Artificial intelligence as we know it is an umbrella term consisting of several types of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. One particularly intriguing aspect of AI that consistently captures the interest of researchers, developers, and enthusiasts is Generative AI.

Picture a technology adept at comprehending patterns and data and endowed with the capability to craft entirely novel content. Generative AI is truly innovative, offering a peek into the boundless realms of artificial creativity. This blog post will discuss the basics of generative AI from its basics to core principles for both novices curious about the nuances of artificial intelligence and AI enthusiasts seeking a deeper understanding.

Generative AI defined

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, photos, audio, and datasets. The recent buzz around generative AI has been driven both by the rise in popularity of simple new user interfaces for creating high-quality text, graphics, and videos and the creation and perfection of several new generative AI platforms.

However, this technology has been around for decades: Generative AI was first introduced in the 1960s but gained popularity in 2014 with the introduction of generative adversarial networks (GANs). GANs, a type of machine learning algorithm, can create convincingly authentic images, videos, and audio of real people, and it is this type of generative AI that has exploded in popularity today.

Rapid advances in large language models (LLMs) – AI models with billions or even trillions of parameters and inputs – have opened a new era in which generative AI can write engaging text, paint photorealistic images, and even create somewhat entertaining sitcoms on the fly. Still, we are in the early days of using generative AI to create readable text and photorealistic stylized graphics. Still, progress thus far indicates that the inherent capabilities of this generative AI could fundamentally change enterprise technology and how businesses operate.

With the rise in popularity of generative AI comes questions concerning the ethical use of the new tool: This newfound capability has opened up opportunities that can better, for example, advancements in accessibility for the hearing or visually impaired. However, it also unlocked concerns about deepfakes – digitally forged images or videos – and harmful cybersecurity attacks on businesses.

How does generative AI work?

When given a prompt, generative AI algorithms return new content in response, including essays, solutions to problems, or realistic fakes created from pictures or audio of a person. Generative AI models combine various AI algorithms to represent and process content. For example, to generate text, various natural language processing techniques transform raw characters (letters, punctuation, and words) into sentences, parts of speech, entities, and actions. Similarly, images are transformed into various visual elements, also expressed as vectors.

Once developers settle on a way to represent the world, they apply a particular neural network to generate new content in response to a query or prompt. Techniques such as GANs are suitable for generating results requested by a prompt. Recent programs such as Google’s BERT and Bard, OpenAI’s GPT, and Dall-E have also resulted in neural networks that can encode language and images and generate new content.

Popular generative AI use cases

Generative AI can be applied in various use cases to generate virtually any kind of content. Some of the most common recent use cases for generative AI include the following:

  • Creating chatbots for customer service and technical support
  • Improving or creating subtitles and dubbing for movies and educational content
  • Creating photorealistic art
  • Improving product demonstration videos
  • Designing physical products and buildings
  • Optimizing designs
  • Brainstorming and collaboration

Benefits of using generative AI

Generative AI can be applied across many areas of businesses, including:

  • Personalization: Organizations can leverage generative AI to personalize user experiences by tailoring content and recommendations based on individual preferences, enhancing customer engagement and satisfaction
  • Innovative product design: Generative AI aids in product design by exploring and generating new ideas and concepts to accelerate innovation processes
  • Data analysis and pattern recognition: Generative AI can quickly analyze vast datasets and identify patterns, enabling businesses to gain valuable insights, make data-driven decisions, and optimize operations
  • Natural language processing: Businesses can use generative AI for natural language understanding and processing, enhancing customer support through chatbots, sentiment analysis, and automated responses
  • Creative collaboration: Generative AI tools facilitate collaboration by assisting creative teams in brainstorming, ideation, and prototyping, fostering a more efficient and dynamic creative process
  • Cost reduction: Through automation of repetitive tasks and processes, generative AI helps businesses reduce operational costs, allowing humans to focus on higher-value tasks that require creativity and critical thinking
  • Enhanced customer service: Generative AI-powered chatbots provide instant responses to customer queries, improving customer service efficiency and availability, and ensuring a positive customer experience
  • Predictive analytics: Generative AI algorithms can forecast trends and anticipate market changes, assisting businesses in making proactive decisions and staying ahead of the competition
  • Cybersecurity: Generative AI can enhance cybersecurity measures by identifying and mitigating potential threats, detecting anomalies in network behavior, and fortifying a business’s digital infrastructure

Concerns & precautions of using generative AI

Despite their promise, new generative AI tools must always be overseen by human interaction to ensure accuracy, trustworthiness, loss of bias, and anti-plagiarism – Ethical issues that likely will take years to sort out.

Dangerously, the latest generation of generative AI tools sounds more coherent on the surface due to their combination of human-like language and an endless supply of data feeding its responses. However, coherence is not synonymous with human intelligence, and there currently is great debate about whether generative AI models can be trained to have reasoning ability.

The convincing realism of generative AI content brings with it a new set of risks: It’s increasingly difficult to detect AI-generated content and, more importantly, to detect when things are wrong. Many results of generative AI are not transparent, making it difficult to determine if, for example, they infringe on copyrights or if there is a problem with the sources from which they draw results.

Here are some limitations to consider when implementing or using a generative AI app:

  • It may not always identify the source of content
  • It can be challenging to assess the bias of original data sources
  • Realistic-sounding content makes it harder to identify inaccurate information
  • It can be difficult to understand how to tune information into new circumstances
  • Results can gloss over bias, prejudice, and hatred

The rise of generative AI is also fueling various concerns. These relate to the quality of results, the potential for misuse and abuse, and the potential to disrupt existing business models. Here are some of the specific types of problematic issues posed by the current state of generative AI:

  • It can provide inaccurate and misleading information
  • It is more difficult to trust without knowing the source and provenance of information
  • It can promote new kinds of plagiarism that ignore the rights of content creators and artists of original content
  • It might disrupt existing business models built around search engine optimization and advertising
  • It makes it easier to generate fake news
  • It makes it easier to claim that real photographic evidence of wrongdoing was just an AI-generated fake
  • It could impersonate people for more effective social engineering cyber attacks

Best practices for using generative AI

It’s important to consider ensuring accuracy and transparency in any final product created using generative AI. While generative AI tools are incredibly powerful for creating content, it has quickly become obvious when it is overused. In general, generative AI tools should be used sparingly and respectfully – Remember, we’re speaking to humans in our communication, so it’s important messages have a human element. ChatGPT and similar tools are great places for inspiration, but it should never be the final version of your copy. Always edit, change, and update information gathered from generative AI and similar tools.

The following practices help achieve an ethical use of generative AI tools:

  • Clearly label all generative AI content for users and consumers
  • Check the accuracy of generated content using primary sources where applicable
  • Consider how bias might get woven into generated AI results
  • Double-check the quality of AI-generated code and content using other tools
  • Learn the strengths and limitations of each generative AI tool

The future of generative AI

ChatGPT’s ability to generate humanlike text has sparked widespread curiosity about generative AI’s potential and shined a light on the many problems and challenges ahead. In the future, many more generative AI tools may hit the market, using their data to help improve branding and communication.

Generative AI could also play a role in various aspects of data processing, transformation, labeling, and vetting as part of augmented analytics workflows. In the future, generative AI models will be extended to support 3D modeling, product design, drug development, supply chains, and business processes. This will make it easier to generate new product ideas, experiment with different organizational models, and explore various business ideas.

For more content like this, discover the Gartner report Innovation Insight: How Generative AI Is Transforming Data Management Solutions – A deep dive into Garter’s research about the potential of AI to change the future of data management. Discover the Gartner research guide for genAI adoption, showing what to expect from vendors and how to identify potential risks.

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