5 innovative ways to use machine learning
Hi, Habr! I present to you the translation of the article "5 Innovative Uses for Machine Learning" by Aj Agrawal.
They will come into your life, at least into your business life earlier than you think. Although the time horizon of the coming cannot be accurately predicted, artificial intelligence (AI) promises to fundamentally influence modern society, for better or worse. The super level (AI) -machine training has received special attention from experts because of the potentially powerful impact on the most important, global industries. Due to the excitement that has arisen, a huge amount of talent and resources are flowing into this space.
But what is this machine learning and why should we take care of it in the first place? The answer is that, in the broadest sense, AI's machine learning models use self-predictive outcome algorithms. In other words, these models can process gigantic data arrays, extract conclusions and make accurate predictions without the need for significant human intervention.
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Many significant generative consequences are generated as a result of the accelerated development of this technology, and most of them are ready to significantly simplify the business world.
But the five most innovative ways to use machine learning. They will come into your life, at least into your business life — sooner than you expect.
The intensive introduction of autonomous vehicles is a much more efficient form of transportation in the future. Analytical reports suggest that self-driving cars can reduce dangerous traffic (fatal) by as much as 90 percent.
Although we are probably a few years from the beginning of mass production for the consumer, the acceptance of autonomous vehicles is inevitable at this stage in the development of society. However, the time scale for the adaptation of this technology is largely dependent on regulatory actions, which often lie outside the control of the technological world.
Software engineers developing these self-managing “fleets of the future” rely heavily on machine learning technologies to run algorithms that allow vehicles to operate autonomously. These models effectively integrate data from various lidar sensors (a query method using lasers) - radar and camera-control vehicles. These deeply developed learning algorithms are becoming more intelligent over time, which ensures driving safety.
Such an important part of the economy, as the health care industry, still operates on an inefficient, outdated infrastructure. The main problem points are finding ways to maintain confidential patient information and system optimization.
Fortunately, we can use innovative machine learning algorithms (which work without people) to process large amounts of medical data without violating the confidentiality agreement. In addition, we can use these models to better analyze and understand diagnoses, risk factors, and causal relationships.
As Dr. Ed Corbett notes: “It is clear that machine learning will add another arrow to the quiver of clinical solutions. “Machine learning in medicine is now at the top,” said Corbett, a health worker at Health Catalyst. Google has developed a machine learning algorithm to help identify cancers in mammograms. Stanford uses a deep learning algorithm to detect skin cancer. ”
The international retail sector has consistently generated over the past few years a sales result of $ 20 trillion per year. This huge figure covers a gigantic amount of consumer data (demographics, trends and tastes) made up of an endless stream of trading patterns and trends.
Nonetheless, many retailers are trying to realize the perspectives of this valuable information, since the information often comes from disparate data stores. In perspective, there is a huge opportunity for the implementation of machine learning models that will allow retailers to better understand their customers and provide a more personalized approach.
Using previously obtained data, machine learning models can predict everything, from what products to recommend, to those for which to launch discounts. Retailers, in particular, can combine digital behaviors to optimize the user's entire path from the first contact to the purchase.
Content moderation is a serious problem for social media platforms, such as Facebook and Twitter, in the process of providing accurate information to its audience.
In response to a public outcry against “fake news,” Facebook recently announced that it is hiring 3,000 new employees specifically to monitor news content on the platform. Although this anxiety extends far beyond the social networks, technology conglomerates such as Google have invested considerable capital in developing their own content monitoring groups to support their fast-growing markets.
Developing machine learning and AI platforms, such as Orions Systems, provide proprietary systems for “growing and adapting interactions between people and artificial intelligence” for tasks such as general content moderation.
Obviously, these technologies for solving problems of content moderation using innovative tools and resources (analyzing, for example, the context and content of each frame from a video) make it possible to increase employee productivity. This is an important step forward, which prepares machine algorithms for very complex work, the moderation of video materials.
The cost of damage from cybercrime will increase by $ 6 trillion per year by 2021. Experts predict that companies will spend more than $ 1 trillion on cybersecurity services from 2017 to 2021 to protect themselves from the growing threat. Most likely, cybersecurity will be a priority for startups and for large enterprises.
Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing, and protect against cyber attacks. Defense systems are trained using the latest data to quickly react and shield from suspicious activity. Unlike people, these algorithms can work 24 hours a day, seven days a week, without getting tired.
Since these machine learning models have become more accessible to developers, they have steadily begun to gain a large number of endorsements from consumers and businesses. And when a techno furor happens, it will be interesting to see which techno models have conquered the summit.
They will come into your life, at least into your business life earlier than you think. Although the time horizon of the coming cannot be accurately predicted, artificial intelligence (AI) promises to fundamentally influence modern society, for better or worse. The super level (AI) -machine training has received special attention from experts because of the potentially powerful impact on the most important, global industries. Due to the excitement that has arisen, a huge amount of talent and resources are flowing into this space.
But what is this machine learning and why should we take care of it in the first place? The answer is that, in the broadest sense, AI's machine learning models use self-predictive outcome algorithms. In other words, these models can process gigantic data arrays, extract conclusions and make accurate predictions without the need for significant human intervention.
')
Many significant generative consequences are generated as a result of the accelerated development of this technology, and most of them are ready to significantly simplify the business world.
But the five most innovative ways to use machine learning. They will come into your life, at least into your business life — sooner than you expect.
Large-scale use of autonomous vehicles
The intensive introduction of autonomous vehicles is a much more efficient form of transportation in the future. Analytical reports suggest that self-driving cars can reduce dangerous traffic (fatal) by as much as 90 percent.
Although we are probably a few years from the beginning of mass production for the consumer, the acceptance of autonomous vehicles is inevitable at this stage in the development of society. However, the time scale for the adaptation of this technology is largely dependent on regulatory actions, which often lie outside the control of the technological world.
Software engineers developing these self-managing “fleets of the future” rely heavily on machine learning technologies to run algorithms that allow vehicles to operate autonomously. These models effectively integrate data from various lidar sensors (a query method using lasers) - radar and camera-control vehicles. These deeply developed learning algorithms are becoming more intelligent over time, which ensures driving safety.
More effective health care
Such an important part of the economy, as the health care industry, still operates on an inefficient, outdated infrastructure. The main problem points are finding ways to maintain confidential patient information and system optimization.
Fortunately, we can use innovative machine learning algorithms (which work without people) to process large amounts of medical data without violating the confidentiality agreement. In addition, we can use these models to better analyze and understand diagnoses, risk factors, and causal relationships.
As Dr. Ed Corbett notes: “It is clear that machine learning will add another arrow to the quiver of clinical solutions. “Machine learning in medicine is now at the top,” said Corbett, a health worker at Health Catalyst. Google has developed a machine learning algorithm to help identify cancers in mammograms. Stanford uses a deep learning algorithm to detect skin cancer. ”
Embedded Retail Management System
The international retail sector has consistently generated over the past few years a sales result of $ 20 trillion per year. This huge figure covers a gigantic amount of consumer data (demographics, trends and tastes) made up of an endless stream of trading patterns and trends.
Nonetheless, many retailers are trying to realize the perspectives of this valuable information, since the information often comes from disparate data stores. In perspective, there is a huge opportunity for the implementation of machine learning models that will allow retailers to better understand their customers and provide a more personalized approach.
Using previously obtained data, machine learning models can predict everything, from what products to recommend, to those for which to launch discounts. Retailers, in particular, can combine digital behaviors to optimize the user's entire path from the first contact to the purchase.
Optimizing content moderation
Content moderation is a serious problem for social media platforms, such as Facebook and Twitter, in the process of providing accurate information to its audience.
In response to a public outcry against “fake news,” Facebook recently announced that it is hiring 3,000 new employees specifically to monitor news content on the platform. Although this anxiety extends far beyond the social networks, technology conglomerates such as Google have invested considerable capital in developing their own content monitoring groups to support their fast-growing markets.
Developing machine learning and AI platforms, such as Orions Systems, provide proprietary systems for “growing and adapting interactions between people and artificial intelligence” for tasks such as general content moderation.
Obviously, these technologies for solving problems of content moderation using innovative tools and resources (analyzing, for example, the context and content of each frame from a video) make it possible to increase employee productivity. This is an important step forward, which prepares machine algorithms for very complex work, the moderation of video materials.
Enhanced Cybersecurity
The cost of damage from cybercrime will increase by $ 6 trillion per year by 2021. Experts predict that companies will spend more than $ 1 trillion on cybersecurity services from 2017 to 2021 to protect themselves from the growing threat. Most likely, cybersecurity will be a priority for startups and for large enterprises.
Researchers are developing clever ways to implement machine learning models to detect fraud, prevent phishing, and protect against cyber attacks. Defense systems are trained using the latest data to quickly react and shield from suspicious activity. Unlike people, these algorithms can work 24 hours a day, seven days a week, without getting tired.
Since these machine learning models have become more accessible to developers, they have steadily begun to gain a large number of endorsements from consumers and businesses. And when a techno furor happens, it will be interesting to see which techno models have conquered the summit.
Source: https://habr.com/ru/post/347788/
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