What is the probability of assembling a team that combines all these properties? Extremely low.
Posted: Thu Jan 23, 2025 7:25 am
The first is when an applied problem is solved by adding neural network functionality, accelerating processes and achieving a variety of results.
The second is when the entire business can be built solely on the work of neural networks and machine learning, connected in clusters, each of which automates a specific stage of the business process.
It is not worth discussing the georgia whatsapp resource effectiveness of any approach or specific neural network now, because, firstly, using them is no easier than formulating the right question to find the correct answer, and secondly, they are developing and what generative networks gave just a year ago is no longer commensurate with what they give today. And what will happen tomorrow?
What makes a person intelligent? Not so much the ability to achieve a result, but the ability to predict the probability of achieving it as accurately as possible, realizing where to start the path of discovery. Literally all manifested scientific phenomena are called discoveries, which means that everything already exists, and you just need to find the key and open the "container" with the answer.
Neural networks are in some way keys, since our thinking is too broad and abstract and we do not know how to manage it fully, and when there is specialized artificial "thinking", for example, generating images in a certain style, then this solves several problems at once: expanding the artist's potential, the number of artists in different styles, tuning into contexts, including different eras, mastery of color, its depth, palettes.
But it is much easier to find an operator of such a network that will replace such a highly specialized team, and it is also possible to learn how to do this. Thus, the task of any business is to discover the ability to divide processes into stages that can be serviced by neural networks, and this is currently called digital transformation of business. This also requires its own specialists who can see the entire process as a whole, determine what is fed to the input of a specific stage and what is obtained at the output with specified properties and technical characteristics.
For example, let's take the creative process. Now an idea is a product of a creator, that is, a specialist who comes up with cool things most often intuitively, but he can be 1 in millions, and the fight for such a specialist is almost doomed, since a person remains a person and has a tendency to emotional distortions, what we call a creative crisis, which can be caused by any socio-psychological factors. But business cannot wait for a person to enter the resource. And here neural networks can come to the rescue again, which will take the totality of the experience of all creators, generalize, find abstract connections corresponding to time and culture, and give out options for ideas, from which you just need to choose someone who knows how to choose, and this is also a separate task. But machine learning and big data can help here too. Let's ask the system what the audience responded to best: historical facts, the cultural heritage of the region, social values or futurism, and it will give out answer options that are worth comparing with the generated ideas, and now we have a set of hypotheses that are determined by a system of meanings, so to speak, a cloud of key terms and concepts accessible to the majority, and then it's time to test them.
The second is when the entire business can be built solely on the work of neural networks and machine learning, connected in clusters, each of which automates a specific stage of the business process.
It is not worth discussing the georgia whatsapp resource effectiveness of any approach or specific neural network now, because, firstly, using them is no easier than formulating the right question to find the correct answer, and secondly, they are developing and what generative networks gave just a year ago is no longer commensurate with what they give today. And what will happen tomorrow?
What makes a person intelligent? Not so much the ability to achieve a result, but the ability to predict the probability of achieving it as accurately as possible, realizing where to start the path of discovery. Literally all manifested scientific phenomena are called discoveries, which means that everything already exists, and you just need to find the key and open the "container" with the answer.
Neural networks are in some way keys, since our thinking is too broad and abstract and we do not know how to manage it fully, and when there is specialized artificial "thinking", for example, generating images in a certain style, then this solves several problems at once: expanding the artist's potential, the number of artists in different styles, tuning into contexts, including different eras, mastery of color, its depth, palettes.
But it is much easier to find an operator of such a network that will replace such a highly specialized team, and it is also possible to learn how to do this. Thus, the task of any business is to discover the ability to divide processes into stages that can be serviced by neural networks, and this is currently called digital transformation of business. This also requires its own specialists who can see the entire process as a whole, determine what is fed to the input of a specific stage and what is obtained at the output with specified properties and technical characteristics.
For example, let's take the creative process. Now an idea is a product of a creator, that is, a specialist who comes up with cool things most often intuitively, but he can be 1 in millions, and the fight for such a specialist is almost doomed, since a person remains a person and has a tendency to emotional distortions, what we call a creative crisis, which can be caused by any socio-psychological factors. But business cannot wait for a person to enter the resource. And here neural networks can come to the rescue again, which will take the totality of the experience of all creators, generalize, find abstract connections corresponding to time and culture, and give out options for ideas, from which you just need to choose someone who knows how to choose, and this is also a separate task. But machine learning and big data can help here too. Let's ask the system what the audience responded to best: historical facts, the cultural heritage of the region, social values or futurism, and it will give out answer options that are worth comparing with the generated ideas, and now we have a set of hypotheses that are determined by a system of meanings, so to speak, a cloud of key terms and concepts accessible to the majority, and then it's time to test them.