Scale knowledge management use cases with generative AI

This could benefit various media, education, podcasting, video generation, and marketing businesses, as they can create appealing content to attract potential audiences. From real-time image, video, and art creation to Yakov Livshits gene sequencing, it provides endless applications. As we enter 2023, the possibilities of this revolutionary AI are becoming endless, with plenty of exceptional use cases being discovered every day for every sector.

Generative AI can help forecast demand for products, generating predictions based on historical sales data, trends, seasonality, and other factors. This can improve inventory management, reducing instances of overstock or stockouts. Generative AI offers teachers a practical and effective way to develop massive amounts of unique material quickly. Whether it’s quiz questions, reviews of concepts or explanations, this technology can generate brand-new content from existing information to help educators easily create diverse teaching materials for their classes. Another application of generative AI is in software development owing to its capacity to produce code without the need for manual coding.

Comparing two of the most powerful text-to-image AI tools — Dall-E2 and Stable Diffusion.

With recent advances in large language models such as ChatGPT, generative AI has become more powerful and more applicable in business. With the advent of artificial intelligence, our day-to-day life has completely changed. In recent years, AI has revolutionized the way we live and work, and the potential of this technology is only beginning to be fully realized. Wendy’s and Google Cloud have partnered for a pilot program that will leverage Google’s generative AI technology, setting a new industry standard for drive-thru experiences.

In the realm of financial technology (FinTech), the emergence of generative AI, a subset of artificial intelligence (AI), has sparked a wave of excitement and innovation. Its transformative potential is poised to reshape the landscape of FinTech operations. We’ll explore the top 16 key use cases for generative AI in FinTech that exemplify its profound impact on the industry.

An Introduction: Generative AI Use Cases for the Financial Services Industry

Generative AI can augment existing datasets within the FinTech industry, enriching the available data for training and validation purposes. By generating synthetic data points, generative AI helps overcome limitations imposed by scarce or imbalanced datasets. This application improves the performance and robustness of AI models by diversifying the training data and ensuring better generalization to real-world scenarios. With enhanced data augmentation, FinTech companies can make more accurate predictions, detect anomalies, and improve risk assessment. Generative AI plays a pivotal role in fortifying the digital infrastructure of financial technologies, safeguarding them against a myriad of threats and vulnerabilities. Its applications span across cybersecurity, blockchain security, PKI-based identity, DDoS protection, and DNS security.

• Audit programs involve the frequent analysis of large swaths of financial and operational data.
• This technology leverages machine learning models, particularly unsupervised and semi-supervised algorithms, to generate new content based on existing data.
• This creates situations where it hallucinates nonexistent facts that are based structured to look convincing, just like in the aforementioned case.
• Generative AI models like GPT-3 can be trained on large amounts of code from various programming languages to create new code.

From enhancing creativity and automating tasks to personalizing content and improving decision-making, the benefits of generative AI are vast and impactful. Generative AI’s potential is far-reaching, with industries across the spectrum exploring its capabilities. From content creation and design to healthcare diagnostics and robotics, generative AI is poised to revolutionize workflows and enhance outcomes.

The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society. If the company is using its own instance of a large language model, the privacy concerns that inform limiting inputs go away. Finally, it’s important to continually monitor regulatory developments and litigation regarding generative AI.

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.

Synthetic data generation involves creating unique data from the input of the original dataset. This is useful when there is not enough data to train a machine-learning model or when it is difficult to obtain new data. Ultimately, code generated by a generative AI model can speed up the development process and reduce the need for manual coding.

Use cases of generative AI models across domains

Generative AI poses a new possibility for reducing time-to-value in digital transformation initiatives by providing better accessibility of advanced technologies to the general population. So we recommend you make a roadmap of your new implementations, issue the necessary guidelines, and determine the success metrics and people responsible for supervising the process. Transportation companies can also train generative AI with historical data about delays or periods of high demand, enabling it to propose effective mitigation strategies. Healthcare institutions can deploy special generative AI chatbots trained to answer as if they were patients with various medical conditions.

From the Mad Men era to the age of the internet, social media, and hyperconnectedness, marketing has undergone a remarkable transformation. Tripnotes is a data-powered travel planner that simplifies, well… trip planning. Users can paste their travel inspiration from text messages, social media, or blogs, and the app automatically saves and researches each mentioned place leveraging generative AI.

This cutting-edge technology has the power to revolutionize various aspects of customer service, enhancing personalized recommendations and enabling scalability in marketing efforts. Customer experience is a top priority for FinTech companies, and generative AI can play a significant role in enhancing it. By analyzing customer data and preferences, generative AI algorithms can provide personalized recommendations and offers, improving customer satisfaction and loyalty. During the training process, the generator network and the discriminator network play a game against each other, with the generator network trying to produce samples that fool the discriminator network. This adversarial training process helps the generator network improve its ability to generate realistic and coherent outputs. This software can seamlessly update and improve the quality of images and text so that you can provide students with improved learning materials, without losing staff efficiency.

Travel industry

The user will be able to change the voice to male or female, modulation, and more where the user can finalize the one which suits the best for the project. The Audio generation model works on test-to-speech and speech-to-speech where in speech-to-speech the AI tool changes the voice and provides the same content. Generative AI models work by learning the patterns in a dataset and then using that knowledge to create new content similar to the original data.

The UK government also launched a £1.5m program in late 2022 to explore the use of AI in reducing carbon emissions. Generative AI is increasingly being adopted by enterprises in the automotive and vehicle manufacturing industry. The market size for AI in manufacturing is currently estimated to be worth USD 2.3 billion in 2022. It is projected to grow at a compound annual growth rate (CAGR) of 47.9% from 2022 to 2027, reaching a market size of USD 16.3 billion by 2027. Inventory allocation can be optimized by predicting demand and adjusting stock levels accordingly using Generative AI.

One example of how media outlets can utilize generative AI for their content is BuzzFeed. In February 2023, they launched their first “Infinity Quizzes,” which create personalized quizzes for users based on a few inputs. Marketing tourist destinations and services requires a significant amount of multimedia content, with video being the most popular format at the moment. One example is Runway, which offers over 30 integrated AI tools to facilitate smooth and accessible video editing for everyone, regardless of their previous knowledge and video editing skills. Generative Artificial Intelligence, or generative AI, is a categorical or descriptive term ascribed to algorithms using machine learning to create or “generate” new content.