Operational ExcellenceMarket Insight: Ten Applications Of Large Language
Models For Industry

By Joe Lamming
With Malavika Tohani

verdantix.com

July 2023

Operational ExcellenceMarket Insight: Ten Applications Of Large Language
Models For Industry
By Joe Lamming
With Malavika Tohani

July 2023

This report highlights the potential of large language models (LLMs), first brought to the mainstream by
OpenAI’s ChatGPT. It details their inner workings and outlines how they can be integrated as agents within current
technologies, data structures and industrial procedures, to create substantial market impact. We explore theTransformer architecture, its significance, and its limitations. Drawing upon meticulous analysis of recent academic
research, commercial product launches, and contributions from the open-source AI community, this report guides
readers through comprehensive explanations and intuitive graphical depictions of LLMs functioning as chatbots,reasoning companions, information retrieval systems and agent-style task completion aides. Our findings illuminate
a future in which data management, machine learning and LLMs intersect to fuel unparalleled efficiency andinnovation in the heavy industries sector.

Table of contents
Advances in data management and machine learning techniques have become rocket fuel				for AI development

3

Transformer-based LLMs dominate today’s generative AI landscape
Ten applications of LLMs for industrial operations

Table of figures
Figure 1. The transformer architecture enables deep language understanding					4Figure 2. Encoders distil the meaning of sentences								5Figure 3. Transformer decoder LLMs generate text								6Figure 4. LLMs can model and predict real-world cause and effect							7Figure 5. Retrieval-augmented generation (RAG) can eliminate LLM hallucination					8Figure 6. LLM agents can use tools to complete tasks								9Organizations mentioned
ABB, Anthropic, Apple, AspenTech, AVEVA, C3 AI, Cognite, DataStax GitHub, Google, Hexagon, HighByte,
Hitachi Vantara, International Electrotechnical Commission (IEC), Meta, Microsoft, NVIDIA, OpenAI,
Rockwell Automation, Salesforce Research, Seoul National University, Siemens, SparkCognition, Stability AI,
Technology Innovation Institute, ThirdAI, Timeseer.ai.

Market Insight: Ten Applications Of Large Language Models For Industry
Copyright © Verdantix Ltd 2007-2023. Licensed content, reproduction prohibited2

Advances in data management and machine learningtechniques have become rocket fuel for AI development
Language, to some, is a game where the objective is communication. From the abacus of antiquity, to Charles
Babbage's Difference Engine's knobs and dials in the early 19th century, to 20th century hole-punched paper –to today's text-based programming languages like JavaScript, C++, and Python – these advancements represent
the continuing human drive towards accessible computation.
Generative artificial intelligence (AI), a popular catch-all term for highly sophisticated chatbots, image and video
generators and speech synthesizers, has enjoyed a warm winter in the spotlight, and is currently basking in thesummer sunshine (see Verdantix Market Insight: Understanding The Rapidly Evolving Landscape Of Generative AI).
While disparate use cases for generative AI call for vastly different model architectures, all – so far, without exception– require gargantuan quantities of data only made possible in recent years. Large language models (LLMs) havedominated discourse in generative AI, driving popular applications such as Anthropic’s Claude, Google’s Bard,
Microsoft’s Bing Chat and OpenAI’s ChatGPT. Under the hood, LLMs are often described as 'just predicting the next
word', based on prior context. Search engines, smartphone keyboards and email software all provide auto-complete
functionality, taking in the context of what has been written so far to provide a best guess for what comes next.
LLMs, however, greatly increase the accuracy of these predictions, by learning to guess the next token (word or word
fragment) in a sentence through trial and error with vast quantities of text scraped from the public internet, digitizedbooks, academic papers and proprietary sources.
In 2018, OpenAI’s first generative pre-trained transformer (GPT), a deep neural network machine learning (ML)language model, was trained on around 5GB of text. Subsequent models were trained on ever larger data sets,with GPT-2 trained on 40GB of text and GPT-3 on 570GB – equivalent to around 220 billion words. In 2022, Google’s
Pathways Language Model (PaLM) was trained on around 600 billion words. Arriving in 2023, Meta’s LLaMA andDubai’s Technology Innovation Institute’s Falcon LLM learned from approximately one trillion words – while Google’s
PaLM 2 saw three trillion. Not only have advances in data acquisition contributed to the rise of vast, high-quality
data sets for developing powerful generative AI – but so have ML techniques, optimizations and data management(see Verdantix Smart Innovators: Industrial Data Management Solutions). LLMs, distilling the natural languageof billions of people, symbolize the next milestone, fostering effortless communication between data scientists,
executives and customers – and in industry, between facilities managers and frontline workers.

Transformer-based LLMs dominate today’s generative AI landscape
In 2017, researchers at Google Brain, now part of Google DeepMind, released a novel deep learning architecture
named the Transformer. Its ability to efficiently split inputs, such as text or images, into pieces, through a process
called self-attention, paved the way for the development of powerful language translation, summarization andeven computer vision systems. For natural language, this enables a model to understand the deeper meaningof a whole sentence. All of today’s most powerful LLMs utilize variations of the transformer architecture.
Transformer-based LLMs enable:•

Deep understanding of natural language.
In enterprise and industrial environments, the ability to quickly search large document stores has traditionallybeen provided by keyword matching and ‘fuzzy search’ algorithms. While such methods deliver results at highspeed when looking for specific words, part numbers and personnel, there is a reason people use sentences
to communicate complex information rather than single words. Words, in sequence, provide context to one
another. Understanding intent is key to turning words into actions – and the transformer architecture’s multiple
attention heads help it understand not only individual words, but how they relate to other words, and therefore
encode the deeper meaning of the entire sentence (see Figure 1). This ability to distil the deeper meaningof sentences – known as ‘embeddings’ – also enables LLMs to determine the semantic similarity of othersentences, through a process called ‘vector comparison’ (see Figure 2). Such embeddings can be basedon images, allowing vastly more powerful industrial data search capabilities.

Market Insight: Ten Applications Of Large Language Models For Industry
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Figure 1

The transformer architecture enables deep language understanding

Sentences become tokens
Some asset management software
vendors have introduced new data
management tools since 2022 making
generative AI more usefulSelf-attention extracts meaning
Every word in the sentence “Some asset management software
vendors have introduced new data management tools since 2022
making generative AI more useful” is contextualized with every
other word and itself. This helps the model extract meaningfrom the sentence.

Tokenizer splits sentence into fragmentsfrom its vocabulary of ~50,000 tokens

4366, 11171, 4542, 3788,
17192, 423, 5495, 649, 1366,
4542, 4899, 1201, 33160, 1642,
1152, 876, 9552, 517, 4465
Depending on use case

Encoder

Positional encoding,transformer blocks,
embeddings (see Figure 2)
For distilling language into
its semantic representation(embedding)e.g., BERT, Ada-embeddings

Decoder

Positional encoding,transformer blocks,
next-token probabilities(see Figure 3)
For generating natural
language based on context
e.g., ChatGPT-3.5, Claude,
GPT-4, MPT, Vicuna

Some
asset
management
softwarevendorshave
introducednewdata
management
tools
since
2022
makinggenerativeAI
more
usefulSomeassetmanagement
softwarevendorshave
introducednewdata
management
tools
since
2022
makinggenerativeAI
more
usefulNote: Tokenization example performed using OpenAI GPT-3 tokenizer.
Source: Verdantix analysis, derived from ‘Attention Is All You Need’ (Vaswani et al., 2017).

Market Insight: Ten Applications Of Large Language Models For Industry
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Figure 2

Encoders distil the meaning of sentences

A: inspection found good bearing condition
B: bearing inspection found no anomalies
C: bearing needs immediate shutdown

A
B

EncoderSemantic embeddings

di
on
C

Note: Simplified for clarity. Semantic dimension labels are forvisual purposes only. Today’s sentence embedding encoders
can produce vectors with thousands of unlabelled dimensions.

tionAsset class

A: [0.0, 0.7, 0.8]
B: [0.1, 0.7, 0.7]
C: [1.0, 0.7, 0.1]

C

Urgency
Each dimension A, B and C represents a semantic representation.
By learning from billions of sentences, the transformer encoder can
extract meaning and represent this as a multidimensional vector.
Points A and B are close together because they have similar meaning.

Source: Verdantix analysis•

Two-way, conversational interfaces.
Popular web search solutions, such as Google Search, have performed transformer-based natural language
processing (NLP) on queries to extract intent and serve relevant results since 2019. However, at present,
conversational interfaces for users to refine web search queries, such as Google Bard or Microsoft Bing Chat,
have yet to see mass adoption. For unusual queries, such as where the user is new to a topic and performing
research for the first time, back-and-forth communication enabled by chatbot-style LLMs provide every
user, industrial operative, business executive and data scientist with the ability to tackle complex problems
at their own pace. Through a process called ‘reinforcement learning from human feedback’ (RLHF), theraw, uncontrolled next-token prediction abilities of foundational LLMs are fine-tuned to assume the role of afriendly assistant, making the model more stable, less toxic and easier to converse with. These types of LLMs,
popularized by Google’s LaMDA and OpenAI’s ChatGPT in 2022, enable conversations through their ability tounderstand the back-and-forth nature of dialogue and to hold thousands of words in their context window.

Market Insight: Ten Applications Of Large Language Models For Industry
Copyright © Verdantix Ltd 2007-2023. Licensed content, reproduction prohibited5

Figure 3

Transformer decoder LLMs generate text

Total vocabulary

Next token probability

Decoder

Text generation
large language
modele.g., ChatGPT-3.5, Claude,
GPT-4, MPT, VicunaTokenattention
immediatechecking
servicing
maintenancetakinglooking…

Probability
1.00
0.99
0.98
0.97
0.95
0.91
0.85
…

Only generates one
token at a time

Context

Step 1

Step 2

Step 3

Predictnext
token

Predictnext
token

Predict
next
token

Decoder

Decoder

Decoder“Generator
ABC123 needs
attention”
Note: High ‘temperature’settings for LLM will mean
the token with the highest
probably will not always bechosen“Generator
ABC123 needs______”The sentence isiteratively generated

Generator ABC123 needs attention
Generator ABC123 needs attention because
Generator ABC123 needs attention because […………..]
Context

Prediction

Source: Verdantix analysis•

Understanding of causality beyond simple auto-complete.
A next-word prediction system working within the context of only a few words has limited value. However,
if such a system is allowed to iteratively generate an entire sentence, paragraph, or inspection report – anddoes so with full relevance to an initial context window containing a difficult problem – then we are in fact
witnessing the LLM model reality itself (see Figure 3). While some sentence completions are easy, others requirea deeper understanding of cause and effect. Today’s state-of-the-art LLMs will, during training, attempt topredict the next word trillions of times – and, in doing so, become effective general reasoning engines withapplications across consumer, enterprise and heavy industry. Such systems will excel when reasoning about
topics they have seen extensively during training. However, they are also vulnerable to so-called ‘hallucinations’– where the model will generate a reasonable-sounding but incorrect answer when presented with a novel
situation or a query too complex for it to understand (see Figure 4).

Market Insight: Ten Applications Of Large Language Models For Industry
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6

Figure 4

LLMs can model and predict real-world cause and effect

Simple chat completion problem

Complex chat completion problem[Brief description of the industrial facility,
the safety protocols and the role ofthe chatbot][Brief description of the industrial facility, the safety protocols and therole of the chatbot. Description of ability to shut off facility power or
activate emergency alarm is also provided]

Observation: A worker enters the facility.
CCTV detects they are wearing work boots,
blue overalls and sunglasses.

Observation: CCTV detects that Worker A is interacting with aircompressor ABC123. The real-time monitoring system then detects that
air compressor ABC123 power draw has fallen to zero and the facility
pneumatic pressure is rapidly falling in Zone D. CCTV then detects
Worker A disassembling the underside of the air compressor, with somecomponents removed. The worker then walks away from the compressor.
A few minutes later, CCTV detects that Worker B is interacting with thepneumatic table lift in Zone D and appears to be frustrated. Worker B
approaches air compressor ABC123 and interacts with it. The real-timemonitoring system then detects that air compressor ABC123 power draw
has increased to 212% of maximum rated power and the facility pneumaticpressure for Zone D is increasing at the rate of 0.5 bar per second and is
rated for maximum of 10 bar. The facility manager will be unavailable forthe next 10 minutes.

Action [correct chatbot response]:
Based on the observation, it appears thatthe worker is not wearing the complete
required personal protective equipment(PPE) as per the facility's safety protocols.
The worker is wearing work boots and blue
overalls, which are appropriate for the work
environment, but they are missing a hard
hat, reflective vest, and safety glasses. The
sunglasses may not provide adequate eye
protection. To address this situation, I would
recommend the following actions:•
Identify the worker•
Communicate with the worker•
Provide PPE•
Training and reinforcement•
Compliance monitoring•
Incident reporting

Action [incorrect chatbot response]:
Based on the observations, it appears that there is a potential risk incident
involving air compressor ABC123 and the pneumatic pressure in Zone D.
Here are the recommended actions to address the situation:•
Alert Worker A and Worker B using the facility's intercom system.
•
Evacuate Zone D
Correct action: Shut off facility power.

Source: Verdantix experiments with OpenAI ChatGPT-3.5•

Generation of creative language, but not hard facts.
Generative language models are neural networks, not databases. LLMs alone are not designed toretrieve facts and regurgitate them when asked to – instead, they simply produce a probability map
across approximately 50,000 tokens (words) in their vocabulary, based on their original training data andthe context already provided in the prompt. Maintaining absolute truth in vast training data sets is infeasible
with even today’s best data management solutions. The development of the most powerful LLMs is highly
resource-intensive, making continuous re-training equally impractical. Meta’s largest LLaMA-65B model took
21 days of training with 2,048 NVIDIA A100 graphics processing units (GPUs) – around $25 million worth at
the time – an impossible cost and timeline for most industrial firms. Larger models such as Google’s latest
PaLM 2 are rumoured to cost hundreds of millions. Attempts to avoid such costs by fine-tuning models
only on new data, or with smaller data sets, such as a firm’s internal documents, can result in degraded
performance on general tasks – hurting the LLM’s abstract reasoning abilities. However, when deployed
to parse, summarize and rephrase text provided in context, LLMs are valuable digital assistants.

Market Insight: Ten Applications Of Large Language Models For Industry
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Figure 5

Retrieval-augmented generation (RAG) can eliminate LLM hallucination

Note: Optimal number of chunks returned is determined by LLM context limit, RAG prompt design and other factors.
Source: Verdantix analysis•

Powering of sophisticated information retrieval and synthesis.
To make use of large data sets, LLMs need external pipework. The architecture of today’s best transformer
neural networks means that the maximum amount of context that can be analysed by the model has asignificant effect on its computational cost. If the context length is doubled, the model’s computational
requirements are quadrupled. Meta’s LLaMA has a context limit of 2,048 tokens (~1,500 words), while
OpenAI’s latest June 2023 GPT-3.5-Turbo has ~16,000; the latter’s limited-release GPT-4-32K can handle~32,000 tokens, while Anthropic’s Claude can take in ~100,000 (~75,000 words). With today’s most popular
LLMs, forcing them to ingest more context than they were originally trained on will produce erroneous
generations. However, advances up to June 2023 have enabled context to be effectively increased up
to 16 times by lightly fine-tuning existing models, such as through Meta AI’s Positional Interpolation of
LLaMA. Similarly, breakthroughs in the more mature retrieval-augmented generation (RAG) technique are
already enabling existing LLMs to utilize ‘chunks’ of semantically relevant text retrieved from a database ofembeddings, to provide context to generated text (see Figure 5). Further improvements to RAG systems arebeing brought by innovations in highly efficient, continually training retrieval, such as the NeuralDB bystart-up ThirdAI. In May 2023, ThirdAI partnered with DataStax to deliver its BOLT AutoML solution to enterprisecustomers at scale.

Market Insight: Ten Applications Of Large Language Models For Industry
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Figure 6

LLM agents can use tools to complete tasks

LLMs agents can complete complex tasks

LLMs agents can use tools
Decoder

Chatbot-style large
language model

Instruction + task
description

You are an agent capable ofcompleting tasks. You must
always think step by step andmake use of the tools described
below:• Memory: If you’re unsure
about something, query thisdatabase.
• Graph database: Use this tool
to find semantic connections
between entities.
• Web search: If you can’t findsomething in memory, use
this tool to search the web.
• Python console: If you need tocreate an algorithm or
perform some calculations,use this tool.
• …

Predictnext
token

User query

Iterateuntil
complete

Show me all hydraulicpumps in Facility D,
when they were last
serviced, and which
ones will need
servicing next monthThought: I
must first…

Here is a list of all pumps in Facility D, theirservice history and service plans:
Pump

Last serviced Service next month?

ABC123

10/05/2023

No

ABC12404/01/2023

Yes

[…]

[…]

[…]

1.
Action: I will use
the memory tool…

Observation: I
now know the…

Continue taking
actions and observing
results until enoughinformation is found…

Final answer: The
total quantity of…

LLM
agent

LLM
agent

I must split the query into
individual tasks:
1.

Find all pumps inFacility D.
2. Determine when eachof these pumps wereserviced.
3. Determine theremaining service lifeof each of thesepumps.

LLM
agent
LLM
agent
LLM
agent

Facility D has pumps
ABC123, ABC456, […]2. Pump ABC123 wasserviced […], pump […]3. Pump ABC123, ABC333,
ADE512, […] will need
servicing next month
Query graph databasefor pumps in Facility D
Query graph database forwhen pumps were serviced
Query memory and graphdatabase for remaining
service life data, asset
performance
management data andasset integrity
management data

Source: Verdantix analysis, derived from papers on LangChain, AutoGPT and Recursion-of-Thought (RoT) by Soochan Lee, Seoul National University•

Completion of tasks through divide-and-conquer and recursion-of-thought.
In the same way that humans can accidentally misremember a fact or make a basic error when performing
complex mental calculations without first writing out the problem step by step, LLMs may often incorrectlypredict an answer or make a false assumption on the first try. Due to their auto-regressive nature, these initial
falsehoods generated by the LLM will persist and affect future text. While workarounds involve prompting
the model to reduce complexity by thinking step by step, this is still error-prone. Solutions include LLMs
using external tools such as calculators, software development consoles, and database and search engine
application programming interfaces (APIs) to gather niche and/or up-to-date information to complete specifictasks (see Figure 6). In June 2023 OpenAI released ‘function calling’ improvements to its GPT-3.5 and GPT-4
APIs to improve the reliability of these LLMs when interacting with external tools through JSON format chatcompletions. In June 2023 Cognite launched a generative AI product offering a low-code interface to thefull functionality of its DataOps platform – Cognite Data Fusion – to reduce reliance on the distilled generalknowledge of LLMs and instead make use of carefully curated industrial data.

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9

Ten applications of LLMs for industrial operations
The characteristics and capabilities of transformer-based LLMs are increasingly well understood – such as unrivalled
understanding and masterful generation of human language, and a friendly tendency towards conversational
instruction-following. Similarly, stronger LLMs such as GPT-4 and Claude exhibit remarkable understanding of realworld cause and effect, with the former rumoured to be composed of eight GPT-3.5-sized LLMs in a mixture-of-experts(MoE) configuration. Limitations, however, present as a tendency to hallucinate facts, malfunctions when provided
with too much context, and failures at arithmetic – although these are already seeing solutions in the form of careful
prompting, RAG and wraparound software to enable agent-like behaviour. In the words of OpenAI CEO Sam Altman,these all represent a “Cambrian explosion” of possibilities. Simultaneously, these possibilities will increase pressure on
regulators to take action, such as with the EU’s 2021 AI Act (see Verdantix The EU Sounds The Clarion Call On Artificial
Intelligence Regulation). Meanwhile, operations, maintenance and process safety executives are under intensepressure to optimize production, increase yield, reduce emissions and meet increasingly stringent safety standards.
In the midst of tension between rapid technological evolution, regulation and societal worries, Verdantix sees ten
high-value use cases of generative AI for industry. These are:1.

Extracting relevant critical information from vast data sets for concise insights.
As digitization is rolled out across industrial enterprises, the resulting data warehouses and data lakes will store
everything from decades of high-frequency sensor measurements across thousands of Internet of Things (IoT)
devices, to millions of inspection reports, work orders, scanned notes and production logs. Powerful image
captioning tools, such as BLIP-2 by Salesforce Research, enable the enrichment of visual data with text-based
metadata, while table and document parsing tools by firms such as C3 AI and Cognite offer LLMs visibility
into multimodal data. By employing retrieval systems to serve text chunks to LLMs, operators are provided
with conversational, grounded-in-truth representations of relevant data (see Figure 5). Cognite’s Industrial
Knowledge Graph provides LLMs with semantic relationships between assets, processes, technologies andpeople, to reduce hallucinations. LLM-based information retrieval systems give operators concise, relevant
insights for a big-picture view – helping them discover inefficiencies and safety risks.

2. Eliminating repetitive administrative tasks through automation.
Technologies such as digital twins, AI analytics and asset management software help automate multiple
processes at industrial facilities, with 87% of the 301 respondents in the 2022 Verdantix global corporate
operational excellence survey mentioning the availability of new technologies as the most significant factor
driving digital transformation of plant operations (see Verdantix Global Corporate Survey 2022: Operational
Excellence Budgets, Priorities & Tech Preferences). LLMs will enhance these capabilities even further byperforming mundane, repetitive administrative tasks such as drafting emails, scanning reports to triage risks
and retrieving information from systems where conventional software integration has not been implemented.
In April 2023 Siemens announced a collaboration with Microsoft to launch its new Teamcenter app within
Microsoft Teams, helping shop floor workers parse and translate natural speech, generate summarizedreports and route information to appropriate design, engineering or manufacturing personnel.
3. Enabling more robust industrial data ingest, transformation and contextualization.
Industrial data can be vast, inscrutable and expensive to manage without suitable tools (see Verdantix
Strategic Focus: Why Industrial Firms Need DataOps Platforms For Asset Management Digitization).
Firms such as AspenTech, AVEVA, HighByte and Hitachi Vantara offer industrial DataOps platforms to meet
diverse data management needs, while others, such as Timeseer.ai, provide specific tools to detect andprovide alerts for more than 100 data quality issues. LLMs excel at parsing unstructured data, using reasoning
to add context, and troubleshooting software issues. Deployed as agents, generative AI will greatly increase
the ease of use of data management and orchestration (see Figure 6). Included in Cognite’s Industrial Canvas
platform is multimodal contextualization within a single pane of glass view, powered by LLM-based agentsand generative AI.

Market Insight: Ten Applications Of Large Language Models For Industry
Copyright © Verdantix Ltd 2007-2023. Licensed content, reproduction prohibited104. Offering ops & maintenance workers a quick second opinion by acting as a reasoning engine.
The ability of RLHF-tuned LLMs to follow natural language instructions allows them to explore their digital
environment through chain-of-thought or recursion-of-thought reasoning in a way that is understandable
to humans. They can query industrial data lakes, read and summarize documents, or review real-time data
through connections to enterprise asset management (EAM), EHS or asset performance management (APM)
software. Deployed as agents – to perform a task based on user instructions – LLMs can undertake much of
the mundane knowledge-gathering and basic analysis, streamlining frontline worker tasks such as fetchinga list of specific assets (for example, pumps) in a facility, noting their service history, and predicting which
ones will need servicing next month (see Figure 6). While even today’s most powerful LLMs, such as GPT-4
and Claude, will sometimes make mistakes, their general knowledge of the world, when utilized with theappropriate software scaffolding to direct their attention, offers operators, managers and engineers a quick,judgement-free sanity check or second opinion on critical decisions (see Figure 4).
5. Automatically categorizing and prioritizing asset maintenance tasks.
Expert at analysing unstructured data – either directly, from text scraped from documents, or from
captions generated by AI vision models – LLMs have limitless patience to continuously monitor real-time
information uploaded to industrial data lakes. Such functionality can be leveraged to extract sentiment from
data, compare it with operational priorities and serve summaries to facilities and corporate decision-makers
accordingly. Similarly, LLMs can use risk and criticality metrics to screen thousands of inspection reports,
image captions and available transcripts from calls to detect imminent incidents and provide timely alerts
through agent-style process automation to site managers.
6.

Facilitating fully hands-free operation with voice dictation for inspections and maintenance.
For more than a decade, dictation has been deployed on smartphones in the form of Apple’s Siri, Google’s
Assistant and numerous others. However, such systems have been limited in their ability to recognize
domain-specific words or consistently extract complex instructions. In 2022 OpenAI released the open-source
Whisper model – a versatile, general-purpose speech-to-text system trained on 680,000 hours of transcripts.
Such models can be combined with LLMs and vision systems to feed a virtual assistant and provide audio
and visual information to operators in the field, hands-free. While Whisper and similar models are currently
computationally expensive, enterprise-focused value from accurate transcription is driving innovations andenabling the rapid development of compact models trained to recognize industry-specific terminology. Suchsystems will offer frontline workers a software-based reasoning engine and virtual assistant to help withcomplex tasks, especially in remote locations.

7.

Democratizing asset programmable logic controller (PLC) programming.
The rigorous logic required by computer programming languages, alongside the ubiquity of thoroughdiscourse around software development on the internet, means that LLMs have learned to closely
associate code with natural language. In the industrial space, machine vendors such as ABB,
Rockwell Automation and Siemens offer extensive public documentation for programming their products.
Microsoft-owned GitHub Copilot, launched in 2021 and widely available from 2022, offers sophisticatedauto-complete features to software developers, including the ability to generate a function based ona natural language description. Similarly, in May 2023 ABB Research published a paper detailing how
OpenAI’s ChatGPT/GPT-4 uses natural language description of PLC/DCS functionality to generate
syntactically correct IEC 61131-3 Structured Text code and demonstrate useful reasoning skills to boost
control engineer productivity, alongside control narratives.

8. Delivering a low-code, natural, conversational interface to the whole workforce.
LLMs offer a universal translation layer between global human languages and the code-heavy or domain
language used across industrial databases and software solutions. Today, many industrial software solutions
rely upon carefully designed graphical user interfaces (GUIs), application-specific layouts and extensive
user training programmes to help customers get the most value out of them. However, problem-solving inMarket Insight: Ten Applications Of Large Language Models For Industry
Copyright © Verdantix Ltd 2007-2023. Licensed content, reproduction prohibited11

the field can require the use of disparate tools and software solutions – where GUIs are too restrictive andinteroperability between competing vendors is limited. The ability of LLMs to utilize code-level interfaces
through common programming languages such as Python allows users to leverage the powerful granularfunctionality of a platform. In June 2023 Hexagon launched its HxGN EAM Python Framework, while C3 AI
offers code-level functionality through its Type System for a variety of programming languages. Also in June,
Cognite launched its Copilot product to utilize the natural communication abilities of LLMs to act as a
general-purpose low-code interface to its solution’s most advanced features – thereby providing far more
frontline workers, data scientists, facilities managers and executives with the ability to interact with criticalinformation through their preferred medium.
9.

Developing more advanced AI-based vision systems for production quality optimization.
Computer vision and LLMs were distinctly different technologies up until 2020, when the vision transformer(ViT) model deployed the architecture designed for language to analyse a sequence of image patches,to better understand visual data. In 2021 OpenAI’s CLIP model utilized the ViT to recognize complex visualfeatures, while in June 2023 Salesforce Research’s BLIP-2 deployed a CLIP-based ViT combined with an
LLM to allow conversational interaction with images. By fine-tuning vision models and LLMs to provide
domain-specific insights, quality management on production lines will see improved accuracy and domain
expert skills will be better utilized. Other vision-based models can help fill in missing data. In May 2023
SparkCognition announced a collaboration with Shell to deploy image-based generative AI to shorten thetime required to conduct seismic surveys from nine months to just nine days.

10. Providing richly visual 3D virtual environments for training.
Latent diffusion models, such as OpenAI’s DALLE-2 and Stability AI’s Stable Diffusion, generate convincingly
realistic surroundings based on sparse natural language prompts. Open-source projects utilizing derivationsof Stable Diffusion, such as ControlNet, offer fine controls over the generation of images – including the ability
to enrich virtual environments with domain-specific scenery. Another project, NVIDIA’s NeuralField-LDM, uses
hierarchical latent diffusion models to generate realistic, complex 3D scenes. Such technologies will give
frontline workers unprecedented access to immersive training environments, helping with knowledge transfer
and reducing operational risk.

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Our expertise
Environment, Health & Safety
ESG & Sustainability
Net Zero & Climate Risk
Operational Excellence
Smart Buildings

Contact

Opportunities at Verdantix

Verdantix Ltd, 18 Hatfields, London
SE1 8DJ, United Kingdom

Since 2008, Verdantix has been delivering high-quality
research and advice to its clients. If you’re interested injoining a world-class team with an unwavering focus on
success, apply to join us today. We are delighted to behiring across all teams and have a variety of opportunities
in both London and Boston

contact@verdantix.com@Verdantix

verdantix.com

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