or experiment/interpretation cycle) will invalidate some of the hypotheses, thereby excluding incorrect discoveries. The experiment selection process aims to choose the experiment most likely to refute the most hypotheses. This iterative process allows irrelevant experiments to be avoided, potentially saving both laboratory time and the cost of using unnecessary reagents and biological materials."
Ross King CV:
http://www.pdfdownload.org/pdf2html/pdf2html.php?url=http%3A%2F%2Fusers.aber.ac.uk%2Frdk%2Fcv.pdf&images=yes
"A laboratory robot called Adam has been hailed as the first machine in history to have discovered new scientific knowledge independently of its human creators.
Adam formed a hypothesis on the genetics of bakers’ yeast and carried out experiments to test its predictions, without intervention from its makers at Aberystwyth University.
The result was a series of “simple but useful” discoveries, confirmed by human scientists, about the gene coding for yeast enzymes. The research is published in the journal Science.
Professor Ross King, the chief creator of Adam, said robots would not supplant human researchers but make their work more productive and interesting."
The Paper, "Automation of Science":
"The Automation of Science
Ross D. King,1* Jem Rowland,1 Stephen G. Oliver,2 Michael Young,3 Wayne Aubrey,1 Emma Byrne,1 Maria Liakata,1 Magdalena Markham,1 Pinar Pir,2 Larisa N. Soldatova,1 Andrew Sparkes,1 Kenneth E. Whelan,1 Amanda Clare1
The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge."
http://www.sciencemag.org/cgi/content/full/324/5923/85
Robot Scientist Website: http://www.aber.ac.uk/compsci/Research/bio/robotsci/intro/
"The Robot Scientist is perhaps the first physical implementation of the task of Scientific Discovery in a microbiology laboratory. It represents the merging of increasingly automated and remotely controllable laboratory equipment and knowledge discovery techniques from Artificial Intelligence.
The robot in our lab
Automation of laboratory equipment (the "Robot" of Robot Scientist) has revolutionised laboratory practice by removing the "drudgery" of constructing many wet lab experiments by hand, allowing an increase in both the scope and scale of potential experiments. Most lab robots only require a simple description of the various chemical/ biological entities to be used in the experiments, along with their required volumes and where these entities are stored. Automation has also given rise to significantly increased productivity and a concomitant increase in the production of results and data requiring interpretation, giving rise to an "interpretation bottleneck" where the process of understanding the results is lagging behind the production of results.
The research fields of Computational Scientific Discovery and Bioinformatics have emerged in part as a response to this bottleneck. Both disciplines use computational approaches from Statistics and Machine Learning to provide an "automated understanding" of the experimental results.
It has become typical practice in Bioinformatics to separate the data collection or experimentation process and the understanding process, where large numbers of experiments are conducted and then specially designed data mining tools are used to identify correlations in the data that might represent hitherto undiscovered scientific knowledge.
This knowledge will initially correspond to the goals of the scientific task, but increasingly the internet repositories that are often constructed to store the data have become the focus of less directed scientific study, where "hidden" knowledge not originally anticipated by the goals of the scientific task may be found. However, this "scrapyard" approach is partly a result of overexperimentation where many unnecessary experiments were conducted along with the potentially informative ones.
PC and Sciclone
The Robot Scientist makes use of an iterative approach to experimentation, where knowledge aquired from a previous iteration is used to guide the next experimentation step. This is a process known as Active Learning, where the learner can plan its own agenda, i.e. decide how best to improve its knowledge base and how to go about acquiring this information. The Robot Scientist uses the laboratory robot to execute the experiment(s) selected as most informative; has a plate reader to analyse the experiments, generating data corresponding to the scientific observations; uses abductive logic programming to generate valid hypotheses that explain the observations; and uses these hypotheses to determine the next most informative experiment. At the beginning of any investigation, the Robot Scientist has not discovered any information, therefore all possible hypotheses are equally valid. As the directed discovery process continues, each new observation (or experiment/interpretation cycle) will invalidate some of the hypotheses, thereby excluding incorrect discoveries. The experiment selection process aims to choose the experiment most likely to refute the most hypotheses. This iterative process allows irrelevant experiments to be avoided, potentially saving both laboratory time and the cost of using unnecessary reagents and biological materials."
Ross King CV:
http://www.pdfdownload.org/pdf2html/pdf2html.php?url=http%3A%2F%2Fusers.aber.ac.uk%2Frdk%2Fcv.pdf&images=yes
Monday, May 18, 2009
Machine controls Bacteria : Sylvain Martel
Researchers in Canada have created a solar-powered micro-machine that is no bigger than the period at the end of this sentence. The tiny machine can carry out basic sensing tasks and can indirectly control the movement of a swarm of bacteria in the same Petri dish.
Sylvain Martel, Director of the NanoRobotics Laboratory at the École Polytechnique de Montréal, previously showed a way to control bacteria attached to microbeads using an MRI machine. His new micro-machine, which measure 300x300 microns and carry tiny solar panels, will be presented this week at ICRA '09 in Japan.
http://www.technologyreview.com/blog/editors/23533/
Sylvain Martel website
http://www.polymtl.ca/recherche/rc/en/professeurs/details.php?NoProf=122
Sylvain Martel Nanorobotics Lab
http://www.nano.polymtl.ca/
Sylvain Martel, Director of the NanoRobotics Laboratory at the École Polytechnique de Montréal, previously showed a way to control bacteria attached to microbeads using an MRI machine. His new micro-machine, which measure 300x300 microns and carry tiny solar panels, will be presented this week at ICRA '09 in Japan.
http://www.technologyreview.com/blog/editors/23533/
Sylvain Martel website
http://www.polymtl.ca/recherche/rc/en/professeurs/details.php?NoProf=122
Sylvain Martel Nanorobotics Lab
http://www.nano.polymtl.ca/
Tuesday, May 12, 2009
Sean Gourley: Mathematician of War
How Sean Gourley predicts war:
http://www.wired.com/dangerroom/2009/05/physicists-fool-proof-war-forumla-just-add-media-accounts/
Sean Gourley predicting war at TED:
http://www.ted.com/index.php/talks/sean_gourley_on_the_mathematics_of_war.html
The Mother (Nature) of All Wars?
Modern Wars, Global Terrorism, and Complexity Science
Sean Gourley in the American Physical Society:
http://www.aps.org/publications/apsnews/200611/backpage.cfm
Sean Gourley on LinkedIn:
http://www.linkedin.com/in/sgourley
A critique by Drew Conway:
http://www.drewconway.com/zia/?p=577
Sean Gourley also has a startup predictor, YouNoodle
http://younoodle.com/static/about
Sean Gourley CV from http://younoodle.com/people/sean_gourley
New Zealander, Rhodes Scholar at Oxford University, PhD in Physics specializing in 'networks and complexity', just finished a research fellowship at Oxford in the quantitative analysis of war and terrorism.
Headline: Scientist
Work status: Living The Dream
Website: http://www.twitter.com/sgourley
Industries: Cleantech, Computing, Financial, Media, Nanotech
Skills: Business, Design, Entrepreneurship, Management, Product design, Writing
Location: Oxford
Groups: Global Entrepreneurship Week
Visas: Europe, United States and New Zealand/Australia
Interested in: Brainstorming, Consulting opportunities, Offering Expertise, Patenting my idea, Professional opportunities, Promoting my startups, Recruiting for my startup, Sharing my projects
Tags: Artificial Intelligence, complexity, conflict, data analysis, data mining, Strategy, track and field, war
Schools: University of Canterbury, Christchurch, University of Oxford
Sean Gourley STARTUP
YouNoodle
YouNoodle YouNoodle
YouNoodle is a place to discover and support the hottest early-stage companies and university innovation.
* Startup type: Company
* Status: Active
* Stage: Beta
Sean Gourley WORK EXPERIENCE
Employer: Said Business School Oxford
Position: Research Fellow
Time period: October 2006 - April 2008
Description: Conducted novel research into the quantitative analysis of wars and terrorism
Employer: NASA
Position: Scientist
Time period: June 2004 - January 2005
Description: Research into the design of self repairing nanocircuits
Sean Gourley EDUCATION
University: University of Oxford
Time period: 2002 - 2007
Degree: Physics: Complex Systems, PhD
University: University of Canterbury, Christchurch
Time period: 2001 - 2002
Degree: Physics: Nanotechnology, MSc
PUBLICATIONS
Articles: http://www.telegraph.co.uk/earth/main.jhtml?xml=/earth/20...
http://www.aps.org/publications/apsnews/200611/backpage.cfm
http://www.guardian.co.uk/science/2006/oct/24/iraq.intern...
http://www.johnbohannon.org/NewFiles/socialscience.pdf
INFORMATION
Sports: Track and Field, Decathlon, Surfing
Awards: Rhodes Scholarship
TED fellow 2009
http://www.wired.com/dangerroom/2009/05/physicists-fool-proof-war-forumla-just-add-media-accounts/
Sean Gourley predicting war at TED:
http://www.ted.com/index.php/talks/sean_gourley_on_the_mathematics_of_war.html
The Mother (Nature) of All Wars?
Modern Wars, Global Terrorism, and Complexity Science
Sean Gourley in the American Physical Society:
http://www.aps.org/publications/apsnews/200611/backpage.cfm
Sean Gourley on LinkedIn:
http://www.linkedin.com/in/sgourley
A critique by Drew Conway:
http://www.drewconway.com/zia/?p=577
Sean Gourley also has a startup predictor, YouNoodle
http://younoodle.com/static/about
Sean Gourley CV from http://younoodle.com/people/sean_gourley
New Zealander, Rhodes Scholar at Oxford University, PhD in Physics specializing in 'networks and complexity', just finished a research fellowship at Oxford in the quantitative analysis of war and terrorism.
Headline: Scientist
Work status: Living The Dream
Website: http://www.twitter.com/sgourley
Industries: Cleantech, Computing, Financial, Media, Nanotech
Skills: Business, Design, Entrepreneurship, Management, Product design, Writing
Location: Oxford
Groups: Global Entrepreneurship Week
Visas: Europe, United States and New Zealand/Australia
Interested in: Brainstorming, Consulting opportunities, Offering Expertise, Patenting my idea, Professional opportunities, Promoting my startups, Recruiting for my startup, Sharing my projects
Tags: Artificial Intelligence, complexity, conflict, data analysis, data mining, Strategy, track and field, war
Schools: University of Canterbury, Christchurch, University of Oxford
Sean Gourley STARTUP
YouNoodle
YouNoodle YouNoodle
YouNoodle is a place to discover and support the hottest early-stage companies and university innovation.
* Startup type: Company
* Status: Active
* Stage: Beta
Sean Gourley WORK EXPERIENCE
Employer: Said Business School Oxford
Position: Research Fellow
Time period: October 2006 - April 2008
Description: Conducted novel research into the quantitative analysis of wars and terrorism
Employer: NASA
Position: Scientist
Time period: June 2004 - January 2005
Description: Research into the design of self repairing nanocircuits
Sean Gourley EDUCATION
University: University of Oxford
Time period: 2002 - 2007
Degree: Physics: Complex Systems, PhD
University: University of Canterbury, Christchurch
Time period: 2001 - 2002
Degree: Physics: Nanotechnology, MSc
PUBLICATIONS
Articles: http://www.telegraph.co.uk/earth/main.jhtml?xml=/earth/20...
http://www.aps.org/publications/apsnews/200611/backpage.cfm
http://www.guardian.co.uk/science/2006/oct/24/iraq.intern...
http://www.johnbohannon.org/NewFiles/socialscience.pdf
INFORMATION
Sports: Track and Field, Decathlon, Surfing
Awards: Rhodes Scholarship
TED fellow 2009
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