Since the launch of ChatGPT, there has been a lot of talk about AI.But with the advent of ChatGPT, the debate about whether and when AI will take over human jobs has been rekindled. In my opinion, which is in line with several other experts, we will not face this direct risk in most sectors in the coming years. However, there is something that is already a fact: professionals who do not have knowledge about AI, especially how to take advantage of it to increase their productivity and knowledge, will be out of the market and replaced by another human. Of course, this is already happening today. Those who dedicate themselves more, study and are willing to learn new things can take advantage. But AI has accelerated this learning and/or productivity curve.

There are studies that indicate that a developer’s productivity, for example, can increase by up to 40% with the use of AI in code production and learning new programming languages. Using tools to reduce manual labor leaves more time for humans to do what they do best, which is to create something truly new and different. . Ah! But it “creates” images or text, yes it does, but this creation is nothing more than a composition of what it has learned before, and not something 100% new. Well, but that is not the reflection I intend to make, as this discussion is broad and complex, we will leave it for another article.

We know that AI is a dense subject with many variants and disciplines, I simplified some of them here because my focus was to discuss the simple power of AI for the professional prepared for it.

How does generative AI work?

A family of AI technologies called Machine Learning (ML) powers the specific technologies behind GenAI. ML uses algorithms that continuously and automatically improve their performance based on data. The type of ML that has led to many AI advances, such as facial recognition, is Artificial Neural Networks (ANNs). ANNs are inspired by the workings of the human brain and its synaptic connections between neurons. There are many types of ANNs. Text and image generative AI technologies build on a suite of AI technologies available to researchers for years. For example, ChatGPT uses a Generative Pre-trained Transformer (GPT), while image GenAI typically uses Generative Adversarial Networks (GANs).

How GenAI Text Models Work

Text generative AI uses a type of ANN known as a General Purpose Transformer and a type of General Purpose Transformer called a Large Language Model.

This is the third iteration of their GPT, with the first released in 2018 and the most recent, GPT-4, in March 2023 (see Table 2).

Each OpenAI GPT has improved by iterating and revamping the previous one through advances in AI architectures, training methods, and optimization techniques. A well-known facet of its continued progress is the use of increasing amounts of data to train its exponentially growing number of “parameters.”

Researchers can tweak these parameters, which include the model’s “weights,” to improve GPT’s performance. The weights are numerical parameters determining how the model processes input and produces output. In addition to advances in optimizing AI architectures and training methods, the rapid renewal of generative AI has resulted from the massive amounts of data and improvements in computing resources available to large enterprises.

Once researchers train GPT, generating a text response to a request involves the following steps:

While GPTs and their ability to automatically generate text have been available to researchers since 2018, what made the launch of ChatGPT so groundbreaking was its free access through an easy-to-use interface, meaning anyone with internet access could explore the tool.

The launch of ChatGPT sent shockwaves around the world and quickly caused other global tech companies to catch on, along with several startups launching their own similar systems or building new tools based on them.

July 2023, some of the alternatives to ChatGPT include the following:

• Alpaca : An improved version of Stanford University’s Meta Llama that aims to address misinformation, social stereotypes, and toxic language in LLMs.
• Bard : An LLM from Google, based on their Lambda and Palm 2 systems, that has real-time internet access, meaning it can provide up-to-date information.
• Chat sonic: Created by Writesonic, it is based on ChatGPT and also tracks data directly.
• Ernie (also known as Wenxin Yiyan文心一言): A bilingual LLM from Baidu, still in development, that integrates deep knowledge with massive datasets to generate text and images. Additionally, all the data used to train its models is open source.
• Jasper: A set of tools and APIs that, for example, can be trained to write in a user’s preferred style. It can also generate images.
• Llama: An open source LLM from Meta that requires less compute power and fewer resources to test new approaches, validate the work of others, and explore new use cases.
• Open Assistant: An open-source approach designed to allow anyone with sufficient knowledge to develop their own LLM. It was developed based on training data curated by volunteers.
• Tongyi Qianwen (通义千问):  An Alibaba LLM that can respond to requests in English or Chinese. It is being integrated into Alibaba’s business toolset.
• You Chat: An LLM that incorporates real-time search capabilities to provide additional context and insights to generate more accurate and reliable results.

Most of them are free to use (within certain limits), while some are open source. Many other products are being released based on one of these LLMs. Examples include the following:

• ChatPDF: Summarize and answer questions about sent PDF documents.
• Elicit The AI ​​Research Assistant:  Aims to automate parts of researchers’ workflows by identifying relevant documents and summarizing key information.
• Perplexity:  Provides a “knowledge center” for people looking for fast, accurate answers tailored to their needs.

Similarly, LLM-based tools are being incorporated into other products, such as web browsers. For example, ChatGPT-based Chrome browser extensions include the following:

• WebChatGPT: Gives ChatGPT access to the internet to enable more accurate and up-to-date conversations.
• Compose AI:  Automatically complete sentences in emails and elsewhere.
• TeamSmart AI:  Provides a “team of virtual assistants”.
• Wiseone: Simplifies online information.

The potential impact of GenAI in various industries

GenAI holds transformative potential across numerous industries. In healthcare, it streamlines patient care through predictive analytics and personalized treatment plans. By analyzing vast amounts of data, GenAI helps physicians make informed decisions quickly.

In the financial sector, this technology enhances risk assessment and fraud detection. Automated analysis of transactions allows for rapid identification of suspicious activities, thus safeguarding assets effectively.

Retail businesses are experiencing a revolution too. With GenAI, companies can tailor marketing strategies based on consumer behavior insights. This creates more engaging shopping experiences that drive sales up.

Manufacturing also benefits from smart automation solutions that optimize production processes while minimizing waste. These advancements lead to increased efficiency and reduced costs across various operations in multiple sectors.

Ethical concerns surrounding the use of AI for transformation

As GenAI technology advances, ethical concerns have emerged that demand careful consideration. One major issue is the potential for bias in AI algorithms. If not properly managed, these biases can lead to unfair treatment of certain groups.

Privacy is another significant concern.

As businesses implement GenAI solutions, they require large amounts of data. This raises questions about how organizations handle and protect personal information.

Automation may enhance productivity but could also displace workers, creating socioeconomic challenges.

Transparency in AI decision-making processes remains elusive.

There’s a risk of over-relying on automation, which can diminish human creativity and intuition. Balancing innovation with ethical considerations will be essential as we navigate this transformative landscape.

Conclusion

While GPTs and their ability to automatically generate text have been available to researchers since 2018, what made the launch of ChatGPT so groundbreaking was its free access through an easy-to-use interface, meaning anyone with internet access could explore the tool.

The transformative power of GenAI is undeniable. As we’ve seen through various case studies, it holds the potential to revolutionize industries and elevate business operations to new heights.

From enhancing productivity in a corporate setting to streamlining processes across diverse fields, its applications are vast and promising. However, with great innovation comes responsibility. The ethical implications surrounding the use of GenAI must be carefully navigated.

As organizations continue to explore this advanced technology, striking the right balance between leveraging its capabilities and addressing ethical concerns will be key. The journey of transformation with GenAI is just beginning, and its future impact could redefine our world in ways we can only imagine.

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