As the master code of life, DNA can do a lot of things. Inheritance. Gene therapy. Wipe out an entire species. Solve logic problems. Recognize your sloppy handwriting.
Wait, What?
In a brilliant study published in Nature, a team from Caltech cleverly hacked the properties of DNA, essentially turning it into a molecular artificial neural network.
Which future are you going to pick?
Today, I would like to tell you two short stories describing what your far future might look like, depending on the choices that you though not only you will make in the near future. Feel free to leave a comment to let others know which one you’d rather have as your real future.
Story 1: A day in 2140
The blinds in your bedroom slowly whirr open, as a gentle melody gradually fills the environment. Ferdinand your AI assistant, to whom you decided to give a far less extravagant name than most other people do informs you that it’s 7:30, your bath is ready, and so will be your usual breakfast once you’re done in the bathroom. Getting up that early is never too easy, but your morning walk in the park is always worth it, because it puts you in a good mood.
Agriculture uses 70 percent of the water in the world, and this appears to be an upward trend regarding water needs. As the demand in other industry sectors is also increasing, and the effects of climate change exacerbate water shortages, water saving measures have become an unavoidable challenge for maintaining the sector and preserving life.
Agronomy researcher Rafael González has developed a model to predict in advance the water that users will need each day. This tool came about from a drive to ally with water resource sustainability.
The model applies artificial intelligence techniques including fuzzy logic, a system used to explain the behavior of decision making. It also mixes variables that are easier to measure, like agroclimatic ones or the size of the plot of land to be watered, with other more complicated variables, like traditional methods in the area and holidays during watering season.
The enzyme laccase is able to alter the chemical structure of wood on its surface and thus facilitate biochemical modifications without changing the structure of the material. By attaching functional molecules, Empa researchers develop waterproof or antimicrobial wood surfaces, for instance. Also it is possible to make adhesive wood fibers, which can be pressed to fiberboards without any chemical binding agents. These solvent-free fiberboards are used for insulation of eco houses.
The problem: There are many variants of laccase, which differ in the architecture of the chemically active center, and not all of them react with the desired substrate. As it is extremely difficult to predict whether or not a particular laccase will react with a specific substrate, costly and time-consuming series of experiments are required to identify suitable laccase-substrate pairs. Molecular simulations could solve the problem: You simply need a precise structural analysis of the laccase to simulate the chemical reaction mechanism for every desirable combination on the computer. However, this requires a high computer computing—capacity and, even then, would be extremely time-consuming and expensive.
But there is a shortcut: “deep learning.” A computer program is trained to recognize patterns with data from the literature and own experiments: Which laccase oxidizes which substrate? What might be the best conditions for the desired chemical process to take place? The best thing about it: The search works even if not all details about the chemical mechanism are known.
Title is a bit misleading — atheism is only unpolular with totalitarian regimes (and Templeton Foundation?) — interesting.
Although Johnson said he found the team’s research useful and important, he was unimpressed by their claim to have outperformed previous predictive methods. “Linear regression analysis is not very powerful for prediction,” he said. “I was a little surprised by the strength of their claims.” He cautioned that we should be skeptical about the word prediction in relation to this type of model. Opinion might be better.
“It’s great to have as a tool,” he said. “It’s like, you go to the doctor, they give an opinion. It’s always an opinion, we never say a doctor’s prediction. Usually, we go with the doctor’s opinion because they’ve seen many cases like this, many humans who come in with the same thing. It’s even more of an opinion with these types of models, because they haven’t necessarily seen many cases just like it—history mimics the past but doesn’t exactly repeat it.”
The silver lining here is that if the power of the models is being overstated then so, too, is the ethical concern.
NASA has recently announced it would give funds to a California-based 3D printing company for finding ways to turn asteroids into giant, autonomous spacecrafts, which could fly to outposts in space, the media reported.
Made In Space’s project, known as RAMA (Reconstituting Asteroids into Mechanical Automata), could one day enable space colonization by helping make off-Earth manufacturing efficient and economically viable, Space.com reported.
The company plans to use 3D printing to turn the asteroids into self-flying vehicles by 2030.
About the future death of explainability to understand AI thinking, the writing is on the wall…
These divergent approaches, one regulatory, the other deregulatory, follow the same pattern as antitrust enforcement, which faded in Washington and began flourishing in Brussels during the George W. Bush administration. But there is a convincing case that when it comes to overseeing the use and abuse of algorithms, neither the European nor the American approach has much to offer. Automated decision-making has revolutionized many sectors of the economy and it brings real gains to society. It also threatens privacy, autonomy, democratic practice, and ideals of social equality in ways we are only beginning to appreciate.
At the simplest level, an algorithm is a sequence of steps for solving a problem. The instructions for using a coffeemaker are an algorithm for converting inputs (grounds, filter, water) into an output (coffee). When people say they’re worried about the power of algorithms, however, they’re talking about the application of sophisticated, often opaque, software programs to enormous data sets. These programs employ advanced statistical methods and machine-learning techniques to pick out patterns and correlations, which they use to make predictions. The most advanced among them, including a subclass of machine-learning algorithms called “deep neural networks,” can infer complex, nonlinear relationships that they weren’t specifically programmed to find.
Predictive algorithms are increasingly central to our lives. They determine everything from what ads we see on the Internet, to whether we are flagged for increased security screening at the airport, to our medical diagnoses and credit scores. They lie behind two of the most powerful products of the digital information age: Google Search and Facebook’s Newsfeed. In many respects, machine-learning algorithms are a boon to humanity; they can map epidemics, reduce energy consumption, perform speech recognition, and predict what shows you might like on Netflix. In other respects, they are troubling. Facebook uses AI algorithms to discern the mental and emotional states of its users. While Mark Zuckerberg emphasizes the application of this technique to suicide prevention, opportunities for optimizing advertising may provide the stronger commercial incentive.
