Building remarkable functionality

Artificial intelligence is disrupting the field of engineering. We are leading this transformation by inventing AI-orchestrated systems.

This shift is driving the creation of the new discipline of AI Engineering at Carnegie Mellon. These systems are developed by simultaneously designing a system’s functionality, including both its AI algorithms and the platform on which the AI runs, to create powerful systems that are more adaptable, resilient, and trustworthy.

AI Engineering principles

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We invent AI algorithms that leverage the constraints of the systems they are embedded in to run better and faster.

These systems are built to be more adaptable, resilient, and trustworthy.

Realizing the potential of AI depends not just upon new advances in AI algorithms and technology—it depends critically on the engineering of AI into systems.

  • AI must be engineered in from the start, not bolted on, for it to be effective.
  • Domain expertise is key and provides a basis for developing AI algorithms and technology that use system characteristics to work dramatically better
  • Solutions must be developed within ethical constraints that ensure system scalability, efficiency, robustness, equity, and trustworthiness

AI Engineering pillars

Engineering of AI mechanisms

  • Engineering compute, sensors, architectural constructs, specialized algorithms and software to orchestrate AI into systems

AI in engineered systems

  • Build AI-orchestrated products and services that are more functional, safe, secure, and resilient

Redefining the engineering discipline with AI

  • Much as computer-aided design and modeling did in the past, AI is the process of reshaping the way engineers work and the way they approach problems.

Teaching AI Engineering

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Core areas of AI research

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Generative AI: There will continue to be a significant evolution in generative AI and the way it is developed to integrate with systems. Engineered systems need to provide constraints on system behavior, including trust, safety, resiliency, and responsibility.

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Autonomous physical systems: AI will enable radical transformation in our cyber and physical worlds. As we advance toward the promise of autonomous vehicles, our engineers are addressing today’s traffic woes by leveraging AI to combine multiple data streams into one automated system that predicts traffic delays and alleviates congestion proactively. These revolutionary changes also come with risk. We are preparing future engineers to not only use AI as a tool for problem solving, but also to solve the challenges that arise with the integration of AI itself.

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Future of manufacturing: Carnegie Mellon University is combining its strengths in AI and machine learning with metals additive manufacturing to make the technology viable to scale up for industry. The complex functional mapping of AI is an avenue to solve the challenges around speeding up printing, predicting and preventing defects, and allowing printing to occur around the clock.

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New device connectivity: CMU Engineering researchers are designing next-generation intelligent edge networks that are efficient, reliable, robust, and secure. They are exploring tools and techniques to ensure that wireless edge networks are self-healing and self-optimized. These networks will make AI more efficient, interactive, and privacy-preserving for applications in sectors such as intelligent transportation, remote health care, distributed robotics, and smart aerospace.

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Human life assistance: AI has already unlocked new opportunities to improve human lives. Engineering researchers are exploring its application to offer advances in human health for bioprinting, cancer treatment, drug delivery, precision rehabilitation, understanding disease, improving diagnosis, detecting viruses and antibodies, predicting pregnancy complications, enhancing human capabilities, and better understanding our brains and decision-making.

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Energy and environment: In addition to health and medical applications, our researchers are using artificial intelligence to solve pressing challenges such as increasing energy and water needs. They are leveraging AI to search for catalysts for renewable energy, materials discovery for safer batteries and nuclear materials, and developing efficient and effective water desalination techniques.

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Cybersecurity: Researchers are focused on advancing research in machine learning and artificial intelligence (AI), in which computers can “learn” trends from massive collections of data. This research is being conducted and tested in various applications ranging from facial recognition to systems that can autonomously find and fix software bugs before they are exploited.

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Human+AI design: Big design problems require creative and exploratory decision making, a skill at which humans excel. Engineers have traditionally applied AI to a problem within a defined set of rules. However, CMU researchers are applying an AI framework that learns human design strategies through observation of human data to generate new designs without explicit goal information, bias, or guidance. Human+AI design employs AI as a tool, a manager, or a partner in the design process based on which mode is best for the problem.

Artificial intelligence videos