What are the most significant limitations of GenAI today? How do you maintain awareness of these limitations when considering applications and possible use cases?
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Despite its impressive capabilities, GenAI has the potential to produce inaccurate or misleading outputs, perpetuate biases, and require significant computational resources and data. To address these challenges, it is important to verify output accuracy, detect and mitigate biases, and use the technology responsibly by updating models with diverse data, monitoring performance, and providing human supervision.
Understanding of company specific processes and it takes a lot of training to make Gen AI smarter with your company specific information. Also the real value of Gen AI comes when it can combine company data with general process knowledge and these implementations are not easy
tribal knowledge (both genAI, humans got limitations here) and niche functional expertise.
GenAI has shown great promise in a variety of applications, but it also has significant limitations. Here are some of the most notable ones:
1. Non-Determinism: GenAI can produce different outputs even when given the same input multiple times, leading to unpredictability in its results. This unpredictability can be a serious drawback in industries where reliability is paramount.
2. Hallucinations: Large GenAI models can 'hallucinate,' posing significant risks as 'hallucinations' are convincingly presented as truths. This can be particularly problematic in areas like news dissemination, education, healthcare, and legal advice where accuracy is crucial.
3. Limited Use Cases: Despite their impressive output capabilities, GenAI applications are limited in their ability to tackle complex, multi-dimensional societal issues. They excel in defined, narrow tasks but lack the general understanding needed to address broader challenges such as strategic decision-making or ethical dilemmas.
4. Bias: GenAI can also be biased if the data it was trained on is biased.
5. Resource-Intensive: GenAI can be resource-intensive.
6. Ethical Concerns: There are ethical concerns related to the use of GenAI.
7. Limited Traceability and Irreproducibility: There are concerns about limited traceability and irreproducibility of GenAI outcomes.
8. Confidentiality Issues: There can be confidentiality issues when using GenAI.
When considering applications and possible use cases for GenAI, it's important to maintain awareness of these limitations. This can be done by staying up to date with the latest research and developments in the field, understanding the specific characteristics and constraints of the GenAI models being used, and carefully considering the ethical and practical implications of using GenAI in different contexts.