Understanding the intricacies of rapidly advancing technology is crucial. Generative AI, a groundbreaking subset of artificial intelligence, is at the forefront of this evolution. But what exactly is generative AI, and how can it transform your business?
What is generative AI?
Generative AI describes deep-learning models that generate content that’s similar, but not exactly the same, as the data they were trained with. This could be text, images, audio, video, or code, for example.
Although it’s gained momentum and mainstream recognition in recent years, generative AI has existed for longer than you might realize. It was used in the first chatbot, ELIZA, created in 1966 by Joseph Weizenbaum. Weizenbaum used a script inspired by what a psychotherapist might say, but was perplexed when users began to genuinely confide in ELIZA. He saw generative AI as a tool to further what humans could do, not as a replacement for humans themselves.
How does generative AI work?
The process starts with a prompt given by the user. This could be very basic or very detailed – indeed, there’s been plenty of discussion about optimizing prompts to get the best out of generative AI. The nature of the prompt will depend on the output you want. For example, you might submit an audio recording if you’d like a written transcript of it.
The AI algorithms will respond with new content based on the prompt. The user can then provide feedback if the first draft doesn’t match their brief, so the generative AI can edit the response accordingly.
What are some generative AI examples?
Chat GPT
Perhaps the most well-known example, ChatGPT is a generative AI chatbot that launched in late November 2022. It presents as a conversation between a chatbot and the user, using machine learning algorithms to process the data needed to respond – all in a matter of seconds.
DALL-E
First built in 2021, this model creates images based on written prompts. Even if what you’re trying to generate doesn’t exist, DALL-E can produce it by relying on its ability to process language and visuals.
Gemini
Google’s contribution to generative AI, Gemini, is a chatbot that’s already undergone multiple iterations. Originally called Bard, the first iteration of Gemini was released in early 2024 and is a large language model (LLM). Similar to ChatGPT, it presents as a chat where you can ask a question, with Gemini returning an answer. It’s built to understand text, image, audio, video, and code.
Jasper
Jasper is a writing generation tool. The user inputs the type of content they want (a summary paragraph, for example, or a blog post introduction), and Jasper produces it. They can fill in a form with extra details, such as key points they want to include or keywords they want to target, in order to get a response that better fits what they need.
PodcastAI
PodcastAI is a collection of AI tools designed to speed up podcast production. The tools can generate episode transcripts; split the episodes into chapters; write a description and title; produce video clips for sharing on social media; create a website for the podcast; host it, and distribute it on platforms like Apple and Spotify.
What are the benefits of generative AI?
Generative AI can be applied to many industries, in many different ways. This versatility is one reason why there’s been such rapid growth. Businesses can leverage this versatility to enhance products, personalize customer experiences, or create unique marketing campaigns
By automating tasks that traditionally require significant effort and time, generative AI allows businesses to focus on what truly matters. It can speed up everyday errands, such as replying to emails, and summarize long, complicated paragraphs into a shorter, more coherent explanation. Entrepreneurs can generate business insights, streamline customer service through AI-powered chatbots, and accelerate data processing.
Generative AI plays a crucial role in improving accessibility, making products and services more inclusive. It can convert written content into audio for visually impaired users or translate text into multiple languages, making your business more accessible to a global audience. Additionally, it helps create more intuitive user interfaces, ensuring that all users, regardless of their abilities, can interact with your digital platforms seamlessly.
With generative AI, productivity sees a marked improvement as it takes over routine tasks, allowing employees to concentrate on higher-level functions. For example, AI can assist in drafting reports, managing schedules, and even conducting preliminary data analysis, thereby freeing up employees to engage in more meaningful and impactful work. This not only improves efficiency but also job satisfaction, as people are empowered to do more of the work they enjoy.
Challenges, risks and limitations of generative AI
While there are plenty of benefits to generative AI, this exciting technology also brings challenges with it. Understanding these is crucial in mitigating risks and making informed decisions.
Cybersecurity
As AI systems become more sophisticated, they also become more attractive targets for cyber attacks. Generative AI models can potentially be manipulated to produce malicious outputs, such as deepfakes, impersonate business leaders or employees, or leak sensitive data. Business owners need to ensure robust cybersecurity measures are in place to protect AI systems from threats. This can include implementing advanced encryption protocols, regularly updating software, and conducting thorough security audits to safeguard against vulnerabilities.
Accuracy
Generative AI models are only as good as the data they are trained on. Ensuring the accuracy of AI-generated outputs relies heavily on the quality and comprehensiveness of training data. Inaccurate or outdated data can lead to unreliable results, potentially affecting business decisions and customer satisfaction. Business owners must invest in maintaining and updating data sets and employ rigorous testing and validation processes to ensure AI models perform accurately and consistently.
Bias
Bias in AI is a significant concern, particularly in generative models. AI systems can inadvertently perpetuate existing biases present in the training data, leading to unfair outcomes and decisions. For business owners, addressing bias requires a proactive approach to identifying and mitigating potential sources of bias in AI models. This can involve diversifying training data, employing fairness algorithms, and conducting regular audits to assess and correct biases in AI systems.
Sourcing
The sourcing of data used in generative AI models raises ethical and legal considerations, as the source of the content isn’t always cited. Additionally, the content might sound accurate, which means users don’t investigate any further. But information can change, so what was correct when the source was written might not be applicable today.
Business owners must ensure that data is sourced ethically and complies with relevant laws and regulations. Transparency in sourcing and data usage is essential to build trust with customers and stakeholders. Clear documentation of data sources and AI processes can help demonstrate accountability and integrity in AI-driven operations.
Hallucinations
Hallucinations pose a significant challenge for generative AI systems, particularly large language models (LLMs). These AI hallucinations occur when models generate content that is factually incorrect, nonsensical, or entirely fabricated, despite appearing plausible on the surface. The phenomenon stems from various factors, including insufficient or biased training data, overfitting, and the inherent limitations of pattern-based learning.
As AI systems become more integrated into critical applications across industries, the consequences of these hallucinations can range from spreading misinformation to causing reputational damage and even posing safety risks. Addressing this issue is crucial for improving the reliability and trustworthiness of AI-generated content, especially in fields like healthcare, finance, and academic research where accuracy is paramount.
While researchers are exploring various mitigation strategies, such as improved model architectures, human oversight, and data quality enhancements, completely eliminating hallucinations remains a complex challenge that continues to hinder the wider adoption and effectiveness of generative AI technologies.
How can you effectively use generative AI for business?
Generative AI that has access to public data is very useful for generic queries (for example, finding top criminals globally). However, generative AI on its own won’t be very helpful to a business if it doesn’t have access to its internal data. If you’d like more specific questions answered (for example, finding risky suppliers in your customer supply chain), you need a generative AI suite, tool, co-pilot or agent that can query your proprietary data.
There are a couple of aspects to think about when adopting generative AI for your business.
The accuracy of your data. The results you get from generative AI will only be accurate if you have an accurate view of your data. Entity resolution can help, as it takes multiple data points that reference the same real-world thing and resolves them into one distinct entity, connecting data in a meaningful way, revealing context and duplicates.
Regulatory requirements and ethical considerations. There’s no general statutory regulation of AI in the UK at present, but existing laws impact how AI can be used. For example, the Equality Act 2010 prohibits discrimination against protected characteristics, but AI can show bias if there’s any in the training data. There are also some restrictions on using AI for surveillance purposes, with employees protected by the Human Rights Act 1998.
RAG. RAG (retrieval-augmented generation) is a capability crucial for accurately querying data outside of the data the LLM was trained on (other knowledge bases or proprietary data). The LLM does not need to be retrained, yet it can understand and generate intelligence from your internal systems. Using RAG also helps to mitigate risks of hallucination, bias and inaccuracy.
The table below shows a comparison of what type of queries generative AI can respond to with and without RAG.
NO RAG RAG "What is the most profitable energy supplier in the UK"? "What is the most profitable account in my customer portfolio?” Security measures. Implement robust security protocols to protect sensitive data and prevent potential misuse or unauthorized access to the LLM system.
Business use cases for generative AI
Generative AI can, and is, used by businesses across a range of industries. Although it can take years to develop and improve how new technology is used, organizations are already seeing changes, or at least the potential for change. Notable use cases include:
Customer experience and behavior
Using an AI chatbot on an organization’s website, to speed up customer support services and handle routine queries, freeing up the support team to focus on more complex issues.
Using natural language processing (NLP) models to analyze customer sentiment from chat logs or reviews.
Automating booking, payment processing, or follow-ups to improve customer experience workflows.
Analyzing customers’ financial history to identify what activity is normal and what is unusual, in order to detect fraud.
Creating personalized customer recommendations for products or services, boosting customer satisfaction and loyalty.
Content creation
Writing or improving email drafts before they’re sent out.
Dubbing audio or video content in different languages.
Writing video scripts.
Optimizing blog content for SEO (search engine optimization).
Writing personalized ad copy for social media campaigns.
Writing social media captions.
Creating images and visuals to support pitches for product ideas.
Automating reports for quarterly updates.
While these processes are quick, it’s important to add a human element to fact check any information included and align with your brand’s voice.
Making decisions
Processing large volumes of data, identifying patterns, and providing actionable insights that aid decision making.
Assisting with product prototyping.
Generating multiple design variations to simplify decision making.
Generating visual dashboards to interpret data and help with quick decision making.
Predicting trends by analyzing historical sales patterns and planning accordingly.
Setting up targeted email campaigns offering discounts on items your customers are most likely to buy.
Using AI to scan resumés and shortlist the best candidates based on specific requirements. (Note that your system will need to be set up to avoid bias – one way of doing this is by excluding names from your scanning process.) This streamlines the hiring process.
Threat Detection and Prevention
Detecting financial crime. By using generative AI to analyze high-volume datasets of their customers and counterparties in seconds, organizations can faster detect, monitor and investigate unusual behavior and suspicious activity.
Detecting fraud, such as identity theft and tax refund fraud. By integrating generative AI and machine learning, organizations can analyze large datasets and identify patterns of fraudulent behavior.
Detecting, predicting and responding to cybercrime attempts. AI can do this faster than humans, which means potential attacks can be dealt with quickly and free up employees to focus on other tasks.
No matter how your business decides to use generative AI, it’s vital to keep best practices in mind:
Check the accuracy and quality of all AI-generated content and remove any bias before publishing.
Label any content which has been generated by AI so users can see.
Test different AI tools to see how they work and what their strengths and limitations are before committing to use one on a regular basis.
Generative AI model architectures and their evolution
The early stages
Generative AI began with simpler models like Markov chains and Hidden Markov Models, which were used for generating sequences based on learned probabilities. They’re relatively simple compared to today’s technology, but laid the groundwork for more complex architectures.
The rise of GANs
One of the most significant breakthroughs in generative AI came with the introduction of GANs (Generative Adversarial Networks) by Ian Goodfellow and his team in 2014. GANs consists of two neural networks – a generator and a discriminator – that play against each other to improve the quality of generated data, creating remarkably realistic images and videos. This has enabled advancements in fields such as art, gaming, and healthcare.
The age of transformers
Following GANs, the introduction of transformer models, like OpenAI's GPT series, revolutionized the field of natural language processing (NLP). Unlike previous architectures that relied heavily on sequential processing, transformers utilize attention mechanisms, allowing them to process textual content with better context awareness. Transformers have drastically improved machine translation, chatbots, content creation and more.
Integrating reinforcement learning
The integration of reinforcement learning has furthered generative AI capabilities, as models now not only generate content but can also optimize outcomes based on specific goals. Reinforcement learning supports actions which help the user get closer to their goal, while ignoring those that do not.
Current trends and future directions
Generative AI is being utilized to spark creativity in art, music, and design. It acts not just as a creator but as a collaborator, providing unique inputs that artists and designers can integrate into their workflows.
With advancements in generative AI, businesses can offer more personalized experiences. For example, customization in advertising, where AI generates product visuals tailored to individual consumer preferences, is becoming increasingly feasible.
The evolution of generative AI brings ethical considerations, from potential biases in generated content to the need for transparency in AI-generated art or news articles. Responsible deployment and usage are essential to harness its power positively.
The future of generative AI looks promising—research continues to enhance model efficiencies, real-time processing capabilities, and multimodal AI systems that can incorporate and generate across different types of content simultaneously.
How does Quantexa use generative AI?
Quantexa is leveraging the power of Gen AI to help its users interact with all the data, context & insight within the Quantexa Platform for KYC, Customer Intelligence, Fraud, AML, and Risk Management solutions.
The generative AI Platform capability Q Assist leverages large language models (LLMs) of your choice but ensures their effectiveness by grounding them in an organization’s trusted data from Quantexa’s Decision Intelligence Platform.
It performs key tasks like query investigations, summarizing data, generating reports, and identifying risks and opportunities using natural language processing. To address common generative AI challenges, Q Assist ensures data security, transparency, and accuracy by maintaining user access rights, clearly showing data sources, and using contextual Retrieval Augmented Generation (RAG) to minimize incorrect outputs.
By integrating with existing LLMs, Q Assist delivers faster efficiency, trustworthy insights, and quicker time-to-value, allowing organizations to make informed decisions with confidence.
FAQs
What is generative AI vs AI?
| Generative AI | Traditional AI |
|---|---|
| Takes user inputs and uses them to create new content | Uses predefined rules to produce the desired output, but doesn’t create anything new |
| Requires neural network techniques | Uses different techniques:
|
| Better for use with unstructured data | Better for use with structured data |
| Better suited to tasks that require natural language processing | Better suited to repetitive tasks that require rule-based processing |
What is the difference between OpenAI and generative AI?
OpenAI is a non-profit research organization, not a type of AI. It was set up with the aim to encourage safe AI use and offer access to relevant research, as well as carrying out research of their own. This research covers a number of different areas of AI, such as natural language processing. OpenAI is responsible for developing the GPT series, which is a form of generative AI (and what ChatGPT was built on).
Is ChatGPT a generative AI?
Yes, ChatGPT is a form of generative AI.
What is the main goal of generative AI?
The main goal of generative AI is to produce new content based on a prompt provided by the user.
What is the difference between self-learning AI and generative AI?
Self-learning AI uses data to train itself, looking for patterns so it can come to conclusions about what is correct and what isn’t – a trial-and-error approach. Generative AI is trained extensively in order to produce content, creating entirely new data.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about Generative AI Browse these handy sources to learn more.