Yezhou Yang is an assistant professor at Arizona State University’s School of Computing and Augmented Intelligence, where he heads the Active Perception Group
Yezhou Yang is an assistant professor at Arizona State University’s School of Computing and Augmented Intelligence, where he heads the Active Perception Group.
Courtesy of Yezhou Yang

Foiling AI hackers with counterfactual reasoning

Amazon Research Award recipient Yezhou Yang is studying how to make autonomous systems more robust.

Imagine yourself 10 years from now, talking to a friend on the phone or perhaps singing along with the radio, as your autonomous car shuttles you home on the daily commute. Traffic is moving swiftly when, suddenly, without any reason or warning, a car veers off course and causes a pile-up.

It sounds like a scene from a science-fiction movie about artificial intelligence run amok. Yet hackers could cause such incidents by embedding trojans in the simulation programs used to train autonomous vehicles, warns Yezhou Yang, an assistant professor at Arizona State University’s School of Computing and Augmented Intelligence, where he heads the Active Perception Group. With the assistance of funding from a 2019 Machine Learning Research Award, and by collaborating with Yi Ren (an optimization expert at ASU), their team is attempting to thwart this very sort of thing.

Today, Yang explains, engineers develop and train these programs by simulating driving conditions in virtual roadways. Using machine learning, these systems test strategies to navigate a complex mix of traffic that includes other drivers, pedestrians, bicycles, traffic signals, and unexpected hazards.

Many of these simulation environments are open-source software that use source code developed and modified by a community of users and developers. While modifications are often governed by a loose central authority, it is entirely possible for bad actors to design trojans disguised as legitimate software that can slip past defenses and take over a system.

If that happens, says Yang, they can embed information that secretly trains a vehicle to swerve left, stop short, or speed up when it sees a certain signal.

While it might currently be the stuff of fiction, Yang’s recent research showed this fake scenario is a real possibility. Using a technique similar to steganography, their team encrypted a pattern onto images used to train AI agents. While human eyes cannot not pick out this pattern, AI can — and does. Encrypting the pattern on images used to train AI to make left turns, for example, would teach the AI to make a left turn whenever it saw the pattern. Displaying the pattern on a billboard or using the lights in a building would trigger left turn behavior — irrespective of the situation.

"Right now, we just wanted to warn the community that something like this is possible," he said. "Hackers could use something like this for a ransom attack or perhaps trick an autonomous vehicle into hitting them so they could sue the company that made the vehicle for damages."

Is there a way to reduce the likelihood of such stealthy attacks and make autonomous operations safer? Yang says it’s possible by utilizing counterfactual reasoning. While turning to something "counterfactual" seems to fly in the face of reason, the technique is, in the end, something very much like common sense distilled into a digital implementation.

Active perception

Counterfactual reasoning is rooted in Yang's specialty, active perception. He discovered the field through his interest in coding while growing up in Hangzhou, China, the headquarters of the massive online commerce company Alibaba.

"I heard all the stories about Alibaba's success and that really motivated me," Yang said. "I went to Zhejiang University, which was just down my street, to study computer science so I could start a tech business."

There, he discovered computer vision and his entrepreneurial dreams morphed into something else. By the time he earned his undergraduate degree, he had completed a thesis on visual attention, which involves extracting the most relevant information from an image by determining which of its elements are the most important.

That led to a Ph.D. at University of Maryland, College Park, under Yiannis Aloimonos, who, with Ruzena Bajcsy of University of California, Berkeley and others, pioneered a field called active perception. Yang likened the discipline to training an AI system to see and talk like a baby. 

Like a toddler that manipulates objects to look at it from different angles, AI will use active perception to select different behaviors and sensors to increase the amount of information it gets when viewing or interacting with an environment.

Yang gave the following example: Imagine a robot in a room. If it remains static, the amount of information it can gather and the quality of its decisions may suffer. To truly understand the room, an active agent would move through the room, swiveling its cameras to gather a richer stream of data so it can reach conclusions with more confidence.

Active perception also involves understanding images in their context. Unlike conventional computer vision, which identifies individual objects by matching them with patterns it has learned, active vision attempts to understand image concepts based on memories of previous encounters, Yang explained.

Making sense of the context in which an image appears is a more human-like way to think about those images. Yang points to the small stools found in day care centers as an example. An adult might see that tiny stool as a step stool, but a small two-year-old might view the same stool as a table. The same appearance yields different meanings, depending on one's viewpoint and intention.

"If you want to put something on the stool, it becomes a table," Yang said. "If you want to reach up to get something, it becomes a step. If you want to block the road, it becomes a barrier. If we treat this as a pattern matching problem, that flavor is lost."

Counterfactual

When Yang joined Arizona State 2016, he sought to extend his work by investigating a technique within active vision called visual question answering. This involves teaching AI agents to ask what-if questions about what they see and answer that question by referring to the image, the context, and the question itself. Humans do this all the time.

"Imagine I'm looking at a person," Yang said. "I can ask myself if he is happy. Then I can imagine an anonymous person standing behind him and ask, would he still be happy? What if the smiling person had a snack in his hand? What if he had a broom? Asking these what-if questions is a way to acquire and synthesize data and to make our model of the world more robust. Eventually, it teaches us to predict things better."

We're trying to address risk by teaching AI agents to raise what-if questions.
Yezhou Yang

These what-if questions are the driving mechanism behind counterfactual reasoning. "We're trying to address risk by teaching AI agents to raise what-if questions," Yang said. "An agent should ask, 'What if I didn't see that pattern? Should I still turn left?’"

Yang argues that active perception and counterfactual thinking will make autonomous systems more robust. "Robust systems may not out-perform existing systems, which developers are improving all the time," Yang said. "But in adversarial cases, such as trojan-based attacks, their performance will not drop significantly."

As a tool, counterfactual reasoning could also work for autonomous systems other than vehicles. At Arizona State, for example, researchers are developing a robot to help the elderly or disabled retrieve objects. Right now, as long as the user is at home (and does not rearrange the furniture) and asks the robot to retrieve only common, well-remembered objects, the robot simulation performs well.

Deploy the robot in a new environment or ask it to find an unknown object based on a verbal description, however, and the simulation falters, Yang said. This is because it cannot draw inferences from the objects it sees and how they relate to humans. Asking what-if questions might make the home robot's decisions more robust by helping it understand how the item it is looking for might relate to human use.

Thwarting hackers

Yang noted that most training simulators accept only yes-or-no answers. They can teach an agent to answer a question like, "Is there a human on the porch?" But ask, "Is there a human and a chair on the porch?" and they stumble. They cannot envision the two things together.

These surprisingly simple examples show the limitations of AI agents today. Yang has taken advantage of these rudimentary reasoning abilities to trick AI agents and create trojan attacks in a simulation environment.

Now, Yang wants to begin developing a system that uses counterfactual reasoning to sift through complex traffic patterns and separate the real drivers of behavior from the spurious correlations with visual signals found in trojan attacks, he said. The AI would then either remove the trojan signal or ignore it.

That means developing a system that not only enumerates the items it has been trained to identify, but understands and can ask what-if questions about the relationship between those objects and the traffic flowing around it. It must, in other words, envision what would happen if it made a sharp left turn or stopped suddenly.

Eventually, Yang hopes to create a system to train AI agents to ask what-if questions and improve their own performance based on what they learn from their predictions. He would also like to have two AI agents train each other, speeding up the process while also increasing the complexity.

Even then, he is not planning to trust what those agents tell him. "AI is not perfect," he said. "We must always realize its shortcomings. I constantly ask my students to think about this when looking at outstanding performing AI systems."

Related content

US, CA, Santa Clara
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 Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an 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 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. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solutions 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: San Francisco, CA, USA | Santa Clara, CA, USA
US, NY, New York
We are looking for a motivated and experienced Senior Data Scientist with experience in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture with deep understanding in advertising businesses, to be part of a team of talented scientists and engineers to innovate, iterate, and solve real world problem with cutting-edge AWS technologies. In this role, you will take a leading role in defining the problem, innovating the ML/AI solutions, and information the tech roadmap. You will join a cross-functional, fun-loving team, working closely with scientists and engineers in a daily basis. You will innovate on behalf of our customers by prototyping, delivering functional proofs of concept (POCs), and partnering with our engineers to productize and scale successful POCs. If you are passionate about creating the future, come join us as we have fun, and make history. Key job responsibilities - Define and execute a research & development roadmap that drives data-informed decision making for marketers and advertisers - Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions - Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale - Partner with product managers and stakeholders to define forward-looking product visions and prospective business use cases - Drive and lead of culture of data-driven innovations within and outside across Amazon Ads Marketing orgs About the team Marketing Decision Science provides science products to enable Amazon Ads Marketing to deliver relevant and compelling guidance across marketing channels to prospective and active advertisers for success on Amazon. We own the product, technology and deployment roadmap for AI- and analytics-powered products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right team to help them make the data-informed advertising decisions. Our science-based products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Seattle
Are you excited about developing models to revolutionize automation, robotics and computer vision? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for scientists, engineers and program managers for a variety of roles. The Amazon Robotics software team is seeking a collaborative Applied Scientist to focus on computer vision machine learning models. This includes building multi-viewpoint and time-series computer vision systems. It includes building large-scale models using data from many different tasks and scenes. This work spans from basic research such as cross domain training, to experimenting on prototype in the lab, to running wide-scale A/B tests on robots in our facilities. Key job responsibilities * Research vision - Where should we be focusing our efforts * Research delivery – Proving/dis-proving strategies in offline data or in the lab * Production studies - Insights from production data or ad-hoc experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
Looking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities The Senior Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy. About the team NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. 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 our sensitive contents detection and mitigation. 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. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. We are open to hiring candidates to work out of one of the following locations: Bangalore, KA, IND
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 extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are 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 imagine 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. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. We are open to hiring candidates to work out of one of the following locations: London, GBR
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. We are open to hiring candidates to work out of one of the following locations: London, GBR