What is Generative AI? Everything You Need to Know

Unless you've been living under a rock, you've probably heard about generative AI by now. It's the buzzword on everyone's lips, from tech enthusiasts to business leaders, and even your average Joe. But what exactly is generative AI, and why is it causing such a stir? In this comprehensive guide, we'll dive deep into the world of generative AI, exploring its history, its potential, and its implications for the future.

On this page:

• What is Generative AI
• How Generative AI Works
• Applications of Generative AI
• Implications and Challenges
• Future Trends and Possibilities
• Conclusion

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What is Generative AI

Generative AI is hardly new technology. As early as the 1950s there were computer programs that used simple algorithms to produce rudimentary musical compositions. Then text-generating programs known as "natural language generators" emerged in the 1960s and 1970s, producing sentences and short paragraphs of text based on predefined rules and templates. 

In the decades since tech giants like Meta and Alphabet went to work building more advanced generative AI models. With the advent of generative adversarial networks (GANs) in 2014, generative AI pushed the tech forward dramatically, enabling researchers to create realistic and high-quality images, videos, and audio. But with GANs came concerns about deepfakes, a controversial application of GANs that is used to create realistic fake videos.

When ChatGPT, a free-to-use generative AI tool, burst onto the scene in 2022, it changed everything. Like GANs, ChatGPT uses neural networks, specifically deep learning models, to learn from data and generate new human-like text, such as reports, articles, summaries, or product descriptions, that closely resembles what a human would create. And also, like GANs, ChatGPT was met with mixed reactions. 

Types of Generative AI Tools:

Generative AI Tool	Text Generation	Image Generation	Audio Generation
ChatGPT	✓	✓
Claude	✓	✓
Gemini (formerly Bard)	✓	✓
DALL-E		✓
Midjourney		✓
Stable Diffusion		✓
WaveNet			✓

 

Some applauded how OpenAI’s chatbot would revolutionize everything from content creation to how doctors treat their patients. Others feared the ethical and social implications of integrating this type of artificial intelligence into our lives, and indeed there are many questions that remain to be answered: How do we ensure the algorithms remain unbiased? Can we properly regulate it? Are we able to control what and how AI learns and what it chooses to do with the data? 

Despite mixed reactions, it’s clear that in a few short years, user curiosity has won out, and everyone wants in. Generative AI adoption has skyrocketed, with ChatGPT alone reaching over 100 million users, according to a report by McKinsey. This is just the tip of the iceberg, with more individuals and businesses poised to start using generative AI tools like ChatGPT and image generator DALL-E which are already changing how a range of jobs and hobbies are performed. 

This is a critical moment in time in the development of AI, which many believe has the potential to shift society towards a more utopian future, where work burdens are reduced. But at the same time, as it becomes more integrated into our lives, we'll have to navigate new challenges and learn to adapt.

How Generative AI Works

At the core of generative AI are neural networks, which are computational models inspired by the human brain's structure and function. These networks consist of interconnected nodes that process information and learn from it through a process called deep learning. Generative AI uses neural networks and deep learning to ingest large sets of raw data that contain examples of the content or artifacts to be generated. The generative AI models examine the data and learn patterns which it then uses to create new, similar content or artifacts based on the original patterns it learned from. 

As you might imagine, the data required to train on a model like ChatGPT is immense. The OpenAI GPT-3 model has been trained on about 45 terabytes of text data from sources, including Wikipedia and books, and over 175 billion parameters. To put that in perspective, 1 TB equals approximately 6.5 million pages of documents stored in standard file formats like .pdfs. Even though that sounds impressive (and it is), expect new AI models to get even bigger. Google has already revealed its developing a model said to exceed one trillion parameters.

Number of Parameters per AI Model:

AI Model	Number of Parameters
OpenAI GPT-3	175 billion
Google's upcoming model	1 trillion+
Microsoft Megatron-Turing NLG	530 billion
NVIDIA Megatron-LM	8.3 billion
DeepMind Gopher	280 billion
Anthropic Constitutional AI (Claude)	Undisclosed, estimated at 175 billion
Baidu ERNIE 3.0 Titan	260 billion
Yandex YaLM-100B	100 billion

 

It’s important to note that humans still have a significant role to play in advancing generative AI, however. As of now, large language models can’t endlessly train themselves to become more intelligent. Yes, the incredibly large volume of data used to train these algorithms allows them to appear creative and intelligent, but the outputs are not always accurate. Whether it’s creating images out of context or incorrect elements (like too many fingers on a hand or incorrect plants in a landscape), it’s clear the models still have a ways to go. Many companies rolling out generative AI services are relying on human supervision to monitor progress, evaluate performance, and make adjustments by tuning hyperparameters and modifying training data. 

Human supervision is essential for evaluating the quality of the output generated by these models. By providing feedback to the model, we can help improve its performance and ensure that the generated content meets the desired standards. That includes addressing ethical and legal considerations related to the use of generative AI and large language models, such as ensuring that the models do not produce harmful or misleading content and comply with relevant laws and regulations.

Data-Collecting Devices for AI Training:

Applications of Generative AI

Skepticism around using ChatGPT (and AI and machine learning more broadly) is understandable given the current constraint. But the fact that so many industries are already benefitting from generative AI is a testament to its potential for good. Art therapy, medical imaging analysis, and high-resolution weather forecasts are a few seemingly unconventional ways AI is already being used to the benefit of many. 

In the business world, it’s individual workers who are largely spearheading the adoption of generative AI. Research from Salesforce discovered that three out of five workers (61%) are already using or intend to use generative AI. In light of this, organizations across the board are rushing to integrate generative AI tools into their business strategies, aiming to secure a share of a substantial prize. According to McKinsey research, generative AI applications could contribute up to $4.4 trillion to the global economy annually. It's conceivable that within the next three years, anything in the technology, media, and telecommunications sectors not linked to AI may be seen as outdated or ineffective.

As companies jump on the bandwagon, let’s look at some of the ways individuals and businesses are already applying generative AI to their work: 

Arts and Creative Fields

Large language models, such as ChatGPT, excel in generating text content, including poetry, and copywriting for web pages, advertisements, books, and screenplays. They can also assist in preparing speeches, although AI can sometimes produce irrelevant or repetitive text, despite user instructions. The language generated can also sometimes be quirky, unexpected, or flat-out incorrect, like using French and English in the same phrase.

The world of graphical content is also changing with the emergence of Google Gemini and DALL-E showcasing the ability to create unique images that cannot be found through traditional internet searches.

Healthcare and Medicine

Few industries (and humanity by extension) stand to benefit from AI quite like healthcare. Clinicians are already using new platforms to turn patient interactions into clinician notes in seconds. Doctors can now use the ChatGPT mobile app to record patient visits. The app translates the information in real time, identifying any gaps and prompting the clinician to fill them in, effectively turning the dictation into a structured note with conversational language. 

After the appointment, the doctor reviews the AI-generated notes, makes their edits, and submits them to the patient’s electronic health record (EHR). That near-instantaneous process makes the manual and time-consuming note-taking and administrative work obsolete by comparison. 

Other potential applications of generative AI in the medical field include:

• Claims management: ChatGPT can generate summaries of manual and denied claims issues and come up with solutions. It can also help detect fraudulent claims by analyzing patterns in claims data and identifying anomalies that may indicate fraudulent activity.
• Improving patient customer service: Generative AI-powered chatbots can provide instant support to patients, answering common questions, scheduling appointments, and providing information about services and procedures. This is especially useful for answering custom coverage summaries for specific benefits questions.
• Providing value-based care: By analyzing patient data to predict health outcomes and identify patients at risk of developing certain conditions, AI can help providers to intervene early to prevent dangerous (and costly) health complications.

Using generative AI in healthcare does introduce the potential for bias, as these models learn from the data they are trained on. That means biased or unrepresentative can lead to biased outcomes, which can have serious implications for patient care.

Gaming and entertainment

Gaming technology is not yet at the point where it can adapt as it is being played. But that day may come, and AI will be central to that feat. AI is already being used to enhance graphics by improving rendering techniques, enhancing visual effects, and optimizing performance based on hardware capabilities. Upscaling lower-resolution textures and images to higher resolutions can improve image quality without the need for higher-resolution source assets.

Fraud detection and security

Financial services are built upon text and numbers—two things that generative AI and large language models can analyze at a high level. Because LLMs can inspect vast amounts of datasets at lightning speed, it's indispensable to finance and eCommerce companies handling a high volume of transactions. AI can identify suspicious financial activities and stop them to mitigate economic losses. Generative AI’s ability to learn and adapt makes it indispensable in the fight against fraudsters who are constantly evolving their tactics. 

Using artificial intelligence in finance does raise regulatory concerns, as models may require access to sensitive data, such as financial transactions or personal information. Regulatory frameworks, such as GDPR in Europe or CCPA in California, require strict adherence to data protection principles, so organizations using generative AI to detect fraud must ensure compliance with these guidelines to avoid penalties.

Software Engineering

Coders are relishing generative AI’s ability to assist and automate certain aspects of software development. Notably, it's especially useful for detecting bugs and suggesting fixes. While platforms like ChatGPT can handle coding to some extent, they may not match the proficiency of highly skilled programmers. However, if engineers can effectively communicate coding requirements, ChatGPT can produce satisfactory results, akin to how human coders interpret client requirements. Many teams are already generating code based on high-level specifications, reducing the need for manual coding and speeding up development.

Implications and Challenges

Generative AI, while offering numerous benefits, raises significant ethical concerns, particularly regarding deepfake videos and misinformation. Deepfakes, created using Generative Adversarial Networks (GANs), can manipulate videos to make it appear as though individuals are saying or doing things they did not. This poses a threat to reputation and can be used for malicious purposes.

• Ethical Considerations: Ethical responsibility is crucial when deploying AI tools, such as chatbots, as these systems can make decisions that are not always transparent or easily understood. Concerns about AI accountability and bias in training data highlight the importance of ethical AI development and deployment practices.
• Intellectual Property: Another challenge is the complexity surrounding copyright and intellectual property laws. AI-generated content based on existing works raises questions about ownership and ethical usage. As AI continues to advance, ensuring ethical standards in its development and use will be critical to address these challenges and prevent misuse.
• Security and Regulation: As mentioned before, businesses have to adhere to data protection laws, consumer protection laws, and industry-specific regulations. Ensuring the use of AI is compliant with these regulations is essential to avoid legal issues and penalties. However, generative AI models are often so complex that explaining their decisions is next to impossible. Regulatory frameworks may require AI systems to be transparent and explainable, otherwise companies may risk incurring hefty penalties. 

Future Trends and Possibilities

While there are many challenges associated with the adoption of generative AI, there are many reasons to be optimistic. Society is incredibly adaptable, and we can be hopeful for a future where AI enhances productivity, leading to more leisure time and a shift toward a more utopian society. There is a future where generative AI is seamlessly integrated into society, enhancing human capabilities and quality of life by enabling humans to work more efficiently and automating repetitive tasks. This will free us up to focus on more creative and strategic endeavors.

Ideally, AI tools will make information and services more accessible to everyone, bridging gaps in education, healthcare, and communication. This technology will optimize resource use, reduce waste, and develop sustainable solutions for environmental challenges. Along those lines, it will revolutionize healthcare by enabling earlier and more accurate diagnoses, and personalized treatment plans and enable researchers to discover life-saving drugs and treatments faster than ever.

The future of generative AI holds immense potential, with advancements expected in model architectures, training techniques, and applications. As the technology matures, we can expect to see more sophisticated and realistic content generation. However, there will also be increased scrutiny and regulation surrounding its ethical use to mitigate potential risks and ensure a positive societal impact. Generative AI has the potential to revolutionize industries, drive innovation, and shape the way we interact with technology in the coming years. To get there, we have to make sure that AI is developed and deployed ethically, with safeguards in place to ensure fairness, transparency, and accountability in its use.

Conclusion

The introduction of ChatGPT was a watershed moment for humanity, democratizing access to generative AI and sparking widespread curiosity. While some lauded its potential to revolutionize content creation and healthcare, others raised ethical and social questions. As generative AI adoption skyrockets, with millions of users leveraging ChatGPT alone, its integration into our professional and personal lives is reshaping how we perform all kinds of tasks, from the mundane to the most exciting. 

Generative AI's core, neural networks, and deep learning enable it to learn from vast datasets and generate human-like content. Nevertheless, ongoing human involvement is essential to ensure the outcomes are accurate and ethical. 

As the technology matures, we can anticipate increased scrutiny and regulation to ensure its ethical use and positive societal impact. Generative AI has the potential to revolutionize industries, drive innovation, and shape our interaction with technology, but achieving this requires careful development and deployment with ethical considerations at the forefront.