People often make a judgment based on past experience and cognition.
So what is the machine made?
For ordinary computer algorithms, the algorithm makes a judgment through a programming language.
Here we use the if and else statements to give a simple example (for the sake of simplicity and clarity, there is no strict programming language to judge the conditions and output, so don't quarrel).
if (reader x voted for recommendation):
Reader x looks handsome
else:
Reader x looks average
For ordinary computer algorithms, through the above judgment statements, it knows that the readers who voted for recommendations are handsome.
But if the programmer finds out one day, some readers who voted for recommendations seem to look quite average, so he optimized the algorithm and changed the judgment conditions to (reader x voted for recommendations and reader x voted for monthly votes).
At this time, for the algorithm, only readers who voted for recommendation and monthly votes were handsome.
Later, programmers will find that no matter how they add judgment conditions, it seems that one or two readers will always jump out to overturn his conclusions. It is very difficult for him to manually formulate rules to make computers accurately identify handsome readers.
At this time, machine learning algorithms are needed.
So how do machine learning algorithms be made?
We only need to randomly select a certain number of readers (the field of machine learning is called training data) and make a table that records some attributes of these readers, such as whether they have voted for recommendations, whether they have voted for monthly votes, and whether they have left comments,
Whether you like it, whether you have rewarded it, etc. (the field of machine learning is called features), and the conclusion is recorded in the last column, that is, whether it is handsome or not (the field of machine learning is called label).
Such training data is provided to the machine learning algorithm, and after training, it will learn a model about the relationship between the reader's characteristics and whether the reader is handsome.
Now let it determine whether a new reader is handsome, and he will give the probability of being handsome and not being handsome based on this training model.
Obviously, as long as the amount of training data given to machine learning is larger, the higher the probability of getting the correct conclusion after learning.
Because of this, machine learning is widely used in many work scenarios. For example, after taking photos of the road camera, it is directly recognized whether the driver is driving normally, smoking, making a phone call, holding the steering wheel with one hand, chatting with the passenger or doing other things.
thing.
With such powerful learning ability combined with the same advanced artificial intelligence technology, it is no wonder that people are worried about the so-called intelligent machinery crisis.
However, there is a big difference between the machine learning mentioned in this paper and the machine learning learned by Gu Feng based on his previous life cognition.
Based on the world's more advanced artificial intelligence technology and digital life technology, machine learning can directly implement language teaching and behavioral teaching.
This is the same as parents teaching their children what pigs are, and how to write.
In fact, this process essentially returns to the most primitive algorithm, that is, programmers use algorithms to let the computer do something.
It is just in this machine learning in the field of digital life, digital life can automatically recognize what it hears and sees, and then generate a new piece of code on its own. This new piece of code is its words or some kind of
Cognition generated by phenomenon learning.
Although the first digital life has not yet been born, people are much more at ease with the artificial intelligence born under this technology than the artificial intelligence in previous lives.
After all, under the limitations of digital life, this form of artificial intelligence cannot be learned quickly by freely shuttled through the Internet. It must be like people, and needs to listen, watch, and do it in order to gradually learn and improve one's own.
Cognition and ability.
Gu Feng gave the scientists a big thumbs up in his heart.
Such technology is simply a waste of resources without being in the game field.
In his previous life, more than ten years ago, many games had already used the gimmicks of APC to attract players, that is, NPC, which is known as AI.
In fact, this is understandable. After all, although players don’t care much, if the NPCs in the game can be more humane and more flesh-and-blood, who is willing to face someone who can always say a few repetitive sentences?
According to the information Gu Feng has learned, digital life technology may be able to mature to commercial use in just two years.
Since the bottleneck of technology has been confirmed to be not a problem, Gu Feng’s super IP plan can finally start planning, and strive to implement it as soon as possible.
Opening a blank document, Gu Feng gently tapped the title on the first line - Pokémon World.
Gu Feng wrote a planning book this time, which was much faster than before when he wrote a game design book. At this time, he was like a small online article with great suffering and deep hatred. He had a lot of thoughts when he thought about it, and he really wrote it.
At the time, I often think about it for a long time before I can put a short sentence on it.
Gu Feng is not very clear whether Pokémon was in front of the previous life or the game was in front of the previous life, but it doesn't matter. Since you want to maximize the value of IP, the game must not be done before the animation.
Comic adaptations and game adaptations are completely different.
In addition to animation and games, Gu Feng's plan also considers novels and movies.
The movie here certainly does not refer to an animation theatrical version, but movies like "Pokemon: Detective Pikachu". After all, the animation theatrical version is still the group of people who watch the animation, and "Pokemon"
Only second-creation movies like Dream: Detective Pikachu can attract more people who don’t like watching animations.
However, the movie plan is not particularly necessary, so you don’t need to put too high priority in the planning.
As for animation, Gu Feng also has a bold idea, he does not intend to use the original animation.
The reason is very simple. Although the original work is classic, it is still a little bit young after all. Sometimes he even doubts that if Pokémon animation was born twenty years later, would it still become popular? Maybe it may not be.
As for the theme of Pokémon, it can be more mature in story arrangement and character creation. After all, the ultimate goal is the game. In theory, although the player group that VR Pokémon is expected to be targeted is not different from all age groups.
Gender, but in fact Gu Feng knew that the real core player group must be young players in their 18 to 20s.
Strong competitive spirit, strong energy, and high enthusiasm for game research are all the characteristics of players of this age. Since Gu Feng wants to build it into an epoch-making competitive masterpiece, it is naturally the top priority to capture the hearts of this group of players.
.
Therefore, giving up the original work and making the animation more mature has become an imperative choice.