1.The world isn't broken. It's working exactly as it was designed to work. And we're the ones who designed it. Which means we fucked up.
The author keeps emphasizing this sentence, "It was designed to work that way." In the previous article, the author cited many examples of design destroying many things. Most of these include unauthorized data collection or unannounced network experiments. Even knowingly committed an example, I think of a description, With adequate profit, capital is very bold. A certain 10 percent. will ensure its employment anywhere; 20 percent. certain will produce eagerness; 50 percent, positive audacity; 100 percent. will make it ready to trample on all human laws; 300 percent; and there is not a crime at which it will scruple, nor a risk it will not run, even to the chance of its owner being hanged. If turbulence and strife will bring a profit, it will freely encourage both. Smuggling and the slave-trade have amply proved all that is here stated."（PJ Dunning, lc, p. 35.) Is the power of design very weak in the face of capital? Is it because the design was originally designed to serve economic activities in order to gain a competitive advantage.
2.We're coming in to solve a problem, because we believe it needs to be solved and its' worth solving. But we work for the people being affected by that problem.
This statement is quite moving. We are always told that the design is to solve the problem, but the author emphasizes the direction of our problem. We are not solving the boss’s problem, but the problem of those who are not in this room. The designer and the boss are no longer in a subordinate relationship, but a cooperative relationship.
3.When all these amazing people, some of whom we don't understand at all, go online, they are going to behave as inefficiently in there as they do out there. That is awesome.
That is awesome! I was also trapped in that designer should make everything simple and efficient. But we should embrace diversity, as the author said, 'To make sure none of these incredible voices get lost.
1.First the general one: The information delivered by a feedback loop can only affect future behavior; it can't deliver the information, and so can't have an impact fast enough to correct behavior that drove the current feedback. A person in the system who makes a decision based on the feedback can't change the behavior of the system that drove the current feedback; the decisions he or she makes will affect only future behavior.
'There will always be delays in responding. It says that a flow can’t react instantly to a flow. It can react only to a change in a stock.' The author explained the general laws of the system, and I think that the economic system can also be explained in the same way. Delay can cause poor information, and poor information can generate profits.
2.Over recent history world capital, like world population, has been dominated by its reinforcing loop and has been growing exponentially. Whether in the future it grows or stays constant or dies off depends on whether its reinforcing growth loop remains stronger than its balancing depreciation loop.
This statement is interesting: ‘the greater the stock of physical capital (machines and factories) in the economy, the greater the production efficiency (output per unit of capital), and the more output (goods and services) that can be produced each year. The more output produced, the more capital can be invested to obtain new capital. This is an enhanced cycle. ’So where did the economic crisis come from? What can break this enhanced cycle is the black swan event, or more and more resources and technologies are needed to maintain the cycle, and the development of technology (productivity) cannot keep up with this growth rate.
3.Resilience is not the same thing as being static or constant over time. Resilient systems can be very dynamic. Short-term oscillations, or periodic outbreaks, or long cycles of succession, climax, and collapse may in fact be the normal condition, which resilience acts to restore!
Systems that feel resilient are more adaptable, but more difficult to maintain balance. The human system mentioned in the article can indeed be regarded as amazing, and through our wisdom, we can maintain the balance of this system for decades. As far as I know, the lifespan of most warm-blooded animals is much lower than that of humans. Cold-blooded animals have poor methods of maintaining system stability, but many of them have long life spans, which is interesting.
1.The practicing writer, the writer-at-work, the writer immersed in his or her project, is not an entity at all, let alone a person, but some curious melange of wildly varying states of mind, clustered toward what might be called the darker end of the spectrum: indecision, frustration, pain, dismay, despair, remorse, impatience, outright failure. To be honored in midstream for one's labor would be ideal, but impossible;
This statement is very creative, magnifying the artist in the creation, focusing on thinking and spiritual world. These emotions are very real, and artists often draw the source of creation in pessimism and failure. A large number of accomplished artists (after they left) are in a less comfortable environment, and failures have created great works of art. The last sentence reflects the contradiction. If the respect is gained in the middle, the artwork may no longer be successful. Artists can achieve remarkable results under time pressure and survival pressure, but once they enter a relaxed environment, the value of their works begins to decline.
2.Is the artist secretly in love with failure, one might ask. Is there something dangerous about "success," something finite and limited and, in a sense, historical: the passing over from striving, and strife, to achievement?
I believe that success does contain danger, and it may come from the'unknown'. There may not be a path to success, and you need to find it yourself. No one knows whether it is correct before reaching it. I guess it is because we often rely on the experience of our predecessors. If we don’t, we will not feel safe.
3.Palinurus, a skillful pilot of the ship of Aeneas, fell into the sea in his sleep, was three days exposed to the tempests and waves of the sea, and at last came to the sea shore near Velia, where the cruel inhabitants of the place murdered him to obtain his clothes: his body was left unburied on the seashore.
Quite like the declarative sentence.
1.“In 2016 a team at the University of Washington set out to create a deliberately faulty husky-versus-wolf classifier. Their goal was to test a new tool called LIME, which they’d designed to detect mistakes in classifier algorithms. They collected training images in which all the wolves were photographed against snowy backgrounds and all the husky dogs against grassy backgrounds. Sure enough, their classifier had trouble telling wolves from huskies in new images, and LIME revealed that it was indeed looking at the backgrounds rather than at the animals themselves.”
The problem of overfitting is very interesting. I think it is one of the most important problems in deep learning training. These examples of cheating machine learning are very common. I recently discovered that many captcha images have a lot of noise in the background. Although this does not confuse the human eye, it can effectively prevent robot image recognition. It also shows that AI is not looking at the object itself. AI may just separate objects from background.
2.First, the team found a way to plot the word vector so that gender bias was visible—with male-associated words on the left and female-associated words on the right.
I am very interested here. In some languages, words are divided into three attributes. What is the origin of this classification? He assigns male-associated to the left and female-associated to the right. Is this habit or accident? As far as I know, there are also sayings in China that men are on the left and women are on the right. Why are men on the left?
3.How do we stop AIs from unintentionally copying human biases? One of the main things we can do is expect it to happen. We shouldn’t see AI decisions as fair just because an AI can’t hold a grudge. Treating a decision as impartial just because it came from an AI is known sometimes as mathwashing or bias laundering.
I agree with this point of view. The bias of AI algorithms comes from people. Perhaps better data collection or analysis methods can be applied, or AI can evolve itself to obtain an unbiased view.
1.Furthermore, digital products often have temporal logic where a linear narrative is replaced by a set of complex states and transitions.
I fully agree with this statement. When I was learning to design the interactive interface on the screen, I found that even for a few very simple pages, after considering different operation modes and user possibilities, there are many pages that need to be designed. , If you consider the transition between elements, the workload will be greater. When facing digital products, the consideration is no longer a page, but a timeline.
2.Perceptually uniform color spaces allow us to align numbers in our code with the visual effect perceived in our viewers.
In some cases, perceptually uniformity is essential.
The chapter where this part is located solves the problems I felt before. In the photos, I found that darker photos give people a stronger sense of layering, while photos with higher brightness or being overexposed give people a kind of The content on the photo feels squashed.
3.The Brooklyn-based chocolate producer Mast Brothers is famous for their colorful packaging designs where colored patterns are used to denote the flavor profile of the chocolate.
I really like this packaging style, and the taste experience is conveyed visually, which feels great.