Since
the release of ChatGPT and other natural language processing (NLP) and large
language model (LLM) tools, there has been a never-ending stream of articles
and information about artificial intelligence in our industry. This advancement has
been in the works for years. However, it is new to many of us, and the
development of this technology has set the world on fire.

Albert
Einstein and Isaac Newton have already said, “What we don’t know is much more
than what we know,” and “What we know is a drop. What we don’t know is an
ocean.” So with those words of wisdom, it is critical for us to take a beat,
learn all we can, and try to understand the implications of this new technology — at
least as best we can today.

HITEC
2024 was abuzz with the potential uses of NLP; all the while, Google,
Microsoft, Adobe and many more technology giants have already implemented and
integrated NLP into their software. If you use any of these platforms, you may
have seen the transformation as search engines reveal more relevant data, cloud-based
productivity platforms seem to know what you want to say before you say it (and
often it certainly says it better!) and graphic platforms are leveraging works
in progress to teach NLP platforms. 

With
all that going on in the background, it can be a slippery slope for data
providers to ensure the protection of personal data or intellectual property.
For creatives, we are already seeing a shift. We have seen companies like Adobe update the terms of service for
their generative AI products, stating that Adobe may use techniques such as
machine learning to analyze user content to improve its services and software.

While
the reaction to this has been mixed at best, the reality is that we will never
know the exact nature of how NLP is being integrated into our daily lives
unless we are part of the changes being implemented. What we can see is the
results and, hopefully, how those results make our lives better. 

What
we do need is assurance that the advancements in development work for the human
good. To do that, we require humans to play a key role in these developments. 

What
is human-in-the-loop in AI & ML?

Many
of us in the hospitality industry have never heard of this term. I read an
interesting article written long before ChatGPT made its appearance in the
market. Here’s a brief primer.

Human-in-the-loop (HITL) machine
learning is a
collaborative approach that integrates human input and expertise into the
life cycle of machine learning (ML) and artificial intelligence systems. Humans
actively participate in the training, evaluation or operation of ML models,
providing valuable guidance, feedback and annotations. Through this
collaboration, HITL aims to enhance the accuracy, reliability and adaptability
of ML systems, harnessing the unique capabilities of both humans and machines.

While
ML models possess remarkable capabilities, they can benefit from human
expertise in areas requiring judgment, contextual understanding, and handling
incomplete information. HITL bridges this gap by incorporating human input and
feedback into the ML pipeline. 

This
human collaboration enhances adaptability and allows models to evolve with
changing user preferences and real-world scenarios. By integrating the human
element, we empower ML systems to navigate the complexities and nuances that
often challenge purely algorithmic approaches. This pairs our ability to
contextualize, think critically and sift through the noise with algorithmic
machine learning models’ incredible ability to process and quickly synthesize
huge amounts of data so the strengths of both humans and machines shine
through.

The importance of HITL in revenue management

There
is a lot of concern that AI and NLP advancements will replace jobs. Just as the
internet, the cloud and mobile devices have changed our lives and replaced the
way we used to do things, AI and NLP will advance our society further, and it
seems much faster. For revenue management professionals, it will be incredibly
important to stay connected to the data and rationalize/explain the outputs. 

While
they are great “copilots,” AI-based systems do not have all the answers.
As a human revenue manager, it is critical to be able to question and/or
challenge the data or outputs and validate accuracy and relevance. In fact,
revenue managers have a great advantage with AI since they are already data
custodians and have been using AI-driven RM systems.

Will
people lose their jobs because of AI? No, but they will lose their jobs to
people who know how to use AI tools and systems most effectively. That is no
different than someone not knowing how to use a property management system to check-in guests or
refusing to engage with email – it is the future, and the more people embrace
it, the more they will be equipped to take advantage of it.

We
will see AI architects in the future who will look at the right utilization of
AI across an organization — how can AI be deployed to be most effective across
the entire business? What insights can be derived from a system that eventually
has access to all accessible data? These are questions that remain to be seen,
but certainly there are many clear benefits to keeping humans in the loop. 

Enhanced
accuracy and reliability require human input and oversight to significantly
improve the accuracy and reliability of ML models. Bias mitigation needs human
involvement to help identify and mitigate potential biases in data and
algorithms, promoting fairness and equity in ML systems. 

Increased
transparency and “explainability” are crucial. Human insights help explain
behind-model decisions, enhancing their transparency and interpretability. This
also improves user trust. The inclusion of human feedback and collaboration
fosters trust among end-users, increasing their confidence in ML systems.

Finally,
continuous adaptation and improvement are necessary. Feedback gathered during
HITL serves as a valuable source for ongoing model improvement and adaptation
to evolving real-world conditions. 

Will there be a time when humans aren’t required?

Remember the advent of the internet, email and cloud computing?
Remember the pains we went through to understand these advancements? Did we
understand how these technologies would change our lives? Certainly not. Who
knew then that we could order food, a ride or check our home security alarm
from another location through a device in our pocket? Who knew we could go
online and order anything from anywhere at any time? 

The
entire premise of AI and NLP is to help humans be more productive and
efficient. But with great change comes great responsibility. Data companies are
leveraging these advances to ensure users can interact with data more easily
and quickly. It removes a lot of the “button pushing” and changes our
relationship with data. It will be imperative to build safety protocols to
protect sensitive and proprietary data.

The
list goes on, but when we look back, technological advancements have been
moving forward full steam ahead for decades. The emergence of a tool that
speaks our language shouldn’t surprise us. Those who adopt it, learn to use it
and engage fully with its potential will be the game changers and innovators of
tomorrow.