"This technology will be transformative in ways we can barely comprehend"

A judge and some of the finalists from the Alexa Prize Grand Challenge 3 talk about the competition, the role of COVID-19, and the future of socialbots.

Human beings are social creatures, and conversations are what connect us—they enable us to share everything from the prosaic to the profound with the people that matter to us. Living through an era marked by pandemic-induced isolation means many of those conversations have shifted online, but the connection they provide remains essential.

So what happens when you replace one of the human participants in a conversation with a socialbot? What does it mean to have an engaging conversation with an AI assistant? How can that kind of conversation prove to be valuable, and can it provide its own kind of connection?

Application period for next Alexa Prize challenge opens

The Amazon Alexa Prize team encourages all interested teams to apply for the Grand Challenge 4 by 11:59 p.m. PST on October 6, 2020.

The participants in this year’s Alexa Prize contest are driven by those questions. Amazon recently announced that a team from Emory University has won the 2020 Alexa Prize. We talked to that team, along with a judge from this year’s competition, as well as representatives from the other finalist teams at Czech Technical University, Stanford University, University of California, Davis, and University of California, Santa Cruz. We wanted to learn what drives them to participate, how COVID-19 has influenced their work and what they see as the possibilities and challenges for socialbots moving forward.

Alexa Prize Grand Challenge 3 winners share their work | Amazon Science

Q: What inspired you to participate in this year’s competition?

Sarah Fillwock, team leader, Emora, Emory University: We had a group of students who were interested in dialogue system research, some of whom had actually participated in the Alexa Prize in its previous years, and we all knew that the Alexa Prize offers a really unique opportunity for anyone interested in this type of work. It is really exciting to use the Alexa device platform to launch a socialbot, because we are able to get hundreds of conversations a day between our socialbot and human users, which really allows for quick turnaround time when assessing whether or not our hypotheses and strategies are improving the performance of our dialogue system.

Marilyn Walker, faculty advisor, Athena, University of California, Santa Cruz: In our Natural Language and Dialogue Systems lab, our main research focus is dialogue management and language generation. Conversational AI is a very challenging problem, and we felt like we could have a research impact in this area. The field has been developing extremely quickly recently, and the Alexa Prize offers an opportunity to try out cutting-edge technologies in dialogue management and language generation on a large Alexa user population.

Amazon Alexa Prize Finalists 2020
The five Alexa Prize finalist teams: Czech Technical University in Prague; Emory University; Stanford University; the University of California, Davis; and the University of California, Santa Cruz.

Vrindavan (Davan) Harrison, team leader, Athena, UCSC: As academics, our primary focus is on research. This year’s competition aimed at being more research-oriented, allowing the teams to spend more time on developing new ideas.

Kai-Hui Liang, team lead, Gunrock, University of California, Davis: Our experience in last year’s competition motivated us to join again as we realized there is still a large room for improvement. I’m especially interested in how to find topics that engage users the most, including trying different ways to elicit and reason about users’ interests. How can we retrieve content that is relevant and interesting, and make the dialog flow more naturally?

Jan Pichl, team leader, Alquist, Czech Technical University: Since the first year of the Alexa Prize competition, we have been developing Alquist to deliver a wide range of topics with a closer focus on the most popular ones. The first Alquist guided a user through the conversation quite strictly. We learned quickly that we needed to introduce more flexibility and let the user be "in charge". With that in mind, we have been pushing Alquist in that direction. Moreover, we want Alquist to manage dialogue utilizing the knowledge graph, and suggest relevant information based on the previously discussed topics and entities.

Christopher D. Manning, faculty advisor, Chirpy Cardinal, Stanford University: It was our first time doing the Alexa Prize, and the team really hadn’t done advance preparation, so it’s all been a wild ride—by which I mean a lot of work and stress for everyone on the team. But it was super exciting that we were largely able to catch up with other leading teams who have been doing the competition for several years.

Hugh Howey, judge and science fiction author: Artificial intelligence is a passionate interest of mine. As a science fiction author, I have the freedom to write about most anything, but the one topic I keep coming back to is the impact that thinking machines already have on our lives and how that impact will only expand in the future. So any chance to be involved with those doing work and research in the field is a no-brainer for me. I leapt at the chance like a Boston Dynamics dog.

Q: What excites you about the potential of socialbots?

Hugh Howey (Judge): This technology will be transformative in ways we can barely comprehend. Right now, the human/computer interface is a bottleneck. It takes a long time for us to tell our computers what we want them to do, and they'll generally only do that thing the one time and forget what it learned. In the future, more and more of the trivial will be automated. This will free up human capital to tackle larger problems. It will also bring us together by removing language barriers, by helping those with disabilities, and eventually this technology will be available to anyone who needs it.

Jinho D. Choi, faculty advisor, Emory: It has been reported that more than 44 million adults in US have mental health issues such as anxiety or depression. We believe that developing an innovative socialbot that comforts people can really help those with mental health conditions, who are generally afraid of talking to other human beings. You may wonder how artificial intelligence can convey a human emotion such as caring. However, humans have used their own creations, such as arts and music, to comfort themselves. It is our vision to advance AI, the greatest invention of humankind, to help individuals learn more about their inner selves so they can feel more positive about themselves, and have a bigger impact in the world.

Ashwin Paranjape, co-team leader, Stanford: As socialbots become more sophisticated and prevalent, increasing numbers of people are chatting with them regularly. As the name suggests, socialbots have the potential to fulfill social needs, such as chit-chatting about everyday life, or providing support to a person struggling with mental health difficulties. Furthermore, socialbots could become a primary user interface through which we engage with the world—for example, chatting about the news, or discussing a book.

Sarah Fillwock, Emory: Our experience in this competition has really solidified this idea of the potential of socialbots being value to people who need support and are in troubling situations. I think that the most compelling role for socialbots in global challenges is to provide a supportive environment to allow people to express themselves, and explore their feelings with regard to whatever dramatic event is going on. This is especially important for vulnerable populations, such as those who do not have a strong social circle or have reduced social contact with others, prohibiting them from being able to achieve the feeling of being valued and understood.

Q: What are the main challenges to realizing that potential?

Abigail See, co-team leader, Stanford: Currently, socialbots struggle to make sense of long, involved conversations, and this limits their ability to talk about any topic in depth. To do this better, socialbots will need to understand what a particular user wants—not only in terms of discussion topics, but also what kind of conversation they want to have. Another important challenge is to allow users to take more initiative, and drive the conversation themselves. Currently, socialbots tend to take more initiative, to ensure the conversation stays within their capabilities. If we can make our socialbots more flexible, they will be much more useful and engaging to people.

Sarah Fillwock, Emory: One major challenge facing the field of dialogue system research is establishing a best practice for evaluation of the performance of dialogue approaches. There is currently a diverse set of evaluation strategies that the research community uses to determine how well their new dialogue approach performs. Another challenge is that dialogues are more than just a pattern-matching problem. Having a back-and-forth dialogue on any topic with another agent tends to involve planning towards achieving specific goals during the conversation as new information about your speaking partner is revealed. Dialogues also rely a lot on having a foundation of general world knowledge that you use to fully understand the implications of what the other person is saying.

Amazon releases Topical Chat dataset

The text-based collection of more than 235,000 utterances will help support high-quality, repeatable research in the field of dialogue systems.

Marilyn Walker, UCSC: There’s a shortage of large annotated conversational corpora for the task of open-domain conversation. For example, progress in NLU has been supported by large annotated corpora, such as Penn Treebank, however, there are currently no such publicly available corpora for open-domain conversation. Also, a rich model of individual users would enable much more natural conversations, but privacy issues currently make it difficult to build such models.

Hugh Howey (Judge): The challenge will be for our ethics and morality to keep up with our gizmos. We will be far more powerful in the future. I only hope we'll be more responsible as well.

Q: What role has the COVID-19 pandemic played in your work?

Jurik Juraska, team member, UCSC: The most immediate effect the onset of the pandemic had on our socialbot was, of course, that it could not just ignore this new dynamic situation. Our socialbot had to acknowledge this new development, as that was what most people were talking about at that point. We would thus have Athena bring up the topic at the beginning of the conversation, sympathizing with the users' current situation, but avoiding wallowing in the negative aspects of it. In the feedback that some users left, there were a number of expressions of gratitude for the ability to have a fun interaction with a socialbot at a time when direct social interaction with friends and family was greatly restricted.

Kai-Hui Liang, UC Davis: We noticed an evident difference in the way Alexa users reacted to popular topics. For example, before COVID-19, many users gave engaging responses when discussing their favorite sports to watch, their travel experiences, or events they plan to do over the weekend. After the breakout of COVID-19, more users replied saying there’s no sports game to watch or they are not able to travel. Therefore, we adapted our topics to better fit the situation. We added discussion about their life experience during the quarantine (eg. how their diet has changed or if they walk outside daily to stay healthy). We also observed more users having negative feelings potentially due to the quarantine. For instance, some users said they feel lonely and they miss their friends or family. Therefore, we enhanced our comforting module that expresses empathy through active listening.

Abigail See, Stanford: As the pandemic unfolded, we saw in real time how users changed their expectations of our socialbot. Not only did they want our bot to deliver up-to-date information, they also wanted it to show emotional understanding for the situation they were in.

Sarah Fillwock, Emory: When COVID became a significant societal issue, we tried two things: we had an experience-oriented COVID topic where our bot discussed with people how they felt about COVID in a sympathetic and reassuring atmosphere, and we had a fact-oriented COVID topic that gave objective information. What we observed was that people had a much stronger positive reaction to the experience-oriented COVID-19 approach than the fact-oriented COVID-19 approach, and seemed to prefer it when talking. This really gave us some empirical evidence that social agents have a strong potential to be helpful in times of turmoil by giving people a safe and caring space to talk about these major events in their life since people responded positively to our approach at doing this.

Q: Lastly, are there any particular advancements in the fields of NLU, dialogue management, conversational AI, etc., that you find promising?

Jan Pichl, Czech Technical University: It is exciting to see the capabilities of the Transformer-based models these days. They are able to generate large articles or even whole stories that are coherent. However, they demand a lot of computation power during the training phase and even during the runtime. Additionally, it is still challenging to use them in a socialbot when you need to work with constantly changing information about the world.

Abigail See, Stanford: As NLP researchers, we are amazed by the incredible pace of progress in the field. Since the last Alexa Prize in 2018, there have been game-changing advancements, particularly in the use of large pretrained language models to understand and generate language. The Alexa Prize offers a unique opportunity for us to apply these techniques, which so far have mostly been tested only on neat, well-defined tasks, and put them in front of real people, with all the messiness that entails! In particular, we were excited to explore the possibility of using neural generative models to chat with people. As recently as the 2018 Alexa Prize, these models generally performed poorly, and so were not used by any of the finalist teams. However, this year, these systems became an important backbone of our system.

Sarah Fillwock, Emory: The work people have been putting into incorporating common sense knowledge and common sense reasoning into dialogue systems is one of the most interesting directions of the current conversational AI field. A lot of the common sense knowledge we use is not explicitly detailed in any type of data set as people have learned them through physical experience or inference over time, so there isn’t necessarily any convenient way to currently accomplish this goal. There have been a lot of attempts to see how far a language modeling approach to dialogue agents can go, but even using huge dialogue data sets and highly complex models still results in hit-and-miss success at common sense information. I am really looking forward to the dialogue approaches and dialogue resources that more explicitly try to model this type of common sense knowledge.


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Job summaryAmazon is looking for a creative Senior Applied Scientist to tackle some of the most interesting problems on the leading edge of natural language processing (NLP) and machine learning (ML) with our Alexa Artificial Intelligence (AI) team. Alexa AI aims to reinvent search and information retrieval for a voice-forward, multi-modal future. We enable customers to interact with unstructured and semi-structured content via a broad range of customer experiences including question answering, summarization, search, and multi-turn dialogues.Key job responsibilitiesIf you are looking for an opportunity to develop innovative solutions to deep technical problems having a massive customer impact, this might be the role for you! As a Senior Applied Scientist, you will work with smart, passionate colleagues in a fast-paced environment. You will develop and help deploy novel, scalable algorithms to advance the state-of-the-art in technology areas at the intersection of NLP and ML. You will keep up with relevant research in the field of NLP and publish your work in top-tier conferences. You will contribute to a multi-year research roadmap, enabling the team to focus on the right technical challenges to delight our customers.
US, NY, New York
Job summaryAmazon AI is looking for an experienced Data Scientist with abackground in the intersection of linguistics, phonetics, or NLP andstatistics/machine learning to help build industry-leading speech andlanguage processing services.As part of our AI team in Amazon Web Services, you will work alongsideapplied scientists, software engineers, and language engineers todefine data requirements for training AI services, ensure dataquality, develop suitable evaluation metrics for novel AI features andservices, and analyze systems' input/output behavior. You will be responsiblefor implementing and maintaining data analysis tools and pipelines, creatinginsightful analyses of complex production systems, and communicating resultsto scientists, product managers, and customers.Your work will directly impact millions of our customers in the formof products and services that make use of speech and languagetechnology. You will gain hands on experience with Amazon’sheterogeneous speech, text, and structured data sources, andlarge-scale computing resources to accelerate advances in spokenlanguage processing.Inclusive Team CultureHere 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 BalanceOur 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 GrowthOur 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.
GB, Cambridge
Job summaryAmazon is looking for a creative Applied Scientist to tackle some of the most interesting problems on the leading edge of Machine Learning (ML), Natural Language Processing (NLP), and Information Retrieval (IR) with our Device Design Group (DDG). Amazon’s Device Design Group has launched revolutionary products like Echo, Fire TV, Alexa Communications, and more. Are you interested in joining the team to lead Amazon’s next innovation?The successful candidate will develop novel ML/NLP/IR/Deep Learning technologies to make Alexa smarter. They will have a true passion for working in a collaborative, cross-functional environment that encourages thinking about optimized solutions to unique problems that do not have yet a known science solution.If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast-paced environment working within a smart and passionate team, this might be the role for you. You will develop and implement novel algorithms and modeling techniques to leverage and advance the state-of-the-art in technology areas that are found at the intersection of ML, NLP, IR, and Deep Learning. Your work will directly impact Amazon products and services that make use of speech and language technology. You will gain hands on experience with Alexa and large-scale computing resources.In this role you will:· Work collaboratively with scientists and developers to design and implement automated, scalable MT models;· Drive scalable solutions from the business, to prototyping, production testing and through engineering directly to production;· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential.
US, VA, Arlington
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently? The Robotics AI team is creating intelligent products for Amazon's supply chain, at the scale of Amazon's supply chain.The Robotics AI software team is seeking a senior scientist to help with our Robotic Perception systems. This includes building Computer Vision systems and sensor fusion to handle the most complex situations that come with large scale. It also includes end-to-end ownership of decision explanation, fault detection, monitoring, A/B testing, large scale model training, simulation, hardware integration, and more. This work spans prototypes in the lab as well as wide-deployment systems. As a Senior scientist, you will help drive where to focus our research, coaching other scientists and engineers, performing your own studies, and building algorithms for us in production.Key Responsibilities* Research vision - Where should we be focusing our efforts* Coaching - Developing the skills of other scientists and engineers* Studies - Insights from production data or ad-hoc experimentation.* Production algorithms - Models that perform the core job of driving our robots in completing their work.Inclusive Team CultureHere at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. 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. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust.FlexibilityIt isn’t about which 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 offer flexibility and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthWe care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it.
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLC, an Amazon.com CompanyTitle: Economist IIWorksite: Seattle, WAPosition Responsibilities:Work with the economists, scientists and/or senior management on key business problems faced in retail, international retail, third party merchants, search, and/or operations. Apply the frontier of economic thinking to experiment design, forecasting, program evaluation and other areas. Build econometric models using data systems. Apply economic theory to solve business problems. Own the development of economic models and manage the data analysis, modeling and experimentation necessary to estimate and validate the models, in collaboration with scientists and engineers. Develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. Apply tools from applied micro-econometrics (e.g. experimental design, difference-in-difference, regression discontinuity) and forecasting (essential time series models). Leverage big data tools for data extraction. Work closely with business partners to communicate the intuition, implication and detail of economic analyses/modeling and incorporate feedback. Write up and present analysis for distribution to various levels of management at Amazon.Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, MA, North Reading
Job summaryAre you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even image yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun.We seek a talented and motivated engineer to tackle broad challenges in system-level analysis. You will work in a small team to quantify system performance at scale and to expand the breadth and depth of our analysis (e.g. increase the range of software components and warehouse processes covered by our models, develop our library of key performance indicators, construct experiments that efficiently root cause emergent behaviors). You will engage with growing teams of software development and warehouse design engineers to drive evolution of the AR system and of the simulation engine that supports our work.This role is a 6 month co-op to join AR full time (40 hours/week) from January-June 2022. Come join us in North Reading, MA, or in our newly expanded innovation hub in Westborough, MA!Both campuses provide a unique opportunity for co-ops to have direct access to robotics testing labs and manufacturing facilities.
US, CA, Palo Alto
The Query Understanding team in Amazon Search connects customer queries with billions of products through a common knowledge graph. Understanding shopping mission is the key to building an intelligent shopping assistant on Amazon. We are working on many challenging science problems in NLP, Graph Mining, Common-Sense Knowledge Extraction, and Unsupervised Learning. We are solving engineering challenges such as automatic ML training and running large BERT models within milliseconds.We are looking for a senior science manager to lead our mission to lift Amazon's search engine from a text based information retrieval engine to a knowledge based shopping assistant. In this role, you will manage teams of passionate, talented, and inventive scientists, to develop industry-leading Natural Language Processing (NLP) and Data Mining algorithms, and drive them successfully to production for the benefit of amazon customers. You will identify research directions, create roadmaps for forward-looking research and communicate them to senior leadership, and work closely with engineering teams to bring research to production. You will work with teams of talented scientists, and fill the ranks by attracting the best scientists in NLP, and Data Mining, by representing Amazon Search at international science conferences. You will work with talented peers and leverage Amazon’s heterogeneous data sources and large-scale computing resources.
US, WA, Seattle
Job summaryWe are constantly making Alexa the best voice assistant in the world. Amazon’s Alexa cloud service and Echo devices are used every day, by people you know, in and about their homes. The Alexa Monetization team is hiring talented and experienced Sr. Applied Scientists to help building the next generation products for Alexa across multiple channels and domains. We are seeking an experienced, entrepreneurial, big thinker for a confidential new initiative within Alexa. You will be joining a team doing innovative work, making a direct impact to customers, showing measurable success, and building with the latest natural language processing systems. If you are holding out for an opportunity to:Make a huge impact as an individual· Be part of a team of smart and passionate professionals who will challenge you to grow every day· Solve difficult challenges using your expertise in coding elegant and practical solutions· Create applications at a massive scale used by millions of people· Work with machine learning systems to deliver real experiences, not just researchAnd you are experienced with…· Drive applied science (machine learning) projects end-to-end ~ from ideation, analysis, prototyping, development, metrics, and monitoring· Conduct deep analyses on massive user and contextual data sets· Propose viable modeling ideas to advance optimization or efficiency, with supporting argument, data, or, preferably, preliminary results· Design, develop, and maintain scalable, Machine Learning models with automated training, validation, monitoring and reporting· Stay familiar with the field and apply state-of-the-art Machine Learning techniques to NLP and related optimization problems· Produce peer-reviewed scientific paper in top journals and conferencesAnd you constantly look for opportunities to…· Innovate, simplify, reduce waste, and increase efficiencies· Use data to make decisions and validate assumptions· Automate processes otherwise performed by humans· Learn from others and help grow those around you...then we would love to chat!In 2021, we have the opportunity to build new products and features from the ground up and we are looking for strong, bias for action engineering leaders who are not afraid of taking bold bets and trying new things to improve customer experience for Alexa.As part of a new and growing team, you will be iterating on new features and products to help drive innovation and expansion. You will work on cross-functional and cross-domain opportunities; tackle challenging projects aim to accelerate experimentations in Alexa; and build out operating mechanisms and technology to enable novel customer experiences. You will be instrumental in setting the team culture, quality bar, engineering best practices, and norms. Mentoring and growing the team around you will be one of the primary ways you measure your own success. You will have the opportunity to contribute and develop deep expertise in the areas of distributed systems, machine learning, conversational technologies, user interfaces (including voice and natural user interfaces), data storage and data pipelines.This role is exciting for scientists who love to apply startup mindset to their day-to-day, enjoy working cross-functionally to master both business and technology knowledge, and are passionate about building engineering best practices. If you are looking for opportunity to learn, grow and lead, this is the position for you.
US, WA, Seattle
Job summaryWhy this job is awesome?· This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site.· MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers.· We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process.· You will help the Delivery Experience organization to build causal inference framework and analyze the long-term effect on business· Your work will support the Amazon leadership to make visionary business decisions.· Do you want to join an innovative team of scientists and engineers who use machine learning and statistical inference techniques to deliver the best delivery experience on every Amazon-owned site?· · Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems?· · Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company?· · Do you like to innovate and simplify?If yes, then you may be a great fit to join the Delivery Experience Machine Learning team.Major responsibilities:· Research and implement causal inference techniques to create scalable and effective models in Delivery Experience (DEX) systems· Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites.· Design and develop machine learning framework to measure the long-term effect of all models in DEX systems· Design and develop search ranking, recommendation and personalization models to improve Amazon customer experience· Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities· Establishing scalable, efficient, automated processes for large scale data analysis and causal inference