Keep travel in a “humanistic cycle”

Since the release of ChatGPT and other Natural Language Processing (NLP) and Large Language Model (LLM) tools, there has been a constant flow of articles and information about AI in our industry. This advancement has been years in the making. However, for many of us, it is still new, and the development of this technology has shocked the world.

Both Einstein and Newton said, “There is more we don’t know than we do know,” and “What we know is a drop of water; what we don’t know is an ocean.” So it is critical for us to take these words of wisdom and pause, learn as much as we can, and try to understand the implications of this new technology — at least as best as we can today.

HITEC 2024 was filled with potential uses for NLP; meanwhile, Google, Microsoft, Adobe, and many other tech giants have already implemented and integrated NLP into their software. If you use any of these platforms, you may have seen this shift as search engines surface more relevant data, cloud-based productivity platforms seem to know what you want to say before you say it (and usually it definitely says it better!), and graphics platforms are leveraging ongoing work to teach NLP platforms.

With all this happening behind the scenes, it can be a dangerous trend for data providers to ensure the protection of personal data or intellectual property. For creatives, we have seen a shift. We have seen like Adobe Updated the terms of service for its generative AI products to note that Adobe may use techniques such as machine learning to analyze user content to improve its services and software.

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

What we really need is to ensure that development progress benefits humanity. To do that, we need humans to play a key role in these developments.

What is human-computer interaction in artificial intelligence and machine learning?

Many of us in the hospitality industry have never heard of this term. I read an interesting article written long before ChatGPT appeared on the market. Here is a quick primer.

Human-Computer Interaction (HITL) Machine Learning HITL is a collaborative approach to infuse human input and expertise into the lifecycle 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 improve the accuracy, reliability, and adaptability of ML systems, leveraging the unique capabilities of both humans and machines.

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

This human-machine collaboration enhances adaptability and allows models to evolve as user preferences and real-world scenarios change. By incorporating the human element, we enable machine learning systems to address complexities and nuances that are often difficult to address with purely algorithmic approaches. This combines our ability to contextualize, think critically, and sift through the noise with the amazing ability of algorithmic machine learning models to process and quickly synthesize large amounts of data, allowing the best of both humans and machines to emerge.

The Importance of HITL in Revenue Management

There is a lot of concern about advancements in AI and NLP replacing jobs. Just as the internet, cloud, and mobile devices have transformed our lives and replaced the way we did things in the past, AI and NLP will further advance our society, and seemingly at a much faster pace. It is important for revenue management professionals to stay connected to the data and rationalize/interpret the output.

While they make excellent co-pilots, AI-based systems don’t want has all the answers. As a human revenue manager, it is critical to be able to question and/or challenge data or outputs and verify accuracy and relevance. In fact, revenue managers have a huge advantage in AI because they are already data custodians and have been using AI-driven RM systems.

Will people lose their jobs to AI? No, but they will lose jobs to those who know how to use AI tools and systems most effectively. It’s no different than someone not knowing how to use a property management system to check in a guest or refusing to use email – it’s the future, and the more people embrace it, the better equipped they will be to take advantage of it.

In the future, we will see AI architects looking at how to properly use AI across the organization – how can AI be deployed across the enterprise to achieve the greatest effect? ​​What insights can be gained from a system that ultimately has access to all accessible data? These questions remain to be seen, but there are undoubtedly many clear benefits to having humans involved.

Improving accuracy and reliability requires human input and supervision to significantly improve the accuracy and reliability of ML models. Reducing bias requires human involvement to help identify and reduce potential biases in data and algorithms, and promote fairness and impartiality in ML systems.

Improving transparency and “explainability” is critical. Human insight helps explain the reasoning behind model decisions, making them more transparent and explainable. This also improves user trust. Incorporating human feedback and collaboration can increase end-user trust and strengthen their confidence in ML systems.

Finally, continuous adaptation and improvement are essential. Feedback collected during HITL is a valuable resource for continuously improving the model and adapting to changing real-world conditions.

Will there come a day when humans are no longer needed?

Remember the advent of the internet, email, and cloud computing? Remember the pain we went through to understand these advances? Did we understand how these technologies would change our lives? Of course not. Who knew we could order food, hail a ride, or check our home security alarm from another location, all from the device in our pocket? Who knew we could go online and order anything, anytime, anywhere?

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

The list goes on, but looking back, technological progress has been moving at full speed for decades. The emergence of a tool that can communicate with us shouldn’t surprise us. Those who adopt it, learn to use it, and use it to its full potential will be the game changers and innovators of tomorrow.