Amazon Consumer Science Summit goes virtual

COVID-19-induced trend toward virtual conferences may change how science is conducted.

COVID-19 has caused massive disruption around the globe. That includes the myriad of science conferences held each year. With the pandemic now forcing the cancellation of nearly all in-person events, these conferences have gone virtual.

Amazon is a participant in this trend. In late September, for instance, Amazon’s consumer science organization held its seventh annual Consumer Science Summit, which focused on science applications for economic decision-making, with topics ranging from operations research and econometrics, to statistics and machine learning.  Originally planned as a 200-person conference to be held at a resort in Washington state, event organizers considered how best to proceed. Their final decision: move the summit online.

No one was sure quite what to expect, but the outcome was surprisingly positive. Without the need to limit attendees due to space constraints, and without the need for participants to build travel days into their schedule or worry about acquiring a US visa, attendance was more than four times that of previous in-person events. Moreover, prominent scientists who otherwise might have been unable to attend in person were able to deliver virtual keynote talks.

Ping Xu, forecasting science director, standing outside in front of a rainbow.
Ping Xu, forecasting science director within Amazon's Supply Chain Optimization Technologies (SCOT) organization, was one of the organizers of this year's Consumer Science Summit.

“In this age of isolation and uncertainty, it turns out this event helped us return to the roots of science — the exchange of ideas,” said Ping Xu, forecasting science director within Amazon’s Supply Chain Optimization Technologies organization.

In fact, scientists everywhere are discovering that a well-run virtual event can have benefits that extend beyond an in-person conference.

The Scientist magazine, for instance, reported on an August conference held by the Society for Mathematical Biology and the European Society for Mathematical and Theoretical Biology. Originally set for Heidelberg, Germany, the virtual conference created virtual space so it would provide for socializing, networking, and mentoring, as well as hearing talks, seeing posters, and visiting the meeting’s corporate sponsors.  

Plus, the roughly 1,800 attendees represented more than 90 countries — two to three times as many as at previous in-person meetings. 

The move to virtual scientific conferences poses some intriguing questions. Will it make science more collaborative and multidisciplinary? Or will people lose the chance for serendipitous connections that are the staple of in-person event?

In an effort to make up for the lack of in-person interaction, Consumer Science Summit organizers took advantage of online tools to facilitate collaboration among virtual attendees. The conference had its own Slack channel, for instance, and networking coffee breaks occurred over Chime. The four-day conference included the presentation of 208 papers and abstracts, 5 keynote, 28 lightning, and 12 technical talks, and 2 fireside chats. 

The keynote talks included presentations by Ming Lin, a computer science professor  at the University of Maryland; Anna Nagurney, the John F. Smith Memorial Professor within the Department of Operations and Information Management at the University of Massachusetts Amherst Isenberg School of Management; Susan Athey, an economics of technology professor at Stanford University, and senior fellow of the Stanford Institute for Economic Policy Research; Nassim Taleb, distinguished professor of risk engineering at the NYU Tandon School of Engineering, and author of several books, including “The Black Swan”; and Gerard Cachon, a professor within the Operations, Information and Decisions Department at the University of Pennsylvania Wharton School. The two fireside chats were with Scott Aronson, the David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin, and Swami Sivasubramanian, vice president, AWS machine learning. Below are three of those keynote talks.

Nassim Taleb: Statistical consequences of fat tails
In this keynote talk, Professor Taleb discusses concepts from the first version of his Technical Incerto: Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications.
Gerard Cachon: Human vs. robot workers in fulfillment center pick processes
In his keynote talk, Professor Cachon talks about new research he has conducted with Omar Besbes, a professor at Columbia University Business School.
Anna Nagurney: Optimization of food supply chain networks: Why quality, trade instruments, and labor all matter
In her keynote talk, which she dedicated to essential workers, Professor Nagurney discusses her latest research on food supply chain networks.

“It was much better than I expected, given my first exposure to the technology that we would rely upon,” says Robert Stine, senior principal scientist within SCOT. “Some aspects, such as the recorded lightning talks worked very well.  This format was better than the awkward in-person version — and could be combined nicely with online meetings and poster sessions.”

Adds Mauricio Resende, a principal scientist within Amazon’s Transportation Services organization: “I attended the previous four summits, and was pleasantly surprised at how well this virtual conference ran. We had a few glitches where people had to refresh their browsers, but otherwise there were no delays, no problems with the live presentations — or the recorded ones.  And we had a much larger audience.”

Ping Xu, the event’s sponsor, agrees.

“I thought it went really well,” she says. “I was really glad to see that we could draw a wider audience by going virtual. Plus, we learned a lot about how to manage a virtual conference. For instance, one thing I learned is that the virtual format encourages a broader engagement but an in-person format encourages a deeper engagement. For our 2021 conference, we are entertaining a hybrid approach where we can offer online talks for a larger audience but still allow in-person celebrations and discussions.

It was hard at times not to miss the more social aspects of an in-person conference, where scientists can connect with peers they’ve worked with before and perhaps make new connections.

“I still prefer in-person conferences,” says Resende. “They give me a chance to bump into old friends and make new ones. I have begun many a collaboration or paper on the basis of unexpected meetings at conferences.”

In this age of isolation and uncertainty, it turns out this event helped us return to the roots of science – the exchange of ideas
Ping Xu, forecasting science director

Some event organizers are aware of that shortcoming and have developed clever ways to address it.  One conference, for instance, asked participants to write a short description of their work and research interests. That information was then run through an algorithm that matched up attendees with similar interests.   

Prior to the COVID-19 pandemic, there already was a movement within the machine learning science community to allow remote paper and poster presentations at scientific conferences.

Turing Award winner Joshua Bengio is a supporter of the movement allowing more virtual presentations of papers and poster sessions, saying in a blog post that he believes “we should rethink these events with the objective of eliminating the resulting carbon footprint.”

In the post, Bengio suggests the science community could consider decentralized conferences, where instead of having a conference at one location, meeting places could be established — at least one on each continent — so that scientists could attend their “local” meeting.

Some think scientists working remotely more often would have greater impact than having science conferences adopt a completely remote model.

“I think the impact of working virtually reduces carbon footprint more than the impact of going from an in-person conference model to a virtual model,” says Resende. “I have worked remotely two or three days a week since 1988. Virtual conferencing reduces the need to see people face-to-face, but doesn’t eliminate it.”

Other drawbacks include sometimes having too much to choose from, reducing focus. And virtual conference-goers have the same challenge as remote workers everywhere: giving full attention to a conference speaker when emails are coming in and phones are ringing.

Nonetheless, most scientists expect to be attending more virtual conferences in the foreseeable future, if largely because of COVID-19.

On the plus side, a world of virtual conferences will improve as conference organizers grow accustomed to virtual formats, and new conferencing tools become available.  Events are apt to draw more people — as the Amazon summit did — and can take advantage of a wider pool of speakers.

But will that improve science?

“Perhaps,” says Stine. “But there already are lots of ways to exchange ideas virtually, such as ArXiv for sharing manuscripts. And universities now post lectures and seminars on-line. That all might change, of course, with a new generation of scientists who have grown up on virtual campuses.”

No matter if in-person or virtual, the key is the exchange of ideas.  In opening the Consumer Science Summit, Xu, the forecasting science director who helped organize the consumer summit, quoted George Bernard Shaw.

“If you have an apple and I have an apple and we exchange these apples, then you and I will each have an apple. But if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.”

Whether virtual or in-person, the primary purpose of science conferences remains the same: the exchange of ideas as researchers stand on the shoulders of the giants who preceded them, seeking to advance the science.

Related content

GB, Cambridge
Amazon Devices is an inventive research and development company that designs and engineer high-profile devices like Echo, Fire Tablets, Fire TV, and other consumer devices. We are looking for exceptional scientists to join our Applied Science team to advance the state-of-the-art in developing efficient multimodal language models across our product portfolio. Through close hardware-software integration, we design and train models for resource efficiency across the hardware and software tech stack. The Silicon and Solutions Group Edge AI team is looking for a talented Sr. Applied Scientist who will lead our efforts on inventing evaluation methods for multimodal language models and agents for new devices, including audio and vision experiences. Key job responsibilities - Collaborate with cross-functional engineers and scientists to advance the state of the art in multimodal model evaluations for devices, including audio, images, and videos - Invent and validate reliability for novel automated evaluation methods for perception tasks, such as fine-tuned LLM-as-judge - Develop and extend our evaluation framework(s) to support expanding capabilities for multimodal language models - Analyze large offline and online datasets to understand model gaps, develop methods to interpret model failures, and collaborate with training teams to enhance model capabilities for product use cases - Work closely with scientists, compiler engineers, data collection, and product teams to advance evaluation methods - Mentor less experienced Applied Scientists A day in the life As a Scientist with the Silicon and Solutions Group Edge AI team, you'll contribute to innovative methods for evaluating new product experiences and discover ways to enhance our model capabilities and enrich our customer experiences. You'll research new methods for reliably assessing perception capabilities for audio-visual tasks in multimodal language models, design and implement new metrics, and develop our evaluation framework. You'll collaborate across teams of engineers and scientists to identify and root cause issues in models and their system integration to continuously enhance the end-to-end experience. About the team Our Edge AI science team brings together our unique skills and experiences to deliver state-of-the-art multimodal AI models that enable new experiences on Amazon devices. We work at the intersection of hardware, software, and science to build models designed for our custom silicon.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with generative AI (GenAI) and multi-modal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop algorithms and modeling techniques to advance the state of the art with multi-modal systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s large-scale computing resources to accelerate development with multi-modal Large Language Models (LLMs) and GenAI in Computer Vision. About the team The AGI team has a mission to push the envelope with multimodal LLMs and GenAI in Computer Vision, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As an Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to lead the development of algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
The Economics Science team in the Amazon Manager Experience (AMX) organization builds science models supporting employee career-related experiences such as their evaluation, learning and development, onboarding, and promotion. Additionally, the team conducts experiments for a wide range of employee and talent-related product features, and measures the impact of product and program initiatives in enhancing our employees' career experiences at Amazon. The team is looking for an Economist who specializes in the field of macroeconomics and time series forecasting. This role combines traditional macroeconomic analysis with modern data science techniques to enhance understanding and forecasting of workforce dynamics at scale. Key job responsibilities The economists within ALX focus on enhancing causal evaluation, measurement, and experimentation tasks to ensure various science integrations and interventions achieve their goals in building more rewarding careers for our employees. The economists develop and implement complex randomization designs that address the nuances of experimentation in complex settings where multiple populations interact. Additionally, they engage in building a range of econometric models that surface various proactive and reactive inspection signals, aiming toward better alignment in the implementation of talent processes. The economists closely collaborate with scientists from diverse backgrounds, as well as program and product leaders, to implement and assess science solutions in our products.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities Develop ML models for various recommendation & search systems using deep learning, online learning, and optimization methods Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps Stay up-to-date with advancements and the latest modeling techniques in the field Publish your research findings in top conferences and journals A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences.
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. About our team SPB Ad Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Applied Scientist with machine learning engineering background who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine learning systems. We are looking for a talented Applied Scientist with a strong background in machine learning engineering to join our team and help us grow the business. In this role, you will partner with a team of engineers and scientists to build advanced machine learning models and infrastructure, from training to inference, including emerging LLM-based systems, that deliver highly relevant ads to shoppers across all Amazon platforms and surfaces worldwide. Key job responsibilities As an Applied Scientist, you will: * Develop scalable and effective machine learning models and optimization strategies to solve business problems. * Conduct research on new machine learning modeling to optimize all aspects of Sponsored Products business. * Enhance the scalability, automation, and efficiency of large-scale training and real-time inference systems. * Pioneer the development of LLM inference infrastructure to support next-generation GenAI workloads at Amazon Ads scale.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities As a Data Scientist, you will • Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges • Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production • Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder • Provide customer and market feedback to product and engineering teams to help define product direction About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. The Applied Scientist will be in a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in Natural Language Processing (NLP) or Computer Vision (CV) related tasks. They will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. They will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Their work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. Key job responsibilities - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve solutions powering customer experience on Alexa+. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership.
US, CA, Sunnyvale
As a Principal Scientist within the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).