#DNN developer

[dynnamite11]

Our experience as a DNN developer has given the DyNNamite team unique insight and capabilities in DNN custom module development.

[i4technolab]

DNN is an ideal solution for creating wide range of commercial applications and websites that allows using third-party products and add-ons or custom tools. This is highly recommended by C# software development companies USA. DNN eases the work of developers, designers, administrators, editors, and other users of the system or website. As DNN’s skinning engine is extremely flexible, it is easy to get better and as per user requirement attractive UI. DNN has the ability for the administrations tools to fully integrate into the websites which makes it easy to modify things. It is used to make professional websites, social intranets, community portals, etc. and mostly preferred for medium sized eCommerce.

[materialsscienceandengineering]

Image denoising using a diffractive material

While image denoising algorithms have undergone extensive research and advancements in the past decades, classical denoising techniques often necessitate numerous iterations for their inference, making them less suitable for real-time applications. The advent of deep neural networks (DNNs) has ushered in a paradigm shift, enabling the development of non-iterative, feed-forward digital image denoising approaches. These DNN-based methods exhibit remarkable efficacy, achieving real-time performance while maintaining high denoising accuracy. However, these deep learning-based digital denoisers incur a trade-off, demanding high-cost, resource- and power-intensive graphics processing units (GPUs) for operation.

Read more.

[gg-selvish]

rules rambling: stats and intention

i think if i had played it safer and kept rules about georgenap-centric dream exclusion in dnn and the infidelity resulting from that it would have done better and the punishment scene with dream after the christmas streams was just too far and made a lot of people give up on it. like thats kinda what i expect happened based on what ive seen in the stats especially compared to similar yet different but comparable works of mine (home security no angst with smut ratio vs best friend eater heavy angst and no smut ratio)

but at the same time from my perspective as a writer... that's where it all started. my original idea for rules was this one passed around screenshot from the christmas streams of dnf at one end of the kitchen and sapnap at the other and everyone was sharing it gassing up dnf and i was like what if it was secretly georgenap/dnn and george got mad at dream for being extra pda-y on camera with him and 'excluding' sapnap like the fans say. that was the whole premise of the fic it came from this idea that george would have those feelings and the way his and dream's characters developped it lead pretty naturally to an unhealthy punishment scene

and then of course there's the ridiculous fact that i was just going to write that general idea and suddenly georgenap were having the UK trip. and i had to get to christmas 2022. it got a little out of my hands.

rules isnt porn with plot the porn IS the plot all of the gritty shit that gives it substance and makes it interesting happens mostly within the smut scenes because thats kind of how my brain works as a smut writer. also its only natural that george, someone who doesnt communicate properly in his relationships, would rely heavily on physical touch and intimacy to articulate his love (and anger in dream's case) and that's the whole issue, right? i touched on love languages a little bit in another rules post so i wont get distracted.

dream's punishment was fucked. and he was way more effected by it in the moment than i intended but while i was writing the scene it just kept getting darker and darker and i had to keep an eye on sapanap's reactions kind of like a timer. so since it took sapnap so long to speak up the punishment just gets so fucking cruel. and dream as noted in the fic is such an under-experienced sub who relies heavily on praise to keep his head in-scene... it was fucked. it was fucked up.

but he does get to chew george out for it! completely tears him to shreds and give him a taste of how it feels to be miserable like that, and then later in a better headspace they explore that vulnerability of subbing/being penetrated too. the ending of rules is so fucking good. sure georgenap is slightly unresolved but dnf really have an incredible dynamic that i thought came out really good. it's a shame not everyone who had their heart muscles strained from the nonstop 60k of angst got the tiger balm soothing of resolution, but maybe i didn't tag the happy ending soon enough.

[dreamnotnapss]

i’ve been having dnkn thoughts that i was thinking of somehow incorporating into a fic but probably not so i am now unleashing them onto your inbox. really in love with the idea of karl being the “missing piece” so to speak that breaks down their walls and makes them finally start acting on their feelings for each other. like dnn all have individually thought about their friendship being something more, but they don’t want to mess up what they already have by pushing on invisible boundaries, so for the most part they keep it to themselves and just carry on as normal. but then in swoops karl, who so openly and easily gives affection that it becomes more comfortable for all of them. it starts with sapnap, and then george, and finally dream, and just as the three of them are starting to actually navigate their feelings for each other they start to realize their developing feelings for karl as well, which is briefly terrifying for both parties (dteam because despite the fact that they’re now happy with each other, they’re all now in love with another one of their friends who’s also partially the reason they got together in the first place, and poor karl who slowly falls in love with not one but three of his best friends who he’s also convinced are perfect for each other already) but then they all end up confessing to each other and it turns out they had nothing to worry about bc they all love each other anyways. thats really all i got to say dnkn in gay love yippeee🎉��️‍🌈🫶

dnkn in gay love yippeee !!!

oh anon :') ugh that's so so cute. gonna be honest i LOVE the concept of Karl swooping in on an established relationship and the relationship having a gay panic bc kind, funny, and physically affectionate Karl just meshes so well with them? like they thought they truly found what they've been looking for but then Karl comes along and suddenly their relationship + Karl becomes what they were looking for and it's scary but it's so easy for them to talk about bc Karl is so blatant that they can cast it off as a joke:

"did you see the way Karl was hanging off of Sapnap today?"

"yeah. kinda hot ngl"

"oh. you too huh"

dnn realize they're all in love with Karl over a late night conversation and they just have to laugh bc they were getting all worried but wow there's something to say about the fact that they all have similar tastes

one of my favorite things to think about is cuddly dnkn all squished in bed together :') Sapnap and George getting competitive about where they are in the pile. Karl and George giggling and messing around between dreamnap. George accidentally kicking Dream while rolling around, trying to muffle his laughter. Dream hoisting George on the other side of him to separate them. sometimes Sapnap joins in on the shenanigans and Dream just gives up bc there's no way he can control all three of them (he can barely control George and Sapnap when they get going)

i could rant about it all day honestly :')

[nekul-nahuel]

hey, tell me that you want me to stay

Happy (late) Valentine’s Day, @dreamnotnapss! I hope you two have had an amazing and full of love month, enjoying (hopefully? haha) this gift for you. For now it's in Spanish (my first language), but in two or three days the translation should already be done:)

5 times where George unexpectedly popped in Dream/Sapnap’s streams, 1 time where the same thing happened, but in a different way. You’ll see why.

5 ocasiones en donde George apareció de improviso en las transmisiones de Dream y Sapnap, 1 ocasión en donde ocurrió lo mismo, pero de manera diferente.

Rating: T Relationships: DNN (Dream/Sapnap/George) Main tags: Polyamory, Developing Relationship, Chatting & Messaging, Domestic, 5+1 Things Language: Spanish - Español, in a few days the translation should be ok!

[Leer en AO3]

[itonlinetraining]

PyTorch has developed over the past few years into a well-liked and often used framework for deep neural networks (DNN) training. about Pytorch 2.0 PyTorch’s popularity is credited to its ease of use, first-rate Python integration, and imperative programming approach.To know more about PyTorch 2.0, check out the Python training course.

[softwarefrim]

Hire DotNetNuke Developers

Hire DotNetNuke Developers at YES IT Labs to transform your ideas into exceptional DNN web solutions, built with precision and care.

[techahead-software-blog]

Revolutionizing Industries With Edge AI

The synergy between AI, cloud computing, and edge technologies is reshaping innovation. Currently, most IoT solutions rely on basic telemetry systems. These systems capture data from edge devices and store it centrally for further use. Our approach goes far beyond this conventional method. 

We leverage advanced machine learning and deep learning models to solve real-world problems. These models are trained in cloud environments and deployed directly onto edge devices. Deploying AI models to the edge ensures real-time decision-making and creates a feedback loop that continuously enhances business processes, driving digital transformation.  

The AI in edge hardware market is set for exponential growth. Valued at USD 24.2 billion in 2024, it is expected to reach USD 54.7 billion by 2029, achieving a CAGR of 17.7%. 

The adoption of edge AI software development is growing due to several factors, such as the rise in IoT devices, the need for real-time data processing, and the growth of 5G networks. Businesses are using AI in edge computing to improve operations, gain insights, and fully utilize data from edge devices. Other factors driving this growth include the popularity of social media and e-commerce, deeper integration of AI into edge systems, and the increasing workloads managed by cloud computing.

The learning path focuses on scalable strategies for deploying AI models on devices like drones and self-driving cars. It also introduces structured methods for implementing complex AI applications.

A key part of this approach is containerization. Containers make it easier to deploy across different hardware by packaging the necessary environments for various edge devices. This approach works well with Continuous Integration and Continuous Deployment (CI/CD) pipelines, making container delivery to edge systems smoother.

This blog will help you understand how AI in edge computing can be integrated into your business. These innovations aim to simplify AI deployment while meeting the changing needs of edge AI ecosystems.

Key Takeaways:

The integration of AI, cloud computing, and edge technologies is transforming innovation across industries. Traditional IoT solutions depend on basic telemetry systems to collect and centrally store data for processing. 

Advanced machine learning and deep learning models elevate this approach, solving complex real-world challenges. These models are trained using powerful cloud infrastructures to ensure robust performance.

After training, the models are deployed directly onto edge devices for localized decision-making. This shift reduces latency and enhances the efficiency of IoT applications, offering smarter solutions.

What is Edge AI?

Edge AI is a system that connects AI operations between centralized data centers (cloud) and devices closer to users and their environments (the edge). Unlike traditional AI that runs mainly in the cloud, AI in edge computing focuses on decentralizing processes. This is different from older methods where AI was limited to desktops or specific hardware for tasks like recognizing check numbers.

The edge includes physical infrastructure like network gateways, smart routers, or 5G towers. However, its real value is in enabling AI on devices such as smartphones, autonomous cars, and robots. Instead of being just about hardware, AI in edge computing is a strategy to bring cloud-based innovations into real-world applications.

AI in edge computing technology enables machines to mimic human intelligence, allowing them to perceive, interact, and make decisions autonomously. To achieve these complex capabilities, it relies on a structured life cycle that transforms raw data into actionable intelligence.

The Role of Deep Neural Networks (DNN)

At the core of AI in edge computing are deep neural networks, which replicate human cognitive processes through layered data analysis. These networks are trained using a process called deep learning. During training, vast datasets are fed into the model, allowing it to identify patterns and produce accurate outputs. This intensive learning phase often occurs in cloud environments or data centers, where computational resources and collaborative expertise from data scientists are readily available.  

From Training to Inference

Once a deep learning model is trained, it transitions into an inference engine. The inference engine uses its learned capabilities to analyze new data and provide actionable insights. Unlike the training phase, which requires centralized resources, the inference stage operates locally on devices. This shift enables real-time decision-making, even in remote environments, making it ideal for edge AI deployments in industries like manufacturing, healthcare, and autonomous vehicles.  

Real-World Applications

Edge AI operates on decentralized devices such as factory robots, hospital equipment, autonomous cars, satellites, and smart home systems. These devices run inference engines that analyze data and generate insights directly at the point of origin, minimizing dependency on cloud systems.  

When AI in edge computing encounters complex challenges or anomalies, the problematic data is sent to the cloud for retraining. This iterative feedback loop enhances the original AI model’s accuracy and efficiency over time. Consequently, Edge AI systems continuously evolve, becoming more intelligent and responsive with each iteration.  

Why Does the Feedback Loop Matters?

The feedback loop is a cornerstone of Edge AI’s success. It enables edge devices to identify and address gaps in their understanding by sending troublesome data to centralized systems for refinement. These improvements are reintegrated into the edge inference engines, ensuring that deployed models consistently improve in accuracy and performance.  

What Does Edge AI Look Like Today?

Edge AI integrates edge computing with artificial intelligence to redefine data processing and decision-making. Unlike traditional systems, AI in edge computing operates directly on localized devices like Internet of Things (IoT) devices or edge servers. This minimizes reliance on remote data centers, ensuring efficient data collection, storage, and processing at the device level. 

By leveraging machine learning, AI in edge computing mimics human reasoning, enabling devices to make independent decisions without constant internet connectivity.

Localized Processing for Real-Time Intelligence

Edge AI transforms conventional data processing models into decentralized operations. Instead of sending data to remote servers, it processes information locally. This approach improves response times and reduces latency, which is vital for time-sensitive applications. Local processing also enhances data privacy, as sensitive information doesn’t need to leave the device.

Devices Empowered by Independence

Edge AI empowers devices like computers, IoT systems, and edge servers to operate autonomously. These devices don’t need an uninterrupted internet connection. This independence is crucial in areas with limited connectivity or for tasks requiring uninterrupted functionality. The result is smarter, more resilient systems capable of decision-making at the edge.  

Practical Application in Everyday Life

Virtual assistants like Google Assistant, Apple’s Siri, and Amazon Alexa exemplify edge AI’s capabilities. These tools utilize machine learning to analyze user commands in real-time. They begin processing as soon as a user says, “Hey,” capturing data locally while interacting with cloud-based APIs. AI in edge computing enables these assistants to learn and store knowledge directly on the device, ensuring faster, context-aware responses.  

Enhanced User Experience

With AI in edge computing, devices deliver seamless and personalized interactions. By learning locally, systems can adapt to user preferences while maintaining high performance. This ensures users experience faster, contextually aware services, even in offline scenarios.  

What Might Edge AI Look Like in the Future?

Edge AU is poised to redefine how intelligent systems interact with the world. Beyond current applications like smartphones and wearables, its future will likely include advancements in more complex, real-time systems. Emerging examples span autonomous vehicles, drones, robotics, and video-analytics-enabled surveillance cameras. These technologies leverage data at the edge, enabling instant decision-making that aligns with real-world dynamics.

Revolutionizing Transportation

Self-driving vehicles are a glimpse into the transformative power of AI in edge computing. These cars process visual and sensor data in real time. They assess road conditions, nearby vehicles, and pedestrians while adapting to sudden changes like inclement weather. By integrating edge AI, autonomous cars deliver rapid, accurate decisions without relying solely on cloud computing. This ensures safety and efficiency in high-stakes environments.  

Elevating Automation and Surveillance

Drones and robots equipped with edge AI are reshaping automation. Drones utilize edge AI to navigate complex environments autonomously, even in areas without connectivity. Similarly, robots apply localized intelligence to execute intricate tasks in industries like manufacturing and logistics. Surveillance cameras with edge AI analyze video feeds instantly, identifying threats or anomalies with minimal latency. This boosts operational security and situational awareness.  

Unprecedented Growth Trajectory

The AI in edge computing ecosystem is set for exponential growth in the coming years. Market projections estimate the global edge computing market will reach $61.14 billion by 2028. This surge reflects industries’ increasing reliance on intelligent systems that operate independently of centralized infrastructures.  

Empowering Smarter Ecosystems

Edge AI will enhance its role in creating interconnected systems that adapt dynamically. It will empower devices to process and act on complex data. This evolution will foster breakthroughs across sectors like healthcare, automotive, security, and energy.  

The future of edge AI promises unmatched efficiency, scalability, and innovation. As its adoption accelerates, edge AI will continue to drive technological advancements, creating smarter, more resilient systems for diverse industries. 

Understanding the Advantages and Disadvantages of Edge AI

Edge computing and Edge AI are shaping the future of data flow management. With the exponential rise in data from business operations, innovative approaches to handle this surge have become essential.  

Edge computing addresses this challenge by processing and storing data near end users. This localized approach alleviates pressure on centralized servers, reducing the volume of data routed to the cloud. The integration of AI with Edge computing has introduced Edge AI, a transformative solution that maximizes the benefits of reduced latency, bandwidth efficiency, and offline functionality.  

However, like any emerging technology, Edge AI has both advantages and limitations. Businesses must weigh these factors to determine its suitability for their operations.  

Key Advantages of Edge AI

Reduced Latency

Edge AI significantly reduces latency by processing data locally instead of relying on distant cloud platforms. This enables quicker decision-making, as data doesn’t need to travel back and forth between the cloud and devices. Additionally, cloud platforms remain free for more complex analytics and computational tasks, ensuring better resource allocation.  

Optimized Bandwidth Usage

Edge AI minimizes bandwidth consumption by processing, analyzing, and storing most data locally on Edge-enabled devices. This localized approach reduces the volume of data sent to the cloud, cutting operational costs while improving overall system efficiency.  

Enhanced Security and Privacy

By decentralizing data storage, Edge AI reduces reliance on centralized repositories, lowering the risk of large-scale breaches. Localized processing ensures sensitive information stays within the edge network. When cloud integration is required, redundant or unnecessary data is filtered out, ensuring only critical information is transmitted.  

Scalability and Versatility

The proliferation of Edge-enabled devices simplifies system scalability. Many Original Equipment Manufacturers (OEMs) now embed native Edge capabilities into their products. This trend facilitates seamless expansion while allowing local networks to operate independently during disruptions in upstream or downstream systems.  

Potential Challenges of Edge AI

Risk of Data Loss

Poorly designed Edge AI systems may inadvertently discard valuable information, leading to flawed analyses. Effective planning and programming are critical to ensuring only irrelevant data is filtered out while preserving essential insights for future use.  

Localized Security Vulnerabilities

While Edge AI enhances cloud-level security, it introduces risks at the local network level. Weak access controls, poor password management, and human errors can create entry points for cyber threats. Implementing robust security protocols at every level of the system is essential to mitigating such vulnerabilities.  

Limited Computing Power

Edge AI lacks the computational capabilities of cloud platforms, making it suitable only for specific AI tasks. For example, Edge devices are effective for on-device inference and lightweight learning tasks. However, large-scale model training and complex computations still rely on the superior processing power of cloud-based AI systems.  

Device Variability and Reliability Issues

Edge AI systems often depend on a diverse range of devices, each with varying capabilities and reliability. This variability increases the risk of hardware failures or performance inconsistencies. Comprehensive testing and compatibility assessments are essential to mitigate these challenges and ensure system reliability.  

Edge AI Use Cases and Industry Examples

AI in edge computing is transforming industries with innovative applications that bridge cloud computing and real-time local operations. Here are key cases and practical implementations of edge AI.

Enhanced Speed Recognition

Edge AI enables mobile devices to transcribe speech instantly without relying on constant cloud connectivity. This ensures faster, more private communication while enhancing user experience through seamless functionality.  

Biometric Security Solutions

Edge AI powers fingerprint detection and face-ID systems, ensuring secure authentication directly on devices. This eliminates latency concerns, enhancing both security and efficiency in personal and enterprise applications.  

Revolutionizing Autonomous Vehicles

Autonomous navigation systems utilize edge AI for real-time decision-making. AI models are trained in the cloud, but vehicles execute these models locally for tasks like steering and braking. Self-driving systems improve continuously as data from unexpected human interventions is uploaded to refine cloud-based algorithms. Updated models are then deployed to all vehicles in the fleet, ensuring collective learning.  

Intelligent Image Processing

Google’s AI leverages edge computing to automatically generate realistic backgrounds in photos. By processing images locally, the system achieves faster results while maintaining the quality of edits, enabling a seamless creative experience for users.  

Advanced Wearable Health Monitoring

Wearables use edge AI to analyze heart rate, blood pressure, glucose levels, and breathing locally. Cloud-trained AI models deployed on these devices provide real-time health insights, promoting proactive healthcare without requiring continuous cloud interactions.  

Marter Robotics

Robotic systems employ edge AI to enhance operational efficiency. For instance, a robot arm learns optimized ways to handle packages. It shares its findings with the cloud, enabling updates that improve the performance of other robots in the network. This approach accelerates innovation across robotics systems. 

Adaptive Traffic Management

Edge AI drives smart traffic cameras that adjust light timings based on real-time traffic conditions. This reduces congestion, improves flow, and enhances urban mobility by processing data locally for instant action.  

Difference Between Edge AI Vs Cloud AI

The evolution of edge AI and cloud AI stems from shifts in technology and development practices over time. Before the emergence of the cloud or edge, computing revolved around mainframes, desktops, smartphones, and embedded systems. Application development was slower, adhering to Waterfall methodologies that required bundling extensive functionality into annual updates.

The advent of cloud computing revolutionized workflows by automating data center processes. Agile practices replaced rigid Waterfall models, enabling faster iterations. Modern cloud-based applications now undergo multiple updates daily. This modular approach enhances flexibility and efficiency. Edge AI builds on this innovation, extending these Agile workflows to edge devices like smartphones, smart appliances, and factory equipment.  

Modular Development Beyond the Cloud

While cloud AI centralizes functionality, edge AI brings intelligence to the periphery of networks. It allows mobile phones, vehicles, and IoT devices to process and act on data locally. This decentralization drives faster decision-making and enhanced real-time responsiveness.  

Degrees of Implementation

The integration of edge AI varies by device. Basic edge devices, like smart speakers, send data to the cloud for inference. More advanced setups, such as 5G access servers, host AI capabilities that serve multiple nearby devices. LF Edge, an initiative by the Linux Foundation, categorizes edge devices into types like lightbulbs, on-premises servers, and regional data centers. These represent the growing versatility of edge AI across industries.  

Collaborative Edge-Cloud Ecosystem

Edge AI and cloud AI complement each other seamlessly. In some cases, edge devices transmit raw data to the cloud, where inferencing is performed, and results are sent back. Alternatively, edge devices can run inference locally using models trained in the cloud. Advanced implementations even allow edge devices to assist in training AI models, creating a dynamic feedback loop that enhances overall AI accuracy and functionality.  

Enhancing AI Across Scales

By integrating edge AI, organizations capitalize on local processing power while leveraging cloud scalability. This symbiosis ensures optimal performance for applications requiring both immediate insights and large-scale analytics. 

Conclusion

Edge AI stands as a transformative force, bridging the gap between centralized cloud intelligence and real-time edge processing. Its ability to decentralize AI workflows has unlocked unprecedented opportunities across industries, from healthcare and transportation to security and automation. By reducing latency, enhancing data privacy, and empowering devices with autonomy, Edge AI is revolutionizing how businesses harness intelligence at scale.  

However, successful implementation requires balancing its advantages with potential challenges. Businesses must adopt scalable strategies, robust security measures, and effective device management to fully realize its potential.  

As Edge AI continues to evolve, it promises to redefine industries, driving smarter ecosystems and accelerating digital transformation. Organizations that invest in this technology today will be better positioned to lead in an era where real-time insights and autonomous systems dictate the pace of innovation.  

Whether it’s powering autonomous vehicles, optimizing operations, or enhancing user experiences, Edge AI is not just a technological shift; it’s a paradigm change shaping the future of intelligent systems. Embrace Edge AI today to stay ahead in the dynamic landscape of innovation.

Source URL: https://www.techaheadcorp.com/blog/revolutionizing-industries-with-edge-ai/

[jcmarchi]

Unmasking Bias in Artificial Intelligence: Challenges and Solutions

New Post has been published on https://thedigitalinsider.com/unmasking-bias-in-artificial-intelligence-challenges-and-solutions/

Unmasking Bias in Artificial Intelligence: Challenges and Solutions

The recent advancement of generative AI has seen an accompanying boom in enterprise applications across industries, including finance, healthcare, transportation. The development of this technology will also lead to other emerging tech such as cybersecurity defense technologies, quantum computing advancements, and breakthrough wireless communication techniques. However, this explosion of next generation technologies comes with its own set of challenges.

For example, the adoption of AI may allow for more sophisticated cyberattacks, memory and storage bottlenecks due to the increase of compute power and ethical concerns of biases presented by AI models. The good news is that NTT Research has proposed a way to overcome bias in deep neural networks (DNNs), a type of artificial intelligence.

This research is a significant breakthrough given that non-biased AI models will contribute to hiring, the criminal justice system and healthcare when they are not influenced by characteristics such as race, gender. In the future discrimination has the potential to be eliminated by using these kinds of automated systems, thus improving industry wide DE&I business initiatives. Lastly AI models with non-biased results will improve productivity and reduce the time it takes to complete these tasks. However, few businesses have been forced to halt their AI generated programs due to the technology’s biased solutions.

For example, Amazon discontinued the use of a hiring algorithm when it discovered that the algorithm exhibited a preference for applicants who used words like “executed” or “captured” more frequently, which were more prevalent in men’s resumes. Another glaring example of bias comes from Joy Buolamwini, one of the most influential people in AI in 2023 according to TIME, in collaboration with Timnit Gebru at MIT, revealed that facial analysis technologies demonstrated higher error rates when assessing minorities, particularly minority women, potentially due to inadequately representative training data.

Recently DNNs have become pervasive in science, engineering and business, and even in popular applications, but they sometimes rely on spurious attributes that may convey bias. According to an MIT study over the past few years, scientists have developed deep neural networks capable of analyzing vast quantities of inputs, including sounds and images. These networks can identify shared characteristics, enabling them to classify target words or objects. As of now, these models stand at the forefront of the field as the primary models for replicating biological sensory systems.

NTT Research Senior Scientist and Associate at the Harvard University Center for Brain Science Hidenori Tanaka and three other scientists proposed overcoming the limitations of naive fine-tuning, the status quo method of reducing a DNN’s errors or “loss,” with a new algorithm that reduces a model’s reliance on bias-prone attributes.

They studied neural network’s loss landscapes through the lens of mode connectivity, the observation that minimizers of neural networks retrieved via training on a dataset are connected via simple paths of low loss. Specifically, they asked the following question: are minimizers that rely on different mechanisms for making their predictions connected via simple paths of low loss?

They discovered that Naïve fine-tuning is unable to fundamentally alter the decision-making mechanism of a model as it requires moving to a different valley on the loss landscape. Instead, you need to drive the model over the barriers separating the “sinks” or “valleys” of low loss. The authors call this corrective algorithm Connectivity-Based Fine-Tuning (CBFT).

Prior to this development, a DNN, which classifies images such as a fish (an illustration used in this study) used both the object shape and background as input parameters for prediction. Its loss-minimizing paths would therefore operate in mechanistically dissimilar modes: one relying on the legitimate attribute of shape, and the other on the spurious attribute of background color. As such, these modes would lack linear connectivity, or a simple path of low loss.

The research team understands mechanistic lens on mode connectivity by considering two sets of parameters that minimize loss using backgrounds and object shapes as the input attributes for prediction, respectively. And then asked themselves, are such mechanistically dissimilar minimizers connected via paths of low loss in the landscape? Does the dissimilarity of these mechanisms affect the simplicity of their connectivity paths? Can we exploit this connectivity to switch between minimizers that use our desired mechanisms?

In other words, deep neural networks, depending on what they’ve picked up during training on a particular dataset, can behave very differently when you test them on another dataset. The team’s proposal boiled down to the concept of shared similarities. It builds upon the previous idea of mode connectivity but with a twist – it considers how similar mechanisms work. Their research led to the following eye-opening discoveries:

minimizers that have different mechanisms can be connected in a rather complex, non-linear way

when two minimizers are linearly connected, it’s closely tied to how similar their models are in terms of mechanisms

simple fine-tuning might not be enough to get rid of unwanted features picked up during earlier training

if you find regions that are linearly disconnected in the landscape, you can make efficient changes to a model’s inner workings.

While this research is a major step in harnessing the full potential of AI, the ethical concerns around AI may still be an upward battle. Technologists and researchers are working to combat other ethical weaknesses in AI and other large language models such as privacy, autonomy, liability.

AI can be used to collect and process vast amounts of personal data. The unauthorized or unethical use of this data can compromise individuals’ privacy, leading to concerns about surveillance, data breaches and identity theft. AI can also pose a threat when it comes to the liability of their autonomous applications such as self-driving cars. Establishing legal frameworks and ethical standards for accountability and liability will be essential in the coming years.

In conclusion, the rapid growth of generative AI technology holds promise for various industries, from finance and healthcare to transportation. Despite these promising developments, the ethical concerns surrounding AI remain substantial. As we navigate this transformative era of AI, it is vital for technologists, researchers and policymakers to work together to establish legal frameworks and ethical standards that will ensure the responsible and beneficial use of AI technology in the years to come. Scientists at NTT Research and the University of Michigan are one step ahead of the game with their proposal for an algorithm that could potentially eliminate biases in AI.

[predictiveanalyticsaiproject]

Introduction

In the digital age, data-driven decisions have become the cornerstone of successful businesses. Predictive analytics, powered by deep learning, offers unprecedented insights, enabling companies to anticipate trends and make informed choices. Our project, "Predictive Analytics on Business License Data Using Deep Learning Project," serves as a comprehensive introduction to deep neural networks (DNNs) and their application in real-world scenarios. By analyzing data from 86,000 businesses across various sectors, this project not only demystifies deep learning concepts but also demonstrates how they can be effectively utilized for predictive analytics.

The Importance of Predictive Analytics in Business

Predictive analytics uses historical data to forecast future events, helping businesses anticipate market changes, optimize operations, and enhance decision-making processes. In this project, we focus on business license data to predict the status of licenses, offering valuable insights into compliance trends, potential risks, and operational benchmarks.

Project Overview

Our project is designed to teach participants the fundamentals of deep neural networks (DNNs) through a hands-on approach. Using a dataset of business licenses, participants will learn essential steps such as Exploratory Data Analysis (EDA), data cleaning, and preparation. The project introduces key deep learning concepts like activation functions, feedforward, backpropagation, and dropout regularization, all within the context of building and evaluating DNN models.

Methodology

The project is structured into several key phases:

Data Exploration and Preparation:

Participants begin by exploring the dataset, identifying key features, and understanding the distribution of license statuses.

Data cleaning involves handling missing values, standardizing categorical variables, and transforming the data into a format suitable for modeling.

Building Baseline Models:

Before diving into deep learning, we create baseline models using the H2O framework. This step helps participants understand the importance of model comparison and sets the stage for more complex DNN models.

Deep Neural Networks (DNN) Development:

The core of the project involves building and training DNN models using TensorFlow. Participants learn how to design a neural network architecture, choose activation functions, implement dropout regularization, and fine-tune hyperparameters.

The model is trained to predict the status of business licenses based on various features, such as application type, license code, and business type.

Model Evaluation:

After training, the DNN model is evaluated on a test dataset to assess its performance. Participants learn to interpret metrics like accuracy, loss, and confusion matrices, gaining insights into the model's predictive power.

Results and Impact

The DNN model developed in this project demonstrates strong predictive capabilities, accurately classifying business license statuses. This model serves as a valuable tool for businesses and regulators, enabling them to anticipate compliance issues, streamline operations, and make data-driven decisions. Beyond the immediate application, participants gain a solid foundation in deep learning, preparing them for more advanced projects in the field of AI and machine learning.

Conclusion

The "Predictive Analytics on Business License Data Using Deep Learning" project offers a practical and educational journey into the world of deep learning. By engaging with real-world data and building predictive models, participants not only enhance their technical skills but also contribute to the broader field of AI-driven business analytics. This project underscores the transformative potential of deep learning in unlocking valuable insights from complex datasets, paving the way for more informed and strategic business decisions. You can download "Predictive Analytics on Business License Data Using Deep Learning Project (https://www.aionlinecourse.com/ai-projects/playground/predictive-analytics-on-business-license-data-using-deep-learningund/complete-cnn-image-classification-models-for-real-time-prediction)" from Aionlinecourse. Also you will get a live practice session on this playground.

[tamanna31]

New Tools and Technology Development will drive ENT Devices Market in coming years

ENT Devices Industry Overview

The global ENT devices market size was estimated at USD 25.93 billion in 2023 and is expected to grow at a compound annual growth rate (CAGR) of 5.54% from 2024 to 2030. This growth can be attributed to several factors, such as high prevalence of ENT-related disorders, an increase in the usage of minimally invasive ENT procedures, and rising geriatric population. Technological advancements also play a crucial role in driving the market growth.

The demand for advanced ENT devices, like robot-assisted endoscopes, is more in developed countries such as the U.S. while it is less in developing countries due to their high cost. The ENT devices market penetration is anticipated to grow significantly due to increasing healthcare spending by other governments and a rise in per capita income. Sales are expected to increase rapidly in developing economies due to high occurrences of ENT diseases such as hearing loss and sinusitis. Additionally, there is an increase in efforts to provide better access to healthcare facilities in these regions.

Gather more insights about the market drivers, restrains and growth of the ENT Devices Market

Hearing loss or impairment is a common condition among patients, particularly in industrialized countries. The World Health Organization reported, more than 430 million people worldwide, approximately 5% of the world's population, have a disabling hearing loss. This number is projected to rise to over 700 million, or one in every ten people, by 2050. The main reasons for this increase are growing life expectancy and noise pollution, leading to more age-related hearing loss cases. In low-income countries, infections such as middle ear infections, measles, or meningitis are the common causes of hearing loss. Moreover, vascular disorders, noise exposure, chronic inflammation, genetic susceptibility, physiological aging of the ear contributing to hearing impairment.

Rising technological advancements such as the adoption of AI and ML and innovations in auditory products are propelling industry growth. For instance, in September 2023, ELEHEAR Inc., an AI-powered hearing aids and audio solutions provider, introduced ELEHEAR Alpha Pro and ELEHEAR Alpha hearing aid devices. It is incorporated with AI noise reduction and extraction, which predicts daily users and their actions to minimize the effect of noise in typical audio environments such as public transit, offices, restaurants, homes, and busy streets. In March 2023, Oticon Medical A/S introduced new features in the processing chip Polaris R, which uses an onboard Deep Neural Network (DNN) for an entirely new method of sound processing. The updated processing chip features include sudden sound stabilizer and Wind & Handling Stabilizer.

Browse through Grand View Research's Medical Devices Industry Research Reports.

• The global intrauterine devices market size was estimated at USD 6.25 billion in 2023 and is projected to grow at a CAGR of 3.66% from 2024 to 2030.

• The global dual chamber prefilled syringes market size was valued at USD 167.3 million in 2023 and is projected to grow at a CAGR of 5.8% from 2024 to 2030.

Key ENT Devices Company Insights

Some of the key market players include, Cochlear Ltd., Demant A/S, Stryker, and KARL STORZ.

Cochlear Ltd. (Cochlear) engages in developing and commercializing cochlear implants, bone conduction implants, & acoustic implants to treat hearing-impaired individuals. Cochlear Ltd. is a global company with major manufacturing facilities in Sweden and Australia.It has a global presence in more than 180 countries.

Demant A/S (Demant) is a global company that develops, manufactures, and commercializes hearing implants, traditional hearing instruments, personal communication devices, & diagnostic instruments. The group operates in over 30 countries and sells its products in over 130 countries.

Nemera., Nico Corporation, and Rion Co., Ltd. are the emerging market participants.

Nemera, founded in 2003, is a medical equipment manufacturer specializing in a diverse product portfolio, including Ear, Nose, Throat, Nasal spray pumps, drug delivery devices, ophthalmic, and others. In 2021, Nemera established an operational base in Brazil and expanded its product and service offerings throughout Latin America.

NICO Corporation is a medical technology company that specializes in developing minimally invasive surgical solutions, particularly in the fields of neurosurgery and otolaryngology (ear, nose, and throat or ENT)

Key ENT Devices Companies:

The following are the leading companies in the ENT devices market. These companies collectively hold the largest market share and dictate industry trends. Financials, strategy maps & products of these ENT devices companies are analyzed to map the supply network.

Ambu A/S

Cochlear Ltd.

Demant A/S

GN Store Nord A/S

Karl Storz SE & Co.

Olympus Corporation

Pentax of America, Inc.

Richard Wolf GmbH

Rion Co., Ltd.

Smith & Nephew plc

Sonova

Starkey Laboratories, Inc.

Stryker

Nico Corporation

Nemera

Recent Developments

In April 2023, Unitron, a brand of Sonova launched Vivante, a platform aimed to enhance listener’s experience through personalized hearing control. This platform offers improved sound performance and new designs to enhance the hearing experience, integrating  experience innovations and the remote plus app to offer a customized hearing experience.

In February 2023, Cochlear Ltd. announced a partnership with Amazon.com, Inc. to expand audio streaming for hearing aids for people with Cochlear's hearing implants to provide comfortable entertainment.

In November 2022, Cochlear Ltd. announced the expansion of its manufacturing facility plant in Kuala Lumpur, Malaysia. The expansion involved an asset of more than USD 6.28 million (RM 30 million) to help the growing demand for acoustic and cochlear hearing implants

Order a free sample PDF of the ENT Devices Market Intelligence Study, published by Grand View Research.

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AI Voice Cloning: Innovations and Implications for the Tech Industry

Artificial intelligence (AI) has advanced fast over the last decade, pushing the limits of what technology is capable of. One of the most intriguing advancements in this field is AI voice cloning. This technology enables the creation of very realistic and customizable synthetic voices, revolutionizing industries ranging from entertainment to customer service. In this blog article, we'll look at the advances driving AI voice cloning, the potential ramifications for the IT industry, and the growing trend of free AI voice cloning tools. Understanding AI Voice Cloning AI voice cloning uses deep learning algorithms to analyze and reproduce a person's voice. By processing large datasets of recorded speech, AI systems may develop synthetic voices that imitate the actual speaker's tone, pitch, and intonation with fantastic accuracy. This approach includes several critical technologies: Deep Neural Networks (DNNs): DNNs model the complexity of human speech, allowing AI to generate real-sounding voices. Natural Language Processing (NLP): NLP aids in comprehending and generating human language, enabling AI to produce coherent and contextually relevant speech. Generative Adversarial Networks (GANs): GANs enhance synthetic voices, increasing authenticity and minimizing artificial undertones. Innovations in AI Voice Cloning Improved Realism and Accuracy The increased realism and accuracy of synthetic voices is one of the most significant advances in AI voice cloning. Early attempts at voice synthesis frequently produced monotone, artificial speech. However, with improvements in machine learning, today's AI-generated sounds are nearly identical to human voices. Google, Amazon, and Microsoft have created voice cloning technology that can duplicate minor differences in speech, such as emotional tones and accents. Customisation and Personalisation AI voice cloning offers an excellent level of customization. Users can customize synthetic voices to reflect unique features, making them more personalized and engaging. This is especially beneficial in applications like virtual assistants, where a personalized voice may significantly improve the user experience. Businesses can also build brand-specific voices that correspond with their identity, guaranteeing consistency across all customer interactions. Real-time Voice Cloning Another ground-breaking breakthrough is real-time voice copying. This technique allows for real-time speech generation by creating synthetic voices on the fly. Real-time voice cloning has essential implications for live broadcasts, video games, and interactive applications, where immediate speech synthesis can improve the immersion experience. Free AI Voice Cloning Tools The democratization of AI technology has resulted in the creation of free AI voice cloning tools. These solutions give individuals and small organizations access to advanced voice copying capabilities without requiring a significant financial investment. Open-source programs and platforms such as Resemble AI, Descript, and iSpeech provide free or freemium approaches, allowing users to experiment with and integrate voice cloning into their projects. Applications of AI Voice Cloning in the Tech Industry Entertainment and Media AI voice cloning is revolutionizing the entertainment industry by allowing the generation of synthetic voices for animated characters, dubbing, and voiceovers. This technology enables the seamless integration of voices from multiple languages and places, making material more accessible worldwide. Furthermore, voice cloning can resurrect the voices of deceased actors, ensuring continuity in long-running series or posthumously released works. Customer Service In customer service, AI voice cloning can improve the capabilities of virtual assistants and chatbots. These AI-powered systems can provide better customer experiences by using a more human-like voice and responding to requests with greater empathy and efficiency. Personalized voices can also strengthen consumer relationships, increasing satisfaction and loyalty. Healthcare AI voice cloning has potential applications in healthcare, especially for individuals with speech disability. Patients can regain functional communication skills by developing synthetic voices that resemble their native voices. Additionally, telemedicine can use voice cloning to add a personal touch to remote consultations. Education AI voice cloning can help educators develop interactive and engaging learning experiences. Synthetic voices can narrate educational information, provide feedback, and aid in language learning by producing consistent and precise pronunciations. This technology can also create personalized learning aids tailored to each student's needs. Implications of AI Voice Cloning Ethical Consideration The rise of AI voice cloning raises numerous ethical concerns. One of the main issues is the possibility of abuse, such as making deepfake audio clips that can be used to deceive or manipulate. Robust legislative frameworks and explicit permission and data privacy norms must be in place to ensure this technology is used ethically. Intellectual Property AI voice cloning creates issues about intellectual property rights. Who owns the rights to a synthetic voice, especially if it sounds like a natural voice? Establishing legal protections and procedures will be critical in dealing with these challenges and avoiding the unauthorized use of cloned voices. Impact on Employment The broad deployment of AI voice cloning could impact voice acting and customer service jobs. While AI can complement human capabilities, job displacement is risky. Examining strategies for reskilling and upskilling employees to adapt to the changing landscape is critical. Future of AI Voice Cloning As AI voice cloning technology advances, we should expect more improvements in realism, customization, and accessibility. More sophisticated algorithms and processing power will enable progressively more convincing synthetic voices to be fabricated. Furthermore, the trend towards open AI voice cloning tools will increase access, allowing for more experimentation and innovation. The future of AI voice cloning holds enormous promise for improving human-computer interactions and generating more immersive and personalized experiences. We may use this technology to create beneficial change in various industries by addressing ethical and legal issues. Conclusion AI voice cloning is a revolutionary discovery that is altering the IT sector. This technology has numerous applications, from improving customer service to revolutionizing entertainment and media. With the introduction of free AI voice cloning tools, more people and organizations may investigate and benefit from this technology. However, careful consideration of the ethical and legal ramifications is required to ensure responsible and equitable use. As we look to the future, AI voice cloning promises to open up new possibilities and change how we engage with technology. Read the full article