DataScience&AITrends Hong Kong Summit
Data and AI are front and center in optimizing business decisions and driving the next wave of enterprise growth. While investment into AI is accelerating in the race to realize new capabilities and use cases, potential risks and regulations are also emerging.
As Data and AI leaders embark on the multi-year journey to realize business outcomes from AI initiatives, they are shifting to more focused and strategic approaches. Having a robust foundation in data and analytics, adequate infrastructure, and efficient governance are keys to progressing from experimentation to achieving meaningful ROI.
The inaugural DataScience&AITrends Asia Summit aims to provide a platform for data, AI, digital, and IT leaders to explore challenges and opportunities to implement data-centric AI. The Summit, which will feature a blend of insightful presentations and panel discussions, aims to solve the business problems of data and AI while exploring the latest tech stacks that accelerate the unlocking of data value.
This Summit is for all professionals involved in digital, data, cybersecurity, transformation, and IT, including:
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Chief Data Officers
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Chief Analytic Officers
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Chief AI Officers
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Chief Digital Officers
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Chief Information Officers
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Chief Technology Officers
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Chief Transformation Officers
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Chief Innovation Officers
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Chief Customer Officers
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Heads of AI
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Heads of Data Science and Analytics
AGENDA
The democratization of Generative AI has ignited a fervor around AI in enterprises. Yet to progress from experimentation to durable usage with widespread business impacts necessitates a transformation in enterprise AI strategy. Charlie Dai, VP, Principal Analyst from Forrester will address the technology trends and business impact of generative AI, with strategic recommendations on how to transform enterprise AI strategy to drive real business growth.
Facilitating the next generation of AI requires massive computing power and cost-effective data storage. Stringent data privacy requirements also increase the needs of Edge AI. This session will explore the needs to rethink the IT infrastructure behind the data, to help CDOs unlock the potential of AI.
Generative AI has been in the spotlight. Behind its vast potential, it has been plagued with security, privacy, and accuracy concerns. This panel will explore:
- Early enterprise use cases of GenAI: the low hanging fruits
- Ensuring trust and reliability of GenAI
- Localizing GenAI with proprietary first-party data
- CAIO role change: considerations for AI solutions as AI developers vs AI users of open source and SaaS solutions
Moderator:
Whether for AI training or testing, many organisations struggle to obtain realistic data sets. To overcome data availability, readiness, and regulatory challenges, synthetic data can be an answer. Find out how synthetic data can be more accessible and flexible while providing better privacy and higher utility than real data.
To produce robust, reusable AI systems, at scale and efficiently, enterprises are shifting from a model- and code-centric approach to being data-centric. This panel will discuss:
- Synthetizing data to trained machine learning models effectively
- Solving data accessibility, volume, and quality challenges
- Overcoming the complexity of producing and maintaining robust AIs
- Producing scalable, multi-objective, and practical AI
- Realigning IT infrastructure to enable data-centric AI
Moderator:
Enterprises are relying on AI and analytics to enhance customer engagement and boost revenue. Yet, to realize their data and AI visions, enterprises must modernize their digital cores and bridge the gap to existing infrastructure and data architecture.
At this executive luncheon, Red Hat has invited the data expert from SAS to stage a fireside chat that illustrates the underlying business challenges of facilitating cloud-native AI, data analytics, and data management for organizations that are modernizing to run on-premise and in multicloud and hybrid cloud environments. They will share how CDOs may operationalize AI and analytics to gain real intelligence responding to evolving business and market changes. There will also be plenty of quality networking time over lunch to for peer-to-peer mutual learning.
Digital transformations across the global economy have resulted in an explosion of structured and unstructured data, with enterprises of all types sitting on troves of information – internal communications, emails, files, financial data, and more. Large Language Models (LLMs) are designed to build many applications by leveraging the power of this data. To make LLMs practical for enterprises, Retrieval Augmented Generation, or RAG, was developed. This session is to share how this AI technique helps LLMs more practical for enterprise, how it overcomes limitation of LLMs and specific to the enterprise by using the enterprise’s data.
As AI production accelerates, and enterprises increasingly rely on AI for decisioning, having better governance to prevent adverse consequences is crucial. This panel will discuss:
- Establishing a responsible and explainable AI culture and structure
- Operationalizing AI Trust, Risk and Security Management (TRiSM)
- Preventing AI hallucination, data drift, and model drift
- Adopting a risk-proportional approach to realizing AI value
Moderator:
SPEAKERS
