Top 9 Generative AI Applications and Tools

Specifically, it can produce standardized reports (such as in the figure below) that offer consistency in how findings are presented. Generative AI can analyze historical sales data and generate forecasts for future sales. So, sales teams can optimize their sales pipeline and allocate resources more effectively. Generative AI can be used to generate contracts based on pre-defined templates and criteria. This can save time and effort for procurement departments and help to ensure consistency and accuracy in contract language.

Text-to-code conversion is made possible by generative AI, and AI-powered code generators optimized for various programming languages are capable of code completion and custom model suggestions. This technology has many applications, including audiobooks, voiceovers for movies and documentaries, as well as advertisements. HR departments often need to come up with a set of questions to ask job candidates during the interview process, and this can be a time-consuming task. AI can be used to generate interview questions that are relevant to the job position and that assess the candidate’s qualifications, skills, and experience.

Vector embeddings — numerical representations of data, including but not limited to text, audio and image data — allow AI algorithms to better understand the relationships between different types of data and their semantic relevance to each other. Yakov Livshits That’s useful for, say, recommendation engines, which can tap embeddings to find data similar to other data (e.g. similar movies and TV shows). But the use cases extend beyond that — think things like fraud detection and typo correction.

Business-function-specific Generative AI Applications

Generative AI is a broad label that’s used to describe any type of artificial intelligence (AI) that can be used to create new text, images, video, audio, code or synthetic data. Video Generation involves deep learning methods such as GANs and Video Diffusion to generate new videos by predicting frames based on previous frames. Video Generation can be used in various fields, such as entertainment, sports analysis, and autonomous driving. Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent.

You can ask ChatGPT Code Interpreter to perform certain analysis tasks and it will write and execute the appropriate Python code. As an AI language model, ChatGPT can assist in maintaining test scripts by identifying outdated or redundant code, suggesting improvements, and even automatically updating scripts when provided with new requirements or changes in the application. These can be useful for mitigating the data imbalance issue for the sentiment analysis of users’ opinions (as in the figure below) in many contexts such as education, customer services, etc.

Discover with Generative AI

It can automatically fill in the information where necessary, speeding up the process of creating these documents. By leveraging generative AI to create a variety of fashion models, fashion companies can better serve their diverse customer base and accurately display their products in a more authentic manner. They can use such models for virtual try-on options for customers or 3D-rendering of a garment. From creating innovative styles to refining and optimizing existing looks, the technology helps designers keep up with the latest trends while maintaining their creativity in the process. This can be done by a variety of techniques such as unique generative design or style transfer from other sources.

The capabilities in AlloyDB can be added to any AlloyDB deployment by installing the relevant extensions at no additional charge, Google says. Additionally, as part of our commitment to an open approach to AI development, we’re also announcing new AI partnerships and programs that make it easier for startups, developers, and enterprises to accelerate their AI projects. If you are interested in updates on our early access opportunities, please join our technical community, Google Cloud Innovators.

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.

With generative AI, users can transform text into images and generate realistic images based on a setting, subject, style, or location that they specify. Therefore, it is possible to generate the needed visual material in a quick and simple manner. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. Whether your company should use generative AI tools is a question only your leadership, your tech team, and the rest of your employees can answer. If there’s a specific use case or way in which a generative AI tool can improve your internal processes, it’s a great idea to invest in one of these tools while they’re still free or relatively low-cost.

• Satisfied that the Pixel 7 Pro is a compelling upgrade, the shopper next asks about the trade-in value of their current device.
• Some of the most popular uses are in customer service, where generative apps can contribute to increasing revenue, customer satisfaction, and customer loyalty.
• An audio recording and editing platform with integrated AI tools that helps you create clear, super-smooth recordings that sound as if they’ve been edited professionally, automating tasks like cleaning up messy sounds and creating transcripts.
• Understanding the requirements described in plain language can translate them into specific commands or code snippets in the desired programming language or test automation framework.
• Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Artificial Intelligence (AI) is going through something of a “hot topic” moment, as applications such as ChatGPT show the world just how powerful and capable it is becoming. A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. One surprising result is that baby boomers report using gen AI tools for work more than millennials. In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts.

For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. Future Adobe Firefly models will leverage a variety of assets, technology and training data from Adobe and others.

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. An audio-related application of generative AI involves voice generation using existing voice sources.

It is essential for decision makers and loan applicants to understand the explanations of AI-based decisions, including why the loan applications were denied. A conditional GAN is a useful tool to create applicant-friendly denial explanations as in the figure below. One advantage of using generative AI to create training data sets is that it can help protect student privacy. A data breach or hacking incident can reveal real-world data containing personal information about school age children. It can allow students to interact with a virtual tutor and receive real-time feedback in the comfort of their home.

Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data. As you can see above most Big Tech firms are either building their own generative AI solutions or investing in companies building large language models. Enabling multimodal search across text, images and video within the enterprise is a key aspect of the search experiences in Gen App Builder. In addition to providing high-quality search results, Gen App Builder can conveniently summarize the results and provide corresponding citations in a natural, human-like fashion. Gen App Builder also automatically extracts key information from the data and enables personalized results for users. Watch this demo to see how these capabilities can come together to transform the search experience for employees at a financial services firm.

Now it is going even further with Einstein Copilot, which Salesforce says offers multiple AI agents that can complete a range of CRM and application-specific tasks on their own. Salesforce was early to the enterprise AI game, launching Einstein in 2016 to aid with customer outreach, search, segmentation, and product recommendations. Salesforce’s enterprise customer administrators can also customize how Einstein Copilot works and what data of theirs it can access and reference, as well as harness third-party LLMs such as OpenAI’s GPT-3.5, using a new Einstein Copilot Studio. Customer relationship management software (CRM) leader Salesforce is announcing a big update today to its artificial intelligence (AI) suite, Einstein, at its annual Dreamforce 2023 conference in San Francisco. Once a generative AI algorithm has been trained, it can produce new outputs that are similar to the data it was trained on. Because generative AI requires more processing power than discriminative AI, it can be more expensive to implement.

This type is commonly used in chatbots and virtual assistants, which are designed to provide information, answer questions, or perform tasks for users through conversational interfaces such as chat windows or voice assistants. The TTS generation has multiple business applications such as education, marketing, podcasting, advertisement, etc. For example, an educator can convert their lecture notes into audio materials to make them more attractive, and the same method can also be helpful to create educational materials for visually impaired people. Aside from removing the expense of voice artists and equipment, TTS also provides companies with many options in terms of language and vocal repertoire.