Generative AI In Marketing: 5 Use Cases

You’ve likely already dabbled in the technology, asking your friendly neighborhood AI virtual assistant what meal to prep for the week or how to draft an email to your boss. Additionally, manufacturing companies can leverage generative AI to improve machine maintenance by providing tailored recommendations based on the device’s usage patterns and specific needs. Generative AI chatbots can greatly improve travel companies’ service by providing customers with all the necessary information on available trips, transportation and accommodation. It can analyze the items in a customer’s cart and their purchase history to suggest other products they may like.

Generative AI is crucial in automating repetitive tasks, increasing productivity, and improving decision-making across several industries. From healthcare and manufacturing to real estate, finance, and entertainment, Generative AI use cases are plentiful. This AI technology can effectively create unique and engaging user experiences by automating creative tasks like content creation and addressing other vital purposes, such as predictive analysis. Generative AI has the potential to revolutionize various industries, and companies that leverage this technology efficiently will be well-positioned to increase revenue, reduce costs, and improve efficiency.

Generative AI Applications

This technology leverages machine learning models, particularly unsupervised and semi-supervised algorithms, to generate new content based on existing data. Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs. Researchers appealed to GANs to offer alternatives to the deficiencies of the state-of-the-art ML algorithms. GANs are currently being trained to be useful in text generation as well, despite their initial use for visual purposes.

This is the only way to ensure customer loyalty and retention in the present times. According to a survey by BCG, 41% of CMOs harness the power of generative AI for better targeting. All in all, a content marketer can learn about the topics, subjects, and words their audience searches for online and cater to the same with relevant content. Since generative AI cannot fully understand human emotions and culture, it might produce responses that are offensive to certain groups of people. Funnily enough, even though it is wrong, the output is framed in a way that sounds just right. Marketers can do away with old content types and experiment with fresh ideas that might improve conversions.

Part 1: The Risks and Ethical Issues Associated with Large Language Models

The availability of numerous readymade tools, frameworks, and blueprints makes it easier for developers to create new games, which traditionally requires building things from the ground up. It is also possible to generate realistic human-like voices using AI tools, which can be used for video game avatars and animations. With the latest advancements in generative AI capabilities, personal Yakov Livshits productivity tools like email and word processing can now be augmented with automation to improve efficiency and accuracy. One notable example of the power of generative AI is Microsoft’s use of GPT-3.5 in the premium version of Teams. This powerful tool enhances meeting recordings by automatically dividing them into sections, generating titles, and adding personalized markers.

It can generate hyper realistic images and mockups that are literally impossible to distinguish from actual photographs. Lokalise AI is an automated localization and translation platform designed for various applications, including web apps, customer service, documents, mobile apps, games, and marketing assets. With its advanced features like contextual translation, alternative variants, rephrasing, and concise adaptations, it enables seamless communication with global audiences in different languages. This creates situations where it hallucinates nonexistent facts that are based structured to look convincing, just like in the aforementioned case.

Training a custom Entity Linking model with spaCy

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.

Firefly can create high-quality images and stunning text effects from just textual inputs. The generative AI technology can help automate software programming tasks using LSTM (Long Short-Term Memory) network, which generates new code based on existing code. You can leverage generative AI for marketing and sales campaigns to create personalized content without compromising users’ privacy. Chatbots can enhance your overall customer experience and give your customer support teams more time to focus on other important tasks, ultimately boosting operational efficiency. Although there are risks involved with using generative AI in marketing, one cannot ignore the benefits.

• If code passes testing, DevOps teams can automatically deploy it using generative AI as part of workflow or process automations.
• Customer personas have sort of revolutionized marketing, enabling marketing organizations to build targeted marketing campaigns.
• This is the only way to ensure customer loyalty and retention in the present times.

Yooz is an automated AI solution designed to assist accounting and finance leaders in managing invoices. The solution aims to streamline and automate the invoice processing workflow, reducing manual effort and enhancing overall efficiency. Gradescope is an AI-powered tool that simplifies assessment grading for teachers. It efficiently grades both digital and paper-based assignments, providing quick and accurate results. Additionally, Gradescope offers valuable insights into students’ knowledge levels across various subjects. There’s no doubt that education today faces many challenges, including unequal access, outdated methods, and the need for personalized learning.

For example, Simcenter has a generative AI tool that helps
discover the optimal system architectures. The model scans through thousands of
possibilities based on the input product characteristics and then suggests the
best-fit system architecture pattern. Since LLMs come pre-trained and are subsequently fine tuned to specific tasks, they create a number of issues and security risks (e.g. insecure code).

Prior to the rise of generative AI, many AI systems were intended for data analytics and data-driven decision-making, such as predictive analysis and business forecasting. Generative AI models go a step further, producing new content such as text, videos, code and images based on their training data in response to user queries. From music generation to video editing and voice synthesis, Generative AI can be leveraged to its fullest potential in film/music production, fashion and gaming. Some AI tools are used in video production and editing to add special effects and generate new videos including animations and even complete movies. It simplifies video editing and saves time for content creators and social media influencers.

This will allow students to practice their conversational and diagnosing skills. Prepare to be surprised by the collective power of human intelligence and Custom Generative AI for Enterprise. Music Yakov Livshits composition is an art form that requires creativity and emotional expression. Generative AI is now capable of composing original pieces of music that align with different genres, styles, and moods.

The most significant of these is the self-attention mechanism, which allows the model to weigh the relevance of a word in a sentence to other words when generating an output. This mechanism allows the model to handle long-range dependencies in text more effectively than previous models. The Transformer model also introduced the concept of positional encoding, which allows the model to consider the position of words in a sentence.