9 Challenges of Generative AI
The realm of artificial intelligence is evolving rapidly, showcasing the immense potential of conceptual AI tools. However, alongside their capabilities, concerns about potential negative impacts are also surfacing.
We have identified several challenges associated with conceptual AI tools based on insights from various sources and our own analysis. These challenges span different categories including superior power and capabilities, data accuracy, social implications, and technical hurdles. Let’s delve into these issues.
Challenge 1:
Bias Inside and Out
Exploring Accuracy and Quality Control
One of the primary issues with relational AI is its tendency to perpetuate biases present in its training data. Instead of mitigating biases, these tools often amplify or perpetuate them, raising questions about the reliability of their applications and giving rise to ethical concerns.
Challenge 2:
The Enigma of Black Boxes
Focusing on Legal and Ethical Standards
The lack of transparency in the decision-making processes of generative AI poses a significant obstacle to their widespread adoption. These AI systems struggle to explain their decisions due to complex reasoning mechanisms, especially in critical scenarios, hindering trust and accountability.
It’s essential to acknowledge that this challenge extends beyond conceptual AI to AI systems in general.
Challenge 3:
Escalating Maintenance and Training Expenses
Address Technical Complexity and Challenges
Training sophisticated conceptual AI models like the large language model (LLM) ChatGPT entails exorbitant costs, often reaching millions of dollars due to high computational requirements. For instance, Sam Altman, the former CEO of OpenAI, revealed that training ChatGPT-4 incurred a staggering $100 million expense.
Challenge 4:
Mimicry Without Insight
Focusing on Accuracy and Quality Control
Generative AIs are bound by the data and patterns they are trained on, limiting their outputs to existing knowledge domains and potentially hindering their adaptability to diverse scenarios.
Challenge 5:
Alignment with Human Values
Addressing Legal and Ethical Considerations
Unlike humans, generative AIs lack the ability to assess the ethical implications of their decisions in alignment with human values. This disparity raises concerns about the misuse of technologies like deepfakes for malicious purposes, emphasizing the need for ethical frameworks to guide their development and deployment.
Challenge 6:
Energy Consumption Concerns
Focusing on Technical Complexity and Challenges
The substantial energy consumption associated with generative AI models, such as ChatGPT, raises economic and environmental concerns. The power consumption of processing units can be equivalent to that of thousands of households, highlighting the need for energy-efficient solutions in AI development.
Challenge 7:
Fabrications and Hallucinations
Exploring Accuracy and Quality Control
Conceptual AI models have been observed generating false information or images when confronted with data gaps, raising doubts about the reliability of their outputs and potential consequences.
For instance, a promotional video featuring Google Bard inaccurately claimed that the James Webb Space Telescope captured images of planets beyond Earth’s orbit.
Challenge 8:
Copyright and Trademark Issues
Addressing Legal and Ethical Considerations
The unauthorized use of copyrighted material by certain relational AI tools infringes on the rights of creators and underscores the importance of ethical data practices. Initiatives like OpenAI’s Copyright Shield aim to compensate for copyright infringements by providing legal support to affected parties.
Challenge 9:
Data Persistence
Focusing on Technical Complexity and Challenges
Sustaining conceptual AI models necessitates significant time and computational resources, posing a notable technical challenge. However, models designed for incremental updates offer a potential solution to this persistent issue.
