How Generative AI Is Changing Creative Work
What is Generative AI? Definition & Examples
Major tech companies are in a race against startups to harness the power of AI applications, whether it’s rewriting the rules of search, reaching significant market caps, or innovating in other areas. The competition is fierce, and these companies are putting in a lot of work to stay ahead. That’s what I use it for,” Jordan Harrod, a Ph.D candidate at Harvard and MIT and host of an AI-related educational YouTube channel, told Built In. In fact, she used an AI text-generator to help write a speech for Gen AI, a generative AI conference recently hosted by Jasper. “That did not end up being the final talk, but it helped me get out of that writer’s block because I had something on the page that I could start working with,” she said.
Once fed with requirements, parameters, and constraints, an AI system can produce multiple variations and options, helping designers explore possibilities and innovations. Generative AI can also be a valuable tool for designers, architects, artists, and scientists. Videos can easily be created and adapted to address the needs and circumstances of different segments or even individuals. So far, we have been used to AI running in the background—monitoring, collating, analysing, and predicting. However, with tools such as ChatGPT, we can interact with AI in a way that goes beyond that.
Machine Learning & Generative AI
Generative AI is applicable to various data types, including text, images, audio, and video. Text generation models, for instance, can produce realistic and coherent paragraphs, while image generation models can create unique visuals based on learned patterns from the training data. DALL-E is an AI model designed to generate original images from textual descriptions. Unlike traditional image generation models that manipulate existing images, DALL-E creates images entirely from scratch based on textual prompts. The model is trained on a massive dataset of text-image pairs, using a combination of unsupervised and supervised learning techniques.
Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields. Generative AI, as noted above, often uses neural network techniques such as transformers, GANs and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system.
Generative AI Industry Examples
Widespread AI applications have already changed the way that users interact with the world; for example, voice-activated AI now comes pre-installed on many phones, speakers, and other everyday technology. Generative AI promises to help creative workers explore variations of ideas. Artists might start with a basic design concept and then explore variations. Architects could explore different building layouts and visualize them as a starting point for further refinement.
Transformers allow models to draw minute connections between the billions of pages of text they have been trained on, resulting in more accurate and complex outputs. Without transformers, we would not have any of the generative pre-trained transformer, or GPT, models developed by OpenAI, Bing’s new chat feature or Google’s Bard chatbot. Image generation- Deep learning algorithms like GANs and Stable Diffusion create new images that look similar to real photos. This can be used for data augmentation, creating art, generating product images and more. Platforms like MidJourney and DALL-E use image generation to produce realistic images. Generative AI takes this a step further by creating new content that follows the patterns it has learned.
Using generative AI for business: Use cases
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
” The answer, of course, is that they weigh the same (one pound), even though our instinct or common sense might tell us that the feathers are lighter. See how much more you can get out of GitHub Codespaces by taking advantage of the improved processing power and increased headroom in the next generation of virtual machines. While these models aren’t perfect yet, they’re getting better by the day—and that’s creating an exciting immediate future for developers and generative AI.
Generative AI is the technology to create new content by utilizing existing text, audio files, or images. With generative AI, computers detect the underlying pattern related to the input and produce similar content. This is in contrast to most other AI techniques where the AI model attempts to solve a problem which has a single Yakov Livshits answer (e.g. a classification or prediction problem). We have already seen that these generative AI systems lead rapidly to a number of legal and ethical issues. “Deepfakes,” or images and videos that are created by AI and purport to be realistic but are not, have already arisen in media, entertainment, and politics.
It utilizes sophisticated algorithms and neural networks to produce diverse outputs, which find applications in areas like art, music, education, business, and more. Generative AI models work by using neural networks inspired by the neurons in the human brain to learn patterns and features from existing data. These models can then generate new data that aligns with Yakov Livshits the patterns they’ve learned. For example, a generative AI model trained on a set of images can create new images that look similar to the ones it was trained on. It’s similar to how language models can generate expansive text based on words provided for context. In simple terms, they use interconnected nodes that are inspired by neurons in the human brain.
However, according to VentureBeat, privacy and security concerns have emerged as the primary factors leading survey participants to resist utilizing AI in their workplaces. To address these reservations, Gartner advises IT leaders to emphasize that the implementation of AI is not intended to replace or displace the workforce. Instead, the goal is to demonstrate how AI can enhance workers’ effectiveness and enable them to focus on more valuable tasks. However, AI implementation in manufacturing and marketing remains relatively low due to the importance of human instincts and individual decision-making in these areas, making them less conducive to AI adoption. The adoption of AI spans across various industries, with notable utilization in service operations, corporate finance, and strategy, where approximately 20 percent of industries report its use. The financial services sector leads in employing AI in product development, with over 30 percent of respondents indicating its utilization in 2023.
How Artificial Intelligence of Things (AIoT) Will Transform Homes
As described earlier, generative AI is a subfield of artificial intelligence. Generative AI models use machine learning techniques to process and generate data. Broadly, AI refers to the concept of computers capable of performing tasks that would otherwise require human intelligence, such as decision making and NLP. Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences. Underpinned by deep learning, these AI models tend to be adept at NLP and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models.
Another critical difference between generative AI and other types of AI is that generative models are typically unsupervised, meaning they do not require pre-labeled data to learn from. This makes generative AI particularly useful in applications where structured or organized data is scarce or difficult to obtain. As good as these new one-off tools are, the most significant impact of generative AI will come from embedding these capabilities directly into versions of the tools we already use. Again, the key proposed advantage is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere. That said, manual oversight and scrutiny of generative AI models remains highly important. Hugging Face Transformers is an open-source library of pre-trained generative AI models, including GPT-2 and GPT-3, that can be fine-tuned for specific use cases.
- Then again, extremely simplistic and hollow chat robot programs have done this for decades.
- It is a form of Artificial Intelligence, that can craft unprecedented creations.
- Microsoft implemented this so that users would see more accurate search results when searching on the internet.
- In theory at least, this will increase worker productivity, but it also challenges conventional thinking about the need for humans to take the lead on developing strategy.