In less than a week of its launch, Amazon Q — Amazon’s Copilot competitor — is already facing a threat to its survival as a new report suggests the generative AI assistant could be hallucinating.
Q is grappling with inaccuracies and privacy issues, including hallucinations and data leaks, The Platformer reported, citing leaked documents. Significantly, the report comes as two major studies showed that large language models (LLMs) are highly inaccurate when connected to corporate databases and are becoming less transparent.
However, according to an Amazon spokesperson, Amazon Q has not leaked any confidential information.
“Some employees are sharing feedback through internal channels and ticketing systems, which is standard practice at Amazon. No security issue was identified as a result of that feedback. We appreciate all of the feedback we’ve already received and will continue to tune Q as it transitions from being a product in preview to being generally available,” the spokesperson said.
Despite Amazon’s tall claims of being a work companion for millions and millions of people, Amazon Q may not be ready for corporate usage, according to analysts tracking the industry.
“If hallucinations are present, you cannot use it for decision-making in a corporate setting,” said Pareekh Jain, CEO of EIIRTrend & Pareekh Consulting. “It’s fine for personal use or obtaining information, but not for decision-making processes.”
More testing needed
Amazon may face substantial testing challenges before its generative AI assistant is ready for commercial release. Jain emphasized the importance of conducting extensive internal trials to ensure readiness.
“I think they need to do more testing with internal employees first,” Jain added. “Obviously, that is what they are doing now. In the end, nobody from external sources has reported these issues. There are two things here: one is the data, and the other is algorithms. They have to see if it’s an issue with the data or with the algorithm.”
Q leverages 17 years of AWS’ data and development proficiency and is designed to work as a versatile tool for enterprises. Given the direction of the industry, much is at stake for Amazon with this AI offering.
While hallucinations don’t undermine the potential of generative AI for consumer and enterprise use cases, proper training is essential, according to Sharath Srinivasamurthy, associate vice president at market research firm IDC.
“Training the models on better quality data, prompt augmentation (guiding users with predefined prompts that the model can understand easily), ongoing finetuning the models on organization or industry-specific data and policies, augmentating a layer of human check in case of response being suspicious are some of the steps that need to be taken to make the best use of this emerging technology,” Srinivasamurthy said.
Will hallucinations prompt urgency to regulate?
Reports of hallucinations raise concerns about the need for regulations and the severity of rules that might kick in at some point. However, Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research pointed out that any resulting regulations can be counterproductive.
“Any regulation can, in fact, slow down the exchange and utilization of data,” Gogia said. “So, for now, the less regulated this industry is, the better. This allows for easier and smoother use of data. Take OpenAI’s GPT as an example – if there were stringent guardrails around what data could be captured, then it wouldn’t have taken off.”
Jain also suggested that setting external boundaries may not be a feasible idea, but more effort may be made by the companies themselves.
“Regulations may exist, but the focus is primarily on self-regulation,” Jain explained. “While regulations and guidelines are necessary, there’s a limit to how much auditing can be enforced. The emphasis should be on responsible AI, where the logic can be explained to customers instead of creating ‘black box’ systems. However, there is a threshold beyond which, in my view, the responsibility shifts more towards how enterprises perceive and implement these measures as a matter of security.”
All eyes on Amazon
While such insights shed light on the necessity for more robust internal testing and a tilt towards self-regulation, the path to deploying AI in enterprise environments is fraught with complexity. The onus is now on Amazon to navigate these challenges, especially because of its late entry into the segment.
“AWS is somewhat of a laggard in this space, with Microsoft and Google currently leading,” Jain added. “Therefore, people have higher expectations, particularly regarding chatbots and other related technologies.”
(This story has been updated with comments from an Amazon spokesperson.)
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