“Ambient intelligence" will accelerate advances in general AI

Alexa’s chief scientist on how customer-obsessed science is accelerating general intelligence.

As the world has become more connected, and computing has permeated our surroundings, a new AI paradigm is emerging: ambient intelligence. In this paradigm, our environment responds to our requests and anticipates our needs, provides information or suggests actions, and then recedes into the background.

Rohit Prasad.jpg
Rohit Prasad, Alexa head scientist and senior vice president at Amazon.

This vision of ambient intelligence is not that different from the one on Star Trek. But for most of the last decade, the focus has been reactive assistance — for example, ensuring that customer-initiated requests to Alexa meet customers’ expectations.

In the ambient-intelligence vision, an AI service such as Alexa makes sense of the state of your environment, including devices, sensors, objects, people, and activity around you, to help you in every situation where you need assistance — either reactively (customer initiated) or proactively (AI initiated).

Realizing the ultimate potential of ambient intelligence requires Alexa to bring the best of machine-intelligence capabilities together with the best of human-intelligence capabilities, which is the barometer of general intelligence today.

The most pragmatic definition of general intelligence is the ability to (1) learn multiple tasks jointly, versus modeling each task independently; (2) continually adapt to changes within a set of known tasks, without explicit human supervision; and (3) learn new tasks directly by interacting with end users.

While these general-intelligence characteristics apply to all types of AI systems, for interactive AI services such as Alexa, two more attributes are critical: (1) multisensory and multimodal intelligence — the ability to process data from multiple input sensors (e.g., microphones, cameras, ultrasound), fuse sensor data for improved understanding of customer goals, and generate output in different modalities (e.g., speech, text, image, video); and (2) interaction skills — the ability to converse in a human-like manner, which encompasses not just command of natural language but also the ability to recognize and respond to affect.

What this means for our customers is that Alexa will become

  • More competent: Alexa’s functionalities and skills will expand much faster through multitask intelligence. Additionally, Alexa will improve through self-learning, becoming less reliant on labeled data;
  • More natural and conversational: Alexa interactions will be as free flowing as human interactions through multisensory intelligence, generalizable language models, commonsense reasoning, and affect modeling; 
  • More personalized: Alexa will adapt to each individual using speech and computer vision. Further, customers will be able to directly personalize Alexa explicitly and implicitly;  
  • More insightful and proactive: Alexa will anticipate customer needs through awareness of the shared environment, make suggestions, and even act on customers’ behalf;  
  • More trustworthy:  Alexa will have the same attributes that we cherish in trustworthy people, such as discretion, fairness, and ethical behavior.

In the past year, Alexa has made considerable progress on all these fronts.

More competent

Alexa receives billions of requests per month, and it is critical for it to answer each of these requests to customers’ satisfaction. In 2021, through advances in automatic speech recognition (ASR), natural-language understanding (NLU), and action resolution, Alexa has become 13% more accurate than the previous year — even as the complexity of customer requests has increased.

Alexa has more than 130,000 third-party skills, whose diversity is a testament to their developers’ creativity. Further, it is available in more than 15 language variants across more than 80 countries, most recently Khaleeji Arabic in Saudi Arabia.

Through advances in large pretrained language models, we are making it easier to expand Alexa’s functionality in terms of both skills and languages. Specifically, we have trained an “Alexa Teacher Model,” a large, pretrained, multilingual model with billions of parameters that encodes language as well as salient patterns of interactions with Alexa. Instead of building new task-specific NLU models (e.g., a skill, a feature, or a language) from scratch on task-specific data, we can build them by fine-tuning the Alexa Teacher model, which provides substantial gains in performance from the same amount of task-specific training data.

While today, the Alexa Teacher Model itself is impractical for real-time language understanding, once it is distilled and fine-tuned, it is compact enough to run in real time but remains more accurate than a similar-sized model trained from scratch. The capacity to generalize across tasks, which the language model enables, is one of the hallmarks of general intelligence.

ATM pipeline.png
The Alexa Teacher Model (AlexaTM) pipeline. The Alexa Teacher Model is trained on a large set of GPUs (left), then distilled into smaller variants (center), whose size depends on their uses. The end user adapts a distilled model to its particular use by fine-tuning it on in-domain data (right).

Models derived from the Alexa Teacher Model have helped reduce customer friction in several locales and will help facilitate and scale multilingual and multimodal use cases in coming years.

Still, faster deployment of new functionality is not sufficient. Customer interactions with Alexa are ever evolving, so Alexa needs to improve continuously. To that end, we have expanded Alexa’s self-learning capability — in particular, its ability to automatically learn from implicit feedback, e.g., when a customer cuts Alexa off in order to rephrase a query.

Currently, we have two methods for learning from implicit feedback. One is a mechanism that learns to automatically reformulate the ASR output to ensure a more accurate response, and the other automatically annotates interaction data to enable the retraining of NLU models with minimal human involvement.

At this year’s Conference on Empirical Methods in Natural Language Processing (EMNLP), Alexa AI researchers presented papers reporting our progress on both these fronts.

Learning how to rewrite customer requests requires identifying which successful requests are rephrases of unsuccessful ones. Past work on rephrase detection considered sentences in pairs, determining the likelihood that one is a rephrase of the other. In our EMNLP paper, we explain how to use temporal features of the dialogue history to better identify rephrases, with an accuracy improvement of 28% on one test dataset.

Earlier rephrase detection models computed similarity scores between pairs of queries (right), which could lead to inaccuracies. A new model instead uses full dialogue context (left) to more accurately detect rephrases by leveraging session-level semantic information. From “Contextual rephrase detection for reducing friction in dialogue systems”.

In the other paper, we describe a scalable framework for using automatically annotated data to continually update our NLU models. This paper shows how to operationalize our previous work on automatic annotation, to deliver immediate results to our customers.

More natural and conversational

As magical as it is to interact with Alexa by simply saying its name, repeating the name during longer interactions feels unnatural: when we’re talking to other people, we don’t use their names on every turn.

This year, we took a major step toward making interactions with Alexa more natural through Conversation Mode, which leverages Echo Show 10’s camera to enable wake-word-free interactions by improving the detection of device directedness (i.e., the intent of addressing Alexa) — even when there are multiple people in the room, conversing with each other as well as with Alexa.

Conversation Mode uses novel computer vision algorithms to gauge customers’ physical orientations toward the device, which indicate whether they’re addressing Alexa or each other. The combination of visual and audio information dramatically improves device-directed-speech detection relative to either modality used independently. Further, on-device speech recognition using fully neural recurrent-neural-network transducers ensures that Alexa recognizes conversational speech with low latency.

We have also started extending Alexa’s conversational memory, going beyond anaphoric references within an interaction session (e.g., “What is its resolution?” while shopping for TVs) to temporarily maintain memory across sessions in certain situations. For example, for high-consideration purchases such as TVs, Alexa remembers your last interaction and starts off your next interaction where you left off. This capability required us to extend Alexa Conversations, which trains deep-learning-based models on synthetic data automatically generated from a small amount of developer-provided data.

As effective as large neural transformer-based language models are for generating textual responses, they lack the commonsense and knowledge grounding they need to be truly useful in large-scale human-machine interactions. This fall, to help foster the type of invention needed to overcome these challenges, we released the commonsense dialogue dataset, which consists of more than 11,000 newly collected dialogues. In each dialogue, successive turns are related by relationship triples in the public commonsense knowledge graph Conceptnet, such as <doctor, LocateAt, hospital> or <specialist, TypeOf, doctor>.

Commonsense dialogue.png
In each dialogue in the commonsense-dialogue dataset, successive turns are related by relationship triples in the public commonsense knowledge graph Conceptnet, such as <piano, RelatedTo, musical> or <musical, RelatedTo, violin>.

Another way to inject common sense into dialogue models is to enable them to import information from online or other sources as needed, on the fly. At the NeurIPS Workshop on Efficient Natural Language and Speech Processing (ENLSP) earlier this month, Alexa researchers won a best-paper award for doing just that. They propose a few-shot-learning approach to training a knowledge-seeking-turn detector, which can recognize customer questions that can’t be answered through existing API calls.

This year, we also published several papers on affect modeling. At the International Conference on Acoustics, Speech, and Signal Processing, we presented the use of contrastive unsupervised learning to improve emotion recognition when training data is scarce; and at the Spoken Language Technologies conference, we described the adaptation of pretrained language models, which have been so successful at natural-language-processing tasks, to the problem of social and emotional commonsense reasoning.

On the flip side, when human speakers recognize shifts in the emotional states of people they’re talking to, they modify the affect in their responses. At the Speech Synthesis Workshop (SSW11) this summer, we extended our previous work on prosody variation to modify the affective characteristics of synthesized speech.

More personalized

AI’s ability to conform to customers as opposed to the other way around differentiates it from other technological advancements. This fall, we launched multiple new services that allow our customers to personalize AI in a self-serve fashion.

With preference teaching, customers can explicitly teach Alexa which skills should handle weather-related questions, which sports teams they follow, and which cuisines they prefer.

A two-dimensional projection of embeddings produced through Custom Sound Event Detection. New sounds are identified by their location in the embedding space.

With Custom Sound Event Detection, customers can train Alexa to recognize new sounds — such as a doorbell ringing — from just a handful of examples. Custom Sound Event Detection uses proximity in a neural network’s representational space to recognize instances of the same sound.

Custom Event Alerts for Ring Video Doorbell cameras and Spotlight cameras works in a similar way. With just a few examples, customers can train their devices to recognize certain states of affairs in the world — such as a shed door that has been left open.

In August, we introduced adaptive volume for Alexa, which lets Echo devices adjust their volume according to ambient-noise levels, so that the perceived noise level stays consistent for the customer. One of the key elements of the approach is algorithmically separating the speech signal and the noise signal, so that they’re separate inputs to the volume adaptation model.

We also launched adaptive listening for US English, an opt-in feature that gives customers more time to finish speaking before Alexa responds, making Alexa a more accessible, patient listener. For speakers with certain speech impediments, adaptive listening has reduced the friction in their Alexa interactions by more than two-thirds.

Finally, Alexa customers can choose to interact with celebrity personalities such as Amitabh Bachchan, Melissa McCarthy, Samuel L. Jackson, or Shaquille O'Neal. At the end of the year, we even brought holiday cheer to Alexa interactions by launching the festive personality of Santa Claus.

More insightful and proactive

Today, one in four smart-home interactions is initiated by Alexa, due to the expansion of its predictive and proactive features such as hunches and routines.

Since 2018, Alexa hunches have recognized anomalies in customers’ daily routines and suggested corrections — noticing that a light was left on at night and offering to turn it off, for instance. This year, we gave customers the option of making hunches more proactive, so Alexa can act on their behalf. When proactive hunches are enabled, Alexa will turn that light off for you without asking first.

Routines let you initiate a sequence of actions with a single trigger word, rather than issuing the same instructions over and over again. Previously, customers had to specify which actions they wanted to string together. But this year, we began phasing in inferred routines. With inferred routines, Alexa recognizes sequences of actions that customers commonly repeat — such as, say, turning on the kitchen lights, starting the coffee maker, and playing the “Wake Up!” playlist — and suggests combining them into a routine. To save the routine, the customer simply accepts Alexa’s suggestion.

We have also continued to expand latent-goal prediction, where Alexa recognizes the larger customer need implied by an initial request and suggests actions or skills to fulfill that need. For instance, a customer asks, “Who won the Celtics game?”, and after answering, Alexa asks, “Would you like to know when the Celtics are playing next?”

Latent-goal prediction uses pointwise mutual information to measure the likelihood of an interaction pattern in a given context relative to its likelihood across all Alexa traffic, and it uses bandit learning to track whether recommendations are helping or not and suppress underperforming experiences.

We have also introduced visual ID on our latest Echo device, Echo Show 15. With visual ID, Alexa shows notes and other reminders just for you (e.g., “Leave a note for Jack that his new passport has arrived”). Visual ID is also available on Astro, an Alexa-enabled home robot that extends environment and state awareness to your physical space. Astro can follow you playing media or find you to deliver calls, messages, timers, alarms, or reminders. With a Ring Protect prosubscription, Astro can also proactively patrol your home and investigate anomalous activities.

More trustworthy

Preserving customer privacy is an uncompromisable tenet for us and an invention area. Differential privacy in particular is one of our key areas of focus. This year, we won a best-paper award at the annual meeting of the Florida Artificial Intelligence Research Society (FLAIRS) for an approach to improving the performance of machine learning models while still meeting the privacy standards imposed by differential-privacy analysis.

At the Conference of the European Chapter of the Association for Computational Linguistics, we presented a method for protecting privacy by automatically rephrasing training text while preserving their semantic sense, in a way that, again, meets differential-privacy standards.

Biased language models still.jpg
Alexa AI researchers constructed a dataset of more than 23,000 text generation prompts, each consisting of six to nine words of a sentence on Wikipedia. The prompts can be used to test language models for bias.
Credit: Glynis Condon

We want Alexa to work equally well for everyone. To that end, in addition to our partnership with the National Science Foundation in the area of fairness in AI, we are pursuing research into detecting and mitigating inappropriate bias. At the ACM Conference on Fairness, Accountability, and Transparency (FAccT) and the Conference of the European Association for Computational Linguistics, we published a pair of papers on measuring bias in language models and detecting bias in datasets for training models that recognize unreliable news.

The path ahead

I recognize that there are multiple paths to general AI, each with years of fundamental research ahead of it. I believe Alexa and its underlying vision of ambient intelligence offer a pragmatic path to general AI— one where every advancement makes Alexa more useful for our customers in their daily lives.

I am in awe at the rate of invention from the Alexa team in the most difficult circumstances. As we wrap up yet another year of the COVID pandemic, I hope the advances the worldwide community of AI researchers is making in every discipline of AI will help us prevent future pandemics.

Related content

CA, BC, Vancouver
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Amazon ML Solutions Lab team helps AWS customers accelerate the use of machine learning to solve business and operational challenges and promote innovation in their organization. We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help develop solutions by pushing the envelope in Time Series, Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV).Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. As a ML Solutions Lab Applied Scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data and develop novel models to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. You will apply classical ML algorithms and cutting-edge deep learning (DL) approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, and event detection among others. The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML/DL models to solve diverse challenges and opportunities across industriesInteract with customer directly to understand their business problems, and help them with defining and implementing scalable ML/DL solutions to solve themWork closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new algorithmsThis position requires travel of up to 20%.
US, WA, Seattle
Are you a Ph.D. interested in the fields of machine learning, deep learning, automated reasoning, speech, robotics, computer vision, optimization, or quantum computing? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way? If this describes you, come join our science teams at Amazon. As an Applied Scientist, you will have access to large datasets with billions of images and video to build large-scale systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. We are looking for smart scientists capable of using a variety of domain expertise to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person.Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel.CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, IL, Chicago
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist ILocation: Chicago, IllinoisPosition Responsibilities:Build the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. Tackle cutting-edge, complex problems such as predicting the optimal location for new Amazon stores by bringing together numerous data assets, and using best-in-class modeling solutions to extract the most information out of them. Work with business stakeholders, software development engineers, and other data scientists across multiple teams to develop innovative solutions at massive scale.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Seattle
Note that this posting is for a handful of teams within Amazon Robotics. Teams include: Robotics, Computer Vision, Machine Learning, Optimization, and more.Are you excited about building high-performance robotic systems that can perceive and learn to help deliver for customers? The Amazon Robotics team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.Amazon Robotics is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. We will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Come join us!A day in the lifeAs an intern you will develop a new algorithm to solve one of the challenging computer vision and manipulation problems in Amazon's robotic warehouses. Your project will fit your academic research experience and interests. You will code and test out your solutions in increasingly realistic scenarios and iterate on the idea with your mentor to find the best solution to the problem.
US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, WA, Bellevue
Employer: Amazon.com Services LLCPosition: Research Scientist IILocation: Bellevue, WA Multiple Positions Available1. Research, build and implement highly effective and innovative methods in Statistical Modeling, Machine Learning, and other quantitative techniques such as operational research and optimization to deliver algorithms that solve real business problems.2. Take initiative to scope and plan research projects based on roadmap of business owners and enable data-driven solutions. Participate in shaping roadmap for the research team.3. Ensure data quality throughout all stages of acquisition and processing of the data, including such areas as data sourcing/collection, ground truth generation, data analysis, experiment, evaluation and visualization etc.4. Navigate a variety of data sources, understand the business reality behind large-scale data and develop meaningful science solutions.5. Partner closely with product or/and program owners, as well as scientists and engineers in cross-functional teams with a clear path to business impact and deliver on demanding projects.6. Present proposals and results in a clear manner backed by data and coupled with conclusions to business customers and leadership team with various levels of technical knowledge, educating them about underlying systems, as well as sharing insights.7. Perform experiments to validate the feature additions as requested by domain expert teams.8. Some telecommuting benefits available.The pay range for this position in Bellevue, WA is $136,000-$184,000 (yr); however, base pay offered may vary depending on job-related knowledge, skills, and experience. A sign-on bonus and restricted stock units may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, dependent on the position offered. This information is provided by the Washington Equal Pay Act. Base pay information is based on market location. Applicants should apply via Amazon's internal or external careers site.#0000
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Amazon.com Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Amazon.com Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000