Kevin O. Woghiren

The following are my “favorite” plausible end of world scenarios posted in a Guardian article by science correspondent, Alok Jha. To add to the “Gamma Rays From Space” scenario, our sun also emits gamma rays during solar flares; a big enough solar flare would do the trick as supposed to waiting for a nearby star to go supernova.

MEGA TSUNAMI

Geologists worry that a future volcanic eruption at La Palma in the Canary Islands might dislodge a chunk of rock twice the volume of the Isle of Man into the Atlantic Ocean, triggering waves a kilometre high that would move at the speed of a jumbo jet with catastrophic effects for the shores of the US, Europe, South America and Africa.

Danger sign: Half the world’s major cities are under water. All at once.

GEOMAGNETIC REVERSAL

The Earth’s magnetic field provides a shield against harmful radiation from our sun that could rip through DNA and overload the world’s electrical systems. Every so often, Earth’s north and south poles switch positions and, during the transition, the magnetic field will weaken or disappear for many years. The last known transition happened almost 780,000 years ago and it is likely to happen again.

Danger sign: Electronics stop working.

GAMMA RAYS FROM SPACE

When a super-massive star is in its dying moments, it shoots out two beams of high-energy gamma rays into space. If these were to hit Earth, the immense energy would tear apart the atmosphere’s air molecules and disintegrate the protective ozone layer.

Danger sign: The sky turns brown and all life on the surface slowly dies.

RUNAWAY BLACK HOLE

Black holes are the most powerful gravitational objects in the universe, capable of tearing Earth into its constituent atoms. Even within a billion miles, a black hole could knock Earth out of the solar system, leaving our planet wandering through deep space without a source of energy.

Danger sign: Increased asteroid activity; the seasons get really extreme.

French mathematician Emile Borel was one of the first few intellectuals to pose these questions (not in original form): How many monkeys would it take to successfully reproduce a work of Shakespeare (or any other literature) and how long would the process take? And, if infinite variables, what is the probability of success? The method: They are all typing randomly on 50-key standard typewriters.

To give the scale of the task, I will invoke some statistics and quotes from Seth Lloyd’s Programming the Universe. The following stats assume a 50-key standard typewriter. Ignoring capitalization, the probability of randomly typing ‘h’ is 1 in 50…typing ‘ha’ is 1 in 2500…typing ‘ham’ is 1 in 125,000…typing ‘hamlet. act i, scene i’ would take a magnitude of 10^-38 (approximately, “it would take a billion billion monkeys, each typing ten characters per second, for each of the roughly billion billion seconds since the universe began”).

A large number of experiments have been carried out to answer Emile Borel’s question using both real and virtual monkeys, but they have all, for the most part, failed or come to a stand-still. One of the latest researchers to try the experiment is Jesse Anderson, an American programmer. Equipped with the Hadoop programming tool and Amazon’s cloud, EC2, Mr. Anderson set out to create the virtual project in August and has recently reported a 99.990% completion rate of Shakespeare’s collections (~3,695,990 characters) using millions of virtual monkeys. How is this possible within such a short time period? Mr. Anderson’s success isn’t due to intelligent algorithms or a secret access to quantum computers, but because he has established very convenient constraints in his program. One constraint is the disregard for punctuation and spaces, while another is the production limit of 9-character text strings per monkey at each time interval. The latter constraint enables Mr. Anderson to sift through each produced text strings for characters that match those within Shakespeare’s collections, which explains his high success rate.

Without any constraints, ‘Borel’s’ project is nearly impossible to simulate using contemporary computers. Without the ubiquity of quantum computing, the answer to Emile Borel’s question will continuously be settled at ‘infinity’. It has been suggested by various computer scientists and physicists that it would be far easier for randomly typing monkeys to recreate computer programs, which are often shorter, less imaginative, and less coherent than literature, than to create masterpieces. So this begs the question: How many monkeys would it take to randomly write Jesse Anderson’s computer program and how long would it take?

Random Fact: There has been an average of 10 to 11 million motor vehicle accidents annually in the US since 2004 (Source: U.S. Census Bureau)

Another Random Fact: Only ~12% of a car’s energy use goes toward providing momentum/moving the passenger (Source: Hofstra)

While both facts/issues have garnered the interests of academia and industry folks, some of the solutions proposed thus far have been counter-intuitive, but expectantly diverse. Academia seems preoccupied with creating smart-phone applications and providing the driver with more responsibilities and distractions, while on the other hand, private enterprises are leaning more toward car automation and yielding less responsibilities to the driver. In a not-too-recent article, UC Berkeley and IBM announced plans of a partnership to create a smart-phone app that would be the equivalent of a “prediction” model for daily traffic in order to combat congestion and fuel inefficiency, given a driver’s GPS data history. And on the East Coast, researchers at MIT and Princeton were reported to having developed a smart-phone traffic app that provides real-time traffic signals in advance to drivers for the sake of improving fuel efficiency. The catch: its a crowd-sourcing app that relies on high traffic activity in order to be effective.

The private enterprise approach has been the more costlier model but, in the long run, it proves to be more effective in reducing motor vehicle accidents and improving fuel efficiency. I remember, a while back, reading an article about a Google project aimed at fully automating the car driving experience. Although the project is still far from being market-ready, the effort is definitely a step forward. However, with that being said, I do not think a human driver will ever be fully replaced when it comes to the ubiquitous automobile given the multitude of changing environmental variables on any given route and on any given day that a computer may not simply be capable of accurately assessing 100% each time. I liken the scenario to GPS-guided smart bombs that can become error-prone due to electronic noise. In addition to its autonomous vehicle project, Google has recently partnered with Ford Motors on a project similar to UC Berkeley and IBM’s “prediction model”. The only difference is that the Google and Ford project would integrate a car’s computer with the cloud, thus providing the car with real-time decision-making abilities, instead of relying on a smart-phone app.

In my opinion, the private enterprise is correct in focusing on further automating the car driving experience.