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Perplexity AI vs Google Gemini Advanced search results compared

Perplexity AI vs Google Gemini Advanced search results compared

When it comes to finding information online, the tools we use can make a huge difference in our efficiency and effectiveness. Two notable options in the realm of search technology are Perplexity AI and Google Gemini Advanced. These platforms are at the cutting edge of search engine development, each offering a unique set of features designed to interpret and produce language in a way that’s remarkably human-like. But which one is the right fit for your specific needs?

Google Gemini Advanced is the latest innovation from the search engine behemoth, aiming to transform the way we discover information on the web. It stands toe-to-toe with Perplexity AI, a competitor that also brings a host of advanced capabilities to the table. The choice between them hinges on several factors, including how they integrate with tools you might already be using, their user interfaces, and the additional functionalities they offer.

For those deeply embedded in the Google ecosystem, Gemini Advanced might be the more appealing option. It’s designed to work seamlessly with Google Workspace, which means you could use its search features within familiar applications like Google Docs and Sheets. This could be a significant advantage for those looking to streamline their workflow and boost productivity.

Perplexity AI vs Google Gemini Advanced

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Both Perplexity AI and Google Gemini Advanced come with a professional tier priced at $20 USD, making them accessible options for those seeking top-tier search technology. However, it’s important to understand the nuances of what each platform offers. When running the same search queries, Gemini Advanced presents its findings in a clear, text-based format, while Perplexity AI opts for a more visual approach, incorporating images and maps that might help some users better understand the results.

Extensions can also play a crucial role in enhancing the capabilities of a search platform. Google Gemini Advanced supports such add-ons, which could lead to further integration with other Google services, like Flights and Hotels. The potential for an extension marketplace means that Gemini’s functionality could expand significantly, much like how the Chrome Web Store has allowed users to customize their browsing experience.

When it comes to practical problem-solving, both language models are competent. For instance, if you’re looking to remove a grape juice stain from a wool rug, either platform can assist you. However, their strengths become apparent in different scenarios. Perplexity AI might be the better choice for those who benefit from visual aids, while Gemini Advanced could be preferred by users who value the integration with Google’s suite of services.

Search Capabilities

Perplexity AI: Perplexity AI is designed to provide intuitive and context-aware search results. It focuses on understanding the user’s query in depth and aims to deliver precise answers by leveraging its understanding of complex questions and natural language. This service is particularly strong in handling nuanced queries that benefit from a deep semantic understanding of the question.

Google Gemini Advanced: Gemini Advanced, as a product of Google, leverages Google’s extensive database and search technology. It is expected to provide highly relevant search results by accessing a wide range of data sources. Gemini Advanced might also incorporate Google’s expertise in search algorithms and data indexing, offering robust search capabilities across a broad spectrum of queries.

User Interface

Perplexity AI: The interface is likely designed to be clean and user-friendly, focusing on delivering answers in a straightforward manner. Perplexity AI might prioritize a minimalistic design to reduce distractions and focus on the content of the search results.

Google Gemini Advanced: Given Google’s history with user interface design, Gemini Advanced is expected to feature a highly intuitive and functional interface. It might include advanced UI elements that highlight sources, categorize information, and visually distinguish between different types of data (e.g., images, text, and videos). The description suggests a particular emphasis on associating data with its sources clearly and efficiently.

Integration with Other Services

Perplexity AI: While specific details on integration were not mentioned, as a newer service, Perplexity AI might initially offer limited integration with external platforms. However, its development trajectory could include expanding these capabilities, focusing on enhancing user experience through seamless service interoperability.

Google Gemini Advanced: Being a part of the Google ecosystem, Gemini Advanced is expected to offer extensive integration with Google services such as Docs, Sheets, and potentially YouTube. This integration can significantly enhance productivity and user experience by allowing direct access to a wide range of tools and information within the same ecosystem.

Results Presentation

Perplexity AI: The service may focus on presenting information in a more text-oriented format, prioritizing direct answers to queries. This approach can be beneficial for users who prefer concise, straightforward information without the need for additional multimedia content.

Google Gemini Advanced: In contrast, Gemini Advanced seems to prioritize a more diverse presentation of search results, including text, images, and videos. This variety can be particularly useful for queries where visual or multimedia content provides additional value or context. The emphasis on cleanly associating data with its sources enhances transparency and credibility.

As you consider which platform to choose, think about your preferences for user interfaces, whether you favor visual support or text-based information, and how crucial it is for the tool to integrate with your existing productivity software. The possibility of enhancing the platform’s utility through extensions is another aspect to keep in mind.

Ultimately, the decision between Perplexity AI and Google Gemini Advanced comes down to what you value most in a search experience. Both are capable of elevating your ability to find information, but they cater to different user preferences in how they present that information. Reflect on which platform aligns best with your requirements. You might also be interested in the release of Gemini Advanced’s integration with Google Workspace. Providing an easy way to use artificial intelligence throughout all your Google products.

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Perplexity AI vs Google Search results tested and compared

Perplexity vs Google Search results compared

If you are interested in learning more about the results you can receive from Perplexity AI and Google Search to determine if Perplexity AI could be considered a superior alternative to Google. This Perplexity vs Google comparison guide created by Corbin Brown explores the functionality and user experience of both search tools by inputting identical search queries and analyzing the results.

The world of online search is buzzing with the arrival of Perplexity AI, a new player that’s challenging the dominance of Google Search. For years, Google has been the primary tool for internet users to find information, offering a comprehensive index of links and integrating with other Google services. Its algorithms have been honed to provide the most relevant web pages for any search query, thanks to extensive data and user feedback.

Perplexity AI is shaking things up with its conversational AI approach. Instead of presenting users with a list of links, it engages them in an interactive dialogue, allowing them to refine their searches on the fly. This is particularly useful for academic searches, where users can get summaries of papers rather than just links to the full texts.

Perplexity vs Google

The user experience is at the heart of what makes Perplexity AI stand out. It aims to make searching more efficient by summarizing information and giving direct access to sources, which could save users time and the hassle of sifting through multiple links.

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This new approach could also have a big impact on how content is optimized for search engines, a practice known as search engine optimization (SEO). While Google has set the standard with its Semantic SEO, the way Perplexity AI summarizes responses might require a new strategy for content creators to get their information seen.

When tested with identical queries, Perplexity AI showed it could provide more focused and tailored results compared to Google. This could be a significant advantage for professionals in academia and research who need to process information quickly.

It’s too early to say if Perplexity AI will dethrone Google, but it’s clear that its conversational search and summarization technology are important developments in the search engine industry. Google still has a massive infrastructure and a wide range of services that users rely on, but Perplexity AI is offering a new, interactive way to search the web.

Perplexity AI is an exciting development for those looking for a more dynamic and streamlined way to access information online. It might not replace Google Search completely, but it’s adding a new layer to the world of search technology. As we continue to navigate the digital world, Perplexity AI is a trend to keep an eye on, as it could change the way we search for information on the internet.

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Google unveils Circle to Search (Video)

Gogole Circle to Search

Google is launching a new way to search on your smartphone called Circle to Search, this is a new feature that lets you circle a party of photos on your device and then Google will search for whatever you have circled on your device.

Samsung just launched their new Galaxy S24 range of smartphones and a partnership with Google Cloud for a range of AI tools, one of the tools the handsets will come with is the new Circle to Search feature. You can see this new search feature in action in the video below, you can also highlight and scribble on the item as well as circling it.

For example, maybe you need help identifying a few items a creator wore in their “Outfit of the Day” video, but they didn’t tag the brands. Just long press the home button or navigation bar on your Android phone to activate Circle to Search. From there, you can select any item that you see with your preferred gesture — like circling their sunglasses — to quickly find similar, shoppable options from retailers across the web. You could scribble the bag and tap on the boots to look those up, too — all without leaving where you are. When you’re done, simply swipe away and you’re right back where you started.

You can find out more details about the new Google Circle to Search feature over at Google’s website at the link below, it certainly looks like a really cool feature and it will be interesting to see exactly how it works.

Source Google

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Codeqai AI powered coding assistant designed for semantic code search

Codeqai AI powered coding assistant

Developers and programmers are always on the lookout for tools that can make their work easier and more efficient. Enter Codeqai, an artificial intelligence AI coding assistant that’s changing the way developers interact with their code. This AI assistant is not just another addition to the toolbox; it’s a sophisticated partner that offers a new level of understanding and interaction with your codebase.

Codeqai

At the heart of Codeqai is a powerful local vector database that it uses to analyze and understand your code. This isn’t a static tool; it’s dynamic, constantly evolving to provide the most relevant insights as you code. What makes Codeqai stand out is its ability to support a wide range of programming languages, including popular ones like TypeScript, JavaScript, and Java. This means that no matter what language your project is in, Codeqai is equipped to help.

Search your codebase semantically or chat with it from cli. Keep the vector database superfast up to date to the latest code changes. 100% local support without any dataleaks. Built with langchain, treesitter, sentence-transformers, instructor-embedding, faiss, lama.cpp, Ollama. You can also install codeqai through PyPI with “pip install codeqai”. However, it is recommended to use pipx instead to benefit from isolated environments.

AI powered coding assistant

Using Codeqai is as simple as having a conversation. It features a command prompt and a chat interface that allows you to ask questions in plain language and receive detailed, contextually accurate answers. This ease of use is made possible by advanced technologies such as Tree-sitter and Sentence Transformers, which give Codeqai the ability to understand and interpret code with remarkable accuracy.

But Codeqai’s AI coding assistant capabilities don’t stop there. It also integrates with various frameworks and toolkits, including Lang chain and Hugging Face’s model hub. These integrations open up a world of language models and embeddings, enabling you to tailor Codeqai to your specific coding needs.

Getting started with Codeqai is straightforward. All you need is Python 3.9 or newer to install it. Once you’re set up, you’ll have access to an exclusive Discord community where you can connect with other developers and discover a wealth of AI tools and resources. For businesses looking to harness the power of AI, Codeqai offers consulting services to help integrate AI solutions seamlessly.

For developers who are serious about enhancing their coding skills and efficiency, Codeqai is an invaluable tool. Its ability to understand and interact with your codebase semantically, combined with its continuous updates and support for multiple languages, makes Codeqai an essential asset for any developer looking to refine their coding process.

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How to use Consensus ChatGPT research search engine custom GPT

How to use the Consensus ChatGPT scientific search engine custom GPT

Since the launch of the OpenAI GPT Store earlier this month thousands of custom GPTs have been made available for ChatGPT Plus, Team and Enterprise users to adopt to improve their productivity and workflows. One such custom GPT that has taken the GPT store by storm is Consensus a search engine that uses AI to find insights in research papers.

Searching for vetted, unbiased information use to be a long been an arduous and painful process. Consensus has been specifically designed to change this harnessing the power of artificial intelligence. Whether you are a lifelong researcher or an everyday consumer, Consensus aims to be your go-to source in the search for expert knowledge.

Consensus GPT

The Consensus GPT provides an easy way to refine the way we seek out and validate scientific knowledge. ChatGPT’s Consensus custom GPT is a sophisticated search engine that is quickly becoming an essential resource for researchers, students, and professionals who require reliable, science-based information. Unlike traditional search engines, Consensus is designed to cater specifically to research-oriented inquiries, providing answers that are not just accurate but deeply rooted in scholarly work.

Consensus custom GPT

The integration of Consensus into the ChatGPT OpenAI Store has been met with enthusiasm from the academic community. Its ability to draw from an expansive database of over 200 million academic papers makes it a standout tool for anyone looking to support their research with authoritative sources. The feature’s popularity stems from its unique capability to deliver answers that are backed by scientific research, ensuring that users have access to the most credible information available.

How to use the Consensus custom GPT

To begin using Consensus, one must access the feature through the OpenAI Store. The key to harnessing the full potential of this tool lies in posing clear and concise questions. The precision of the questions asked directly influences the accuracy and comprehensiveness of the answers provided. Academic research papers, which form the backbone of Consensus’s database, are typically subjected to a stringent peer-review process. By leveraging these documents, Consensus offers direct quotations that not only enhance the credibility of your research but also ensure that your citations are precise.

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Navigating Consensus is straightforward, even for those with minimal technical expertise. Once inside the ChatGPT interface, users can input their queries and expect to receive responses that are grounded in scientific evidence. The system is adept at sifting through its vast database to present information that is not only pertinent but also up-to-date with the latest research findings.

For inquiries that extend beyond the scope of academic literature, Consensus also incorporates Bing to facilitate broader searches. This integration provides users with a more comprehensive search experience, ensuring that all possible avenues of information are explored.

Direct quotes for academic papers

One of the most significant advantages of using Consensus is its ability to provide direct quotes from academic papers. This feature is particularly beneficial for those who require exact citations, as it helps maintain the integrity of their work and ensures that the information used is accurate.

Consensus GPT can help you research in a wide variety of different areas

Crafting effective questions is crucial when using Consensus. The specificity of a query leads to more targeted and definitive answers. The tool is engineered to keep searches focused and relevant, ensuring that the information provided is not just accurate but also directly related to the user’s inquiry. Consensus GPT allows users to find answers, search for papers, and draft content grounded in scientific research by searching our database of 200M+ papers directly within the ChatGPT interface.

ChatGPT’s Consensus feature is a powerful resource that can significantly enhance the research process. By tapping into a vast repository of academic knowledge, users can secure science-based answers that will elevate the quality of their research endeavors. Whether you are a seasoned researcher or a student just beginning to navigate the world of academic inquiry, Consensus offers a reliable and efficient way to access the wealth of scientific knowledge that is available at our fingertips. With its intuitive design and robust database, Consensus is poised to become an indispensable tool for anyone seeking to substantiate their research with solid, scholarly evidence.

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Google’s Generative AI in Search expanded to more countries

Google

Google has announced that its Generative AI in Search feature has now been expanded to more than 120 countries and territories around the world, and it is also now supported by four more languages.

The new Languages include Spanish, Portuguese, Korean, and Indonesian and some of the new countries include Mexico, Brazil, South Korea, Indonesia, Nigeria, Kenya South Africa, and more.

Already, generative AI in Search is allowing us to serve a wider range of information needs, including those that benefit from multiple perspectives. With SGE, we’re showing more links, and links to a wider range of sources on the results page, creating new opportunities for content to be discovered.

As we’ve continually improved the experience, we’ve also expanded internationally beyond the United States with recent launches in India and Japan. So far, the vast majority of feedback in all three countries has been positive, and people are finding generative AI particularly useful for complex questions they wouldn’t typically think to search.

You can find out more information about Google’s Generative AI Search and its expansion to more countries and also to more languages around the world over at the Google website at the link below.

Source Google

Image Credit: Kai Wenzel

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Using Arduino and Elasticsearch to build search powered projects

Arduino and Elasticsearch

The integration of Elasticsearch with Arduino for IoT applications is a significant development in the field of technology. This partnership between Elastic, a leading platform for search-powered solutions, and Arduino, a popular open-source electronics platform, has opened up new possibilities for IoT applications. The collaboration has resulted in the development of an Elasticsearch client library that runs on Arduino modules, enabling direct communication with an Elasticsearch server from an Arduino board.

The partnership between Arduino and Elastic has been instrumental in the development of this new technology. The collaboration has led to the creation of a simple Elasticsearch client library that can run on Arduino modules. This library allows for direct communication with an Elasticsearch server from an Arduino board, thus simplifying the process of data transmission and storage.

IoT applications

The potential of this technology was tested by developing an IoT device that sends temperature data to Elastic Cloud every five minutes. This innovative application of the technology could lead to a solution that provides the current average temperature from all sensors within a 5 km radius, thanks to Elasticsearch’s geo features. This geolocation-based temperature reporting could be particularly useful in industries such as agriculture, where real-time temperature data can be crucial.

What is Elasticsearch?

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Arduino Pro’s industrial-grade offerings, including Cloud services, software libraries, and a variety of components, are compatible with the entire Arduino ecosystem. This compatibility ensures that the integration of Elasticsearch with Arduino can be seamlessly implemented across a wide range of IoT applications.

A use case was designed for a company managing multiple IoT devices in Italy, with each device sending sensor data to Elastic Cloud. The company can manage any scale of IoT devices without needing a dedicated infrastructure, and can adjust internal parameters of each device based on the average temperature of neighboring devices within a 100 km range. This use case demonstrates the scalability and flexibility of the integrated system.

Search powered projects

Elasticsearch provides multiple feedback using search features like filtering, aggregation, multi-match, geospatial, vector search (kNN), semantic search, and machine learning. These features can be used to analyze and interpret the data collected from the IoT devices, providing valuable insights and facilitating decision-making.

Kibana, the UI available in Elastic Cloud, allows for the creation of a dashboard to monitor data from all devices, including geo-data representation on a map. This visualization tool can be particularly useful in monitoring and managing multiple IoT devices.

Setting up Elastic Cloud is a straightforward process. Users need to create an account, choose the size of the Elasticsearch instances they want to use, and generate an API key of Elasticsearch. An index needs to be created to store data from the Arduino boards, including temperature values, device position using geo-location, a device identifier name, and a timestamp. This preparation of the Elasticsearch index for data storage is a crucial step in the integration process.

The integration of Elasticsearch with Arduino for IoT applications is a significant development that offers numerous benefits. The partnership between Arduino and Elastic, the development of the Elasticsearch client library for Arduino modules, and the potential for geolocation-based temperature reporting are just a few of the many exciting aspects of this integration. With the use of Elasticsearch’s search features for feedback and Kibana for data monitoring and visualization, this integration promises to revolutionize the way we manage and utilize IoT devices.

Source &  Image Source :  AB

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