Unlocking Creativity: AI Tools for Rapid Work Generation

As customer expectations continue to shift, brands need to be creative in meeting them. AI can help by enabling rapid work generation and freeing up time for creative innovation.

From an image generator to a writing assistant, these AI tools can elevate your workflows. Just be sure to test them with a discerning eye before relying on them.

1. Generative AI

Generative AI is one of the most exciting developments in artificial intelligence. This type of AI creates and produces new outputs like text, images, music, and videos based on input prompts or data from its training sets. This is helpful for business processes such as product design and customer support. For example, generative AI models can take a set of data about a particular client and produce a new, personalized service for that client.

Generative models are able to produce new data that is similar to the original data in a way that is not possible with other machine learning techniques. This is possible because generative AI uses an adversarial training process that trains a generator network and a discriminator network. The generator network tries to produce data that is indistinguishable from real data and the discriminator network tries to tell if it’s real. The generator network will continue to produce data until it is unable to fool the discriminator network.

Some examples of generative AI are text generators that create unique, human-sounding text based on input prompts and art and image generators that produce artwork or photography based on a specific text-based prompt. However, this technology has also caused concerns of plagiarism and other forms of intellectual property abuse because the generated content often uses previous work by other artists and creators without crediting them.

Nevertheless, generative AI is making significant strides as businesses adopt it to automate and enhance processes. Generative AI is used to generate new products and services, create new images and 3D objects, improve text and speech, write code, and facilitate data augmentation for machine learning models. For example, generative AI can transform satellite imagery into map views for exploring new areas or create photo-realistic medical images. In addition, generative AI can simplify the process of creating regulatory submissions (which can be hundreds of pages long) and automate the writing of clinical trial reports.

Another example of generative AI is knowledge management, which involves collecting information throughout the organization and ensuring that it can be easily found by employees when needed. With generative AI, companies can automate these tasks and free up resources to focus on more strategic objectives.

2. Deep Learning

Deep learning is a subset of machine learning and uses artificial neural network architecture to mimic the human brain. It allows machines to learn by observation and experience without being programmed explicitly. It is particularly well-suited for tasks where a lot of data is available, and it excels at pattern recognition and feature extraction.

Today’s most common AI tools use deep learning, from the smart speakers on your mantle with Alexa or Google voice assistant built-in to AI chatbots like ChatGPT, Bing Chat, and Google Bard. These tools use natural language processing (NLP), a subset of deep learning, to understand typed and spoken text and answer questions.

NLP uses unsupervised machine learning to teach computers how to process language and recognize patterns in it. They analyze billions of sentences to learn how to make sense of the words and concepts we use when talking, writing, and reading. They also use recurrent neural networks to retain past knowledge of conversations, allowing them to “remember” what they’ve already heard and respond accordingly.

These tools are used in a wide range of ways to improve productivity. Productivity tools like Microsoft 365 Copilot use NLP and deep learning to automate repetitive tasks. They can be triggered by a simple question, such as ‘email the team about the status of project x’ and then automatically gather relevant information from emails, documents, and other sources to create and send a message for you.

Generative AI can produce a huge range of credible, original writing in seconds. It can write an entire essay on nationalism and fascism or explain how to remove a peanut butter sandwich from a plastic bag, for example. It can also produce a variety of visual content, such as images or video, and even translate between different languages.

While there is debate about how fast generative AI will replace human jobs, there’s no doubt that it can significantly accelerate the speed of work in many sectors. As a result, companies are investing in generative AI to reduce their production time. NVIDIA Hopper and Ampere GPUs powered by tensor cores enable developers to easily build, train, and deploy generative AI models with the power of FP32 mixed precision matrix multiplication.

3. Natural Language Processing

Natural language processing is the technology that allows computers to understand, manipulate and interpret human speech and text. It’s the core technology behind virtual assistants like Oracle Digital Assistant, Siri, Cortana and Alexa. Most consumers have interacted with NLP on a daily basis without even realizing it.

NLP is a rapidly growing field that is becoming increasingly important for businesses to understand. NLP tools are used for a number of tasks including translation, information extraction, summarization, question answering and sentiment analysis. NLP is currently one of the fastest-growing areas of AI research.

Traditionally, NLP has been based on rule-based models that were created by linguists. The 1990s saw a change in approach as the computer became faster and more capable of performing calculations. This allowed engineers to create rules based on linguistic statistics and eliminate the need for a linguist to develop them. The emergence of deep learning has transformed NLP into a more intuitive, data-driven approach. Deep learning algorithms examine large sets of labeled data to identify patterns that can be used to improve an NLP program.

This has made it possible for programs to write articles for The Guardian and generate computer games from text prompts—a feat that would have been impossible only a few years ago. Increasingly, NLP programs are being used to automate routine tasks and free employees to focus on higher value work.

While NLP is widely-adopted in business, its most valuable applications are still yet to be fully realized. NLP is most often used to perform classification and translation tasks—identifying and interpreting the meaning of words. It’s also being used to power chatbots and digital assistants that can understand a wide range of user requests, match them to the appropriate entry in a corporate database and then respond appropriately.

Businesses should begin identifying their potential use cases for language-based AI tools by determining what types of tasks can be automated using this technology. For example, many sectors and divisions within an organization have highly specialized vocabularies. In these situations, a model trained on data from that sector or division will be more effective than a generalized one.

4. Machine Learning

Machine learning is a subset of AI that allows computers to learn from past data without being explicitly programmed. It’s used for data analysis that would otherwise require human judgment or trial and error. Examples include identifying probabilistic matches between disparate sets of data (as in GE’s use of machine learning to manage supplier data) and providing more accurate search results on web pages or mobile devices. It can also automatically label data, as in the example of Google’s algorithm that recognizes text within images. It’s also employed in smart robots, like the ones GM is testing, and in pattern or image recognition.

The success of generative AI tools such as ChatGPT and DALL-E has sparked interest in how the technology can be leveraged to support creative work. For example, generative AI could help artists explore variations on an initial idea. Industrial designers could generate product designs using a variety of colors and shapes, or architects could generate different layouts for building projects.

Generative AI can also democratize aspects of creative work that were previously difficult to do at scale, such as generating large volumes of high-resolution imagery or analyzing complex data sets. This is made possible by advances in deep learning that allow computers to learn how to do a specific task, or how to solve a specific problem, on their own.

MIT computer science professor Aleksander Madry notes that while many people see the promise of generative AI, it’s important to remember that the technology is only as good as the data it’s trained on. He adds that companies should avoid “looking at the technology and trying to backport it into a business need” that might not be suited for generative AI.

To ensure that AI is successful in their businesses, companies should focus on building up capabilities in three areas: automating business processes; gaining insight through data analysis; and engaging with customers and employees. By focusing on these three key areas, companies can unlock creativity in their operations and make the most of their investments. Ultimately, this will improve productivity and free up human workers to be more innovative in their work.