What is generative AI and how does it work?

ChatGPT, Bing, Bard, YouChat, DALL-E, Jasper…chances are good you’re leveraging some version of generative artificial intelligence on a regular basis.

Or, if you’re not yet acquainted with generative AI systems for whatever reason, you’re still dazzled by this game changer.

Or, at the very least, because you aren’t living under a rock and you read the news, you’re vaguely aware of this new content-creation phenomenon that relies on large datasets.

Whatever your level of exposure to and interest in AI solutions, this technology isn’t going anywhere but up. Its breakthroughs have been likened by Gartner to the inventions of the steam engine, electricity, and the Internet in terms of its projected impact.

With that perspective, here’s our guide to the basics, including a few salient points offered up by our contributing reporter, ChatGPT.

What is generative AI?

While various applications of AI have come on the scene, generative AI technology is the darling transforming humans’ lives at the moment.

“Regular” AI (also known as discriminative) is focused on distinguishing between types of informational input.

Generative AI sounds like a creative form of AI, and it’s that in spades. It mimics human creativity, coming up with high-quality generated content (supposedly material that hasn’t existed, but that, practically, could amount to rephrased or repurposed information) — text, images, answers to questions, videos, songs, report summaries, diagrams, poems, marketing copy, webinars, essays, computer code, and you name it.

The genesis of generative

Generative AI models aren’t new per se — they’ve been a useful tool for analyzing data for years. Everything changed with advances in deep learning. In 2013, deep-learning models called variational autoencoders (VAEs) were commonly used to generate realistic speech and images, leading to more ways the models could be used. Almost 10 years later, things decidedly hit a fever pitch, with an abundance of enterprise-level platforms (Google, Microsoft, Amazon, IBM) and smaller, specialized types of generative AI applications, some of which are open source.

Now, apps such as OpenAI ChatGPT (for text generation) and DALL-E (for generated images), plus Midjourney (for images) are household names. And by 2032, the generative AI market is expected to balloon to more than $191 billion.

How generative AI models work

Generative AI algorithms’ “brain” power is built with the help of deep learning (also called deep neural networks), a subset of machine learning. The generative AI process starts with feeding a large language model (LLM) huge amounts of data — pretraining dataset content — books, web pages, company information — whatever aligns with the information to be generated. LLMs utilize transformers (the T in ChatGPT stands for them), which turn sentences and data sequences into numerical representations known as vector embeddings.

With the ingested data converted to vectors, it can be classified and organized according to how near it is to similar vectors in the vector space. This helps determine how words are related. The effectiveness of the vectorization ultimately determines how well the model can produce output similar to what’s in its training data (but not identical, of course).

To reach the point where a model can turn out results that make sense, the data must go through a huge number of computational processing steps. One machine-learning framework used with generative AI is a generative adversarial network (GAN), which works by pitting neural networks against each other. For the most part, the model’s learning is an automatic process, but humans must fine-tune the training data to make sure it’s accurate.

Then, easily produced by people’s text “prompts”, the interface’s output looks and sounds natural, like a human is writing or saying it. Like you’re talking to or texting with a caring language-savvy chatbot or virtual assistant.

Use cases for generative AI

The ways generative AI can be utilized for various creative purposes are relatively unlimited, and include:

  • Writing pretty much anything. Gen AI makes the often-arduous process of coming up with text, such as when composing an email or updating a resume, inherently easier. Even if the content isn’t perfect on the first try, you can lightly edit and streamline it instead of toiling and taxing your brain with a workflow to build it from scratch.
  • Designing art and creation of images. You can create synthetically generated new images for marketing campaigns (e.g., product images), produce realistic photos, come up with cartoons for class projects, design logos, illustrate books.
  • Streamlining ecommerce business. Use of generative AI can help employees and sellers craft product descriptions for ecommerce platforms, make personalized product recommendations based on online shopping customer preferences, and facilitate excellent shopping experiences that improve customer engagement; it can also automate various tasks, such as inventory management forecasting and keeping track of sales data, for online retailers.
  • Improving online customer support. Another win for the ecommerce industry: conversational AI chatbots that companies have been integrating in websites are being upgraded with generative AI so they seem more natural and conversational to their customers.
  • Composing music. Who writes the songs that make the whole world sing? Maybe your generative AI app, and (still original, but) in the style of Taylor Swift.

The best thing about genAI

In the spirit of exploring generative AI’s extensive abilities, let’s take this opportunity to prompt ChatGPT, the friendly interface for language models GPT-3 and GPT-4, for its opinion on the technology’s best benefit.

Like a thoughtful human thinking in real time, the chatbot doesn’t answer this loaded question directly but pragmatically cites multiple best things depending on one’s needs and perspectives: novel creativity, innovation, customer satisfaction, personalization, efficiency through automation.

For one thing, “generative AI-powered chatbots and recommendation systems can provide 24/7 support and enhance the overall customer experience,” it says.

For another, in terms of personalization for ecommerce companies, search-result content and product recommendations can be tailored to individual preferences by generative AI, creating a more rewarding user experience that can thereby boost ROI.

When it comes to efficiency, with generative AI doing the heavy lifting of content generation, human colleagues can spend considerably less time and exert less effort dealing with previously work-intensive tasks, and companies can thereby reduce labor costs.

The generative AI creative muscle can “inspire new ideas and innovations in various fields,” adds ChatGPT. In medicine and finance, it can aid in professionals’ decision-making processes. “Generative AI is helping researchers discover new drug compounds, predict disease outbreaks, and improve medical image analysis, leading to advancements in healthcare,” it explains.

Trust but verify

As you’ve probably heard, at this point, while implementing a wonderous new way of doing things, ChatGPT and other gen AI can’t be completely trusted in terms of what they tell us. “Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers,” says Gartner.  That means it needs a human manager to review its handiwork, fact check it, and strive to ensure that it hasn’t, sounding authoritative, made stuff up (known aptly as “hallucinating”).

This weakness could be significant, for instance, in arenas where the information being disseminated must be 100% true, such as the aforementioned analyzing of medical images, plus in social media, where fake news put out by rogue content providers can so easily spread. Generative AI’s appropriation by people to create damaging deepfakes, such as real news anchors reporting fake stories, raises fears of how public opinion could be malevolently influenced.

One positive caveat: feedback offered to generative AI bots about incorrect content may be enthusiastically taken under advisement. With ChatGPT, anyway, you can inform it that its LLM is getting something wrong, and it not only won’t be offended or defensive, it will actively listen to your input and strive to correct itself.

How many fingers?

When it comes to original-image generation (for instance, using DALL-E 2), things aren’t necessarily hunky-dory either. Blatant artistry problems, such as too many digits and holiday-themed items placed in a birthday card design, while laughable, are indicative of the unrefined state of this media.

The wild west

Innocent creative-genius mistakes aside, there’s also gen AI’s propensity to trample copyright laws and inadvertently plagiarize authors’ work as part of its wholesale gobbling up of available information, processing it, and spitting out AI-generated content as original work.

Is there a way to prevent this sort of thing? Our reporter ChatGPT acknowledges that “striking the right balance between automation and human oversight is key to realizing the full potential of generative AI while mitigating potential risks.” That’s nice, but trying to keep AI from misbehaving is uncharted, murky territory. With any luck, new laws will address these significant concerns.

Generating a promising future

While generative AI is considered by some “overhyped”, and it could face a reckoning in 2024, it’s still expected to make serious inroads in multiple areas, from product design on down to customer support. Here are some of Gartner’s predictions for optimization from generative models in specific domains:

  • Marketing: The percentage of synthetically generated outbound marketing messages from large companies, which was around 2% in 2022, will hit 30% by 2025.
  • Pharmaceuticals: Generative AI will factor into the creation of more than 30% of new drugs and materials by 2025.
  • Website and mobile app design: 60% will be automated using generative AI by 2026.
  • Teamwork: More than 100 million people will work alongside robots by 2026.

defensive UX banner

Can gen AI improve your search?

Enterprise search is another area in which gen AI can streamline the customer experience. ChatGPT, why would a website manager want to know more about Algolia?

Leverage cutting-edge generative AI to enhance your customers’ shopping experience, boost conversions, and drive growth. Say goodbye to frustrating search results and hello to personalized, lightning-fast product discovery.

👉 Learn More About Algolia

Let’s make shopping an unforgettable experience for your customers together! 💪

Thanks; I like your team-spirit approach and emphasis on personalized shopping experiences. Except for the broken “Learn more” link, you’re somewhat on target!

At any rate, we at Algolia hope you’ve learned something fascinating about generative AI tools from this post.And if you want to improve the personalized experiences for shoppers or subscribers on your website, as ChatGPT notes, our API can helpReach out to our humans and let’s generate a plan for improving your conversion rates and revamping your online store with NeuralSearch.

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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