At a glance, Gemini’s ability to generate text and images gives it a serious advantage over GPT4 with respect to the range of content that it can produce.

However, perhaps the most threatening differentiator between the two is Google’s vast array of proprietary training data. Google Gemini can process data taken across services, including Google Search, YouTube, Google Books, and Google Scholar.

The use of this proprietary data in training the Gemini models could result in a distinct edge in the sophistication of the insights and inferences that it can take from a data set. This is particularly true if early reports that Gemini is trained on twice as many tokens as GPT4 are correct.

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The launch of ChatGPT last November shook Google to its foundations. The popular chatbot posed such a threat to the company’s business that it had to declare a code red and began investing in catching up on the generative AI bandwagon. This effort has not only resulted in the release of Google Bard but also Gemini.

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Gemini is a set of large language models (LLMs) that combine GPT-4 with training techniques taken from AlphaGo, such as reinforcement learning and tree search, which has the potential to unseat ChatGPT as the most dominant generative AI solution on the planet.

The company says the robot’s artificial intelligence shows how robots could have a role to play in business.

Companies are struggling with where to start with generative AI. The authors’ case studies, based on their growing global community of over 3,000 GenAI practitioners, point to a new category of work, more precise and actionable than “knowledge work.” They call it WINS Work — the places where tasks, functions, possibly your entire company or industry — are dependent on the manipulation and interpretation of Words, Images, Numbers, and Sounds (WINS). This framework can help leaders identify how vulnerable their business is to changes from this new technology and plan their response.

Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.362921 data-title= Where Should Your Company Start with GenAI? data-url=/2023/09/where-should-your-company-start-with-genai data-topic= AI and machine learning data-authors= Paul Baier; Jimmy Hexter; John J. Sviokla data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/09/Sep23_09_AlexWilliam-383x215.jpg data-summary=

Understand where your company stands — and what it needs to do.

Though artificial intelligence has been making inroads into the enterprise, the rise of generative AI is accelerating the pace of adoption. It’s time for enterprise CXOs to consider building systems of intelligence that complement systems of record and systems of engagement.

In the last two decades, enterprises have invested in building solid foundations for managing data and information. Relational databases such as Oracle and Microsoft SQL Server became the cornerstone of information systems. Built on this foundation were customer relationship management, human resources management, supply chain management and other line of business applications that quickly became the digital backbone of… More.

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This context, when combined with advanced prompt engineering, helps enterprises build intelligent AI-based assistants on the lines of Microsoft Copilot or Google Duet AI.

The foundation models become the core of systems of intelligence. The contextual information generated via semantic search is fed to these generative AI models, which deliver rich insights and accurate information to users. The use cases aligned with SOI go beyond typical chatbots. Different teams within an organization will use them to handle a range of scenarios, from marketing to sales forecasting.

The next generation of platforms, tools, and cloud services will be focused on enabling businesses to build and consume systems of intelligence. Platform and cloud providers are rapidly developing tools and services to support this trend.