DeveloperWeek 2025

Mithun A. Sridharan, Think Insights, Founder

A principles-based framework for AI governance provides a compass to navigate the ethical and responsible development and use of Artificial Intelligence. Unlike rigid rulebooks, these frameworks establish core principles that guide decision-making throughout the AI lifecycle to address the unique challenges posed by different AI systems.

Several prominent examples of principles-based frameworks exist. The European Union's Ethics Guidelines for Trustworthy AI, the Bletchley Declaration, SR 117, and ISO/IEC 42001 are a few frameworks that provide a comprehensive set of principles for organizations to manage AI throughout its lifecycle.

The principles-based approaches provide a strong foundation for navigating the complexities of AI development and implementation. As AI technology continues to evolve, these frameworks can be revisited and refined to address new challenges. By focusing on core ethical principles, they ensure that AI development remains responsible and human-centric. 

Shambhavi Srivastava, Appen, AI Solutions Architect

Data annotation involves assigning relevant information to raw data to enhance machine learning (ML) model performance. While this process is crucial, it can be time-consuming and expensive. The emergence of Large Language Models (LLMs) offers a unique opportunity to automate data annotation. However, the complexity of data annotation, stemming from unclear task instructions and subjective human judgment on equivocal data points, presents challenges that are not immediately apparent.

In this session, Chris Stephens, Field CTO and Head of AI Solutions at Appen will provide a overview of an experiment that the company recently conducted to test the tradeoff between quality and cost of training ML models via LLMs vs human input. Their goal was to differentiate between utterances that could be confidently annotated by LLMs, and those that required human intervention. This differentiation was crucial to ensure a diverse range of opinions or to prevent incorrect responses from overly general models. Chris will walk audience members through the dataset used as well as methodology for the experiment, as well as the company’s research findings.

 

Parth Shukla, Cequence Security, Security Analyst
Khyati Ganatra, Cequence Security,  Manager, Applied ML

In the presentation titled "AI Frontiers: Shielding Digital Gateways from Bot Invasions," we delve into the forefront of cyber defense against bot-driven threats that exploit API vulnerabilities. This comprehensive study explores how advanced AI and ML models are being harnessed to fortify digital defenses, offering a detailed analysis of API communication patterns and the evolving landscape of bot attacks. Through a series of real-world case studies, we illuminate the mechanisms of sophisticated bot strategies—ranging from data breaches and account takeovers to shopping bots that deplete inventories. The narrative progresses to unveil how AI/ML technologies serve as the cornerstone of innovative defense mechanisms. We dissect the architecture of AI-driven systems tailored to detect and counteract anomalous behaviors indicative of bot activities, leveraging vast datasets to train ML models that adeptly differentiate between legitimate user interactions and malicious bot intrusions. The discussion further navigates through the technical and operational nuances of implementing AI/ML defenses, emphasizing predictive analytics for preemptive action, machine learning for dynamic threat adaptation, and the overarching impact of such technologies in securing digital ecosystems against the insidious threats posed by automated attacks. This presentation not only highlights the challenges but also showcases the resilience and adaptability of AI/ML solutions in the ever-evolving battle against digital villains.

Alison Cossette, Neo4j, Data Science Strategist, Advocate, Educator

As generative AI continues to push boundaries, creating novel content by learning from massive datasets, we are faced with complex issues around intellectual property, privacy, and the ethical use of data. Current systems of copyright, fair use, and data protection lack the scope to fully address the unique challenges posed by AI pattern recognition and generation.

This pivotal talk introduces the pioneering concept of "Pattern Rights" - a holistic ethical framework to inform the development and deployment of generative AI technologies. Pattern Rights serves as an umbrella construct, encompassing principles of copyright, fair use, training data transparency, privacy, data ownership, and accountability.

We will explore how Pattern Rights can ensure appropriate attribution and compensation when AI models learn from copyrighted works or personal data. It establishes guidelines around consent, anonymization, and ethical data sourcing practices.

As the AI industry is rapidly evolving, we urgently need governance to foster innovation while upholding rights and safeguarding against misuse. Pattern Rights provides a roadmap to navigate this uncharted territory responsibly and equitably.

Dan Edwards, Crowe, Managing Director

In an era where artificial intelligence (AI) and machine learning are rapidly evolving, the accounting profession stands at the precipice of significant transformation. This session aims to demystify the impact of AI technologies, such as Microsoft Copilot, on the future of accounting.

Join us for an insightful exploration into the capabilities of AI in the accounting space, where we will delve into the current state of AI tools like Microsoft Copilot, their potential to automate complex accounting tasks, and the implications for accounting professionals.

This session is designed for accounting professionals at all levels who are seeking to understand the impact of AI on their careers and how to strategically position themselves for success in a rapidly changing technological landscape. Whether you're a seasoned CPA or a recent graduate, this session will provide valuable insights into the future of accounting.

Utkarsh Contractor, Aisera, VP of AI

Enterprises are on the cusp of a transformative shift. They are moving beyond task-specific AI to embrace agentic systems capable of autonomous decision-making and actions through probabilistic reasoning. This evolution marks a new era of autonomy, where AI not only augments human capabilities but also drives business outcomes by:
- Optimizing Operations: Self-regulating systems enhance efficiency, ensure compliance, and proactively mitigate risks.
- Accelerating Innovation: Data-driven insights fuel strategic decision-making, enabling rapid market adaptation.
- Enhancing Customer Experience: Intelligent agents anticipate customer needs, delivering personalized solutions.

Although the potential benefits are immense, successful implementation demands a strategic approach that encompasses ethical considerations, security, and complexity management.

Join Utkarsh Contractor, VP of AI/ML at Aisera, as we explore the opportunities and challenges of this paradigm shift and how organizations can harness agentic AI to achieve sustained competitive advantage.

Anton McGonnell, SambaNova Systems, VP of Product

This session, led by Anton McGonnell, Head of Software Products at SambaNova Systems, presents an in-depth exploration of the Composition of Experts approach in AI - a model made up of many models that work in conjunction with each other - and in particular the benefits to individual developers. Instead of building one monolithic model, this strategy composes workflows using many specialised, independent "expert" models. This proves to be more efficient, modular, and extensible.

The talk will provide a deep dive into the advantages of this technique, which promotes reusability, saves on alignment tax, and enables granular access control. By detailing real-world implementations, attendees will learn best practices for breaking down problems, assigning appropriate experts, and orchestrating data flow between them. McGonnell will discuss how an architecture with a large memory capacity, enables this approach by hosting and serving thousands of models in parallel on a single node without performance degradation. This makes the Composition of Experts approach highly scalable and reduces the total cost of ownership for inference.

Additionally, the session will offer crucial hardware considerations for executing Composition of Experts at scale. It will examine how specialised architectures can allow hosting thousands of models in parallel on a single node. With companies increasingly relying on AI, reliability and cost-efficiency are paramount concerns. Composition of Experts tackles these challenges through modularity and inference optimisation. By attending, developers and engineers can expect to gain highly applicable skills for improving the scalability, performance, and governance of AI in production environments.