Posted on October 28, 2024 by Kopius Editorial Board with Rob Carek
Rob Carek Explains Why CodeOps is a Win for Businesses, Customers, and Developers
Across the enterprise, in every industry vertical and every operational and functional area, organizations are racing to take advantage of Generative AI (GenAI). It’s already in use in 65% of organizations, according to a McKinsey Global Survey. For software companies, much of the focus has been on using GenAI to generate code. But the approach has challenges. Depending on the tool developers use, the code accuracy rate is only between 31% and 65%, according to a Bilkent University study. The general consensus is it’s buggy and poses hidden security risks.
But software companies and developers now have another meaningful approach to GenAI at their disposal—CodeOps. GenAI fueled CodeOps is an approach that now enables developers to reuse internally owned, fully approved, modular coding building blocks—systematically. And it’s driving a transformational shift that creates business and customer value, unburdens developers of mundane and repetitive coding, and enables them to innovate.
We sat down with Rob Carek, Vice President of Client Solutions at Kopius, to introduce you to CodeOps.
Tell me about CodeOps. What is it and what problem does it solve?
Modern software development processes are wildly inefficient. A fundamental challenge, at least for companies with more than one product or application, is that there’s no practical way to reuse code. So, if you have a suite of 20 products, and every single one of them has a similar feature, your development teams have built that feature 20 different times—and they do it differently, every single time. In theory, a human could pour over requirements and search code repositories to find commonalities and reuse existing code, but that’s just not practical—it would be far more work than just rebuilding it.
But with the advent of GenAI, code reuse is NOW an addressable problem.
CodeOps is a code reuse strategy, and GenAI is not only the enabler, but also the accelerator. The idea is that companies can now develop reusable, modular code and store it in a library or repository. Then, GenAI can be used to search for existing code to use or build on instead of developing everything from scratch.
What are the big benefits of CodeOps?
There are four big benefits that I see: efficiency, innovation, faster time to market, and security. From an efficiency standpoint, since existing code is being repurposed, companies can save a ton of development and testing time. And when you think about how that is amplified across a whole suite of products—well, the gains are almost exponential. And all the time they save, they can spend innovating—building new features and enhancements that are unique to a given product and require original code. It’s the more challenging and interesting part of a developer’s job and where they really want to spend their time, so there’s a human benefit. It also means that things that really move the needle get to market and in customers’ hands sooner.
From a security standpoint, anything in the library is proven code—you know it meets organizational security and compliance standards. But, again, the impact really comes at scale. If you push a patch, everything updates, every vulnerability is closed wherever the code is in use.
Is CodeOps compatible with DevOps and DevSecOps?
Absolutely. The goal of DevOps is to break down silos between development and operations so new products, features, and enhancements get to market faster, more efficiently, and with fewer issues. DevSecOps prioritizes security at every step of the process. But both practices are focused on code development at the product or team level. CodeOps addresses a need at the organizational level, across multiple products. By reusing code wherever possible, CodeOps amplifies DevOps and DevSecOps outcomes—new things get to market even faster, even more efficiently, and with even fewer issues.
How can organizations get started with CodeOps?
Many of the major code platforms are starting to explore CodeOps and looking for ways to integrate it into their solutions, but it’s still very early days. I anticipate the first place they will start is using LLMs to identify commonalities in requirements. That doesn’t account for developing code that fulfills those requirements, and it’s going to be a long while before we see integrated, searchable code libraries. But that doesn’t mean you have to wait until they figure it out to get started.
At Kopius, we’ve developed a solution companies can use to adopt CodeOps today. We use GenAI to look at your backlog and identify commonalities in new requests and aggregate them. Then, we develop requirements and develop code to address them and validate it. The code is pushed to your code repository so when you’re ready to work those requests into a sprint, your developers can access it. It’s a more organic way to build a library of existing, pre-approved code that doesn’t require your teams to operate any differently than they do now.
What will it take to get developers to adopt CodeOps?
Modern development practices are simply not designed for content reuse at scale—there’s no precedent for it. And culturally, developers will look at someone else’s code and think, “I wouldn’t have done it that way.” So, like DevOps, getting developers to adopt CodeOps is going to take cultural change. Kopius’ solution takes that into consideration. It’s a hybrid human / technology approach that builds trust and buy-in by actively engaging developers in reviewing requirements and code and providing feedback. That way, they’ve contributed to it and have more confidence in it.
And as I mentioned earlier, CodeOps frees developers from the repetitive and mundane—things that are table stakes, so they have more time for developing things that are truly innovative. It’s a win-win.
What’s the single, most important thing companies should know about GenAI-fueled CodeOps?
GenAI-fueled CodeOps isn’t just an incremental improvement. It’s a truly transformational shift that will enable organizations to develop code at speed and scale, drive value into customers’ hands at speed, and free developers from the burden of repetitive, mundane work so they can focus on innovating.
Ultimately, GenAI-fueled CodeOps makes the most of what both technology and humans bring to the table—and rapidly scales it.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
Every technology project starts with an outcome—a business goal that needs to be achieved. But to achieve that goal, you need to define a set of deliverables, establish a timeline, and determine a budget. Rarely do the timeline and budget line up with the work that needs to be done. There are seldom enough dollars to put the number of people on the project necessary to bring the deliverables to life within the timeline. This is often because of how challenging it is to fully scope a project up front. No matter how thorough you are, new requirements come to light, resulting in scope creep.
Many companies will lean into project management to make everything come together. Smart—a solid PMO practice is the foundation on which all successful technology projects are built.
But you can’t always project manage your way out of this type of problem. That said, there are some things you can do.
Two Key Approaches to Use When Time and Budget are Out of Sync with Project Scope
One of the most complex technology consulting programs I’ve worked on was for a new company in the healthcare space. The budget was a swag from an investor’s presentation deck and was completely out of alignment with the six-month timeline for standing up ERP manufacturing system, provider and patient registration and management portals, and an ecommerce app. Rescoping the project wasn’t an option—if every compliance parameter wasn’t met within the given timeframe, the client would have to wait an entire year to reapply with the organization that had program oversight. In the end, we met the timeline, stayed on budget, and our client was awarded the contract they were after.
We used two key approaches to make it happen. First, we brought all the right stakeholders to the table early to develop standard operating procedures (SOPs). And second, we used those SOPs, as well as compliance guidelines, to build and validate wireframes before standing up MVPs. Then we validated those before coding the actual apps.
Engage the Hive Mind
At the beginning of the initiative, we brought all the key stakeholders together for a series of workshops—one for each app we had to deliver. Every department that had a say was represented—product, engineering, sales, marketing, manufacturing, and legal. Not only did we have a binder on hand detailing hundreds of pages of compliance regulations, but we also had someone on hand who knew them inside and out. Collectively, we walked through every aspect of each app, developing standard operating procedures, strawmen, and requirements.
This hive mind approach meant we could problem solve, make decisions and come to agreements at speed and minimized our chances of going down the wrong path.
My Take
When timeline and budget aren’t in line with the work that needs to be done, you can’t afford to make mistakes. Get the people who hold the answers to your questions in a room and map out your requirements. At Kopius, we call these JumpStarts, and they may take a few days or a few weeks. Then, continue to check in with the same stakeholders at every critical juncture to validate your work.
“Measure once. Cut twice.”
For me, the project management equivalent of “measure once, cut twice,” is wireframes first, MVP second, coding third. And at each of these stages, you need to bring your stakeholders together to validate your work. For the healthcare project, once we had a thorough list of requirements, our UI/UX developed wireframes that we validated with the same group of stakeholders we initially brought to the table. This allowed us to identify and work through any potential issues up front. Then, once the wireframes were validated, we stood up MVPs for each app so stakeholders could walk through the basics of each process and validate it. Only then did we dive deep into coding all the features and functionality for the first release.
My Take
When timeline and budget aren’t in line with the work that needs to be done, the inclination can be to jump right into coding. A better approach is to double down on validating your path forward through JumpStart workshops and wireframing. This will minimize errors—and added time and costs—in the long run.
Expect the Unexpected
No matter how thorough you are in developing your requirements, there are going to be some “ahas” along the way. You have to expect the unexpected and remain flexible. But being flexible doesn’t mean saying yes to everything. Scope creep can derail a project from both a timeline and budget standpoint. For this project, we managed that by getting everyone to agree to a light phase one for each application, then planned to iterate, releasing new features every two weeks after launch.
Looking Ahead: The GenAI Approach
Like many technology companies, Kopius is actively integrating generative AI (GenAI) into our processes, and I’m working on a set of custom GPTs that I believe can make a difference when time and budget are out of sync with requested deliverables. By entering business and technical requirements into it—maybe even a transcript from a discovery session—and asking it to generate common use cases that serve as starting points for designing application features, we can streamline the work involved in building new apps. The prompt engineering requires a lot of up-front effort, but once that initial lift is done, we’ll be able to use it again and again.
Undoubtedly GenAI will deliver thousands of small efficiencies like this, but it’s only part of the equation. The time / budget / scope challenge is an inherent part of software development, and solving it is always going to take a multi-faceted approach.
JumpStart Your Technology Project—and Stay on Track—with Kopius!
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
Generative AI (GenAI) adoption is surging. Sixty five percent of respondents to the McKinsey Global Survey on the State of AI in Early 2024 indicate their businesses are using generative AI in at least one functional area. Yet, more than half of individual GenAI adopters use unapproved tools at work, according to a Salesforce survey. Clearly, businesses want and need to implement the technology to meet their business goals, but in the absence of a clear path forward, employees are finding ways to adopt it anyway, perhaps putting sensitive data at risk. Organizations need to move fast, put a strategy in place, and implement pilot projects with impact.
But what’s the best way to get started?
We get this question often at Kopius. Maybe you have a problem you need to solve in mind or a general use case, or maybe that’s not yet clear. You might understand the possibilities but haven’t narrowed down an opportunity or area of impact. Regardless of which camp you’re in, when we peel back the onion, we find that most companies need to step back and address fundamental issues with their data foundation before they can begin to tackle GenAI.
At Kopius, we have a detailed framework for walking you through the things you need to take into consideration to identify a GenAI pilot project and build a data foundation to support. But asking—and answering questions like the ones below—is at the root of it.
What problem are you trying to solve?
In a survey of Chief Data Officers (CDOs) by Harvard Business Review, 80% of respondents believed GenAI would eventually transform their organization’s business environment, and 62% said their organizations intended to increase spending on it. But no company can afford to make investments that don’t deliver on outcomes.While there is value in just getting started, it’s both worthwhile and necessary to define an initial use case. Not only do you want your program to have impact, but the GenAI ecosystem is so broad that without some sort of use case, you will be unable to define what type of outputs need to be generated.
Some companies will have a clear use case, while others will have a more general sense of where they’re headed. Still others are working with an “AI us” request from senior leadership to explore the landscape.Wherever you are in this process, our framework is designed to help you identify a meaningful pilot project.
What are your data sources? What do you need to capture? Next, you’ll need to take stock of your data sources, so you have a solid understanding of the full set of data you’re working with. What inputs do you have coming in and what inputs do you need to get to your end goal? Often, there is a project behind a project here. If you don’t have the data you need to solve the business challenge, then you’ll have to develop and implement a plan to get it. For instance, say you want to measure the impact of weather conditions on fleet performance, and you’re planning on using IoT data from your vehicles. You’ll also need to determine what weather data you need and put a solution in place to get it.
What is the state of your data? Is it relevant, quality, and properly housed and structured?
With GenAI, your ability to get quality outputs that deliver on business outcomes depends on the quality of your inputs. That means data must be current, accurate, and appropriately stored and structured for your use case. For instance, if you’re developing a GenAI enabled chatbot that employees can query to get information about policies, procedures, and benefits, you’ll need to make sure that information is current and accurate.
At this point, you’ll also need to consider where the data is being stored and what format it’s in. For instance, JSON documents sitting in non-relational database or tables sitting in a SQL database are not necessarily a model for GenAI success. You may have to put your raw data in a data lake, or if you already have a data lake, you may need to warehouse and structure your data so that it’s in the right format to efficiently deliver the output you want.
What governance and security measures do you need to take? Data governance is about putting the policies and procedures in place for collecting, handling, structuring, maintaining, and auditing your data so that it is accurate and reliable. All these things impact data quality, and without quality data, any outputs your GenAI solution delivers are meaningless. Another important aspect of data governance is ensuring you are compliant with HIPPA or any other regulatory mandates that are relevant to your organization.
Data security, in this context, is a subset of data governance. It is about protecting your data from external threats and internal mishandling, including what user groups and/or individuals within your organization can access what. Do you have PPI in your system? Salary data? If so, who can modify it and who can read it? Your answers to these questions may inform what data platform is best for you and how your solution needs to be structured.
What is your endgame? What types of outputs are you looking for?
The problem you’re trying to solve is closely tied to the types of outputs you are looking for. It’s likely that exploration of the former will inform conversation of the latter. Are you building a chatbot that customers can interact with? Are you looking for predictive insights about maintaining a fleet or preventing accidents? Are you looking for dashboards and reporting? All this is relevant.This also gets into questions about your user profile—who will be using the solution, when and where will they be using it, what matters most to them, and what should the experience be like?
A Rapidly Evolving Data Platform Landscape Drives Complexity
Getting started with GenAI is further complicated by how complex the third-party GenAI, cloud, and data platform landscapes are and how quickly they are evolving. There are so many data warehouse and data lake solutions on the market—and GenAI foundational models—and they are advancing so rapidly that it would be difficult for any enterprise to sort through the options to determine what is best. Companies that already have data platforms must solve their business challenges using the tools they have, and it’s not always straightforward. Wherever you land on the data maturity spectrum, Kopius’ framework is designed to help you find an effective path forward, one that will deliver critical business outcomes.
Do You Have the Right Data Foundation in Place for GenAI?
In the previously mentioned survey by Harvard Business Review, only 37% of respondents agreed that their organizations have the right data foundation for GenAI—and only 11% agreed strongly. But narrowing in on a business problem and the outcomes you want and defining a use case can be useful in guiding what steps you’ll need to take to put a solid data foundation in place.
One last thought—there are so many GenAI solutions and data platforms on the market. Don’t worry too much about what’s under the hood. There are plenty of ways to get there. By focusing on the business problem and outcomes you want, the answers will become clear.
JumpStart Your GenAI Initiative by Putting a Solid Data Foundation in Place
At Kopius, we harness the power of people, data and emerging technologies to build innovative solutions that help our customers navigate continual change and solve formidable challenges. To accelerate our customers’ success, we’ve designed a JumpStart program to prioritize digital transformation together.
A large language model (LLM) is a deep learning algorithm pre-trained on massive amounts of data. LLMs use transformer models — a set of neural networks that includes an encoder and decoder with self-attention capabilities. Essentially, the encoder and decoder identify meanings from text and understand the relationships between the words and phrases in it.
This article provides an overview of LLMs, including how they work, their applications, and future innovations. It also highlights the advantages of implementing LLMs for your business and how to use them for success.
Large Language Models Explained
Large language models are foundational models that use natural language processing and machine learning models to generate text. Natural language processing is a branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
By combining computational linguistics with statistical machine learning and deep learning models, LLMs can process human language in the form of voice data or text to understand its whole meaning, including user intent and sentiment.
There are different types of large language models, such as:
Generic or raw language models: Trained to predict the next word based on the language in the training data, typically used to perform information retrieval tasks.
Instruction-tuned language models: Trained to predict responses to the instructions given in the input, allowing them to perform sentiment analysis or generate code or text.
Dialog-tuned language models: Trained to have a dialogue by predicting future responses. Examples include chatbots and virtual assistants.
The goal of LLMs is to predict the text likely to come next. LLMs are pre-trained on vast amounts of data to understand the complexities and linkages of language. The sophistication and performance of an LLM can be judged by the number of parameters it has — or the factors it considers when generating output.
Generative AI vs. Large Language Models
Generative AI is an umbrella term that refers to AI models capable of generating content. LLMs are a specific category of generative AI models with a specialized focus on text-based data. Essentially, all large language models are generative AI. The main differences between generative AI vs. LLMs include:
Training: Generative AI undergoes extensive training on large datasets to connect patterns and relationships present within that data. Once trained, they can generate new content that aligns with the characteristics of the training data. In contrast, LLMs are trained on vast volumes of text data, from books and articles to code. After training, LLMs can complete text-related tasks.
Scope: While generative AI uses many models to create new content beyond textual data, LLMs excel at understanding language patterns to predict and generate text accurately.
Type of content: As mentioned, generative AI creates images, music code, and other content beyond text. They are a good fit for creative fields like music, art, and content creation. LLMs are best suited for text-based tasks and applications like chatbots, language translation, and content summarization.
When used together, generative AI and LLMs can enhance various applications like content personalization, storytelling, and content generation. For example, a generative AI model trained on artwork datasets could be improved by LLMs trained on art history by generating descriptions and analyses of artwork. A business could use that combination to create marketing images and phrasing that improves user intent, ultimately helping boost sales.
How Do Large Language Models Work?
A transformer model is the most common basis for a large language model, consisting of an encoder and a decoder. The transformer model processes data by tokenizing the input and conducting mathematical equations to discover the relationships between the tokens, or words. This process allows the computer to see patterns a human would if given the same query.
Before working from a transformer model, LLMs must undergo training to ensure they can fulfill general functions, and fine-tune their skills to perform specific tasks. Large language models are often trained on massive textual datasets like Wikipedia, containing trillions of words.
During training, the LLM engages in unsupervised learning, which is processing datasets given to it without specific instructions. This stage allows the LLM’s AI algorithm to decipher the meaning of words and the relationships between words. It also learns to distinguish words based on context. For example, it would learn whether “right” means “correct” or the opposite of “left.”
Key Components of LLMs
Large language models consist of several neural network layers — recurrent, embedding, attention, and feedforward layers — that work together to process input text and generate output content. Here’s how these components work:
Embedding layer: The embedding layer consists of vectors representing words in a way the machine learning model can quickly process. This part of the large language model is working on dissecting the meaning and context of the input.
Feedforward layer: The feedforward layer consists of various connected layers that transform the input embeddings. This allows the model to glean higher-level abstractions or understand the user’s intent with the text input.
Recurrent layer: The recurrent layer analyzes each word in a sequence provided in the input, capturing the relationship between words in a sentence.
Attention mechanism: The attention mechanism enables the language model to focus on single parts of the input text that are relevant to the task at hand. This layer allows the model to generate the most accurate outputs.
Business Applications of Large Language Models
Large language models have numerous applications in business environments. Key examples include:
1. Content Creation
LLMs can help generate valuable content spanning many formats, from articles and blog posts to product descriptions and social media posts — saving your company plenty of time and resources. As writing assistants, large language models can also provide real-time grammar, spelling, and phrasing suggestions.
Further, language models can help your company generate fresh outlines by analyzing existing content and trending topics, helping you develop relevant content that resonates with your target group.
Suggesting relevant keywords to enhance visibility in search results
Identifying common search queries to tailor your content to match user intent
Helping structure content to improve ranking in search results
Conducting SEO audits to analyze your website’s speed and areas for improvements
Using LLMs’ recommendations about SEO strategy can help improve user engagement and improve your site’s visibility.
3. Customer Service
Large language models can help improve the customer service experience by automating various interactions. For instance, chatbots can respond to customer inquiries, help with troubleshooting, and provide relevant information 24/7. Additionally, virtual sales assistants can engage with customers, answer product questions, and guide them through the sales process.
4. Virtual Collaboration
You can also use LLMs to enhance staff productivity and effectiveness. The AI tool can help facilitate collaboration and streamline routine tasks. Examples of functions LLMs can perform include:
Generate meeting summaries and transcriptions
Provide real-time translations for multilingual teams
Facilitate knowledge sharing
Document company and project-related processes
Assist team members with disabilities, such as vision or hearing impairment
5. Sales
Large language models can also support sales professionals with various processes, including:
Lead identification: LLMs can identify potential leads by analyzing massive amounts of data to understand customer preferences. This can help your sales teams target high-quality leads with a higher likelihood of conversion.
AI-powered chatbots: AI chatbots can engage with website visitors, collect information, and provide teams with customer insights to generate more leads.
Personalized sales outreach: Using customer information and data, LLMs can help craft personalized sales outreach messages, such as customized emails and product recommendations.
Customer feedback analysis: AI strategies can also analyze customer feedback and pain points to help sales teams personalize their approach and build stronger relationships.
6. Fraud Detection
Large language models also offer fraud detection capabilities. They can analyze textual data, identify patterns, and detect issues to help your company fight against fraud. These AI strategies provide real-time monitoring, such as financial transactions or customer interaction. They can quickly identify suspicious patterns and generate real-time alerts to jumpstart an investigation.
Applications and Use Cases in Other Industries
With various applications, you can find uses for LLMs in several fields, such as:
Marketing and advertising: LLMs excel in generating high-quality content, making them a good fit for personalized marketing, chatbots, content creation, ad targeting, and measuring the effectiveness of marketing campaigns.
Retail and e-commerce: Large language models can analyze customer data to generate personalized recommendations for products and services. They can also help answer customer inquiries, assist in purchases, and detect fraud.
Health care: Large language models are being used in health care to improve medical diagnoses, patient monitoring, drug discovery, and virtual reality training. LLMs are revolutionizing the health care industry to improve patient satisfaction and health outcomes.
Science: LLMs can understand proteins, molecules, and DNA. They can potentially be used in the development of vaccines, finding cures, and improving preventative care medicines.
Tech: Large language models are widely used in the tech industry, from allowing search engines to respond to queries to assisting developers with writing code.
Finance: LLMs are used in finance to improve the efficiency, accuracy, and transparency of financial markets. They can complete risk assessment tasks, assist in trading and fraud detection, and help financial institutions comply with regulations.
Legal: These AI strategies have helped lawyers, paralegals, and legal staff search massive textual datasets and generate legal phrasing. LLMs can streamline tasks like research and document drafting to save time.
Benefits of Large Language Models
The benefits of rolling out large language models for your business include:
Deeper Levels of Comprehension
Unlike earlier chatbots and automated systems that relied on keyword matching and rigid scripts, LLMs can better understand the context, sentiment, and intent behind queries. This allows better customer-support chatbots, virtual assistants, and search engines. For example, in e-commerce, when an online shopper has a question for the online assistant, AI can dissect the question and reveal its context to provide a relevant and accurate response.
Saved Time
Large language models can produce almost anything text-related, from quick suggestions to lengthy essays. As a result, marketers, journalists, and even employees who aren’t tasked with writing are using LLMs to streamline their work and create professional content. This saved time and effort can be channeled into personalizing the content.
Enhanced Efficiency and Accuracy
Traditional methods of text processing and analysis methods can be daunting and prone to errors, especially when working with vast datasets. By contrast, with their deep-learning algorithms, LLMs can analyze data at unparalleled speeds, reducing or eliminating manual work altogether. For example, businesses can use LLMs to scour customer reviews, identify common issues and areas they’re doing well, and respond to customers quickly — saving a lot of time in the process.
Personalized Experiences
By collecting data and analyzing customer behavior and preferences, LLMs offer personally tailored recommendations and experiences. For instance, LLMs can work as product recommendation engines that suggest items to shoppers based on browsing and purchasing history. This increases the likelihood of conversions and a better customer experience.
Considerations When Implementing LLMs
While LLMs provide many advantages across business applications, they also come with a few considerations to note. This technology is still growing and changing, meaning companies will need to be aware of risks like:
Hallucinations or falsehoods generated as a result of poorly trained LLMs
Biases when the datasets aren’t diverse enough
Security issues, such as cybercriminals using the LLM for phishing and spamming
Challenges in scaling and maintaining LLMs
Using LLMs for Business Success
LLMs will continue developing and learning, offering various innovations for businesses. With improvements like better accuracy, audiovisual training, and enhanced performance of automated virtual assistants, you’ll want to get ahead of the competition and use AI to transform your workplace. While it can be challenging to implement LLMs without technical expertise, the right consultants can guide your LLM strategy, ensuring it drives success for your company.
By considering your unique objectives and resources, the experts at Kopius can help you implement AI and machine learning (ML) solutions to empower your team and strengthen your company for the long term.
Our focus areas include:
Customer service automation
Data analytics and business intelligence
Process automation and optimization
AI and ML strategy development
Bias mitigation and fairness
Personalization and marketing automation
Churn prevention and customer retention
Supply chain optimization
Talent acquisition
At Kopius, our AI and ML solutions can transform inefficiencies in your company and improve your decision-making. When working with us, our experts will highlight the areas of your business that could grow the most with artificial intelligence and machine learning.
Explore LLM Opportunities With Kopius
LLMs can unlock exciting possibilities for your business, including streamlining tedious administrative tasks, generating fresh content, enhancing your marketing efforts, and personalizing the customer experience. When you’re ready to implement AI and machine learning for your business, Kopius is here to help with digital technology consulting services.
While it can be challenging to implement these strategies on your own, our consultants have the knowledge and expertise to help you get the most out of technology and drive real results. We consider your unique needs and goals to develop a plan that works best for you. To get started, contact us today.
Generative artificial intelligence (AI) adoption has grown exponentially in recent years, with more than half of United States employees already using AI to complete work-related tasks. Generative AI presents many possibilities for creation, from producing music and art to entire virtual worlds. It also has practical uses, including optimizing various business processes.
In this guide, you’ll learn how generative AI works, its applications, and how to leverage it for business success.
Generative AI is defined as a type of artificial intelligence technology that can create various types of content, including text, video, images, music, and audio. Types of generative AI include:
Text: Generative AI can produce essays, scripts, blogs, news articles, and even poetry. The training process involves consuming massive amounts of text from articles, books, and websites to find patterns and relationships in human languages. Examples of generative AI that can create text include Perplexity AI and ChatGPT.
Imagery: This type of AI learns by analyzing image datasets with text descriptions or captions. This way, it can understand different concepts and merge them together to create an image. These image technologies can produce diverse images in various mediums, from oil painting style to animation.
Sound: AI music generators are trained on various music tracks and metadata to find patterns and features across music genres. They can also learn lyrics to songs and create music.
Coding: Generative AI technology can be exposed to large code datasets in various program languages, like Java. Through this training, they can detect patterns, structures, and practices in these languages to write and improve code.
Video: Generative AI can create video from visual, text, and audio sources. They can even be trained on how to use video editing software and apply effects to existing videos.
Research discovery: Many generative AI strategies can automate the research process and decipher complex texts. This type of AI can analyze research patterns and identify key information or produce summaries.
How Does Generative AI Differ From Other AI Technologies?
Here are the main differences between generative AI and other AI technologies:
Generative AI vs. predictive AI: In contrast to generative AI, predictive AI uses patterns in past data to forecast outcomes and insights. Many organizations have used this technology to sharpen decision-making and develop data-driven strategies.
Generative AI vs. conversational AI: Conversational AI helps AI systems like chatbots interact with humans in a natural way. It uses natural language processing and machine learning to understand language and provide human-like text or speech responses.
The Evolution of Generative AI in Recent Years
Over several decades, we’ve seen several advances in generative AI. The language modeling techniques that help form the foundation of generative AI can be traced back to the 1950s and 1960s. In the 2010s, generative adversarial networks (GANs) — a type of machine learning algorithm — were introduced, helping AI create convincingly real images, videos, and audio of real people.
Transformers, a type of machine learning, have also led to breakthrough language models. Transformers have made it possible for researchers to train larger models without needing to label the data beforehand. As a result, newer models are trained on larger datasets — generating answers with more depth.
Transformers also allow these models to discover connections between words across pages or books rather than in individual sentences. They can even be used to track connections to analyze chemicals, code, proteins, and DNA.
How Does Generative AI Work?
Generative AI is powered by machine learning models or neural network techniques to learn the patterns and relationships of human-created content. Training involves tuning the model’s parameters for different use cases and then fine-tuning those results. For instance, to create a chatbot for an e-commerce site, you might train it on common questions customers ask and the responses often given to them.
Additionally, there are different types of AI models that work in different ways, such as discriminative models. While generative AI models dive deep into the underlying distribution of input data to generate new samples that closely resemble training data, discriminative models focus on learning the decision boundary separating classes within the input data. Rather than modeling the dataset, they target the conditional likelihood distribution of labels from the input data.
Key Mechanisms Behind Generative AI
Generative AI technology specifically relies on algorithms like Variational Autoencoders (VAE) and GANs that are trained to capture underlying structures and probabilistic distributes that define the data. It then uses these learned patterns to generate new content.
VAEs: VAEs consist of two neural networks, referred to as the encoder and decoder. The encoder is responsible for converting the input into a smaller representation of the data. With this compressed version, the decoder can then reconstruct the original input data and discard irrelevant information to generate novel data.
GANs: GANs pit two neural networks against one another — a generator that produces new examples and a discriminatory one that learns to distinguish the new content as real (from the domain) or fake (generated).
As the models get smarter, they will produce better content, and the discriminator will get better at spotting generated content. The procedure then repeats, pushing improvements until the generated content is indistinguishable from the existing content.
It’s also important to learn about the architecture of AI generative models. The most common is the transformer network, which consists of multiple layers — self-attention, feed-forward, and normalization layers. These layers work together to decipher and predict streams of data, which could include text, protein sequences, or even images.
Generative AI Applications
Generative AI has the potential to enhance the customer experience, speed up product development, and improve employee productivity, spanning use cases from retail to research and development.
Here are just a few ways industries are leveraging generative AI for success:
Customer Service
Generative AI has led to improvements in customer operations, improving the customer experience and employee productivity through digital self-service and augmenting agent skills. One study found that a company with over 5,000 customer service agents saw increased issue resolution by 14% per hour and reduced time spent handling issues by 9%. Examples of operational improvements generative AI can make include:
Customer self-service
Reduced response time
Resolution during initial contact
Increased sales
Generative AI-fueled chatbots can provide immediate and personalized responses to customer inquiries, regardless of the complexity of the problem, or the language and location of the customer. Generative AI can also enhance coaching and quality assurance by gathering user insights, thereby increasing productivity and sales.
Ultimately, automation through generative AI can improve the quality and effectiveness of interactions, freeing up time for customer care teams to respond to inquiries only solvable by human agents.
Marketing
Generative AI can also transform marketing processes with efficient and effective content creation, SEO optimization, and product discovery. Here are some use cases for this technology in the marketing industry:
Rapid content creation: Generative AI can reduce the time required to come up with content, saving teams time and effort. It can also ensure a uniform brand voice, writing style, and format, helping teams personalize marketing messages for different customer segments and demographics.
Search engine optimization (SEO): Generative AI can lead to higher conversion for a reduced cost through SEO optimization. The technology can synthesize key SEO words, support content creation, and distribute the targeted content to customers.
Product discovery: Generative AI can be personalized with text, images, and speech and a deep understanding of customer profiles. It can leverage customer user insights to help customers discover relevant products, helping companies achieve higher website conversion rates.
Sales
Generative AI also has the possibility to change how B2B and B2C companies approach sales by:
Increasing probability of sale: Generative AI technology can identify and prioritize sales leads by gathering customer data, creating profiles, and suggesting actions to improve client engagement.
Improving lead development: This technology can also help sales teams nurture leads by integrating relevant product sales and customer profiles to create discussion scripts for customer conversation. It can also automate follow-ups with customers, nurturing leads until the client is ready to interact with a human sales agent.
Software Engineering
Software engineering plays a significant role in many companies, and with generative AI, software engineers can use augmented coding and train large language models to generate code. Generative AI can lead to cost savings for companies by accelerating coding processes like creating initial code drafts and generating new system designs.
One study found that software developers using generative AI completed tasks 55.8% faster than those not using it. Another study found that by reducing the time needed for these tasks, engineers reported a better work experience, citing improvements in flow, happiness, and fulfillment.
Research and Development
The life sciences and chemical industries are already using generative AI foundation models in research and development. These foundation models can generate candidate molecules and accelerate drug development. In addition to increasing productivity in producing candidate designs, generative AI can optimize manufacturing designs, leading to cost reductions in production and logistics.
Generative AI can also be used to optimize health care processes, such as appointment scheduling and analytics, to help improve efficiency over time. Digital AI solutions can ultimately lead to improved patient care and hospital efficiency.
Limitations and Challenges of Generative AI
Since generative AI is so new, there are some inherent risks involved in using it — some recognized and some yet to be discovered. Examples of generative AI risks include:
Accuracy and reliability concerns: While the information generative AI produces sound convincing, sometimes the information can be wrong. These models are still in the early stages of development, so it’s important to assess responses for appropriateness, usefulness, and accuracy before using them to distribute information.
Copyright issues: Generative AI models are trained on large amounts of publicly available data. They are not designed to be compliant with copyright laws, making it important to pay close attention to your company’s use of AI.
Bias: It’s also important to enact policies or controls that can detect bias within the AI outputs. That way, you can deal with them in a way that is consistent with your company policy and legal requirements.
Cybersecurity and fraud: It’s important to be prepared for misuse of generative AI for cyber and fraud attacks, such as those that use deep fakes.
There are several ways to mitigate risks when implementing generative AI in businesses. By committing to guidelines and having safeguards in place, you can ensure the technology solutions are accurate, safe, and trusted to help your team flourish.
The Future of Generative AI
In the coming years, generative AI technology will continue to evolve, revolutionizing how we work. Reports predict that we will see $42 billion in annual spending by 2030 on generalized AI use cases, such as writing, researching, and summarizing strategies. More than 50% of this spending will be on communications platforms and chatbots — driving significant improvements in the customer and employee experience.
These tech solutions will make advancements in translation, drug discovery, and the generation of new content, from video and text to music and fashion design. We’ll also see a significant impact when integrating these capabilities with existing tools, such as grammar checkers and design programs.
More and more businesses will customize generative AI using their own data to improve communication and branding. Programmers will also use generative AI to enforce company-specific best practices for formatting and writing more readable, consistent code.
In the future, generative AI models will likely be expanded to support 3D modeling, product design, drug development, and business processes — making it easier to generate new product ideas and explore business ideas. When used for training, generative AI can automatically identify best practices to help train employees more efficiently in one part of an organization.
Preparing for What’s Next
Generative AI solutions are setting the pace for innovation and strategic leadership in the business world, fostering a culture of informed decision-making, optimized marketing strategies, and enhanced efficiency. With numerous advancements on the horizon for generative AI, you can stay ahead of the competition by implementing generative AI for your business.
To recognize the full potential of generative AI, consider collaborating with digital technology consultants. At Kopius, we can translate business problems into AI solutions. Collaborating with us can provide a competitive advantage, as we ensure AI aligns with your business objectives. We’ll identify areas where AI and machine learning can offer benefits, such as enhancing product offerings, improving customer service, or optimizing operational efficiency.
We can also help you mitigate generative AI risks like privacy concerns and potential biases in AI algorithms. Being proactive about these risks can enhance your organization’s reputation and give you a competitive advantage.
Partner With Kopius for Generative AI Innovation
Generative AI is much more than a technological strategy — it’s a transformative force reshaping the business landscape. As AI continues to evolve, its impact on executive leadership and business strategy will only increase. By effectively implementing AI, you can position your organization for success in the competitive business environment of the future.
Kopius can help you use generative AI to reach your objectives and achieve significant growth. Our consultants have the knowledge and expertise to maximize the benefits of AI and machine learning, allowing you to drive real and impactful results. By considering your unique needs and goals, we’ll develop a plan that works best for your organization. We can also help mitigate security risks associated with data breaches, regulatory violations, and data quality.
At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success. Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Rapidly prototype solutions
And, fast-forward success
Gather your best and brightest business-minded individuals and join our experts for a hands-on workshop that encourages innovation and drives new ideas.
The core of future-proofing your business lies in the incorporation of cutting-edge technological trends and strategic digitization of your business operations. Combining new, transformative solutions with tried-and-true business methods is not only a practical approach but an essential one when competing in this digital age. Using the latest digital transformation trends as your guide, start envisioning the journey of future-proofing your business in order to unlock the opportunities of tomorrow.
#1 Personalization
The importance of personalized customer experiences should not be understated. More than ever, consumers are faced with endless options. To stand out from competitors, businesses must use data and customer behavior insights to curate tailored and dynamic customer journeys that both delight and command their audience. Analyze purchasing history, demographics, web activity, and other data to understand your customer, as well as their likes and dislikes. Use these insights to design customized customer experiences that increase conversion, retention, and ultimately, satisfaction.
#2 Artificial Intelligence
AI is everywhere. From autonomous vehicles and smart homes to digital assistants and chatbots, artificial intelligence is being used in a wide array of applications to improve, simplify, and speed up the tasks of everyday life. For businesses, AI and machine learning have the power to extract and decipher large amounts of data that can help predict trends and forecasts, deliver interactive personalized customer experiences, and streamline operational processes. Companies that lean on AI-driven decisions are propelled into a world of efficiency, precision, automation, and competitiveness.
#3 Sustainability
Enterprises, particularly those in the manufacturing industry, face increasing pressure to act more responsibly and consider environmental, social, and corporate governance (ESG) goals when making business decisions. Digital transformations are one way to support internal sustainable development because they lead to reduced waste, optimized resource use, and improved transparency. With sustainability in mind, businesses can build their data and technology infrastructures to reduce impact. For example, companies can switch to more energy-efficient hardware or decrease electricity consumption by migrating to the cloud.
#4 Cloud Migration
More and more companies are migrating their data from on-premises to the cloud. In fact, by 2027, it is estimated that 50% of all enterprises will use cloud services1. What is the reason behind this massive transition? Cost saving is one of the biggest factors. Leveraging cloud storage platforms eliminates the need for expensive data centers and server hardware, thereby reducing major infrastructure expenditures. And while navigating a cloud migration project can seem challenging, many turn to cloud computing partners to lead the data migration and ensure a painless shift.
Future-Proof Your Business Through Digital Transformation with Kopius
Innovating technology is crucial, or your business will be left behind. Our expertise in technology and business helps our clients deliver tangible outcomes and accelerate growth. At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.
Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.
Winning the interest and loyalty of customers means more than just offering a superior product or service. The secret lies in a powerful strategy called personalization – a dynamic approach that tailors the customer experience to meet individual needs and preferences. As businesses across industries strive to create lasting connections with their customers and meet their evolving expectations, the importance of personalization in the customer experience should not be overstated. Read on to explore the compelling case for customer personalization and a step-by-step guide on how your business can embark on this journey to elevate the customer experience.
Let’s face it, generic offerings are outdated. Today, customers yearn for something more; they want an experience that resonates with their unique tastes. Personalization is the magic ingredient that taps into this desire. By tailoring products, services, and interactions to individual preferences, businesses create a sense of connection that fosters lasting loyalty. And beyond that, research from McKinsey found that companies who implemented a personalization strategy generated 40% more revenue than their counterparts who placed less emphasis on this approach. All signs point to tailored customer journeys.
Data lies at the heart of personalization, offering insights into customer behaviors. More than ever, companies have access to a wealth of customer information, such as past purchases and browsing habits, that act as the building blocks to these insights. Leveraging advanced analytics and artificial intelligence, businesses can uncover valuable patterns and trends, guiding them to craft personalized experiences for their customers.
Building a successful personalization strategy requires thoughtful consideration and calculated execution. If you are just getting started, follow these steps to build an improved and tailored customer experience that will drive remarkable results for your business:
Step 1: Gather as Much Customer Data as Possible.
At the core of every successful personalization strategy lies a deep understanding of your customers. To lay this solid foundation, start by gathering valuable data from multiple touchpoints along their journey, including website interactions, purchase history, and customer feedback. Take advantage of powerful tools like customer relationship management (CRM) software, website analytics, and social media insights to gain a holistic view of your customers’ preferences, behaviors, and pain points.
Step 2: Divide Your Customers Into Audience Segments.
With an abundance of data at your fingertips, it is time to move on to segmentation. Divide your customers into distinct groups based on shared traits like demographics, purchase behavior, and interests. Audience segmentation empowers you to personalize your messaging or offerings, address individual customer needs with accuracy, and create a sense of relevance.
Step 3: Get Personal With Your Messaging.
Now that you have completed the segmentation process, it’s time to get personal! Start by creating interesting content with tailored product recommendations, and design exclusive offers that cater specifically to the unique preferences of each of your audience segments. By doing so, you will create truly personalized experiences that captivate your audience and leave an impression.
Step 4: Automate Dynamic Content Delivery.
Offer real-time digital experiences that resonate with your customers’ interests and past interactions. Embracing innovative technologies like artificial intelligence allows you to analyze customer data, predict behavior, and implement an effective personalization strategy that delivers tailored experiences on the fly. AI-powered chatbots take personalized support a step further, offering instant assistance to resolve customer concerns and boost overall customer satisfaction levels.
Step 5: Track Your Personalization Campaigns.
Monitor the impact of your personalization strategy on customer engagement, satisfaction, and business performance. Evaluate key metrics like conversion rates and customer retention to assess their effectiveness. Utilize any insights gained to identify areas for improvement and modify your approach accordingly.
The possibilities for designing a personalized digital experience are limitless. AI-powered chatbots provide real-time personalized support, making customers feel valued and cared for. Dynamic content delivery ensures website experiences are based on individual preferences. Personalization will enrich the customer journey, increasing engagement and conversion rates. If you are ready to deliver personalized experiences, Kopius is here to help. Let’s team up to create extraordinary customer experiences for your business!
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At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.
Our JumpStart program fast-tracks business results and platform solutions. Connect with us today to enhance your customer satisfaction through a data-driven approach, drive innovation through emerging technologies, and achieve competitive advantage.
Augmented Intelligence (AI) and Machine Learning (ML) were already the technologies on everyone’s radar when the year started, and the release of Foundation Models like ChatGPT only increased the excitement about the ways that data technology can change our lives and our businesses. We are excited about these five industries that are winning at artificial intelligence.
As an organization, data and AI projects are right in our sweet spot. ChatGPT is very much in the news right now (and is a super cool tool – you can check it out here if you haven’t already).
There are a few real-world examples of how five organizations are winning at AI. We have included those use cases along with examples where our clients have been leading the way on AI-related projects.
You can find more case studies about digital transformation, data, and software application development in our Case Studies section of the website.
Brands are helping customers to visualize the outcome of their products or services using computer vision and AI. Consumers can virtually try on a new pair of glasses, a new haircut, or a fresh outfit, for example. AI can also be used to visualize a remodeled bathroom or backyard.
We helped a teledentistry, web-first brand develop a solution using computer vision to show a customer how their smile would look after potential treatment. We paired the computer vision solution with a mobile web application so customers could “see their new selfie.”
Consumer questions can be resolved faster and more accurately
Customer service can make or break customer loyalty, which is why chatbots and virtual assistants are being deployed at scale to reduce average handle time average speed-of-answer, and increase first-call resolutions.
We worked with a regional healthcare system to design and develop a “digital front door” to improve patient and provider experiences. The solution includes an interactive web search and chatbot functionality. By getting answers to patients and providers more quickly, the healthcare system is able to increase satisfaction and improve patient care and outcomes.
Finance: Preventing fraud
There’s a big opportunity for financial services organizations to use AI and deep learning solutions to recognize doubtful transactions and thwart credit card fraud which help reduce cost. Also known as anomaly detection, banks generate huge volumes of data which can be used to train machine learning models to flag fraudulent transactions.
Agriculture: Supporting ESG goals by operating more sustainably
Data technologies like computer vision can help organizations see things that humans miss. This can help with the climate crisis because it can include water waste, energy waste, and misdirected landfill waste.
The agritech industry is already harnessing data and AI since our food producers and farmers are under extreme pressure to produce more crops with less water. For example, John Deere created a robot called “See and Spray” that uses computer vision technology to monitor and spray weedicide on cotton plants in precise amounts.
We worked with PrecisionHawk to use computer vision combined with drone-based photography to analyze crops and fields to give growers precise information to better manage crops. The data produced through the computer vision project helped farmers to understand their needs and define strategies faster, which is critical in agriculture. (link to case study)
Healthcare: Identify and prevent disease
AI has an important role to play in healthcare, with uses ranging from patient call support to the diagnosis and treatment of patients.
For example, healthcare companies are creating clinical decision support systems that warn a physician in advance when a patient is at risk of having a heart attack or stroke adding critical time to their response window.
AI-supported e-learning is also helping to design learning pathways, personalized tutoring sessions, content analytics, targeted marketing, automatic grading, etc. AI has a role to play in addressing the critical healthcare training need in the wake of a healthcare worker shortage.
Artificial intelligence and machine learning are emerging as the most game-changing technologies at play right now. These are a few examples that highlight the broad use and benefits of data technologies across industries. The actual list of use cases and examples is infinite and expanding.
Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success.
JumpStart Your Success Today
Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
As AI becomes even more integrated into business, so does AI bias.
On February 2, 2023, Microsoft released a statement from Vice Chair & President Brad Smith about responsible AI. In the wake of the newfound influence of ChatGPT and Stable Diffusion, considering the history of racial bias in AI technologies is more important than ever.
The discussion around racial bias in AI has been going on for years, and with it, there have been signs of trouble. Google fired two of its researchers, Dr. Timnit Gebru and Dr. Margaret Mitchell after they published research papers outlining how Google’s language and facial recognition AI were biased against women of color. And speech recognition software from Amazon, Microsoft, Apple, Google, and IBM misidentified speech from Black people at a rate of 35%, compared to 19% of speech from White people.
In more recent news, DEI tech startup Textio analyzed ChatGPT showing how it skewed towards writing job postings for younger, male, White candidates- and the bias increased for prompts for more specific jobs.
If you are working on an AI product or project, you should take steps to address AI bias. Here are four important questions to help make your AI more inclusive:
Have we incorporated ethical AI assessments into the production workflow from the beginning of the project? Microsoft’s Responsible AI resources include a project assessment guide.
Are we ready to disclose our data source strengths and limitations? Artificial intelligence is as biased as the data sources it draws from. The project should disclose who the data is prioritizing and who it is excluding.
Is ourAI production team diverse? How have you accounted for the perspectives of people who will use your AI product that are not represented in the project team or tech industry?
Have we listened to diverse AI experts? Dr. Joy Buolamwini and Dr. Inioluwa Deborah Raji, currently at the MIT Media Lab, are two black female researchers who are pioneers in the field of racial bias in AI.
“AI research must also acknowledge that the problems we would like to solve are not purely technical, but rather interact with a complex world full of structural challenges and inequalities. It is therefore crucial that AI researchers collaborate closely with individuals who possess diverse training and domain expertise.”
Ready to JumpStart AI in Your Business?
Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success.
Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
The rise of generative models such as ChatGPT and Stable Diffusion has generated a lot of discourse about the future of work and the AI-assisted workplace. There is tremendous excitement about the awesome new capabilities such technology promises, as well as concerns over losing jobs to automation. Let’s look at where we are today, how we can leverage these new AI-generated text technologies to supercharge productivity, and what changes they may signal to a modern workplace.
That’s the question on everyone’s mind. AI can generate images, music, text, and code. Does this mean that your job as a designer, developer, or copywriter is about to be automated? Well, yes. Your job will be automated in the sense that it is about to become a lot more efficient, but you’ll still be in the driver’s seat.
First, not all automation is bad. Before personal computers became mainstream, taxes were completed with pen and paper. Did modern tax software put accountants out of business? Not at all. It made their job easier by automating repetitive, boring, and boilerplate tasks. Tax accountants are now more efficient than ever and can focus on mastering tax law rather than wasting hours pushing paper. They handle more complicated tax cases, those personalized and tailored to you or your business. Similarly, it’s fair to assume that these new generative AI tools will augment creative jobs and make them more efficient and enjoyable, not supplant them altogether.
Second, generative models are trained on human-created content. This ruffles many feathers, especially those in the creative industry whose art is being used as training data without the artist’s explicit permission, allowing the model to replicate their unique artistic style. Stability.ai plans to address this problem by enabling artists to opt out of having their work be part of the dataset, but realistically there is no way to guarantee compliance and no definitive way to prove whether your art is still being used to train models. But this does open interesting opportunities. What if you licensed your style to an AI company? If you are a successful artist and your work is in demand, there could be a future where you license your work to be used as training data and get paid any time a new image is generated based on your past creations. It is possible that responsible AI creators can calculate the level of gradient updates during training, and the percentage of neuron activation associated to specific samples of data to calculate how much of your licensed art was used by the model to generate an output. Just like Spotify pays a small fee to the musician every time someone plays one of their songs, or how websites like Flaticon.com pay a fee to the designer every time one of their icons is downloaded. Long story short, it is likely that soon we’ll see more strict controls over how training datasets are constructed regarding licensed work vs public domain.
Let’s look at some positive implications of this AI-assisted workplace and technology as it relates to a few creative roles and how this technology can streamline certain tasks.
As a UI designer, when designing web and mobile interfaces you likely spend significant time searching for stock imagery. The images must be relevant to the business, have the right colors, allow for some space for text to be overlaid, etc. Some images may be obscure and difficult to find. Hours could be spent finding the perfect stock image. With AI, you can simply generate an image based on text prompts. You can ask the model to change the lighting and colors. Need to make room for a title? Use inpainting to clear an area of the image. Need to add a specific item to the image, like an ice cream cone? Show AI where you want it, and it’ll seamlessly blend it in. Need to look up complementary RGB/HEX color codes? Ask ChatGPT to generate some combinations for you.
Will this put photographers out of business? Most likely not. New devices continue to come out, and they need to be incorporated into the training data periodically. If we are clever about licensing such assets for training purposes, you might end up making more revenue than before, since AI can use a part of your image and pay you a partial fee for each request many times a day, rather than having one user buy one license at a time. Yes, work needs to be done to enable this functionality, so it is important to bring this up now and work toward a solution that benefits everyone. But generative models trained today will be woefully outdated in ten years, so the models will continue to require fresh human-generated real-world data to keep them relevant. AI companies will have a competitive edge if they can license high-quality datasets, and you never know which of your images the AI will use – you might even figure out which photos to take more of to maximize that revenue stream.
Software engineers, especially those in professional services frequently need to switch between multiple programming languages. Even on the same project, they might use Python, JavaScript / TypeScript, and Bash at the same time. It is difficult to context switch and remember all the peculiarities of a particular language’s syntax. How to efficiently do a for-loop in Python vs Bash? How to deploy a Cognito User Pool with a Lambda authorizer using AWS CDK? We end up Googling these snippets because working with this many languages forces us to remember high-level concepts rather than specific syntactic sugar. GitHub Gist exists for the sole purpose of offloading snippets of useful code from local memory (your brain) to external storage. With so much to learn, and things constantly evolving, it’s easier to be aware that a particular technique or algorithm exists (and where to look it up) rather than remember it in excruciating detail as if reciting a poem. Tools like ChatGPT integrated directly into the IDE would reduce the amount of time developers spend remembering how to create a new class in a language they haven’t used in a while, how to set up branching logic or build a script that moves a bunch of files to AWS S3. They could simply ask the IDE to fill in this boilerplate to move on to solving the more interesting algorithmic challenges.
An example of asking ChatGPT how to use Python decorators. The text and example code snippet is very informative.
For copywriters, it can be difficult to overcome the writer’s block of not knowing where to start or how to conclude an article. Sometimes it’s challenging to concisely describe a complicated concept. ChatGPT can be helpful in this regard, especially as a tool to quickly look up clarifying information about a topic. Though caution is justified as demonstrated recently by Stephen Wolfram, CEO of Wolfram Alpha who makes a compelling argument that ChatGPT’s answers should not always be taken at face value.. So doing your own research is key. That being the case, OpenAI’s model usually provides a good starting point at explaining a concept, and at the very least it can provide pointers for further research. But for now, writers should always verify their answers. Let’s also be reminded that ChatGPT has not been trained on any new information created after the year 2021, so it is not aware of new developments on the war in Ukraine, current inflation figures, or the recent fluctuations of the stock market, for example.
In Conclusion
Foundation models like ChatGPT and Stable Diffusion can augment and streamline workflows, and they are still far from being able to directly threaten a job. They are useful tools that are far more capable than narrowly focused deep learning models, and they require a degree of supervision and caution. Will these models become even better 5-10 years from now? Undoubtedly so. And by that time, we might just get used to them and have several years of experience working with these AI agents, including their quirks and bugs.
There is one important thing to take away about Foundation Models and the future of the AI-assisted workplace: today they are still very expensive to train. They are not connected to the internet and can’t consume information in real-time, in online incremental training mode. There is no database to load new data into, which means that to incorporate new knowledge, the dataset must grow to encapsulate recent information, and the model must be fine-tuned or re-trained from scratch on this larger dataset. It’s difficult to verify that the model outputs factually correct information since the training dataset is unlabeled and the training procedure is not fully supervised. There are interesting open source alternatives on the horizon (such as the U-Net-based StableDiffusion), and techniques to fine-tune portions of the larger model to a specific task at hand, but those are more narrowly focused, require a lot of tinkering with hyperparameters, and generally out of scope for this particular article.
It is difficult to predict exactly where foundation models will be in five years and how they will impact the AI-assisted workplace since the field of machine learning is rapidly evolving. However, it is likely that foundation models will continue to improve in terms of their accuracy and ability to handle more complex tasks. For now, though, it feels like we still have a bit of time before seriously worrying about losing our jobs to AI. We should take advantage of this opportunity to hold important conversations now to ensure that the future development of such systems maintains an ethical trajectory.
JumpStart Your Success Today
Kopius supports businesses seeking to govern and utilize AI and ML to build for the future. We’ve designed a program to JumpStart your customer, technology, and data success.
Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders, equip teams with the skills and mindset needed to:
Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
