Generative AI is transforming how we utilize and interact with technology. Rather than a pure tool, advancements in artificial intelligence allow generative models to take a more proactive role in the creative process.
From art to literature to advanced programming, Generative AI stands at the forefront of innovation. Models like DALL-E, BARD, and ChatGPT have gained global popularity and sparked debates on the same scale.
This article will delve into these discussions and analyze the various applications of content-focused AI models.
What is Generative AI?
Generative AI is a subset of deep learning used to output new assets. It goes beyond the limits of traditional artificial intelligence and is designed to create content like text, code, music, digital art, and video media.
Traditional AI models include strict programming and instructions. These features make them highly proficient at supplementing prompts through predictive reasoning, pattern analysis, and research.
However, Generative AI utilizes deep learning to take the next step and operate in the creative space. These models create a mathematical structure of "ideas" and can apply them imaginatively and uniquely.
For example, Traditional AI can recite the following line of Shakespearean literature, but Generative AI can dissect the recurring themes and create a new but similar story.
How Does Generative AI Work?
Generative Adversarial Networks (GAN) are the foundation of Generative AI. GAN is a procedure that pits two machine learning models against each other. These are called the "generator network" and the "discriminator network."
The generator creates content that the discriminator recognizes as accurate. At first, this output is randomly made and fails to pass the discriminator's tests. Both models gradually learn from each interaction until the discriminator can no longer determine if the content is fake.
Generative AI Models and Examples
It's hard to think of a discipline immune to Generative AI's potential. The benefits aren't restricted to fields looking for complete products. While generated content can often be used "out of the box," it's similarly useful as an inspirational tool or for counsel.
Some of the most impactful Generative AI models across creative forms include DALL-E, BARD, and, most famously, ChatGPT.
What is ChatGPT?
ChatGPT is a text-based generative AI. The "GPT" stands for its natural language model architecture, Generative Pre-trained Transformer. While it's far from the first chatbot that's popped up, it provides an unrivaled conversational quality and informational depth.
ChatGPT has lured in 180.5 million users, with 100 million using it weekly. Its many benefits and features have quickly cemented the AI's use in both creative and formal endeavors, including:
- Creative writing
- Business writing
- Programming
- Research
It's important to remember that ChatGPT isn't always right. A study at Purdue found that the AI is wrong more than half the time. These faults are typically due to the bot not picking up on contextual clues from the conversation.
The elegant and convincing nature of ChatGPT's responses often convinces users to believe the answer. ChatGPT is an incredible Generative AI capable of high-level conversation and creation. However, it still requires some human intervention for responsible use.
What is DALL-E?
DALL-E is a groundbreaking Generative AI model focused on image creation and manipulation. By constructing a mathematical structure for ideas like "bowl" or "car," DALL-E creates complex images without blending objects unnaturally.
The AI's process is simple. Users enter a prompt into the text box, and another machine learning model matches the text to corresponding visuals that capture the right concept. Once the AI has the matching components, it maps them to the space.
The most exciting aspect of DALL-E is one of its supporting machine learning models: CLIP (Contrastive Language-Image Pre-training). CLIP compares a text caption against thousands of images and learns how it relates to each image.
This model compares the strength of each relation and can more precisely learn what connects the text prompt to a visual representation. The program is currently on its second major iteration, DALL-E 2, but has already announced a third upcoming version.
What is Google Gemini?
Google Gemini is the company's next step into AI after retiring its previous chatbot, BARD. The project consists of three models known as Gemini Nano, Pro, and Ultra. This development line is Google's response to OpenAI's dominance in the AI field over the past few years.
Unlike other AI tools that engage with prompts in a single format, Gemini is multimodal and can work with text, images, audio, videos, and code. These capabilities allow users to reference varying types of content when creating prompts.
For example, someone can ask, "What are the musical notes in this song?" ChatGPT cannot fulfill your request because it doesn't know what song you're referring to. However, Gemini models include an input for users to attach audio files and supplement the prompt.
Creative Examples of Generative AI Use
Before using Generative AI, many consider it a convenient way to skip content creation. While AI content is often indistinguishable from human-made products, it still has quirks and awkward moments. Quality variations make it difficult to rely on Generative AI for standalone and prolonged use.
So, a growing number of writers, artists, musicians, and the like are using AI models as support. They help overcome artistic blocks by providing unique next steps and can fill gaps in an individual's skill set.
In less than a year, Sci-Fi author Tim Boucher released 97 books with the help of Generative AI. Boucher reported that his books, which include accompanying pictures, only take about 8 hours to complete.
The author's blazing speeds are achieved using Midjourney, an AI image generator, combined with ChatGPT and Claude.ai to complete text.
What is OpenAI?
Founded in December 2015, OpenAI is an AI research company responsible for industry titans like ChatGPT and DALL-E 2. Its self-described aim is to "ensure that artificial intelligence benefits all of humanity."
Unlike the name described, OpenAI does not create open-source products. This was true in the past, but it has since transitioned to a closed-source, profit-driven development. OpenAI's models train with a transformer architecture that pits two AIs against each other to improve output.
The company's incredible contributions to Generative AI cannot be understated. Its efforts have pushed artificial intelligence from a futuristic curiosity into a recognizable part of creative processes.
OpenAI's success has pushed countless developers and companies to push the boundaries of artificial intelligence. It showed the profitability of these endeavors and provided a previously open-source roadmap of the steps.
Securing the Future: The Intersection of Generative AI and IT Security
As Generative AI evolves to create increasingly sophisticated content, there's an increasing need to protect its applications against abuse.
AI presents distinctive and considerable challenges to cybersecurity. Its ability to create nearly indistinguishable content opens the door for mass-produced and highly flexible attack strategies. One of the most looming threats is improvements to email phishing.
Several security firms have already used AI-generated phishing emails in penetration testing. The results show that these emails are more convincing and successful than most. This is just one example of how Generative AI's streamlined capabilities will massively improve cybercriminals' timelines in countless attack patterns.
Opportunities and Innovations
Despite the emerging challenges, Generative AI also opens avenues for innovative security solutions.
One of the most easily implemented measures is to use Generative AI to create secure passwords. Removing the burden of crafting complex passwords will help employees maintain vital online hygiene in the long run. These practices will better secure them against tactics like credential stuffing.
Additionally, models train on massive historical databases. They can identify patterns and trends and generate documentation for security products to resist probable attacks. This allows businesses to respond to threats almost immediately, even if an attack has no historical basis.
OpenAI's IT Security and Generative AI
One of OpenAI's goals at its inception was to make AI safe for humans. This mission statement has an innate role of safeguarding the technology against cybercrime.
OpenAI interacts with the cybersecurity community through its Bug Bounty Program. The company invites ethical hackers to probe its technology and report vulnerabilities. So far, the program has located 71 issues and has an average payout of $1150.
Conclusion
Generative AI is a technological frontier that's reshaping how we create. Starting from low-capacity chatbots, new models stand on par with humans in the conversational arena.
It's not only text-based AI that has skyrocketed in use. DALL-E can accurately create unique images by merging the concepts behind an object rather than objects found through training. Other Generative AIs like Google Genesis assimilate multiple creative fields into the prompt submission box.
There's no end in sight to how Generative AI will improve the creative process in the future. Learning how to use it today will help you more quickly adapt to future advancements, configure your IT security against the attack patterns it enables, and stay ahead of the latest cyber threats.
