#Google Gemini API

[unculturedai]

Experiment #2.0 Concluded: A Shift in Focus Towards a New AI Venture

A few weeks ago, I shared my excitement about Experiment #2.0: building a multi-platform app for the Google Gemini API competition. It was an ambitious project with a tight deadline, aiming to revolutionize how we achieve long-term goals. Today, I’m announcing a change in direction. I’ve decided not to participate in the competition. Why the Change? While the app idea held immense potential, I…

[govindhtech]

How the Google Gemini API Can Supercharge Your Projects

Google has revealed two big updates for Gemini 1.5 Pro and the Gemini API, which greatly increase the capabilities of its premier large language model (LLM):

2 Million Context Window With Gemini 1.5 Pro, developers may now take advantage of a 2 million context window, which was previously limited to 1 million tokens. This makes it possible for the model to generate content that is more thorough, enlightening, and coherent by enabling it to access and analyse a far wider pool of data.

Code Execution for Gemini API With this new functionality, developers can allow Python code to be generated and run on Gemini 1.5 Pro and Gemini 1.5 Flash. This makes it possible to undertake activities other than text production that call for reasoning and problem-solving.

With these developments, Google’s AI goals have advanced significantly and developers now have more control and freedom when using Gemini. Let’s examine each update’s ramifications in more detail:

2 Million Context Window: Helpful for Difficult Assignments

The quantity of text that comes before an LLM generates the next word or sentence is referred to as the context window. A more expansive context window enables the model to comprehend the wider context of a dialogue, story, or inquiry. This is essential for jobs such as:

Summarization Gemini can analyse long documents or transcripts with greater accuracy and information by using a 2M context window.

Answering Questions Gemini are better able to comprehend the purpose of a question and offer more perceptive and pertinent responses when they have access to a wider background.

Creative Text Formats A bigger context window enables Gemini to maintain character development, continuity, and general coherence throughout the composition, which is particularly useful for activities like composing scripts, poems, or complicated storylines.

The Extended Context Window’s advantages include Enhanced Accuracy and Relevance Gemini can produce outputs that are more factually accurate, pertinent to the subject at hand, and in line with the user’s goal by taking into account a wider context.

Increased Creativity Geminis may be more inclined to produce complex and imaginative writing structures when they have the capacity to examine a wider range of data.

Streamlined Workflows The enlarged window may eliminate the need for developers to divide more complex prompts into smaller, easier-to-handle portions for tasks needing in-depth context analysis.

Taking Care of Possible Issues

Cost Increase Higher computational expenses may result from processing more data. To address this issue, Google built context caching into the Gemini API. This reduces the need to repeatedly process the same data by enabling frequently used tokens to be cached and reused.

Possibility of Bias A wider context window may exacerbate any biases present in the training data that Gemini uses. Google highlights the value of ethical AI development and the use of diverse, high-quality resources for model training.

Code Execution: Increasing Gemini’s Capabilities Gemini’s ability to run Python programmes is a revolutionary development. This gives developers the ability to use Gemini for purposes other than text production. This is how it operates:

The task is defined by developers

They use code to define the issue or objective they want Gemini to solve.

Gemini creates code Gemini suggests Python code to accomplish the desired result based on the task definition and its comprehension of the world.

Iterative Learning Programmers are able to examine the generated code, make suggestions for enhancements, and offer comments. Gemini may then take this feedback into consideration and gradually improve its code generating procedure.

Possible Uses for Code Execution Data Analysis and Reasoning Gemini can be used for tasks like data analysis and reasoning, such as creating Python code to find trends or patterns in datasets or carry out simple statistical computations.

Automation and scripting

By creating Python scripts that manage particular workflows, Gemini enables developers to automate time-consuming tasks.

Interactive apps Gemini may be able to produce code for basic interactive apps by interacting with outside data sources.

The advantages of code execution Enhanced Problem-Solving Capabilities With this feature, developers can use Gemini for more complex tasks involving logic and reasoning than just text production.

Enhanced Productivity Developers can save significant time and improve processes by automating code generation and incorporating feedback.

Reducing Entry Barrier Gemini may become more approachable for developers with less programming knowledge if it can produce Python code.

Security Points to Remember Sandbox Execution Google stresses that code execution takes place in a safe sandbox environment with restricted access to outside resources. This lessens the possibility of security issues.

Focus on Particular Tasks At the moment, the Gemini API is primarily concerned with producing Python code for user-specified tasks. This lessens the possibility that the model may be abused or used maliciously.

In summary The extension of Gemini’s capabilities by Google is a major turning point in the development of LLMs. While code execution creates opportunities for new applications, the 2 million token window allows for a richer grasp of context. We anticipate a rise in creative and potent AI applications as the Gemini ecosystem develops and developers investigate these new features.

Other Things to Think About The technological features of the update were the main topic of this essay. You can go into more detail about the consequences for various sectors or particular use cases. Provide contrasts with other LLMs, such as OpenAI’s GPT-4, emphasising the special advantages of Gemini. Talk about any moral issues that might arise from using code execution capabilities in LLMs.

Read more on Govindhtech.com

[adi-barda]

Chapter 4 - Gemini API Developer Competition - Fighting game & Android Export

As planned, I spent the last days on adding fighting game capability to the engine and Android exporting feature. The fighting game has much more details in the puzzle for the AI agent to cope with. There are complex animations for the player and for the opponent, they need to constantly look at each other, you need to be able to demo their kick, punch, block animations, the player needs to be able to move in 3D space etc. Overall I'm very pleased with the results so far. The user can speak freely enough with the AI, get instant results and funny reactions. What's more, I've been able to add Android exporting of the game and automatically open it in Android studio. It was challenging because the Java code worked different on PC and on the mobile device specifically handling of Zip files and all kind of Gradle dependency hell. ChatGPT was on my side all the way, assisting me to resolve configuration issues and coding problems such as selecting the best Zip 3rd party library.

This video clip, demonstrates the current status of the project. It shows a complete story from the user perspective - you have a conversation with the AI, a game is created and finally you export it to Android studio for deployment in Google play store or any other market place.

What's next

Better and shorter presentation

Prepare the installation of all the components as well as SceneMax3D dev studio

Get feedback from the community

Prepare documentation for the architectural strategies, entities diagram etc.

So far I'm getting very good vibes from the game dev. community, and friends on various WhatsApp groups.

[gazetadoleste]

Google se prepara para integrar Gemini aos aplicativos do Android

O Google está preparando uma integração mais profunda da sua inteligência artificial Gemini no Android, uma novidade que promete tornar o uso do sistema operacional ainda mais conveniente. Uma API encontrada na versão de testes do Android 16 voltada para desenvolvedores revela que o Gemini poderá agora atuar dentro de aplicativos, ampliando suas capacidades e funcionalidades. O recurso,…

[7ooo-ru]

Google Gemini сможет выполнять задачи в приложениях, не открывая их

Искусственный интеллект Google Gemini получит новые возможности благодаря API «app functions», позволяющему выполнять действия в приложениях без их открытия. По данным The Verge, новая функция обнаружена в коде Android 16 для разработчиков и может стать доступна для всех пользователей уже в следующем году. Источник изображения: Solen Feyissa / Unsplash

Подробнее https://7ooo.ru/group/2024/11/23/217-google-gemini-smozhet-vypolnyat-zadachi-v-prilozheniyah-ne-otkryvaya-ih-grss-358531349.html

[isfeed]

Google is prepping Gemini to take action inside of apps

Gemini could spring into action soon. | Illustration by Alex Castro / The Verge Google released the first Android 16 developer preview earlier this week and keen-eyed observers are already uncovering interesting tidbits, including one that hints about a much more useful future for Google’s AI assistant. In Android Authority, Mishaal Rahman writes about a mysterious new API in Android 16 called…

[antboom]

الفرق بين Gemini وChatGPT: مقارنة شاملة  مع تطور تقنيات الذكاء الاصطناعي بوتيرة متسارعة، ظهرت العديد من الأنظمة المتقدمة التي تهدف إلى تسهيل حياة المستخدمين وحل مشاكلهم بطرق مبتكرة. من بين هذه الأنظمة، برز كل من Gemini وChatGPT كأدوات رئيسية تُستخدم في مجموعة متنوعة من المجالات، بدءًا من إنشاء المحتوى وحتى دعم العملاء. في هذا المقال، سنتناول مقارنة شاملة بين هذين النظامين من حيث التكنولوجيا، التطبيقات، والمزايا، لمساعدتك في فهم أيهما الأنسب لاحتياجاتك. ما هو ChatGPT؟ ChatGPT هو نموذج لغة كبير (LLM) تم تطويره بواسطة OpenAI. يعتمد على بنية GPT (Generative Pre-trained Transformer) وهو مصمم لفهم النصوص المكتوبة، تحليلها، وتوليد ردود طبيعية تشبه لغة الإنسان. تم تحسين ChatGPT عبر تدريب مكثف باستخدام مجموعات بيانات ضخمة تحتوي على نصوص متعددة اللغات من مختلف المصادر. الوظائف الرئيسية لـ ChatGPT: توليد النصوص: يمكنه كتابة مقالات، نصوص تسويقية، قصص، وحتى برمجة الأكواد. الدعم الفني وخدمة العملاء: يساعد في الرد على أسئلة المستخدمين بشكل فعال. التعليم: يقدم شروحاً مبسطة للمفاهيم المعقدة في العديد من المجالات. المزايا: واجهة سهلة الاستخدام: يمكن للمستخدمين التفاعل معه بسهولة عبر الدردشة. تعدد اللغات: يدعم العديد من اللغات، مما يجعله مناسباً لجمهور عالمي. قابلية التكيف: يمكن تخصيصه لتقديم حلول متخصصة في مجالات محددة. ما هو Gemini؟ Gemini هو منتج طورته Google ضمن مشروع DeepMind، وهو يُعد منافساً مباشراً لنماذج مثل ChatGPT. تم تصميم Gemini ليكون أكثر تكاملاً من خلال دمج فهم أعمق للبيانات مع القدرة على التفاعل مع الأنظمة الأخرى بكفاءة. الوظائف الرئيسية لـ Gemini: دمج متعدد الوسائط: يتعامل Gemini مع النصوص، الصور، والفيديوهات، مما يجعله أكثر شمولية من بعض النماذج الأخرى. التكامل مع خدمات Google: يدعم Gemini التكامل مع منتجات مثل Google Docs، Gmail، وGoogle Search، مما يعزز الإنتاجية. تطبيقات الذكاء الاصطناعي المتقدمة: يستخدم خوارزميات تعلم عميق متقدمة لتحليل البيانات وتقديم نتائج دقيقة. المزايا: تعدد المصادر: يعتمد Gemini على معلومات محدثة من الإنترنت، مما يضمن توفير إجابات أكثر دقة. التعلم السياقي: يمكنه فهم السياق بشكل أفضل لتحسين جودة التفاعلات. التكامل السلس: يتيح العمل بسلاسة مع الأنظمة الأخرى، مما يجعله أداة قوية للشركات. المقارنة بين Gemini وChatGPT العنصر                Gemini                     ChatGPT                 المطور      Google DeepMind                OpenAI  دعم الوسائط  المتعددة           يدعم النصوص، الصور، والفيديو يدعم النصوص فقط (مع تحسينات تدريجية) التكامل مع الأنظمة      مدمج بشكل عميق مع منتجات Google يمكن دمجه عبر واجهات API مع أنظمة متعددة تحديث المعلومات يعتمد على بيانات حديثة مباشرة من الإنترنت يعتمد على بيانات التدريب حتى تاريخ معين سهولة الاستخدام موجه أكثر نحو المحترفين والمستخدمين المهتمين بالتكامل واجهة بسيطة وسهلة الاستخدام للمستخدمين العاديين أي النظامين تختار؟ يعتمد الاختيار بين Gemini وChatGPT على احتياجاتك الفعلية: اختر ChatGPT إذا: كنت بحاجة إلى أداة سهلة الاستخدام لإنشاء النصوص والمحادثات. تعمل على مشاريع تتطلب دعمًا متعدد اللغات. تبحث عن حل بسيط للمستخدم الفردي أو للمشاريع الصغيرة. اختر Gemini إذا: تحتاج إلى نظام أكثر شمولية يمكنه التعامل مع الصور والفيديوهات بجانب النصوص. تبحث عن تكامل أعمق مع أدوات Google. تعمل في بيئة تتطلب تحليلًا متقدمًا للبيانات أو حلولًا للشركات. نظرة مستقبلية يتوقع أن يشهد كل من Gemini وChatGPT تحسينات كبيرة في المستقبل، مع تطورات تشمل: Gemini: توسيع نطاق تكامله مع المزيد من الأدوات وتطوير الذكاء السياقي بشكل أكبر. ChatGPT: تحسين دعم الوسائط المتعددة وزيادة قدرته على تقديم إجابات مستندة إلى بيانات حديثة. الخلاصة سواء كنت مهتماً بتطوير المحتوى، التعليم، أو حتى إدارة الأعمال، يقدم كل من Gemini وChatGPT حلولاً مبتكرة تلبي احتياجات مختلفة. Gemini يتميز بقدرته على التعامل مع وسائط متعددة وتكامل عميق مع أنظمة Google، بينما يقدم ChatGPT تجربة مستخدم متميزة وسهولة في التعامل مع النصوص. اختيار الأنسب يعتمد بشكل رئيسي على نوع المهام التي تريد تنفيذها ومدى تعقيدها. via Blogger https://ift.tt/KvM0Nwm November 18, 2024 at 05:18PM

[unculturedai]

Experiment #2.2 Doubling Down: Two Google Gemini AI Apps in 30 Days – My Journey

Hello everyone! 👋 Yesterday, I shared my pivot from my initial app idea due to a saturated market. This led me to explore new horizons with the Google Gemini API. Today, I’m thrilled to announce an even bolder challenge: developing two apps in the next 30 days! Two Apps, Two Purposes Public Project: Your Guide to AI App Development. My original concept, a goal-setting app, will continue…

[education30and40blog]

Google AI Studio | Gemini API | Google for Developers  | 

Get started with the Gemini API on Google AI Studio. Quickly develop prompts for Gemini 1.5 Flash and 1.5 Pro with 2 million token context window.

[govindhtech]

New Cloud Translation AI Improvements Support 189 Languages

189 languages are now covered by the latest Cloud Translation AI improvements.

Your next major client doesn’t understand you. 40% of shoppers globally will never consider buying from a non-native website. Since 51.6% of internet users speak a language other than English, you may be losing half your consumers.

Businesses had to make an impossible decision up until this point when it came to handling translation use cases. They have to decide between the following options:

Human interpreters: Excellent, but costly and slow

Simple machine translation is quick but lacks subtleties.

DIY fixes: Unreliable and dangerous

The problem with translation, however, is that you need all three, and conventional translation techniques are unable to keep up. Using the appropriate context and tone to connect with people is more important than simply translating words.

For this reason, developed Translation AI in Vertex AI at Google Cloud. Its can’t wait to highlight the most recent developments and how they can benefit your company.

Translation AI: Unmatched translation quality, but in your way

There are two options available in Google Cloud‘s Translation AI:

A necessary set of tools for translation capability is the Translation API Basic. Google Cloud sophisticated Neural Machine Translation (NMT) model allows you to translate text and identify languages immediately. For chat interactions, short-form content, and situations where consistency and speed are essential, Translation AI Basic is ideal.

Advanced Translation API: Utilize bespoke glossaries to ensure terminology consistency, process full documents, and perform batch translations. For lengthy content, you can utilize Gemini-powered Translation model; for shorter content, you can use Adaptive Translation to capture the distinct tone and voice of your business. By using a glossary, improving its industry-leading translation algorithms, or modifying translation forecasts in real time, you can even personalize translations.

What’s new in Translation AI

Increased accuracy and reach

With 189-language support, which now includes Cantonese, Fijian, and Balinese, you can now reach audiences around the world while still achieving lightning-fast performance, making it ideal for call centers and user content.

Smarter adaptive translation

You can use as little as five samples to change the tone and style of your translations, or as many as 30,000 for maximum accuracy.

Choosing a model according to your use case

Depending on how sophisticated your translation use case is, you can select from a variety of methods when using Cloud Translation Advanced. For instance, you can select Adaptive Translation for real-time modification or use NMT model for translating generic text.

Quality without sacrificing

Although reports and leaderboards provide information about the general performance of the model, they don’t show how well a model meets your particular requirements. With the help of the gen AI assessment service, you can choose your own evaluation standards and get a clear picture of how well AI models and applications fit your use case. Examples of popular tools for assessing translation quality include Google MetricX and the popular COMET, which are currently accessible on the Vertex gen AI review service and have a significant correlation with human evaluation. Choose the translation strategy that best suits your demands by comparing models and prototyping solutions.

Google cloud two main goals while developing Translation AI were to change the way you translate and the way you approach translation. Its deliver on both in four crucial ways, whereas most providers only offer either strong translation or simple implementation.

Vertex AI for quick prototyping

Test translations in 189 languages right away. To determine your ideal fit, compare NMT or most recent translation-optimized Gemini-powered model. Get instant quality metrics to confirm your decisions and see how your unique adaptations work without creating a single line of code.

APIs that are ready for production for your current workflows

For high-volume, real-time translations, integrate Translation API (NMT) straight into your apps. When tone and context are crucial, use the same Translation API to switch to Adaptive Translation Gemini-powered model. Both models scale automatically to meet your demands and fit into your current workflows.

Customization without coding

Teach your industry’s unique terminology and phrases to bespoke translation models. All you have to do is submit domain-specific data, and Translation AI will create a unique model that understands your language. With little need for machine learning knowledge, it is ideal for specialist information in technical, legal, or medical domains.

Complete command using Vertex AI

With all-inclusive platform, Vertex AI, you can use Translation AI to own your whole translation workflow. You may choose the models you want, alter how they behave, and track performance in the real world with Vertex AI. Easily integrate with your current CI/CD procedures to get translation at scale that is really enterprise-grade.

Real impact: The Uber story

Uber’s goal is to enable individuals to go anywhere, get anything, and make their own way by utilizing the Google Cloud Translation AI product suite.

Read more on Govindhtech.com

[newspatron]

Google Gemini: The Ultimate Guide to the Most Advanced AI Model Ever

We hope you enjoyed this article and found it informative and insightful. We would love to hear your feedback and suggestions, so please feel free to leave a comment below or contact us through our website. Thank you for reading and stay tuned for more

Google Gemini: A Revolutionary AI Model that Can Shape the Future of Technology and Society. Artificial intelligence (AI) is one of the most exciting and rapidly evolving fields of technology today. From personal assistants to self-driving cars, AI is transforming various aspects of our lives and society. However, the current state of AI is still far from achieving human-like intelligence and…

[tumnikkeimatome]

OpenAIライブラリを使ってGeminiへAPIアクセス可能に！サードパーティライブラリ不要で移行が簡単に！既存コードのアクセス先をGeminiに切り替えるためのコード実装例を紹介

Geminiが実装したOpenAIライブラリ互換機能の概要 GoogleのGeminiモデルがOpenAIのライブラリとREST APIを通じたアクセスに対応しました。 既存のOpenAIライブラリを使用したプロジェクトから、わずかなコード変更でGeminiモデルへの移行が実現できます。 Chat Completions APIとEmbeddings APIの両方に対応しており、開発者の選択肢が大幅に広がりました。 Python環境での実装方法 Pythonでの実装は非常にシンプルです。 OpenAIクライアントのインスタンス生成時に、APIキーとベースURLを指定するだけで設定が完了します。 from openai import OpenAI client =…

[ai-news]

Author(s): Devi Originally published on Towards AI. Part 1 of a 2-part beginner series exploring fun generative AI use cases with Gemini to enhance your photography skills! In this blog post, I’ll show you how to build a Photo Critique and Enhanceme #AI #ML #Automation

[ujjinatd]

La API de Google Gemini y AI Studio obtienen una función de 'conexión a tierra con la búsqueda de Google' para desarrolladores Google está agregando una nueva car... https://ujjina.com/la-api-de-google-gemini-y-ai-studio-obtienen-una-funcion-de-conexion-a-tierra-con-la-busqueda-de-google-para-desarrolladores/?feed_id=819741&_unique_id=6726533e27b96