In today's rapidly evolving technological landscape, the reliable, secure, and transparent operation of AI is no longer a luxury but a fundamental requirement for enterprise success. This conference is meticulously designed for enterprise AI professionals, including data scientists, machine learning engineers, AI architects, IT decision-makers, and business leaders, to provide the essential knowledge and actionable strategies for designing, developing, and maintaining AI systems that meet the highest standards of reliability, security, and trustworthiness, drawing inspiration from "Mil-Spec" principles. Over the next three days, we will delve into the critical aspects of engineering unwavering AI reliability, fortifying systems against adversity, ensuring inherent trust, and operationalizing these principles for future-proof deployments.

Primary Audience for Conference Attendees

Core AI & Data Practitioners:

• Data Scientists & Analysts: Professionals working with large datasets, machine learning, and AI, seeking to understand the impact of new AI platforms, advanced analytics tools, and the reliability/trustworthiness aspects of AI in mission-critical contexts.

• AI/ML Engineers & Developers: Individuals building and deploying AI models, interested in the latest advancements in MLOps, robust model development, adversarial resilience, and the integration of AI into specialized, high-stakes domains.

• AI Architects: Those responsible for designing the overall structure and infrastructure of AI systems, with a keen interest in resilient architectures, distributed systems, security protocols, and scalability for mission-critical applications.

Enterprise & Business Leaders:

• IT Decision-Makers (CIOs, CTOs, VP of IT): Executives responsible for technological strategy and infrastructure, focused on the operational stability, security, and governance of AI deployments within their organizations.

• Business Leaders (CEOs, VPs, Directors of Business Units): Leaders seeking to understand how to leverage AI for core business processes, the risks associated with AI, and how to ensure AI systems deliver reliable and trustworthy outcomes that align with business objectives and regulatory requirements.

• Heads of Digital Transformation: Leaders driving the adoption of new technologies across the enterprise, specifically interested in the practicalities and challenges of integrating mission-critical AI.

• Project Managers (AI/ML/Data Science): Professionals overseeing AI projects, focusing on best practices for project execution, risk management, and ensuring the delivery of reliable and trustworthy AI solutions.

Governance, Risk & Compliance (GRC):

• Technology Ethicists & Policy Makers: Researchers, legal professionals, government officials, and policymakers focused on the societal impact, ethical implications, data privacy, and regulatory frameworks for emerging AI technologies, specifically responsible AI, explainability, and bias mitigation in critical systems.

• Risk Management Professionals: Individuals responsible for identifying, assessing, and mitigating risks within an organization, particularly those related to AI failures, security breaches, and ethical concerns.

• Compliance Officers: Professionals ensuring adherence to industry regulations and standards, with a focus on AI governance, auditability, and ethical guidelines in sensitive domains (e.g., finance, healthcare).

• Auditors (Internal & External): Professionals needing to understand AI system design, data provenance, model explainability, and operational controls to ensure compliance and accountability.

Specialized Domain Experts:

• Defense & Security Analysts: Professionals from national security, intelligence, and defense sectors interested in the implications of AI for mission-critical operations, cybersecurity, autonomous systems, and maintaining AI integrity in contested environments.

• Financial Services AI Leads: Experts from banking, trading, and insurance focusing on AI in high-frequency transactions, fraud detection, risk assessment, and regulatory compliance, where reliability and accuracy are paramount.

• Healthcare AI Specialists: Professionals involved in deploying AI for diagnostics, patient care, drug discovery, and medical imaging, with a critical need for explainability, accuracy, and unwavering availability.

• Manufacturing & Industrial AI Engineers: Specialists applying AI to autonomous manufacturing, supply chain optimization, and predictive maintenance in industrial settings, emphasizing continuous operation and safety.

• Robotics & Autonomous Systems Engineers: Specialists in autonomous vehicle technology, industrial robotics, and other critical autonomous systems, focusing on the resilience, safety, and trustworthiness of embedded AI.

Research & Development:

• Academics & Researchers: Professors, post-doctoral researchers, and graduate students across computer science, engineering, AI, ethics, law, and social sciences, seeking to present their latest findings, engage in interdisciplinary discussions, and identify new research avenues related to resilient and trustworthy AI.

• R&D Directors & Innovation Leaders: Executives and managers responsible for strategic technology adoption, R&D roadmaps, and identifying future AI areas for their organizations, particularly those focused on long-term stability and ethical deployment.

Infrastructure & Operations:

• IT & Cloud Architects: Professionals designing and managing data infrastructure, interested in scalable platforms, multi-cloud strategies, and robust infrastructure for deploying and monitoring mission-critical AI and emerging technologies.

• DevOps/MLOps Engineers: Specialists in automating the AI lifecycle, including continuous integration, deployment, monitoring, and ensuring the operational resilience and maintainability of AI systems.

Investment & Media:

• Venture Capitalists & Investors: Individuals and firms looking for insights into the next wave of disruptive AI technologies, focusing on market potential, technological maturity, and the inherent risks and opportunities in mission-critical AI solutions.

• Journalists & Tech Writers: Media professionals specializing in technology, science, and societal impact, seeking to deepen their understanding of complex emerging AI topics, particularly regarding the challenges and solutions for building reliable and ethical AI.

Main Conference Subject Areas

Building truly resilient, trustworthy, and mission-critical AI systems involves adopting a comprehensive approach that prioritizes reliability, robustness, and security.

1. Enhanced Reliability and Uptime

Enhanced reliability and uptime for AI systems means pushing beyond standard "five nines" availability to ensure continuous operation, even in extreme conditions. This involves designing systems that can withstand failures, self-heal, and proactively address potential issues, much like military-grade technology. In an enterprise setting, this translates to AI systems that consistently drive core business processes without interruption, ensuring critical workflows remain operational.

1.1 Mil-Spec Focus: Designed for Continuous Operation in Extreme Conditions, Where Failure is Not an Option

The military-grade focus on continuous operation under extreme conditions serves as a blueprint for building AI systems that are inherently stable and reliable. This means engineering AI to perform flawlessly in high-stakes environments, such as controlling critical infrastructure or operating autonomous vehicles, where any downtime or malfunction could have catastrophic consequences. The principles gleaned from this approach inform the design of AI that prioritizes unwavering performance and immediate response capabilities.

1.1.1 Enterprise Application: Mission-Critical Workflows

For enterprises, this means AI driving core business processes like financial transaction processing, fraud detection, supply chain optimization, autonomous manufacturing robots, or critical healthcare diagnostics. These applications demand uninterrupted service and absolute accuracy, making the AI system’s reliability a direct determinant of business continuity and success. The integration of AI into these vital functions necessitates a design philosophy that mirrors the stringent demands of military specifications, ensuring that the AI can consistently deliver under pressure.

1.1.2 Redundancy and Failover

Implementing active-active or active-passive AI model deployments, geographically distributed systems, and robust failover mechanisms are essential to ensure continuous operation even if a server or data center goes down. This multi-layered approach to redundancy provides critical safeguards, ensuring that if one component or location experiences an issue, another is ready to seamlessly take over without any disruption to the AI's functionality. This proactive design minimizes the risk of single points of failure, crucial for mission-critical applications.

1.1.3 Self-Healing Systems

Designing AI infrastructure that can automatically detect and recover from errors or performance degradation minimizes human intervention and maximizes uptime. These self-healing capabilities allow the AI system to autonomously identify and resolve internal inconsistencies or operational slowdowns, maintaining optimal performance without constant oversight. This leads to more robust and autonomous AI deployments, significantly reducing the need for manual troubleshooting and enabling AI to operate with greater independence and resilience.

1.2 Predictive Maintenance for AI Infrastructure

Predictive maintenance for AI infrastructure involves using AI to monitor the health of the underlying hardware and software that supports AI models, anticipating potential failures before they occur. This proactive approach helps prevent unexpected downtime, ensuring that the AI systems remain operational and continue to deliver consistent performance. By leveraging AI to oversee its own foundational components, organizations can significantly enhance the stability and longevity of their AI deployments.

2. Robustness to Data Anomalies and Adversarial Attacks

Ensuring robustness to data anomalies and adversarial attacks is paramount for AI systems that need to be trustworthy and mission-critical. This involves building AI that can not only identify and filter out corrupted or unusual data but also defend against malicious attempts to manipulate its behavior. This multi-faceted defense ensures the integrity of AI decisions and prevents vulnerabilities that could be exploited in real-world scenarios.

2.1 Mil-Spec Focus: Resilient to Sensor Noise, Jamming, and Intentional Attacks

The military-grade focus on resilience to sensor noise, jamming, and intentional attacks provides a critical framework for designing AI systems capable of operating under compromised conditions. This principle emphasizes the need for AI to maintain its functionality and accuracy even when confronted with distorted or malicious inputs. Such resilience is vital for any AI system deployed in unpredictable or hostile environments, ensuring it can process information reliably despite deliberate interference or environmental challenges.

2.1.1 Enterprise Application: Data Validation and Cleansing

Implementing highly stringent data validation pipelines to identify and correct anomalies, outliers, and corrupted data before it feeds into AI models prevents "garbage in, garbage out" scenarios. This rigorous preprocessing ensures that AI models are trained and operate on clean, accurate data, which is fundamental to maintaining their reliability and trustworthiness. By meticulously validating and cleansing data, organizations can significantly reduce the risk of incorrect outputs and flawed decisions from their AI systems.

2.1.2 Adversarial Robustness

Training AI models to be resistant to adversarial attacks, such as subtle changes to input data that fool the model, is crucial to prevent misuse for fraud, data manipulation, or denial-of-service in a business context. This defense mechanism hardens AI against sophisticated attempts to compromise its integrity, ensuring that its decisions remain reliable even when confronted with malicious inputs. By proactively building in adversarial robustness, organizations can safeguard their AI systems from exploitation and maintain trust in their automated processes.

2.1.3 Model Drift Detection

Continuously monitoring AI model performance for "drift," where the model's accuracy degrades over time due to changes in real-world data distribution, and automatically triggering retraining or recalibration is vital for maintaining accuracy. This proactive detection and adjustment mechanism ensures that AI models remain relevant and effective as the data they process evolves. By quickly identifying and addressing model drift, organizations can ensure their AI systems consistently deliver optimal performance and reliable outcomes.

2.2 Bias Mitigation

Rigorous testing and ongoing monitoring for algorithmic bias are essential to ensure fairness and prevent discriminatory outcomes in critical applications like hiring, loan approvals, or customer targeting. This commitment to bias mitigation helps build ethical AI systems that treat all individuals equitably, fostering trust and complying with regulatory requirements. By actively addressing potential biases, organizations can deploy AI that supports inclusive practices and avoids perpetuating or amplifying societal inequalities.

3. Stringent Security and Data Integrity

Stringent security and data integrity are fundamental pillars for building truly mission-critical AI systems, mirroring the exacting standards found in military applications. This encompasses a holistic approach to protecting all aspects of the AI lifecycle, from data at rest and in transit to the models themselves, ensuring unauthorized access and manipulation are prevented. Implementing these measures guarantees that AI systems are not only robust in their function but also impenetrable to cyber threats, safeguarding sensitive information and maintaining operational credibility.

3.1 Mil-Spec Focus: Protecting Sensitive Information and Operational Capabilities from Cyber Threats and Unauthorized Access

The military-grade focus on protecting sensitive information and operational capabilities from cyber threats and unauthorized access sets the benchmark for AI security. This principle emphasizes the imperative to safeguard every component of an AI system, from its data to its decision-making logic, against external attacks and internal compromises. By adopting this rigorous security posture, organizations can ensure that their AI systems are fortified against espionage, sabotage, and data breaches, preserving both their integrity and their strategic value.

3.1.1 Enterprise Application: Data Encryption (at rest and in transit)

Encrypting all data used by AI models, from training data to inferences, both when stored and when moving across networks, is a foundational security measure. This ensures that even if unauthorized access occurs, the data remains unreadable and unusable without the proper decryption keys. By implementing robust encryption protocols, organizations can significantly reduce the risk of data breaches and maintain the confidentiality of sensitive information processed by their AI systems.

3.1.2 Access Control and Authentication

Implementing granular access controls and multi-factor authentication for AI models, data, and pipelines ensures only authorized personnel and systems can interact with them. This layered security approach meticulously regulates who can access, modify, or deploy AI components, thereby preventing unauthorized operations and maintaining the integrity of the AI system. Strong access controls are crucial for safeguarding intellectual property and preventing malicious tampering with critical AI functions.

3.1.3 Supply Chain Security for AI

Verifying the security of all components, libraries, and pre-trained models used in AI development is essential to prevent hidden vulnerabilities or malicious code from being introduced. This meticulous approach to supply chain security minimizes the risk of backdoors or compromised elements being embedded within the AI system during its development. By ensuring the integrity of every part of the AI supply chain, organizations can build trusted systems free from hidden threats.

3.2 Audit Trails and Logging

Comprehensive logging of all AI activities, decisions, and data access is crucial for auditability, compliance, and post-incident analysis. These detailed audit trails provide a transparent record of how the AI system operates, enabling organizations to trace back actions, identify anomalies, and demonstrate adherence to regulatory requirements. This capability is invaluable for debugging issues, validating AI decisions, and ensuring accountability in mission-critical applications.

Experiencing Alta/Snowbird

Alta and Snowbird are two world-renowned ski resorts located in the Wasatch Mountains of Utah, connected by a single lift ticket. They are known for their exceptional snowfall, challenging terrain, and breathtaking scenery.

Alta is a historic ski area known for its steep slopes and deep powder snow. It's a popular destination for advanced skiers and snowboarders. Alta is also known for its no-frills atmosphere and its commitment to preserving the traditional skiing experience.

Snowbird is a larger resort offering a wider variety of terrain, including beginner and intermediate slopes. It's also home to the Cliff Lodge, a luxurious hotel with stunning mountain views. Snowbird is popular for families and groups of friends.

Both resorts are part of the Cottonwood Canyons, renowned for incredible skiing and snowboarding conditions. They are just a few miles apart and easily accessible from Salt Lake City International Airport, a short 45-minute drive away. 
 

Here are some cool adventures:

Heli-Skiing Adventure: Experience the ultimate powder skiing adventure with a helicopter tour to remote, pristine snowfields.

Hiking: Explore the stunning alpine scenery on trails ranging from easy to challenging.

Mountain Biking: Tackle thrilling downhill trails or cruise on scenic cross-country routes.

Scenic Tram Rides: Soar to the summit of Hidden Peak for breathtaking views of the Wasatch Mountains.

Alpine Slide: Race down the mountain on a thrilling alpine slide.

Wildflower Viewing: Witness the vibrant beauty of wildflowers in bloom throughout the canyons.

Heli-Skiing Adventure (Optional)

Experience the ultimate powder skiing adventure with a helicopter tour to remote, pristine snowfields.

Join us for a thrilling day of heli-skiing. We will fly you to remote, pristine snowfields where you will experience the ultimate powder skiing adventure. Our experienced guides will lead you to the best terrain, ensuring a safe and unforgettable experience.

Call for Presentations & Papers

The Data Science Association (DSA) invites submissions of original research papers, innovative presentations, and interactive workshops for the "Building Resilient, Trustworthy, and Mission-Critical AI Systems" conference. This is a premier platform for leading experts, researchers, and practitioners to disseminate groundbreaking findings, share practical experiences, and foster collaborative advancements in the field of enterprise AI. We specifically seek contributions that illuminate the path towards developing AI systems with unwavering reliability, robust security, and inherent trustworthiness, drawing inspiration from "Mil-Spec" principles.

We'll move past theoretical discussions to focus on the engineering discipline required to deploy AI in high-stakes environments where failure is not an option.

We invite you to contribute your expertise and join a community dedicated to building trustworthy, enterprise-grade AI. We are seeking compelling presentations, insightful papers, interactive workshops, and concise lightning talks that showcase original research, practical implementations, and forward-looking analyses.

Enhanced Reliability and Uptime

This track focuses on pushing AI system reliability beyond standard industry benchmarks, drawing inspiration from military-grade (mil-spec) technology. We encourage submissions on:

• Mil-Spec Focus: Continuous Operation in Extreme Conditions:
•  
  • The engineering principles for designing AI systems that perform flawlessly in high-stakes environments.
  • Strategies for creating AI that prioritizes unwavering performance and immediate response capabilities.
•  
• Enterprise Application: Mission-Critical Workflows:
•  
  • Case studies of AI driving core business processes like financial transaction processing, supply chain optimization, and critical healthcare diagnostics.
  • Discussions on how the reliability of an AI system directly impacts business continuity and success.
•  
• Redundancy and Failover:
•  
  • Implementing active-active or active-passive AI model deployments and geographically distributed systems to ensure continuous operation.
  • Best practices for designing robust failover mechanisms to eliminate single points of failure.
•  
• Self-Healing Systems:
•  
  • Designing AI infrastructure that can automatically detect and recover from errors or performance degradation.
  • Techniques for building autonomous AI deployments that minimize human intervention.
•  
• Predictive Maintenance for AI Infrastructure:
•  
  • Using AI to monitor the health of underlying hardware and software to anticipate and prevent failures before they occur.

Robustness to Data Anomalies and Adversarial Attacks

This track addresses the critical need for AI systems to be resilient against corrupted data and malicious manipulation, a core tenet of building trustworthy AI. We are particularly interested in submissions on:

• Mil-Spec Focus: Resilient to Sensor Noise and Intentional Attacks:
•  
  • Frameworks for designing AI systems that maintain functionality and accuracy even when confronted with distorted or malicious inputs.
•  
• Enterprise Application: Data Validation and Cleansing:
•  
  • Implementing stringent data validation pipelines to identify and correct anomalies, outliers, and corrupted data.
  • Methodologies for ensuring AI models are trained and operated on clean, accurate data.
•  
• Adversarial Robustness:
•  
  • Training AI models to be resistant to adversarial attacks, such as subtle changes to input data that can lead to misuse.
  • Defenses to prevent fraud, data manipulation, or denial-of-service in a business context.
•  
• Model Drift Detection:
•  
  • Continuously monitoring AI model performance to detect "drift," where accuracy degrades over time due to changes in real-world data, and automatically triggering retraining.
•  
• Bias Mitigation:
•  
  • Rigorous testing and ongoing monitoring for algorithmic bias to ensure fairness and prevent discriminatory outcomes in critical applications.

Stringent Security and Data Integrity

This track will explore a holistic approach to protecting the entire AI lifecycle, ensuring that systems are not only robust in function but also impenetrable to cyber threats. We welcome submissions on:

• Mil-Spec Focus: Protecting Sensitive Information and Operational Capabilities:
•  
  • Strategies for safeguarding every component of an AI system—from data to decision-making logic—against cyber threats, espionage, and sabotage.
•  
• Enterprise Application: Data Encryption (at rest and in transit):
•  
  • Implementing robust encryption protocols for all data used by AI models to maintain confidentiality.
•  
• Access Control and Authentication:
•  
  • Implementing granular access controls and multi-factor authentication to ensure only authorized personnel and systems can interact with AI models and data.
•  
• Supply Chain Security for AI:
•  
  • Verifying the security of all components, libraries, and pre-trained models used in AI development to prevent hidden vulnerabilities.
•  
• Audit Trails and Logging:
•  
  • Comprehensive logging of all AI activities, decisions, and data access for auditability, compliance, and post-incident analysis.

Submission Types

We invite diverse contributions to enrich our program:

• Oral Presentations (20 minutes): Share your research findings, innovative applications, or case studies in a focused presentation.
•  
• Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that will undergo a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
•  
• Experience & Insight Papers (4-6 pages, formatted for readability): This category is for practitioners, industry leaders, and security experts to share valuable lessons learned, practical implementations, and insightful perspectives on the challenges and successes of engineering mission-critical AI. Submissions will be peer-reviewed for clarity, relevance, and practical value.
•  
• Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.
•  
• Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within the field of secure and reliable AI.
•  
• Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to AI reliability, robustness, or security.

Submission Guidelines

• Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
• Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
• Keywords: Provide 3-5 relevant keywords that best describe your submission.
• Originality: Submissions must represent original work that has not been previously published or is not currently under review elsewhere.
• Audience Consideration: Presenters should be prepared to convey complex technical or theoretical concepts clearly to a diverse audience, including both technical and non-technical attendees.
• Formatting: Specific formatting guidelines for full papers will be provided upon the submission portal opening.

Review Process

All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in AI engineering, cybersecurity, and data science. Submissions will be evaluated based on:

• Relevance to conference themes
• Originality and novelty of contributions
• Technical merit and soundness (for technical papers)
• Clarity, organization, and presentation quality
• Potential impact and practical applicability

We look forward to your valuable contributions and to a stimulating and collaborative conference!