Do you know how fast the AI market is growing? If we believe the analysts of Grand View Research, the industry's value by 2030 will exceed $1.8 trillion, which is more than the annual budget of some countries.
High development speed is caused by the demand for technology in IT and other industries. With the release of ChatGPT into the market, progress has become evident, even to those who are very mediocre in the IT field.
Moqod experts have prepared for you a little excursion into the world of AI and trend solutions that shook the industry. Today we will tell you about the features of ChatGPT, its predecessors, and successful and not-so-successful debuts of AI.
ChatGPT: AI Path From Idea to a Comprehensive Solution
The bot has come a long way to the state of the working product, which the OpenAI experts showed us on November 30, 2022. Initially, its concept was based on NLP (Natural Language Processing) and ML (Machine Learning) technologies, but over time it was upgraded to GPT (Generative Pre-trained Transformer).
There were three iterations of the GPT development in total:
- AI was trained in general text comprehension.
- AI learned to reproduce information.
- AI learned to analyze data.
All three stages of GPT modernization are based on a key idea of OpenAI, namely AGI (Artificial General Intelligence). The experts' task was (and remains) the total integration of AI into all spheres of human activity: social, industrial, economic, scientific, etc.
So far, the final goal has yet to be reached, but the work is active, and ChatGPT is being updated. Now with the help of users testing and training AI.
Background of ChatGPT
Before ChatGPT, there were several attempts to realize AI of this level. Since 1956, work has been underway to determine the vector for the development of artificial intelligence. The technology to create comprehensive AI did not exist in those days, but there was some progress in the late 1990s and early 2000s.
First of all, IBM and its DeepBlue supercomputer are worth mentioning. It was developed and trained using observation and analysis of many chess matches. As a result, it managed to beat the legendary Garry Kasparov. Isn't that a sign of the dominance of AI?
After 2006 the world saw technologies such as RPA (Robotic Process Automation), Big Data, Data Lakes, Neural Network, NLP (Natural Language Processing), and many others. They were the beginning of the level of AI that we see in ChatGPT.
By the way, OpenAI had come a long way before it implemented ChatGPT. Few people know, but it has released several AIs that have helped develop the bot. The most famous of these are:
- DALL-E 2, a system for generating images from text descriptions.
- Whiper, a neural network for processing natural language and interpreting it into code.
- Alignment, a complex project for optimizing AI perception of human logic.
These three projects have not received serious publicity and popularity for several reasons. First of all, because of their specifics, but with ChatGPT, the situation is different. The hype, which rose because of the bot, is enormous, and its popularity is spreading daily among professionals in various fields.
The Hype Around ChatGPT: AI Investment or Struggle Between Microsoft and Google?
Not so long ago, Microsoft announced its intention to invest $10 billion in OpenAI, particularly ChatGPT. It would seem that the tech giant decided to help its younger colleague. But it is not that simple.
Microsoft and Google have been competing for decades. In addition to competition in the OS market, they also compete in the technical nature of their search engines. Google is constantly upgrading its search engine by introducing modern technologies such as AI.
Microsoft has its own alternative, Bing. For several reasons, they failed to implement AI in time, missing their chance to take over the industry's leadership. With the release of ChatGPT, a new perspective opened up for Microsoft.
After appreciating the benefits of the bot, the company's management decided to invest actively in its development. Most likely, it was due to the desire of leading Microsoft experts to integrate ChatGPT algorithms into Bing and other Microsoft products. Will they succeed? We will find out in the coming months.
Departed Competitors of ChatGPT
The AI industry has already seen failed products that came too early. Too early for users who are not mature enough to use them. History has seen two key examples of such debuts and falls:
- Microsoft Tay. It was released in 2016, presenting it to the general public. In the end, the work of the AI led to unexpected consequences: users quickly taught it negative things: hatred of women and racism. It took 24 hours, after which a scandal erupted. The project's authors had nothing to do but close the bot and apologize to the public.
- BlenderBot by Meta. Implemented in mid-2022, shut down a few days after its release. Users pulled the same trick as with Microsoft Tay, training the AI to think its creator (Mark Zuckerberg) was too manipulative. It wasn't without its political scandals. For example, the AI believed that Trump was the eternal president of the United States.
These are not the best examples of AI, though with very cautionary tales. OpenAI developed the Moderation API, or information verification system, to avoid such mishaps. ChatGPT algorithms analyze input data, check it and then generate a relevant result blocking malicious information.
By the way, the OpenAI experts focused on the safe use of AI and preventing potential risks.
ChatGPT AI in IT: A Full-Fledged Replacement for Developers or a Good Helper for Newcomers?
ChatGPT was initially positioned as an AI for working with text data. As it turned out in practice, the bot successfully copes even with code.
The information is spreading in the network that ChatGPT will soon replace traditional developers from the market because it will write code. Is this a myth or a real threat to the industry? Let's find out.
Indeed, ChatGPT can implement entire blocks of code that a user asks for just by describing the desired function. But its performance is still very much in question. And so is the confidentiality of information.
The data that is entered into the bot is not only stored but also reproduced in one form or another. That is, corporate information, analytics, and author code are all available to all bot users without exception. Is it dangerous? Partially, yes.
But if you use ChatGPT wisely, you can greatly improve productivity in development at the expense of:
- creating template code;
- researching technologies, development languages, and frameworks;
- describing code, its functions, and actions;
- automatic code commenting;
- experimenting with development methods;
- quick creation of test cases and their description;
- creating technical documentation;
- implementation of regular expressions;
- code optimization and cleanup;
- searching for and describing errors in code.
ChatGPT's capabilities don't end there. The potential of AI is hard to measure, as it has absorbed more than 570 GB of data in the closed-loop training phase alone. The algorithms use more than 175 billion parameters, which are actively expanding.
The bot is learning, gaining new skills, and acquiring digital intelligence. Yes, it cannot think independently, but it can already analyze information, structure data, and help programmers, marketers, copywriters, business people, etc.
ChatGPT Is Not Alone: Competitors and Counterparts to Test or Work With
The AI industry has a pretty good range of working bots, which for several reasons, are less popular and well-known than ChatGPT. But that's no reason to ignore them. We've prepared the top 5 ChatGPT analogs for programmers and IT experts. The bots won't do all the work for you, but they'll help you speed up the development.
Top 5 AI-based bots:
- BLOOM. AI, trained in 46 communication and 13 development languages. You can edit program code, find and correct errors, comment lines, etc.
- LAMDA. AI from Google, designed to perform a wide range of work: from text content generation to full-fledged development of programs and extensions.
- Chinchilla. A modern AI with similar functionality to ChatGPT but with fewer parameters, namely 70 billion vs. 175 billion. Performs the same tasks as ChatGPT.
- CodeGen. AI-based on NLP and GPT. Helps to create code by describing functionality, specifics, and tasks.
- AlphaCode. AI by DeepMind, which helps to generate, analyze and optimize code. Its functionality is enough for basic development and speeds up error detection.
There are more than enough AI options to help developers. The only question is how much they will simplify the workflow and how it will affect the quality of the code.
Stagnation or Development of the AI Industry: Future Options Based on Analyst Predictions
The future of AI is predetermined: technology is destined to become the crowning achievement of human hands and then replace it altogether in all key areas. It sounds gloomy, doesn't it?
A massive investment inflow in the AI industry is expected in the next decade. Even now, the AI market is estimated at almost $300 billion, and by 2030 its value will be close to $2 trillion. During this period, several new players in the segment will emerge, including tech giants like Apple, Microsoft, Xiaomi, and Huawei.
Technology development will focus on RPA upgrades, AI ChatGPT training, and security work. It is the latter that is the cornerstone of all modern AI. And if you altogether remove moderation from control, allowing users to train bots without restriction, bad things could happen.
The risk of using AI for fraudulent purposes is exceptionally high, and the damage they can do to humanity can be fatal. Therefore, AI security will likely be a significant trend for the next decade.
Predictions may come true or not — it depends on factors beyond our control. One thing is sure, Moqod experts will continue to monitor the high-tech market, analyze it, and share exciting information with you.