Pros and cons of AI_ Promise vs Peril.pptx

Advantages and disadvantages of AI

1. 2025/ 08 / 05 AI: Promise vs Peril

2. CONTENTS 01. AI Overview 02. Advantages of AI 03. Drawbacks of AI 04. Balanced Future

3. 01 AI Overview

4. What Artificial Intelligence Really Means 01 Definition and Types Artificial Intelligence (AI) refers to systems that perform tasks requiring human-like perception, reasoning, and learning. It ranges from narrow AI, designed for specific tasks, to general AI with broader capabilities, and potentially super AI surpassing human intelligence. 02 Historical Context AI traces its roots back to the 1956 Dartmouth Conference. It has evolved through phases like expert systems, machine learning, and deep learning, with current advancements driven by data-driven statistical approaches and generative models.

5. Core Technologies Driving AI Today 1 Learning Paradigms Supervised learning uses labeled data to train models, unsupervised learning finds patterns in unlabeled data, and reinforcement learning optimizes actions based on rewards. These methods form the backbone of modern AI systems. 2 Neural Networks and Transformers Neural networks, with techniques like back-propagation, enable complex pattern recognition. Transformers, a type of neural network, power state-of-the-art models in natural language processing, revolutionizing language understanding and generation. 3 Enabling Infrastructure Big data, powerful GPUs, and cloud computing provide the necessary resources for training large models. The fusion of computer vision, NLP, and robotics demonstrates AI's multidisciplinary impact across various industries.

6. 02 Advantages of AI

7. Productivity Surge and Cost Reduction Automation of Repetitive Tasks AI automates routine cognitive tasks in sectors like finance, customer service, and manufacturing, reducing human error and enabling 24/7 operation, leading to significant labor cost savings. Scalable Efficiency Cloud-based AI APIs allow small businesses to access advanced analytics without heavy infrastructure investment, democratizing technology and fostering innovation at scale. Micro-Second Decision Making AI systems can process vast amounts of data and make decisions in microseconds, optimizing processes and improving efficiency in real-time applications. Redirecting Human Resources By automating repetitive tasks, AI frees up human resources to focus on creative and strategic roles, enhancing overall productivity and innovation within organizations.

8. Accelerated Scientific Discovery Protein Structure Prediction AI models can predict protein structures in days rather than years, revolutionizing drug discovery and speeding up the development of new treatments for various diseases. Material Science Advancements AI accelerates material genome projects, leading to the discovery of better batteries, catalysts, and other materials, driving innovation in renewable energy and sustainable technologies.

9. Personalized Services for All 01 Adaptive Tutoring AI-driven adaptive tutoring systems adjust difficulty levels based on individual student performance, providing personalized education that enhances learning outcomes. 02 Early Disease Detection Wearable devices powered by AI can detect early signs of diseases, enabling timely interventions and improving healthcare outcomes through personalized monitoring. 03 Real-Time Translation AI facilitates real-time translation, breaking down language barriers and promoting global communication and collaboration across diverse communities.

10. 03 Drawbacks of AI

11. Job Displacement and Skills Gap Susceptibility of Routine Jobs Many routine cognitive roles in accounting, logistics, and legal review are at risk of being automated by AI, leading to significant job displacement in these sectors. Reskilling Challenges Studies project that millions of workers will need to reskill by 2030. The current education and training systems may struggle to meet this demand, exacerbating the skills gap. Polarization of Employment AI may lead to a polarization of jobs into high-skill creative roles and low-wage service jobs, widening inequality and disproportionately affecting certain demographics and regions.

12. Bias Amplification and Fairness Failure 01 Historical Bias in Data Training data often contains historical biases related to gender, race, and socioeconomic status, which AI models can inadvertently amplify, leading to unfair outcomes. 02 Credit Scoring Disparities AI-driven credit scoring systems may perpetuate discrimination, unfairly denying loans to certain groups based on biased data and algorithms. 03 Facial Recognition Accuracy Gaps Facial recognition technologies have been shown to have accuracy gaps across different demographic groups, leading to potential misuse and perpetuation of bias in law enforcement and other applications. 04 Opacity of Deep Models The complexity of deep learning models makes it difficult to audit and correct biases, posing significant challenges for ensuring fairness and accountability in AI systems.

13. Privacy Erosion and Security Threats Massive Data Harvesting AI systems require vast amounts of personal data for training, raising concerns about privacy erosion and the potential misuse of sensitive information. Security Vulnerabilities AI introduces new security threats, such as deepfake technology and adversarial attacks on critical systems, which can undermine trust and safety in digital environments.

14. 04 Balanced Future

15. Ethical Frameworks and Governance Emerging Guidelines Frameworks like the EU AI Act and OECD principles provide guidelines for ethical AI development, emphasizing transparency, accountability, and risk management. Lifecycle Accountability Ensuring accountability throughout the AI lifecycle, from design to deployment, is crucial for building trustworthy systems that align with societal values. Multi-Stakeholder Audits Conducting multi-stakeholder audits and algorithmic impact assessments can help identify and mitigate biases, ensuring that AI systems are fair and inclusive.

16. Human-in-the-Loop Design 01 Meaningful Human Control Maintaining meaningful human control in high-stakes decisions ensures that AI systems augment human capabilities rather than replacing them entirely. 02 Hybrid Teams Creating hybrid teams that combine machine speed with human judgment can optimize decision-making processes and maintain accountability in complex environments.

17. Upskilling and Lifelong Learning 01 Public-Private Partnerships Public-private partnerships can fund reskilling programs, enabling workers to transition to new roles as AI transforms the job market. 02 Micro-Credential Ecosystems Micro-credential ecosystems offer flexible learning opportunities, allowing workers to acquire new skills and adapt to the evolving demands of the AI-driven economy. 03 Early Education Integration Embedding computational thinking, ethics, and adaptability into early education prepares the next generation to thrive in an AI-integrated world. 04 Continuous Learning Culture Promoting a culture of continuous learning ensures that individuals remain adaptable and relevant as AI continues to evolve and reshape industries.

18. Conclusion: Shape AI Before It Shapes Us Proactive Stewardship AI is a powerful tool whose impact depends on human choices. Proactive regulation, inclusive design, and continuous education are essential to harness its benefits while mitigating risks, ensuring that AI augments humanity rather than replacing it.

19. 2025/ 08 / 05 THANK YOU FOR READING ！