ChatGPT Started A New Kind Of AI Race, Making Text Boxes Cool

It’s extremely evident that quite a number of people didn’t anticipate the viral acceptance of ChatGPT, not even OpenAI was able to predict the craze. Before it made history as the consumer tool with the fastest growth rate, before it popularized the term “generative pre-trained transformers,” and before every company you could imagine was rushing to implement its underlying paradigm, ChatGPT debuted in November as a “research preview.”

The very first blog post describing ChatGPT as just a simple hilarious project has since become an irrelevant reference because it probably undermines the essence of what it has become today. The post introduced “ChatGPT as a sibling model to InstructGPT, which is trained to follow instructions in a prompt and provide a detailed response. We are excited to introduce ChatGPT to get users’ feedback and learn about its strengths and weaknesses.” No waxing poetic about how technology is profoundly altering the way we interact with it, not even a syllable about how awesome it is. Just a research preview, really.

Only four months into the hottest innovation, ChatGPT is actively altering the way the world views technology. Because of the direction things are headed, the metaverse or glitzy interfaces are not the technology of the future. “Type commands into a text box on your computer” is what it is. The command line is back, but it’s much smarter this time.

Indeed, generative AI is moving in two directions at once. First, which adds new tools and capabilities to the things you already use, is considerably more infrastructure-focused. Massive language models, such as GPT-4 and Google’s LaMDA, will assist you in writing emails and notes, improving your presentation decks automatically, fixing any errors in your spreadsheets, editing your images more effectively than you can, and, in many cases, create your code for you.

In general, this has been AI’s course for a while, right? During the past few years, Google has been incorporating various types of AI into its products, and even companies like Salesforce have developed robust AI research initiatives. These models could revolutionize corporate efficiency, but they are expensive to develop, expensive to train, and expensive to query. AI improvements in goods you now use are and will continue to be a major industry, or at least are receiving significant investment.

The second AI trend, in which interacting with the AI becomes a consumer product, was far less evident. Now thinking about it again, it makes more sense, who wouldn’t want to interact with a robot who is knowledgeable about movies, cuisine, things to do in Tokyo, and more? Who would have guessed that typing into a chat window would become the newest big thing in user interfaces before ChatGPT grabbed the entire planet by storm and before Bing and Bard tried to build their own products out of it? This is, in a sense, a return to an extremely old concept. For a long time, the majority of users only used computers to type commands into blank screens using the command line.

But then, we created better user interfaces! The issue with the command line was that you had to be extremely precise in what you typed and in what order to get the computer to do what you wanted. It was a lot simpler to point and click on large icons, and it was also much simpler to explain to people what the computer could accomplish using images and icons. The GUI still has supremacy Despite not being the most sophisticated interface, messaging may be the most expendable. Consider Slack: you generally think of it as a messaging application, but you can integrate links, editable documents, interactive polls, educational bots, and much more in that back-and-forth interface. WeChat is well known for being a complete platform—basically, the entire internet—condensed into a messaging app. Starting with a message allows you to travel far over the command line, which was replaced by it.

That said, developers never gave up attempting to make the chat UI function. An excellent example is WhatsApp, which has spent years attempting to understand how customers might utilize chat to communicate with businesses. One of Google’s numerous unsuccessful messaging apps, Allo, hoped you would talk to an AI assistant while chatting with your friends. Several extremely intelligent people believed that messaging apps were the future of everything during the initial chatbot mania, which peaked around 2016.

The message interface, or “conversational AI,” has a certain allure. It all starts with the fact that we all know how to use messaging applications, which are where we spend a lot of time and effort because they’re how we stay in touch with the people we care about most. While you might not know how to discover your frequent flyer number in the Southwest app or how to traverse the Uber app’s hidden areas, practically everyone can understand the concept of “text these words to this number.” Messaging can greatly simplify experiences in a market where users don’t want to download apps and mobile websites are still generally bad.

But, so many of these applications experience the same problems. Chat is ideal for short information exchanges, like during work hours; you can ask a question and receive a quick response. But looking through a catalogue in the form of messages? Not at all. Purchasing an airline ticket after exchanging 1,000 messages? No, thanks. It’s the same as voice assistants, and if you’ve ever tried to use Alexa to even make a basic purchase. If you ask me, a  dedicated visual user interface is quite effective than a messaging window for the majority of complex tasks.

And things quickly become difficult when talking about ChatGPT, Bard, Bing, and the rest. These models are intelligent and cooperative, but in order to acquire what you want, you still need to know exactly what to ask for, how to ask, and in what order. The concept of a “prompt engineer,” a person you pay to have the expert knowledge necessary to get the ideal image from Stable Diffusion or to have ChatGPT write the ideal Javascript, may seem absurd, but it is an absolutely essential component of the solution. It is comparable to the early days of computing when only a select few were able to instruct a machine on what to do. There are already websites where you can buy and trade really good prompts, prompt experts, and books about prompts. I also imagine Stanford is already working on a major in prompt engineering that will be available to everyone in the near future.

Generative AI is amazing since it seems to be capable of practically everything. That is also the root of the issue. What do you do when you have unlimited options? How do you begin? When all you have to look at to see what it’s capable of is a blinking cursor, how can you possibly learn how to utilize it? These businesses may eventually provide more interactive, visual tools that aid in people’s understanding of what they can accomplish and how everything functions. This is one reason to pay attention to ChatGPT’s new plug-ins system, which is currently rather simple but may soon broaden the functionality of the chat window. The greatest notion any of them have right now is to make a few recommendations for what you might type.

AI was intended to be AI a feature, but now it has managed to transform into a product. As a result, the text box has returned. Messaging has become the interface once more.