Saturday, May 23, 2009

Robot Scientist; Ross King, Adam, & "Automation of Science"

Robot achieves scientific first
"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."

Robot Scientist Website:

"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:

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.

Sylvain Martel website

Sylvain Martel Nanorobotics Lab

Tuesday, May 12, 2009

Sean Gourley: Mathematician of War

How Sean Gourley predicts war:

Sean Gourley predicting war at TED:

The Mother (Nature) of All Wars?
Modern Wars, Global Terrorism, and Complexity Science
Sean Gourley in the American Physical Society:

Sean Gourley on LinkedIn:

A critique by Drew Conway:

Sean Gourley also has a startup predictor, YouNoodle

Sean Gourley CV from

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
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 is a place to discover and support the hottest early-stage companies and university innovation.

* Startup type: Company
* Status: Active
* Stage: Beta

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

Sports: Track and Field, Decathlon, Surfing
Awards: Rhodes Scholarship
TED fellow 2009

Wednesday, May 6, 2009

Shmatikov and Narayanan De-anonymize Flickr and Twitter


Excerpt from article: "Operators of online social networks are increasingly
sharing potentially sensitive information about users and
their relationships with advertisers, application developers,
and data-mining researchers. Privacy is typically protected
by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and
anonymity in social networks and develop a new
re-identification algorithm targeting anonymized socialnetwork
graphs. To demonstrate its effectiveness on realworld
networks, we show that a third of the users who
can be verified to have accounts on both Twitter, a popular
microblogging service, and Flickr, an online photo-sharing
site, can be re-identified in the anonymous Twitter graph
with only a 12% error rate.
Our de-anonymization algorithm is based purely on the
network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all
existing defenses, and works even when the overlap between
the target network and the adversary’s auxiliary information
is small."

Vitaly Shmatikov
faculty page:

Arvind Narayanan's Live Journal:

Apparently Arvind Narayanan's has a start up, "like Pandora for videos"

Arvind Narayanan's research blog:

Full text of De-anonymizing Social Networks below:

De-Anonymizing Social Networks Shmatikov Narayanan

Friday, May 1, 2009

DigitalGlobe Satellite Service sets IPO terms

" NEW YORK, April 29 (Reuters) - DigitalGlobe Inc (DGI.N: Quote, Profile, Research), a satellite imagery company serving the military and large corporations, set the terms on Wednesday for its planned $250 million initial public offering and scheduled its pricing for mid-May.

The Longmont, Colorado-based company plans to sell 14.7 million shares at between $16 and $18 each, in a deal led by underwriters Morgan Stanley and JP Morgan, according to a regulatory filing."

More detailed:
"Not by strength, by guile."