Building Trustworthy AI for Adversarial Environments

Building Trustworthy AI for Adversarial Environments

Table of Contents

  1. Introduction
  2. The Advancements in AI-Based Systems
    1. Machine Learning-Based Systems
    2. Generative Adversarial Networks
    3. Deep Speech Two
    4. AlphaGo and AlphaGo Zero
    5. Libratus and Pluribus
    6. Self-Driving Cars
    7. GPT-3 and Large Language Models
    8. AlphaFold and Protein Folding
  3. The Challenge of Trustworthy AI
  4. Industry vs. DOD Efforts
  5. Building Trustworthy AI: Theory, Engineering, and Human Context
    1. Theory: Understanding the Limits and Challenges
    2. Engineering: Developing Robust and Reliable Systems
    3. Human Context: Interacting and Coordinating with Humans
  6. DARPA's Efforts in Building Trust
    1. The GUARD Program: Defending Against Adversarial Attacks
    2. The ANSWER Program: Advancing the Third Wave of AI
    3. The Machine Common Sense Program: Building Robotic Systems with Proprioception
    4. The Symbiotic Design for Cyber-Physical Systems Program: Assisting in Engineering Unmanned Underwater Vehicles
    5. The ITM Program: Developing Adaptable and Resilient Autonomous Systems
  7. Bridging the Gap Between Industry and DOD's Needs
  8. Addressing the Unsustainability of Computing Resources in AI
  9. Prioritizing DARPA's Resources
  10. Conclusion

Artificial Intelligence and Trustworthy AI: Advancements, Challenges, and DARPA's Efforts

Artificial Intelligence (AI) has witnessed remarkable advancements in the past decade, thanks to machine learning-based systems that analyze extensive amounts of data to identify Patterns. This breakthrough has opened up possibilities for AI to be utilized in various domains, such as media synthesis, speech recognition, game playing, self-driving cars, language processing, and even protein folding.

However, with these advancements come the pressing need for trustworthy AI. Despite the remarkable progress, there are concerns about the reliability and ethical implications of AI systems. The Defense Advanced Research Projects Agency (DARPA) recognizes these challenges and aims to address them by promoting the development of trustworthy AI systems that can be relied upon in critical applications.

The Advancements in AI-Based Systems

Machine Learning-Based Systems

Machine learning-based systems have been at the forefront of the AI revolution. By utilizing large amounts of data and compute-intensive algorithms, these systems have achieved unprecedented capabilities. One notable example is generative adversarial networks (GANs), which generate realistic media, such as images of people. The ability to Create indistinguishable images of people has sparked the media synthesis revolution.

Deep Speech Two

Deep Speech Two introduced a neural net-based approach to speech recognition, replacing the traditional pipeline of HAND-engineered stages. This new pipeline enabled better speech recognition, even in the presence of accents, background noise, and different languages. The system was trained to understand raw audio and produce accurate transcriptions, surpassing previous methods.

AlphaGo and AlphaGo Zero

AlphaGo made headlines in 2016 by defeating the world champion Go player, Lee Sedol. Go is an ancient game with a complex state space, making it challenging for computers to compete. AlphaGo's success was a breakthrough, demonstrating that AI systems could excel in strategic and complex games. AlphaGo Zero, an enhanced version, surpassed its predecessor by using reinforcement learning to train itself, achieving unprecedented performance.

Libratus and Pluribus

Libratus, developed by Carnegie Mellon University, defeated human experts in poker. This achievement showcased the ability of AI systems to outperform humans in an imperfect information game. Pluribus, another AI poker player, took it a step further by beating a team of human professionals, demonstrating the adaptability and effectiveness of AI in complex decision-making scenarios.

Self-Driving Cars

Tesla's advancements in self-driving cars have showcased the potential of AI in transportation. While self-driving technology has improved, challenges remain, such as unpredictable failures and the need for human drivers to take responsibility. Tesla's Autopilot, for example, occasionally crashes into emergency vehicles, highlighting the importance of ensuring the predictability and reliability of AI systems.

GPT-3 and Large Language Models

GPT-3, a large language model, has gained Attention for its ability to generate Fluent and coherent text. It has demonstrated state-of-the-art performance in natural language processing tasks, including translation and question answering. However, the model's success is primarily due to its ability to generate text that convincingly mimics human conversation, rather than deep understanding of the context or content.

AlphaFold and Protein Folding

DeepMind's AlphaFold made significant strides in the field of biology by solving a 50-year-old problem related to protein folding. Proteins are described by sequences of amino acids, but understanding their intricate three-dimensional structure is crucial for studying their interactions within biological systems. AlphaFold's predictions provided laboratory-level accuracy, opening doors for revolutionary advancements in biological science.

The Challenge of Trustworthy AI

Despite these remarkable achievements, the question remains: is AI on track to be trustworthy? The answer, unfortunately, is no. Several challenges, including the unpredictability of self-driving cars, the limitations of large language models, the vulnerability to adversarial attacks, and the ethical implications of AI decision-making, hinder the trustworthiness of AI systems.

Industry vs. DOD Efforts

While industry has made significant strides in developing AI technologies, their motivations primarily revolve around commercial success and competition. In contrast, the Department of Defense (DOD) is concerned with national security and protecting the country from potential threats. Therefore, relying solely on industry's efforts may not fully address the requirements of trustworthy AI in critical defense applications.

Building Trustworthy AI: Theory, Engineering, and Human Context

Addressing the challenge of trustworthy AI requires a multifaceted approach. DARPA's initiatives encompass three key thrusts: theory, engineering, and the human context.

Theory: Understanding the Limits and Challenges

Developing a theoretical understanding of AI systems' capabilities and limitations is crucial. This involves identifying problems that are particularly challenging for AI, such as reasoning with uncertainty and partial information, and determining the potential of low-data regimes and adversarial environments. By establishing theoretical foundations, it becomes possible to build AI systems that are both trustworthy and efficient.

Engineering: Developing Robust and Reliable Systems

Engineers play a vital role in building trustworthy AI systems. Using principles derived from theoretical insights, engineers can develop models and algorithms that are robust, reliable, and efficient. By leveraging innovations in hardware and software, engineers can enhance the performance and integrity of AI systems, reducing the risk of failures and vulnerabilities.

Human Context: Interacting and Coordinating with Humans

Creating AI systems that seamlessly Interact and coordinate with humans is crucial for trustworthiness. AI systems need to understand human intentions, emotions, and cultural references to facilitate effective communication and collaboration. Moreover, these systems must possess ethical frameworks and reasoning capabilities to make sound judgments aligned with human values and goals.

DARPA's Efforts in Building Trust

DARPA has launched several programs to develop trustworthy AI systems and address the challenges associated with their development:

  1. The GUARD Program: Defending Against Adversarial Attacks - This program aims to enhance the resilience of AI systems to adversarial attacks, such as image manipulations that deceive classifiers. By developing defensive mechanisms, DARPA aims to ensure the robustness and reliability of AI systems in real-world scenarios.

  2. The ANSWER Program: Advancing the Third Wave of AI - This program focuses on combining symbolic reasoning with statistical machine learning techniques to achieve the best of both worlds. By leveraging symbolic approaches for explainability and reasoning, and statistical techniques for flexibility and learning, DARPA aims to develop AI systems that can be trusted and understood.

  3. The Machine Common Sense Program: Building Robotic Systems with Proprioception - This program aims to develop robotic systems that possess proprioception, i.e., self-awareness of their own states and positions. By utilizing internal sensors and simulation-based training, these systems can adapt to unpredictable situations and perform complex tasks.

  4. The Symbiotic Design for Cyber-Physical Systems Program: Assisting in Engineering Unmanned Underwater Vehicles - This program aims to assist engineering teams in designing unmanned underwater vehicles (UUVs) capable of operating in challenging environments. By leveraging AI to explore design possibilities and predict system performance, DARPA aims to enhance the efficiency and reliability of UUVs.

  5. The ITM Program: Developing Adaptable and Resilient Autonomous Systems - This program focuses on developing autonomous systems that can reason, adapt, and make complex decisions without a single right answer. DARPA aims to study human decision-making in complex scenarios, model human values and decision-making processes, and embed them in AI systems to ensure alignment and trustworthiness.

Bridging the Gap Between Industry and DOD's Needs

DARPA actively collaborates with industry to bridge the gap between industry efforts and the defense department's needs. DARPA works with both large companies and small businesses, fostering partnerships and providing funding opportunities to support research and development initiatives. Initiatives such as the Embedded Entrepreneurship and Small Business Innovation Research (SBIR) programs further encourage collaboration and innovation.

Addressing the Unsustainability of Computing Resources in AI

As the demand for AI systems grows, concerns regarding the sustainability of computing resources arise. DARPA acknowledges the necessity of innovative approaches to effectively utilize resources. This involves finding ways to reduce resource requirements and leveraging models that achieve comparable results with fewer parameters. By optimizing resource usage, DARPA aims to ensure the long-term viability and scalability of AI systems.

Prioritizing DARPA's Resources

DARPA prioritizes its resources based on the potential impact, urgency, and significance of each initiative. Program managers play a vital role in driving the prioritization process, advocating for initiatives that demonstrate high-value returns and Align with DARPA's mission. DARPA assesses the potential return on investment, considering the potential for revolutionary advancements in technology and their relevance to national security.

Conclusion

Building trustworthy AI systems is a fundamental concern for DARPA, given the rapid advancements and potential implications of AI. By embracing a multidisciplinary approach, encompassing theory, engineering, and the human context, DARPA aims to address the challenges and promote the development of AI systems that are reliable, ethical, and aligned with human values. Collaborations with industry and academia are crucial in achieving this mission, and DARPA actively seeks partnerships to drive innovation and ensure the creation of trustworthy AI systems.

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