The Unpredictable Experiment of Releasing Language Models
In a bold move that challenges conventional practices, generative AI companies have adopted a cutting-edge approach to quality assurance: releasing large language models (LLMs) directly onto the untamed realms of the internet. This audacious experiment leverages the collective power of the online community to uncover bugs, glitches, and unexpected features, eliminating the need for tedious testing phases. Every internet user becomes an unintentional participant in the grand beta test of the century, as we discover the quirks and peculiarities of LLMs one prompt at a time. With the vast expanse of the internet as our safety net, who needs traditional quality control measures, right? However, caution should not be disregarded when overlooking the potential for misuse.
“The chaotic race to release or utilize gen AI LLM models seems like handing out fireworks — sure, they dazzle, but there’s no guarantee they won’t be set off indoors!” – Unknown
Mistral, for example, recently launched its 7B model under Apache 2.0 licenses. However, in the absence of explicit constraints, concerns arise regarding the potential for misuse. Adjustments to parameters behind the scenes can lead to completely different outcomes. Biases embedded in algorithms and the data they learn from can perpetuate societal inequalities. CommonCrawl, the web-crawler used by platforms like GPT-3 and LLaMA, constitutes a significant portion of the training data. While it benefits language modeling, it lacks comprehensive quality control measures. Therefore, the responsibility of selecting quality data falls squarely on the developers. Recognizing and mitigating these biases are crucial steps towards ethical AI deployment. It is essential to make ethical software development mandatory, rather than discretionary.
“Developing ethical software should not be discretionary, but mandatory.” – Unknown
If a developer chooses to deviate from ethical guidelines, limited safeguards are currently in place. The responsibility of ensuring the equitable and unbiased application of gen AI lies not only on developers but also on policymakers and organizations.
“The onus lies not just on developers but also on policymakers and organizations to guarantee the equitable and unbiased application of gen AI.” – Unknown
The Challenges and Ethical Concerns of Gen AI
The terms of service for gen AI offerings often do not guarantee accuracy or assume liability. This poses significant challenges as many users rely on these services for learning purposes or work-related tasks and may struggle to differentiate between credible and hallucinated content.
“You’re entering the labyrinth of limited liability. Abandon all hope, ye who read this (or don’t).” – Unknown
Inaccuracies in gen AI can have real-world implications. For example, Google’s Bard chatbot incorrectly claimed that the James Webb Space Telescope had captured the world’s first images of a planet outside our solar system, leading to a drop in Alphabet shares. As gen AI models are increasingly involved in decision-making processes, determining responsibility for errors becomes a complex matter. Should it fall on the LLM provider, the entity offering value-added services using LLMs, or the user for potential lack of discernment?
“In the fantastical land of legal jargon where even the punctuation marks seem to have lawyers, the terms of services loosely translate to, ‘You’re entering the labyrinth of limited liability. Abandon all hope, ye who read this (or don’t).’” – Unknown
Furthermore, issues arise when addressing deletion requests. LLMs do not operate like traditional databases, making it challenging to selectively remove specific pieces of information. These models generate responses based on learned patterns, making the deletion of individual data pieces difficult.
Legal frameworks struggle to keep up with the expansive capabilities of gen AI. Questions surrounding compensation for content creators whose work drives LLM algorithms have resulted in lawsuits involving companies like OpenAI, Microsoft, Github, and Meta. Content creators should have the option to opt-out or monetize their content within the context of LLMs.
Quality standards for LLMs are still being recalibrated. While a 2% crash rate in applications like Amazon Prime Music may be acceptable, the consequences of failures in healthcare, public utilities, or transportation would be catastrophic. Differentiating between AI breakdowns and hallucinations presents a unique challenge due to the abstract nature of these occurrences.
Comprehensive frameworks that integrate legal, ethical, and technological considerations are necessary as gen AI continues to push the boundaries of innovation. Countries like China have already proposed detailed rules to address the issues associated with gen AI, and it is expected that other governmental organizations worldwide will follow suit.
Once released, gen AI can be difficult to contain. While platforms like Facebook and Twitter have struggled to combat the prevalence of fake news, the genie of AI innovation should not go unchecked. LLM providers must take responsibility for ensuring the ethical utilization of their models instead of relying solely on committees or users.
“‘Till then, fasten your seat belt.” – Unknown
By Amit Verma, Head of Engineering/AI Labs and Founding Member at Neuron7.
