If you look at software today, through the lens of the history of engineering, it’s certainly engineering of a sort—but it’s the kind of engineering that people without the concept of the arch did. Most software today is very much like an Egyptian pyramid with millions of bricks piled on top of each other, with no structural integrity, but just done by brute force and thousands of slaves.
—Alan Kay, from A Conversation with Alan Kay
To any headphone jack, all audio is raw in the sense that it exists as a series of voltages that ultimately began as measurements by some tool, like a microphone or an electric guitar pickup or an EKG. There is no encryption or rights management, no special encoding or secret keys. It’s just data in the shape of the sound itself, as a record of voltages over time. When you play back a sound file, you feed that record of voltages to your headphone jack. It applies those voltages to, say, the coil in your speaker, which then pushes or pulls against a permanent magnet to move the air in the same way it originally moved the microphone whenever the sound was recorded.
Smartphone manufacturers are broadly eliminating headphone jacks going forward, replacing them with wireless headphones or BlueTooth. We’re going to all lose touch with something, and to me it feels like something important.
The series of voltages a headphone jack creates is immediately understandable and usable with the most basic tools. If you coil up some copper, and put a magnet in the middle, and then hook each side of the coil up to your phone’s headphone jack, it would make sounds. They would not be pleasant or loud, but they would be tangible and human-scale and understandable. It’s a part of your phone that can read and produce electrical vibrations.
But perhaps 5% of designers and engineers have a different view — they sense that users have interests or aims which go beyond their immediate goals or apparent preferences. Thus a product which helps with goals, or which satisfies preferences, could nonetheless be a waste of a user’s time. And a user could — despite responding to many emails and seeing many photos — eventually regret using such a product, because the product derailed a deeper concern the user has.
What is even more important to a person than their current goals or preferences? The process of refining, discovering, and clarifying those goals and preferences.
“It shows some of the unreasonable effectiveness and strange inner workings of deep learning systems. The unique characteristics of the human voice are learned and generated as well as hallucinations of a system trying to find images which are not there.”
Most reasonable scientists flocked to a theory put forth by Penn State astronomer Jason Wright: alien megastructures. As it turns out, the unusual dimming is almost certainly the result of a massive collection of solar panels known as a Dyson swarm. While SETI observations of the star found no unusual radio or laser signals that might indicate a technologically hyper-advanced civilization, most astrophysicists accept some version of the theory that the aliens are either harvesting solar energy from the past via a n-d timebridge, or, somewhat less likely, from the security of a half-dimensional “spacetime fjord.” Unfortunately, confirming either possibility will have to wait until the launch of the James Webb Space Telescope in 2018.
It may be fortuitous that the trolley problem has trickled into the world of driverless cars: It illuminates some of the profound ethical—and legal—challenges we will face ahead with robots. As human agents are replaced by robotic ones, many of our decisions will cease to be in-the-moment, knee-jerk reactions. Instead, we will have the ability to premeditate different options as we program how our machines will act. For philosophers like Lin, this is the perfect example of where theory collides with the real world—and thought experiments like the trolley problem, though they may be abstract or outdated, can help us to rigorously think through scenarios before they happen. Lin and Gerdes hosted a conference about ethics and self-driving cars last month, and hope the resulting discussions will spread out to other companies and labs developing these technologies.
Next, they designed an abuse-evading algorithm to help the robot avoid situations where tiny humans might gang up on it. Literally tiny humans: the robot is programmed to run away from people who are below a certain height and escape in the direction of taller people. When it encounters a human, the system calculates the probability of abuse based on interaction time, pedestrian density, and the presence of people above or below 1.4 meters (4 feet 6 inches) in height. If the robot is statistically in danger, it changes its course towards a more crowded area or a taller person. This ensures that an adult is there to intervene when one of the little brats decides to pound the robot’s head with a bottle (which only happened a couple times).
Human logic, unlike that of the machines which we program and use every day, isn’t perfect. We make mistakes, we establish bad mental habits, and we have many cognitive biases that negatively impact our ability to be successful engineers. I want to go over five of the most common biases that I see on a regular basis as a software engineer.
In an era of breathtaking, earth-changing engineering projects, this has been billed as the biggest of them all. Three times as long and almost twice as deep as its rival in Panama, Nicaragua’s channel will require the removal of more than 4.5bn cubic metres of earth – enough to bury the entire island of Manhattan up to the 21st floor of the Empire State Building. It will also swamp the economy, society and environment of one of Latin America’s poorest and most sparsely populated countries. Senior officials compare the scale of change to that brought by the arrival of the first colonisers.
“It’s like when the Spanish came here, they brought a new culture. The same is coming with the canal,” said Manuel Coronel Kautz, the garrulous head of the canal authority. “It is very difficult to see what will happen later – just as it was difficult for the indigenous people to imagine what would happen when they saw the first [European] boats.”
For the native Americans, of course, that first glimpse of Spanish caravels was the beginning of an apocalypse. Columbus’s ships were soon followed by waves of conquistadores whose feuding, disease and hunger for gold and slaves led to the annihilation of many indigenous populations.
The belief that agency can be distributed is hard to internalize even after you’ve been intellectually convinced. I nodded along as I read Mike’s posts last year, but I keep catching myself acting in violation of these beliefs. As an example but without intending to get bogged down in politics, it’s easy to read about congressional corruption and gain a sense that all congressmen are bad people. Then you might read a story about a specific congressman and think, “hmm, he wasn’t so bad.” Ok, so maybe he’s an exception. Or today’s congressmen are more corrupt. But there’s a third possible synthesis that the mind shies away from: perhaps the system made them that way. Perhaps sequences of simple actions that are each beyond reproach can cause the group as a whole to grow hostile toward the people who form it or who caused it to be formed.
Famous though the slogan might be, its meaning has never been clear. In the 2004 IPO letter, founders Larry Page and Sergey Brin clarify that Google will be “a company that does good things for the world even if we forgo some short term gains.” But what counts as “good things,” and who constitutes “the world?” The slogan’s significance has likely changed over time, but today it seems clear that we’re misunderstanding what “evil” means to the company. For today’s Google, evil isn’t tied to malevolence or moral corruption, the customary senses of the term. Rather, it’s better to understand Google’s sense of evil as the disruption of its brand of (computational) progress.
It really sucks that when Bozhidar Batsov put his big talk about how shitty EmacsWiki is into action, the only thing we got out of it was another shitty wiki. It bugs the everliving hell out of me that even though engineering is supposed to be a rigorous discipline, we throw all kinds of shit at the wall to see what sticks, without ever looking at the walls in the last four or five rooms to see what the hell worked the last time.
Product failure is deceptively difficult to understand. It depends not just on how customers use a product but on the intrinsic properties of each part—what it’s made of and how those materials respond to wildly varying conditions. Estimating a product’s lifespan is an art that even the most sophisticated manufacturers still struggle with. And it’s getting harder. In our Moore’s law-driven age, we expect devices to continuously be getting smaller, lighter, more powerful, and more efficient. This thinking has seeped into our expectations about lots of product categories: Cars must get better gas mileage. Bicycles must get lighter. Washing machines need to get clothes cleaner with less water. Almost every industry is expected to make major advances every year. To do this they are constantly reaching for new materials and design techniques. All this is great for innovation, but it’s terrible for reliability.