What developers need to know about generative AI
These models empower artists, designers, storytellers, and innovators to push the boundaries of creativity and open new possibilities for content creation. AI generative models are designed to learn from vast amounts of data and generate new content that resembles the original data distribution. These models go beyond simple classification or prediction tasks and aim to create new samples that exhibit artistic, intellectual, or other desirable qualities. Apart from that, from DALL-E 2 to Stable Diffusion, all use Generative AI to create realistic images from text descriptions. In video generation too, Runway’s Gen-1, StyleGAN 2, and BigGAN models rely on Generative Adversarial Networks to generate lifelike videos.
Because Generative AI technology like ChatGPT is trained off data from the internet, there are concerns with plagiarism. Its function is not so simple as asking it a question or giving it a task and copy pasting its answer as the solution to all your problems. Generative AI is meant to support human production by providing useful and timely insight in a conversational manner. Similarly, Generative AI is susceptible to IP and copyright issues as well as bias/discriminatory outputs.
IBM Research’s latest analog AI chip for deep learning inference
Fashion designers and brands can leverage these capabilities to stay ahead of the curve and offer truly innovative products. By 2030, AI will enhance the world economy by a projected $15.7 trillion, or 26%. Despite the fact that AI will automate certain industries, studies indicate that any employment losses caused by automation Yakov Livshits will likely be more than countered in the long term. This is due to the larger economic impacts these new technologies have made possible. Gartner suggests that in order to gain a competitive edge, businesses should use generative AI immediately by adjusting their workforce dynamics, business processes, and tools.
Another potential use case of generative AI refers to large language models or LLMs, which can be trained on billions and trillions of parameters. LLMs have created a new era for helping generative AI models to create engaging text and realistic images. On top of it, the developments in multimodal AI could help teams in generating content through different types of media. It’s a powerful technology that uses machine learning to generate new, original data. With applications ranging from content creation to data enhancement, it’s already driving innovation in various industries.
By industry
Bing’s Image Generator is Microsoft’s take on the technology, which leverages a more advanced version of DALL-E 2 and is currently viewed by ZDNET as the best AI art generator. Generative AI is used in any AI algorithm or model that utilizes AI to output a brand-new attribute. The most prominent examples that originally triggered the mass interest in generative AI are ChatGPT and DALL-E. The purpose of generative AI is to create content, as opposed to other forms of AI, which might be used for different purposes, such as analyzing data or helping to control a self-driving car. Our goal is to deliver the most accurate information and the most knowledgeable advice possible in order to help you make smarter buying decisions on tech gear and a wide array of products and services.
Yakov Livshits
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.
Moreover, AI can help retailers make more informed business decisions by analyzing vast amounts of data and providing insights into customer preferences and market trends. As the field of artificial intelligence continues to evolve, generative AI is increasingly being used by businesses, researchers, and creators to drive innovation in a variety of fields. From e-commerce to entertainment, the possibilities of generative AI are seemingly endless. With numbers like that in mind, companies have raced to adopt marketing technologies that will allow them to create the tailored online experiences that customers so obviously want.
Using generative AI, individuals may convert words into visuals and produce realistic graphics based on a specified context, topic, or place. It is important to apply these graphic elements for strategic reasons, such as designing marketing campaign creatives. This “Christmas miracle” of sorts occurs because the technology black-boxes its inner working (which relies on heavy-duty data crunching and sophisticated analyses) and presents only the end results.
We can enhance images from old movies, upscaling them to 4k and beyond, generating more frames per second (e.g., 60 fps instead of 23), and adding color to black and white movies. If we have a low resolution image, we can use a GAN to create a much higher resolution version of an image by figuring out what each individual pixel is and then creating a higher resolution of that. Although some users note that on average Midjourney draws a little more expressively and Stable Diffusion follows the request more clearly at default settings. On top of that, transformers can run multiple sequences in parallel, which speeds up the training phase. Both a generator and a discriminator are often implemented as CNNs (Convolutional Neural Networks), especially when working with images. Mathematically, generative modeling allows us to capture the probability of x and y occurring together.
According to Accenture’s 2023 Technology Vision report, 97% of global executives agree that foundation models will enable connections across data types, revolutionizing where and how AI is used. To operate in tomorrow’s market, businesses will need to lean on the full capabilities that generative AI provides. Secondly, Generative AI enables automated learning and decision-making capabilities. This potential of AI to learn from data and create something new presents an exciting prospect for businesses aiming to improve efficiency and introduce innovative solutions. Gaming studios can develop new and appealing content for their users without any rise in developer workload.
