2.1 What is generative AI?
Generative AI is a type of artificial intelligence designed to create new content, such as text, images, audio, video, or even code, based on patterns it has learned from existing data. Unlike traditional AI, which typically focuses on recognizing patterns or analyzing data, generative AI produces entirely new outputs. It does this by using machine learning models, particularly neural networks, that are trained on vast amounts of data.
Some well-known examples include OpenAI's GPT (Generative Pre-trained Transformer), which generates human-like text, and image-generation models like DALL·E, which can create new images from textual descriptions. These systems are used in various applications, from writing articles to generating art and even designing products.
Generative AI's ability to innovate and create makes it valuable for businesses, entertainment, design, and many other fields. However, it also raises ethical considerations, such as the potential for creating misleading content like deepfakes or biased outputs based on the data it has learned from.
Some well-known examples include OpenAI's GPT (Generative Pre-trained Transformer), which generates human-like text, and image-generation models like DALL·E, which can create new images from textual descriptions. These systems are used in various applications, from writing articles to generating art and even designing products.
Generative AI's ability to innovate and create makes it valuable for businesses, entertainment, design, and many other fields. However, it also raises ethical considerations, such as the potential for creating misleading content like deepfakes or biased outputs based on the data it has learned from.
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LearningTranscript
00:00Generative AI models refer to artificial intelligence that can autonomously create
00:05or generate new content. In my previous course about developing an AI-first mindset,
00:11we discussed how AI is used for analysis, classification, optimization, and recommendation.
00:18Now, these new advanced AI models can understand patterns and relationships in data,
00:24and then use that understanding to comprehend and then produce or synthesize new information.
00:32This massive advancement in AI was made possible due to a number of factors, but arguably the three
00:38most important ones were the development of innovative deep learning architectures,
00:43such as transformers and diffusions, which are facilitating more complex and efficient AI models.
00:49Number two is the rapid expansion and access to computing power,
00:54especially cloud computing, which has made compute more accessible and cost-effective
00:58for AI researchers and companies, like OpenAI, who rely on it for developing and training their
01:03models. And number three is the ability to learn and train on an unprecedented data scale,
01:11essentially the entire internet. Take, for example, an AI model that's trained on thousands
01:17of paintings from famous artists. The AI model will learn the style, the patterns, and techniques
01:23from these paintings, and then utilize this knowledge to create new and sometimes unique
01:28works of art. Check out, for example, a photo I took of the sky of New York and how I recreated
01:35it in the style of a Renaissance painter while adding some science fiction elements to it.
01:40That's pretty cool, right? Art is just one example. Let's talk about customer support.
01:48The latest AI models are already trained on customer support best practices from every
01:54knowledge base available. Applying it within the setting of your own business can already
02:01enhance your customer experience materially. And if you're using a conversational AI model,
02:07customers might not even realize that they're talking to a machine unless you tell them. Now,
02:12imagine you can fine-tune it with your own data and include the uniqueness of each customer,
02:18your business model, your desired operating system, and your principles. You can then utilize
02:24this knowledge and establish a deeper connection with your customers and help provide the best
02:29service that matches your brand. Imagine building unique customer support models for healthcare or
02:35travel or government or utility companies. It's astonishing how extensible this technology can be.
02:42AI can be used to create new products or designs. It could be used to reimagine user experiences,
02:47to reinvent workflows, and to improve decision making. The list is endless. And as a result,
02:53AI has the potential to significantly benefit and disrupt businesses across a wide range of sectors.
03:00Now, the disruption part might sound concerning, but the more you know, the better position you'll
03:06be in to leverage this technology positively for your business and for your customers.
03:11If you do have concerns or reservations, write them down, and hopefully we'll be able to address
03:16them by the end of this course. In the next few sections, I'll share several key components that
03:21are typically involved in every AI project. I cover some of them in more depth in my previous
03:26course in case you want to check it out. But for the purposes of this course,
03:31you only need to know the components mentioned in this section. So let's get started.