When science fiction jumped from pulp magazines to full books during the mass-market paperback revolution of the 1950s, publisher Ace Books used an innovative new format known as Tête-bêche. It packaged two novels back to back, so readers could finish one, flip the book over, and start the next. A number of prominent science fiction novelists launched their careers on the backs of these little books, including Ursula K. Le Guin, Samuel R. Delany, and Philip K. Dick.
Over half a century later, high-end publisher Folio Society is reviving the format, releasing a gorgeous Tête-bêche book that pairs Dick’s classic novels Do Androids Dream of Electric Sheep? and A Scanner Darkly.As with theirotherbooks, Folio Society has included some…
Artificial intelligence is rapidly developing and is already starting to change the world, at a pace that is worrying to some experts. Huge personalities in the tech industry often lament the dangers of unfettered development of AI systems: people like Elon Musk and Stephen Hawking, who warn of a future where AI reigns supreme. Whether or not their concerns are unfounded, there certainly is a value on keeping tabs on the progress of AI innovation.
AI is getting good and in a lot of cases, way better than experts imagined. AlphaGo, Google’s game playing AI, has been beating the world’s best players for a while now, something that wasn’t thought to be possible for at least a decade. Elon Musk’s OpenAI is doing even better than that by beating the world’s greatest eSports players at DOTA 2, a game that is much more complex and involves tricking opponents. It is easy to see how an argument can be made saying that we need to more closely and accurately map the development of AI systems.
There are now multiple efforts to track AI and analyze its current and potential impact. One, called the AI Index, is being headed up by the nonprofit lab SRI International. They are seeking to track what forms of AI receive the most interest from researchers, as well as the number of engineers and dollars flowing into AI-based companies. Their goal is to release a comprehensive report on the state and rate of progress of AI development by the end of 2017.
Another, led by the Electronic Frontier Foundation (EFF), is compiling data from AI research to assemble an open source resource of progress. “We want to know what urgent and longer term policy implications there are of the real version of AI, as opposed to the speculative version that people get overexcited about,” says Peter Eckersley, EFF’s chief computer scientist, to Wired.
While some are still unsure what the value of this data will be, Eckersley believes that its use will become clear over time; he cited the example that tracking might lend real data to arguments over whether automation is taking away human jobs. In his data, he also already sees how vital it will be to track this technology as it advances. “The data we’ve collected supports the notion that the safety and security of AI systems is a relevant and perhaps even urgent field of research,” he says.
AI has the potential to be the single greatest human achievement, yet critics can also argue that it has the potential to be the most destructive. Having a clear-eyed view of the developmental process can give a certain level of assurance that AIs will not suddenly turn on us. However, there is no guarantee that this will be enough. While we are currently evaluating the need to place a speedometer on AI, as Wired put it, we may also need to consider if we need to be setting a speed limit.
The Hyperloop One’srecent speed record of 308 kmh (192 mph) is an important step (however small) toward surpassing the first goal of the Hyperloop: to achieve quicker transit than other alternatives. But, while the hyperloop was initially designed to achieve 1,200 km/h (750 mph) with a chic micro-craft built for three passengers, it is developing into something quite different.
In his original outline, Musk illuminated some glaring problems at the conceptual stage of several other “high speed” rail systems — namely the high expense per mile, the cost of operation, and that other propositions were less safe than flying by two orders of magnitude.
No one thought the proposal would come so far a mere four years after Elon Musk released his initial plans for Hyperloop system. But with tubes 3.3 meters (11 feet) in diameter, the craft looks more like the cargo version from Musk’s original concept. Instead of a bobsled, we’re seeing something more like an ordinary train. Additionally, the thin concrete pylons planned for minimal terrestrial footprint will be significantly larger. Since this is more on the scale of a train or highway, the disruptive potential of compact tubes would seem, alas, reneged.
The environmental pitch of Hyperloop was simple. Having speed, high acceleration and deceleration, and a high frequency of available stops would give the world’s population centers incentive to switch away from “traditional” modes of transportation. This would mean less greenhouse gases emitted, potentially slowing the advance of global climate change.
However, the recent Hyperloop One test shows multiple branching routes that resemble more of a linear track than a loop, which was a key factor for energy efficiency of the system. Without high-speed winds that travel in a constant direction, the main form of propulsion would seem to default to the magnetic levitation system, omitting the complex on-boarding/off-boarding feature that made Hyperloop feel not only innovative, but feasible.
But last month Musk moved back towards that feasible direction when he announced that Boring Company’s boring (if not mysterious) tunnels could create a Hyperloop vacuum-tunnel betwixt New York and Washington, D.C., with a transit time of 29 minutes. He then met with Hawthorne, Calif., Mayor Alex Vargas to explain the physics, and (presumably) the economics of implementing the Hyperloop, which on the scale of the state of California, was estimated to cost $ 7.5 billion.
It may sound cynical, but — at its core — engineering is physics with compromise. And as these compromises mount, it’s difficult to keep sight of the final goal. But as with any technological revolution, it takes a prolonged and sober engagement with the real-world drawbacks, and even failures, to predict the final outcome.