Dan Kohn's Blog

The week of Dec. 6-12 will be Computer Science Education Week, as declared by President Barack Obama. To celebrate, the computer science department will host an open house Thursday, Dec. 10, from 2 until 6 p.m. in Dunn Hall. The event is free and open to the public. Refreshments will be served.

The open house includes an interactive presentation, “Discovering Computer Science at the U of M,” from 4 to 5 p.m. in Dunn Hall, Room 351. Attendees will learn “What is computer science?” “What are the career opportunities for computer science majors?” and “What courses are required for the minor, major or double major?” Tours of research labs, with researchers giving short demonstrations about their current research projects and allowing hands-on activities, will be from 2 to 4 p.m. and 5 to 6 p.m. Read more…

An IBM Research-led cognitive computing team claims advances in large-scale cortical simulation and a new algorithm that synthesizes neurological data, two major milestones that indicate the feasibility of building a cognitive computing chip.

By Suzanne Deffree, Managing Editor, News — Electronic News, 11/18/2009

IBM today at super computing conference SC 09 claimed significant progress toward creating a computer system that simulates and emulates the brain’s abilities for sensation, perception, action, interaction, and cognition in low-power form and compact size.

An IBM Research-led cognitive computing team announced it achieved advances in large-scale cortical simulation and a new algorithm that synthesizes neurological data. According to IBM, these are two major milestones that indicate the feasibility of building a cognitive computing chip.

Scientists at IBM Research – Almaden and in collaboration with colleagues from Lawrence Berkeley National Lab said they performed the first near real-time cortical simulation of the brain that exceeds the scale of a cat cortex and contains 1 billion spiking neurons and 10 trillion individual learning synapses.

To perform the cortical simulation, IBM explained the team built a cortical simulator that incorporates a number of innovations in computation, memory, and communication as well as biological details from neurophysiology and neuroanatomy. The simulation was performed using the cortical simulator on Lawrence Livermore National Lab’s Dawn Blue Gene/P supercomputer with 147,456 CPUs and 144 terabytes of main memory.

In collaboration with researchers from Stanford University IBM further developed an algorithm that exploits the Blue Gene supercomputing architecture in order to noninvasively measure and map the connections between all cortical and sub-cortical locations within the human brain using magnetic resonance diffusion weighted imaging. IBM said that mapping the wiring diagram of the brain is crucial to untangling its vast communication network and understanding how it represents and processes information.

IBM believes the two advancements could move its team closer to its goal of building a compact, low-power synaptronic chip using nanotechnology and advances in phase change memory and magnetic tunnel junctions. The team’s work could break the mold of conventional von Neumann computing, in order to meet the system requirements of the instrumented and interconnected world of tomorrow, Big Blue claimed.

“Learning from the brain is an attractive way to overcome power and density challenges faced in computing today,” said Josephine Cheng, IBM fellow and lab director of IBM Research – Almaden, in a statement. “As the digital and physical worlds continue to merge and computing becomes more embedded in the fabric of our daily lives, it’s imperative that we create a more intelligent computing system that can help us make sense the vast amount of information that’s increasingly available to us, much the way our brains can quickly interpret and act on complex tasks.”

In recognition of their work, IBM and its university partners have been awarded $16.1 million in additional funding from DARPA (Defense Advanced Research Projects Agency) for the agency’s Systems of Neuromorphic Adaptive Plastic Scalable Electronics initiative.

Google Launches New Programming Language: Go
By: Darryl K. Taft 2009-11-10

Google has introduced a new programming language, known as Go, that is aimed at being fast, productive and fun for developers to use.

Google officials described Go as an “experimental language” that attempts to combine the development speed of working in a dynamic language like Python with the performance and safety of a compiled language like C or C++.

Moreover, in a blog post by the Google Go team, team members Robert Griesemer, Rob Pike, Ken Thompson, Ian Taylor, Russ Cox, Jini Kim and Adam Langley said: “In our experiments with Go to date, typical builds feel instantaneous; even large binaries compile in just a few seconds. And the compiled code runs close to the speed of C. Go is designed to let you move fast.”

Meanwhile, an FAQ on the Go language on the language’s web site addressed who should use the language. “We hope adventurous users will give it a try and see if they enjoy it,” the FAQ said. “Not every programmer will, but we hope enough will find satisfaction in the approach it offers to justify further development.”

Indeed, in that FAQ, Google admits that Go is not being used internally at Google, at least not for production systems. “The Go project was conceived to make it easier to write the kind of servers and other software Google uses internally, but the implementation isn’t quite mature enough yet for large-scale production use,” the FAQ said. “While we continue development we are also doing experiments with the language as a candidate server environment. It’s getting there.”

The Google Go team also said in its blog post:

“Typical builds feel instantaneous; even large binaries compile in just a few seconds. And the compiled code runs close to the speed of C. Go lets you move fast.

“Go is a great language for systems programming with support for multi-processing, a fresh and lightweight take on object-oriented design, plus some cool features like true closures and reflection.”

Among the trends behind the origin of Go are:

· Computers are enormously quicker but software development is not faster.

· Dependency management is a big part of software development today but the “header files” of languages in the C tradition are antithetical to clean dependency analysis—and fast compilation.

· There is a growing rebellion against cumbersome type systems like those of Java and C++, pushing people towards dynamically typed languages such as Python and JavaScript.

· Some fundamental concepts such as garbage collection and parallel computation are not well supported by popular systems languages.

· The emergence of multicore computers has generated worry and confusion.

“We believe it’s worth trying again with a new language, a concurrent, garbage-collected language with fast compilation,” Google said on its FAQ about Go.

Go follows on the heels of a Java-like programming language Google introduced in September, known as Noop.

FAQ for Go