How Generative AI Is Changing Creative Work

With the advancements of technology, such as the famous GPT-3 which we covered in a different article, many people are simply stunned. If you want to see it for yourself, there are web pages with images of people who never existed. This idea is completely different from the traditional MPEG compression algorithms, as when the face is analysed, only the key points of the face are sent over the wire and then regenerated on the receiving end. The results are impressive, especially when compared to the source images or videos, that are full of noise, are blurry and have low frames per second.

Generative AI  is a broad term that describes when computers create new content — such as text, photos, videos, music, code, audio and art — by identifying patterns in existing data. Manufacturers are starting to turn to generative AI solutions to help with product design, quality control, and predictive maintenance. Generative AI can be used to analyze historical data to improve machine failure predictions and help manufacturers with maintenance planning. According to research conducted by Capgemini, more than half of European manufacturers are implementing some AI solutions (although so far, these aren’t generative AI solutions).

Introducing McKinsey Explainers: Direct answers to complex questions

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end.

Programming teams will use generative AI to enforce company-specific best practices for writing and formatting more readable and consistent code. Ian Goodfellow demonstrated generative adversarial networks for generating realistic-looking and -sounding people in 2014. OpenAI, an AI research and deployment company, took the core ideas behind transformers to train its version, dubbed Generative Pre-trained Transformer, or GPT.

What is generative AI and why is it suddenly everywhere? Here’s how tools like ChatGPT and Dall-E work

A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) have the potential to change how a range of jobs are performed. DALL-E is an example of text-to-image generative AI that was released in January 2021 by OpenAI.

• Among the dozens of music generators are AIVA, Soundful, Boomy, Amper, Dadabots, and MuseNet.
• 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.
• In our case we did an interview with AI and it sounded really interesting and natural.

In fact, 96% of developers surveyed reported spending less time on repetitive tasks using GitHub Copilot, which in turn allowed 74% of them to focus on more rewarding work. In marketing, content is king—and generative AI is making it easier than ever to quickly create large amounts of it. A number of companies, agencies, and creators are already turning to generative AI tools to create images for social posts or write captions, product descriptions, blog posts, email subject lines, and more. Generative AI can also help companies personalize ad experiences by creating custom, engaging content for individuals at speed. Writers, marketers, and creators can leverage tools like Jasper to generate copy, Surfer SEO to optimize organic search, or albert.ai to personalize digital advertising content. Open source has powered software development for years, and now it’s powering the future of AI as well.

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.

To be sure, it has also demonstrated some of the difficulties in rolling out this technology safely and responsibly. But these early implementation issues have inspired research into better tools for detecting AI-generated text, images and video. Industry and society will also build better tools for tracking the provenance of information to create more trustworthy AI. Larger enterprises and those that desire greater analysis or use of their own enterprise data with higher Yakov Livshits levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity.

Other challenges cited by the survey respondents include data training, technical capabilities, regulatory and legal oversight, and implementation costs. Generative AI could tap models that use the creative work of others without permission. If that work is challenged, then it companies that use the AI could be vulnerable to lawsuits from the proper rights holders. Christofferson said this is indeed an impediment to generative AI, but it’s not as big as the others. It might be hard to see how AI could become more than half of the work in game development, but Christofferson noted that AI is already accounting for a lot of programming work at companies, and programming is a huge part of game production.

What does Gartner predict for the future of generative AI use?

Starting today, anyone, whether they are in Adobe’s beta program or not, can head to Firefly.Adobe.com and begin creating content using Firefly. With simple text prompts in one of over 100 supported languages, Firefly will generate four images with customizable art styles, colors, and visual themes. Businesses and the world at large will show little patience to apply the new emerging technologies to promote swiftly our level of productivity and content generation. So, be prepared to invest significant time and effort to master the art of creativity in a world dominated by generative AI. Codifying, digitizing, and structuring the knowledge you create will be a critical value driver in the decades to come. Generative AI and large language models enable knowledge and skills to transmit more easily across teams and business units, accelerating learning and innovation.

Another consequence of how Firefly has been designed, implemented, and released is that the platform is commercially viable. This means that companies can use Adobe Firefly to create content without concerns over content ownership, a situation that is much murkier with some competing generative AI models. Additionally, these tools can help embolden aspiring creators to enter Yakov Livshits into an artistic field that might otherwise have been outside of their capabilities. Someone without any graphic design experience can use Dall-E or similar programs to generate a beautiful image, without having any understanding of color, shape or form. In industrial settings, generative AI has several uses, particularly in the production and design of products.

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This has led to specialized models of BERT — for biomedical content (BioBERT), legal content (Legal-BERT), and French text (CamemBERT) — and GPT-3 for a wide variety of specific purposes. This deep learning technique provided a novel approach for organizing competing neural networks to generate and then rate content variations. This inspired interest in — and fear of — how generative AI could be used to create realistic deepfakes that impersonate voices and people in videos. It operates on AI models and algorithms that are trained on large unlabeled data sets, which require complex math and lots of computing power to create. These data sets train the AI to predict outcomes in the same ways humans might act or create on their own. Further development of neural networks led to their widespread use in AI throughout the 1980s and beyond.

Generative AI models work by using neural networks to identify patterns from large sets of data, then generate new and original data or content. These models are trained on huge datasets consisting of hundreds of billions of words of text, based on which the model learns to effectively predict natural responses to the prompts you enter. One emerging application of LLMs is to employ them as a means of managing text-based (or Yakov Livshits potentially image or video-based) knowledge within an organization. The labor intensiveness involved in creating structured knowledge bases has made large-scale knowledge management difficult for many large companies. However, some research has suggested that LLMs can be effective at managing an organization’s knowledge when model training is fine-tuned on a specific body of text-based knowledge within the organization.

Tom Stein, chairman and chief brand officer at B2B marketing agency Stein IAS, says every marketing agency, including his, is exploring such opportunities at high speed. But, Stein notes, there are also simpler, faster wins for an agency’s back-end processes. To learn more about what artificial intelligence is and isn’t, check out our comprehensive AI cheat sheet. Experience AI built into the flow of work, for any workflow, user, department, and industry. Save time and drive efficiency with AI-powered predictions and generative AI across the Customer 360 with Salesforce Einstein.