Wider adoption of generative AI - DALL-E Tutorial

- Another significant advancement of AI has been the ability to run complex generative AI models on mobile devices. This breakthrough has made advanced AI tools more accessible to the general public. For example, Stable Diffusion, a powerful text-to-image generation model that we covered earlier in the course, can now be accessed on smartphones, enabling users to create high quality visuals with quite modest, and actually no hardware. This accessibility has revolutionized creative industries by democratizing powerful AI tools. Artists and designers can now generate artworks, enhance photos, and create graphics directly from their mobile devices. This ease of access has invited more people to engage in creative processes, transforming how creative self-expression is produced and shared. Aside from phone availability of generative AI, cloud-based solutions have been pivotal in the widespread adoption of generative AI as well. Major technology companies like Google, Microsoft, Nvidia, Amazon have integrated AI capabilities into their cloud platforms, making it much easier for businesses to leverage AI without significant upfront investments in infrastructure. For instance, Google Cloud's AI platform, Microsoft Azure AI, Nvidia's Picasso and Edify, provide comprehensive environments for developing, training, and deploying AI models. These platforms offer pre-trained models and tools for fine tuning, enabling developers to integrate AI functionalities into their applications much more easily compared to last year. This integration has facilitated a wide range of applications, from customer service automation to sophisticated data analytics. Both of these, I call it mainstreamification, technological infrastructure advancements, significantly contributed to the public perception of generative AI. Initially, there was considerable apprehension about the potential negative impacts of AI, such as job displacement, ethical concerns leading up to people feeling like they may be irrelevant in the future. However, thankfully, in the past year, with the widespread adoption of AI, we have started demystifying generative AI, and started evaluating its tangible benefits in productivity, which shifted the public sentiment towards a more positive outlook. Beyond advancements in technical infrastructure, the rise in computation power, and the heightened focus on generative AI in research cycles have significantly improved the quality and accuracy of creative AI productions as well. Developers have harnessed this increased output quality, combined it with a mission to create tools that address real world problems. And this synergy transformed the use of generative AI from a mare, a hobbyist tool into a powerful professional asset. For instance, Kubrick, the tool that I mentioned in the former video, is now utilized in professional film, VFX, and animation productions. This transformation illustrates how generative AI has evolved from a novelty to a crucial solution in creative industries, significantly enhancing production efficiency and artistic craftsmanship possibilities. Another example of the jump between hobbyist to professional results of generative AI is the latest advancements made in video, text-to-video models. When I recorded my first course, the first examples of text-to-video looked like, I would say, very bad pixelated clip art. And today, with OpenAI's Sora model, we can create sequences that are almost indistinguishable from reality. The creative AI tools that we've talked about in this video only scratch the surface of possibilities. As we continue to explore and develop advanced technologies, generative AI's role in our daily and professional lives will only grow more significant and transformative.