Gopalan Oppiliappan

Gopalan Oppiliappan

Head, AI Centre of Excellence at Intel India

    Gopalan is a highly accomplished thought leader in AI, and he brings a wealth of experience in AI, ML, Analytics and Enterprise Resource Planning (ERP). He currently heads the AI Center of Excellence (AI CoE) at Intel India, where he leads a team of AI Research Scientists and Machine Learning Engineers to deliver transformational AI capabilities for various business functions within Intel. His core competencies include Generative AI, machine learning, Graph Neural networks, natural language processing, optimization, data mining, and data visualization. The AI CoE has been recognized globally, in conferences like NeurIPS, IEEE and in IECS. The CoE has also filed couple of patents in advanced AI techniques. Gopalan is also a prolific speaker and a panelist on AI. He has done several talks in NASSCOM, Indian School of Business (ISB), IIM Udaipur, IIM Shillong with many other institutions in India and globally. Gopalan is a Mechanical Engineer from PSG College of Technology, Coimbatore and holds an MBA from IIM, Bangalore. In addition, Gopalan holds several certifications in AI and ML from renowned universities including Stanford, MIT, and University of Texas at Austin. He is also a Heartfulness Meditation trainer and practitioner.

    All Sessions by Gopalan Oppiliappan

    Day 2 04/24/2024
    10:40 am - 11:10 am

    Advancing Ethical Natural Language Processing: Towards Culture-Sensitive Language Models

    <span class="etn-schedule-location"> <span class="firstfocus">Responsible AI</span>

    Natural Language Processing (NLP) systems play a pivotal role in various applications, from virtual assistants to content generation. However, the potential for biases and insensitivity in language models has raised concerns about equitable representation and cultural understanding. This talk explores the development of Culture-Sensitive Language Models (LLMs) as a progressive step towards addressing these issues. The core principles involve diversifying training data to encompass a wide range of cultures, implementing bias detection and mitigation strategies, and fostering collaboration with cultural experts to enhance contextual understanding. Our approach emphasizes the importance of ethical guidelines that guide the development and deployment of LLMs, focusing on principles such as avoiding stereotypes, respecting cultural diversity, and handling sensitive topics responsibly. The models are designed to be customizable, allowing users to fine-tune them according to specific cultural requirements, fostering inclusivity and adaptability. The incorporation of multilingual capabilities ensures that the models cater to global linguistic diversity, acknowledging the richness of different languages and cultural expressions. Moreover, we propose a feedback mechanism where users can report instances of cultural insensitivity, establishing a continuous improvement loop. Transparency and explainability are prioritized to enable users to comprehend the decision-making process of the models, promoting accountability. Through this multidimensional approach, we aim to advance the field of NLP by developing culture-sensitive LLMs that not only understand and respect diverse cultural nuances but also contribute to a more inclusive and ethical use of language technology.

    Day 2 04/24/2024
    10:40 am - 11:10 am

    Advancing Ethical Natural Language Processing: Towards Culture-Sensitive Language Models

    <span class="etn-schedule-location"> <span class="firstfocus">Responsible AI</span> </span>

    Natural Language Processing (NLP) systems play a pivotal role in various applications, from virtual assistants to content generation. However, the potential for biases and insensitivity in language models has raised concerns about equitable representation and cultural understanding. This talk explores the development of Culture-Sensitive Language Models (LLMs) as a progressive step towards addressing these issues. The core principles involve diversifying training data to encompass a wide range of cultures, implementing bias detection and mitigation strategies, and fostering collaboration with cultural experts to enhance contextual understanding. Our approach emphasizes the importance of ethical guidelines that guide the development and deployment of LLMs, focusing on principles such as avoiding stereotypes, respecting cultural diversity, and handling sensitive topics responsibly. The models are designed to be customizable, allowing users to fine-tune them according to specific cultural requirements, fostering inclusivity and adaptability. The incorporation of multilingual capabilities ensures that the models cater to global linguistic diversity, acknowledging the richness of different languages and cultural expressions. Moreover, we propose a feedback mechanism where users can report instances of cultural insensitivity, establishing a continuous improvement loop. Transparency and explainability are prioritized to enable users to comprehend the decision-making process of the models, promoting accountability. Through this multidimensional approach, we aim to advance the field of NLP by developing culture-sensitive LLMs that not only understand and respect diverse cultural nuances but also contribute to a more inclusive and ethical use of language technology.

    Open Data Science

     

     

     

    Open Data Science
    One Broadway
    Cambridge, MA 02142
    info@odsc.com

    Privacy Settings
    We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
    Youtube
    Consent to display content from - Youtube
    Vimeo
    Consent to display content from - Vimeo
    Google Maps
    Consent to display content from - Google