What is generative AI? Ultimate guide

Generative AI refers to AI models trained on extensive datasets that autonomously generate content such as text, images, audio, and video by predicting subsequent words or pixels. Users provide prompts, and algorithms create content accordingly.

"It's AI capable of creating content... People are thrilled due to the output quality."

The inception of OpenAI's ChatGPT in 2022 has evolved into a rapidly expanding subfield of artificial intelligence. Major tech players like Microsoft, Google, and Amazon are embracing it. Sarah Nagy, CEO of Seek AI, describes it as AI capable of producing content, with output quality so impressive that it often resembles human creation, fueling excitement in the field.

Training generative AI models

Generative AI models undergo training by feeding them large datasets that are preprocessed and often labeled, although unlabeled data might also be utilized. A prevalent method involves diffusion models, which introduce noise to training data and learn to reconstruct it accurately. Previously, generative adversarial networks were widely used for training. Evaluation is essential after each iteration to assess how closely the generated data matches the training data. Teams can fine-tune parameters, incorporate additional training data, and introduce new datasets to expedite the progress of generative AI models.

How does generative AI function?

TRANSFORMERS

Transformers represent a category of machine learning models enabling AI systems to comprehend natural language. By facilitating the analysis of vast text corpora, transformers enable models to establish intricate connections, leading to more precise and sophisticated outputs. Notably, without transformers, the development of generative pre-trained transformer (GPT) models by OpenAI, Bing's recent chat feature, and Google's Gemini chatbot would not have been possible.

GENERATIVE ADVERSARIAL NETWORKS

The breakthrough came around 2014 with the advent of generative adversarial networks, or GANs. These machine learning models feature two neural networks engaged in a competitive process to enhance their prediction accuracy. One network generates counterfeit outputs resembling real data, while the other discerns between artificial and authentic data. Both networks utilize deep learning methods to refine their techniques. GANs have paved the way for AI-generated images, videos, and audio.

LARGE LANGUAGE MODELS

The crucial element in generative AI is large language models (LLMs), featuring billions or even trillions of parameters. LLMs enable AI models to produce coherent, grammatically accurate text, marking them as one of the most successful implementations of transformer models.

Overall, the rapid advancement and widespread adoption of generative AI represent a revolutionary development in technology. This momentum shows no signs of slowing down any time soon.

What kinds of results can generative AI generate?

Generative AI's reputation for generating:

Applications of generative AI

The integration of generative artificial intelligence is reshaping our work, lifestyle, and creativity. It serves as a source of entertainment, inspiration, and convenience. If a domain involves code, language, images, or audio, there's potential for generative AI. Experts speculate that this technology could become as essential to everyday life as the cloud, smartphones, and the internet.

Examples of Generative AI Applications:

• Troubleshoot code
• Generate speeches
• Craft song lyrics
• Stimulate idea generation
• Personalize email content
• Generate social media posts
• Design 3D objects for gaming
• Expedite game development with code assistance

Software developers increasingly rely on generative AI tools like Tabnine, Magic AI, and Github Copilot not only for specific coding queries but also for bug fixes and code generation. AI text generators simplify writing tasks across various formats such as blogs, songs, and speeches.

Jordan Harrod, a Ph.D. candidate at Harvard and MIT, and host of an AI-focused YouTube channel, uses AI text generators for sparking creativity and ideation. She utilized one to draft a speech for Gen AI, a generative AI conference hosted by Jasper. AI-generated text serves as a valuable resource for teams requiring scalable written content production, including marketing and sales teams.

Srinath Sridhar, co-founder and CEO of Regie.ai, a sales-focused generative AI startup, emphasizes the significance of AI in automating tasks such as personalized emails and call scripts for sales teams. Regie.ai and similar tools streamline sales workflows through the application of generative AI technology.

The accessibility of content creation

Generative AI has impacted the gaming industry, which has a history of embracing artificial intelligence. It's revolutionizing game development, testing, and gameplay. Sony's Haven Studios and Electronic Arts are integrating this technology into game creation. Roblox also intends to introduce generative AI features to its Roblox Studio building tool.

"We're witnessing remarkable potential, where individuals can simply describe things in natural language as they normally would and then bring them to life."

Stefano Corazza, head of Roblox Studio, expressed the aim to "democratize content creation," eliminating the typical technological barriers in game development and enabling anyone to become a content creator, regardless of their background or age.

According to Corazza, generative AI presents immense potential, allowing individuals to describe concepts in natural language and bring them to life seamlessly.

Roblox intends to introduce generative AI code-completion features to expedite game development alongside the natural language interface. Corazza emphasized the platform's focus on real-time collaboration for world-building, coding, and experience creation, aiming to simplify and accelerate content creation with the help of generative AI.

Benefits of generative AI

Cost Efficiency

Generative AI's speed and automation not only expedite results but also have cost-saving potential for businesses. Accelerated product development and task completion enhance customer experiences, resulting in increased revenue and ROI.

User-Friendly

Previous iterations of this technology often necessitated data submission through APIs or complex procedures. Developers had to acquaint themselves with specialized tools and code applications using languages like Python. Presently, utilizing a generative AI system typically involves a simple plain language prompt of a few sentences. Moreover, users can usually customize and edit generated outputs.

Enhanced Productivity

Undoubtedly, the potential for increased efficiency is a compelling aspect of generative AI. This technology enables the automation of tasks that would typically demand manual effort, such as days of writing and editing or hours of drawing.

Enhanced Decision-Making

For example, Seek enables companies to pose inquiries to their data without directly accessing the data itself. By integrating Seek into their data infrastructure, employees can obtain required information from proprietary data through simple queries, eliminating the need to inundate the data science team with ad-hoc questions. This allows for swift and efficient access to necessary information.

Seek CEO Nagy highlighted that individuals can interact with AI using natural language, facilitating rapid completion of tasks that would otherwise require weeks of manual effort.

Accelerated Business Operations

The speed, efficiency, and user-friendliness facilitated by generative AI render it highly appealing to numerous companies today. This is evident in the efforts of companies like Salesforce, Microsoft, and Google, which are all striving to integrate generative AI into their products. Consequently, businesses are actively seeking ways to incorporate it into their operations.

Sridhar noted that people are eager to utilize this new technology to solve various problems, as it represents a significant advancement from capabilities available just five years ago.

Obstacles in generative AI

However, the widespread adoption of this technology also presents several challenges. Concerns regarding its accuracy, potential biases, and the risk of misuse and abuse are increasingly prevalent.

Accountability issues

Tools like ChatGPT and DALL-E, trained on internet content, raise significant concerns regarding plagiarism. Questions concerning the ownership rights of data used to train AI systems, the copyright of generative engine outputs, and accountability for defamatory or harmful outputs remain unresolved. "It's all sourced from the same training data, so the aspect of creativity and originality diminishes," remarked YouTuber Harrod. "We lack a solid framework for issues such as attribution and compensation or royalty systems in this context."

Limited Functionality and Availability

While significant advancements have been made in generative artificial intelligence, particularly in text and image generation, the development of AI-generated audio and video is still evolving. OpenAI's Jukebox, released in 2020, generates music in various genres and styles, but progress in AI-generated voices and videos remains ongoing. Microsoft's VALL-E, for example, can simulate voices and emotional tones, but much of this technology is not yet widely accessible to the public.

Erroneous Outputs

Generative AI systems are prone to producing inaccurate responses, often resulting in the dissemination of misinformation. Nagy compares generative AI to an improv comedian, explaining that it aims to produce content that fits a given character or context, even if it lacks factual accuracy.

This issue applies to all generative AI systems, as there is currently no built-in mechanism for fact-checking. Models lack the capability to verify their outputs, and users may not necessarily scrutinize them either.

Harrod highlights the challenge of addressing this issue, expressing concern about individuals accepting generative AI outputs as factual without verification.

Limited Oversight and Safeguards

Currently, there are few laws dedicated to governing the creation and utilization of artificial intelligence. Consequently, most related issues will need to be addressed within the framework of existing laws, at least for the time being. Moreover, it falls upon companies to oversee the content generated on their platforms, a considerable undertaking given the rapid pace of development in this field.

Roblox Studio's Corazza emphasized the impending surge in content, highlighting companies' responsibility to ensure that generated content remains respectful and fosters a civil environment for creators.

Rise of Deepfakes

Generative AI platforms can create basic videos or edit existing ones, leading to the emergence of deepfakes used in sophisticated phishing scams. However, this aspect of generative AI is not as advanced as text or still image generation. Harrod notes that while the field is progressing rapidly, it has not reached the point where users can easily generate specific video content, but it's a swiftly evolving domain.

A brief history of generative AI

Generative AI has a rich history dating back to the 1960s, with milestones like ELIZA, a basic chatbot developed by MIT's Joseph Weizenbaum. However, modern generative AI, epitomized by ChatGPT and DALL-E, is far more sophisticated. Advancements in natural language processing enable these systems to process raw data—text, speech, and images—transforming them into vectors using diverse encoding methods.

The future outlook for generative AI

Despite its challenges, the future of generative AI appears promising, especially with OpenAI's recent announcement of API access to ChatGPT. This move is expected to catalyze the development of new chatbots and other generative AI interfaces.

Jordan Harrod expressed hope that future tools would serve beneficial purposes, advancing societal goals. OpenAI's release of GPT-4 in March 2023, which powers ChatGPT, marks a significant milestone. GPT-4 boasts improved accuracy and reduced bias, with the added feature of being multimodal, accepting both text and images as inputs.

Multimodal capabilities are anticipated to revolutionize AI applications, allowing for simultaneous communication through various modes like text, images, and voice-overs. Sridhar predicts that in the coming years, these advancements will integrate seamlessly, enabling more dynamic and versatile interactions.