Generative AI is one of the rarest things in technology – a product that actually lives up to the hype. Though still in its early stages, generative AI already looks to be one of the most promising technologies developed since the start of the new millennium. It has the potential to remake every aspect of our lives and can potentially level the playing field between startups and better-established enterprises by destroying the proverbial moat. As millions of people have discovered over the past several months, there’s no doubt that platforms for generative AI offerings such as ChatGPT and Bard can be extremely helpful in accelerating digital transformation for the enterprise. We have already started to see companies taking advantage of foundation models for process automation, increasing productivity, and driving innovation. These models are helping businesses redefine customer experiences, optimize operations, and create new revenue streams.
However, these large models are not a panacea and as their use increases, it is increasingly important that those who rely on generative AI understand its strengths as well as its limitations. There are a range of challenges and risks regarding their implementation – especially within the enterprise. This means that the businesses that come out ahead won’t be the ones that use generative AI the most, it will be the ones that use it the best.
As an AI practitioner and part of the research community, I recognize that addressing ethical dilemmas, such as bias and fairness, is a top priority for both regulators and researchers in our use of foundation models. Similarly, ensuring robust privacy and data security is critical in the enterprise setting. Anticipating these issues, the next wave of AI advancements aims to solve these challenges including grounding, bias mitigation, and building guardrails. We can expect to see more stringent measures, like using encryption and anonymization of both training data and model outputs to prevent breaches. Regular audits, fine-tuning of models, and solid data regulations will also become a norm to safeguard against the misuse of sensitive information.
It’s crucial to acknowledge that generative AI models, such as GPT are not designed for complex computations or mathematical analyses. They can easily be misguided by user-provided misinformation, often producing inaccurate results. These AI models also have their own set of limitations. They may generate unexpected errors due to their inability to access real-time information and susceptibility to biased or misleading data. For instance, ChatGPT is not internet-enabled, limiting its knowledge to pre-September 2021 data. Challenges also exist with handling multiple languages and dialects and the risk of over-dependence on AI, which can undermine critical thinking and decision-making.
The use of generative AI models for content synthesis, though widespread, poses a significant risk of generating plagiarized or false content, as highlighted by a very recent notable case involving a New York lawyer. The lawyer, unknowingly, referenced non-existent legal cases in a filing after using the AI tool, ChatGPT, for legal research. This event, marked as an “unprecedented circumstance,” led the judge to demand an explanation for the “bogus” cases cited in the brief. The lawyer later expressed regret for his reliance on the AI tool and committed to ensuring the authenticity of any AI-generated content in the future. This case underscores the importance of cautious use and meticulous scrutiny of generative AI models, particularly in critical domains where misinformation can have serious consequences.
As we stand on the threshold of generative AI becoming mainstream, it’s critical that we resolve its inherent challenges – Generative AI technology is indeed revolutionary and transformative, but it’s not without its pitfalls and limitations. As we venture deeper into this new era of technology, it’s vital to use these tools with understanding and caution, leveraging their strengths while keeping aware of their weaknesses. It is not about who uses AI the most, but who uses it best – with wisdom, discernment, and a focus on ethical considerations.
