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"Alexa, what's the weather like today."
Scientists have taken decades to understand natural human speech to the point that voice-activated interfaces such as Alexa, Amazon's natural language processing system, are sufficiently capable to be accepted by consumers. Alexa addresses users of Amazon's Echo products, including Echo, Dot and Tap, as well as Amazon TV and other third-party products. Even since 2012, when the patent was filed For Alexa, which would become Amazon's artificial intelligence system, capabilities have increased dramatically and the merit of this growth goes back to machine learning.
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For something we do every day without thinking, the conversation between machines and humans is complex. So how did Amazon and other players in the sphere such as Google, Apple and Microsoft decipher the code?
ABC of Alexa
More than 30 million smart speakers were sold worldwide last year, and this number is expected to reach nearly 60 million this year. While Amazon remains the leader in the sector of smart speakers, 20 million devices last yearothers (including Google) are developing and starting to catch up. Each has nuances, but let's look under the hood for an echo to see how Alexa works.
Although the Echo cylinder contains features such as speakers, a microphone, and a small computer to wake the system and flash its lights to inform you of its activation, its actual abilities occur as soon as it sends any what you said to Alexa. cloud to interpret by Alexa voice services (AVS).
So when you ask Alexa: "What time will it be today?", The device records your voice. Then, this recording is sent over the Internet to Amazon's Alexa Voice Services, which analyzes the recording in orders that it understands. Then the system returns the corresponding output to your device. When you ask questions about the weather, an audio file is sent back and Alexa tells you the weather without you having any idea that there was any exchange between the systems. This means that if you lose your Internet connection, Alexa will not work anymore.
Echo's ready-to-use skills are impressive for most of us, but Amazon allows and encourages approved developers to access Alexa Voice Services services for free, so they can create new skills. Alexa to improve the skills of the system, as Apple had done with the App Store. . Following this opening, the list of skills that Alexa (currently more than 30,000) can help with continues to grow rapidly. Users can, of course, buy Amazon products, but they can also order pizzas at Domino, ride a car to Uber or Lyft, control their lights, make a payment via the skill. Capital One, get wine / wine deals for dinner and so much more. more.
Constantly learn from human data
Data and machine learning Alexa is the foundation of Alexa's power, which only grows with her popularity and the amount of data she collects. Whenever Alexa is mistaken in interpreting your request, this data is used to make the system smarter next time. The automatic learning is the reason for the rapid improvement of the capabilities of the user interface activated by the voice. For example, Google speech was able to dramatically improve its error rate in a year. now he recognizes 19 out of 20 words that he hears. Understanding natural human speech is a huge problem, and we now have the computing power to improve it as we use it.
The challenges of natural language generation and processing
As a subset of artificial intelligenceNatural Language Generation (LNG) is the ability to obtain written and verbal responses to natural sound based on data entered into a computer system. Human language is quite complex, but current natural language generation capabilities are becoming very sophisticated. Consider NLG as an editor who transforms data into a language that can be communicated.
Natural language processing (NLP) is the reader who takes the language created by NLG and consumes it. Advances in this technology have allowed for a dramatic growth in the number of smart personal assistants such as Alexa.
The voice-based artificial intelligence is so attractive because it promises to provide natural support to human beings. No sweeping or typing necessary. This is also the reason why it is a technical challenge to build. Just think of the non-linearity of your typical conversation.
When people talk, they break off, change topics or repeat themselves, use body language to add meaning, and a wide variety of words with multiple meanings, depending on the context. It's like a parent trying to understand the vernacular language of teenagers, but a lot, much more complicated.
Amazon continues to count on an army of specialists as well as a fleet of machines to further improve Alexa and Alexa Voice Services. Their goal is to make spoken language a user interface as natural as the conversation with another human being.
I can not wait to see what awaits us next.
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"Alexa, what's the weather like today."
Scientists have taken decades to understand natural human speech to the point that voice-activated interfaces such as Alexa, Amazon's natural language processing system, are sufficiently capable to be accepted by consumers. Alexa addresses users of Amazon's Echo products, including Echo, Dot and Tap, as well as Amazon TV and other third-party products. Even since 2012, when the patent was filed For Alexa, which would become Amazon's artificial intelligence system, capabilities have increased dramatically and the merit of this growth goes back to machine learning.
For something we do every day without thinking, the conversation between machines and humans is complex. So how did Amazon and other players in the sphere such as Google, Apple and Microsoft decipher the code?
ABC of Alexa
More than 30 million smart speakers were sold worldwide last year, and this number is expected to reach nearly 60 million this year. While Amazon remains the leader in the sector of smart speakers, 20 million devices last yearothers (including Google) are developing and starting to catch up. Each has nuances, but let's look under the hood for an echo to see how Alexa works.
Although the Echo cylinder contains features such as speakers, a microphone, and a small computer to wake the system and flash its lights to inform you of its activation, its actual abilities occur as soon as it sends any what you said to Alexa. cloud to interpret by Alexa voice services (AVS).
So when you ask Alexa: "What time will it be today?", The device records your voice. Then, this recording is sent over the Internet to Amazon's Alexa Voice Services, which analyzes the recording in orders that it understands. Then the system returns the corresponding output to your device. When you ask questions about the weather, an audio file is sent back and Alexa tells you the weather without you having any idea that there was any exchange between the systems. This means that if you lose your Internet connection, Alexa will not work anymore.
Echo's ready-to-use skills are impressive for most of us, but Amazon allows and encourages approved developers to access Alexa Voice Services services for free, so they can create new skills. Alexa to improve the skills of the system, as Apple had done with the App Store. . Following this opening, the list of skills that Alexa (currently more than 30,000) can help with continues to grow rapidly. Users can, of course, buy Amazon products, but they can also order pizzas at Domino, ride a car to Uber or Lyft, control their lights, make a payment via the skill. Capital One, get wine / wine deals for dinner and so much more. more.
Constantly learn from human data
Data and machine learning Alexa is the foundation of Alexa's power, which only grows with her popularity and the amount of data she collects. Whenever Alexa is mistaken in interpreting your request, this data is used to make the system smarter next time. The automatic learning is the reason for the rapid improvement of the capabilities of the user interface activated by the voice. For example, Google speech was able to dramatically improve its error rate in a year. now he recognizes 19 out of 20 words that he hears. Understanding natural human speech is a huge problem, and we now have the computing power to improve it as we use it.
The challenges of natural language generation and processing
As a subset of artificial intelligenceNatural Language Generation (LNG) is the ability to obtain written and verbal responses to natural sound based on data entered into a computer system. Human language is quite complex, but current natural language generation capabilities are becoming very sophisticated. Consider NLG as an editor who transforms data into a language that can be communicated.
Natural language processing (NLP) is the reader who takes the language created by NLG and consumes it. Advances in this technology have allowed for a dramatic growth in the number of smart personal assistants such as Alexa.
The voice-based artificial intelligence is so attractive because it promises to provide natural support to human beings. No sweeping or typing necessary. This is also the reason why it is a technical challenge to build. Just think of the non-linearity of your typical conversation.
When people talk, they break off, change topics or repeat themselves, use body language to add meaning, and a wide variety of words with multiple meanings, depending on the context. It's like a parent trying to understand the vernacular language of teenagers, but a lot, much more complicated.
Amazon continues to count on an army of specialists and a fleet of machines to further improve Alexa and Alexa Voice Services. Their goal is to make spoken language a user interface as natural as the conversation with another human being.
I can not wait to see what awaits us next.
