#AI business Platform

[tsreviews]

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[jatinsreviews]

AI SPARK Review

How to create a New Online Business by leveraging Whatsapp through AI?

[devilsskettle]

i feel like i’ve been WAITING for the other shoe to drop wrt people’s opinions about watcher for this very reason. not that i think the reaction is completely not understandable but the greater the parasocial relationship, the greater the fallout as soon as public opinion shifts. you don’t have a relationship with these people they’re just content creators, chill

[jcmarchi]

DeepL Revolutionizes Language AI with Launch of DeepL Voice for Real-Time Multilingual Communication

New Post has been published on https://thedigitalinsider.com/deepl-revolutionizes-language-ai-with-launch-of-deepl-voice-for-real-time-multilingual-communication/

DeepL Revolutionizes Language AI with Launch of DeepL Voice for Real-Time Multilingual Communication

DeepL, a global leader in Language AI, has launched DeepL Voice, a cutting-edge voice translation tool designed to facilitate seamless communication across languages. With an estimated valuation of $2 billion, DeepL has earned its place as a premier provider of language solutions for enterprises and individuals alike. This latest product, DeepL Voice, enables multilingual interactions in real-time, bridging language gaps in both virtual and face-to-face settings. This milestone was celebrated during DeepL’s first flagship event, DeepL Dialogues, in Berlin.

What is DeepL Voice?

DeepL Voice is the company’s first product focused on voice-based translation, expanding beyond DeepL’s renowned text translation services. Available in two specialized models—Voice for Meetings and Voice for Conversations—DeepL Voice offers real-time translated captions, empowering global teams to communicate seamlessly without language constraints. DeepL Voice is designed to work across virtual meeting platforms and mobile devices, making it versatile for various business needs, from multinational collaborations to customer-facing roles.

The tool supports numerous languages, including English, German, Japanese, Korean, French, Spanish, and more, with captions available in all 33 languages supported by the DeepL Translator. With these capabilities, DeepL Voice aims to enhance productivity and inclusivity by eliminating language barriers that often hinder global business interactions.

DeepL Voice for Meetings: Empowering Multilingual Virtual Meetings

One of the flagship applications of DeepL Voice is Voice for Meetings, which allows participants to speak in their preferred language during virtual meetings while providing real-time translated captions for others. This enables every attendee to understand and contribute fully, regardless of language fluency. By supporting natural multilingual dialogue, Voice for Meetings promotes clear communication and meaningful engagement, paving the way for more effective global collaboration.

Christine Aubry, Internationalisation Coordinator at Brioche Pasquier, an early adopter of DeepL Voice, shared her experience: “Our teams felt truly connected, opening up new possibilities for collaboration that were previously limited by language constraints. DeepL Voice for Meetings brought our teams closer together.”

DeepL Voice for Conversations: Facilitating One-on-One, In-Person Communication

For in-person conversations, Voice for Conversations operates on mobile devices, enabling real-time translations during one-on-one interactions. This application is designed for customer-facing and frontline roles, where language barriers can have immediate impacts on service quality and operational efficiency. With Voice for Conversations, DeepL provides companies a solution to support their multilingual workforce and improve customer interactions, enhancing both customer satisfaction and employee effectiveness.

Addressing the Challenges of Real-Time Voice Translation

DeepL Voice’s development involved tackling unique technical challenges associated with real-time translation, such as handling incomplete inputs, pronunciation differences, and latency issues. Unlike text translation, real-time voice translation demands instant processing to avoid disruptions and inaccuracies in conversation. To overcome these challenges, DeepL’s engineers leveraged years of data and AI expertise, training their models to account for variations in accents, regional dialects, and environmental factors. The result is a robust solution that provides clear, contextually accurate translations at high speed.

Jarek Kutylowski, CEO and founder of DeepL, explained, “Real-time speech translation introduces a new level of complexity. By building on the AI and linguistic expertise we’ve honed since 2017, we’ve overcome challenges like incomplete input and pronunciation variations, ensuring businesses can engage globally without language constraints.”

Reducing Business Costs from Ineffective Communication

Language barriers can be costly for businesses, and DeepL Voice addresses these inefficiencies directly. Ineffective communication can cost companies up to $54,860 per employee annually, according to Axios HQ. DeepL Voice eliminates these barriers by offering high-quality, real-time translation, reducing the likelihood of miscommunication and enhancing team collaboration.

DeepL: A Leader in Language AI Innovation

Founded in 2017, DeepL has quickly established itself as a trusted provider of language solutions, now used by over 100,000 businesses, including half of the Fortune 500. DeepL’s Language AI platform, known for its accuracy and enterprise-grade security, is built on proprietary language models fine-tuned over years to handle complex linguistic nuances. Today, the platform serves clients across 228 markets, providing them with tools for both written and spoken language needs.

Backed by top-tier investors like Benchmark, IVP, and Index Ventures, DeepL continues to expand its product offerings with a commitment to meeting the highest standards in quality and security. Earlier in 2024, the company released a next-generation language model that surpasses competitors such as OpenAI’s ChatGPT-4, Google, and Microsoft in translation quality. This model, built on DeepL’s proprietary data, is optimized for fewer edits, further enhancing productivity for enterprise users.

Integrating DeepL Voice in Global Operations: Key Use Cases

With its versatility, DeepL Voice has numerous applications across various business sectors:

Global Team Meetings: Virtual meetings can now include participants speaking different languages, enabling a more inclusive environment where everyone can fully engage.

Frontline Customer Service: Customer-facing employees, especially in retail, healthcare, and hospitality, can now offer support in the language most comfortable for each customer, enhancing service quality and customer loyalty.

Manufacturing and Safety Operations: Real-time voice translation allows employees in high-stakes environments to understand critical information without delay, reducing the risks associated with miscommunication.

DeepL’s Broader Impact on Language and Communication

DeepL Voice represents more than just a product launch; it is a significant milestone in DeepL’s mission to eliminate language barriers worldwide. By enabling instant multilingual communication, DeepL Voice empowers businesses to leverage their global talent, foster stronger relationships, and expand into new markets. From multinational corporations to government agencies, DeepL’s language AI tools are designed to streamline operations, improve productivity, and enhance communication across diverse workforces.

What’s Next for DeepL?

DeepL’s journey in language technology is far from over. The company is continuously expanding its capabilities, with plans to introduce additional languages and advanced AI features in future releases. With the next-generation large language model already outperforming industry giants, DeepL is poised to set new standards in the field, offering tools that not only translate but truly bridge gaps in human communication.

Summary

DeepL Voice is a game-changing solution for businesses that face language challenges in daily operations. By facilitating clear, real-time communication across languages, DeepL Voice allows businesses to foster inclusivity, reduce miscommunication costs, and operate with greater efficiency. This innovation is another leap forward in DeepL’s mission to connect the world through language AI, transforming how we communicate, and collaborate.

[devsgames]

Some years ago when everyone was talking about Blockchain, I made a post on Twitter that went viral and somehow resulted in me getting to write an opinion piece for Wireframe (Raspberry Pi's now-defunct gamedev magazine).

I find myself thinking about this article again as I see people have constant circular discussions on AI and generally spreading misinformation about tech in general. I find my thinking is still very much the same - that technology is just a tool, and that the root problems are always something larger. Sometimes I wish we bothered pooling our effort into tackling those instead.

It was originally titled "Blockchain Isn't the Enemy", and you could probably replace a good chunk of "NFT" and "Blockchain" talk with "AI" and it still works.

[glassixma]

Glassix

Software Development Company

Glassix is a top-rated AI customer support and messaging platform, leveraging the advanced capabilities of conversational AI integrated with the GPT-4 engine. It’s designed to empower busy teams to excel in customer support and experience, featuring an AI-powered unified inbox that consolidates communications across all business channels and apps, including WhatsApp, Apple Messages for Business, social media, email, SMS, and more. Complementing this robust unified inbox, Glassix offers an intuitive drag-and-drop chatbot flow builder and templates, making it effortless to craft smart, efficient automation flows and deploy chatbots to any channel with just a single click. The platform's distinctiveness lies in its comprehensive AI suite and omnichannel features, providing users with innovative and modern tools such as auto-suggested replies, automatic tagging of conversations, one-click conversation summaries, and the capability to deploy generative AI chatbots. These features collectively ensure stellar customer support and experience, setting Glassix apart in the realm of customer engagement solutions.

Contact Details

Glassix

One Boston Place, Suite 2600, Boston, MA, USA 02108

Phone- +1 (617) 683-1236

Website- https://www.glassix.com/

Business Email- contact@glassix.com

Business Hours- Mon - Thu: 9AM - 5PM.

Payment Methods- Credit/ Debit Card, PayPal, Apple Pay, Google Pay, Wire Transfer.

Owner Name- Guy Shalom.

Follow On:

Facebook- https://www.facebook.com/GlassixCompany

YouTube- https://www.youtube.com/@Glassix_CX

Instagram- https://www.instagram.com/glassix_cx/

TikTok- https://www.tiktok.com/@glassix.com

LinkedIn- https://www.linkedin.com/company/glassix

[phonesuitedirect]

AI-powered customer service automation are able to handle tedious and time-consuming tasks, such as answering frequently asked questions. Read More...

[fusiondynamics]

HPC cluster management software

HPC

High-performance computing (HPC) is at the core of next-gen scientific and industrial breakthroughs across a broad range of domains, such as medical imaging, computational physics, weather forecasting, and banking.

With the help of powerful computing clusters, HPC processes massive datasets to solve complex problems using simulations and modelling.

Advantages of our HPC product offerings

Intensive Workloads

Fusion Dynamics HPC servers are designed to handle extensive computational requirements, suitable for enterprises working on large amounts of data.

With up to 80/128 cores per processor, our server clusters can handle intensive parallel workloads to meet your computing needs.

Memory Bandwidth and Rapid Storage

Before performing complex calculations rapidly, HPC systems must be able to swiftly store and access vast quantities of data.

Our HPC servers have high memory bandwidth, with each memory channel accessible with uniform low latency speeds across processor cores.

High Throughput

An HPC compute module must allow rapid data transfer from the processor to multiple peripherals like network modules, external storage, and GPUs.

Our HPC solutions support up to 128 PCIe Gen4 lanes per socket, ensuring high throughput data transfer in parallel.

Lower Energy Costs

The trade-off of high computational speeds is energy consumption, and it is vital to maximise the performance-to-energy ratio to maintain an efficient system as workloads increase. Our line of HPC servers offers higher computational performance per watt to minimise your energy costs even as you scale your HPC operations.

Contact Us

+91 95388 99792

aditya@fusiondynamics.io

[navai-official]

AI in Manufacturing: Top 10 Use Cases

The manufacturing industry underwent a major transformation during the Industrial Revolution. Today, it is poised for an even bigger transformation with artificial intelligence. A new age of innovation and efficiency has started. AI will redefine everything, from the assembly line to the boardroom. In 2024, the global AI in manufacturing market size is USD 5.94 billion. It’s expected to touch USD 230.95 billion by 2034. AI in manufacturing will solve many persistent problems and take processes and operations to a whole new level. Let’s first understand the existing bottlenecks in the manufacturing world.

Problems in the Manufacturing Industry

The manufacturing industry is the backbone of many economies. It faces a slew of challenges that hinder it from reaching its full potential. Here are a few of the nasty ones:

1: Quality Control Problems

Every manufacturing company will agree that QC is a big headache. A few defective products slipping through the cracks can cost the reputation of the brand. Ensuring consistent product quality is a constant battle.

2: Maintenance and Repairs

Manufacturing equipment can be unreliable. They give up on you when you need them the most. This leads to downtime, and downtime leads to productivity loss, and that’s how the cookie crumbles. It’s quite difficult to see equipment failures coming, making them a persistent challenge.

3: Supply Chain Issues

Navigating complex supply chains is no less than a logistical nightmare. Disruptions, delays, and shortages can cripple operations.

4: High Energy Consumption

Manufacturing and factories are associated with huge smoke-emitting chimneys. Reducing energy consumption and carbon footprints is a growing concern. It’s critical for reducing expenses as well as environmental safety.

5: Data Overload

The sheer volume of data generated by modern manufacturing facilities can be difficult to analyze and leverage. However, this is now a positive with the advent of AI. Let’s explore how.

Top 10 Use Cases of AI in Manufacturing

The lack of digitization took a toll on manufacturing businesses during the pandemic. The integration of AI in manufacturing is the biggest paradigm shift for the industry ever. Here are 10 very important use cases of AI in manufacturing:

1: Developing New Products

Customers expect companies to create new products and innovate upon the existing ones. However, creating new products runs the risk of unacceptance from the market or high competition.

AI changes the decision-making process altogether by analyzing vast amounts of data on consumer preferences and market trends. It can predict exactly what the customers demand and generate innovative product concepts. Manufacturers can personalize their products to the specific needs of their customers. This level of personalization can drive customer loyalty and increase sales.

AI-driven simulation and testing tools can accelerate the product development process.

2: Real-Time Quality Checks

Computer vision-powered quality control systems are now an effective solution to the drawbacks of manual inspections. They can monitor the manufacturing belts in real-time and detect defects and anomalies with inhuman precision and speed. AI achieves this by analyzing vast amounts of data and learning to recognize patterns.

AI quality control not only increases accuracy but also reduces the labor costs of manual inspections.

For instance, AI can detect minuscule flaws in a car’s paint job or identify irregularities in the texture of a fabric. Apart from detecting defects, AI in manufacturing can ensure that the products meet specific quality standards, ensuring the best output.

3: Predictive Maintenance

Predictive maintenance is another benefit of AI monitoring and computer vision. AI can predict when a machine is likely to malfunction, so you’ll be ready with repairs or a replacement. All it needs for this is data. By analyzing sensor data from machines, it can predict potential failures before they sneak up.

Businesses can embrace a proactive approach, improving equipment reliability and reducing operational costs. Unplanned downtime will be a thing of the past.

For example, AI can analyze vibration patterns in a motor to detect early signs of wear and tear. Or, it can monitor temperature fluctuations in a machine to identify overheating issues. Manufacturers can schedule maintenance during off-peak hours, and the work goes on smoothly.

4: Demand Forecasting

Overstocking and understocking both eat away profits. AI in manufacturing prevents them from happening with demand forecasting. Machine learning algorithms analyze historical data and market conditions to generate accurate demand forecasts. Manufacturers can know exactly how many units of which product to create to reduce stockouts and overstock.

For example, AI can predict fluctuations in demand for a particular product based on seasonal trends or upcoming events. Manufacturers can tweak their manufacturing schedules accordingly, ensuring they have the right amount of inventory on hand to meet demand.

[kazifatagar]

Dell Technologies Expands NativeEdge AI Platform to Simplify Edge Operations and Boost Resilience

Dell Technologies has unveiled significant enhancements to its Dell NativeEdge edge operations software, aiming to streamline AI deployment and improve edge workload resilience. The updates enable organizations to better manage the growing volume of data generated and processed at the edge, with advanced features such as high-availability clustering and automated AI integrations. Meeting the…

[stratosiq]

Turn feedback into action with StratosIQ! Discover insights that matter—like this clothing retailer who improved customer satisfaction by addressing sizing issues. Make smarter decisions, faster.

[simplifysol]

Essential Tools and Technologies for Effective Omnichannel Customer Support

In today’s competitive landscape, omnichannel customer service is essential for meeting customer expectations across multiple touchpoints. This approach allows businesses to deliver consistent and seamless experiences, whether customers interact through social media, chat, email, or phone. For an effective omnichannel strategy, leveraging the right tools and technologies is crucial. Here’s a look at the must-have solutions:

1. AI Chatbots for Business

AI chatbots have become vital for providing instant responses and 24/7 support, enhancing customer satisfaction and retention. An AI chatbot for business can help address common customer queries, route complex issues to human agents, and improve response times. With the ability to personalize interactions based on previous conversations, chatbots ensure a cohesive experience across channels. They not only save valuable time for customer service teams but also help in boosting customer engagement.

2. Customer Experience Management (CXM) Platforms

A customer experience management platform is essential for collecting and analyzing customer data from various touchpoints. By integrating customer insights across channels, a CXM platform enables businesses to understand customer preferences, pain points, and satisfaction levels. This comprehensive data helps companies improve the customer journey, providing valuable insights for refining their omnichannel customer service strategy. CXM platforms also facilitate personalized interactions, which is key to meeting customer expectations in an omnichannel environment.

3. Unified Communication Platforms

Unified communication tools allow seamless switching between channels — enabling customers to start a conversation on one channel (e.g., social media) and continue it on another (e.g., email) without losing context. These platforms integrate phone, chat, email, and social media, providing a holistic view of the customer’s history and interactions. They streamline communication for customer service teams, enhancing efficiency and ensuring consistency across channels.

4. CRM (Customer Relationship Management) Systems

An effective CRM system centralizes customer data, ensuring that all customer-facing teams have access to the same information. This alignment allows agents to provide informed, personalized assistance across various channels, contributing to a positive customer experience. CRMs also support omnichannel strategies by offering analytics that highlight customer trends, enabling businesses to anticipate needs and respond proactively.

5. Analytics and Reporting Tools

For ongoing success in omnichannel customer service, analytics tools are essential. These solutions measure performance across channels, helping businesses identify areas of improvement and understand customer behavior. With insights into response times, customer satisfaction, and engagement metrics, companies can refine their approach to deliver exceptional support.

Conclusion: Investing in the right tools, such as an AI chatbot for business, a customer experience management platform, and other key technologies — is essential for any organization aiming to excel in omnichannel customer service. These tools not only enhance operational efficiency but also provide valuable insights for delivering a superior customer experience. By leveraging these technologies, businesses can ensure they’re ready to meet the evolving demands of today’s customers.

[jcmarchi]

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

New Post has been published on https://thedigitalinsider.com/deepfake-misuse-deepfake-detection-before-its-too-late-cybertalk/

Deepfake misuse & deepfake detection (before it’s too late) - CyberTalk

Micki Boland is a global cyber security warrior and evangelist with Check Point’s Office of the CTO. Micki has over 20 years in ICT, cyber security, emerging technology, and innovation. Micki’s focus is helping customers, system integrators, and service providers reduce risk through the adoption of emerging cyber security technologies. Micki is an ISC2 CISSP and holds a Master of Science in Technology Commercialization from the University of Texas at Austin, and an MBA with a global security concentration from East Carolina University.

In this dynamic and insightful interview, Check Point expert Micki Boland discusses how deepfakes are evolving, why that matters for organizations, and how organizations can take action to protect themselves. Discover on-point analyses that could reshape your decisions, improving cyber security and business outcomes. Don’t miss this.

Can you explain how deepfake technology works? 

Deepfakes involve simulated video, audio, and images to be delivered as content via online news, mobile applications, and through social media platforms. Deepfake videos are created with Generative Adversarial Networks (GAN), a type of Artificial Neural Network that uses Deep Learning to create synthetic content.

GANs sound cool, but technical. Could you break down how they operate?

GAN are a class of machine learning systems that have two neural network models; a generator and discriminator which game each other. Training data in the form of video, still images, and audio is fed to the generator, which then seeks to recreate it. The discriminator then tries to discern the training data from the recreated data produced by the generator.

The two artificial intelligence engines repeatedly game each other, getting iteratively better. The result is convincing, high quality synthetic video, images, or audio. A good example of GAN at work is NVIDIA GAN. Navigate to the website https://thispersondoesnotexist.com/ and you will see a composite image of a human face that was created by the NVIDIA GAN using faces on the internet. Refreshing the internet browser yields a new synthetic image of a human that does not exist.

What are some notable examples of deepfake tech’s misuse?

Most people are not even aware of deepfake technologies, although these have now been infamously utilized to conduct major financial fraud. Politicians have also used the technology against their political adversaries. Early in the war between Russia and Ukraine, Russia created and disseminated a deepfake video of Ukrainian President Volodymyr Zelenskyy advising Ukrainian soldiers to “lay down their arms” and surrender to Russia.

How was the crisis involving the Zelenskyy deepfake video managed?

The deepfake quality was poor and it was immediately identified as a deepfake video attributable to Russia. However, the technology is becoming so convincing and so real that soon it will be impossible for the regular human being to discern GenAI at work. And detection technologies, while have a tremendous amount of funding and support by big technology corporations, are lagging way behind.

What are some lesser-known uses of deepfake technology and what risks do they pose to organizations, if any?

Hollywood is using deepfake technologies in motion picture creation to recreate actor personas. One such example is Bruce Willis, who sold his persona to be used in movies without his acting due to his debilitating health issues. Voicefake technology (another type of deepfake) enabled an autistic college valedictorian to address her class at her graduation.

Yet, deepfakes pose a significant threat. Deepfakes are used to lure people to “click bait” for launching malware (bots, ransomware, malware), and to conduct financial fraud through CEO and CFO impersonation. More recently, deepfakes have been used by nation-state adversaries to infiltrate organizations via impersonation or fake jobs interviews over Zoom.

How are law enforcement agencies addressing the challenges posed by deepfake technology?

Europol has really been a leader in identifying GenAI and deepfake as a major issue. Europol supports the global law enforcement community in the Europol Innovation Lab, which aims to develop innovative solutions for EU Member States’ operational work. Already in Europe, there are laws against deepfake usage for non-consensual pornography and cyber criminal gangs’ use of deepfakes in financial fraud.

What should organizations consider when adopting Generative AI technologies, as these technologies have such incredible power and potential?

Every organization is seeking to adopt GenAI to help improve customer satisfaction, deliver new and innovative services, reduce administrative overhead and costs, scale rapidly, do more with less and do it more efficiently. In consideration of adopting GenAI, organizations should first understand the risks, rewards, and tradeoffs associated with adopting this technology. Additionally, organizations must be concerned with privacy and data protection, as well as potential copyright challenges.

What role do frameworks and guidelines, such as those from NIST and OWASP, play in the responsible adoption of AI technologies?

On January 26th, 2023, NIST released its forty-two page Artificial Intelligence Risk Management Framework (AI RMF 1.0) and AI Risk Management Playbook (NIST 2023). For any organization, this is a good place to start.

The primary goal of the NIST AI Risk Management Framework is to help organizations create AI-focused risk management programs, leading to the responsible development and adoption of AI platforms and systems.

The NIST AI Risk Management Framework will help any organization align organizational goals for and use cases for AI. Most importantly, this risk management framework is human centered. It includes social responsibility information, sustainability information and helps organizations closely focus on the potential or unintended consequences and impact of AI use.

Another immense help for organizations that wish to further understand risk associated with GenAI Large Language Model adoption is the OWASP Top 10 LLM Risks list. OWASP released version 1.1 on October 16th, 2023. Through this list, organizations can better understand risks such as inject and data poisoning. These risks are especially critical to know about when bringing an LLM in house.

As organizations adopt GenAI, they need a solid framework through which to assess, monitor, and identify GenAI-centric attacks. MITRE has recently introduced ATLAS, a robust framework developed specifically for artificial intelligence and aligned to the MITRE ATT&CK framework.

For more of Check Point expert Micki Boland’s insights into deepfakes, please see CyberTalk.org’s past coverage. Lastly, to receive cyber security thought leadership articles, groundbreaking research and emerging threat analyses each week, subscribe to the CyberTalk.org newsletter.

[botsailor]

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[youthchronical]

OpenAI hires Meta's former Orion head to lead its robotics efforts

Jaap Arriens | NurPhoto via Getty Images The former head of Meta’s Orion augmented reality glasses initiative has joined OpenAI to lead the startup’s robotics and consumer hardware efforts. Caitlin “CK” Kalinowski announced her new role Monday in a post on LinkedIn and X, writing, “In my new role, I will initially focus on OpenAI’s robotics work and partnerships to help bring AI into the…

[xx-invadr-ellie-xx]

I can't get over how idiotic musk's plan was for this whole Brazil situation. Like it literally went like this.

Brazil: Your rampant unfiltered hate speech and misinformation on your platform is in violation of the law, you need to do something about it.

musk: I'll just close all our offices in Brazil, now that there's no staff in the country you can't charge us.

Brazil: You need to have a representative operating in the country otherwise you can't do business here. Consult with us in 24 hours or your site will be blocked.

musk: ha look here's an ai generated image of toilet paper with the name of your supreme court judge on it (he actually did this)

Twitter is blocked in Brazil after musk failed to consult with them and appoint a representative.