How I learned to love supercolliders more than space travel and telescopes

UT Physics professor Dr. Steven Weinberg at the 2013 South by Southwest Interactive Conference

“We need to fund supercolliders – and that is why I am here (at SXSWi),” said Nobel Prize-winning physicist Dr. Steven Weinberg. And the South by Southwest Interactive audience applauded with enthusiasm. As much as SxSWi participants love the pursuit of space travel and rocket ships, they love their big data more.

What physicists need in order to get to the next step is more data. Isaac Newton was able to make a transformative discovery about the laws of motion and gravitation— but his theories were based upon hundreds of years of star and sky data. Evolutionist Charles Darwin’s theories drew upon a large collection of naturist data from an aggregate of fossil collections.

Manned space flight might need perhaps a hundred billion in funding to get off the ground. That kind of money is ten times what it would cost to build a device such as the Hadron collider. But, when you are talking about an investment of that magnitude, you need to consider which scientific and engineering initiative would produce the largest amount of data that is of scientific interest and worth?  The answer, according to Weinberg, is the supercollider.

Time travel back to the year 1993 in Texas: what if the U.S. had funded and constructed the superconducting supercollider in Ellis county. It would have pursued many important scientific goals. But there was a funding competition between the supercollider and the International Space Station in Houston. Congress passed the space station, but not the supercollider. (For more information on the history of the superconducting supercollider, see WIKI: )

Dr. Weinberg was there through it all. And he is optimistic about a little project called the International Linear Collider or ILC. (For more information visit here: ) The ILC is the next step that could lead to an avalanche of new data that might reap massive scientific rewards, taking scientists into future discoveries about the nature of the universe as they peek into the building blocks of the world itself.

According to Dr. Weinberg, it should have been built decades ago.

“2,000 years ago, the Greek philosophers theorized about the existence of atoms. It took until the 1900s for chemists to prove that atoms existed. We do not have another 2,000 years to wait to move scientific knowledge forward. Funding is needed,” said Weinberg.

Dr. Weinberg’s areas of research include particle physics, unification of fundamental interactions, and cosmology. (For a summary of his work, see his WIKI: ) Dr. Weinberg is perhaps one of the most brilliant minds working in theoretical physics at this time and that leads to him throwing out remarks of this kind…saying that scientists currently have “strong hints that a unification [unified field theory] may be possible, but the simplicity will be seen in the phenomena when it is a much higher rate of energy —10, 000 trillions times more energy than we can create in a lab.”

He pauses. Then he explains that when he says 10,000 trillion, he is not implying a “big number.” This is the exact number he is talking about. Now that’s a theoretical mathematical physicist.

Dr. Weinberg explained that, historically, 20th century physicists reasoned that there were four main forces of nature. By the 1940s, electromagnetic forces were understood pretty well and there were interim theories about weak nuclear forces, but there was no theory to explain strong forces. The fourth force – gravity – was not well understood.

Could there be simplicity in the equations? By the 1950s, physicists theorized that there might be a particle that carried the weak force – a w particle. (Weinberg jokes “w” for weak, not for Weinberg…but it would not be improbable if a particle was to be named for him, as he is at the forefront of this science.)

Dr. Weinberg treated the audience to a physics lecture that summarized the development of the search for various particles that led to the discovery and proof of the various particles, including the crucial Higgs Boson, a very unstable particle that is pivotal to basic theories of particle physics.  (For detailed information on the discover or the Higgs Boson and it’s importance to theoretical physics, see: .)

The particle was discovered at the Large Hadron Collider (LAC) at CERN in Switzerland. (See: ) Dr. Weinberg was asked to list some experiments he believed should be funded and he listed these:

  • Produce neutrinos which would travel in a mine and that we could detect: electron neutrinos.
  • Look into the decay of the proton. Granted, the average proton’s age is longer than the age of the universe! However, if you have enough protons, you can observe one decaying.
  • Various experiments at the 100 million level range of funding, for example….
  • After the LAC is finished, scientists will need a collider that collides electrons and positrons. This will take an international collaboration to get this funded, which is difficult because few nations want to fund something this is not located in their country.

Listen to the presentation on Soundcloud

Recent news from the CERN laboratory in Geneva has revealed the existence of a heavy unstable particle that had been predicted by the theory that unifies two of the forces of nature. This is the last missing piece of our current theory of known elementary particles, the Standard Model. But there is much left to be done before we have a thoroughly unified theory of all matter and force, and some of this will involve observations from space.– quote from the Youtube video summary of his talk, found here:

Steven Weinberg holds the Josey Regental Chair in Science at the University of Texas at Austin, where he is a member of the Physics and Astronomy Departments. His research on elementary particles and cosmology has been honored with numerous prizes and awards. He is the author of over 200 scientific articles, one of which is the most frequently cited paper on particle physics of the past fifty years. 

Among his books are the prize-winning The First Three Minutes and Dreams of a Final Theory, and the treatises Gravitation and Cosmology and, in three volumes, The Quantum Theory of Fields.   Educated at Cornell, Copenhagen, and Princeton, Dr. Weinberg also holds honorary doctoral degrees from sixteen other universities, including Chicago, Columbia, McGill, Padua, Salamanca, and Yale. He taught at Columbia, Berkeley, M.I.T., and Harvard, where he was Higgins Professor of Physics, before coming to Texas in 1982.

Links: Dr. Steven Weinberg’s bio page on UT’s website

Dr. Steven Weinberg’s book, Lake Views: This World and the Universe

More images from 2013 South by Southwest Interactive

2013 SxSW Interactive Presentations on Soundcloud 

Find out more about the South by Southwest Interactive Conference in Austin Texas

IBM researcher Kevin Nowka talks about the big, big data

Dr. Kevin Nowka is cute. He’s a little nervous to leave his laptop in the AT&T conference room just to go out for a photo shoot. But when he stands in front of those pretty red flowers and start smiling into the sun for the Austin Forum photogs, he looks as so cute I want to hand off all of my personal data to IBM.

Dr. Nowka, a grad of Stanford University and the director of IBM Research, Austin, specializes in high-performance and low-power circuits, processor design, and technology. He works with teams of scientists studying system models, creating faster and ever more efficient VLSI circuits (Very large-scale integrated circuits, see WIKI for more: The short version: they are packing thousands of transistors on a single chip. Go, IBM. Make my phone smarter. Or make my Nest thermostat. And other intelligent whatnot.)

A VLSI integrated-circuit die

The dilemma of big data: we can capture it, but who will put it to effective use? Dr. Nowka discussed the new tech twists that will put the tools for data management into play.

So…big problems cause big data. But, to solve big problems, we need big data. They are interrelated.

Nowka listed off some examples of big data, big problems, and big opportunities:

  • Highway congestion: urban roadways that are broken by being underbuilt and causing congestion cost the U.S. roughly 5.5 billion an hour and 2.9 billion gallons of wasted fuel. (Statistics from Texas Transportation Institute).
  • The U.S. could save $130 billion annually by deploying smart-grid technology to electrical delivery systems.
  • Big data analysis goal is to draw value from data that has variety, velocity, volume, and veracity. Apply this intentionality to law enforcement, traffic control, telecom, manufacturing, and more.
  • Gross waste of resources in government systems could be addressed by clever applications of tech to big data, going after fraud, reducing waste.

Dr. Kevin Nowka at the AT&T Center at the Austin Forum.

The volume of digital data is expected to double every two years. That goes for you, for me, for the US, for the Library of Congress, etc. Just think how much data you personally store; you are probably creating increasing amounts of personal data with no end in sight.  By 2017, the total digital data will surpass the number of stars in the observable universe.

And the more access people have, the more data they create. About two-thirds of the world still does not have access to the Internet, so we can expect our data creation to grow exponentially as more of the world gets connected.

There were 5.9 trillion text messages sent in 2011. That represents five times more data than the voice data sent via phones. (Phone factoid: there are more than 6.3 billion mobile phones out there.)

A picture of the IBM RAMAC disc storage from 1950s. We now can store a thousand times more data on the average memory stick.  ( )

Social interactions as well as mobile communications create almost unimaginable amounts of data. And the type of data is changing: currently 80 percent of the data being created is now unstructured. (Structured data is data in a relational database. Unstructured is…everything else.) And data is connecting to other data, as refrigerators hook up in a horrifying and obscene ways with phones, toasters, tablets, and, ultimately the 2001 Hal computer. (I added that last part, not Dr. Nowka).

Something to think about the next time you take a ride on a plane: it takes a billion lines of code to run the software that runs an airplane. Each engine on a plane generates 10-TB every 30 minutes.

Also, 70 percent of most data is multimedia. Don’t just think images from your phone. More than a billion medical images were generated just in 2012.

Velocity: data is in motion, coming at us in gigabit speed. It can be managed in “real time” models, and used to predict. We can take actions based on what the data tells us. Homeland security requires 50 billion records a day; 320 terabytes of deep analysis.

A scary reality: one in three business leaders polled said they were making business decisions without a clear understanding of what their company data is indicating.

So, how do you make sense of unstructured text data? Since computers got us into this giant data situation, perhaps we can use them to help us make sense of it.

Currently a tiny percentage of potentially useful data is tagged, and less is analyzed. This makes me think of crowd-sourced data tagging, such as crowd corrections of facts in WIKI, in Google maps, in WAZE, a hundred more such loose but effective collaborations.

Tagging data: word based, or topic based tagging. Machine learning is being used to classify words into topics, which can then be mined, to retrieve and analyze the specific data that is relevant to a specific topic or keyword.  Think “Ben Laden.” You probably should not say that phrase on your cell phone or in an email — or in a blog post. Whoops!  JK. But, seriously. Watch what you say, type, blog.

Nowka showed us an IBM application that sifts through Facebook data to find selected topics. He can do the same thing with your tweets, and snag location information, too.

Image data. Computers are making sense out of it. Consider medical image category recognition software: it combs through millions of images to locate images that correlate to a topic of interest on a specific disease. Consider ImageCLEF 2012: a computer attempt to classify images into categories that yielded about 88% correct image classification.  ( )

The next step is having a natural language access to big data. Watson is an open domain question answering system that delivers precise answers to questions, with accuracy. IBM Watson finds, reads, scores, and combines information. It searches structured and unstructured data. It finds potential answers and compares in a scoring engine to determine confidence level in the potential answer.

It is important to know when you do not know. (“There are unknown unknowns – the ones we don’t know we don’t know.” – Donald Rumsfeld, U.S. Secretary of Defense at the time.) A system like WATSON can help us avoid the “unknown unknowns.”

Dr. Nowka vision is big: data analytics taking a variety of high volume, high velocity data of all types, and using natural language accessible systems such as IBM Watson to mine that data for meaning and substance. There is no shortage of problems that we can apply to analytics.

So, questions. How can big data not become…evil? Nowka says, “Knowledge is power. But those in control of data should be making sure that privacy is protected for those whose data is being processed.”

What’s next? How close can we come to AI mimicking the robustness of human analysis? Nowka speaks of IBM Watson and what it can, and cannot do, at this time.

What sort of cool places will IBM go as they play with their big data? Currently, IBM is investing in Smarter Planet. IBM tech is going after big city issues, after safety, petroleum, traffic, after health issues. IBM wants to apply Watson to big health centers such as Sloan Kettering. So much more can be found on their website. ( ).


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