TrueBridge Capital Partners

http://en.wikipedia.org/wiki/Truebridge_capital_Partners

IA Venture Partners

http://www.iaventurepartners.com/

VC focused on "big data"

Afghanistan's Lithium Eureka

http://green.venturebeat.com/2010/06/14/afghanistans-lithium-eureka-a-big-win-for-china-or-another-bolivia/

Startup Reading List

Web 1.0:

Founders at Work: Stories of Startups' Early Days (Recipes: a Problem-Solution Ap)

The Oracle of Oracle: The Story of Volatile CEO Larry Ellison and the Strategies Behind His Company's Phenomenal Success

Hard Drive: Bill Gates and the Making of the Microsoft Empire

Netscape Time: The Making of the Billion-Dollar Start-Up That Took on Microsoft

Web 2.0

The PayPal Wars: Battles With Ebay, the Media, the Mafia, And the Rest of Planet Earth

The Google Story: For Google's 10th Birthday

The Accidental Billionaires: The Founding of Facebook: A Tale of Sex, Money, Genius and Betrayal

Delivering Happiness: A Path to Profits, Passion, and Purpose

GnuBio and David Weitz

http://www.technologyreview.com/printer_friendly_article.aspx?id=25481&channel=biomedicine§ion=

"At a time when the longtime goal of a $1,000 genome is still just out of reach, a Harvard University physicist is promising an even cheaper price--the ability to sequence a human genome for just $30. David Weitz and his team are adapting microfluidics technology that uses tiny droplets, a strategy developed in his lab, to DNA sequencing. While the researchers have not yet sequenced DNA, they have successfully demonstrated parts of the process and formed a startup, GnuBio, to commercialize the technology. Weitz presented the findings at the Consumer Genomics Conference in Boston last week."

RainDance Technology
http://www.raindancetechnologies.com/

GnuBio
http://www.masshightech.com/stories/2010/05/31/daily32-GnuBio-launches-as-open-source-genome-sequencing-startup.html

David Weitz
http://www.seas.harvard.edu/weitzlab/

Siguler Guff & Co. investing in Russian Silicon Valley

http://deals.venturebeat.com/2010/05/31/u-s-private-equity-fund-to-invest-250m-in-russian-tech-center/

Siguler Guff & Co. is expressing a huge amount of confidence in Russia’s plan to modernize the economy.

Could Humans Be Infected by 'Computer Viruses?'

http://www.sciencedaily.com/releases/2010/05/100526095830.htm

Inference Algorithm and Probabilistic Programming at MIT

Historically, building a machine-learning system capable of learning a new task would take a graduate student somewhere between a few weeks and several months, says Daniel Roy, a PhD student in the Department of Electrical Engineering and Computer Science who along with Cameron Freer, an instructor in pure mathematics, led the new research. A handful of new, experimental, probabilistic programming languages — one of which, Church, was developed at MIT — promise to cut that time down to a matter of hours.

http://web.mit.edu/newsoffice/2010/machine-learning-0518.html

KarDo

http://www.technologyreview.com/computing/25351/?a=f

"The new software system, called KarDo, was developed by researchers at MIT. It can automatically configure an e-mail account, install a virus scanner, or set up access to a virtual private network, says MIT's Dina Katabi, an associate professor at MIT.

Crucially, the software just needs to watch an administrator perform this task once before being able to carry out the same job on computers running different software. Businesses spend billions of dollars each year on simple and repetitive IT tasks, according to reports from the analyst groups Forrester and Gartner. KarDo could reduce these costs by as much as 20 percent, Katabi says.

In some respects, KarDo resembles software that can be used to record macros--a set sequence of user actions on a computer. But KarDo attempts to learn the goal of each action in the sequence so it can be applied more generally later, says MIT post-graduate Hariharan Rahul, who codeveloped the system. "

Russia's Silicon Valley

http://www.technologyreview.com/business/25009/?a=f

Marketcetera

http://venturebeatprofiles.com/company/profile/marketcetera

"Marketcetera Platform allows you to build automated trading systems for equities, equity options and currencies, to maximize the effectiveness of your traders and developers. Trade opportunities disappear in milliseconds. Equity options data feeds now reach 1 million messages per second. "

Homepage: http://www.marketcetera.com/site/

Structured Data Start Ups

Data Marketplace

Data Market http://www.crunchbase.com/company/datamarket

Factual
http://searchengineland.com/factual-parting-the-curtains-of-the-invisible-web-27608

InfoChimps
http://blog.infochimps.org/

Others
http://www.crunchbase.com/tag/structured-data

Plasmobot: the slime mold robot

http://www.newscientist.com/article/mg20327245.100-plasmobot-the-slime-mould-robot.html?DCMP=OTC-rss&nsref=robots

"In recent years, single-celled organisms have been used to control six-legged robots, but Andrew Adamatzky at UWE wants to go one step further by making a complete "robot" out of a plasmodium slime mould, Physarum polycephalum, a commonly occurring mould that moves towards food sources such as bacteria and fungi, and shies away from light."


Andrew Adamatzky homepage:
http://uncomp.uwe.ac.uk/adamatzky/

Credit Derivatives: Russia safer investment than California

http://www.bloomberg.com/apps/news?pid=20601109&sid=aypQny1ySDjU

“This would have been impossible to imagine a year ago,” said Dmitry Sentchoukov, an emerging-market credit strategist at Dresdner Kleinwort in London. “Now it’s clear emerging economies are going to outperform the Group of Seven in growth, and that makes investors comfortable with the idea that developing countries can be priced richer than developed.”


"Credit-default swap prices from Turkey to Indonesia are falling as bonds rise amid signs that their economies are recovering faster than developed nations."

International Joint Conference for Artificial Intelligence

The AI State of the Union

http://www.newscientist.com/article/dn17518-smart-machines-whats-the-worst-that-could-happen.html?DCMP=OTC-rss&nsref=robots

Buehler, Zisserman, Everingham Computer Learns Sign Language

"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

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/

Machine Learning and Trading: Fina Technologies

Company applies machine learning to quantitative trading. It is a spin-off of Gene Network Sciences (GNS) of Cambridge, Mass.

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,

An article by Joshua Holden appears in Forbes, "Why Computers Can't--Yet--Beat The Market"
http://www.forbes.com/2009/06/18/fina-financial-markets-opinions-contributors-artificial-intelligence-09-joshua-holden_print.html

"Training a financial trading system to deliver the single best model given the data will most often lead to models that fit the past at the expense of predicting the future. What are needed are systems that are flexible, adapt to changing circumstances and are, at their core, probabilistic rather than deterministic. By having distributions of possible models for the state of the world, we can balance the competing desires for certainty and flexibility. By retraining the models automatically when results begin to drift relative to expectations, we can achieve some of the adaptability that humans exhibit in the face of shifts. The path to beating the markets lies in building systems that understand, but do not emulate, the persistent biases in human nature."

"If our goal is to build intelligent systems to beat the markets, we cannot simply ignore irrationality. As Keynes famously remarked, "the markets can remain irrational for longer than you can remain solvent." Longer, too, than can an AI trading system."

In Forbes, "Man vs. Machine on Wall Street" http://www.forbes.com/2008/11/22/supercomputers-biology-quants-biz-wall-cx_mh_1124quants_print.html

Origin of the applying machine learning to finance: "The idea comes out of systems, or network, biology. Genes and proteins interconnect in a complex web. By drawing these connections, companies hope to invent better drugs. Merck in particular has put technology similar to that used by GNS at the center of its approach.

This computerized approach to biology attracted investors who were, in some cases, quants. Two years ago, Hill was having drinks at an upscale Manhattan bar with an investor and a GNS board member named Thomas Paul, then chief investment officer for Fortress Investment Group. For years, they'd been toying with the idea of using GNS' technology to trade stocks. That night, for some reason, the idea finally stuck.

Paul graduated from MIT in 1993 with bachelor's and master's degrees in engineering and computer science, and, like many of his peers, went to Wall Street, working first at Goldman Sachs and then at Deutsche Bank before starting an $800 million fund at Fortress.

He was prepared for the odd world of quants, he said, by playing on the MIT blackjack team--a different version of the team portrayed in the movie 21, in which a group of students figured out that with investor backing, they could consistently beat the house in Las Vegas by counting cards."

Founder of GNS and Fina is Colin Hill: "Colin Hill brings years of hands-on scientific experience to his role, with expertise in the areas of computational physics and systems biology. Hill is a frequent speaker at international scientific and industry conferences and has appeared in numerous publications and television segments including The Wall Street Journal, CNBC Morning Call, Nature, Forbes, Wired, and the Economist. He also serves as a board member of AesRx, a biopharmaceutical company dedicated to the development of a new treatment for sickle cell disease (http://www.aesrx.com). In 2004, Hill was named to MIT Technology Review's TR100 list of the top innovators in the world under the age of 35. He graduated from Virginia Tech with a degree in physics and earned master's degrees in physics from McGill University and Cornell University."

Hedge Fund Startups

Hedge Fund Startups

The excel spreadsheet is available here: http://www.scribd.com/doc/16561645/Hedge-Fund-Startup-Investments

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