Embarking on the Generative AI Journey - View from a Chemical Industry Participant

Embarking on the Generative AI Journey - View from a Chemical Industry Participant

With so many headlines spinning these days around the pros, cons and ethics of Generative AI, it was valuable to spend a little time to learn more. There are numerous courses available via online LinkedIn learning, highly recommended.

As a recent novice, it was relatively easy to grasp the basics, become an amateur prompt engineer and start putting these models to use. I can only imagine the potential in more experienced hands.

Whether we like it or not, agree or disagree with its advancement, Generative AI looks set to have a profound impact on so much around us. This not least in the workplace, in industry and through the various commercial channels which many of us are so very familiar with. Productivity is so often referred to.

It's particularly worthwhile to get comfortable with the basics, inform your own views and consider the implications - good, bad or otherwise. Timely to put suitable governance oversight, policies, training and guidance in place for folks in key sectors and companies before rapid adoption takes hold.

Many articles have been published on the topic of AI. These perhaps felt trend like and futuristic only a few years ago, but they are all so real today. So I thought it would be entertaining to ask Chat GPT itself for an opinion about the use and proliferation of Generative AI. The responses were insightful, with a longer list of arguments against its own use than in support. A curious set of outcomes perhaps which you can check out below for a light-hearted introduction to this topic.


?? For General Application

When I asked Chat GPT for a short evaluation of the pros and cons of generative AI, it concludes that its use should be approached with careful consideration of the following arguments, taking into account ethical, legal, and societal implications. It says balancing the benefits with the potential drawbacks is essential to responsible and effective deployment of this technology.

Arguments FOR the use of Generative AI

  • Efficiency | Helps automate the creation of content, such as text, images & videos, saving time & resources.
  • Creativity | Can generate novel & creative content, aiding artistic & innovative endeavors.
  • Personalisation | Can be used to personalize user experiences, like recommending products or content tailored to individual preferences.
  • Cost Savings | Businesses can cut costs by automating tasks that would otherwise require human labour.
  • Data Augmentation | Generative models can create synthetic data, which can be valuable for training other AI systems when real data is scarce.
  • Innovation | Can foster innovation by enabling the rapid development of new products, services & applications.
  • Consistency | AI-generated content can maintain a consistent style or tone, which can be challenging for human creators to achieve over time.
  • Accessibility | AI-generated content can make information more accessible through translations, summaries & other forms of content generation.
  • Scalability | Businesses can easily scale content production to meet growing demands with the help of generative AI.

Arguments AGAINST the use of Generative AI

  • Bias & Ethics | Generative AI can perpetuate & even amplify biases present in the training data, leading to unfair or harmful outputs.
  • Loss of Jobs | Automation through generative AI can lead to job displacement, particularly in industries reliant on content creation.
  • Quality Control | AI-generated content may lack the quality & accuracy of human-produced content, potentially damaging a brand's reputation.
  • Security Risks | Generative AI can be used maliciously to create deepfake videos or generate convincing fake news.
  • Legal & Copyright Issues | Determining ownership & copyright of AI-generated content can be complex & contentious.
  • Dependency | Overreliance on AI for creative or critical tasks may erode human skills & decision-making capabilities.
  • Environmental Impact | Training large generative models requires significant computational power & can contribute to increased energy consumption & carbon emissions.
  • Transparency | AI-generated content often lacks transparency, making it difficult to understand how a particular output was generated, which can be a concern for critical applications.
  • Creative Stagnation | Over-reliance on AI creativity may stifle human creativity & discourage original thinking.
  • Privacy Concerns | The use of generative AI in applications like deepfakes can compromise individual privacy & lead to personal data misuse.
  • Unintended Consequences | Generative AI can produce unexpected & unintended results, which can be challenging to predict or prevent.
  • Regulatory Challenges | The rapid development of generative AI has outpaced regulatory frameworks, leading to uncertainties around its use & oversight.
  • Erosion of Authenticity | As AI generates more content, there is a risk of eroding the authenticity & genuineness of human experiences & interactions.
  • Resource Intensive | Training & deploying advanced generative models can require substantial computational resources, limiting access to smaller organizations & individuals.

Generative AI offers a range of benefits, from efficiency and creativity to personalization and cost savings. However, it also raises significant ethical, social, and practical concerns, including bias, job displacement, and privacy issues. Striking a balance between harnessing the potential of generative AI and addressing its challenges is essential to its responsible and beneficial use in various domains.


?? Domain - Chemicals Industry

When I probed Chat GPT further on the potential application of generative AI across the chemicals industry as a specific domain, it raised some additional points of note.

Arguments FOR the use of Generative AI:

  • Drug Discovery | Generative AI can accelerate drug discovery by predicting molecular structures and properties, potentially reducing the time and cost of developing new pharmaceuticals.
  • Material Design | AI can help design new materials with specific properties, such as lightweight and durable composites or advanced coatings for various industrial applications.
  • Process Optimisation | Generative AI can optimize chemical manufacturing processes, leading to increased efficiency, reduced waste, and lower energy consumption.
  • Environmental Impact | AI can be used to develop more environmentally friendly chemicals and processes, helping the industry reduce its ecological footprint.
  • Safety Enhancement | AI can predict and prevent accidents by analyzing data and identifying potential hazards in chemical production and handling.
  • Customisation | AI can help design chemicals and formulations tailored to specific customer or industry needs, allowing for greater customization.
  • Speed & Cost Efficiency | Generative AI can significantly reduce the time and cost required for research and development in the chemicals industry.
  • Competitive Advantage | Companies that adopt generative AI early can gain a competitive advantage by developing innovative products and solutions faster than their competitors.
  • Predictive Maintenance | AI can predict when equipment and infrastructure in chemical manufacturing facilities need maintenance or repair, minimizing downtime and production interruptions.

Arguments AGAINST the use of Generative AI:

  • Safety Concerns | The application of AI in chemical processes raises safety concerns, as errors or vulnerabilities in AI systems could lead to accidents or disasters.
  • Complexity & Expertise | Developing and implementing generative AI systems in the chemicals industry requires significant expertise, which can be a barrier for many companies.
  • Ethical Considerations | The use of AI in designing chemicals, especially pharmaceuticals, raises ethical concerns about transparency, accountability, and the potential for unintended consequences.
  • Quality Control | AI-generated chemical designs may not always meet the required quality and safety standards, leading to regulatory and product quality issues.
  • Job Displacement | With AI automating tasks in the chemicals industry, there may be concerns about job displacement for workers in traditional roles.
  • Data Privacy | The use of AI in chemical research relies on extensive data, which can lead to privacy and security risks if not handled properly.
  • Regulatory Challenges | AI-generated chemicals may pose regulatory challenges, as existing regulations may not be well-equipped to address novel AI-designed substances.
  • Environmental Impact | While AI can help develop environmentally friendly solutions, it can also consume significant computational resources, contributing to its own environmental impact.
  • Lack of Data | In some areas of the chemicals industry, there may be a lack of high-quality data to train generative AI models effectively.
  • Reliability & Accountability | If an AI-generated chemical or material fails, it can be challenging to determine accountability, potentially leading to legal and liability issues.
  • Resistance to Change | The chemicals industry has historically been slow to adopt new technologies, and there may be resistance to implementing generative AI solutions.
  • Loss of Human Expertise | An overreliance on AI could lead to a decline in the development of human expertise in chemical research and development.
  • Security Risks | The use of AI in chemical processes may expose vulnerabilities to cyberattacks and industrial espionage.
  • Sustainability | While AI can contribute to sustainability efforts, there is a risk that it may be used to develop chemicals and processes that prioritize profit over environmental concerns.


?? Domain - Specialty Chemicals Industry

When I took the enquiry of Chat GPT one level further to the potential application of generative AI across the specialty chemicals industry as a more specific domain, it tailored its response to these further points below.

Arguments for the Use of Generative AI:

  • Efficient Molecular Design | Generative AI can help chemists design new molecules more efficiently. It can generate a vast number of molecular structures and predict their properties, potentially leading to the discovery of novel specialty chemicals with desirable characteristics.
  • Accelerated R&D | AI-driven algorithms can significantly speed up the research and development process. This can reduce the time and resources required to bring new specialty chemicals to market, improving competitiveness.
  • Cost Reduction | By automating certain aspects of chemical research, generative AI can reduce labor costs and the need for expensive laboratory equipment. This can make R&D more cost-effective for specialty chemical companies.
  • Customisation | Generative AI can be used to tailor specialty chemicals to specific customer needs. This level of customization can lead to higher customer satisfaction and increased market share.
  • Predictive Analytics | Generative AI can analyze vast amounts of chemical data, allowing for better predictive analytics. This can help specialty chemical companies anticipate market trends, customer preferences, and potential issues, leading to more informed decisions.
  • Reduced Environmental Impact | AI can optimize chemical processes, minimizing waste and energy consumption. This can align with sustainability goals and reduce the environmental footprint of the specialty chemicals industry.
  • Drug Discovery & Healthcare | In cases where specialty chemicals intersect with pharmaceuticals, generative AI can accelerate drug discovery by suggesting potential compounds for various medical applications, potentially leading to breakthrough medications.

Arguments Against the Use of Generative AI:

  • Safety Concerns | AI-generated molecules may have unforeseen safety or environmental risks. Without thorough human oversight and testing, there's a risk of releasing harmful chemicals into the environment.
  • Lack of Creativity | While AI can generate numerous possibilities, it may lack the creativity and innovation that human chemists bring to the table. Some breakthroughs in specialty chemicals may rely on intuition and serendipity.
  • Ethical Concerns | The use of AI in chemical research can raise ethical questions, particularly regarding intellectual property. Determining ownership and patent rights for AI-generated chemical designs can be complex and contentious.
  • Data Dependency | Generative AI relies on large datasets to make accurate predictions and designs. If a company lacks sufficient data or has poor-quality data, the effectiveness of AI may be limited.
  • Regulatory Challenges | The specialty chemicals industry is subject to strict regulations. Implementing AI in R&D may require significant regulatory approvals and compliance efforts.
  • Quality Control and Validation | Ensuring the quality and safety of AI-generated specialty chemicals can be challenging. Rigorous validation and testing processes are necessary to guarantee that these chemicals meet regulatory standards and perform as expected.
  • Human Expertise | AI may not fully replace the expertise of human chemists. Human judgment, intuition, and the ability to adapt to unexpected challenges are crucial in specialty chemical development, and over-reliance on AI may diminish these skills.
  • Data Privacy | Handling sensitive chemical data with AI can pose data privacy risks. Protecting proprietary information and customer data becomes increasingly critical as AI systems process and generate large datasets.
  • Long-Term Viability | The specialty chemicals industry may face challenges in maintaining AI systems over the long term. Evolving technology, changing regulations, and shifts in market demand can make it difficult to adapt AI solutions over extended periods.
  • Initial Implementation Costs | Implementing AI in R&D and production processes can require a significant upfront investment in infrastructure, software, and talent. Smaller specialty chemical companies may find this cost-prohibitive.

In summary, the use of generative AI across the specialty chemicals industry offers the potential for faster innovation and cost savings but also presents challenges related to safety, creativity, ethics, data, and regulation. Companies should carefully consider these factors when deciding whether and how to implement AI in their operations. A balanced approach that combines AI capabilities with human expertise and experience may offer the best outcomes in this domain.


Closing Comment ??

I have been both staggered, inspired and somewhat frightened in equal measure by the sheer power and capabilities of these AI models. They have certainly come of age, the power of these models looks set to advance dramatically over the next 18-24 months if you listen to experts in the field. Where will this take us?

Image not created with Generative AI ! | On the road in Scotland | copyright Colin Morton



Ian Johnston

Head of Product Management - AISEA

12 个月

Great insights Colin Morton. You have definitely sparked my interest in this new space.

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Simon Griffiths

Retired... at None right now

12 个月

Colin Morton great read and an area we all need to get up the curve on. It’s coming for us and our children whether we like it or not.

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Trevor Gauntlett

I help chemical companies sell into the US $100 billion+ per annum Lubricants industry

12 个月

One of the things we have to beware of is placing a personality onto the AI. It would be easy to say or write that the AI is "aware of" or "acknowledges" its' shortcomings or disadvantages. It's simply collated them. I've not joined the AI debate so far, but its arrival makes it even more important for our educators to start considering how we teach critical thinking to children in the age of misinformation. Unfortunately, I think it will be designed into AI-based tools before our education systems can be changed and should become a USP of those AI-based tools. If anyone ever sees a patent for an AI-based tool utilising critical thinking, please remember this post. I'll give you 10% of my royalties after we challenge the patent ??

Lady Tina Reading Dip DigM MCIPR

Magazine Editor (x3!) LinkedIn Storyteller passionate about accessibility, community & sustainability

12 个月

Very interesting article. I have just written a piece for the mag about AI but this is far more extensive than mine! (Love the picture too - my sister-in-law lives in Perth).

Charu Tripathi

Marketing Director | Driving innovation and growth through cutting-edge marketing automation solutions

12 个月

Enriching article, Colin Morton! AI can be a blessing if used appropriately. The potential is huge.

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