No technology, especially in its early adoption phase, is flawless. Even with the popularity generative AI has gained in the eyes of businesses and consumers, its imperfections cannot be glossed over. Hallucinations and biases in training data, among other issues, cause business owners to be hesitant when considering this solution.
Although some early adopters have found a way to adopt Large Language Models (LLM) as they exist today, many feel that they are essentially left with two options. Wait until improvements arrive or government guidelines are put in place to ensure the safe use of the technology, potentially left behind, or adopt it now and without letting AI touch critical business systems. None of these options are really viable, so what can businesses do from here? Diving beneath the surface of Generation AI coverage to understand both its advantages and disadvantages will help modern businesses determine where they can safely implement tomorrow’s LLMs.
What are the pitfalls of the AI generation?
Each iteration of today’s most popular chatbots draws on a much larger data set, delivering a better solution with each evolution. However, given the black-box nature of generative AI, the characteristics of this technology also change as the underlying dataset evolves. A recent update to ChatGPT responded to concerns about “lazy” and “weird” response behaviors that users had started reporting, with Open.AI citing that one potential reason was ChatGPT’s reinforcement learning process.
While AI developers are still fine-tuning the auditing process, to better understand how and why generative AI programs act the way they do, the hallucinations caused by these programs will require human monitors to act as controls. quality assurance. For businesses, keeping humans informed can seem like a trap, as they are not able to fully convey process AI. In reality, these checks should remain part of the design process.
Business leaders are interested and investing in AI, but implementation hasn’t really caught up
Providing world-class customer service, especially in the digital age, is a priority for all business leaders. To create an exceptional customer journey, you need to be present everywhere, from mobile to social media. As headlines have begun to shift customer expectations for what top-notch customer service should look like, business leaders know that even the most advanced AI available today is not the tomorrow’s dream customer support agent. One of the the main factors preventing businesses trust is key to full AI integration. Developers have noticed and are starting to respond by being transparent around their AI safeguards and responsible practices, but it will still take some time to close the implementation gap.
The impact of the AI generation on businesses is likely to evolve rapidly and for a long time
In the near term, increased attention from developers, businesses, and government regulators will focus on responsible AI development. By creating technical guardrails and enforcing regulations that help ensure the safe and secure use of AI generation at an industrial level, companies looking to integrate AI can ease their concerns. That said, looking to the future, there is little evidence that the innovation curve for generative AI technology has peaked or is slowing down. By continually expanding the training data on which these tools are developed, we will only see the advent of more accurate and secure platforms for businesses. The only question remaining for businesses? How much evidence is needed for them to know it’s time to implement AI, and where can it make the biggest difference?
Finding the Balance Between Live and Virtual Team Members
The AI generation will see the need for oversight by human teams in the near future, and probably forever, but that doesn’t mean there aren’t business use cases that can benefit from it today. Using LLM to generate content for existing virtual agents is one example, allowing businesses to get chatbots up and running faster without increasing risk to the business. Using LLMs is like brewing a powerful potion: each ingredient adds to its potency, but the wrong combination can be disastrous, so make sure you have the right tools needed to mix your potion.
Rasmus Hauch is CTO at Boost.ai. Rasmus brings a wealth of technology leadership experience to boost.ai as Chief Technology Officer. Previously, he was CTO at 2021.AI, leading teams to deliver best-in-class AI/ML solutions. His advisory roles at Proprty.ai, Ryver.ai and Capsule, as well as his long tenure at IBM, reflect his extensive expertise in the field of AI. At boost.ai, Rasmus aims to advance our technology front, aligning with our mission to innovate in conversational AI.
