The dilemma of Carbon is a natural state of advancement of technology in an industry. The person who explores the path is always more difficult than the person who walks. Similarly, if this pathfinder finds a new path, he has the opportunity to gain the greatest value.

In Mo Hui's concept, the obstacles faced by Kaben are basically engineering and technical obstacles, and there are very few theoretical obstacles. At this stage of human development, life service robots are actually mature in various major basic theoretical fields, and there are nothing more than a lot of difficulties in engineering and technology.

To make a simple analogy, the theoretical model of internal combustion engines has been established very early, but the subsequent generations of internal combustion engines are constantly being updated and the technology is constantly advancing forward. In fact, it is not a theoretical breakthrough, but the engineering technology of internal combustion engines is becoming more and more perfect.

If we put aside the advances in peripheral disciplines such as material technology and talk about the technological progress of internal combustion engines alone, we will find that its progress has not deviated from the original theoretical model in principle. It is nothing more than the continuous improvement of thermal efficiency and the continuous improvement of power. These are all advances in engineering technology.

This is the problem we are facing in the field of robots. Theoretical and technical technologies in the main fields are no longer obstacles. Now we only need to make breakthroughs in engineering technology. They are theoretically feasible, and we must also implement them in engineering.

Several key areas where robots are stuck now, image recognition, voice recognition, artificial intelligence, positioning and navigation, are not exactly stuck, but the implementation of the existing technology is not good.

Just like the early steam engines, the pressure, sealing, transmission, and mechanical structure were not good, which resulted in very low overall efficiency and could only be responsible for drainage in the mine. The application scenarios and market acceptance were greatly restricted.

The current robot is in this state. Overall, there are technologies in each field that can be used, but the performance is not very good. The combination of them seems worse and it is often quite expensive, but it is fresh in real use, with poor application and work efficiency.

To put it bluntly, there are too many fields that need to be strengthened in life service robots today. The technology in these fields is too low, which has always been unable to improve the overall application performance of robots.

However, one advantage is that all related technologies are available, so there will be no technical gap in the field that is currently completely unsolvable. Are there any problems that have been solved and are now solving the problem of whether it is good or not.

For example, image recognition technology has existed for a long time. Many related application technologies have been extended from this technology, such as Baidu's image search, face recognition, three-dimensional reconstruction, etc., all of which have been extended from this technology.

Kuka faces standardized designable scenarios, while cardbooks face random uncontrollable scenarios, and there are many emergencies, so relatively speaking, the technical difficulty faced by cardbooks is much higher than that of Kuka. However, Kuka tends to be precision and efficiency, while cardbooks tend to be usability and intelligence.

The acquisition cost of cardbooks is not high, and it has gone the farthest in the field of life service robots. One of Mo Hui’s main considerations for getting cardbooks is to try to use ultrabooks to accelerate the R&D process.

At present, the computing power of ultrabooks is extremely extraordinary and the intelligence is not bad. Although it may not be the strongest, it is at least considered the first echelon among the research and development of major laboratories.

The possible help that ultrabooks can provide to card books should be simulation and troubleshooting, such as image recognition. If you want to obtain an image recognition technology with better application effects, the key lies in the intelligence of algorithms and unsupervised self-learning.

Mo Hui adopted a stupid method. After Kapin was acquired, Kapin team asked to provide an evolutionary model in the field of image recognition. This model is actually a training model, training and learning artificial intelligence through massive samples. At the same time, they were asked to provide all possible algorithms in the field of image recognition, regardless of the effectiveness of this algorithm.

Mo Hui had quite a lot of resources to call based on the hand of God. He not only asked for card bookings, but also contacted many scientific and commercial institutions that were conducting research in this field to provide similar things.

What Mo Hui did is actually to try to exhaust the algorithms in this field, and at the same time exhaust the research methods in this field, and then use a large number of samples to allow the ultrabooks to be infinitely compared and combined.

Strictly speaking, this is not scientific research. He uses the computing power advantages of ultrabooks to continuously arrange and combine, exhaust all possibilities, and find the possible paths in it.

Although this method is very stupid, it can indeed have an effect in a certain field. In fact, the various rules and correlations extracted from big data are all used to use various algorithms to find hidden possible relationships through similar methods. Theoretically, some scientific research is similar to exhaustiveness. The invention of incandescent lamps is actually to exhaust all possible materials and finally choose tungsten.

The first scientific research of the ultrabook was carried out with the help of Azhu, the artificial intelligence. Fortunately, the ultrabook's computing speed is very fast, and the technology will give results in an instant, no matter how large the sample library it faces.

So Mo Hui's scientific research progress was very fast. He was able to adjust the calculation model dozens of times a day, constantly try and correct the problem and collided, and find the right path.

Using image recognition technology as a whetstone, Mo Hui constantly sharpens the application methods of ultrabooks for scientific research, constantly adjusts and attempts, and in the continuous interactive adjustment, ultrabooks slowly exert their super performance.

When the scientific research method of ultrabooks basically took shape, an image recognition with poor application performance was released. Its algorithm was formed after countless derivation and evolution. Its intelligent module has been trained by hundreds of millions of pictures and videos and has sufficient adaptability.

This image recognition technology quickly conducted an application test for the Kaban team, and it was found that it could identify most commonly used items in the family through photos and videos, be able to distinguish pets, and be able to identify moving objects.

To achieve this, in fact, the visual recognition module can almost meet the needs of life robots. What needs to be done later is to extend other functions based on this technology, such as distance judgment, path planning, self-positioning, etc.

For Mo Hui, the biggest gain is not this image recognition technology, but the scientific research model and method explored by Ultrabook in the process of developing this technology.

Just take the first step, Mo Hui program it into a new module with self-learning ability and quite intelligent scientific research 10 to be continued.

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