Keeping a Human in the Loop: How AI Depends on Creative Input


Keeping a Human in the Loop: How AI Depends on Creative Input

As those in the language services sector know, delivering a message involves much more than syntax, diction, or personalization. Ultimately, it’s a matter of tone, feeling, and understanding. However, given its capacity for increasingly novel facsimiles,  that does not preclude or compartmentalize AI.

While businesses may be uncertain about their exact niche, it’s seen plenty of global investments, estimated at just under a trillion USD between 2013 and 2022. It even prompted head-turning predictions by Goldman Sachs of a 7% and $7 trillion increase in GDP by 2033.

Build It as You Drive

Such figures paint a telling picture of the general landscape. But what does the terrain look like for those working directly with AI? Beyond a “less vs. more” perspective, as though there’s some competition between human and machine output, a more nuanced view is essential to find the exact balance between each element.

That’s especially true for language services professionals who, perhaps more than anyone, are ushering AI into daily life. Language is where AI’s role in society has most rapidly taken shape. A closer look reveals that users and developers are not just playing a role in AI’s development but wholly defining its value and utility.

Finding Your Niche Means Finding Your Footing First

A human-in-the-loop (HITL) approach to AI isn’t a matter of preference or desire. It’s part and parcel of its development and application at every level.

AI dramatically reduces the total amount of labor required for a fully functioning language prompt. However, it leaves the question of what type of labor is fully open.

There needs to be more debate about whether AI streamlines and hastens the technical side of digital invention. Rather than diminish human involvement, however, this only increases the potential for creative human input—and this goes well beyond mere machine oversight or error correction.

If the creation of new value-adding tools becomes more accessible and faster, there will be a surplus of time to use and refine them.

The Human Machine Interface (HMI) Goes Viral

By any measure, human oversight is essential to shaping the value of any finished AI product or content-production system. Human input is also intrinsic to AI’s development—increasingly so. After all, AI must mine growing repositories of human-generated content to exist and improve.

The need to mine content is most accurate for user-facing content, for which wordsmiths and graphic designers continually vie (or preferably collaborate) for a more significant share of human attention. But in their infancy, almost purely textual formats have made for the most popular AI tools, and by a wide margin.

Beyond any value judgments, this tells us that text-based engagement with AI is more helpful in determining where in the technological loop humans are and will likely remain in the foreseeable future.

A New Spin on “Human Resources”

According to one survey, 97% of business owners believe front-running artificial intelligence tools will benefit them. However, as much as companies may want to entrust business functions to AI, determining the right strategy remains open-ended. After all, the pros and cons of any AI strategy can and will scale to dramatic proportions.

Thus, business owners and developers (and even many consumers) are paying more attention to the fact that “garbage in, garbage out” is alive and well in the AI era. As the programming adage goes, the quality of a software’s output depends on the quality of its input. There’s no getting around that, just as AI users can’t likely eliminate the need to condition data.

This requires overcoming learning curves associated with specific AI system prompts, evaluating the results, and then altering prompts and input data accordingly—i.e., machine training  (MT). It can even be challenging to determine where and when output data originated, especially when using free apps (which usually restrict access to the latest data repositories).

That’s not even addressing the need to protect a company legally and ethically. The topic of plagiarism and error correction requires significant foresight. As with any technology, curated products require a certain proportion of trust and oversight, especially when brand new.

Whether onboarding staff or equipment, businesses must focus on developing and refining their automation tools. It can feel like hitting a moving target as the tools continually change. But it’s a target most business leaders consider worth their time and effort.

Demonstration of Value: The Antidote to Speculation

Above all, businesses must ensure the cost of AI does not exceed its value. As they level their sights on a continually morphing horizon, what are some of the most promising language applications for AI?

Internally, AI is already very effective as a research aid. Quickly summarizing large volumes of data can make all the difference in maintaining a market edge, allowing businesses to outpace competitors still reliant on manual methods.

Large language models (LLMs) that emphasize HITL use further refine the efficacy of data summarization by improving their systems in trade-specific ways. It also gives businesses an impetus to develop their proprietary training models with proven ROI. Over time, the HITL for a given application will shrink and become faster—but only if companies commit to it.

Where Global Becomes Local

HITL strategies are also indispensable to modern translation services, for which even more conditioned MT inputs are necessary. This aspect is true for any culturally dependent application, where the ability to automate localization services has a high potential for replicating and scaling serious mistakes (not just spelling and grammar).

When maintaining an audience hinges on impeccable cultural sensitivity and relevancy, poor translation issues could turn audiences away. Incorrect geolocation services could also be less-than-endearing, as it may create a depersonalizing effect. In the language services field, playing your cards close is often far better than taking an inept swing, primarily when serving international markets.

However, this need for caution should encourage those who’ve achieved confidence in a given artificial intelligence application or strategy. After all, automation services have driven sales to epic proportions in the early 2000s and 2010s. What chatbots have done for support services, integrated AI functions will likely also do for sales and marketing.

Purpose Driven and Innately Human

AI is inherently in flux, but its groundwork is firmly established and available. Individuals and organizations can use innumerable AI apps to integrate digital functions and test the results. As AI impacts language services of all kinds, we’ll undoubtedly see an influx in strategies for integrating client-facing communications with tried-and-true sales and marketing automation.

Embracing a HITL philosophy will counterintuitively accelerate AI development by honing the LLM and MT systems on which it depends. Integrating AI into manual workflows more, not less, will soften the learning curve and reveal new, value-adding ways to derive more engagement with fewer resources.

Software tools have become much easier to use. But they are by no means self-directing.

An Unbroken Cycle Between Humans and Technology

AI’s advent has sparked speculation over the role humans will take in its less-than-autonomous development. However, this narrative implies that the opposite is possible or even likely. A better question would be: who wants a closed-loop AI system devoid of human input, and to what ends?

Sure, it minimizes heavy labor. People understand the advantages of automation implicitly. However, most routine users of AI are more than happy to invest time and attention into it. After all, judging the merits of what an AI program produces innately feeds its development, even if we hardly lift a finger.

AI serves as a phenomenal starting point for language projects of any kind. It’s even a reliable assistant through most of a project’s production. But the finishing touches almost always depend on a human’s say-so, just as they have with any tool throughout human history.

Vistatec thrives on collaboration because, for us, language service is more than a trade. It’s a labor of love. Contact us with any insights about the role of AI in your language field, and be a part of the conversation.