This is a presentation on Generative AI vs Human Intelligence.

1. Department of Computer Science & Engineering
Technical Seminar (18CSS84) Presentation
On
“Comparing the Ideation Quality of Humans With
Generative Artificial Intelligence”
Presented by
Shubham Kokila
1AY20CS154
Under the guidance of
Mrs. Bhavyashree S P
Assistant Professor
Acharya Institute of Technology
Bangalore

2. Contents
1. Introduction
2. Applications
2. Objectives
3. Literature survey
4. System Requirements
5. System architecture
6. Advantages
7. Disadvantages
8. Challenges
9. Conclusion
10. References
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence “
Dept of CSE

3. Introduction
1. The innovation domain is embracing AI, particularly large language models (LLMs) with
conversational interfaces.
• Example: Companies like Google, OpenAI, and Microsoft are developing advanced AI
models such as GPT-3 and GPT-4 to assist in various tasks, including ideation.
Scholars and practitioners are integrating generative AI into the ideation phase of innovation.
• Example: Research studies have shown how AI systems like GPT-3 can generate
creative ideas in domains such as product development and marketing strategies.
2. Creativity is defined as combining originality (novelty) and effectiveness (usefulness),
essential for innovation.
3. Professionals traditionally lead innovation but may face limitations in idea diversity and
time constraints.
4. Businesses encounter challenges in generating new ideas through human ideation
due to the need for continuous innovation and shortening innovation .
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

4. Application
Business and Organization:
• Brand Strategy
• Competitive Analysis
• Strategic Planning
• Customer Experience Enhancement
• Risk Management
Individuals:
• Personal Finance
• Travel Planning
• Health and Wellness
• Career Development
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

5. Objectives
• Compare professional and AI-generated ideas in terms of novelty, customer
benefit, feasibility, and overall quality.
• Analyze strengths and weaknesses of AI-generated and human-generated ideas to
understand AI's role in ideation.
• Emphasize the importance of human creativity and judgment in innovation
processes.
• Provide practical insights for optimizing innovation strategies through effective
integration of AI capabilities.
• Explore the potential of generative AI tools like ChatGPT to enhance idea
generation efficiency and diversity.
• Emphasize the importance of teamwork between humans and AI for effective
idea generation and innovation.
• Provide practical advice for businesses to improve their innovation strategies by
using AI responsibly and ethically.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

6. Literature and Survey
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE
Authors Title Publication Key Findings
N. Haefner, J.
Wincent, V.
Parida, and
O. Gassmann
Artificial intelligence and
innovation management: A
review, framework, and
research agenda
Technological
Forecasting and
Social Change, vol.
162, p. 120392,
2021
Framework for
AI-human
replacement,
AI's impact on
innovation
management
R. Verganti, L.
Vendraminell
i, and M.
Iansiti
Innovation and Design in
the Age of Artificial
Intelligence
Journal of Product
Innovation
Management, vol.
37, no. 3, pp. 212-
227, 2020
AI
transformation
, Human role
shift
J. Just, T.
Ströhle, J.
Füller, and K.
Hutter
AI-based novelty detection
in crowdsourced idea
spaces
Innovation, pp. 1-28,
2023
Semantic
Representatio
n, AI-based
Language
Models, Text
Embeddings

7. System Requirement
Hardware Requirements:
• Processor :Any Processor more than 500MHz
• Ram :4GB
• Hard Disk :4GB
• Input Device :Keyboard and Mouse
• Output Device :High resolution Monitor
Software Requirements:
• Operating System :Windows 7 or higher
• Programming :Python and Related Libraries
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

8. System Architecture
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

9. Advantages
• Enhanced Idea Diversity: Integration of generative AI expands the range of ideas generated
beyond human limitations, leading to a more diverse pool of innovative solutions.
• Time and Cost Efficiency: AI-driven ideation processes significantly reduce the time and
resources required for generating ideas compared to traditional brainstorming sessions, thereby
increasing productivity and cost-effectiveness.
• Rapid Idea Generation: Generative AI tools like ChatGPT can quickly produce numerous
ideas within a short timeframe, accelerating the innovation process and enabling organizations
to stay ahead in competitive markets.
• Scalability: AI-powered ideation processes can be easily scaled to accommodate varying levels
of idea generation needs, from small-scale projects to large-scale innovation initiatives, without
compromising quality or efficiency.
• Continuous Availability: AI tools are available 24/7, allowing for idea generation at any time,
catering to global teams and tight deadlines.
• Augmented Human Intelligence: AI augments human intelligence by complementing human
creativity and expertise, leading to more robust and innovative solutions.
• Error Reduction: AI minimizes errors in idea generation and evaluation through automated
processes, improving the overall quality of outcomes.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

10. Disadvantages
• Dependence on Technology: Over-reliance on AI for idea generation may lead to a
decreased emphasis on human creativity and intuition, potentially stifling innovation in the
long run.
• Lack of Human Touch: AI-generated ideas may lack the human touch and emotional
intelligence present in ideas generated through human collaboration, potentially impacting
customer resonance and engagement.
• Limited Context Understanding: AI may struggle to understand nuanced contextual
factors, cultural sensitivities, or domain-specific knowledge, resulting in less relevant or
practical ideas in certain situations.
• Ethical Concerns: The use of AI in idea generation raises ethical concerns related to data
privacy, ownership of ideas, and transparency in decision-making, requiring careful
consideration and ethical oversight.
• Skill Requirements: Implementing AI-driven ideation processes requires specialized
technical skills for development, maintenance, and troubleshooting, posing challenges for
organizations lacking in-house expertise.
• Cost of Implementation: Integrating AI tools into the innovation process incurs initial setup
costs, licensing fees, and ongoing maintenance expenses, which may be prohibitive for some
organizations, especially smaller businesses.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

11. Challenges
1. Ethical concerns: AI ideation raises issues of data privacy, bias, and
unintended consequences.
2. Human-AI collaboration: Challenges in effective teamwork and balancing
automated and human decision-making.
3. Quality control: Ensuring relevance and quality of AI-generated ideas
requires human oversight.
4. Adaptability: AI tools may struggle to adapt to diverse problem domains and
require continuous refinement.
5. Adoption barriers: Resistance to AI integration due to organizational inertia
and concerns about job displacement.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

12. Conclusion
• Maximizing Innovation : Integration of generative AI enhances innovation
outcomes by expanding idea diversity, improving efficiency, and mitigating biases.
• Addressing Critical Challenges: Despite its benefits, challenges such as ethical
concerns, effective collaboration, and quality control need to be addressed.
• Continuous Improvement: Regularly updating AI integration strategies to align
with evolving technological advancements and organizational needs.
• Human-AI Collaboration: Fostering synergy between human creativity and AI
capabilities to harness the combined strengths for innovative ideation processes.
• Human-Centric Ideation: While AI can enhance idea generation, human input
remains crucial for providing context, empathy, and emotional intelligence to the
innovation process.
• Ethical Oversight: Human supervision is vital to ensure AI-generated ideas adhere
to ethical standards and societal norms, reducing the risk of bias or harmful
consequences.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE

13. References
• S. G. Bouschery, V. Blazevic, and F. T. Piller, "Augmenting human innovation
teams with artificial intelligence: Exploring transformer-based language models,"
Journal of Product Innovation Management, vol. 40, no. 2, pp. 139-153, 2023.
• V. Bilgram and F. Laarmann, "Accelerating Innovation with Generative AI: AI-
augmented Digital Prototyping and Innovation Methods," IEEE Engineering
Management Review, vol. 51, no. 2, pp. 1-5, 2023.
• F. Barron, "The disposition toward originality," The Journal of Abnormal and
Social Psychology, vol. 51, no. 3, pp. 478-485, 1955.
• M. A. Runco and G. J. Jaeger, "The Standard Definition of Creativity," Creativity
Research Journal, vol. 24, no. 1, pp. 92-96, 2012.
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence ”
Dept of CSE

14. THANK YOU!!
“Comparing the Ideation Quality of Humans With Generative Artificial Intelligence”
Dept of CSE