These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization. It’s able to produce text and images, spanning blog posts, program code, poetry, and artwork (and even winning competitions, controversially).
The ability to generate images from text highlights the potential of artificial intelligence as a resource. That’s why neuroflash now combines the No. 1 German-language text generator with a new function, the text to image generation. This makes neuroflash the first company in the DACH region to offer its customers the opportunity to try out AI image generation for themselves completely free of charge. Scroll down to check out some prompt examples and the awesome pictures that neuroflash created from them in comparison to DALLE-2.
Generative AI models are a type of artificial intelligence model that can generate new content, such as text, images, music, or even videos, similar to the data they were trained on. These models understand the structures and patterns found in the training data using machine learning techniques, and then they apply that information to produce new, original material. As we continue to advance these models and scale up the training and the datasets, we can expect to eventually generate samples that depict entirely plausible images or videos.
The new models, called the Granite series models, appear to be standard large language models (LLMs) along the lines of OpenAI’s GPT-4 and ChatGPT, capable of summarizing, analyzing and generating text. IBM provided very little in the way of details about Granite, making it impossible to compare the models to rival LLMs — including IBM’s own. But the company claims that it’ll reveal the data used to train the Granite series models, as well as the steps used to filter and process that Yakov Livshits data, ahead of the models’ availability in Q3 2023. Large Language Models (LLMs) were explicitly trained on large amounts of text data for NLP tasks and contained a significant number of parameters, usually exceeding 100 million. They facilitate the processing and generation of natural language text for diverse tasks. Each model has its strengths and weaknesses and the choice of which one to use depends on the specific NLP task and the characteristics of the data being analyzed.
Organizations that rely on Yakov Livshits should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content. Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand. If you’re looking for a way to create stunning images with AI, this I hope this tutorial on AI image prompt examples helped to give you some guidance and ideas.
Clients receive 24/7 access to proven management and technology research, expert advice, benchmarks, diagnostics and more. “We’re not scraping the internet with our models, if that’s your question,” Benioff told me Wednesday afternoon. When you imagine what you want to see, remember that concrete things are easier for the AI to represent than abstract words. So if you work with concrete words, you can get more predictable images.
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.
GANs reach the generative model by dividing the problem into 2 networks; the generator and the discriminator. 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. Heretofore, however, the creation of deepfakes required a considerable amount of computing skill. OpenAI has attempted to control fake images by “watermarking” each DALL-E 2 image with a distinctive symbol. More controls are likely to be required in the future, however — particularly as generative video creation becomes mainstream.
These products and platforms abstract away the complexities of setting up the models and running them at scale. As an evolving space, generative models are still considered to be in their early stages, giving them space for growth in the following areas. Additionally, diffusion models are also categorized as foundation models, because they are large-scale, offer high-quality outputs, are flexible, and are considered best for generalized use cases. However, because of the reverse sampling process, running foundation models is a slow, lengthy process. The goal for IBM Consulting is to bring the power of foundation models to every enterprise in a frictionless hybrid-cloud environment.
Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this type of AI could fundamentally change business. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains. In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs. Examples include OpenAI Codex. While we live in a world that is overflowing with data that is being generated in great amounts continuously, the problem of getting enough data to train ML models remains. Acquiring enough samples for training is a time-consuming, costly, and often impossible task.
Based on the comparison, we can figure out how and what in an ML pipeline should be updated to create more accurate outputs for given classes. In the intro, we gave a few cool insights that show the bright future of generative AI. The potential of generative AI and GANs in particular is huge because this technology can learn to mimic any distribution of data. That means it can be taught to create worlds that are eerily similar to our own and in any domain. We just typed a few word prompts and the program generated the pic representing those words. This is something known as text-to-image translation and it’s one of many examples of what Yakov Livshits do.
It has also found applications in photo editing and video post-production, allowing for creative enhancements and artistic interpretations. Style transfer models continue to evolve, giving users more control and flexibility to generate personalized and expressive visual content. Generative AI enables users to quickly generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, sounds, animation, 3D models, or other types of data. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models.
They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions. Using generative models to come up with new ideas, we can dramatically accelerate the pace at which we can discover new molecules, materials, drugs, and more. Adobe is introducing a new credit-based model for generative AI across Creative Cloud offerings with the goal of enabling adoption of new generative image workflows powered by the Firefly Image model. Starting today, the Firefly web application, Express Premium and Creative Cloud paid plans now include an allocation of “fast” Generative Credits.
Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. DALL-E is a neural network that creates images from text captions for a wide variety of concepts. It works as a transformer language model that receives both the text and image as a single stream of data. Diffusion models are another type of generative model that can generate high-quality images.