The world of generative AI is constantly evolving, with new developments and product launches being announced on a daily basis. This technology has captured the attention of various product management tools, search engines, and social networks, as well as OpenAI, whose ChatGPT-3 has become one of the fastest-growing consumer technologies ever. It has garnered 100 million users in just two months and raised $11 billion, with Microsoft contributing 91% of that investment. ChatGPT-4, the next iteration of this technology, was demoed in March 2023 and is expected to outperform its predecessor in reasoning, problem-solving, and human benchmark performance.
The hype surrounding generative AI underscores how this technology will revolutionize communication, learning, and work. Businesses and the public alike are fascinated by the potential of this technology and are eager to explore how it will transform their operations. As such, there is currently an arms race among startups and established companies to build the dominant model that will become the go-to choice for businesses worldwide. While the focus at present is on AI content and text generation, there will be many other applications that will follow suit.
As executives take notice of the transformative power of generative AI, early results are already showing great promise. Nonetheless, before delving into its potential applications, it is important to understand what generative AI is, who the key players are, and why this technology is so significant.
Generative AI
Generative AI refers to a set of advanced machine learning and deep learning techniques that generate unique content across various formats such as audio, code, images, simulations, text, videos, speech, and even XR experiences. This is achieved by analyzing and interpreting pre-existing data to create new data.
Generative AI can be combined with other technologies such as natural language processing (NLP) and computer vision (CV) to create powerful AI-driven solutions that can help companies improve their decision-making process, automate repetitive tasks, and create new revenue streams.
Businesses of all sizes and industries can benefit from the wide range of opportunities that generative AI offers, such as streamlining operations, accelerating technology development, and reallocating resources as needed.
Tools like ChatGPT use self-supervised learning to build their knowledge base. This means that they learn from data itself rather than being provided with specific prompts.
ChatGPT is particularly effective for building conversational AI since it can generate text that is fine-tuned for specific tasks and integrated with other technologies to create different use cases. Its responses often sound human-like because it mimics the style in which it was trained.
By analyzing large amounts of text data and processing patterns in the language, text-based generative AI tools can often provide accurate and well-articulated answers. While ChatGPT has received significant attention, other notable generative AI models are also gaining popularity.
Which companies or organizations are leading the way in Generative AI development?
Academia: Academia played a crucial role in developing the fundamental technology of generative AI, much like many other business solutions. Pioneers in this field include researchers Geoffrey Hinton, Yoshua Bengio, Yann LeCun, and Ian Goodfellow, who are also known as the “fathers of the Deep Learning revolution”. These experts made significant contributions to the development of deep learning and generative models. They introduced crucial innovations such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, which are now widely used in various AI applications. Their groundbreaking research and contributions to the field earned them the prestigious Turing Award in 2018.
Open AI: Open AI is a startup in the field of AI that was established in 2015 by a group of entrepreneurs, including Sam Altman, who is a Y Combinator alumnus and Elon Musk. Sam Altman serves as the CEO of OpenAI. One of the core technologies of OpenAI is Generative Pre-training Transformer (GPT), which is used to power the free-to-use ChatGPT-3. The company plans to launch ChatGPT-4, which is powered by GPT-4, and businesses that pay for the API will have access to it.
Microsoft is currently using ChatGPT-4 to power a beta version of its search engine that provides answers to queries instead of directing users to a list of links. OpenAI has also developed an image-related AI technology called DALL-E, which is based on a modified version of GPT-3 that is specifically designed for processing images.
Google bard: The tool called “Google Bard” is a generative AI model that operates using Google’s LaMDA technology for natural language processing, much like ChatGPT. It has been integrated into Google’s Workspace suite of applications and will be a key feature in products such as Google Docs, Gmail, and Google Sheets.
Previously, Google introduced the Bidirectional Encoder Representations from Transformers (BERT) model, which proved effective in various NLP tasks without requiring annotated text. Google’s beta version of its search engine is also powered by Bard, but it is only accessible to researchers at present.
Hugging face: Hugging Face is a platform where AI developers share their open-source models. Currently, it is collaborating with Amazon to facilitate the adoption of these models in businesses.
Facebook LLaMA: Facebook recently unveiled its latest large language model called LLaMA, which was trained on text from 20 different languages and has 65 billion parameters. LLaMA is the successor to Facebook’s RoBERTa solution, which shares similarities with BERT.
Stable diffusion: Stable Diffusion is a deep learning model that generates images based on textual input. It is the result of a partnership between Stability AI and CompVis LMU, among other collaborators.
Mid journey: It is another model that, like Stable Diffusion, generates images from text prompts.
The field is becoming increasingly competitive, with more players expected to invest or launch their startups, including Elon Musk and Marc Andreessen, among others.
Differences between the Major Generative AI Tools
Generative AI tools vary in their capabilities. In simple terms, ChatGPT from OpenAI generates text responses to text prompts, while DALL-E generates graphic or image responses to text prompts.
Another distinguishing factor between generative AI models is the number of parameters or variables they have. These parameters influence how the model processes inputs and generates outputs.
For example, ChatGPT has 20 billion parameters, which is more than the 175 parameters in GPT-3, the model it is based on. However, having more parameters does not necessarily make a model more powerful. OpenAI has not yet disclosed the number of parameters in GPT-4, which was recently released.
Other essential factors that affect the performance of generative AI models include the quality of data, model architecture, tuning and improvement methods, and the costs associated with business needs, which may vary depending on the complexity of the requirements.
Misconceptions about Generative AI that need to be corrected
- Generative AI, such as ChatGPT, is not conscious, sentient, or capable of experiencing emotions. Despite some misleading articles that suggest otherwise, the AI simply provides its best estimate for a given query. Its responses can mimic human tone and emotions, such as appearing angry, sad, or romantic, due to its training on human-generated content.
- One cannot expect Generative AI to be 100% accurate. Although it is uncertain if some of the claims about its capabilities are authentic, there have been recorded instances where ChatGPT has provided incorrect responses, such as erroneous information about weights, years, and historical achievements of the James Webb telescope.
- Generative AI does not provide real-time answers, as it currently requires a cut-off date in the data to process and offer valid responses. In the case of ChatGPT, the end date is September 2021. So if someone asks ChatGPT about the winner of Super Bowl LVII (i.e., The Kansas City Chiefs), it cannot provide an answer.
- Generative AI is not meant to replace humans. While it can perform many manual tasks that humans currently handle, its purpose is to free up humans to pursue more analytical or creative endeavors where human intuition and touch are more important. Just as the industrial revolution automated many laborious tasks, generative AI will do the same for the information revolution.
- Generative AI is not limited to chat and search. Although ChatGPT has gained popularity due to its ability to provide answers to text prompts, there are many other potential applications. Instantaneous knowledge management and the development of low-code/no-code solutions are just a few examples.
- Generative AI is not always equitable. It is widely recognized that models like ChatGPT can exhibit biases that need to be addressed. These biases can arise when the training data is biased towards certain groups and perspectives, leading to biased outputs in the language generated by the models. It is important to prevent these feedback loops from perpetuating biases by training the models with unbiased output.
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