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More-autonomous machine learning systems will make Alexa more self-aware, self-learning, and self-service. Three years ago, we deployed a self-learning mechanism that automatically corrects defects in Alexa’s interpretation of customer utterances based purely on implicit signals. In addition to the goal-related labels, our model also labels utterances according to intent (the action the customer wants performed, such as playing music), slots (the data types the intent operates on, such as song names), and slot-values (the particular values of the slots, such as “Purple Haze”).Īs a baseline for slot and intent labeling, we used a RoBERTa-based model that didn’t incorporate contextual information, and we found that our model outperformed it across the board.
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We can set a higher threshold, exceeding human performance significantly, and still achieve much larger annotation throughput than manual labeling does. Our model’s outputs on both tasks were comparable to the human annotators’: our model was slightly more accurate but had a slightly lower F1 score. Our ground truth, for both the model and the baseline, was a set of annotations each of which had been corroborated by three different human annotators. Two of these are goal segmentation, or determining which utterances in a dialogue are relevant to the accomplishment of a particular task, and goal evaluation, or determining whether the goal was successfully achieved.Īs a baseline for these tasks, we used a set of annotations each of which was produced in a single pass by a single annotator. We evaluate the label generation model on several tasks. Prime Video beats previous state of the art on the MovieNet dataset by 13% with a new model that is 90% smaller and 84% faster.
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We have a few programs targeting different applications of self-learning, including automated generation of ground truth annotations, defect reduction, teachable AI, and determining root causes of failure. The great advantages of self-learning are that it doesn’t require data annotation, so it scales better while protecting customer privacy it minimizes the time and cost of updating models and it relies on high-value training data, because customers know best what they mean and want. This can be customer-initiated, as when customers use Alexa’s interactive-teaching capability, or Alexa-initiated, as when Alexa asks, “Did I answer your question?”
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Aggregated across multiple customers, barge-ins and rephrases indicate requests that aren’t being processed correctly.Ĭustomers can also explicitly teach Alexa how to handle particular requests. If a customer interrupts Alexa’s response to a request - a “barge-in”, as we call it - or rephrases the request, that’s implicit feedback. Self-learning system uses customers’ rephrased requests as implicit error signals.Ĭustomer-system interactions can provide both implicit feedback and explicit feedback. Physical/digital activity: If a customer listens only to jazz, “Play music” should elicit a different response than if the customer listens only to hard rock if the customer always makes coffee after the alarm goes off, that should influence the interpretation of a command like “start brewing”.
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Device types: If the device has a screen, it’s more likely that “play Hunger Games” refers to the movie than if the device has no screen.Device state: If the oven is on, then the question “What is the temperature?” is more likely to refer to oven temperature than it is in other contexts.Here are some examples of what we mean by context: By “context”, we mean the set of circumstances and facts that surround a particular event, situation, or entity, which an AI model can exploit to improve its performance.įor instance, context can help resolve ambiguities. In particular, the IOT creates new forms of context for conversational-AI models. And this will mean a shift in our understanding of what conversational AI is.Īlexa’s chief scientist on how customer-obsessed science is accelerating general intelligence. What will the interface with the IOT be? Will you need a separate app on your phone for each connected device? Or when you walk into a room, will you simply speak to the device you want to reconfigure?Īt Alexa, we’re betting that conversational AI will be the interface for the IOT. The most likely answer is the Internet of things (IOT) and other intelligent, connected systems and services. For the most part, you need to physically touch them to use them, which does not seem natural or convenient in a number of situations.
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Then came the laptop, and finally mobile devices so small we can hold them in our hands and carry them in our pockets, which felt revolutionary.Īll these devices, however, tether you to a screen. ( Alexa) is now giving the 3rd #SIGIR2022 keynote.įirst time Ruhi is attending SIGIR, surely not the last one! /KL0SnPq2CS- SIGIR 2022 ? July 14, 2022įor decades, the paradigm of personal computing was a desktop machine.