Generative AI Use Cases for Industries and Enterprises

What used to be a physical process (cameras, actors, studios…) has now transitioned into a fully digital realm, making video creation convenient and accessible to all. 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. It’s a great online tool that helps educators effortlessly transform their text-based documents into an engaging video training featuring a human face, establishing a deeper connection with the viewers.

This has the potential to greatly increase the efficiency and speed of content creation for media organizations. The multilingual support offered by generative AI tools like ChatGPT for customer service involves using the large language model capabilities of the system to provide support to customers who speak different languages. Conversational AI tools Yakov Livshits can be trained on a variety of languages, and it can translate messages from one language to another in real-time. By using machine learning algorithms, manufacturers can predict equipment failures and maintain their equipment proactively. These models can be trained on data from the machines themselves, like temperature, vibration, sound, etc.

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This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities Yakov Livshits the workforce will need, and rethinking core business processes such as retraining and developing new skills. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. 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.

AGI, the ability of machines to match or exceed human intelligence and solve problems they never encountered during training, provokes vigorous debate and a mix of awe and dystopia. AI is certainly becoming more capable and is displaying sometimes surprising emergent behaviors that humans did not program. In a recent Gartner webinar poll of more than 2,500 executives, 38% indicated that customer experience and retention is the primary purpose of their generative AI investments. This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%). What is encouraging for generative service providers is that a further 17% of survey respondents have immediate plans to adopt GAI (i.e., within weeks), which should in theory see the survey sample user base rise to 27% in a short period.

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He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School. Generative AI can be used to provide personalized sales coaching to individual sales reps, based on their performance data and learning style. This can help sales teams to improve their skills and performance, and increase sales productivity.

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.

But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data. 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. Overall, it provides a good illustration of the potential value of these AI models for businesses.

One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do. Generative AI can be used to simulate different risk scenarios based on historical data and calculate the premium accordingly. For example, by learning from previous customer data, generative models can produce simulations of potential future customer data and their potential risks. These simulations can be used to train predictive models to better estimate risk and set insurance premiums.

Tools like ChatGPT can create personalized email templates for individual customers with given customer information. When the company wants to send an email to a customer, ChatGPT can use a template to generate an email that is tailored to the customer’s individual preferences and needs. Generative programming tools can be used to automate game testing, such as identifying bugs and glitches, and providing feedback on gameplay balance. This can help game developers to reduce testing time and costs, and improve the overall quality of their games. Generative AI can generate game content, such as levels, maps, and quests, based on predefined rules and criteria.

Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups. Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.

• Tome is a revolutionary generative AI solution that takes the hassle out of creating presentations.
• Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned.
• Lastly, one of the most recent generative AI use cases has been the enterprise implementation of this technology for streamlined search.
• Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks.
• A GAN consists of a generator and a discriminator that creates new data and ensures that it is realistic.

Moreover, generative AI overcomes some of the previous hurdles to AI adoption in health care. It requires less data, is more adaptable to unfamiliar situations, and can interface better with clinical staff. These features make generative AI more broadly applicable and transferable to different health care tasks. Some companies are exploring the idea of LLM-based knowledge management in conjunction with the leading providers of commercial LLMs.