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The future direction of AI machine learning
The future direction of AI machine learning
We have explained how artificial intelligence (AI) predicts the future, and how it changes the workplace and invents the timing of employment.
While driverless cars and robotics may have captured the headlines, artificial intelligence, deep learning, and similar technologies may have their biggest impact invisible, by simplifying and advancing everyday life and business in countless ways.
In the words of Alex White, vice president of NVIDIA, deep learning heralds a “new era of computing” that will open “strong and far-reaching innovations” for everyone and everything. The company is developing more sophisticated graphics processing units to advance machine learning capabilities while supporting 1,500 startups to understand where machine learning can take people.
Machine learning is not only used in eye-catching areas such as cancer treatment, robotics, and driverless cars, but is also increasingly being used in everyday business operations. Although this subject is very ordinary, it is really important. Deep learning may completely change people's daily work in data disposal, make them more fulfilling and more inventive, and finally let all efforts work to bear fruit. In other words, deep learning can do monotonous things, so you don't have to do it.
Is artificial intelligence the inventor of employment opportunities? For example, if you are engaged in marketing and sales, deep learning can track the interaction between customers and your brand in real time on all social media. It allows you to liberate from the cumbersome data sorting work, to pursue more valuable purposes, to establish a greater job advantage. In this situation, deep learning can be a skill in improving employment. When companies improve, they will grow and invent more opportunities for us to find these people.
At least, in theory. We have full reason to be suspicious, but this theory is very logical. For example, e-commerce sales reached 2 trillion US dollars last year, and it is estimated that this number will double by 2020. Sales and marketing are an important part of the job market, and this market can be refined through deep learning.
White recently spoke at the "Deep Learning Association" event at Nvidia, London. Another outstanding speaker was Dr Anthony Morse, a scholar who, in his words, "let the machine learn like a child." In a video, Dr. Morse introduces an object to an orange star to a humanoid robot. Then, he showed the star together with a red ball, after the robot did not know the red ball.
When asked to pick up a red ball, the robot can infer from the previous command which of the two objects is an orange star, and infer which object is a red ball (this is the only object left on the table) . This relatively simple task reminds a wonderful and complex world. The significance of this experiment is that this humanoid robot is teaching himself with the talents a child may have.
As usual, this technology has taken off and its potential is very ambitious. So, how do these possibilities translate into the ideal life of our ordinary people? Morse said that visual image recognition is often thought of as an example of machine learning, because it is easy for everyone to understand, let alone deep learning in vision. The fact that it is easy to transcend human beings. Anyone who is familiar with Facebook's auto-tagging tools will realize that it is useful to both users and advertisers, although the ability to learn in depth is much more than just sorting social network photos.
Morse thought that the ability to learn deeply is universal and dual: first, to improve the existing degree of automation; second, to promote new consumption lines and the city often thinks that about the former category of products, the development of factory consumption lines It may evolve into more complex systems, and if they are replaced by deep learning, newer and more advanced products will be produced, making new products and markets more attractive. From speech recognition and real-time translation to driverless cars, from early cancer detection to Morse's so-called magic software (which is purchased online immediately after taking someone's clothes), there are things that suit everyone's needs or weird features.
Unlimited possibilities? Indeed, its impact is far-reaching. But there are infinite possibilities? According to Morse, this is not the case. He said: "It can't handle all the problems under the sun." This mood is very different from the many people's perceptions about the grand potential of deep learning.
Dr. Adam Grzywaczewski is similar to Morse's concept, and he attributed the recent “deep learning” to three pauses. First, the availability of big data has increased. People upload 100 hours of video to YouTube every minute and upload 350 million photos to Facebook every day. Second, this range of data has prompted new deep learning techniques to be developed and developed. Finally, the leap in computing power is a huge impetus for these technologies to be completed.
However, deep learning is limited in depth. Morse sighs that the well-marked workout data is usually expensive or non-existent. In addition, you need to weigh such an accusation that demand is alert to the potential of deep learning. Understanding how children learn and apply them to machine learning is no longer a material for science fiction movies. It is now on the verge of a seizure, and it is on a commercial level.
Dr. Grzywaczewski is keen to emphasize that the ecosystem of deep learning is dynamic and it changes from time to time. Alex Wright, vice president of NVIDIA, believes that the research related to machine learning is mainly limited to academic, not commercial, and this wave will soon rush to the shore.
While driverless cars and robotics may have captured the headlines, artificial intelligence, deep learning, and similar technologies may have their biggest impact invisible, by simplifying and advancing everyday life and business in countless ways.
In the words of Alex White, vice president of NVIDIA, deep learning heralds a “new era of computing” that will open “strong and far-reaching innovations” for everyone and everything. The company is developing more sophisticated graphics processing units to advance machine learning capabilities while supporting 1,500 startups to understand where machine learning can take people.
Machine learning is not only used in eye-catching areas such as cancer treatment, robotics, and driverless cars, but is also increasingly being used in everyday business operations. Although this subject is very ordinary, it is really important. Deep learning may completely change people's daily work in data disposal, make them more fulfilling and more inventive, and finally let all efforts work to bear fruit. In other words, deep learning can do monotonous things, so you don't have to do it.
Is artificial intelligence the inventor of employment opportunities? For example, if you are engaged in marketing and sales, deep learning can track the interaction between customers and your brand in real time on all social media. It allows you to liberate from the cumbersome data sorting work, to pursue more valuable purposes, to establish a greater job advantage. In this situation, deep learning can be a skill in improving employment. When companies improve, they will grow and invent more opportunities for us to find these people.
At least, in theory. We have full reason to be suspicious, but this theory is very logical. For example, e-commerce sales reached 2 trillion US dollars last year, and it is estimated that this number will double by 2020. Sales and marketing are an important part of the job market, and this market can be refined through deep learning.
White recently spoke at the "Deep Learning Association" event at Nvidia, London. Another outstanding speaker was Dr Anthony Morse, a scholar who, in his words, "let the machine learn like a child." In a video, Dr. Morse introduces an object to an orange star to a humanoid robot. Then, he showed the star together with a red ball, after the robot did not know the red ball.
When asked to pick up a red ball, the robot can infer from the previous command which of the two objects is an orange star, and infer which object is a red ball (this is the only object left on the table) . This relatively simple task reminds a wonderful and complex world. The significance of this experiment is that this humanoid robot is teaching himself with the talents a child may have.
As usual, this technology has taken off and its potential is very ambitious. So, how do these possibilities translate into the ideal life of our ordinary people? Morse said that visual image recognition is often thought of as an example of machine learning, because it is easy for everyone to understand, let alone deep learning in vision. The fact that it is easy to transcend human beings. Anyone who is familiar with Facebook's auto-tagging tools will realize that it is useful to both users and advertisers, although the ability to learn in depth is much more than just sorting social network photos.
Morse thought that the ability to learn deeply is universal and dual: first, to improve the existing degree of automation; second, to promote new consumption lines and the city often thinks that about the former category of products, the development of factory consumption lines It may evolve into more complex systems, and if they are replaced by deep learning, newer and more advanced products will be produced, making new products and markets more attractive. From speech recognition and real-time translation to driverless cars, from early cancer detection to Morse's so-called magic software (which is purchased online immediately after taking someone's clothes), there are things that suit everyone's needs or weird features.
Unlimited possibilities? Indeed, its impact is far-reaching. But there are infinite possibilities? According to Morse, this is not the case. He said: "It can't handle all the problems under the sun." This mood is very different from the many people's perceptions about the grand potential of deep learning.
Dr. Adam Grzywaczewski is similar to Morse's concept, and he attributed the recent “deep learning” to three pauses. First, the availability of big data has increased. People upload 100 hours of video to YouTube every minute and upload 350 million photos to Facebook every day. Second, this range of data has prompted new deep learning techniques to be developed and developed. Finally, the leap in computing power is a huge impetus for these technologies to be completed.
However, deep learning is limited in depth. Morse sighs that the well-marked workout data is usually expensive or non-existent. In addition, you need to weigh such an accusation that demand is alert to the potential of deep learning. Understanding how children learn and apply them to machine learning is no longer a material for science fiction movies. It is now on the verge of a seizure, and it is on a commercial level.
Dr. Grzywaczewski is keen to emphasize that the ecosystem of deep learning is dynamic and it changes from time to time. Alex Wright, vice president of NVIDIA, believes that the research related to machine learning is mainly limited to academic, not commercial, and this wave will soon rush to the shore.