How To Choose a Good Scientific Problem

When I did experiments, I always like to read some easy papers, such as review, survey, or some academic stuff. Tonight (or, in fact, this early morning) I read an interesting paper:


Uri Alon, How to choose a good scientific problem, Molecular Cell 35, 2009.

Abstract: Choosing good problems is essential for being a good scientist. But what is a good problem, and how do you choose one? The subject is not usually discussed explicitly within our profession. Scientists are expected to be smart enough to figure it out on their own and through the observation of their teachers. This lack of explicit discussion leaves a vacuum that can lead to approaches such as choosing problems that can give results that merit publication in valued journals, resulting in a job and tenure.

This paper gives several suggestions to both the students/post-docs and the mentors (especially those young assistant professors, who start to build their labs). Although the paper was written for people in the biology field, it is helpful to people in any fields.

There are several good suggestions for students and young professors. I pick up three of them:

(1) Thinking over a topic for enough time (e.g. 3 months) before starting to do it. Fully consider the feasibility and the interests of the topic.

(2) Listen to inner voice, not the voice of those who are around you or around the conferences. Namely, choose the topic that you are really interested in, not the one others are interested in.

(3) A research road is not a straight line from the  beginning to the destination. There are many loops and circles (the author called it 'cloud') between your beginning and the destination (as shown in the figure). And most probably, your destination is not the original destination; you find another more interesting problem and start to solve it.

IBM Unveils Cognitive Computing Chips

Continuing the discussion in here, Hasan Al Marzouqi sent me a news from IBM, which arouses me strong interests. Here is it (http://www-03.ibm.com/press/us/en/pressrelease/35251.wss):

ARMONK, N.Y., - 18 Aug 2011: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. The technology could yield many orders of magnitude less power consumption and space than used in today’s computers.

In a sharp departure from traditional concepts in designing and building computers, IBM’s first neurosynaptic computing chips recreate the phenomena between spiking neurons and synapses in biological systems, such as the brain, through advanced algorithms and silicon circuitry. Its first two prototype chips have already been fabricated and are currently undergoing testing.

Called cognitive computers, systems built with these chips won’t be programmed the same way traditional computers are today. Rather, cognitive computers are expected to learn through experiences, find correlations, create hypotheses, and remember – and learn from – the outcomes, mimicking the brains structural and synaptic plasticity.

To do this, IBM is combining principles from nanoscience, neuroscience and supercomputing as part of a multi-year cognitive computing initiative. The company and its university collaborators also announced they have been awarded approximately $21 million in new funding from the Defense Advanced Research Projects Agency (DARPA) for Phase 2 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project.

The goal of SyNAPSE  is to create a system that not only analyzes complex information from multiple sensory modalities at once, but also dynamically rewires itself as it interacts with its environment – all while rivaling the brain’s compact size and low power usage. The IBM team has already successfully completed Phases 0 and 1.

“This is a major initiative to move beyond the von Neumann paradigm that has been ruling computer architecture for more than half a century,” said Dharmendra Modha, project leader for IBM Research. “Future applications of computing will increasingly demand functionality that is not efficiently delivered by the traditional architecture. These chips are another significant step in the evolution of computers from calculators to learning systems, signaling the beginning of a new generation of computers and their applications in business, science and government.”

Neurosynaptic Chips

While they contain no biological elements, IBM’s first cognitive computing prototype chips use digital silicon circuits inspired by neurobiology to make up what is referred to as a “neurosynaptic core” with integrated memory (replicated synapses), computation (replicated neurons) and communication (replicated axons).

IBM has two working prototype designs. Both cores were fabricated in 45 nm SOI-CMOS and contain 256 neurons. One core contains 262,144 programmable synapses and the other contains 65,536 learning synapses. The IBM team has successfully demonstrated simple applications like navigation, machine vision, pattern recognition, associative memory and classification.

IBM’s overarching cognitive computing architecture is an on-chip network of light-weight cores, creating a single integrated system of hardware and software. This architecture represents a critical shift away from traditional von Neumann computing to a potentially more power-efficient architecture that has no set programming, integrates memory with processor, and mimics the brain’s event-driven, distributed and parallel processing.

IBM’s long-term goal is to build a chip system with ten billion neurons and hundred trillion synapses, while consuming merely one kilowatt of power and occupying less than two liters of volume.

Why Cognitive Computing 

Future chips will be able to ingest information from complex, real-world environments through multiple sensory modes and act through multiple motor modes in a coordinated, context-dependent manner. 
For example, a cognitive computing system monitoring the world's water supply could contain a network of sensors and actuators that constantly record and report metrics such as temperature, pressure, wave height, acoustics and ocean tide, and issue tsunami warnings based on its decision making. Similarly, a grocer stocking shelves could use an instrumented glove that monitors sights, smells, texture and temperature to flag bad or contaminated produce. Making sense of real-time input flowing at an ever-dizzying rate would be a Herculean task for today’s computers, but would be natural for a brain-inspired system.   

“Imagine traffic lights that can integrate sights, sounds and smells and flag unsafe intersections before disaster happens or imagine cognitive co-processors that turn servers, laptops, tablets, and phones into machines that can interact better with their environments,” said Dr. Modha.

For Phase 2 of SyNAPSE, IBM has assembled a world-class multi-dimensional team of researchers and collaborators to achieve these ambitious goals. The team includes Columbia University; Cornell University; University of California, Merced; and University of Wisconsin, Madison.

IBM has a rich history in the area of artificial intelligence research going all the way back to 1956 when IBM performed the world's first large-scale (512 neuron) cortical simulation. Most recently, IBM Research scientists created Watson, an analytical computing system that specializes in understanding natural human language and provides specific answers to complex questions at rapid speeds. Watson represents a tremendous breakthrough in computers understanding natural language, “real language” that is not specially designed or encoded just for computers, but language that humans use to naturally capture and communicate knowledge.

IBM’s cognitive computing chips were built at its highly advanced chip-making facility in Fishkill, N.Y. and are currently being tested at its research labs in Yorktown Heights, N.Y. and San Jose, Calif.

For more information about IBM Research, please visit ibm.com/research. 

Look for more compressed sensing algorithms for cluster-structured sparse signals

I am now deriving some algorithms for cluster-structured sparse signals (and block-sparse signals). I plan to do some experiments, comparing mine with existing algorithms. Generally, my algorithms do not need any information about the cluster size, cluster number, cluster partition, etc. So, my algorithms can be used to compare most, if not all, existing algorithms. However, currently, I only compared those classic algorithms, such as group Lasso, overlap group Lasso, DGS, BCS-MCMC, block OMP (and its variants -- I don't know why, these OMP algorithms are very poor, especially in noisy cases). Although there are branch of papers proposed  state-of-the-art algorithms, their codes are not available online. If you, my dear readers, happen to know some good algorithms (and their codes are available online), please let me know. Thank you.

The most beautiful picture

I know this is an academic blog. But forgive me. I want to post this picture to share my greatest happiness with all of you.