Chapter 424 Professor Xu, I have some questions

Resource allocation can greatly improve the utilization efficiency of various resources such as energy.

If artificial intelligence can shine in these fields, human life will be greatly changed in the future.

The last one, competitive games, is relatively the fairest one and can more objectively reflect the computing power of artificial intelligence.

After confirming these details, the date of the competition was set one month later.

"Professor Xu, isn't one month too short?"

Han Shubin knows that in addition to the field of competitive games, we have not yet begun to study the other two fields.

As for IBM and Google, the technology is relatively mature.

"One month is enough, let's set it at this time."

Seeing that Xu You was so confident, Han Shubin stopped asking any more questions.

In fact, even if the deadline is reduced to half a month, Xu You is very confident that he can complete the work.

Xu You can't wait for this quantum competition.

"Let's start with the weather forecasting program."

Although Xu You has not formally conducted research on the field of artificial intelligence weather prediction before.

But Xu You still knows very well how artificial intelligence predicts weather.

In weather prediction, the main methods used by artificial intelligence include intelligent grid forecasting and model analysis technology.

Intelligent grid forecasting uses big data analysis skills to conduct comprehensive mining and analysis of large amounts of weather data.

Through such an intelligent grid system, artificial intelligence can accurately predict various weather conditions.

This weather prediction method is accurate in predicting precipitation. The disadvantage is that the prediction period is relatively short and it is impossible to accurately predict the weather a few days later.

Another method of weather prediction is model analysis technology.

Model analysis technology also uses various big data and artificial intelligence, but the focus is on the need to model a complex meteorological system.

This method of weather prediction may not be as accurate as the first method in the short term, but in the long term, it has a higher prediction accuracy.

The weather within a month can be predicted more accurately.

However, because the amount of calculation is too huge, the performance requirements for the computer are very high.

Even many supercomputers are unable to handle such a huge amount of calculations.

But these are not a problem for a warp calculation quantum computer with extremely high calculation speed.

What's more, Xu You also has the skill of brain simulation to help him complete the modeling work.

In just three days, Xu You completed all the programming and modeling work and taught the mathematician to predict the weather.

"Our weather forecasting system has also learned multiple weather forecasting methods such as intelligent grid forecasting and model analysis technology. It can accurately predict the weather through its own system scoring mechanism. When there is sufficient meteorological data

, the algorithm can predict the weather within 24 hours with almost 100% accuracy. Even for the weather within a month, the prediction accuracy can be increased to more than 95%."

Because the weather is affected by so many factors, it is almost impossible to predict the weather in a few days with 100% accuracy.

A butterfly flapping its wings may change the weather of a certain day.

Not to mention artificial rainfall and other artificial changes in the weather.

But regarding the data given by Xu You, a researcher raised his own questions.

"Professor Xu, I can understand the accuracy of weather forecasting within 24 hours. But... how did you arrive at the accuracy of weather forecasting within a month?"

This question is very normal, because it has only been three days since Xu You made this weather prediction model.

There is no time to calculate the accuracy of the model.

"This data is a theoretical value. We will know the specific accuracy later."

As he spoke, Xu You showed the weather forecast just made by the artificial intelligence on the big screen.

Based on radar and other data provided by the National Meteorological Observatory, artificial intelligence has completed weather forecasts for various parts of the world within one month.

However, compared to the weather forecast given by the meteorological station, the weather forecast calculated by artificial intelligence will have some discrepancies. It even gives a completely different forecast whether it will be sunny or rainy on a certain day in a certain place.

"Professor Xu, if it is just a theoretical value, will this model lack sufficient verification?"

"We will observe it for half a month. If the data does not meet the standards, we will make changes to the model."

In fact, Xu You is very confident. Through the results of Xu You's brain simulation, the accuracy of this model is even higher than the data given by Xu You.

Xu You also understood their suspicions. After all, if normal procedures were followed, multiple verifications and modifications would definitely be required.

"I agree with Professor Xu, we will know the prediction accuracy of the model in a few days," Han Shubin said.

Even Han Shubin couldn't understand how Xu You arrived at the theoretical values ​​predicted by the model.

But as long as this achievement comes from Xu You, there is nothing to doubt.

After completing the weather prediction model, Xu You then studied the task of resource allocation.

Compared with weather prediction, the resource allocation problem is much less contingency, and the main test is the computing power of quantum computers.

For example, in terms of energy allocation, the power load is predicted through the data provided by the power grid, and then predictive maintenance measures are provided to provide accurate power supply and demand solutions.

Or in the field of wind power generation, we can build and train neural network models based on historical power generation data and weather forecast information to optimize wind power generation plans and improve the efficiency of wind power generation.

Two days have passed, and the artificial intelligence has learned to solve various resource allocation problems.

Compared with the previous model, computational artificial intelligence can improve efficiency by anywhere from 20 to 50 percent, making resource allocation more reasonable.

As the past two days have passed, the accuracy of weather prediction calculated by artificial intelligence can also be verified.

"Professor Xu, the accuracy of our weather forecasts around the world in the past two days has reached 99.9%. Many of the locations where the forecasts are inaccurate are caused by artificial rainfall and other man-made behaviors, which affects the accuracy of our forecasts."

A member of the project team said.

This kind of accuracy means that if artificial intelligence predicts the weather a thousand times, it will only make one mistake.

This is already a very high figure for weather forecasts that already have a lot of contingency.
Chapter completed!