Trains moving along the track in an orderly fashion

The characteristics exhibited in a car accident are equally applicable to a walk along a leaf-strewn path on a mundane autumn day. They are both such events, in which the interdependence of countless details prevails over the explanatory power of the rules that determine them. All we can do seems to be to resent or marvel at an outcome as it emerges. Now, our latest paradigm technique, machine learning, may reveal that the everyday world is governed more by chance than by rules. If so, it is because machine learning can step outside the patterns of human perception and instead summarize laws that we cannot understand or apply.

Machine learning concept map

The opaqueness of machine learning systems raises serious concerns about their credibility and tendency to bias. But the fact that they do work may give us a whole new understanding of what the world is and what role we play in it. Machine learning works in a fundamentally different way than traditional programming. In fact, traditional programming is the set of rules we use to make sense of the world based on rules. To take one of the most iconic examples of machine learning: If you are writing software to recognize handwritten numbers, a programmer would traditionally tell the computer that a “1” is made up of a vertical line, and an “8” is made up of a larger circle and a smaller circle above it. and a smaller circle above it, and so on. This approach may work well, but its reliance on the Platonic ideal of handwritten numbers means that the program will misjudge a significant percentage of handwritten numbers. Since the actual numbers come from mortal hands, they cannot be so “perfect”.

A new way of doing machine learning

Machine learning models, on the other hand, know how to learn from examples. To create a machine learning model that can recognize handwritten numbers, developers don’t tell the computer anything we humans know about the shape of numbers. Instead, the developers give it thousands of sample images of handwritten numbers, each different and correctly labeled with the number it represents. The system uses an algorithm to discover statistical relationships between pixels with the same labeled image. A series of pixels on some vertical line will increase the statistical weight of an image as a “1”, decrease the probability of it being a “3”, and so on.

Unknowable, but effective

In real-life machine learning applications, the number of possible answers is in the hundreds of millions, the amount of data to be considered is enormous, and the correlations between data points are so complex that we humans are usually unable to understand them. For example, human metabolism is an extremely complex series of interactions and interdependent effects. So a machine learning system was created that could predict the response of the human system to complex factors, called DeepMetab, and it became a place for doctors, researchers, laypeople and hypochondriacs to ask questions about the human organism and explore relevant ideas. Although we cannot understand how it produces its output, DeepMetab remains the most important source of knowledge about the human body.

The combination of AI and healthcare

As we increasingly rely on machine learning models (MLMs) like DeepMetab that we cannot understand, we may gradually come to accept the following two views: The first view says that in order to obtain useful probabilistic outputs generated by machine learning models, we must often tolerate the disadvantage of inexplicability. The second view is that the difficulty of interpretation is not a drawback, but a real situation. Machine learning models are effective because they are better at reading the world than we are: they generate cognition beyond humans, etc., by counting vast amounts of interconnected data without having to explain to humans how they got to such cognition. Whenever a citizen or regulator cries out in despair because they can’t understand how machine learning works, we get a sense that these models do work.

Concept map for the age of big data

If machine learning models work by giving up on simplifying and explaining complexity with rules that can be understood, then we can feel it in the cries of “It works!” we can feel all the tiny things interacting with each other in their interdependence. And it is these tiny things that are the true essence, rattling in the cosmic sound of harmonious laws. The success of our technology is telling us that the world is a true black box.

Man vs. Machine

From watches to cars, from cameras to thermostats, machine learning is deeply embedded in our daily lives. It is used to recommend videos, try to identify hate speech, guide the movement of vehicles, control the spread of disease, and is critical to mitigating the climate crisis. It’s not perfect and may amplify social biases, but we continue to use it because it works. That machine learning does all of this without applying rules to particular things is surprising and even disturbing. We favor rules over examples so much that we think it’s crazy to have a machine learning system play Go simply by analyzing lots of games and moves without knowing the rules. But that’s how machine learning became the best Go player of all time. In fact, when developers provide a system with data relevant to a domain, they often deliberately hide from it the interrelationships between the data we already know.
Overly specific generalizations?

Now, even people who know a little bit about machine learning can get creeped out by the fact that machine learning models are created by generalizing from data. For example, if a machine learning model for handwritten digit recognition does not generalize from the samples it learns from, it will be a failed model due to overfitting. However, the generalized description of a machine learning model differs from the traditional generalizations we use to explain a particular situation. We like traditional generalizations because (a) we can understand them; (b) they often lead to deductive conclusions; and (c) we can apply them to specific situations. However, (a) generalized descriptions of machine learning models are not always easy to understand; (b) they are statistical, probabilistic, and largely inductive; and (c) we usually cannot apply them unless we run the corresponding machine learning model.

Statistics

Furthermore, generalized descriptions from multilevel machine learning models may be very specific: for example, vascular patterns in a retinal scan may predict an arthritis attack, but only if 50 numerical indicators are satisfied, which in turn may be influenced by interconnections. It’s like trying to figure out how a car avoids serious damage in a multi-vehicle collision: the vehicle necessarily has to overcome many specific conditions, but such events cannot be reduced to a comprehensible rule, nor can such complex rules be migrated and applied to other events. Or, it’s like a clue in a murder case that indicates the killer but is only valid for that one case.

Wall of clues

Machine learning models do not deny the existence of rules or laws. It simply emphasizes that these rules alone are not enough to understand everything that happens in our complex universe. Serendipitous details interact to make the rules inadequate in their explanatory power, even assuming we could know all the rules in the world. For example, if you know the laws of gravity and air resistance, as well as the mass of a coin and the Earth, and you know how high the coin falls from, you can calculate how long it takes for the coin to hit the ground. This is usually good enough for your practical purposes. But the traditional Western scientific framework overemphasizes rules. To fully apply these rules, we must know every factor that affects the fall, including which pigeons disturb the air currents around the coin and the simultaneous effects exerted by the gravity of distant stars on it. (Did you remember to add the influence of a distant comet?) To apply these laws with complete accuracy, we must have a comprehensive and inaccessible knowledge of the universe like that of the Laplace demon.

The coin flip incident

This is not a criticism of the pursuit of scientific laws or practical science. Science is usually empirically based and adequate for our needs — although the actual achievable precision will make us make certain concessions. But this should make us wonder: why does the Western world regard unexplainable chaotic phenomena as purely epiphenomenal, believing that there are laws beneath them that can explain such phenomena? Why do we ontologically prefer something eternal and unchanging to the constant flow of water or dust?

Rewriting the definition of knowledge

These are common topics in the history of Western philosophy and are far beyond the scope of this paper. But it is undeniable that we are attracted to a world simplified by eternal laws, so that we can understand it and thus predict and control it. At the same time, these simple and wonderful laws hide from us the chaos of particular situations, which are not only determined by the laws themselves, but are also influenced by the state of every other particular situation. But now we have a prediction and control technique that stems directly from the many tiny factors that exist simultaneously and interact with each other as a whole. This technique gives us greater control, but does not enhance our understanding. It succeeds in making us focus on things that are beyond our understanding.

The laws of physics

At the same time, for the same reason, machine learning may break the fascination with certainty as the hallmark of knowledge, because machine learning results in probabilities. Indeed, deriving perfectly certain results from a machine learning model raises doubts about that model. The probability of the output of machine learning is intrinsically inaccurate; the real statement about probability is the ability to correctly predict its chance of being wrong.

The Butterfly Effect

We now have a mechanism that shocks us, some model that operates by drawing information from the many details of interconnectedness in an incomprehensible, subtle network. Perhaps we need not regard those chaotic swirls as mere representations that are not yet well understood. Perhaps the complexity and cognitive difficulty of the interplay of all factors will shake the cognitive foundation of Western science that what is most real is most fixed, most universal, and most knowable.

Schematic of the chaotic solution of the three-body problem

Perhaps we will finally accept that the unimaginably complex connections, accidents and coincidences of simple events are the true face of the world. We will also accept that a brain weighing 1.4 kg is not enough to build a complete knowledge of the world. The brutal unknowability of the world is blurring the boundaries of our understanding. If this is happening, it is because we are hearing more specific, tiny, noisy signals through, for example, machine learning models. These signals are generating useful, surprising, probabilistic knowledge based on the incomprehensible connections between all things.

Author: David Weinberger

"By David Weinberger

Translation: Clouds bloom and leaves fall

Reviewer: Ms. Zhou π

Original link: Learn from machine learning"

The post While being shocked by GPT4, how should we view the power of artificial intelligence appeared first on TechGoing.

Use the Command Prompt method

1. Run the Command Prompt as administrator
2. Enter the command “wmic path software licensing service get OA3xOriginalProductKey” in the window.
3. Normally, this command will return a 25-bit product key. This is the product key that is stored in your UEFI firmware or in the BIOS of your computer. IT House understands that if you used a different key to reinstall Windows, you may need another tool to find the key.

Use the Registry Editor method

1. Open Notepad
2. Then copy the following command to Notepad
3. Then click Save
   

1. Then rename the file and change its suffix to “.vbs” format, such as the windows product key VBS Or you can choose Save As, select “All Files”, and rename it to a file with You can also choose Save As, select “All Files” and rename the file with “.vbs” suffix.

5. Double-click to run

The post Win10: How to retrieve your product key for Windows 10 appeared first on TechGoing.

First of all, you need to open the phone house, and then check whether the following functions are open. The several features mentioned are application behavior records, album and social application file protection, secure sharing, clipboard privacy protection, virtual identity ID, and browser privacy protection. When many people talk about the UI of Android phones, MIUI will always have a place. MIUI is loved by many users because of its smooth operation, natural animation transition, and lack of too many redundant functions. Of course, Xiaomi always attaches importance to user information security and privacy security protection, from functional design, technical means, management process and other aspects of a comprehensive and systematic design, the achievement of MIUI’s privacy protection capabilities.

Browser Privacy Protection

According to Xiaomi’s earnings report for the second quarter of 2022, throughout the second quarter, MIUI achieved good results within the global market, successfully hitting a record high in monthly active users both globally and in mainland China. Specifically, in June this year, the number of MIUI active users reached 547 million worldwide, while 140.2 million were active in mainland China.

The post How to protect the privacy and security of Xiaomi phones? appeared first on TechGoing.

Whether the photo looks good, the first glance will look at the composition, whether the subject is clear, whether the coordination between the elements with the overall structure of the photo, lines, texture, light and shadow, etc. whether ornamental.

Today introduces 5 basic composition methods, which can cope with 90% of the shooting scenarios, take a look.

1. The method of composition in thirds
Thirds can be said to be the most basic composition, but also evolved many versions. In short, the picture is divided evenly in accordance with the ratio of 1/3, three parts horizontally and three parts vertically. The main elements are placed on the intersection of two-thirds, which is the visual center of gravity of the picture, more beautiful and attractive.

When there are longer objects such as lines in the photo, you can also arrange them on the three-quarter line, such as horizon lines, buildings, people, etc.. This not only avoids the dullness generated by centering, but also maintains the balance of the center of gravity of the picture, which can be said to kill two birds with one stone.

2. Guiding line composition
The use of lines to guide the line of sight to the subject, forming a strong sense of visual convergence, the line can be a real line, but also a regular change in the extension of objects generated by the imaginary line, this composition is suitable for scenes with depth, such as highways, railroad tracks, flower fields and so on.

3. Symmetrical composition
Symmetrical composition refers to the picture of two parts of the scene, a line or point as the axis of symmetry, in size, shape, color and arrangement of the mutual balance of reciprocity in a form of composition. It is possible that the photographed scene is itself a symmetrical structure, or it is possible that the reflection is utilized. Symmetrical compositions produce a sense of serenity and harmony and have the power to turn ordinary scenes into magic.

4. Centered composition
Thirds is to put the object in a third of the position, while the center composition is to put the object in the center of the picture, these two are not contradictory. Some scenery is not suitable for thirds composition, but for more centered composition, such as when shooting close-ups, you can let the object in the center and try to fill the whole picture.

Sometimes a centered composition can make the photo more impactful, allowing the viewer to focus on the subject, in addition to a more symmetrical and balanced image.

5. Frame composition
As the name implies, frame composition involves constructing a frame in the picture so that the scene appears inside the frame. The frame is like a “circle” drawn in the photo, drawing a circle certainly to let you look at the things in the circle ah. There are many foregrounds that can act as frames, including caves, fences, branches, and other elements. Frame compositions can create a sense of strangeness and make people look at them. When using frames, be careful that the chosen frame is aesthetically pleasing and does not weaken the subject.

6. Triangle composition
Let the object in the photo form a triangle, the object may itself have a triangular structure; may also be through a special shooting angle, so that it becomes a triangle; can also be three points of interest in a triangular distribution, forming an imaginary triangle. Triangles have stability, and the picture taken with a positively placed triangle composition also has a sense of stability, while a triangle placed upside down will have a sense of instability.

7. White composition
In the photo to create a large blank area, through the white highlight the subject, to create a mood, is the white composition. The white composition can create a feeling of solitude, emptiness and silence. When using white space composition, we should pay attention to the balance of the picture, and at the same time, there must be a point of interest in the picture that attracts people’s attention, not all the white space.

The post The 7 essential composition methods can cope with most shooting scenarios appeared first on TechGoing.

Honor earlier announced the global release of Magic4 Pro when it was unveiled first at the Mobile World Congress held in February 2022. Magic4 Pro boasts its delivery of smartphone design, display, camera, video, performance, and privacy with its innovative technology.

Honor France listing is yet to announce the price of the smartphone. In addition to that, the early bird sale is providing a discount coupon of 100 Euros on the actual price once the delivery starts. The ongoing early bird sale window will last till 30 May.

Honor Magic4 Pro specifications listed by Honor France

The Magic4 Pro is powered by Qualcomm Snapdragon 8 Gen 1 chipset. The device listed will come with 8 GB RAM and 256 GB internal storage. Additionally, the user interface provided is MagicUI 6.0 which is based on Android 12. Honor’s GPU Turbo X delivers the firm’s first AI Super Rendering technology for an enhanced gameplay experience. This means that the device can run at an incredibly high framerate as well as network connectivity. This will enable the device to produce less heat as well as reduce lag.

The Magic4 Pro features an OLED curved screen that measures 6.81 inches . Additionally, the display comes with a resolution of 2848×1312 pixels providing a pixel density of approx. 460 ppi. The phone has the ability to display 1.07 billion colours and supports 100% DCI-P3. This will allow users to experience more immersive gameplay and colourful cinema.

Magic4 Pro features an innovative Triple Camera System. This includes a 50MP wide-angle camera with a 1.56-inch colour sensor, a 50MP 122- degree ultra-wide camera, and a 64MP periscope camera. The 100x digital zoom listed in its camera feature will allow the device to produce clear images of distant objects.

When it comes to battery capacity, the 4600mAh battery in the HONOR Magic4 Pro packs a punch with 100W fast charging.

Other features of Magic4 Pro as listed include IP68 water resistance body, 5G support, Wifi, and NFC. Additionally, the phone also features 3D face unlock.

Source

The post Honor France lists Magic4 Pro, hinting at global release; specifications listed appeared first on TechGoing.

It shows that the cable is a coaxial cable with 19 wires inside. Six of the wires are tinned copper wires that provide power. There are also two cables that support USB 2.0 transport, making the cable backwards compatible with Thunderbolt 3, USB 3 and USB 2. Additionally, many cables are plated with silver and copper.

The cables themselves also feature premium components contained in a durable brass housing, as well as 24 gold-plated pins per connector. In addition, the connector is equipped with an Intel chip for signal reconstruction and signal jitter reduction inside, and the outside is wrapped with a waterproof and dustproof braid, which increases the durability of the cable.

ChargerLAB notes that the cable performs very well, but may not be useful to the average customer as there are cheaper alternatives.

According to the description on Apple’s webpage, the wire has the following highlights:

Data transfer rate up to 40Gb/s

USB 3.1 Gen 2 data transfer rates up to 10Gb/s

DisplayPort video output (HBR3)

Connect to Thunderbolt 3, Thunderbolt 4, or USB-C and USB-enabled devices and displays

Power supply up to 100 watts

Woven design, not easy to tangle when rolled

The Thunderbolt logo is easy to identify and won’t be confused with other cables

Daisy-chain up to six Thunderbolt 3 devices

The post Apple Thunderbolt 4 Pro cable teardown: tell you why it’s so expensive appeared first on TechGoing.

Apple’s support page now has the option to update MagSafe Battery Packs. To keep your Pack up to date, you can use an iPhone, iPad, or Mac.

How to Update MagSafe Battery Pack’s Firmware

Updating your MagSafe Battery Pack’s firmware is the easiest way to take advantage of mobile charging at 7.5W. When you plug in your iPhone’s battery pack, the process of updating the firmware begins automatically. The firmware takes about a week to update.

While a week just to update is a significant amount of time, there is a way to update much more quickly.

Plugging one end of a Lightning to USB cable into the Lightning connector on the battery pack, and the other end into a Mac or iPad to perform a firmware update on your battery pack. This battery system is compatible with both the iPhone 12 and 13 models.

There haven’t been many fans of the accessory since its release in July 2021. The charger’s ability to deliver only 5W of power left many iPhone users dissatisfied. However, the iPhone 12 Mini’s short battery life has made it a popular choice for iPhone 12 owners. Overall, the 20W power adapter is still the best way to charge. However, the MagSafe Battery Pack is a good option for travellers

To check latest update (version 2.7) installation status, attach the battery pack to your iPhone and go to Settings > General > About > MagSafe Battery Pack.

The post Apple MagSafe Battery Pack charge iPhones faster after new firmware upgrade appeared first on TechGoing.