Tomorrow is Today

It’s Official: Over Time, Common Investment Strategies No Better Than Selecting Stocks at Random

Today, Alessio Biondo from the University of Catania in italy and a few pals test this idea for themselves. These guys have simulated the performance of four traditional strategies using 10 years of historical data from the UK, German and US stock markets. They then compare the results with those from an entirely random strategy.

The traditional approaches are all based on the past performance of the market and include, for example, the “momentum strategy” which measures how fast the price of something has changed in the recent past and then uses this to predict how much it will change in the near future. Another approach is called the “up down strategy” in which the prediction for tomorrow’s market behaviour is exactly the opposite of today’s. The results of this comparison are straightforward and the same for all the markets these guys studied.

They say that standard trading strategies can occasionally be successful over small time windows. But on large timescales, they perform no better than a purely random strategy. What’s more, the results from a random strategy are much less volatile than those from traditional trading strategies and so less risky.

(via Computer Simulations Reveal Benefits of Random Investment Strategies Over Traditional Ones | MIT Technology Review)

DARPA Brain Simulation Upgraded to 100 Trillion Synapses

The Sequoia supercomputer at Lawrence Livermore National Laboratory, recently crowned world champion of supercomputers, just simulated 10 billion neurons and 100 trillion connections among them—the most powerful brain simulation ever. IBM and LLNL built an unprecedented 2.084 billion neurosynaptic cores, which are an IBM-designed computer architecture that is designed to work like a brain.

IBM was careful to say it didn’t build a realistic simuated complete brain— “Rather, we have simulated a novel modular, scalable, non-von-Neumann, ultra-low power, cognitive computing architecture,” IBM researchers say in an abstract (PDF) of their new paper. It meets DARPA’s metric of 100 trillion synapses, which is based on the number of synapses in the human brain. This is part of DARPA’s cognitive computing program, called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE).

(via Simulated Brain Ramps Up To Include 100 Trillion Synapses | Popular Science)

Proteins Could “Remember” The Past to Prepare For the Future

The most efficient machines remember what has happened to them, and use that memory to predict what the future holds. That is the conclusion of a theoretical study by Susanne Still, a computer scientist at the University of Hawaii at Manoa and her colleagues, and it should apply equally to “machines” ranging from molecular enzymes to computers, Nature News reports.

The finding could help to improve scientific models such as those used to study climate change. Information that provides clues about the future state of the environment is useful, because it enables the machine to ‘prepare’ — to adapt to future circumstances, and thus to work as efficiently as possible.

…think of a vehicle fitted with a smart driver-assistance system that uses sensors to anticipate its imminent environment and react accordingly — for example, by recording whether the terrain is wet or dry, and thus predicting how best to brake for safety and fuel efficiency. That sort of predictive function costs only a tiny amount of processing energy compared with the total energy consumption of a car.

But for a biomolecule it can be very costly to store information, so its memory needs to be highly selective. 

…Because biochemical motors and pumps have indeed evolved to be efficient, says Still, “they must therefore be doing something clever — something tied to the cognitive ability we pride ourselves with: the capacity to construct concise representations of the world we have encountered, which allow us to say something about things yet to come”.

To be honest, I’m not sure I fully understand theoretical Biology well enough to know how “real” this is outside of simulation; but it sounds as if this research is helping to improve how biological processes get modeled.

(via Proteins remember the past to predict the future | KurzweilAI)

“Neurobot” Models Human Consciousness in Effort to Improve In-Game AI

The idea behind BotPrize is to foster software capable of navigating physical space in a human-like way. This could be used to create more realistic video-game characters, better simulate crowd behaviour in emergency situations or control robots in the real world.

Neurobot’s performance, in particular, will provide an indication of whether the theory of consciousness that it is based on - global workspace theory (GWT) - can really produce human-like behaviour.

According to GWT, unconscious processing such as the gathering and processing of sights and sounds is carried out by various autonomous brain regions working in parallel. Only when information is deemed important enough can it enter the global workspace - or “consciousness” - and be broadcast to other regions of the brain.

(via AI cyber-fighter: does it feel human, punk? - tech - 06 September 2012 - New Scientist)

Japanese Android Simulates Garrulous Old Man

Powered by air servos, the droid has all the idiosyncratic moves of Beicho performing rakugo, an art in which performers wear kimono and use only a kerchief and hand fan as props.

…it waves its arms, bows its head, and speaks in a gravelly voice like the master while narrating tales. Its mouth isn’t all that expressive but from far away, it’s hard to notice. The robot cracked up a few journalists at a press conference. It took two months to build and cost some $1 million, according to Sankei News.

[The Android] was unveiled as part of an exhibition that combines a retrospective on Beicho’s career with exhibits on cutting edge tech in Osaka.

(via Elderly storytelling android debuts in Japan | Cutting Edge - CNET News)

Researchers Create First Complete Computer Model of Entire Living Organism

researchers have successfully made a computer model of Mycoplasma genitalium, the world’s tiniest free-living bacterium.

…M. genitalium has the smallest genome of any living organism—a mere 525 genes—but even for an organism of its size, it takes that much information to account for every interaction it will undergo in its lifespan. Researchers tallied the number of experimentally determined parameters in the model at more than 1,900; those were split up into 28 algorithms, which stepped in for biological processes.

(via Researchers Build First Complete Computer Model of an Entire Organism | Popular Science)

NASA Tests Collaboration Scenarios With Communication Delays to Prep for Deep Space Missions

The first round of tests, conducted in May, was performed in line with NASA’s current procedures.

Three 2-hour scenarios—a normal return from a mission to an asteroid, a return with a spacecraft system failure, and a return with an onboard medical emergency—were each simulated with time delays of 1.2 seconds, 50 seconds, and 5 minutes. In the second round of tests, the crew had access to software tools currently used only by ground-based planners and spacecraft support teams, as well as a combination of other technologies.

Frank and his team are still analyzing the results, but one surprise stood out: The time delay that posed the biggest challenge was not the longest—the 5-minute delay—but the 50-second delay. “Fifty seconds is just long enough, where your expectations of an immediate response are violated, but it’s not so long that it’s blindingly obvious to you that you are going to have to wait,” says Frank. One of the new techniques that proved popular with simulation subjects was to replace voice communications with text-based “chat” sessions.

Another popular tool was a system that allowed flight controllers to monitor how far along an astronaut had gotten in a procedure without having to interrupt him or her for a status update. With this portable system, crew members stepped through procedures as they worked on a task, and mission control automatically received notifications of each step as the astronauts advanced through procedures.

(via New Communication Tech for Deep-Space Missions - IEEE Spectrum)

“Google and Stanford have created the [digital equivalent of the] visual cortex of an infant human”

Jeff Dean and his team from Google, working with Andrew Ng and Quoc Le from Stanford University, have effectively created a rudimentary, low-resolution digital version of the brain’s visual cortex.

The system, which comprises of a cluster of 1,000 computers (totaling 16,000 processor cores), analyzes 10 million 200×200 still frames from YouTube. Over 3 days, the system’s software builds up a network of hundreds of neurons and thousands (millions?) of synapses. During this period, the system tries to identify features — edges, lines, colors — and then creates object categories based on these features.

The rather intriguing result is that, when the system looks at an image of a cat, a specific (digital) neuron fires — just like in a human brain. Watching the system in action — watching the neurons light up — is almost like performing a virtual, digital MRI scan. In the picture below, you can see the contents of the “human face” neuron, alongside some of the stimuli that successfully trigger the neuron.

(via Google and Stanford create a digital brain that, like an infant, learns to identify a human face from scratch | ExtremeTech)