Category robots

We are more than seals, we enable and unlock new features for robotics. Robots have always fascinated us! The dream of human-like machines that independently perform complex tasks is ancient. What began as a utopia has long since gained a firm place in the automation industry as industrial robots. 6-axis, SCARA, or Delta robots take over physically demanding, unhealthy tasks and tirelessly complete even the most monotonous work steps 24/7. This permanent use in mostly demanding industrial environments requires maximum performance from all robot components. For the seals, this means above all high demands on reliability and service life. Immerse yourself in the universe of robotics and discover how Freudenberg Sealing Technologies can support your company with its customized sealing solutions.

The high flexibility of cobots, not only in adapting to changing tasks but especially in diverse working environments, places stringent demands on the seals employed. To guarantee operational reliability and long-term durability, it is crucial to protect the joints from dust, splash water, and other contaminants. Seals play a pivotal role in this context. Freudenberg Sealing Technologies' high-performance sealing solutions not only prevent leaks and potential failures but also enhance the efficiency of robotics applications by ensuring a long service life. Sealing solutions for cobots from Freudenberg Sealing Technologies.

An AI-powered robot that can prepare cups of coffee in a busy kitchen could usher in the next generation of intelligent machines, a study suggests.

An AI-powered robot that can prepare cups of coffee in a busy kitchen could usher in the next generation of intelligent machines, a study suggests.

An AI-powered robot that can prepare cups of coffee in a busy kitchen could usher in the next generation of intelligent machines, a study suggests.

The compact size, high performance and efficiency of Jetson Thor enables Atlas to run complex, multimodal AI models that work seamlessly with Boston Dynamics’ whole-body and manipulation controllers.

Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and artificial intelligence to analyze their data, they created a simple, inexpensive tool that could expand possibilities for performing chemical analysis.

Organizations are eager to move into the era of agentic AI, but moving AI projects from development to production remains a challenge. Deploying agentic AI apps often requires complex configurations and integrations, delaying time to value. 

Barriers to deploying agentic AI: 

• Knowing where to start: Without a structured framework, connecting tools and configuring systems is time-consuming.
  
  
• Scaling effectively: Performance, reliability, and cost management become resource drains without a scalable infrastructure.
  
  
• Ensuring security and compliance: Many solutions rely on uncontrolled data and models instead of permissioned, tested ones
  
  
• Governance and observability: AI infrastructure and deployments need clear documentation and traceability.
  
  
• Monitoring and maintenance: Ensuring performance, updates, and system compatibility is complex and difficult without robust monitoring.
  

Now, DataRobot comes with NVIDIA AI Enterprise embedded — offering the fastest way to develop and deliver agentic AI. 

With a fully validated AI stack, organizations can reduce the risks of open-source tools and DIY AI while deploying where it makes sense, without added complexity.

This enables AI solutions to be custom-tailored for business problems and optimized in ways that would otherwise be impossible.

In this blog post, we’ll explore how AI practitioners can rapidly develop agentic AI applications using DataRobot and NVIDIA AI Enterprise, compared to assembling solutions from scratch. We’ll also walk through how to build an AI-powered dashboard that enables real-time decision-making for warehouse managers. 

Use Case: Real-time warehouse optimization

Imagine that you’re a warehouse manager trying to decide whether to hold shipments upstream. If the warehouse is full, you need to reorganize your inventory efficiently. If it’s empty, you don’t want to waste resources; your team has other priorities

But manually tracking warehouse capacity is time-consuming, and a simple API won’t cut it. You need an intuitive solution that fits into your workflow without required coding. 

Rather than piecing together an AI app manually, AI teams can rapidly develop a solution using DataRobot and NVIDIA AI Enterprise. Here’s how: 

• AI-powered video analysis: Uses the NVIDIA AI Blueprint for video search and summarization as an embedded agent to identify open spaces or empty warehouse shelves in real time.
  
  
• Predictive inventory forecasting: Leverages DataRobot Predictive AI to forecast income inventory volume.
  
  
• Real-time insights and conversational AI: Displays live insights on a dashboard with a conversational AI interface.
  
  
• Simplified AI management: Provides simplified model management with NVIDIA NIM and DataRobot monitoring.
  
  

This is just one example of how AI teams can build agentic AI apps faster with DataRobot and NVIDIA. 

Solving the toughest roadblocks in building and deploying agentic AI

Building agentic AI applications is an iterative process that requires balancing integration, performance, and adaptability. Success depends on seamlessly connecting — LLMs, retrieval systems, tools, and hardware — while ensuring they work together efficiently. 

However, the complexity of agentic AI can lead to prolonged debugging, optimization cycles, and deployment delays. 

The challenge is delivering AI projects at scale without getting stuck in endless iteration. 

How NVIDIA AI Enterprise and DataRobot simplify agentic AI development

Flexible starting points with NVIDIA AI Blueprints and DataRobot AI Apps

Choose between NVIDIA AI Blueprints or DataRobot AI Apps to jumpstart AI application development. These pre-built reference architectures lower the entry barrier by providing a structured framework to build from, significantly reducing setup time.

To integrate NVIDIA AI Blueprint for video search and summarization, simply import the blueprint from the NVIDIA NGC gallery into your DataRobot environment, eliminating the need for manual setup.

Accelerating predictive AI with RAPIDS and DataRobot

To build the forecast, teams can leverage RAPIDS data science libraries along with DataRobot’s full suite of predictive AI capabilities to automate key steps in model training, testing, and comparison.

This enables teams to efficiently identify the highest-performing model for their specific use case.

Optimizing RAG workflows with NVIDIA NIM and DataRobot’s LLM Playground

Using the LLM playground in DataRobot, teams can enhance RAG workflows by testing different models like the NVIDIA NeMo Retriever text reranking NIM or the NVIDIA NeMo Retriever text embedding NIM, and then compare different configurations side by side. This evaluation can be done using an NVIDIA LLM NIM as a judge, and if desired, augment the evaluations with human input.

This approach helps teams identify the optimal combination of prompting, embedding, and other strategies to find the best-performing configuration for the specific use case, business context, and end-user preferences. 

Ensuring operational readiness

Deploying AI isn’t the finish line — it’s just the start. Once live, agentic AI must adapt to real-world inputs while staying consistent. Continuous monitoring helps catch drift, bugs, and slowdowns, making strong observability tools essential. Scaling adds complexity, requiring efficient infrastructure and optimized inference.

AI teams can quickly become overwhelmed with balancing development of new solutions and simply keeping existing ones. 

For our agentic AI app, DataRobot and NVIDIA simplify management while ensuring high performance and security:

• DataRobot monitoring and NVIDIA NIM optimize performance and minimize risk, even as the number of users grows from 100 to 10K to 10M.

• DataRobot Guardrails, including NeMo Guardrails, provide automated checks for data quality, bias detection, model explainability, and deployment frameworks, ensuring trustworthy AI.

• Automated compliance tools and complete end-to-end observability help teams stay ahead of evolving regulations. 

Deploy where it’s needed 

Managing agentic AI applications over time requires maintaining compliance, performance, and efficiency without constant intervention.

Continuous monitoring helps detect drift, regulatory risks, and performance drops, while automated evaluations ensure reliability. Scalable infrastructure and optimized pipelines reduce downtime, enabling seamless updates and fine-tuning without disrupting operations. 

The goal is to balance adaptability with stability, ensuring the AI remains effective while minimizing manual oversight.

DataRobot, accelerated by NVIDIA AI Enterprise, delivers hyperscaler-grade ease of use without vendor lock-in across diverse environments, including self-managed on-premises, DataRobot-managed cloud, and even hybrid deployments.

With this seamless integration, any deployed models get the same consistent support and services regardless of your deployment choice — eliminating the need to manually set up, tune, or manage AI infrastructure.

 The new era of agentic AI

DataRobot with NVIDIA embedded accelerates development and deployment of AI apps and agents through simplifying the process at the model, app, and enterprise level. This enables AI teams to rapidly develop and deliver agentic AI apps that solve complex, multistep use cases and transform how end users work with AI. 

To learn more, request a custom demo of DataRobot with NVIDIA.

The post DataRobot with NVIDIA: The fastest path to production-ready AI apps and agents appeared first on DataRobot.

Virginia Tech researchers say a true revolution in wireless technologies is only possible through endowing the system with the next generation of artificial intelligence (AI) that can think, imagine, and plan akin to humans. Doing so will allow networks to break free from traditional enablers, deliver unprecedented quality, and usher in a new phase of the AI evolution.

Top 5 AI Apps For Speech Recognition

Artificial Intelligence technology has touched almost every aspect of life and the environment. In particular, Speech/Voice recognition is one of the trending technology belonging to AI that made human-to-machine interactions simple and faster.

Voice recognition or speech recognition is gaining popularity around the world. The rise of AI and voice-controlled intelligent assistants have made virtual interactions over a secured platform easier and more convenient. These cutting-edge digital technologies can convert human speech into machine-understandable code and revert back to human spoken language. Google Assistant, Microsoft Cortana, Apple’s Siri, and Amazon’s Alexa are a few best examples of speech recognition apps that have attained huge popularity in the market.

According to market research reports, 74% of smartphone users or digital customers rely on voice-based assistants to search for products and services, open their favorite playlists, make a shopping list, track the mailbox, etc. Such wide acceptance of AI and speech recognition applications is a profitable note for voice recognition technology companies.

It is expected that the global voice recognition apps market is expected to reach $18 billion by the year 2023, at a compound annual growth rate of 23.89%. So, businesses can either integrate AI features in existing apps or start to invest in AI development to grab market opportunities.

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Whether it’s on a standalone home device or Smartphone, the benefits delivered by digital voice assistants are incredible. Therefore, speech recognition AI will offer unbelievable businesses growth opportunities for organizations. The opportunities of developing AI-powered voice-controlled apps or speech recognition applications has great scope in the future.

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How AI is Used in Speech Recognition: Top 5 AI Apps for Speech Recognition

#1 Google Assistant- One of the Best AI apps in 2022

Google Assistant is one of the best voice recognition applications. It is a popular speech recognition application developed using AI. This Artificial Intelligence (AI) enabled and voice-powered virtual assistant software. It is known as one of the trending and advanced virtual assistants.

Google has partnered with a number of firms to make Google Assistant available on large number of smart devices such as head phones, smart phones, cars, fridges, etc.

Here, you can learn about what is Google Assistant and how does it work?

Google speech recognition app supports both text and voice entry and uses the latest AI technology like NLP (natural language processing). It provides a variety of services, including voice search, voice commands, assists tasks, translates in real-time, sends reminders, finds information online, makes appointment, and many more.

Google speech recognition app has also expanded its virtual assistant services significantly. It supports 10,000 devices across over 1000 brands.

Also Read: Top 10 Artificial Intelligence Technology Trends In 2022

 

#2 Alexa- Top Speech Recognition AI App

No doubt. Alexa is one of the best voice recognition apps for android and is also recognized as one of the top voice recognition apps for iPhone. Alexa is also known as Amazon Alexa, is an AI-based virtual assistant from the multinational company, Amazon. Initially, it was tried with Amazon Echo dot smart speakers and Amazon Echo. Anyway, currently, it is not available on iOS and Android platforms.

Amazon Alexa uses NLP, voice queries, etc. to provide services like music playback and voice interaction. In addition, it will set up alarms, creates to-do lists, plays audiobooks, and streams podcasts. Moreover, Alexa can also give live information about traffic, the latest news and sports, and the weather which are very useful.

You can experience this virtual assistant on Mac and Windows, and it requires some easy steps to setup. Some PCs accept Alexa’s hands-free activation, while others need you to click the Alexa icon.

#3 Siri – Best AI apps for speech recognition:

Can AI do voice recognition? The best answer is Siri-like famous voice recognition applications. Similar to Alexa and Google speech recognition app, Siri is one of the best AI apps for speech recognition.

Siri is one of the Top 5 AI apps for speech recognition applications which is delivering top-notch performance and assisting iPhone users virtually in many ways. It is a well-known virtual assistant recognized speech software from the Apple Company.

Siri uses Voice Questions and the Natural Language User Interface (UI) to work and send text messages, make calls, answer queries and make recommendations. It assigns requests to many Internet services. Besides, Siri adapts to users’ searches, language, and preferences.

#4 Microsoft – Best AI Voice Recognition Software

The most popular voice recognition apps for android and iOS. Another strong AI application that does not require an introduction is Cortana, a virtual assistant from the tech giant, Microsoft. Cortana provides problem-solving, hands-free assistance, answers queries, performs specific tasks, takes notes, gives reminders, and assists in calendar management. Over time, Microsoft’s Cortana “learns” and performs complex tasks.

Cortana uses information from Bing search engines, NLP, and other devices to provide personalized recommendations, and it consists of a speech API that greatly works with Windows and 3rd party applications.
This voice recognition software is available on iOS, Android, Windows Mobile, Microsoft Band, Windows 10, Windows Mixed Reality (MR), Invoke Smart Speaker, and Xbox One.

You can also see Cortana on famous headsets such as the CloudX, HyperX, Logitech G933, Sennheiser GSP350, and Razor Kraken 7.1 V2.

Recommend To Read: Top 10 AI apps for speech recognition

#5 Hound 

It is one of the top 5 AI Apps for speech recognition. The Hound app, Silicon Valley Company named ‘SoundHound’ has launched this Hound application. This virtual assistant app answers the queries of users and completes several tasks such as calculations and recognizing words correctly in the process with 95% accuracy.

The Hound app can send a text message and make calls for you. You can also have information about weather conditions, breaking news, hotels, and restaurants nears by, call Ola or Uber, check the status of the flight, etc. This digital assists app is available on both iOS and Android.

This best AI voice recognition software app uses its Deep Meaning Understanding and Speech-to-Meaning technologies. Hound has an excellent range of customers, e.g., Honda, Mercedes Benz, Hyundai, and Motorola.

Recommended: Top Artificial intelligence Companies in US, India & Europe

These are the top 5 AI apps developed using speech recognition technology. All apps have been crowned as the best AI apps in 2022. Speech recognition in AI is really a gift for voice recognition technology companies to create miracles with technology. Voice recognition technology companies are assisting businesses across different verticals in creating futuristic speech recognition AI software applications.

Best AI Tools for Speech Recognition in 2025

In 2025, there are various AI speech recognition tools that can accurately and rapidly convert speech into text. Some of the well-known ones are:

• Google Speech-to-Text

An extremely accurate tool that can transcribe speech in real-time and has support for various languages.

• Microsoft Azure Speech Service

A strong speech recognition functionality that includes transcription as well as translation. It will be utilized to develop most of the voice-enabling applications.

• Amazon Transcribe

A cloud service for speech-to-text transcription, ideal for customer call, meeting, or media transcription with multi-accents and language support.

• Deepgram

A speech recognition platform powered by AI that prioritizes high accuracy for business use, with features such as real-time transcriptions and training custom models.

• Rev AI

Highly accurate transcription is what Rev AI is best known for, and it is commonly used to transcribe podcasts, interviews, and videos.

These AI tools for speech recognition in 2025 simplify the process of integrating speech recognition capabilities into products for developers and businesses, making it easier to improve accessibility, customer service, and user engagement.

Cost of Building an AI Voice Generator App

The cost of building an AI voice generator app may be $50,000 to $150,000 based on features, platform, and AI complexity. User interface design, voice generation API integration, and voice processing and storage with cloud infrastructure will also impact the overall cost of developing an AI Voice app.

Further, features such as multi-language integration will increase the final cost of developing an AI text-to-speech app. Moreover, licensing AI models or cloud services, such as AWS or Google Cloud, will also be an additional expense to the estimated cost of building an AI voice generator app.

However, additional costs for developing an AI text-to-speech app can be added for fine-tuning the models to achieve high-quality, natural-sounding voices. Maintenance, server prices, and security features also contribute to the overall cost of developing an AI voice app.

How to Build an AI-Based Language Learning App?

To develop voice recognition apps for iPhone or voice recognition apps for Android, AI development companies follow a precise process to ensure the successful app development.

Firstly, to create an AI-driven language learning app, incorporate AI technologies such as natural language processing (NLP) and speech recognition for customized lessons. Machine learning can adjust content to the skill level of the user. Real-time translation, grammar check, and pronunciation feedback are the highlight features that utilize APIs such as Google Translate or IBM Watson.

Second, develop a backend infrastructure for handling user data and tracking advancement. Secure platforms in the cloud, such as AWS or Firebase, make safe data storage possible. Facilitate privacy compliance and provide offline accessibility for lessons. Integration of social capabilities such as native speaker language exchange makes it a better experience. Periodic updates of content and AI updates will make the app efficient and topical.

Final Words

As per the research reports of Trend force, the voice recognition solutions market across the globe is forecasted to reach USD 15.98 billion by the year 2021. Virtual/ speech recognition is becoming an integral part of AI systems and it gives more communication to users.

Thanks to natural language processing, it is constantly learning from us and making necessary changes in the way we live, and perform. Businesses across the globe are expected to invest more in this speech recognition technology as the whole voice infrastructure improves soon.

Planning to Develop an AI-Powered Speech Recognition App for your Business?

USM Business Systems is providing cutting-edge AI solutions to transform your business. We have a team of experienced and professional AI developers who gained unbelievable hands-on best practices in using various advanced technologies.

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The post The Foldable iPhone Revolution: What to Expect from Apple’s Game-Changing Device appeared first on 1redDrop.