A new approach for fighting cancer could result in a successful drug for many kinds of cancers:

The genetic alteration the drug targets has tantalized researchers for decades. Normal healthy cells have a mechanism that tells them to die if their DNA is too badly damaged to repair. Cancer cells have grotesquely damaged DNA, so ordinarily they would self-destruct. A protein known as p53 that Dr. Gary Gilliland of Merck calls the cell’s angel of death normally sets things in motion. But cancer cells disable p53, either directly, with a mutation, or indirectly, by attaching the p53 protein to another cellular protein that blocks it. The dream of cancer researchers has long been to reanimate p53 in cancer cells so they will die on their own.

How Quentin Tarantino writes movies:

I have a writer’s journey going on and a filmmaker’s journey going on, and obviously they’re symbiotic, but they also are separate. When I write my scripts it’s not really about the movie per se, it is about the page. It’s supposed to be literature. I write stuff that’s never going to make it in the movie and stuff that I know wouldn’t even be right for the movie, but I’ll put it in the screenplay. We’ll decide later do we shoot it, do we not shoot it, whatever, but it’s important for the written work.

When I finish the script I want the script to be so good that I’m tempted to stop, I’m tempted not to make the movie, because if I just stop right now, I’m the winner. Now I do make them, but I want the screenplay to be that much of a document. I rarely look at the script after that other than to just go over the dialogue.

That explains why his dialogue is so incredibly tight and lyrical. I wonder if there’s something people in other creative fields can take from this sort of process. Tarantino says he effectively writes his screenplays as novels first, which helps result in beautiful stories and dialogue that may not have happened if he wrote for the screen first. He’s writing for a different medium, one that’s heavily driven by well-sculpted writing and plot, and then turns it into a film. This is a mild reversal of the adage that you should design for the medium you’re working with, but you could think of it as a way of capitalizing on the unique advantages of written word for the benefit of film.

I came across this article by Stacey Higginbotham for GigaOm while doing research for Basil:

An open data standard for food has emerged on the web. With such a tool, restaurants, food apps, grocery stores, the government and other interested parties can tell that arugula is also called rocket salad, no matter where on the web it occurs or what a restaurant menu or recipe app calls it. Right now, that’s an impossible task, which leads to inefficiencies in both consumer-facing apps and the supply chains of restaurants and grocery stores.

A group of folks concerned about sustainable foods have built the seeds of an open food database hosted on Heroku, with the code pertaining to it located at Github.

Really, really cool idea, and something we absolutely need more of. Theoretically, this sort of thing would allow Basil to do a lot of very powerful things. For example, it could have much smarter tagging; rather than just tag recipes with ingredients it uses that happen to be in a built-in list of ingredients or user-added ones, Basil could use the service’s list of ingredients, so you’d get a much fuller tagging system. But it could also be more intelligent about it; if one recipe says it uses “coriander” and another recipe says it uses “cilantro,” Basil could use this service to see oh, they’re the same thing, and to tag both recipes with “cilantro.” Or, if this service ends up providing translations of food names into different languages, Basil could be language-independent: whatever language you save a recipe in, it would display the tags for it in the user’s native language. That’s awesome.

There is huge potential here to do something incredible, and it shows the potential for what open and linked data could do. Imagine if we then take this data-set and link it up to a data-set which provides nutritional information for foods. It would then be quite simple to create a rough estimate of the nutritional content for any recipe, even if the information isn’t provided. From there, we could link up to another data-set which provides the user’s health data (say, Fitbit’s API), and from there, to a service which tells you how many calories, carbohydrates, et cetera someone with that health profile should have each day. All of the sudden, we have a very, very concrete way to recommend recipes to people that meet their health needs. And if they tell Basil that they cooked that recipe today, Basil could update their Fitbit account.

Think about how big a deal that is. We have all of this data already—we just need to unlock it. Open data has the potential to be even more important than the web browser and hypertext.

Earlier this month, I responded to a post by Ben Brooks where he argues that retina displays do indeed significantly change the usability of devices. I argued that while retina displays certainly do make for a better device (reading especially), the contribution it makes doesn’t substantially change the device’s functionality. To illustrate that point, I explained that although I would prefer a retina iPad Mini, the iPad Mini’s smaller size and weight has dramatically changed the usability of the iPad for me. The lack of a retina display hasn’t reduced the iPad Mini’s usefulness; despite the non-retina screen, the iPad Mini is more useful for reading than the full-sized, retina-equipped iPad.

Ben responded to my post:

This is missing the point.

…

The argument isn’t: which is more useful iPad with retina display, or iPad mini. The argument is that retina displays, by themselves, are not a disruptive technology — a notion which I firmly call bullshit on.

Ben, I’m afraid, is missing the point. Ben stated in his original post that “Retina displays, on any device, absolutely change the usability of the device,” and “If you even once find yourself saying: ‘after using technology X, you can’t go back to technology W’ — then you sir have just found a feature that is fundamentally important.”

Ben appears to think that I’m simply arguing that an iPad Mini is more useful than a full-sized retina iPad. That’s true, but that wasn’t what I argued; rather, I was showing that (for me!) the retina display is absolutely something I can live without, and that it did not “absolutely change” the usability of the device. That the iPad Mini—a device with a visibly inferior screen to the iPad 3 or iPad 4—is more usable than its larger sibling puts that into dramatic relief. If the retina screen is such a boon for usability, that wouldn’t be the case. I would have begun using it again for the more usable screen. But my iPad 3 has sat around since the Mini arrived going unused, despite its beautiful screen.

For Ben, though, I’m sure the retina screen is that big of a difference. After seeing a retina display, with its incredibly sharp text and beautiful colors, he can’t stand the thought of going back to a non-retina display. For Ben, there is no going back. And that makes sense—they’re beautiful screens. But that’s part of the point, too: that simply isn’t true for everyone. It isn’t true for me, someone who absolutely loves retina screens, and it isn’t true for many of the people purchasing an iPad Mini rather than its larger sibling. I expected to hate the iPad Mini’s screen. I was wrong.

There’s no doubt that retina displays will replace all non-retina displays going forward, and I am happy for it. No doubt, because they’re simply superior. But it’s also true that they haven’t fundamentally changed our devices’ usability, as Ben originally argued.

Matt Long goes into great detail on how to use the Xcode LLDB to solve bugs. If you work in Xcode, this is a must-read.

Dave Caolo:

Years later, my family vacations, birthday parties and other notable milestones were punctuated by the blazing spotlight on my father’s 8mm camera. He shot everything, observing much of my childhood through a two-inch viewfinder.

Now that I’m a parent, the viewfinder is larger, the camera is smaller and the urge to capture and share is greater than either of those men would ever imagine.

I’m not sure that’s a good thing. Here’s how I came to that realization.

Just a terrifically beautiful piece. If you read one thing this week, read this.

Salman Khan’s university concept is excellent:

In a chapter titled “What College Could Be Like,” Mr. Khan conjures an image of a new campus in Silicon Valley where students would spend their days working on internships and projects with mentors, and would continue their education with self-paced learning similar to that of Khan Academy. The students would attend ungraded seminars at night on art and literature, and the faculty would consist of professionals the students would work with as well as traditional professors.

“Traditional universities proudly list the Nobel laureates they have on campus (most of whom have little to no interaction with students),” he writes. “Our university would list the great entrepreneurs, inventors, and executives serving as student advisers and mentors.”

That’s quite similar to what I wrote earlier this year. Giving students access to leaders in different fields as a key part of the process, and providing seminars on diverse topics, are brilliant ideas. The goal is to get students engaged on their own work, something that’s meaningful to them, and to provide the resources they need to act on them. By doing so, students are actively seeking knowledge they need, and even knowledge that isn’t immediately necessary. When someone’s engaged in creating something of their own, they are much more likely to get something out of seminars on a variety of topics than they would be when sitting in a classroom listening to a lecture to fulfill a general-education requirement.

I don’t think this concept will work for everyone, but I think that’s exactly the problem: we’ve settled on the traditional university as a solution for everyone when it simply isn’t, and due at least in part to political reasons, have made it very difficult for radically different concepts to be explored. We need experiments like this—and many of them—to improve our education system.

Shawn Blanc on Twitterrific 5:

And alas, for me, some of the “missing” elements in Twitterrific are deal breakers. Despite how fast and gorgeous Twitterrific 5 is, I do not want to give up push notifications, mute filters, or the mobilizer web toggle. These 3 features of Tweetbot are so important to how I use Twitter that I won’t be switching to Twitterrific as my one and only Twitter client.

There are no filters or push notifications (push notifications are on the roadmap, though).

It’s a different kind of Twitter client, for sure; my biggest annoyance is not being able to start a new direct message from the direct message view itself (you have to navigate to the person’s profile page).

Twitterrific 5 is out.

The new Twitterrific is simply beautiful. Even if it’s only to study the custom user interface (which you should, if you have any interest in interface design), go get it. It’s stunning.

U.S. city taxi regulators are doing their best to work with Uber while protecting customers:

Taxi regulators from 15 cities, including New York, Los Angeles, San Francisco, Washington and Chicago, were on the committee that drafted the guidelines on new rules. One rule would forbid luxury car services from using a GPS device as a meter for calculating fares based on time and distance, which is the method that Uber uses.

Another rule would forbid any driver from accepting an electronic hail through a smartphone while driving. And one says limousines may not accept a request for a ride that is made less than 30 minutes in advance, which would impede Uber’s primary business model of connecting luxury car drivers with passengers immediately.

And by “doing their best to work with Uber,” I mean doing their best to make inane rules that make it almost impossible for companies like Uber to do business. Because, customer safety.

Jeff Hawkins’ new company focuses on analyzing streaming data for patterns, rather than mining old datasets:

Data storage companies like EMC and Hewlett-Packard thrive on storing massive amounts of data cheaply. Data analysis companies including Microsoft, I.B.M., and SAS fetch that data and crunch the history to find patterns. They and others rely on both the traditional relational databases from Oracle, and newer “unstructured” databases like Hadoop.

Much of this will be a relic within a few years, according to Mr. Hawkins. “Hadoop won’t go away, but it will manage a lot less stuff,” he said in an interview at Numenta’s headquarters in Redwood City, Calif. “Querying databases won’t matter as much, as people worry instead about millions of streams of real-time data.” In a sensor-rich world of data feeds, he is saying, we will model ourselves more closely on the constant change that is the real world.

Interesting premise. I think this sort of thing is going be one of the next big frontiers for the technology industry.

Wagner Custom Skis makes skis specifically tailored to each person and what kind of conditions they want to ski.

Is there any reason this company should have to worry about the manufacturing? They should be able to focus on creating skis that perfectly fit a person, rather than also trying to perfect manufacturing. 3D printing could eventually allow them to do that.

3D printing could mean the softwarization of physical objects:

Researchers at the Cornell University Creative Machines Lab recently developed a machine that they used to print components vital to working electronics, as well as a functioning electromagnet and battery. Home printers are poised to follow a similar development curve. Hod Lipson, the Creative Machines Lab director, says consumer-grade multi-material printers are less than a decade away.

Think about a world where 3D printers are affordable and the instructions for printing objects are distributed online like applications. Prices will decline and development of them will increase, as people iterate on designs at speeds closer to software.

No longer will it be necessary to find suppliers and manufacturers to create a new hardware product—just distribute the instructions online.

Eveline Chao has one of the better overviews of China’s Internet censorship I’ve seen:

Sina Weibo users can post anything they like, and often sensitive posts will even appear in their personal feed, but the post is blocked from search results. In other words, a user might have no idea their post has been “disappeared” and their friends and other users can’t see the post in their feeds. After a term has been unblocked, it quietly reappears in users’ feeds and search results.

Because what’s censored and how it’s censored is not uniform, the effect on speech may be even worse than a strict program for censoring defined topics in every case. Since people aren’t always sure what’s going to be censored (or in some cases, if it is censorship at all), there’s a freezing effect. You can get around a well-defined censorship program, but it’s much harder to get around one that is always changing.

For the last few decades, we have struggled with how to employ manufacturing workers who lost their well-paid job with great benefits due to a globalized economy. When workers in another part of the world are willing to work for a fraction of what it costs to manufacture something in the United States, it’s obvious why companies move their manufacturing operations: it’s a significant cost advantage and, worse, if they don’t, their competitors will. This is only more true today. In January, Charles Duhigg and Keith Bradsher reported for the New York Times that for technology products especially, the labor cost itself is less important. What matters is that Asia—especially China—is the only place where every part of the supply chain exists in one region, that can manufacture quickly and at immense scale.

Manufacturing, too, is increasingly automated. The human’s role in actually putting things together is decreasing. Automation on large scale for identical products, like cars, has been a reality for decades. What’s happening now, though, is that smaller scale, small production runs are being automated as well. Rethink Robotics has created a robot called Baxter that can be “taught” how to do repeating tasks, and can work around humans. Rethink Robotics says Baxter can work for the equivalent of $4 an hour. Vanguard Plastics, a 30-person company in Connecticut, is using Baxter for menial tasks. Vanguard’s president, Chris Budnick, says that workers who did these jobs before are not being laid off, but are now assigned to “higher-level” tasks like training Baxter for each new production run.

Robots like Baxter are a work multiplier. Whereas before Vanguard required humans to do menial tasks, now they only need humans to train robots how to do something. But many more people are required to do the menial tasks than are required to train robots, so while no one may be losing their job now, they will need to find new productive tasks for them in the future—or eliminate their jobs. As robots like Baxter get better, too, manufactures will need even fewer employees to train them.

Other industries face very similar problems. Retail salespersons and cashiers, for example, account for nearly 6 percent of all jobs in the U.S., but are increasingly irrelevant. For many products, shopping online is more convenient and cheaper. Tower Records, Blockbuster and Borders all failed fundamentally because purchasing music, movies and books online is much better than paying more money for the privilege of driving to a store, hoping they have what you want and waiting in line. Even grocery stores are reducing their need for cashiers by employing self-checkout machines, which allow customers to scan and pay for items on their own and require only one employee to monitor several self-checkout machines.

Almost all of the jobs lost due to offshoring and automation have been low or semi-skilled kinds of jobs. Manufacturing jobs required training, but certainly did not require several years of specialty education and training to do. Retail sales and cashier positions require almost zero training. It would appear, then, that since offshoring and automation are eliminating low and semi-skilled jobs, we can re-orient our economy toward “knowledge work,” or work whose primary task is thinking. Examples of these kinds of jobs are software engineers, engineers, lawyers, doctors, accountants, managers and scientists. These kinds of jobs require a tremendous investment in education and training, and therefore seem not to fall prey to offshoring and automation.

In The Lights in the Tunnel, Martin Ford asks a very good question: “What is the likely economic impact of machines or computers that begin to catch up with—and maybe even surpass—the average person’s capability to do a typical job?” Or, more provocatively: If computers can already beat the best chess players in the world, isn’t it likely that they will also soon be able to perform many routine jobs?

Ford argues that not only is this true, as we’re seeing for manufacturing and retail jobs, but that it is also true for highly-skilled knowledge work jobs. Think about what a radiologist does. Much of what they do is read routine x-rays or CT and MRI scans to diagnose issues with patients. Since radiology is increasingly digital, and knowledge of what different conditions and diseases look like can be digitally represented and algorithmically identified, it’s likely that some of what human radiologists do today—the more routine, easy to identify cases—will be handled by computers instead. Doing so will dramatically decrease costs for hospitals because they will have to employ less doctors, which require large salaries, health insurance, vacation and sick days, and have to be hired and managed. Computers don’t.

The same, of course, is true for much of what general practice doctors do as well. Computers like IBM’s Watson could diagnose patients with routine things like the flu and provide a treatment as well. In fact, because Watson would have access to exponentially more medical research, journal articles, studies and patient history (and aggregate patient data), Watson may very well provide better diagnoses and treatments than the average human doctor.

Ford points out this is true for other fields, too, like law. He writes:

Currently there are jobs in the United States for many thousands of lawyers who rarely, if ever, go into a courtroom. These attorneys are employed in the areas of legal research and contracts. They work at law firms and spend much of their time in the library or accessing legal databases through their computers. They research case law, and write briefs which summarize relevant court cases and legal strategies from the past.… Can a computer do the lawyer’s job? (70-71)

Is there any reason to think that computers will never be able to do this kind of basic research and summarization? I don’t think so. What this suggests is that automation will challenge many kinds of knowledge work just as much as low and semi-skilled work. Indeed, companies will have even more reason to automate these kinds of jobs, because they are generally very well-paid jobs.

Manufacturing and retail job elimination, then, is just the first wave of many to come. The question, though, is not how to get those jobs back and protect the ones that still exist. That isn’t going to happen, is counter-productive and a waste of time. The question to ask is, when many of the jobs people depend on our automated, what kind of jobs will they do instead?

That question is, I think, the most important question to answer for the next few decades.

I have some ideas, but for now, I just want to ask the question and want you to think about it. How do we productively employ these people?