How Generative AI is Changing the Way Creatives Work

We can see right now how ML is used to enhance old images and old movies by upscaling them to 4K and beyond, which generates 60 frames per second instead of 23 or less, and removes noise, adds colors and makes it sharp. ML based upscaling for 4K, as well as FPS, enhance from Yakov Livshits 30 to 60 or even 120 fps for smoother videos. All of us remember scenes from the movies when someone says “enhance, enhance” and magically zoom shows fragments of the image. Of course it’s science fiction, but with the latest technology we are getting closer to that goal.

Along with 2022 improvements in image generation capabilities, the release of OpenAI’s latest language model “sparked the current wave of public interest,” Toner said. Given that the pace the technology is advancing, business leaders in every industry should consider generative AI ready to be built into production systems within the next year—meaning the time to start internal innovation is right now. Companies that don’t embrace the disruptive power of generative AI will find themselves at an enormous—and potentially insurmountable—cost and innovation disadvantage. For example, GPT-3.5, a foundation model trained on large volumes of text, can be adapted for answering questions, text summarization, or sentiment analysis. DALL-E, a multimodal (text-to-image) foundation model, can be adapted to create images, expand images beyond their original size, or create variations of existing paintings. Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities.

Data Privacy

This will require governance, new regulation and the participation of a wide swath of society. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly. For a quick, one-hour introduction to generative AI, consider enrolling in Google Cloud’s Introduction to Generative AI. Learn what it is, how it’s used, and why it is different from other machine learning methods. Generative AI’s popularity is accompanied by concerns of ethics, misuse, and quality control.

Generative AI models use a complex computing process known as deep learning to analyze common patterns and arrangements in large sets of data and then use this information to create new, convincing outputs. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time. In simple terms, they use interconnected nodes that are inspired by neurons in the human brain. These networks are the foundation of machine learning and deep learning models, which use a complex structure of algorithms to process large amounts of data such as text, code, or images. The field accelerated when researchers found a way to get neural networks to run in parallel across the graphics processing units (GPUs) that were being used in the computer gaming industry to render video games. New machine learning techniques developed in the past decade, including the aforementioned generative adversarial networks and transformers, have set the stage for the recent remarkable advances in AI-generated content.

Future Challenges Are Around The Corner

GANs are unstable and hard to control, and they sometimes do not generate the expected outputs and it’s hard to figure out why. When they work, they generate the best images; the sharpest and of the highest quality compared to other methods. There are AI techniques whose goal is to detect fake images and videos that are generated by AI. The accuracy of fake detection is very high with more than 90% for the best algorithms. But still, even the missed 10% means millions of fake contents being generated and published that affect real people.

Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response. Seamlessly integrate your preferred predictive and generative partner AI models, train them on data in Data Cloud, and use them to equip Einstein Copilot with more accurate insights and content. No, generative credits don’t roll over to the next month because the cloud-based computational resources are fixed and assume a certain allocation per user in a given month.

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.

Generative AI uses various machine learning techniques, such as GANs, VAEs or LLMs, to generate new content from patterns learned from training data. These outputs can be text, images, music or anything else that can be represented digitally. The number of monthly generative credits each user receives depends on their subscription.

Another factor in the development of generative models is the architecture underneath. A major concern around the use of generative AI tools -– and particularly those accessible to the public — is their potential for spreading misinformation and harmful content. The impact of doing so can be wide-ranging and severe, from perpetuating stereotypes, hate speech and harmful ideologies to damaging personal and professional reputation and the threat of legal and financial repercussions. It has even been suggested that the misuse or mismanagement of generative AI could put national security at risk. Einstein is built on a powerful Trust Layer that safeguards your company’s sensitive customer data. Deploy AI with Ethics by Design, intentionally embedding ethical and humane use guiding principles in the design, development, and delivery of software.

For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content. Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike. As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny.

DALL-E 2 and other image generation tools are already being used for advertising. Nestle used an AI-enhanced version of a Vermeer painting to help sell one of its yogurt brands. Stitch Fix, the clothing company that already uses AI to recommend specific clothing to customers, is experimenting with DALL-E 2 to create visualizations of clothing based on requested customer preferences for color, fabric, and style. Mattel is using the technology to generate images for toy design and marketing. Well, for an example, the italicized text above was written by GPT-3, a “large language model” (LLM) created by OpenAI, in response to the first sentence, which we wrote. GPT-3’s text reflects the strengths and weaknesses of most AI-generated content.

What Kinds of Problems can a Generative AI Model Solve?

These models have largely been confined to major tech companies because training them requires massive amounts of data and computing power. GPT-3, for example, was initially trained on 45 terabytes of data and employs 175 billion parameters or coefficients to make its predictions; a single training run for GPT-3 cost Yakov Livshits $12 million. Most companies don’t have the data center capabilities or cloud computing budgets to train their own models of this type from scratch. In the future, generative AI models will be extended to support 3D modeling, product design, drug development, digital twins, supply chains and business processes.

If cheaply made generative AI undercuts authentic human content, there’s a real risk that innovation will slow down over time as humans make less and less new art and content. Creators are already in intense competition for human attention spans, and this kind of competition — and pressure — will only rise further if there is unlimited content on demand. Extreme content abundance, far beyond what we’ve seen with any digital disruption to date, will inundate us with noise, and we’ll need to find new techniques and strategies to manage the deluge. Ultimately, I believe generative AI will be incorporated as a feature in all sorts of products and be useful across a wide range of industries.

• The explosive growth of generative AI shows no sign of abating, and as more businesses embrace digitization and automation, generative AI looks set to play a central role in the future of industry.
• In fact, the processing is a generation of the new video frames, which are based on the existing ones and tons of data to enhance human face details and object features.
• The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds.
• Like any major technological development, generative AI opens up a world of potential, which has already been discussed above in detail, but there are also drawbacks to consider.
• Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences.
• For example, a chatbot like ChatGPT generally has a good idea of what word should come next in a sentence because it has been trained on billions of sentences and “learned” what words are likely to appear, in what order, in each context.

History demonstrates, however, that technological change like that expected from generative AI always leads to the creation of more jobs than it destroys. The best-known example of generative AI today is ChatGPT, which is capable of human-like conversations and writing on a vast array of topics. Other examples include Midjourney and Dall-E, which create images, and a multitude of other tools that can generate text, images, video, and sound. The landscape Yakov Livshits for neural network research thawed out in the 1980s thanks to the contributions of several researchers, most notably Paul Werbos, whose initial work rediscovered the perceptron; Geoffrey Hinton; Yoshua Bengio; and Yann LeCun. Their combined work demonstrated the viability of large, multilayer neural networks and showed how such networks could learn from their right and wrong answers through credit assignment via a backpropagation algorithm.