Generative AI Innovate Faster with Foundation Models

Google Docs has a feature that attempts to automatically augment text with AI generated content. Photo sessions with real physical human models are expensive and require lots of logistical effort. The results are impressive, especially when compared to the source images or videos, that are full of noise, are blurry and have low frames per second. All of us remember scenes from the movies when someone says “enhance, enhance” and magically zoom shows fragments of the image. Of course it’s science fiction, but with the latest technology we are getting closer to that goal.

Paige.AI, a digital pathology company, is integrating generative AI into its products to improve the accuracy and efficiency of prostate cancer detection. It was the first company to receive FDA approval for AI use in digital pathology and is looking to integrate the resulting information into patient electronic health records along with other clinical data. The recent success of ChatGPT, which demonstrated the ability to create nuanced and articulated content at scale, highlighted the potential value of generative AI across the enterprise. As a result, executives and business users are starting to make generative AI and predictive AI complementary domains. Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries.

Best Generative AI Tools and Platforms

Another website has  more than two million photos, royalty free, of people who never existed but look like real people. You can select different parameters to get images that fit the specific criteria, and all this is generated by AI; none of these people even exist. Looking at the matrix, you can find that there are other opportunities that have received less attention. Like marketing, creating content for learning — for our purposes, let’s use the example of internal corporate learning tools — requires a clear understanding of its audience’s interests, and engaging and effective text. Priming it with existing documentation, you can ask it to rewrite, synthesize, and update the materials you have to better speak to different audiences, or to make learning material more adaptable to different contexts. This is happening already in marketing, where several start-ups have found innovative ways to apply LLMs to generate content marketing copy and ideas, and achieved unicorn status.

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Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models. It does this by learning patterns from existing data, then using this knowledge to generate new and unique outputs. GenAI is capable of producing highly realistic and complex content that mimics human creativity, making it a valuable tool for many industries such as gaming, entertainment, and product design.

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.

• Using generative AI models, applications can automate tedious tasks like video compositions, and animations, adding special effects, editing video snippets, etc.
• The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it.
• ChatGPT code interpreter can convert files between different formats, provided that the necessary libraries are available and the operation can be performed using Python code.
• Generative Adversarial Networks modeling (GANs) is a semi-supervised learning framework.
• Many early users have praised Claude’s abilities when it comes to comedy, creative content generation, and generally absorbing feedback about communication style.

These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. Yakov Livshits A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience.

Generative AI’s potential impact on knowledge work

Third, it would benefit from editing; we would not normally begin an article like this one with a numbered list, for example. The last point about personalized content, for example, is not one we would have considered. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks.

The AI revolution is impacting every sector, including healthcare, education, finance, agriculture, and construction, with new AI solutions emerging daily. 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.

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It provides actionable insights and aids in decision-making and strategy formulation. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. An excellent example of generative AI’s collaboration enhancement capabilities is Microsoft implementing GPT-3.5 in Teams Premium, which uses AI to enhance meeting recordings.

Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks.