#AIServices

[connectinfosoftech]

Artificial Intelligence and Machine Learning Solutions by Connect Infosoft Technologies

We offer customizable AI and ML solutions tailored to meet the specific requirements of each client, ensuring maximum impact and ROI.

Let's make your business more efficient and successful with AI and ML solutions

[xceloreconnect]

How is AI-Centered Product Design Different?

At Xcelore, our AI-Centered Product Design service stands out through the seamless integration of artificial intelligence in every phase of the design process. Unlike traditional approaches, we employ advanced Generative AI algorithms to analyze user data, predict behaviours, and dynamically adapt design elements. Our focus is on delivering a personalized and optimized user experience, anticipating user needs through machine learning insights. With Xcelore's AI-Centered Product Design service, you can expect a product that evolves based on user interactions and feedback, ensuring maximum user satisfaction and overall product effectiveness.

[briskwinits]

At BriskWinIT, we specialize in providing cutting-edge AI services that take advantage of the interaction between these technologies to provide opportunities in a number of industries that were previously imagined.

For more, visit: https://briskwinit.com/generative-ai-services/

[botgochatbot]

Generative AI is rapidly evolving, moving beyond text and images to embrace multimodal capabilities. Models like GPT-4V and Lumiere can now process text, images, and videos simultaneously, delivering richer, more intuitive interactions. Switch to Botgo for cutting-edge AI solutions! Contact us for a free 60-day trial, demo, and quotes at 🌐 https://botgo.io

[blockchain-company]

AI Solutions

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[govindhtech]

How Visual Scout & Vertex AI Vector Search Engage Shoppers

At Lowe’s, Google always work to give their customers a more convenient and pleasurable shopping experience. A recurring issue Google has noticed is that a lot of customers come to their mobile application or e-commerce site empty-handed, thinking they’ll know the proper item when they see it.

Google Cloud developed Visual Scout, an interactive tool for browsing the product catalogue and swiftly locating products of interest on lowes.com, to solve this problem and improve the shopping experience. It serves as an example of how  artificial intelligence suggestions are transforming modern shopping experiences across a variety of communication channels, including text, speech, video, and images.

Visual Scout is intended for consumers who consider products’ aesthetic qualities when making specific selections. It provides an interactive experience that allows buyers to learn about different styles within a product category. First, ten items are displayed on a panel by Visual Scout. Following that, users express their choices by “liking” or “disliking” certain display items. Visual Scout dynamically changes the panel with elements that reflect client style and design preferences based on this feedback.

This is an illustration of how a discovery panel refresh is influenced by user feedback from a customer who is shopping for hanging lamps.Image credit to Google Cloud

We will dive into the technical details and examine the crucial MLOps procedures and technologies in this post, which make this experience possible.

How Visual Scout Works

Customers usually know roughly what “product group” they are looking for when they visit a product detail page on lowes.com, although there may be a wide variety of product options available. Customers can quickly identify a subset of interesting products by using Visual Scout to sort across visually comparable items, saving them from having to open numerous browser windows or examine a predetermined comparison table.

The item on a particular product page will be considered the “anchor item” for that page, and it will serve as the seed for the first recommendation panel. Customers then iteratively improve the product set that is on show by giving each individual item in the display a “like” or “dislike” rating:

“Like” feedback: When a consumer clicks the “more like this” button, Visual Scout substitutes products that closely resemble the one the customer just liked for the two that are the least visually similar.

“Dislike” feedback: On the other hand, Visual Scout substitutes a product that is aesthetically comparable to the anchor item for a product that a client votes with a ‘X’.

Visual Scout offers a fun and gamified shopping experience that promotes consumer engagement and, eventually, conversion because the service refreshes in real time.

Would you like to give it a try?

Go to this product page and look for the “Discover Similar Items” section to see Visual Scout in action. It’s not necessary to have an account, but make sure you choose a store from the menu in the top left corner of the website. This aids Visual Scout in suggesting products that are close to you.

The technology underlying Visual Scout

Many Google Cloud services support Visual Scout, including:

Dataproc: Batch processing tasks that use an item’s picture to feed a computer vision model as a prediction request in order to compute embeddings for new items; the predicted values are the image’s embedding representation.

Vertex AI Model Registry: a central location for overseeing the computer vision model’s lifecycle

Vertex  AI Feature Store: Low latency online serving and feature management for product image embeddings

For low latency online retrieval, Vertex AI Vector Search uses a serving index and vector similarity search.

BigQuery: Stores an unchangeable, enterprise-wide record of item metadata, including price, availability in the user’s chosen store, ratings, inventories, and restrictions.

Google Kubernetes Engine: Coordinates the Visual Scout application’s deployment and operation with the remainder of the online buying process.

Let’s go over a few of the most important activities in the reference architecture below to gain a better understanding of how these components are operationalized in production:Image credit to Google cloud

For a given item, the Visual Scout API generates a vector match request.

To obtain the most recent image embedding vector for an item, the request first makes a call to Vertex AI Feature Store.

Visual Scout then uses the item embedding to search a Vertex AI Vector Search index for the most similar embedding vectors, returning the corresponding item IDs.

Product-related metadata, such as inventory availability, is utilised to filter each visually comparable item so that only goods that are accessible at the user’s chosen store location are shown.

The Visual Scout API receives the available goods together with their metadata so that lowes.com can serve them.

An update job is started every day by a trigger to calculate picture embeddings for any new items.

Any new item photos are processed by Dataproc once it is activated, and it then embeds them using the registered machine vision model.

Providing live updates update the Vertex AI Vector Search providing index with updated picture embeddings

The Vertex AI Feature Store online serving nodes receive new image embedding vectors, which are indexed by the item ID and the ingestion timestamp.

Vertex AI low latency serving

Visual Scout uses Vector Search and Feature Store, two Vertex AI services, to replace items in the recommendation panel in real time.

To keep track of an item’s most recent embedding representation, utilise the Vertex AI Feature Store. This covers any newly available photos for an item as well as any net new additions to the product catalogue. In the latter scenario, the most recent embedding of an item is retained in online storage while the prior embedding representation is transferred to offline storage. The most recent embedding representation of the query item is retrieved by the Feature Store look-up from the online serving nodes at serving time, and it is then passed to the downstream retrieval job.

Visual Scout then has to identify the products that are most comparable to the query item among a variety of things in the database by analyzing their embedding vectors. Calculating the similarity between the query and candidate item vectors is necessary for this type of closest neighbor search, and at this size, this computation can easily become a retrieval computational bottleneck, particularly if an exhaustive (i.e., brute-force) search is being conducted. Vertex AI Vector Search uses an approximate search to get over this barrier and meet their low latency serving needs for vector retrieval.

Visual Scout can handle a large number of queries with little latency thanks to these two services. Google Cloud performance objectives are met by the 99th percentile reaction times, which come in at about 180 milliseconds and guarantee a snappy and seamless user experience.

Why does Vertex AI Vector Search happen so quickly?

From a billion-scale vector database, Vertex AI Vector Search is a managed service that offers effective vector similarity search and retrieval. This offering is the culmination of years of internal study and development because these features are essential to numerous Google Cloud initiatives. It’s important to note that ScaNN, an open-source vector search toolkit from Google Research, also makes a number of core methods and techniques openly available. The ultimate goal of ScaNN is to create reliable and repeatable benchmarking, which will further the field’s research. Offering a scalable vector search solution for applications that are ready for production is the goal of Vertex  AI Vector Search.

ScaNN overview

The 2020 ICML work “Accelerating Large-Scale Inference with Anisotropic Vector Quantization” by Google Research is implemented by ScaNN. The research uses a unique compression approach to achieve state-of-the-art performance on nearest neighbour search benchmarks. Four stages comprise the high-level process of ScaNN for vector similarity search:

Partitioning: ScaNN partitions the index using hierarchical clustering to minimise the search space. The index’s contents are then represented as a search tree, with the centroids of each partition serving as a representation for that partition. Typically, but not always, this is a k-means tree.

Vector quantization: this stage compresses each vector into a series of 4-bit codes using the asymmetric hashing (AH) technique, leading to the eventual learning of a codebook. Because only the database vectors not the query vectors are compressed, it is “asymmetric.”

AH generates partial-dot-product lookup tables during query time, and then utilises these tables to approximate dot products.

Rescoring: recalculate distances with more accuracy (e.g., lesser distortion or even raw datapoint) given the top-k items from the approximation scoring.

Constructing a serving-optimized index

The tree-AH technique from ScaNN is used by Vertex AI Vector Search to create an index that is optimized for low-latency serving. A tree-X hybrid model known as “tree-AH” is made up of two components: (1) a partitioning “tree” and (2) a leaf searcher, in this instance “AH” or asymmetric hashing. In essence, it blends two complimentary algorithms together:

Tree-X, a k-means tree, is a hierarchical clustering technique that divides the index into search trees, each of which is represented by the centroid of the data points that correspond to that division. This decreases the search space.

A highly optimised approximate distance computing procedure called Asymmetric Hashing (AH) is utilised to score how similar a query vector is to the partition centroids at each level of the search tree.

It learns an ideal indexing model with tree-AH, which effectively specifies the quantization codebook and partition centroids of the serving index. Additionally, when using an anisotropic loss function during training, this is even more optimised. The rationale is that for vector pairings with high dot products, anisotropic loss places an emphasis on minimising the quantization error. This makes sense because the quantization error is negligible if the dot product for a vector pair is low, indicating that it is unlikely to be in the top-k. But since Google Cloud want to maintain the relative ranking of a vector pair, they need be much more cautious about its quantization error if it has a high dot product.

To encapsulate the final point:

Between a vector’s quantized form and its original form, there will be quantization error.

Higher recall during inference is achieved by maintaining the relative ranking of the vectors.

At the cost of being less accurate in maintaining the relative ranking of another subset of vectors, Google can be more exact in maintaining the relative ranking of one subset of vectors.

Assisting applications that are ready for production

Vertex AI Vector Search is a managed service that enables users to benefit from ScaNN performance while providing other features to reduce overhead and create value for the business. These features include:

Updates to the indexes and metadata in real time allow for quick queries.

Multi-index deployments, often known as “namespacing,” involve deploying several indexes to a single endpoint.

By automatically scaling serving nodes in response to QPS traffic, autoscaling guarantees constant performance at scale.

Periodic index compaction to accommodate for new updates is known as “dynamic rebuilds,” which enhance query performance and reliability without interpreting service

Complete metadata filtering and diversity: use crowding tags to enforce diversity and limit the use of strings, numeric values, allow lists, and refuse lists in query results.

Read more on Govindhtech.com

[compunnelinc]

Revolutionizing Enrollment and Retention: Compunnel's Data-Driven Success at a Texas Technical College

Read how Compunnel transformed a leading Texas technical college’s enrollment and retention strategies through innovative Data Analytics and Machine Learning. This case study highlights the college's challenges—low retention rates, inaccurate enrollment forecasts, and limited data use—and Compunnel's strategic interventions, including predictive models, robust data frameworks, and real-time dashboards. The results? A 75% increase in enrollment prediction accuracy, a 25% boost in retention rates, and a 40% drop in dropout rates. Download the full case study now to unlock the power of data-driven education for your institution.

[calpionusoftwaretechnologies]

Healthcare Chatbots: A Smart Solution for Healthcare Efficiency

Undoubtedly, technology has changed our lives to an extent where information is available at the click of a mouse or tap on your phone. Google has been the first source of information for all our queries, and with AI integration - ChatGPT, with its latest versions, has adopted a form of search console as well.

When humans fall sick or need urgent assistance regarding their healthcare needs, it is only. Still, it is natural for us to google our symptoms and reach a conclusion; however, with automated bots ready with equipped information and speed of no other, it is a rather reliable solution that healthcare organizations would intend to adopt to their programs.

Chatbots are known for their reliability and quick answers in times of adversity when you need immediate assistance with tasks that do not necessarily involve human intervention. Our latest blog highlights the technological advancements of Chatbots and their role in increasing healthcare efficiency, with patient engagement as a major benefactor.

medical ai chatbots

Patient engagement is at the crux of every healthcare organization due to its increasing impact on patient retention. A healthcare chatbot can improve the patient's experience in various ways, such as making tasks easier for patient appointments, filling prescriptions, as an assistant, etc. Conversational chatbots are all the rage due to hyper-personalization, human-like conversation experience, and speed. 

 Some of the factors that drive the need for chatbots in healthcare are: 

Patients

Providers 

Insurance providers

Bots handle 70% of conversations and do not require human assistance, saving around 2.5 billion hours, which can be further utilized for better care and patient support chatbots.

Use Cases of AI Chatbots in Healthcare

Many people realize that adopting automation into their innovation strategy can be a game changer by cost-effectively improving operations throughout the organization, benefiting employees and patients. Embracing new technologies, such as robotic process automation with chatbots in healthcare, is crucial to achieving the interdependent goals of cost reduction and better patient care.

Appointment Scheduler: Patients can chat with chatbots to book appointments with their desired hospitals without waiting on calls or queues like in old times.

Patient Queries: AI chatbots can answer single and multiple queries by patients with a high response rate and accuracy.

Text Reminders/ IVR calls: Patient text reminders and IVR calls remind patients of their due dates of billing and appointments so there are no-shows or loss of collection for the providers.

Diagnostic Chatbots: Preliminary symptoms can be diagnosed using medical picture analysis. Medical AI chatbots can assist patients in better understanding their conditions, making efforts to adhere to prescribed regimens, and following up with healthcare physicians. 

Patient Billing Chatbots: With Chatbots as patient engagement software, clinicians may easily contact patients and remind them of their monthly statements through E-statements and text reminders. 

A/R calling bots, often known as accounts receivable chatbots, are automated systems that collect outstanding customer payments. Businesses utilize them to automate repetitive tasks like reminder calls, payment schedules, and negotiations. 

Remote Monitoring Assistants: Once a patient has completed their medical consultation and is concerned about the side effects, chatbots are utilized to follow up via remote monitoring patient support with a polite conversation. These bots even provide updates on their sessions, lab results, symptom checks, and future progress. These interactions enable patients to quickly resolve their questions and alleviate worry, thus improving their experience. 

Advantages of Healthcare Chatbots

Healthcare chatbots that apply the use cases above give providers numerous cost and time savings benefits and a competitive advantage. By 2027, chatbots in healthcare are expected to become the major channel for customer service in one-quarter of enterprises. 

24/7 Availability

Data-driven Insights

Resource Allocation

Patient Engagement 

Overall cost reduction

Streamline Operations & Increase ROI

Patient Satisfaction

Reducing workforce via automation

How to Successfully Implement a Healthcare Chatbot

The growth of AI and Machine Learning algorithms makes AI chatbot training and implementation easier, resulting in better healthcare outcomes. Statistics indicate that aural and visual features will be merged in the future. This technology encourages organic interactions, boosts user engagement, and makes chatbots in healthcare more accessible. However, enterprises must take specific procedures before installing a chatbot, and Calpion Inc. is your go-to tech bot. 

Calpion Inc. provides its complete support with the implementation, management, and deployment of a healthcare chatbot in all its stages via the following steps:

Define your chatbot purpose.

Choosing the right model with the customized model 

Regulatory Compliance of HIPAA compliance and SOC-certified. 

Map your Patient Journey

Train & improve your Chatbot.

Integrate within existing systems.

Conclusion

Calpion Inc. consistently deploys, maintains, and manages the solutions to ensure flawless functionality while preserving operational efficiency. Healthcare firms may improve patient experience, staff efficiency, resource allocation, and service quality by tailoring chatbots to specific hospital bottlenecks and maximizing their impact.

If you're looking for an AI-powered chatbot or a customized model with unique business requirements, Calpion Inc. is here to solve your challenges. Contact Calpion to learn how our customized AI solutions have helped our healthcare clients improve their productivity, reduce patients waiting time by 1/10th, and enrich their patient experience.

[pranathisoftwareservices]

Pranathi Software Services excels in exploring and implementing the diverse Uses of Artificial Intelligence. Their approach is transformative, impacting various industries by integrating innovative intelligence into key operations. The company's expertise in AI extends to solving complex business challenges, enhancing efficiency, and driving growth. By harnessing the power of AI, Pranathi Software is at the vanguard of bringing about a technological revolution in the business world.

[connectinfosoftech]

Innovate and Grow with AI and Machine Learning Solutions!

We are your partner in leveraging AI and Machine Learning to drive business success.

Our customized solutions can help you automate processes, gain deep insights, and enhance operational efficiency.

Ready to transform your business? Contact us for a FREE consultation today!

[yespoonamsoni]

AI Development Solutions (Artificial Intelligence)

Pioneering AI Development Solutions, customized to your specific requirements. Our advanced Artificial Intelligence consulting services leverage cutting-edge technology, empowering your business with intelligent, strategic decisions. Stay ahead of your competitors by visiting OnGraph and experience the power of Natural Language Processing, enabling seamless communication and interaction.

[botgochatbot]

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[copperchips]

AI in Healthcare: Pioneering the Future of Medicine

Artificial Intelligence (AI) has emerged as a game-changer in the field of healthcare, reshaping the way we approach medical diagnosis, treatment, and patient care. With the ability to analyze vast volumes of patient data at incredible speeds, AI is ushering in a new era of precision medicine and healthcare innovation.

[govindhtech]

Cloud Run Accelerates AI Application Production Release

Google Cloud Run

It’s no secret that Cloud Run provides one of the easiest methods available for deploying AI-powered applications into production, freeing developers from the burden of managing the underlying infrastructure or scaling from a small number of users to millions. However, did you know that a lot of clients also choose Cloud Run as their go-to platform for giving their AI researchers the resources they require to carry out and scale up their experiments outside of their reliable Python notebooks?

Upon top of the container runtime, Cloud Run offers several services that provide an all-inclusive platform for developing and executing AI-powered apps. Google Cloud outlines several of Cloud Run’s primary capabilities in this blog post, which can expedite the creation of AI-powered applications:

Time to market: by quickly transitioning from Vertex AI Studio prototyping to a deployed containerised application

Observability: by the use of Google Cloud observability technologies and the integrated SLO monitoring of Cloud Run

Rate of innovation: test several iterations of your service concurrently with updates and traffic division

Building RAG implementations by securely and immediately connecting to cloud databases is a relevant and factual approach.

By placing several Cloud Run services in front of a single external global application load balancer, multi-regional deployments and HA are made possible.

From using AI Studio for prototyping to releasing a Cloud Run service

Vertex AI Studio is the starting point for many new AI-based products since it enables quick prototyping on a variety of models without requiring the creation of code. From there, a convenient shortcut for converting experiments into code in a number of well-known programming languages is provided by the “Generate Code” feature.

A script that calls the Vertex AI APIs that provide the AI service makes up the resulting code snippet. The process of converting that script into a web application may be as simple as transforming the hardcoded prompt into a templated string and enclosing everything in a web framework, depending on the kind of application you are attempting to develop. This may be accomplished, for instance, in Python by enclosing the prompt in a little Flask application and parameterizing the request with a straightforward Python f-string:

Google Cloud can already containerise and launch its application with the help of a straightforward package.txt file that contains the necessary requirements. Google Cloud doesn’t even need to supply a Dockerfile describing how Google Cloud containers should be generated because of Cloud Run’s support for Buildpacks.

Use telemetry and SLOs to track the performance of your application

Ensuring that the programme satisfies user expectations and determining the business impact it generates are dependent on the implementation of observability. Out of the box, Cloud Run provides both observability and monitoring of Service Level Objectives (SLOs).

In order to manage your application based on error budgets and use that measure to strike a balance between stability and rate of innovation, it is crucial to monitor SLOs. SLO monitoring can be established using Cloud Run based on configurable metrics, latency, and availability.

In order to gather all the necessary data in one location, traditional observability such as logging, monitoring, and tracing is also readily available out of the box and seamlessly integrates with Google Cloud Observability. In particular, tracing has shown to be quite useful when examining the latency decomposition of AI applications. It is frequently applied to enhance comprehension of intricate orchestration situations and RAG implementations.

Quick invention combined with simultaneous updates and cloud deployment

Numerous AI use cases drastically alter Google Cloud’s problem-solving methodology. The end result is frequently unpredictable due to the nature of LLMs and the effects of variables like temperature or subtleties in prompting. Thus, being able to conduct experiments concurrently can facilitate rapid iteration and innovation.

With Cloud Run, developers may run multiple concurrent versions of different service revisions at once and have fine-grained control over how traffic is shared among them thanks to the built-in traffic splitting feature. This could entail serving various prompt iterations to various user groups and comparing them based on a shared success metric, such as click-through rate or likelihood of purchase, for AI applications.

A managed service called Cloud Deploy can be used to automatically plan the release of several iterations of a Cloud Run service. Additionally, it connects with your current development routines such that push events in source control can initiate a deployment pipeline.

Establishing a connection to cloud databases to incorporate company data

A static pre-trained model may not always be able to produce accurate results due to the absence of the domain-specific context. Retrieval-augmented generation (RAG) and other methods of adding extra data to the prompt frequently help give the model adequate contextual information to improve the relevance of the model’s responses for a given use case.Image Credit to Google Cloud

In order to use cloud databases like AlloyDB or Cloud SQL as a vector store for RAG implementations, Cloud Run offers direct and private connectivity from the orchestrating AI application. Cloud Run may now connect to private database endpoints without the additional step of a serverless VPC connector thanks to direct VPC egress capabilities.

Deployments across several regions and custom domains

Every Cloud Run service by default gets a URL in the form of <service-name>.<project-region-hash>.a.run.app, which can be used to make HTTP queries to the service. Although this is useful for internal services and rapid prototyping, it frequently causes two issues.

Firstly, the domain suffix does not correspond to the service provider, and the URL is not very memorable. As a result, users of the service are unable to determine whether it is a genuine offering. Not even the SSL certificate, which is granted to Google, divulges who owns the said service.

The second issue is that various areas will have different URLs if you grow your service to multiple regions in order to offer HA and lower latency to your distributed user base. This implies that changing service regions is not transparent to users and must be handled at the client or DNS level.

Both of these issues may be resolved with Cloud Run’s support for custom domain names and its ability to combine deployments of Cloud Run across several regions under a single external IP address based on anycast, all behind a global external load balancer. After setting up the load balancer and turning on Cloud launch’s outlayer traffic detection feature, you can launch your AI service with a custom domain, your own certificate, and automated failover in the event of a regional outage.

Let your AI software be powered by Cloud Run

Five key areas were examined by Google Cloud, which makes Cloud Run an ideal place to start when developing AI-powered applications on top of Vertex AI’s robust services.

Read more on govindhtech.com

[focaloid-technologies]

[juneconnects]

AI-powered technology is powering the intelligence of the future. Give your business a boost by integrating artificial intelligence with your administrative process to accelerate growth.

Send a message to know how you can adopt AI to breathe new life into your business! WhatsApp: https://wa.me/919535555225