#artificialinteligence

[sdreatechprivatelimited]

Google's Gemini now comes with Gmail and Messages.

This AI application may help individuals draft emails, summarize data-heavy emails, pinpoint presentation highlights, and set off meeting alarms. . . .

[kaelula-sungwis]

The photographer by T. Chabry Via Flickr: Created with Midjourney

[govindhtech]

​HTTP 429 Errors: Keep Your Users Online And Happy

429 Errors

Avoid leaving your visitors waiting when resources run out: How to deal with 429 errors

429 error meaning

When a client sends too many requests to a server in a specified period of time, an HTTP error known as “Too Many Requests” (Error 429) occurs. This error can occur for many reasons:

Rate-limiting

The server limits client requests per time period.

Security

A DDoS attack or brute-force login attempt was detected by the server. In this instance, the server may block the suspect requestor’s IP.

Limits bandwidth

Server bandwidth is maxed out.

Per-user restrictions

The server has hit its maximum on user requests per time period.

The mistake may go away, but you should fix it to avoid losing traffic and rankings. Flushing your DNS cache forces your computer to acquire the latest DNS information, fixing the issue.

Large language models (LLMs) offer developers a great deal of capability and scalability, a seamless user experience depends on resource management. Because LLMs require a lot of processing power, it’s critical to foresee and manage possible resource depletion. Otherwise, 429 “resource exhaustion” errors could occur, which could interfere with users’ ability to interact with your AI application.

Google examines the reasons behind the 429 errors that LLMs make nowadays and provides three useful techniques for dealing with them successfully. Even during periods of high demand, you can contribute to ensuring a seamless and continuous experience by comprehending the underlying causes and implementing the appropriate solutions.

Backoff!

Retry logic and exponential backoff have been used for many years. LLMs can also benefit from these fundamental strategies for managing resource depletion or API unavailability. Backoff and retry logic in the code might be useful when a model’s API is overloaded with calls from generative AI applications or when a system is overloaded with inquiries. Until the overloaded system recovers, the waiting time grows dramatically with each retry.

Backoff logic can be implemented in your application code using decorators in Python. For instance, Tenacity is a helpful Python general-purpose retrying module that makes it easier to incorporate retry behavior into your code. Asynchronous programs and multimodal models with broad context windows, like Gemini, are more prone to 429 errors.

To show how backoff and retry are essential to the success of your gen AI application, Google tested sending a lot of input to Gemini 1.5 Pro. Google is straining the Gemini system by using photos and videoskept in Google Cloud Storage.

The results, where four of five attempts failed, are shown below without backoff and retry enabled.the results without backoff and retry configured

The outcomes with backoff and retry set up are shown below. By using backoff and retry, all five tries were successful. There is a trade-off even when the model responds to a successful API call. A response’s latency increases with the backoff and retry. Performance might be enhanced by modifying the model, adding more code, or moving to a different cloud zone. Backoff and retry, however, is generally better in times of heavy traffic and congestion.The results with backoff and retry configured

Additionally, you could frequently run into problems with the underlying APIs when working with LLMs, including rate-limiting or outages. It becomes increasingly crucial to protect against these when you put your LLM applications into production. For this reason, LangChain presented the idea of a fallback, which is a backup plan that might be employed in an emergency. One fallback option is to switch to a different model or even to a different LLM provider. To make your LLM applications more resilient, you can incorporate fallbacks into your code in addition to backoff and retry techniques.

With Apigee, circuit breaking is an additional strong choice for LLM resilience. You can control traffic distribution and graceful failure management by putting Apigee in between a RAG application and LLM endpoints. Naturally, every model will offer a unique solution, thus it is important to properly test the circuit breaking design and fallbacks to make sure they satisfy your consumers’ expectations.

Dynamic shared quota

For some models, Google Cloud uses dynamic shared quota to control resource allocation in an effort to offer a more adaptable and effective user experience. This is how it operates:

Dynamic shared quota versus Traditional quota

Traditional quota: In a Traditional quota system, you are given a set amount of API requests per day, per minute, or region, for example. You often have to file a request for a quota increase and wait for approval if you need more capacity. This can be inconvenient and slow. Of course, capacity is still on-demand and not dedicated, thus quota allocation alone does not ensure capacity. Dynamic shared quota: Google Cloud offers a pool of available capacity for a service through dynamic shared quota. All of the users submitting requests share this capacity in real-time. You draw from this shared pool according to your needs at any given time, rather than having a set individual limit.

Dynamic shared quota advantages

Removes quota increase requests: For services that employ dynamic shared quota, quota increase requests are no longer required. The system adapts to your usage habits on its own.

Increased efficiency: Because the system can distribute capacity where it is most needed at any given time, resources are used more effectively.

Decreased latency: Google Cloud can reduce latency and respond to your requests more quickly by dynamically allocating resources.

Management made easier: Since you don’t have to worry about reaching set limits, capacity planning is made easier.

Using a dynamic shared quota

429  resource exhaustion errors to Gemini with big multimodal input, like large video files, are more likely to result in resource exhaustion failures. The model performance of Gemini-1.5-pro-001 with a traditional quota and Gemini-1.5-pro-002 with a dynamic shared quota is contrasted below. It can be observed that the second-generation Gemini Pro model performs better than the first-generation model due to dynamic shared quota, even without retrying (which is not advised).model performance of Gemini-1.5-pro-001 with traditional quota versus Gemini-1.5-pro-002 with dynamic shared quotamodel performance of Gemini-1.5-pro-001 with traditional quota versus Gemini-1.5-pro-002 with dynamic shared quota

Dynamic shared quota should be used with backoff and retry systems, particularly as request volume and token size grow. In all of its initial attempts, it ran into 429 errors when testing the -002 model with greater video input. The test results below, however, show that all five subsequent attempts were successful when backoff and retry logic were used. This demonstrates how important this tactic is to the consistent performance of the more recent -002 Gemini model.

A move toward a more adaptable and effective method of resource management in Google Cloud is represented by dynamic shared quota. It seeks to maximize resource consumption while offering users a tightly integrated experience through dynamic capacity allocation. There is no user-configurable dynamic shared quota. Only certain models, such as Gemini-1.5-pro-002 and Gemini-1.5-flash-002, have Google enabled it.

As an alternative, you may occasionally want to set a hard-stop barrier to cease making too many API requests to Gemini. In Vertex AI, intentionally creating a customer-defined quota depends on a number of factors, including abuse, financial constraints and restrictions, or security considerations. The customer quota override capability is useful in this situation. This could be a helpful tool for safeguarding your AI systems and apps. Terraform’s google_service_usage_consumer_quota_override schema can be used to control consumer quota.

Provisioned Throughput

You may reserve specific capacity for generative AI models on the Vertex AI platform with Google Cloud’s Provisioned Throughput feature. This implies that even during periods of high demand, you can rely on consistent and dependable performance for your AI workloads.

Below is a summary of its features and benefits:

Benefits

Predictable performance: Your AI apps will function more smoothly if you eliminate performance fluctuation and receive predictable reaction times.

Reserved capacity: Queuing and resource contention are no longer concerns. For your AI models, you have a specific capacity. The pay-as-you-go charge is automatically applied to extra traffic when Provisioned Throughput capacity is exceeded.

Cost-effective: If you have regular, high-volume AI workloads, it can be less expensive than pay-as-you-go pricing. Use steps one through ten in the order process to determine whether Provisioned Throughput can save you money.

Scalable: As your demands change, you may simply increase or decrease the capacity you have reserved.

Image credit to Google Cloud

This would undoubtedly be helpful if your application has a big user base and you need to give quick response times. This is specifically made for applications like chatbots and interactive content creation that need instantaneous AI processing. Computationally demanding AI operations, including processing large datasets or producing intricate outputs, can also benefit from provisioned throughput.

Stay away with 429 errors

Reliable performance is essential when generative AI is used in production. Think about putting these three tactics into practice to accomplish this. It is great practice to integrate backoff and retry capabilities into all of your gen AI applications since they are made to cooperate.

Read more on Govindhtech.com

[ukmploymentlawnews]

Translators: Future Proofing Against AI

[techaipulse]

🚀 7 Exciting Breakthroughs in Full Dive Virtual Reality You Need to Know! 🌐 Discover the latest advancements in full dive VR technology and how they’re transforming the future of immersive experiences. From stunning innovations to groundbreaking developments, get the inside scoop on what’s next for VR! 👉 Read more:

[verbotenefruchtdigital]

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Erlebe die fesselnde Geschichte von "Die Schöpferin", die an der Stadtbahnhaltestelle Lutherkirche in Fellbach startet! Begleite Sophie und Hector in eine atemberaubende, futuristische Welt, in der Künstliche Intelligenz die Menschheit herausfordert. Stell dir vor, du tauchst in eine packende Handlung ein, in der unsere Helden versuchen, die KI zu überlisten und die Welt zu retten. Kannst du die Spannung spüren? 🌟🤖

Neugierig geworden? Verpasse nicht dieses unglaubliche Hörspiel-Abenteuer! Hier kannst du dir den Trailer des Hörspiels anhören: https://www.youtube.com/watch?v=-DDEmAHd7ik

[protonshubtechno]

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Join our FREE webinar and learn how to build AI-powered mobile apps that will transform the way you do business! 💻

🗓 Webinar on 22nd April at ⏱ 4:00 PM

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[sdreatechprivatelimited]

Tech alert…⚡️

~ As per a report, a new AI model, "OpenAI Strawberry" will be launched at the end of September. 🚀💡 To know more about tech-related updates, follow our social media page. 📲 . . .

[govindhtech]

Qualcomm AI Hub: Boost AI Device Development

 Qualcomm Technologies is transforming user experiences and allowing developers across a wide range of Snapdragon and Qualcomm platform-powered devices with the Qualcomm AI Hub, cutting-edge research, and an exhibit of AI-enabled commercial goods.

They popularized mobile devices AI with the Snapdragon X Elite for PCs and Snapdragon 8 Gen 3 for cellphones. Durga Malladi, senior vice chairman and director of Qualcomm Technologies, Inc.’s technology strategy and edge solutions, said the Qualcomm AI Hub will allow developers to fully utilize these cutting-edge technologies and create engaging AI-enabled apps. “Based on the assist from the extensive AI model collection offered by the Qualcomm AI Hub, programmers can quickly and effortlessly integrate pre-optimized AI models into their mobile apps, resulting in more rapid, dependable, and confidential user experiences.”

Qualcomm AI Hub helps developers improve on-device AI

Image Credit to Qualcomm 

The Qualcomm AI Hub offers pre-optimized AI models for Snapdragon and Qualcomm platform-powered devices. This package offers developers over 75 AI and generative AI models, including Whisper, ControlNet, Stable Diffusion, and Baichuan 7B. These models improve on-device AI performance, memory use, and battery efficiency and exist in various form factors and runtime packages. Optimizing each model for hardware acceleration across all Qualcomm AI Engine cores (NPU, CPU, and GPU) speeds up inference by 4X. The AI model library works directly with the Qualcomm AI Engine direct SDK, seamlessly translating models from source framework to popular runtimes and optimizing hardware-awarely. Developers can simply incorporate these models into their applications to speed up time to market and provide on-device AI solutions that save money, are fast, reliable, private, and customizable.

Hugging Face,the GitHub, and Qualcomm AI Hub have enhanced models today. In addition to new models, the Qualcomm AI Hub will support various platforms and operating systems. Developers may register today to run Qualcomm AI Hub models on Qualcomm Technologies’ cloud-hosted devices to get early access to new features and AI models.

Hugging Face cofounder and CEO Clement Delangue said, “We are excited to host Qualcomm Technologies’ AI models on Hugging Face.” “These factors popular models for artificial intelligence, tailored for across-device machine learning and prepared to use across Snapdragon & Qualcomm systems, will make possible the next batch of cell phone programmers and edge applications that utilize AI, bringing AI cheaper and more readily available for everyone.”

Innovations in AI Research

Qualcomm AI Research is demonstrating Large Language and Vision Assistant (LLaVA), a 7+ billion parameter large multimodal model (LMM) that can generate multi-turn conversations about an image with an AI assistant and accept text and images for the first time on an Android smartphone. This LMM’s responsive token rate improves device cost, customization, privacy, and reliability. LMMs with verbal understanding and visual comprehension may identify and debate complex visual patterns, objects, and situations, among other uses.

Qualcomm AI Research presents the first Android smartphone Low Rank Adaptation demonstration. Stable Diffusion using LoRA lets users create creative or customized photographs. LoRA reduces AI model trainable parameters, improving on-device generative AI scalability, flexibility, and efficiency. LoRA may be used with customized AI models like massive language models to create personalized assistants, improved language translation, and more. It also lets you customize language vision models (LVMs) for creative styles.

Qualcomm AI Research is demonstrating a 7+ billion parameter LMM on a Windows PC for the first time. This LMM can generate multi-turn talks about audio inputs like music or traffic noises and text inputs.

Fostering Generative AI across All Devices at Mobile World Congress Barcelona

Shown are HONOR Magic6 Pro, OPPO X7 Ultra, and Xiaomi 14 Pro commercial AI smartphones. They all use Snapdragon 8 Gen 3 Mobile Platform. Every device has exciting new generative AI features like photo object eraser (OPPO), video production and calendar creation (HONOR), and AI-generated image magnification (Xiaomi).

Computers

The Snapdragon X Elite’s 45 TOPS NPU allows on-device AI, which will change computer usage. Qualcomm Technologies will demonstrate that you can create any picture and generative AI will build it in 7 seconds, 3 times faster than x86 competitors, using the free image editor GIMP and the Stable Diffusion plug-in.

Automotive

The Snapdragon Digital Chassis Platform showcases both traditional and generative AI capabilities to give drivers and passengers more powerful, effective, private, secure, and customized experiences.

Consumer Internet of Things

Humane’s Snapdragon-powered AI Pin lets consumers carry AI in a novel, conversational, screenless form factor.

Connectivity

The Snapdragon X80 Modem-RF System’s second-generation 5G AI engine boosts speed, coverage, latency, and battery life. They also introduced the Qualcomm FastConnect 7900 Mobile networking System, the first AI-optimized Wi-Fi 7 system that improves local wireless networking performance, latency, battery usage, and flexibility.

5G Infrastructure

Qualcomm Technologies will demonstrate three AI-based network management innovations: an AI-generated generative AI assistant to assist radio access network (RAN) engineers with network and slice management tasks, an AI-powered open RAN application (rApp) to reduce network energy consumption, and an AI-powered 5G network slice lifecycle management suite.

Read more on Govindhtech.com

[delightfulpainterdelusion]

Unveiling the Power of PPnude's NSFW AI: Infinite Erotic Anime Imagery at Your Fingertips

Are you curious about the groundbreaking technology behind PPnude's Anime NSFW AI Generator? This innovative tool is revolutionizing the way we create and enjoy erotic anime imagery. Let's dive into how it works and what makes it so special.

Q: What exactly is the PPnude Anime NSFW AI Generator? A: PPnude's Anime NSFW AI Generator is a state-of-the-art tool designed to produce an unlimited number of erotic anime girl pictures. By harnessing the power of AI, it offers creators and enthusiasts a seamless and efficient way to explore the world of anime art.

Q: How does the AI Generator function? A: The generator utilizes advanced algorithms to analyze vast amounts of anime-related data. This enables it to create highly realistic and diverse anime girls that cater to various tastes and preferences.

Q: What are the benefits of using this AI Generator? A: With the PPnude Anime NSFW AI Generator, users can enjoy a vast library of unique anime girl images with just a few clicks. This not only saves time but also allows for endless creativity and exploration of new artistic styles.

Q: Is there a limit to the content generated by the AI? A: No, the PPnude Anime NSFW AI Generator has no limits. Users can generate an unlimited number of images, ensuring a continuous stream of new and exciting content. Whether you're a seasoned artist or a curious enthusiast, this tool offers endless possibilities.

[kevinsoftwaresolutions]

Revolutionizing Insights: Power BI's Seamless Integration with AI and Machine Learning

Introduction:

In the dynamic world of data analytics, Power BI has emerged as a powerhouse, transforming raw data into meaningful insights. Now, imagine taking that capability to the next level by seamlessly integrating artificial intelligence (AI) and machine learning (ML). Brace yourselves, because the fusion of Power BI with AI and ML is a game-changer, revolutionizing the way we extract value from data.

The Power BI Marvel:

Before we delve into the realm of AI and ML, let's marvel at the capabilities of Power BI. This Microsoft tool empowers organizations to visualize and comprehend data, turning complex information into actionable insights. But what happens when you infuse this already robust platform with the magic of AI and ML? Buckle up – the possibilities are limitless.

Automated Insights:

AI and ML algorithms embedded in Power BI can automatically analyze vast datasets, identifying patterns and trends that might be elusive to the human eye. Say goodbye to manual data crunching and hello to automated insights that drive informed decision-making.

Predictive Analytics:

With the integration of ML models, Power BI transforms into a predictive analytics powerhouse. Anticipate future trends, forecast outcomes, and gain a competitive edge by leveraging the predictive capabilities at your fingertips.

Natural Language Processing (NLP):

The integration of AI-driven NLP allows users to interact with Power BI using natural language. Ask questions, receive instant responses, and explore your data through a conversational interface – making data analysis accessible to everyone, regardless of technical expertise.

Anomaly Detection:

Uncover hidden issues or irregularities in your data with AI-driven anomaly detection. Power BI can automatically highlight deviations, enabling swift identification and response to potential problems before they escalate.

Real-World Impact:

Let's explore how this fusion of Power BI with AI and ML can make a tangible impact across various industries:

Healthcare:

Predict patient outcomes, optimize resource allocation, and streamline operations in healthcare by leveraging AI and ML insights within Power BI.

Finance:

Uncover hidden financial patterns, detect fraudulent activities, and forecast market trends with precision, empowering financial institutions to make informed decisions.

Retail:

Optimize inventory management, predict customer preferences, and enhance the overall shopping experience by harnessing the analytical prowess of Power BI infused with AI and ML.

Conclusion:

As we navigate the data-driven future, the integration of Power BI with AI and ML stands as a beacon of innovation. This synergy not only empowers organizations to glean deeper insights but also democratizes data analysis, making it accessible to a broader audience. So, gear up for a transformative journey as you explore the boundless possibilities at the intersection of Power BI, AI, and ML – where data becomes not just information, but a catalyst for unparalleled success.

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[takk8is]

The era of AI “hallucination”: How generative technology is transforming marketing and design

© 2023 by “Draumar” at Takk™ Innovate Studio, David Cavalcante

The word of the year and why.

The word “hallucinate” was recently elected the word of the year for 2022 by the Collins Dictionary. This is mainly due to the growing use of this word in the context of generative artificial intelligence. AI models capable of generating images, texts, and other content are often described as having the ability to “hallucinate” or imagine things that do not exist.

Understanding generative AI.

What is generative AI?

Generative artificial intelligence refers to AI models trained to produce original outputs based on a prompt or input request. This includes generating entirely new images, text, code, audio, and video.

Famous examples.

The most famous example currently is DALL-E from OpenAI. It can create realistic images of virtually any text description you provide. Other popular examples include ChatGPT from Anthropic, which maintains seemingly human-like conversations, and Claude, also from Anthropic, which generates coherent texts on various topics.

How the technology works.

These AI models are trained on huge datasets to learn patterns and correlations. Afterward, they can receive a prompt and generate new outputs that match the style and context of the original data. It’s as if they can “hallucinate” or imagine entirely new versions of what they learned before.

Transforming marketing and design.

New creative possibilities.

As a marketing and technology professional, I find the rapid development of generative AI models fascinating. These tools have the potential to revolutionize digital content creation, graphic designs, brand communication, and much more.

For example, instead of hiring human designers, companies will be able to simply “ask” an AI to create logos, ads, infographics, and other customized materials for their businesses. This will save a lot of time and money.

More efficiency and scale.

Furthermore, the automated generation of content, such as blog posts, emails, video captions, and website texts, will also be possible. Thus, marketing professionals can scale and customize their communications like never before.

In the future, entire advertising campaigns could even be generated by AI based on strategic goals and target audience profiles. This will bring more efficiency and relevance to communications.

Concerns and challenges.

Ethical and security issues.

However, generative models also raise concerns about copyrights, privacy, and security. We need to be careful not to allow these tools to be used to spread misinformation or generate harmful content.

Moreover, as AI becomes more sophisticated, we will need to deal with complex issues about creativity, authorship, and the nature of human work. We don’t have all the answers yet.

Current limitations.

For now, generative models still have many limitations. They can make false claims, repeat existing biases, and fail completely when asked to perform complex or creative tasks. The technology still has a long way to go before we can truly rely on it for critical tasks.

Future perspectives.

Cautious optimism.

Overall, I am excited to see where this technology can take us. I believe generative models will bring many positive innovations if developed and used ethically and responsibly.

With the right precautions, this new era of “AI hallucination” has the potential to automate mundane tasks, enhance human creativity, and make information more accessible to everyone.

What are the main examples of generative AI today?

Some of the main examples of generative AI today are DALL-E (image generation), ChatGPT (conversational chatbots), Claude (text generation), and Jukebox (music generation).

Can generative AI replace human designers and writers?

Not yet. Generative AI still has many creative and contextual limitations. For now, it works best to automate simple tasks or serve as an assistant to human designers and writers.

Do generative models pose any risk?

Yes. They can be used for spreading misinformation, violating copyrights, or generating inappropriate content. Therefore, we need ethical safeguards as the technology develops.

What is needed to train these AI models?

Enormous datasets (millions of parameters) and significant computational power are required. Large technology companies invest massive resources in this training.

Will generative AI replace marketing and communication professionals?

Not in the next few years. Humans will still be needed for creative strategies, emotional connections, ethics, and supervision. However, many tasks will be automated, allowing professionals to focus on higher-value activities.

— David Cavalcante 𝕚𝕟 https://linkedin.com/in/hellodav 𝕏 https://twitter.com/Takk8IS 𝕨 https://takk.ag