Microsoft introduces a new user interface experience for the computer vision experience with Vision Studio

Microsoft recently introduced a new user interface (UI) for developers called VisionStudio to try it Computer Vision API.

Computer Vision API is part of Microsoft View cognitive services in azure. The application programming interface (API) provides access to advanced algorithms for media processing and information return. Azure’s computer vision algorithms can analyze visual content differently based on user input and choices by loading a media asset or specifying a media asset URL. more Latest version 4.0 from API was Recently released in previewand its capabilities are built into Vision Studio.

Vision Studio aims to allow developers to explore, view, and evaluate features from Computer Vision, regardless of their coding experience. Moreover, they can access the documentation, SDK, REST API and view the supported languages through the user interface.

Developers can try OCR (OCR), spatial analysisAnd the FaceAnd the Image analysis Computer vision services. They can optionally sign in with their Azure account or create one. Next, choose Service and create an optional resource. The latter allows developers to upload their own resources. As Kate Brown, Director of Programs at Microsoft, explains in Cognitive Services Blog Posts:

Each Vision PC feature contains one or more trial experiences in Vision Studio. To use your images in Vision Studio, you will need an Azure subscription and a Cognitive Services resource for authentication. Otherwise, you can try Vision Studio without logging in with our sample images. These experiments help you quickly test features using a no-code approach that provides both text and JSON responses.

Other public cloud vendors, such as AWS and Google, offer similar Computer Vision APIs on their platforms. Get to know AWS It offers free tutorials to try the capabilities, but an AWS account is required. Also, Google has an offer with The vision of artificial intelligence, which can be tested without logging in and by uploading custom images. Finally, according to a verified market source Report“Artificial Intelligence in the Computer Vision Market, Computer Vision Capabilities Market, Expected to Reach $2,05,104.8 Million by 2030, at a CAGR of 37.05% from 2023 to 2030” – hence the investments In this public knowledge services by cloud vendors.

More details about Computer Vision are available at Documents landing page. Instructions on how to use Vision Studio can be found in Microsoft Learn page.

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IBM gives partners access to three new AI libraries

The new libraries provide natural language processing, speech-to-text, and text-to-speech capabilities for partners to add to applications for any hybrid and multi-cloud environment, says Monica Agarwal, IBM’s VP of Ecosystem Building and Technology Partnerships.

IBM has released new AI libraries designed to help IBM partners, customers and developers build and bring AI-powered services to market.

The three new AI libraries for New York-based Armonk, now available publicly, combine innovation from IBM Research and open source technology to provide Natural Language Processing, Speech-to-Text and Text-to-Speech capabilities for partners to add to the new, said Monica Aggarwal, Vice President Head of ISV Build Ecosystem and Technology Partnerships at IBM for CRN, Applications for any hybrid and multi-cloud environment.

Aggarwal said this expanded access to AI will help MSPs, MSSPs and other similar partners to accelerate AI adoption.

“We got feedback from partners, we listened to them, we worked on our strategy and we said, ‘You know what? Let’s build something very convenient for consumption, said Aggarwal. “A simple, easy and flexible format for libraries to embed in a solution. And we have a great program to build with you, go to market with you – let’s take that and win the market.”

What has IBM done for artificial intelligence?

Nalit Patel, CEO of All Solutions, an IBM partner in Livingston, New Jersey, told CRN that he loves IBM’s investment in cutting-edge technology, including artificial intelligence.

He also said he would love to invest more in the IBM Partner Program.

They are becoming more channel friendly,” Patel said.

Meanwhile, the libraries are giving partners access to the same AI libraries that power IBM Watson products, according to IBM. Developers and IT teams can include new libraries in their applications and create custom products without data science expertise, reducing the barrier to AI adoption.

One library, the IBM Watson Natural Language Processing Library, is used to process human language and discover meaning, content, intent and feelings, according to the vendor.

The IBM Watson Speech to Text Library is used for speech transcription for customer service. The IBM Watson Text to Speech library is used to convert typed text to natural voice in a variety of languages and sounds in an application.

Earlier this month, during IBM’s latest quarterly earnings call, IBM Chairman and CEO Arvind Krishna Call Artificial intelligence and hybrid cloud are “the two most transformative enterprise technologies of our time” and described partners with different business models as a “critical component of our strategy” to win customers.

IBM has also been in a series of acquisitions to build its AI capabilities along with innovation from within the company. Recent AI-related acquisitions include Databand.ai And the wig And the new.

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Snap Way to Design Ad Ranking Service Using Deep Learning

Snap geometry recently posted a file Blog Posts on how they designed their ad categorization and targeting service using deep learning.

Showing ads to users is the trend of monetizing social networking platform. Snap’s ad rating system is designed to target the right user at the right time. Snap follows a few principles in designing such a complex system. They focus on providing an excellent user experience while maintaining user privacy and security. The following image shows the high-level structure of Snap Ads Ranking and Targeting.

Snap .’s Comprehensive Ad Classification Service Architecture

This system consists of some microservices. It contains a module called Ad Eligibility Filter. This module applies some filters such as checking ad budget, privacy, and ad policy compliance. Determines whether an advertisement is appropriate to display to the user. Ads from this module are passed to the candidate generation module. The filter generation module identifies relevant ads to users. Usually a machine learning model is very light Re-Call used in this unit. Once hundreds or a few thousand ads are identified, a heavy deep learning model is used to score the likelihood of the ads converting. In the end, the auction is run to select and score ads based on business rules, budget, etc. The feedback loop is used to collect more user interaction data to retrain models.

Ad rating systems face some unique challenges compared to general recommendation and search ranking systems. First of all, size, cost and latency. As mentioned in Blog Posts :

Our ML models work very broadly; Every day we make trillions of predictions using models trained on billions of examples. Such a large scale involves significant training and heuristics costs. These models also operate under strict latency restrictions. A combination of highly optimized training platform, inference platform, and model architectures is needed to keep cost and latency within acceptable limits.

Low prediction error rate is an important factor in choosing an advertisement. The error in predicting the outcome will propagate in the auction phase resulting in low quality data in the model’s training. The rapid change in advertising inventory is also one of the main challenges. This makes it difficult to get good embed Representation of advertisements.

For heavy ml models, in-service deep learning models are used as described in Blog Posts :

We make use of the latest multitasking models like MMoEs And the PLE To jointly predict multiple conversion events (eg, app installs, purchases, and subscriptions). Our models also use the latest high-level feature interaction layers such as DCN And the DCN version 2 as a building stone. We also optimize these models for inference cost and response time by splitting them into multiple towers, ie one for user features processing and one for advertising features. We illustrate these components and their interrelationships in the figure below.

A typical high-level structure used to classify Snap ads

Ad Ranking Systems is a use case recovery systems. Some cloud service providers such as AmazonAnd the The GoogleAnd the Microsoft It also offers these return systems as a service to its customers. There are some open source systems like Ad search systems On Github for further review.

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AI-controlled robotic laser can target and kill cockroaches

A laser controlled by two cameras and a microcomputer powered by an AI model can be trained to target specific types of insects

technology

October 20 2022

by Matthew sparks

Laser device to kill cockroaches

Eldar Rachmotulin

Researchers have created a device that uses machine vision to detect cockroaches and stun them with a laser. They say this method could provide a cheaper and more environmentally friendly alternative to pesticides.

Eldar Rachmotulin At Heriot-Watt University in Edinburgh, UK, he and his colleagues outfitted a two-camera laser and a microcomputer that worked with an artificial intelligence model that could be trained to target specific types of insects.

Rachmotulin says the team chose to conduct experiments with crickets because their resilience is a rigorous test: “If you…

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AI-powered Ericsson Performance Optimizers – top network performance and automation, rolled into one

• The new solution leverages automation, scalability, speed, accuracy, and consistency in network optimization to enable a superior subscriber experience, while reducing operating costs.

• Ericsson and Ooredoo Qatar trialed the software solution at a major football tournament and noted improved uplink capacity along with gains on speed and traffic volume.

• A part of Ericsson’s Cognitive Software portfolio, the solution uses digital twin technology and advanced AI techniques like deep reinforcement learning.

Ericsson (NASDAQ: ERIC) Performance Optimizers is a suite of AI-powered applications, that analyze the CSPs’ Radio Access Network (RAN) to proactively provide mobile network optimization recommendations and resolve specific network performance issues.

Ericsson Performance Optimizers can be deployed to enable a helicopter view of the network as a whole. The solution accounts for the invisible changes in the network caused by each addition to the environment, such as new applications, city growth, new sites or user behavior. It is equipped with digital twin technology and advanced AI techniques like deep reinforcement learning.

Digital twin technology accurately mimics the network behavior upon parameter changes, ensuring an approach that minimizes risk and elevates the optimizer’s quality to telco grade from day one. Reinforcement learning is a machine-learning (ML) technique that learns from the network, where an agent (Performance Optimizer) interacts with the environment and takes actions towards a long-term goal.

Additionally, the solution can also be used to accurately predict network performance improvements and proactively provide one-shot optimization recommendations for targeted cells.
Ericsson plans to periodically add new features to the existing solution to address the challenges brought on by different network issues, thereby increasing the coverage of automated optimization.

With 5G, CSPs are looking to transform essential parts of their operations to achieve optimal performance and return on investment. Vendor agnostic AI-native solutions such as Ericsson Performance Optimizers are key to automate and cope with growing complexity while keeping control of cost.

In November 2021, Ericsson trialed the solution with Ooredoo Qatar for a major football tournament. As part of the partnership Ericsson provided network optimization and event management and 5G connectivity in three stadiums, airports and places of attraction. Massive efficiency gains were noted – where traditional worst cell analysis takes days to complete, the new automated approach with performance optimizers took only 15 minutes, producing hundreds of recommendations.

Juan Manuel Melero, Head of Network Design & Optimization, Ericsson, says: During the trial with Ooredoo Qatar, we noted that 89% of internal uplink interference cases were automatically resolved by the Performance Optimizers. This resulted in 7% improvement in uplink capacity, along with significant gains in speed and traffic volume. We now look forward to leveraging our expertise and experience to support our customers around the world to achieve top network performance and deliver the best user experience.

Günther Ottendorfer, Chief Technology and Infrastructure Officer at Ooredoo Qatar, says: For Ooredoo, customer experience and automation are top priority. We see AI-powered operations as key to deliver on our promise of world-class services to our subscribers. During the major football event held in Qatar in December 2021, Ericsson’s Cognitive Software provided a near real-time automation of optimization actions. The end-result was a superior user experience. before, during and after the matches.

Ericsson Performance Optimizers suite is part of the Cognitive Software pack in Ericsson Operations Engine. It can be implemented through licensing, software as a service (SaaS) or as part of our services packs.