IDG Contributor Network: Pattern matching is not enough

Did you realize something? When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.

Machine learning is not equal to AI

First of all, machines do not understand. This is by far the biggest misconception while discussing AI, in particular in the context of virtual private assistants like Amazon Alexa or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.

To read this article in full, please click here

Did you realize something? When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.

Machine learning is not equal to AI

First of all, machines do not understand. This is by far the biggest misconception while discussing AI, in particular in the context of virtual private assistants like Amazon Alexa or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.

To read this article in full, please click here

IDG Contributor Network: Pattern matching is not enough

Did you realize something? When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.

Machine learning is not equal to AI

First of all, machines do not understand. This is by far the biggest misconception while discussing AI, in particular in the context of virtual private assistants like Amazon Alexa or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.

To read this article in full, please click here

Did you realize something? When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.

Machine learning is not equal to AI

First of all, machines do not understand. This is by far the biggest misconception while discussing AI, in particular in the context of virtual private assistants like Amazon Alexa or Apple Siri. Machines match data to predefined data patterns of understanding. Thus, understanding is a question of the size of a data pool, because the more data is matched to something we can understand the more “understanding” a machine seems to have.

To read this article in full, please click here

IDG Contributor Network: How AI can help to avoid Amazon Web Services disruptions

Back in February 2017, an Amazon S3 service disruption in AWS’ oldest region (US-EAST-1) shut down several major websites and services, such as Slack, Trello, Quora, Business Insider, Coursera and Time Inc.

Other users were reporting that they were also unable to control devices which were connected via the Internet of Things since IFTTT was also down.

These kinds of disruptions are becoming more and more business critical for today’s digital economy. To prevent these situations, cloud users should always consider the shared responsibility model in the public cloud. However, there are also ways where artificial intelligence (AI) can help. An AI-defined infrastructure – specifically, an AI-powered IT management system – can help to avoid service disruptions of public cloud providers.

To read this article in full, please click here

Back in February 2017, an Amazon S3 service disruption in AWS’ oldest region (US-EAST-1) shut down several major websites and services, such as Slack, Trello, Quora, Business Insider, Coursera and Time Inc.

Other users were reporting that they were also unable to control devices which were connected via the Internet of Things since IFTTT was also down.

These kinds of disruptions are becoming more and more business critical for today’s digital economy. To prevent these situations, cloud users should always consider the shared responsibility model in the public cloud. However, there are also ways where artificial intelligence (AI) can help. An AI-defined infrastructure – specifically, an AI-powered IT management system – can help to avoid service disruptions of public cloud providers.

To read this article in full, please click here