Generative AI: What is it, and how is it Being Used?

Generative AI is a type of artificial intelligence that creates new content, such as text, images, or music, by learning patterns from existing, available data. Generative AI models then produce original content that mirrors the data they were trained on. This marks a shift from traditional AI systems primarily designed to analyse and classify data.

The production of new, original content is why the word generative comes into play.

It’s crucial to understand the recent advances in generative AI capabilities since a wide variety of applications now rely on them. These days, you encounter generative AI whenever you interact with a virtual assistant. If you use creative tools online, you’ve likely noticed new options to utilise generative AI features.

McKinsey research also indicates that generative AI applications could contribute up to $4.4 trillion annually to the global economy. With this much money involved, the takeaway is that the generative AI boom is here to stay and will continue transforming our lives.

Gen AI relies on complex algorithms known as neural networks. These mimic the human brain’s ability to ‘learn’ from data through a process called deep learning.

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Simply put, researchers feed these AI models large training datasets, including text, images, audio, or other data types. The models recognise sophisticated patterns and structures within the dataset and can then generate new content based on those patterns.

Given this relationship, the quality and breadth of the training data set significantly impact the AI’s output.

For example, an AI trained in a wide variety of human languages can generate more natural and contextually appropriate text.

Several key concepts and technologies enable generative AI tools to function:

1. Neural networks and deep learning

These are the foundational elements of generative AI. Neural networks simulate the way the human brain processes information, allowing machines to recognise patterns and relationships within data.

Deep learning involves multiple layers of neural networks that can learn hierarchical representations. This enables the generation of complex and nuanced content across various data types like text, images, and audio.

2. Transformers

Transformers are advanced models that help to understand context in human language. This is called natural language processing.

Many first-time users of tools like ChatGPT are impressed by the advances in transformers, which enable these AI tools to generate coherent and contextually relevant text as responses to human queries at speed. (Note: the GPT in ChatGPT stands for generative pre-trained transformer.)

3. Variational autoencoders (VAEs)

VAEs are like artists who study a bunch of paintings and then create new artworks in the same style. They learn the essence of the data (what makes a Van Gogh recognisable as a Van Gogh, for example) and can generate new items that resemble the originals. The output will be similar but crucially not identical to the original dataset.

4. Generative adversarial networks (GANs)

Imagine two players in a game: a generator and a discriminator.

• The generator creates fake images and new data points based on a dataset.
• The discriminator tries to detect real or fake images by comparing them to authentic data.

Over time, the creator gets better at making realistic images to ‘fool’ the detector. Through this adversarial training process, GANs work to produce high-quality images and videos: ai-generated content that can potentially pass as genuine.

5. Diffusion models

These models start with random noise and gradually turn it into meaningful data, like shaping a lump of clay into a sculpture step by step. They refine the data in stages until it becomes a clear and coherent output. Diffusion models have shown exceptional performance in generating high-resolution images and are gaining attention as a viable alternative to GANs.

6. Reinforcement learning

Think of training a dog by giving it treats for good behaviour. Similarly, AI models learn the best actions to take by receiving rewards or penalties, helping them make better decisions over time. In this way, AI experts and engineers can shape the desired performance of AI models, fine-tuning them to achieve evermore specific goals.

For example, AI engineers can use reinforcement learning to reward models for adhering to ethical guidelines when training them.

7. Quantum computing

Quantum computers are super-powerful machines that can process complex calculations faster than regular computers.

While still in the developmental stages, quantum computing has the potential to greatly enhance the capabilities of generative AI by handling larger datasets and more complex models more efficiently.

This could lead to significant advancements in processing power, pushing the boundaries of what generative AI can achieve while lowering environmental costs.

Bringing it all together

Generative AI models can perform tasks and create new content across various domains by leveraging these technologies and concepts. The alignment of AI algorithm advancements and greater computational power pushes the boundaries of what machines can create. It’s this relentless progress within the field that is making generative AI such a potentially transformative force.

Sometimes, it’s best to show rather than tell. Explaining the technologies and concepts above is useful, but for many of us, it’s still abstract until we see it in practice. Here’s a snapshot of how different industries and sectors leverage generative AI.

Healthcare

In healthcare, generative AI contributes to diagnostics, treatment planning, and patient care advancements.

• Medical imaging: Generative models improve the quality of medical images, such as MRI scans, which enhance resolution and reduce noise. This improvement helps doctors make more accurate diagnoses.
• Drug discovery: AI models can generate potential molecular structures for new drugs , accelerating the initial discovery process. This is due to their ability to analyse vast amounts of data to propose new compounds that may be effective against specific diseases.
• Improve efficiencies: AI can help relieve the burden on healthcare professionals by assisting with some administrative tasks. 87% of healthcare leaders believe AI can help reduce burnout issues.

Better imaging and AI-driven, personalised treatment plans lead to better healthcare overall. Doctors recognise AI as a tool they can leverage to boost their service while still being a people-centred profession.

Generative AI also reduces the time and resources required to develop treatments and manage patient care. These efficiencies translate into significant long-term cost savings for healthcare providers and patients.

Yet, even in healthcare, there are drawbacks. First, there is still a question of reliability. We must thoroughly validate AI models to ensure they provide accurate and safe recommendations. Even though humans are fallible, many of us would still prefer to rely on human expertise over an algorithm when it comes to health outcomes.

There’s also a data privacy issue. Handling sensitive patient data requires strict compliance with privacy regulations.

Customer service

Customers across the ANZ region are increasingly expecting quick, efficient, personalised customer service interactions. AI is helping businesses meet these expectations, with 90% of service professionals who use AI saying it saves them time.

• AI-powered chatbots like ChatGPT can handle customer questions and offer personalised recommendations. These can feel like ‘real’ conversations, thanks to the latest advancements.
• AI can generate email replies based on customer queries, making communication more efficient for all stakeholders.

AI agents don’t need breaks; they can simultaneously provide 24/7 support to many customers. When done well, this improves customer satisfaction. Since many companies have access to customer data, AI can offer tailored solutions. Even though you’re talking to a ‘robot,’ the interaction feels more real thanks to the hyper-personalisation made possible through access to data.

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Yet, these customer interactions aren’t perfect. AI may not fully grasp the emotional nuances of human interaction. Since customer service is based on trust, this lack of perceived empathy can be damaging. AI is also capable of making other mistakes. Errors in understanding, whether through poorly phrased input or a misunderstanding of context, can lead to inappropriate or simply factually incorrect statements.

Finance

Companies in the finance sector are in a race to develop tools that use generative AI to predict market trends. They’re also using it to improve access to personalised advice.

• AI models generate trading strategies by analysing historical data, aiming to optimise investment portfolios.
• Generative models simulate various financial scenarios to help institutions assess potential risks (and prepare accordingly).
• AI chatbots can offer financial advice to customers, boosting access to financial advice.

AI can provide insights that lead to better financial strategies, both for corporations and individual investors and savers. With incredible computing power, AI models can complete calculations and simulations more efficiently and accurately than humans can imagine.

Yet, markets are unpredictable. Over-reliance on AI models may be risky due to unforeseen events. For instance, how can these models predict another pandemic that results in a black swan event? Another drawback is that these financial AI applications are currently experiencing unclear regulatory issues.

For more information, read our recent white paper: A Guide to Generative AI in Financial Services. It explores the significant ways AI activates growth, impacts risk management, and improves customer satisfaction in the financial sector.

We’ve referred to specific limitations within each sector above, but there are broader challenges to AI that we need to address as generative AI continues to evolve.

Bias in AI models

Generative AI creates new content based on its training data. As such, the new content reflects the training data. So what if the data is flawed in some way? Logic follows that the output will also be flawed.

The most prevalent issue related to this is bias. Biased training data can lead to unfair or discriminatory outputs. Consider the repercussions if biased data is used in AI tools related to hiring or lending.

Of course, AI professionals see the solution in better AI. Implementing responsible AI practices, such as bias detection algorithms and diverse training datasets, can mitigate this issue.

Computational cost

Training and running advanced AI models require significant resources. ChatGPT costs more than $100,000 a day to run alone.

However, developing more efficient algorithms, utilising cloud computing resources, and potentially accelerating investment in quantum computing research may reduce some of these demands.

Ethical and security concerns

The creation of deep fakes and the alarming rise of disinformation . The spread of deliberately malicious content. Generative AI has lowered the barriers of entry for those looking to sow discontent, and this poses risks to our society.

But sometimes, AI can be harmful even without malicious intent. For example, what would happen if a security camera used facial expressions to identify a scenario as ‘safe,’ but in this case, it was not? Who will be responsible for this?

Determining responsibility for these AI actions is complex. When AI systems make mistakes, it's unclear who is accountable — the AI developers, users, or the AI itself.

The takeaway here is that we need whole new legal frameworks to define accountability as it relates to AI.

Governments are working hard to reign in the AI wild west. It’s often the case that technological advances outpace government reactions. Regulation is needed worldwide to establish ethical guidelines and to curb some of the more harmful AI use.

We see this in Australia, where new laws have been passed to combat deepfakes . Legislators are working toward a broad, all-encompassing Australia AI Act to protect users and prosecute bad actors.

Beyond regulation, part of the solution will be to develop AI tools that can detect and flag AI-generated content reliably.

Promoting digital literacy will also help the public identify and question suspicious content. With AI's ever-growing prominence on our screens, digital and media literacy is likely to be needed to be taught in schools.

Data privacy

Using large amounts of data raises privacy concerns; collecting and processing personal data can infringe on individual privacy rights. In fact, some of the best use cases for generative AI fundamentally rely on data collection.

The solution for companies is to comply fully with European privacy laws, such as GDPR .

Further, to gain public trust, companies should ensure transparency regarding data management practices. In some cases, customers can opt in or out of data collection, understanding that they know how their data can be used by opting in.

Job displacement

Automation through AI may lead to job losses in some sectors. Workers in roles susceptible to automation might face unemployment or the need to reskill, especially if they don’t have protections in place in the form of unions.

A recent report suggests that 1.3 million Australian workers might need to leave their current jobs between now and 2030.

On the other hand, there will be other opportunities, such as a growing need for AI tech workers, with more than 200,000 by 2030 .

Addressing this issue is complex. On the one hand, we could progress to a society where workers have a greater voice in how companies are run or can participate in shared ownership schemes. This would offset the harmful impact of generative AI on the job market.

Alternatively, governments and organisations can invest in reskilling programs, steering workers to positions that require human skills or interaction.

There’s no easy solution to the risk of job displacement due to AI, and your response to the issue likely depends on your philosophical viewpoint on the role work plays within society.

Maybank, one of the largest banks in Southeast Asia with a presence in Indonesia, Malaysia, and Singapore, leverages generative AI to enhance customer experience. Maybank has successfully implemented AI chatbots to answer a range of customer inquiries and provide personalised financial advice.

The AI feature on its app is called “360 Digital Wealth.”

One of the benefits for customers is improved accessibility. Through the app, they can receive instant support at any time of the day from the comfort of their homes. That means no waiting in line in person or listening to music.

AI analyses data from other customer interactions alongside any information they have about the particular client to provide tailored, bespoke services. For many everyday Maybank customers, this represents a significant upgrade to the level of service they receive.

One of the challenges Maybank must overcome is ensuring that the AI understands and can respond accurately in multiple languages and dialects, ensuring it serves a diverse population across its markets.

And, of course, safeguarding sensitive financial data against breaches is essential when finances are involved.

Charles Budiman, the bank’s CEO, is bullish on AI's future at the bank despite any potential obstacles: “We are fully aware that this technology will play an even greater role in the future with regard to how we run our business.”

Generative AI is becoming increasingly accessible, with various AI platforms and tools available for individuals and businesses. Here are some popular options to explore:

• ChatGPT by OpenAI: An AI language model that can generate human-like text. It can help draft emails, write code, do maths or even compose AI content like poetry. It's a prime example of how transformers enable natural language processing.
• DALL·E 3 image generator: Also from OpenAI, this tool generates images from textual descriptions. It's now integrated into ChatGPT. Artists and designers use DALL·E 3 to bring creative concepts to life quickly and easily.
• Google Gemini: A conversational Google AI service that assists with research, drafting content, and answering questions across a wide variety of topics.
• Salesforce Einstein AI: Integrated within Salesforce's CRM platform, Einstein uses AI and machine learning to deliver predictions and recommendations. Businesses can leverage Einstein AI to enhance customer experiences, automate tasks, and make data-driven decisions.

If you don’t feel like diving straight into these generative AI solutions without deepening your understanding, plenty of resources are out there to help.

• Consider AI and machine learning courses on Coursera .
• Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig is a great book offering a comprehensive guide to AI principles and practices.
• If you’re super keen, dive into the latest research papers on transformer models and GANs.

AI Stack Exchange and GitHub are two online communities where you can browse forums and access repositories related to AI.

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The potential of generative AI is vast and continually expanding. Here are some of our predictions for where the technology may lead:

• There will be even more personalisation in areas like education, healthcare, and marketing. AI systems can generate content and recommendations that meet specific needs based on more data analysis.
• AI models are expected to become even better at understanding and generating realistic human language. This will make interactions with virtual assistants and chatbots more natural.
• Combining generative AI with virtual reality (VR), augmented reality (AR), and the Internet of Things (IoT) could create immersive and interactive experiences. For instance, AI-generated environments in VR could adapt in real-time to user inputs.
• Policymakers and industry leaders will need to collaborate to establish guidelines and regulations as generative AI becomes more prevalent.
• Companies will rise to meet the challenge of computational and environmental costs, developing more sustainable practices.

Emphasis on reskilling and education will be crucial to prepare the workforce to work alongside generative AI.

AI has long been integral to the Salesforce Platform. For example, Einstein AI technologies deliver more than 200 billion daily predictions across Einstein 1, helping businesses close deals faster, provide AI-powered human-like conversations for frequently asked questions, and better understand customer behaviour .

Recently, Salesforce announced Einstein GPT, the world’s first generative AI for CRM. From personalised sales emails to auto-generated code, Einstein GPT will deliver AI-created content across every sales, service, marketing, commerce, and IT interaction at hyperscale. And, it’s built for customers in a way that’s relevant to them — Einstein GPT uses data from Data Cloud combined with public data to create content across Einstein 1.

And, it will do so with the same foundation of inclusivity, responsibility, and sustainability at the core of any Salesforce product. Read about generative CRM and what it means for businesses.

Learn more about Einstein GPT and how it marks the next big milestone in Salesforce’s AI journey.