When it’s Great and when it’s Not so Great

Point of departure

On the 12th of August 2023 my dear friend Feliz, his long time EU companion Paulo, and their children invited me for dinner at an Indian restaurant downtown Vientiane (Laos). We had not seen each other for decades! Only kept in touch using chat apps.

Feliz exhibiting one of his old habits started testing me on the slander of a notoriously narcissistic fake socialist who once joined the Dutch labour party to set tag sailing on a grandiose political career. A career of Me, I, and Myself. That kind of career! The hearsay slander and character assassination of
Mr Frans Timmermans landed very badly with me. I understood what had happened and started to get annoyed. Luckily, my friend Feliz quickly changed the topic to technology. A topic I really love … but the feeling was already bad.

Artificial Intelligence … just a hype, or more

We started talking about Artificial Intelligence. Feliz had recently visited a municipal waste incineration plant where he was shown a very clever design of air pollution prevention technology. AI based emission control. “chatGPT is absolutely great”. Feliz was all in favour of AI. Hold on your horses, I thought … and said: “That’s something completely different”. “That’s a technology based on a very smart way of recycling human work,” if not a legally allowed circumventing approach to straight forward plagiarism, I thought.
“I am sorry, you don’ t understand it”, I said. “I will write an article about it.”

A promise made true … be it though a bit late

This is an article about AI and why it’s absolutely great for controlling and fine-tuning stack gas cleaning at a waste incineration plant while it’s applications in human communication are prone to glitches and worse.

chatGPT is a great tool and assistant

We will start using chatGPT as a research tool. Within just a few seconds it will list a brief showing the distinct differences between the use of Large Language Models and the AI technology used to control and mitigate stack gas emissions of air pollutants. After reading you will understand why these two applications of AI fundamentally differ from each other.

A proper prompt

“Tell me the difference between using LLMs for translation and writing content and AI applied to regulate stack gas cleaning at a municipal waste incineration plant.“

Note that this prompt is an informed and educated query directing chatGPT to proper sources of information.

chatGPT’s answer

Using Large Language Models (LLMs) for tasks like translation and content writing is fundamentally different from applying AI to regulate stack gas cleaning at a municipal waste incineration plant in terms of objectives, methods, and technologies involved. Here’s a detailed comparison:

1. Objectives

• LLMs for Translation and Content Writing:
  • Translation: The primary goal is to accurately convert text from one language to another while maintaining the original meaning, context, and nuances.
  • Content Writing: The aim is to generate coherent, contextually appropriate, and engaging text based on given prompts or topics.
• AI for Regulating Stack Gas Cleaning:
  • The objective is to monitor and control the emissions from the incineration process to ensure compliance with environmental regulations, improve efficiency, and reduce pollutants released into the atmosphere.

2. Technologies Involved

• LLMs for Translation and Content Writing:
  • Natural Language Processing (NLP): Techniques to understand and generate human language.
  • Machine Learning Models: Specifically, transformer-based models like GPT-4, BERT, etc., trained on vast amounts of text data.
  • Text Generation Algorithms: Used to create fluent and contextually relevant text.
• AI for Regulating Stack Gas Cleaning:
  • Control Systems Engineering: Involves the design and management of systems to maintain desired outputs.
  • Sensor Data Processing: Utilizes sensors to monitor gas composition, temperature, pressure, and other relevant parameters.
  • rol: Algorithms to predict and adjust the operation of the gas cleaning process in real-time.
  • Predictive Maintenance: AI models to foresee and mitigate potential system failures or inefficiencies.

3. Methods

• LLMs for Translation and Content Writing:
  • Data Training: LLMs are trained on large datasets containing text in multiple languages and contexts.
  • Inference: The model processes input text to produce translated or newly generated text.
  • Fine-tuning: Models can be further refined on specific datasets for specialized applications.
• AI for Regulating Stack Gas Cleaning:
  • Real-time Monitoring: Continuous collection and analysis of data from sensors.
  • Feedback Control Loops: Automated adjustments to the cleaning process based on real-time data.
  • Predictive Modeling: Using historical and current data to predict future states and optimize the process.

4. Challenges

• LLMs for Translation and Content Writing:
  • Context and Nuance: Ensuring the translation or generated text retains the original context and subtle nuances.
  • Bias and Ethics: Mitigating biases present in training data to produce fair and accurate content.
  • Quality and Coherence: Maintaining high quality and logical coherence in longer pieces of text.
• AI for Regulating Stack Gas Cleaning:
  • Accuracy and Reliability: Ensuring the AI system accurately monitors and controls emissions without failure.
  • Integration: Seamlessly integrating AI with existing mechanical and electronic control systems.
  • Regulatory Compliance: Continuously meeting stringent environmental regulations and standards.

5. Impact

• LLMs for Translation and Content Writing:
  • Global Communication: Facilitates cross-lingual communication and understanding.
  • Content Creation: Enhances productivity and creativity in writing, marketing, and other content-driven fields.
• AI for Regulating Stack Gas Cleaning:
  • Environmental Protection: Reduces harmful emissions and helps in protecting the environment.
  • Operational Efficiency: Improves the efficiency and effectiveness of waste incineration plants.
  • Cost Savings: Potentially reduces operational costs through optimized processes and predictive maintenance.

In summary, while both applications involve AI, the nature of the tasks, underlying technologies, methodologies, and impacts are quite distinct. LLMs for translation and content writing focus on understanding and generating human language, whereas AI for stack gas cleaning is centered on monitoring and controlling industrial processes to ensure environmental compliance and efficiency.

What’s missing in the above brief

chatGPT by lack of resources in the LLMs it used, missed out on an absolutely essential difference between training LLMs and training AI for Regulating Stack Gas Cleaning technology.

Unbiased feedback!

The difference is in the feedback. Whereas LLMs are trained and fine-tuned through human input with all it’s biased and erroneous responses, predictive Ai for regulating stack gas cleaning is trained by the unbiased feedback of sensors in a setup that we engineers know very well as the traditional feedback control loop.

Using chatGPT to create content … Do’s and Dont’s

Not so great!

Generating much too beautiful resumés and CVs.

Artificial intelligence is aiding job seekers. But, over-reliance on it is hurting them. Experts told Worklife that job seekers using AI to help write cover letters or resumes are smart to do so. However, copying and pasting the auto-generated content is a mistake.

It’s becoming easier for hiring managers to recognise AI-created content, which is often stilted and repetitive. AI can make mistakes and will often embellish and inflate your career story, too. So, view the software as an assistant more than a be-all, end-all. That will lead to its best usage.

To be extended later on. This is a work in progress.