How Generative AI Is Changing Creative Work

From that perspective, businesses and society will be responsible to decide how much of the creative work will ultimately be done by AI and how much by humans. Finding the balance here will be an important challenge when we move ahead with integrating generative AI in our daily work existence. If the practice of enhanced personalized experiences is applied broadly, then we run the risk to lose the shared experience of watching the same film, reading the same book, and consuming the same news. In that case, it will be easier to create politically divisive viral content, and significant volumes of mis/disinformation, as the average quality of content declines alongside the share of authentic human content. For example, recent lawsuits against prominent generative AI platforms allege copyright infringement on a massive scale. What makes this issue even more fraught is that intellectual-property laws have not caught up with the technological progress made in the field of AI research.

This will make it easier to generate new product ideas, experiment with different organizational models and explore various business ideas. Generative AI could also play a role in various aspects of data processing, transformation, labeling and vetting as part of augmented analytics workflows. Semantic web applications could use generative AI to automatically map internal taxonomies describing job skills to different taxonomies on skills training and recruitment sites. Similarly, business teams will use these models to transform and label third-party data for more sophisticated risk assessments and opportunity analysis capabilities. The recent progress in LLMs provides an ideal starting point for customizing applications for different use cases.

How will generative AI contribute business value?

Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields. The rise of generative AI is largely due to the fact that people can use natural language to prompt AI now, so the use cases for it have multiplied. Across different industries, AI generators are now being used as a companion for writing, research, coding, designing, and more. As a new technology that is constantly changing, many existing regulatory and protective frameworks have not yet caught up to generative AI and its applications.

Engineers can produce more effective and economical designs while reducing the time and resources needed for developing products by employing generative AI for developing things. As the technology continues to evolve, we can expect to see more innovative applications that will change the way we think about content creation and consumption. Conversational Yakov Livshits AI and generative AI have different goals, applications, use cases, training and outputs. Both technologies have unique capabilities and features and play a big role in the future of AI. Some AI experts are also concerned about the dangers posed by future iterations of the technology — a superintelligent “rogue AI” that supersedes human control.

What Can Generative AI Text Create?

Generative AI could be detrimental because of its lack of accuracy, as PolitiFact found when it put ChatGPT to a fact-checking test. Interest spiked again in November 2022, when OpenAI launched ChatGPT, allowing anyone to sign up for free to test it and provide feedback during a research preview. In April, the site had more than 206 million unique visitors, according to data analytics company Similarweb, which noted that growth had flattened since its initial launch. But generative AI, a subset of the field, recently has propelled the technology into public view.

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.

At a high level, generative AI refers to a category of AI models and tools designed to create new content, such as text, images, videos, music, or code. Generative AI uses a variety of techniques—including neural networks and deep learning algorithms—to identify patterns and generate new outcomes based on them. Organizations and people (including software developers and engineers) are increasingly looking to generative AI tools to create content, code, images, and more. Generative AI models use neural networks to identify patterns in existing data to generate new content. Trained on unsupervised and semi-supervised learning approaches, organizations can create foundation models from large, unlabeled data sets, essentially forming a base for AI systems to perform tasks [1]. Organizations can create foundation models as a base for the AI systems to perform multiple tasks.

Web design

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 can’t have genuinely new ideas that haven’t been previously expressed in its training data or at least extrapolated from that data. Generative AI requires human oversight and is only at its best in human-AI collaborations.

Even AI experts don’t know precisely how they do this as the algorithms are self-developed and tuned as the system is trained. Different generative AI tools can produce new audio, image, and video content, but it is text-oriented conversational AI that has fired imaginations. In effect, people can converse with, and learn from, text-trained generative AI models in pretty much the same way they do with humans.

Neural network models use repetitive patterns of artificial neurons and their interconnections. A neural network design—for any application, including generative AI—often repeats the same pattern of neurons hundreds or thousands of times, typically reusing the same parameters. This is an essential part of what’s called a “neural network architecture.” The discovery of new architectures has been an important area of AI innovation since the 1980s, often driven by the goal of supporting a new medium. But then, once a new architecture has been invented, further progress is often made by employing it in unexpected ways.

In 2014, a type of algorithm called a generative adversarial network (GAN) was created, enabling generative AI applications like images, video, and audio. In customer service, earlier AI technology automated processes and introduced customer self-service, but it also caused new customer frustrations. Generative AI promises to deliver benefits to both customers and service representatives, with chatbots that can be adapted to different languages and regions, creating a more personalized and accessible customer experience. When human intervention is necessary to resolve a customer’s issue, customer service reps can collaborate with generative AI tools in real time to find actionable strategies, improving the velocity and accuracy of interactions. The two models are trained together and get smarter as the generator produces better content and the discriminator gets better at spotting the generated content.