ChatGPT and Generative AI – why NOW is the time to understand them?

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ChatGPT and Generative AI – why NOW is the time to understand them?

Generative AI and tools like ChatGPT are evolving at a surprising pace. Now is the time to understand them. Read about their potential in this article.

A quick development of new AI systems may soon change the face of many industries. The potential of the latest GenAI tools, such as ChatGPT, is so great that it cannot be ignored. Companies that do not take advantage of this breakthrough moment may find themselves at a huge disadvantage soon. 

On the wave of technological evolution

ChatGPT and Generative AI is not in its early stages of development. It has long been evolving and shaping the future. With its transformative potential, it is clear that it will revolutionise how individuals and organisations interact with technology

Gartner notes that GenAI's impact is becoming similar to that of the steam engine, electricity and the Internet 

GenAI will likely play a crucial role in changing how we work and impacting our daily lives. In the coming years, we will see industries transform in terms of improving efficiency, accelerating processes and discovering new business models. 

Business leaders need to know how to use ChatGPT and GenAI to maintain their market position or gain a competitive advantage. Now is the time to build a Generative AI strategy.

Benefits of GenAI include:

  • improving productivity, efficiency and workflow by automating (manual or repetitive tasks);
  • streamlining processes
  • removing time barriers in generating content and ideas; 
  • personalisation of customer service experiences;
  • acceleration of research and development works; 
  • analysing or exploring complex data
  • enabling new forms of creation
  • generating synthetic data based on which it is possible to train, improve AI systems and create their new models.
As proof of GenAI's importance to the future of technology, Microsoft has invested over $3 billion in OpenAI, the creator of ChatGPT, and has announced a multi-year $10 billion investment in the technology. It highlights the growing importance of OpenAI tools not only for Microsoft and its competitors (like Google, Meta and Apple) but for many other organisations. Google is also not slowing down. In May 2023, the company announced several new generative AI-powered features, including the Search Generative Experience and a new LLM called PaLM 2 that will power the Bard chatbot, including Google products.

Generative AI, ChatGPT and LLM – key concepts 

What is Generative AI?

GenAI is a set of algorithms capable of creating content based on simple prompts and context. Compared to traditional AI systems that follow predetermined patterns and rules, this technology has the unique ability to make almost anything.

It can generate new content, such as text, sound, and graphics, for 3D objects, and newer models can combine more than one function. GenAI learns from a dataset without explicit instructions while identifying basic patterns for a wide range of tasks.

What is ChatGPT?

ChatGPT is an example of text-to-text GenAI; trained to interact with users using natural language dialogue. Examples of ChatGPT capabilities are:

  • engaging in lengthy and consistent dialogues; 
  • answering questions; 
  • generating a variety of written materials, including business plans, advertising campaigns and computer code; 
  • composing text in different styles or genres, e.g. poem, essay, short story, film script. 

What is LLM?

LLM, or Large Language Model, is a type of machine learning model that is also the algorithmic basis for chatbots such as ChatGPT. This algorithm processes natural language (NLP) input and predicts the next word, and the next, based on what it has already seen until its answer is complete.

It generates and classifies texts, formulates answers to questions conversationally, and translates text from one language to another. It is trained on the basis of a huge number of articles, Wikipedia entries, books, online resources and other inputs. 

LLMs are controlled by parameters that are millions, billions, or even trillions. A parameter is more or less something that helps the LLM decide between different answer options. For example, GPT-3 LLM has 175 billion parameters, and its latest model – GPT-4, has supposedly a trillion of them!

Generative AI in our daily life 

How is GenAI revolutionising the way we work, learn and communicate? 

GenAI is already reshaping the way we and organisations learn and work, both on a global and individual scale. A communication revolution has also begun, which changes the methods of researching, creating and testing content. These changes are happening incomparably faster than before. 

  • In creative industries, copywriters, designers, programmers, and photo/video editors can access generative AI tools that simplify everyday tasks. 
  • In business, it changes how companies interact with customers, driving their development. As a result, GenAI brings business value, such as the ability to increase revenues, reduce costs and better manage risks. 
  • In a daily life it is used for various activities – from planning, and design thinking, through writing texts and outlines to creating travel plans. 

According to a recent Gartner online survey involving over 2,500 business leaders, 38% indicated that customer retention is the primary focus of their investment in GenAI. Next up was: revenue growth (26%), cost optimisation (17%) and business continuity (7%). 

ChatGPT and GenAI – applications in various areas

While GenAI models are still in the early stages of scaling, the first wave of tools based on them is already here. They are used to automate routine tasks such as documenting, coding, editing and improving workflows. As a result, GenAI will not only cause profound changes in various industries and fields but will help solve some of the world's complex problems today. 

With such widespread investment in GenAI, the future of the world looks transformative 

In the longer term, GenAI can empower employees who will improve their ability to invent and improve ideas, projects, processes, services and relationships in collaboration with AI. This symbiotic relationship will accelerate the time to proficiency and significantly expand the scope and competencies of employees in all areas. 

Typical uses include mainly content creation functions: 

  • in the desired style (text tuning); 
  • classification by topics, moods and keywords; 
  • creating shortcuts and summaries (texts, conversations, e-mails, websites). 

Which industries hold the most potential from GenAI?

  • Healthcare – faster identification of potential health problems; analysing research to better diagnose the future development of a disease that might otherwise be overlooked. 
  • Biopharmacy – accelerating the discovery of drugs and new chemical structures, new molecules or proposing new drug compounds for testing. 
According to a Gartner study, it is predicted that by 2025 over 30% of new drugs will be systematically discovered using generative techniques, which looks promising considering the possibility of reducing the cost and time of drug development.
  • Research and development – accelerating R&D cycles, e.g. by generating data on millions of molecules prone to a particular disease and then testing their application. 
  • Climate and meteorology – simulating natural disasters, weather forecasting and modelling various climate scenarios. 
  • Education – creating training materials, lesson plans or online educational platforms, possibly to supplement learning via a chatbot. 
  • Finance – create personalised investment recommendations, analyse market data and test different scenarios to suggest new trading strategies. 
  • Marketing – creating personalised marketing and product, social media, and technical sales content such as text, images, and video. 
A Gartner study predicts that by 2025, 30% of marketing messages coming from large organisations will be generated synthetically.
  • IT/engineering – writing better code, translating software between languages and analysing different types of information, documenting and reviewing code to improve the development process. 
  • Automotive – for simulating and training autonomous vehicles. 
  • Media and entertainment – quickly generate content or to improve creators' work, such as writers and designers, creating new video game levels or generating special effects for movies. 
  • Fashion and interior design industry – for creating virtual designs or anticipating upcoming trends. 
  • Law – answering complex questions and extracting information from enormous legal documentation. Preparation and review of annual reports. 
  • Natural language processing – as the driving force behind chatbots, virtual assistants and advanced writing tools.
  • Other: architecture, manufacturing, aerospace, defence, electronics and energy – by extending core processes with AI models. 

How did we get to ChatGPT?

From simple algorithms to complex generative models

GenAI has a surprisingly long history. It has been slowly sneaking into our lives – from the technology that powers our smartphones, through autonomous driving functions in cars, to the tools used by salespeople. As a result, its progress was almost imperceptible. 

As we face the AI's revolutionary transformations, understanding this technology's history can help us navigate its future. 

AI history outline:The birth of GenAI dates back to the concept of machine learning, i.e. the late 1950s, when scientists introduced the concept of using algorithms to create new data; the breakthrough was made by Alan Turing, the so-called Turing test. The first trainable neural networks (a significant component of the technology underlying GenAI) were invented in 1957 by psychologist Frank Rosenblatt. Machine learning began to flourish in the 1990s, and after 2000 as advanced hardware and digital data became more widely available; the birth of generative AI as we know it today heralded the emergence of a type of machine learning known as neural networks.In 2014, Ian Goodfellow and his colleagues invented a specific type of neural network called a generative adversarial network (GAN), i.e. a generative adversarial network – the type of algorithm from which the creative power of GenAI comes; it enabled the creation of generative AI applications such as images, video and audio. 2014 also saw another significant step with Variation Auto Encoders (VAE) and Recursive Neural Networks (RNNs) starting to show their ability to generate new content. The emergence of these technologies has set the stage for the expansion of Generative AI and the development of advanced language models like ChatGPT in 2023.

GenAI is a testimony to the power of human imagination and technological innovation – from humble beginnings, it has grown into a sophisticated technology capable of producing extraordinary things.

Why it’s time to get interested in Generative AI?

As we look to the future, it is clear that GenAI will shape our world in ways we cannot yet imagine. This technology will have immense implications for companies, capital and regulatory structures. 

The new GenAI model can significantly accelerate the adoption of AI even in organisations that do not have deep knowledge of AI or data science.

Predictions for the next 5 years regarding the use of GenAI according to Gartner: By 2024 – 40% of enterprise applications will have conversational AI built-in (up from about 5% in 2020). By 2025 – 30% of organisations will implement an AI-enabled development and testing strategy (up from 5% in 2021). By 2026 – GenAI will automate 60% of the work related to designing new websites and mobile applications. By 2026 – over 100 million people will hire robo-colleagues to assist them in their work. By 2027 – almost 15% of new applications will be generated automatically by AI without human intervention.

How to start with GenAI at the organisational level? 

Incorporating GenAI into business strategy: 

  • SMBs can gain business value from free versions of tools, open source applications like ChatGPT or by paying lower subscription fees. 
  • Larger companies may consider combining the software with the base model. Especially when they need a more thorough analysis or use of their own corporate data. A dedicated solution is recommended due to the higher level of security and better protection of intellectual property. 

Want to learn more about GenAI? Follow our YouTube channel for regular updates and insight into the evolving world of AI: Beyond AI – YouTube channel.

https://youtu.be/7elTdLJl6sA

Summary 

GenAI, ChatGPT and LLMs are technologies that are transforming the artificial intelligence landscape, taking it to a whole new level. GenAI's explosion and the range of its applications make it a general-purpose technology, opening up new opportunities and changing the game's rules for companies and entire industries. Shortly, it will be invaluable in giving you the competitive edge.

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