"Sustainable AI: Environmental impact of AI and ways to keep it sustainable"

AI's growth has increased energy use and carbon emissions, raising sustainability concerns. Sustainable AI aims to reduce this impact through energy-efficient hardware, optimized algorithms, renewable energy, and green data centers. Promoting low-power AI research and awareness can help align AI development with environmental goals.

20+ Years of experience in the Technology industry.
Top skill -
• Digital Strategy
• Digital Transformation
• IT Strategy
• Cloud Strategy
• Generative AI

"Human and AI collaboration: Governance Strategies for Trustworthy AI and Safe AI system’s"

Governance strategies are essential for ensuring trustworthy and safe AI. These include regulatory frameworks, ethical design, and ongoing monitoring to promote transparency and accountability. Collaboration between policymakers, technologists, and stakeholders is key to building public trust and aligning AI with human values.

I am an AI Engineering Manager at IBM with a PhD in AI and Data Analytics and over 17 years of experience.

I am responsible for delivering innovative solutions for Apple accounts and managing large-scale teams and projects. My expertise includes text and numeric data solutions, using techniques like NLP, generative AI, and geo-spatial analysis. With 11 publications, I am also a guest speaker and passionate about solving real-world problems through AI and data-driven methods.

"Designing Scalable AI Architectures: Best Practices and Frameworks"

I am a valuations professional with over 19 years of experience, currently leading a specialized advisory team that helps clients optimize their capital and corporate finance strategies for competitive advantage and increased returns.

My role focuses on capital advisory, including capital allocation, optimization, raising, and distribution, as well as valuations within the M&A space.

"The Future of AI - innovations and Ethical Implications"

I leverage AI and machine learning to drive quantitative analytics with a focus on cybersecurity. Leading my team, we transform complex digital and financial data into innovative solutions for strategic decision-making. With strengths in leadership, communication, and data mining, I guide the team to deliver actionable insights, advancing data-driven business strategies while ensuring regulatory compliance.

As Executive Director at Wells Fargo, I apply my expertise in AI and machine learning to enhance quantitative analytics, particularly in cybersecurity. Together with my team, we tackle complex digital and financial data, turning challenges into innovative solutions that drive strategic decision-making. Our collaborative work in developing and validating AI/ML models has been instrumental in strengthening the organization’s analytical capabilities.

"PromptOps: Mastering AI-Powered SDLC with Reusable Prompts"

Ph.D. Research Scholar(NeuroSciences) in Data Science by Education, a Data Scientist by Passion and Profession, and a Leader by experience. Blended 15 years of work experience in Technical, Functional, and Leadership skills in the areas of Data Science, Machine Learning, and Deep Learning with a strong Research mindset, which helped me set up frameworks and solutions from scratch. An Influencer, Motivator, and Supporter of all the Good Causes.

"Securing AI: Navigating Privacy, Security, and Compliance"

This topic explores the critical intersection of privacy, security, and compliance in AI development. It highlights the challenges organizations face in protecting sensitive data while ensuring regulatory adherence, and discusses strategies for implementing robust security measures to safeguard AI systems.

Data Science professional with 17 years of experience. Works on Data Science/Machine Learning using Python/R.

"Building scalable AI systems"

This talk addresses the challenges of building large-scale AI systems, especially with Large Language Models (LLMs) and generative AI, divided into two parts:

### Part 1: General Challenges in Scaling AI

1. **Computational Resources**: Significant computational power is required, driving up costs.
2. **Data Quality**: Acquiring high-quality, domain-specific data is complex and resource-intensive.
3. **Fine-Tuning**: Adapting LLMs for specific domains demands extensive time and expertise.
4. **Explainability**: Transparency becomes crucial as models increase in complexity.
5. **Integration**: Merging AI models with legacy systems poses technical challenges.
6. **Model Updates**: Regular retraining is costly and labor-intensive.
7. **Regulatory Compliance**: Meeting industry regulations is essential.
8. **Talent Scarcity**: A shortage of skilled professionals hampers scaling efforts.

### Part 2: Challenges in the Legal Domain

Applying LLMs in the legal field introduces unique challenges:
1. **Data Volume**: Vast amounts of complex data complicate analysis.
2. **Data Quality**: Legal documents must be cleaned and standardized.
3. **Handling Precedents**: Legal AI needs to accurately interpret past rulings.
4. **Ambiguity**: The nuanced nature of legal language challenges LLMs.
5. **Jurisdictional Differences**: Models must adapt to varying laws.
6. **Adversarial Scenarios**: Balancing opposing legal arguments is complex.
7. **Continuous Updates**: Legal AI must stay current with new regulations.

### Startup Strategies

To overcome these challenges, the startup adopted strategies such as:
1. **Focusing on a Niche**: Concentrating on a specific area of law for manageable development.
2. **Leveraging Open-Source Models**: Using pre-trained models to reduce costs.
3. **Efficient Data Pipeline**: Streamlining data collection and cleaning.
4. **Collaboration with Experts**: Partnering with legal professionals for accuracy.
5. **Public Legal Databases**: Utilizing publicly available resources for training.
6. **Robust Privacy Measures**: Implementing strong protections for compliance.

As the AI field evolves, ongoing adaptation to new challenges is crucial.

Prof S Chandrasekhar is Senior Prof & Director Business Analytic at IFIM Business School B “Lore since Nov 2013.

He was chair Professor (July 1998-Feb 2013) & officiating Director at FORE School of Management, New Delhi (April 2009 to Jan 2010). He joined FORE School in 1988 as Senior Professor. He also headed the software development at Fore School of Management which are developing proto type of products in the area of Business Intelligence, Risk Management, and Customer Relations Management. He has a total of about 34years of experience in R&D, Academic & Industry in the area of Quantitative Techniques & IT. Taught courses in the area of IT, QT, Adv. Market Research at Post Graduate level.

Prior to this he worked at Indian Institute of Management, Lucknow for about ten years(1988-98) as Professor in the area of Computers & information Systems. Member Secretary of IIM L Governing Board for about three years.