#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

[nshtn]

about me [2]

tags

media

Some of the things I enjoyed watching are listed below, in case you want to talk about them with me or just know more about my personality! I love to chat with other people. I am an open book.

Bold is something I love.

- movies/shows -

sci-fi > 2001: A Space Odyssey, Avatar: Rise of the Na'vi, Black Mirror: Hated in the Nation, Jurassic Park and Jurassic World, Person of Interest, Resident Evil, Snowpiercer, Tomorrowland, V for Vendetta, Wall-E

fantasy > My Little Pony, Puella Magi: Rebellion, Steven Universe, The Batman 2022, Witcher

- anime -

Demon Slayer, Dungeon Meshi, Puella Magi Madoka Magica, Terror in Resonance, Magia Record, My Hero Academia

- misc. fandom -

Create for Minecraft, Dano's Rose Garden, Friendship is Optimalverse, Human Domestication Guide, KoboldAI, Nova Sector, OpenPony for Second Life, Open Source Free Realms, OS/OR Objectum [Disembodied AI], Riddler Year One, Retro Demoscene, Retro Text to Speech Software/Hardware

- youtube -

💚entertainment:

Trixie Mattel, Upper Echelon, WigWoo1, oompaville

🖥️ tech :

hacking > F11snipe, John Hammond, Kevin Fang, Loi Liang Yang, The PC Security Channel, Tyler Ramsbey

news > Computer Clan, Cybernews, Digital Trends, Gamers Nexus, Seytonic, Techquickie, ThrillSeeker

repair > Connor Leschinsky [Vintage Animatronics], EEVBlog, Louis Rossmann, northwestrepair, Paul Daniels

scambaiting > Jim Browning, Kitboga, NanoBaiter, Scammer Payback, The Hoax Hotel

production > Berry Bunny [Crystal Frost Viewer], DankPods [MP3 Player 'Nugget's], Engineered Arts [AMECA], Jauwn [Cryptogame Scammers], pacarena abel cirilo [OsakaOS]

🎮 games :

minecraft > Mischief of Mice, Mr. Beardstone, PitFall, Qwuiblington, SalC1, slicedlime, The Duper Trooper, TheMisterEpic, Zaypixel, zman1064

mmorpgs > Force Gaming, Josh Strife

misc > Acai, CrabBar, Freylaverse, habie147, H.O.D, Laacer, Leaf, Let's Game It Out, Luke Stephens, MONI, penguinz0, Shirley Curry, SidAlpha, Sorenova, SunlessKhan, Vinesauce Joel

production > Muno, Rebelzize [Skyblivion], TeamFOLON [Fallout: London], Unitystation

🎥 'edu'tainment :

docuseries > Ahoy, PleaseBee, Computerphile, Journey to the Microcosmos, Moth Light Media, Past Eons Productions, Quinton Reviews, Technology Connections, danooct1

education > Josh's Channel, Branch Education, SciShow, Kurzgesagt, 3Blue1Brown, Alan Zucconi, Art of the Problem, Defunctland, Dodoid, Lily's Cutie World, Philosophy Tube

programming > Black Hat, Code Bullet, sentdex, OALabs, RetroDemoScene, Sun Knudsen, Tech Rules, Travis Baldree

entertainment > Captain Disillusion, Michael MJD, Harry101UK, Jim Abernethy, K Klein, Legal Eagle, LEMMiNo, Lindsay Ellis, NHRL, Strange Parts, William H Baker, Coffeezilla, The Official Channel, Kira

🥣 cooking :

BORED, emmymade, Haphazard Homestead, The Nature Nerd, The Pasta Queen, Townsends

technology

I own a Steam Deck, gaming PC and laptop. Technology is my special interest; I hyperfixate on machine learning, hardware repair, programming and hacking.

👾 programming :

I know a little bittle of Assembly, CSS, C#, C++ and JS, and a lottle of HTML and LSL!

I've written a Google Gemini API bridge for Second Life. I've also written a worn headpatter that lets people feed you snacks and a dynamic terrain footstep "library", and heavily modified the Ostiabs' Elevenlabs TTS.

I wrote a feed-forward non-parallel reinforcement network before - it was a pet doggy! I've modified pretrained genetic perceptrons. I selfhost an ethically trained 20b fp32 8-bit quant'd gguf GPT-3, and sometimes an ethically trained 13b 8bit gguf llama3 GPT-3.5.

Closing thoughts: my beliefs on AI are strong, just because I love machine learning doesn't mean I have to capitulate my values... I don't like company solutions (barring, say, runbox). I take little issue with ethically trained models save for AI art models. We just don't live in a society where they are capable of being ethically or morally feasible.

🤫 hacking :

I once got to have a conversational spot with a cybersecurity analyst who is famous on YouTube!!

I've done a miniscule amount of reverse-engineering for the ForgeLight engine, its' packet opcodes and its' asset server hierarchy.

I've reverse-engineered one of the scantly mentioned annoying crash exploits performed by Minecraft masscan botnets... it intentionally crashes Fabric servers in a magically stupid way via a popular library!

I love WinDBG and am well-versed in reading dmp files from it, and you should totally send me your full untampered memory dump right now. [/j]

🕹️gaming :

singleplayer > Audiosurf 2, Flight Rising, Fallout New Vegas, Fallout 4, FATE: The Cursed King, GemCraft, Jurassic World 2, Orwell, Planet Zoo, Resident Evil, Sims 2

multiplayer with mod > Creatures: Docking Station, Peggle, Rimworld, Sims 3, Sims 4, Skyrim, Slime Rancher, Subnautica, Webkinz

multiplayer > ARK:SE, Bloons TD 6, Depth, Dino Run SE, Distance, Don't Starve Together, Dying Light, Fall Guys, Golf With Your Friends, Guild Wars 2, Minecraft, MultiVersus, Path of Titans, Party Animals, Palworld, Risk of Rain 2, Second Life, Space Station 13, Space Station 14, Starbound, The Isle, Tower Unite, Unitystation

🔎 misc. factoids :

I have autism, C-PTSD, OCD, chronic pancreatitis and gastroparesis. I don't have a gallbladder. I used to have some malingering psychosis dx when I was younger, but for some reason I grew out of experiencing visual or physical hallucinations and only get sound ones or the feeling of being watched (good thing I made it Weird. That's a great coping mechanism actually, highly recommend) and I don't really think I fit in that dx at all.

Part of my autism is being hyperspecific and logorrheic. It's a big part of my life, sorry!

I am transmasc, and I don't have fallopian tubes, ovaries* or a uterus. 😎

I love matcha green tea, soymilk, maple, sweet red bean, setsumaimo and honey. My favorite food is avocado sushi or veggie cabbage dumplings.

I love medicine, especially biotechnology and pharmacology. I love reading medical papers. I love pirating them!! Shout out to Elsevier for hosting such piratable ones!!!

I'm super bodily transhumanist. I have a Freestyle Libre and a PEG-J feeding tube!

I'm completely immune to the entire class of benzodiazepines and my body doesn't process morphine. I have no idea if it's just not demethylating or I have opioid receptor polymorphism, but the latter is more likely than the former. That makes me tough :3c

I totally fully believe everything my favorite characters do is morally correct and will imitate it in real life, and I fully condone all of their horrible actions! /j/j/j/j

Thanks for reading, I hope we can be friends!

*I have one free-floating ovary, technically...

[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.

[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

[y2fear]

Grounding with Google Search available in Google AI Studio, Gemini API

[prcg]

La API Gemini de Google y AI Studio se ponen a tierra con la Búsqueda de Google

A partir de hoy, los desarrolladores que utilizan la API Gemini de Google y su Estudio de IA de Google para crear servicios basados ​​​​en inteligencia artificial y los robots podrán basar los resultados de sus indicaciones con datos de la Búsqueda de Google. Esto debería permitir respuestas más precisas basadas en datos más recientes. Como ha sido el caso antes, los desarrolladores podrán probar…

[technonews-biz]

Demystifying generative AI - 4 major misconceptions

AI is replacing the lawyer, AI writes essays so well that it fools professors, AI is putting artists out of work because anyone can design a magazine cover or write music for a movie. These examples have hit the headlines in recent months, especially statements about the impending obsolescence of intelligent professions and managers. However, AI is not an innovation in the sense that it has been around for a very long time. Since the mid-1950s, there have been successive waves of anxiety and fantasy, each time with the same prophecy: humans will be replaced forever by machines. And each time, those predictions have failed to come true. But this time, as we see the use of new AI multiplying, can we reasonably believe that things will be different?

Technology Revolution?

Talking about artificial intelligence conjures up a picture of the coming “future” in many people's imaginations. The news and media talk about a technological breakthrough happening right before our eyes. But is this really the case?

The algorithms used by ChatGPT or DALL-E are similar to those that have been known and used for years. If the innovation is not in the algorithms, then perhaps a major technological breakthrough will allow us to process large amounts of data in a more “intelligent” way? Not at all! The advances we are seeing are the result of relatively continuous and predictable progress. Even the much-discussed generative AI, i.e. the use of algorithms trained to generate many possible answers, is not new either - although improving results are making it increasingly usable.

What has happened over the past year is not a technological revolution at all, but a breakthrough in usage. Until now, AI giants have kept these technologies to themselves or released only limited versions, thus limiting their use to the general public. The newcomers (OpenAI, Stable.AI, and Midjourney), on the other hand, have decided to allow people to freely dispose of their algorithms. The real breakthrough lies precisely in making AI publicly available.

Big tech companies are technologically obsolete

As mentioned above, big companies like Google, Apple, and Meta are as good at owning these technologies as anyone else, but keep them in highly restricted access. They maintain very tight control over their AI for two reasons.

First, it's their image: if ChatGPT or DALL-E create racist, discriminatory or offensive content, the mistake will be justified because they are startups that are still in the learning process. This “right to make a mistake” does not extend to Google, whose reputation would be severely damaged (not to mention potential legal problems).

The second reason is strategic. Training and exercising AI algorithms is incredibly expensive (we're talking millions of dollars). These staggering costs benefit GAFAMs, which are already well established. Opening up access to their AI means giving up this competitive advantage. However, this situation will seem paradoxical when you consider that these same companies grew by liberating the use of technology (search engines, web platforms) while other established players of the time jealously guarded them under tight control. Beyond the scientific demonstration, one of the reasons Facebook made its Llama model available was precisely to put pressure on the biggest players. Now that this market is being explored by new players, the digital giants are rushing to offer their “ChatGPT” to the market (hence the new version of Microsoft Bing with Copilot and Google Gemini).

OpenAI is open source AI

Another myth that is important to dispel is the openness of new companies' AI. Indeed, the use of their technology is pretty wide open to promise. For example, ChatGPT's “GPT API” allows anyone (for a fee) to incorporate queries into algorithms. Others make the models themselves available, allowing them to be modified at will. However, despite this accessibility, AI remains closed: open or collaborative learning is out of the question here. Updates and new training are done exclusively by OpenAI and the companies that created them. Most of these updates and protocols are kept secret by the startups.

If neural network learning were open and collaborative, we would see battles (e.g., using “bots”) to influence the learning of the algorithm, which would negatively impact the performance of the system. Similarly, on Wikipedia, the collaborative encyclopedia, there have been attempts to influence what is presented as “collective truth” for many years. There is also the issue of the right to use data.

Shutting down AI seems very logical. But it actually raises a fundamental question about the credibility of content. The quality of information is uncertain. AI can be biased or partial, and poor training can lead to dangerous “behavior.” Since the general public is unable to assess these parameters, the success of AI depends on trust in companies - as is already the case with search engines and other “big tech” algorithms. Such “open” AI completely redefines ethics, responsibility and regulation. These pre-trained modules are easy to share and, unlike centralized AI platforms such as OpenAI's GPT, are virtually impossible to regulate. Typically, in the event of an error, we will be able to determine exactly which part of the training caused it? Was it the initial training or one of hundreds of subsequent training sessions? Could it be that the machine was trained by different people?

Many people will lose their jobs

Another myth associated with new AI concerns the issue of the impact on employment. Despite fears that generative AI will replace humans in a range of occupations, it is currently too early to think about such a prospect. No matter how effective AI may seem for solving everyday tasks and automating processes, it is not capable of replacing an expert or a specialist. ChatGPT or DALL-E can produce very good “drafts”, but they still need to be tested, selected and finalized by a human.

Also, we should not forget that the “creativity” of AI and its deep analysis abilities are a kind of illusion. Generative AI is not “Intelligence” in the literal sense of the word, but an algorithm that selects the most relevant answers. In reality, the intrinsic quality of the results is questionable. The explosion of information, content and activities that will result from the widespread and open use of AI will make human experience more necessary than ever. This is the rule of digital revolutions: the more we digitize, the more human experience is required.

Summary

There are many myths and tall tales surrounding AI, especially since the emergence of generative AI such as DALL-E.

In reality, these AIs do not represent a technological revolution in the sense of innovation, as their existence predates the emergence of ChatGPT.

First of all, we are witnessing a hiatus in usage, thanks to startups that have “opened” access to AI to the general public.

In reality, the training protocols of these AIs are kept secret by the companies, but the programming interfaces give users the illusion of owning the algorithm.

Despite concerns, this widespread and open use of AI will make human expertise more necessary than ever.

The emergence of generative AI has sparked much discussion and created many myths about the future of the technology and the impact on human endeavor. However, the reality is that AI is not a technological revolution, but the result of incremental progress and changes in the way we use already known algorithms. The secrecy of learning protocols, limited access to these technologies, and the illusion of openness all emphasize that humans remain a key element in the management and control of AI. Rather than replacing human expertise, the development of AI only reinforces its importance, making us important participants in this new digital age.

[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…

[kurshub]

Google представила обновленные ИИ-модели Gemini 1.5 Pro и Flash! Улучшения производительности, сокращение задержек и снижение цен на API до 64% делают их идеальными для анализа данных и масштабных задач. Подробнее по ссылке https://kurshub.ru/journal/news/google-predstavila-uluchshennye-modeli-gemini-1-5/ #Google #GeminiAI #ИскусственныйИнтеллект #МоделиИИ #РазработкаИИ #ТехнологииБудущего #ОбработкаДанных #МашинноеОбучение #НейронныеСети #ТехНовости

[govindhtech]

Google Vertex AI API And Arize For Generative AI Success

Arize, Vertex AI API: Assessment procedures to boost AI ROI and generative app development

Vertex AI API providing Gemini 1.5 Pro is a cutting-edge large language model (LLM) with multi-modal features that provides enterprise teams with a potent model that can be integrated across a variety of applications and use cases. The potential to revolutionize business operations is substantial, ranging from boosting data analysis and decision-making to automating intricate procedures and improving consumer relations.

Enterprise AI teams can do the following by using Vertex AI API for Gemini:

Develop more quickly by using sophisticated natural language generation and processing tools to expedite the documentation, debugging, and code development processes.

Improve the experiences of customers: Install advanced chatbots and virtual assistants that can comprehend and reply to consumer inquiries in a variety of ways.

Enhance data analysis: For more thorough and perceptive data analysis, make use of the capacity to process and understand different data formats, such as text, photos, and audio.

Enhance decision-making by utilizing sophisticated reasoning skills to offer data-driven insights and suggestions that aid in strategic decision-making.

Encourage innovation by utilizing Vertex AI’s generative capabilities to investigate novel avenues for research, product development, and creative activities.

While creating generative apps, teams utilizing the Vertex AI API benefit from putting in place a telemetry system, or AI observability and LLM assessment, to verify performance and quicken the iteration cycle. When AI teams use Arize AI in conjunction with their Google AI tools, they can:

As input data changes and new use cases emerge, continuously evaluate and monitor the performance of generative apps to help ensure application stability. This will allow you to promptly address issues both during development and after deployment.

Accelerate development cycles by testing and comparing the outcomes of multiple quick iterations using pre-production app evaluations and procedures.

Put safeguards in place for protection: Make sure outputs fall within acceptable bounds by methodically testing the app’s reactions to a variety of inputs and edge circumstances.

Enhance dynamic data by automatically identifying difficult or unclear cases for additional analysis and fine-tuning, as well as flagging low-performing sessions for review.

From development to deployment, use Arize’s open-source assessment solution consistently. When apps are ready for production, use an enterprise-ready platform.

Answers to typical problems that AI engineering teams face

A common set of issues surfaced while collaborating with hundreds of AI engineering teams to develop and implement generative-powered applications:

Performance regressions can be caused by little adjustments; even slight modifications to the underlying data or prompts might cause anticipated declines. It’s challenging to predict or locate these regressions.

Identifying edge cases, underrepresented scenarios, or high-impact failure modes necessitates the use of sophisticated data mining techniques in order to extract useful subsets of data for testing and development.

A single factually inaccurate or improper response might result in legal problems, a loss of confidence, or financial liabilities. Poor LLM responses can have a significant impact on a corporation.

Engineering teams can address these issues head-on using Arize’s AI observability and assessment platform, laying the groundwork for online production observability throughout the app development stage. Let’s take a closer look at the particular uses and integration tactics for Arize and Vertex AI, as well as how a business AI engineering team may use the two products in tandem to create superior AI.

Use LLM tracing in development to increase visibility

Arize’s LLM tracing features make it easier to design and troubleshoot applications by giving insight into every call in an LLM-powered system. Because orchestration and agentic frameworks can conceal a vast number of distributed system calls that are nearly hard to debug without programmatic tracing, this is particularly important for systems that use them.

Teams can fully comprehend how the Vertex AI API supporting Gemini 1.5 Pro handles input data via all application layers query, retriever, embedding, LLM call, synthesis, etc. using LLM tracing. AI engineers can identify the cause of an issue and how it might spread through the system’s layers by using traces available from the session level down to a specific span, such as retrieving a single document.Image credit to Google Cloud

Additionally, basic telemetry data like token usage and delay in system stages and Vertex AI API calls are exposed using LLM tracing. This makes it possible to locate inefficiencies and bottlenecks for additional application performance optimization. It only takes a few lines of code to instrument Arize tracing on apps; traces are gathered automatically from more than a dozen frameworks, including OpenAI, DSPy, LlamaIndex, and LangChain, or they may be manually configured using the OpenTelemetry Trace API.

Could you play it again and correct it? Vertex AI problems in the prompt + data playground

The outputs of LLM-powered apps can be greatly enhanced by resolving issues and performing fast engineering with your application data. With the help of app development data, developers may optimize prompts used with the Vertex AI API for Gemini in an interactive environment with Arize’s prompt + data playground.

It can be used to import trace data and investigate the effects of altering model parameters, input variables, and prompt templates. With Arize’s workflows, developers can replay instances in the platform directly after receiving a prompt from an app trace of interest. As new use cases are implemented or encountered by the Vertex AI API providing Gemini 1.5 Pro after apps go live, this is a practical way to quickly iterate and test various prompt configurations.Image credit to Google Cloud

Verify performance via the online LLM assessment

With a methodical approach to LLM evaluation, Arize assists developers in validating performance after tracing is put into place. To rate the quality of LLM outputs on particular tasks including hallucination, relevancy, Q&A on retrieved material, code creation, user dissatisfaction, summarization, and many more, the Arize evaluation library consists of a collection of pre-tested evaluation frameworks.

In a process known as Online LLM as a judge, Google customers can automate and scale evaluation processes by using the Vertex AI API serving Gemini models. Using Online LLM as a judge, developers choose Vertex AI API servicing Gemini as the platform’s evaluator and specify the evaluation criteria in a prompt template in Arize. The model scores, or assesses, the system’s outputs according to the specified criteria while the LLM application is operating.Image credit to Google Cloud

Additionally, the assessments produced can be explained using the Vertex AI API that serves Gemini. It can frequently be challenging to comprehend why an LLM reacts in a particular manner; explanations reveal the reasoning and can further increase the precision of assessments that follow.

Using assessments during the active development of AI applications is very beneficial to teams since it provides an early performance standard upon which to base later iterations and fine-tuning.

Assemble dynamic datasets for testing

In order to conduct tests and monitor enhancements to their prompts, LLM, or other components of their application, developers can use Arize’s dynamic dataset curation feature to gather examples of interest, such as high-quality assessments or edge circumstances where the LLM performs poorly.

By combining offline and online data streams with Vertex AI Vector Search, developers can use AI to locate data points that are similar to the ones of interest and curate the samples into a dataset that changes over time as the application runs. As traces are gathered to continuously validate performance, developers can use Arize to automate online processes that find examples of interest. Additional examples can be added by hand or using the Vertex AI API for Gemini-driven annotation and tagging.

Once a dataset is established, it can be used for experimentation. It provides developers with procedures to test new versions of the Vertex AI API serving Gemini against particular use cases or to perform A/B testing against prompt template modifications and prompt variable changes. Finding the best setup to balance model performance and efficiency requires methodical experimentation, especially in production settings where response times are crucial.

Protect your company with the Vertex AI API and Arize, which serve Gemini

Arize and Google AI work together to protect your AI against unfavorable effects on your clients and company. Real-time protection against malevolent attempts like as jailbreaks, context management, compliance, and user experience all depend on LLM guardrails.

Custom datasets and a refined Vertex AI Gemini model can be used to configure Arize guardrails for the following detections:

Embeddings guards: By analyzing the cosine distance between embeddings, it uses your examples of “bad” messages to protect against similar inputs. This strategy has the advantage of constant iteration during breaks, which helps the guard become increasingly sophisticated over time.

Few-shot LLM prompt: The model determines whether your few-shot instances are “pass” or “fail.” This is particularly useful when defining a guardrail that is entirely customized.

LLM evaluations: Look for triggers such as PII data, user annoyance, hallucinations, etc. using the Vertex AI API offering Gemini. Scaled LLM evaluations serve as the basis for this strategy.

An instant corrective action will be taken to prevent your application from producing an unwanted response if these detections are highlighted in Arize. The remedy can be set by developers to prevent, retry, or default an answer such “I cannot answer your query.”

Utilizing the Vertex AI API, your personal Arize AI Copilot supports Gemini 1.5 Pro

Developers can utilize Arize AI Copilot, which is powered by the Vertex AI API servicing Gemini, to further expedite the AI observability and evaluation process. AI teams’ processes are streamlined by an in-platform helper, which automates activities and analysis to reduce team members’ daily operational effort.

Arize Copilot allows engineers to:

Start AI Search using the Vertex AI API for Gemini; look for particular instances, such “angry responses” or “frustrated user inquiries,” to include in a dataset.

Take prompt action and conduct analysis; set up dashboard monitors or pose inquiries on your models and data.

Automate the process of creating and defining LLM assessments.

Prompt engineering: request that Gemini’s Vertex AI API produce prompt playground iterations for you.

Using Arize and Vertex AI to accelerate AI innovation

The integration of Arize AI with Vertex AI API serving Gemini is a compelling solution for optimizing and protecting generative applications as businesses push the limits of AI. AI teams may expedite development, improve application performance, and contribute to dependability from development to deployment by utilizing Google’s sophisticated LLM capabilities and Arize’s observability and evaluation platform.

Arize AI Copilot’s automated processes, real-time guardrails, and dynamic dataset curation are just a few examples of how these technologies complement one another to spur innovation and produce significant commercial results. Arize and Vertex AI API providing Gemini models offer the essential infrastructure to handle the challenges of contemporary AI engineering as you continue to create and build AI applications, ensuring that your projects stay effective, robust, and significant.

Do you want to further streamline your AI observability? Arize is available on the Google Cloud Marketplace! Deploying Arize and tracking the performance of your production models is now simpler than ever with this connection.

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…

[toollistai]

What is AiAssistWorks?

AiAssistWorks is an AI-powered tool designed for Google Sheets, allowing users to automate and enhance spreadsheet tasks without writing complex formulas. It integrates with over 50 AI models, including GPT, Claude, and Gemini, to perform actions like content generation, data analysis, translation, and data cleaning.

Features:

AI Integration: Access multiple AI models directly in Google Sheets.

Simplified Automation: No need for complex formulas; automate tasks with ease.

Multi-language Support: Works with any language.

Affordable Pricing: Offers a free plan and a low-cost upgrade for advanced features.

Uses: Ideal for users who want to enhance productivity in Google Sheets by automating repetitive tasks, generating content, and analyzing data efficiently.

[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