#cisco chatbot

[jcmarchi]

The modern ROI imperative: AI deployment, security and governance

New Post has been published on https://thedigitalinsider.com/the-modern-roi-imperative-ai-deployment-security-and-governance/

The modern ROI imperative: AI deployment, security and governance

Ahead of the TechEx North America event on June 4-5, we’ve been lucky enough to speak to Kieran Norton, Deloitte’s US Cyber AI & Automation leader, who will be one of the speakers at the conference on June 4th. Kieran’s 25+ years in the sector mean that as well as speaking authoritatively on all matters cybersecurity, his most recent roles include advising Deloitte clients on many issues around cybersecurity when using AI in business applications.

The majority of organisations have in place at least the bare minimum of cybersecurity, and thankfully, in most cases, operate a decently comprehensive raft of cybersecurity measures that cover off communications, data storage, and perimeter defences.

However, in the last couple of years, AI has changed the picture, both in terms of how companies can leverage the technology internally, and in how AI is used in cybersecurity – in advanced detection, and in the new ways the tech is used by bad actors.

As a cybersecurity tool, AI can be used in network anomaly detection and the smart spotting of phishing messages, among other uses. As a business enabler, AI means that the enterprise has to be proactive to ensure AI is used responsibly, balancing the innovation AI offers with privacy, data sovereignty, and risk.

Considered a relatively new area, AI, smart automation, data governance and security all inhabit a niche at present. But given the growing presence of AI in the enterprise, those niches are set to become mainstream issues: problems, solutions, and advice that will need to be observed in every organisation, sooner rather than later.

Governance and risk

Integrating AI into business processes isn’t solely about the technology and methods for its deployment. Internal processes will need to change to make best use of AI, and to better protect the business that’s using AI daily. Kieran draws a parallel to earlier changes made necessary by new technologies: “I would correlate [AI] with cloud adoption where it was a fairly significant shift. People understood the advantages of it and were moving in that direction, although sometimes it took them more time than others to get there.”

Those changes mean casting the net wide, to encompass the update of governance frameworks, establishing secure architectures, even leveraging a new generation of specialists to ensure AI and the data associated with it are used safely and responsibly. Companies actively using AI have to detect and correct bias, test for hallucinations, impose guardrails, manage where, and by whom AI is used, and more. As Kieran puts it: “You probably weren’t doing a lot of testing for hallucination, bias, toxicity, data poisoning, model vulnerabilities, etc. That now has to be part of your process.”

These are big subjects, and for the fuller picture, we advocate that readers attend the two talks at TechEx North America that Kieran’s to give. He’ll be exploring both sides of the AI coin – issues around AI deployment for the business, and the methods that companies can implement to deter and detect the new breed of AI-powered malware and attack vectors.

The right use-cases

Kieran advocates that companies start with smaller, lower-risk AI implementations. While some of the first sightings of AI ‘in the wild’ have been chatbots, he was quick to differentiate between a chatbot that can intelligently answer questions from customers, and agents, which can take action by means of triggering interactions with the apps and services the business operates. “So there’s a delineation […] chatbots have been one of the primary starting places […] As we get into agents and agentic, that changes the picture. It also changes the complexity and risk profile.”

Customer-facing agentic AI instances are indubitably higher risk, as a misstep can have  significant effects on a brand. “That’s a higher risk scenario. Particularly if the agent is executing financial transactions or making determinations based on healthcare coverage […] that’s not the first use case you want to try.”

“If you plug 5, 6, 10, 50, a hundred agents together, you’re getting into a network of agency […] the interactions become quite complex and present different issues,” he said.

In some ways, the issues around automation and system-to-system interfaces have been around for close on a decade. Data silos and RPA (robotic process automation) challenges are the hurdles enterprises have been trying to jump for several years. “You still have to know where your data is, know what data you have, have access to it […] The fundamentals are still true.”

In the AI era, fundamental questions about infrastructure, data visibility, security, and sovereignty are arguably more relevant. Any discussions about AI tend to circle around the same issues, which throws into relief Kieran’s statements that a conversation about AI in the enterprise has to be wide-reaching and concern many of the operational and infrastructural underpinnings of the enterprise.

Kieran therefore emphasises the importance of practicality, and a grounded assessment of need and ability as needing careful examination before AI can gain a foothold. “If you understand the use case […] you should have a pretty good idea of the ROI […] and therefore whether or not it’s worth the pain and suffering to go through building it.”

At Deloitte, AI is being put to use where there is a clear use case with a measurable return: in the initial triage-ing of SOC tickets. Here the AI acts as a Level I incident analysis engine. “We know how many tickets get generated a day […] if we can take 60 to 80% of the time out of the triage process, then that has a significant impact.” Given the technology’s nascence, demarcating a specific area of operations where AI can be used acts as both prototype and proof of effectiveness. The AI is not customer-facing, and there are highly-qualified experts in their fields who can check and oversee the AI’s deliberations.

Conclusion

Kieran’s message for business professionals investigating AI uses for their organisations was not to build an AI risk assessment and management programme from scratch. Instead, companies should evolve existing systems, have a clear understanding of each use-case, and avoid the trap of building for theoretical value.

“You shouldn’t create another programme just for AI security on top of what you’re already doing […] you should be modernising your programme to address the nuances associated with AI workloads.” Success in AI starts with clear, realistic goals built on solid foundations.

You can read more about TechEx North America here and sign up to attend. Visit the Deloitte team at booth #153 and drop in on its sessions on June 4: ‘Securing the AI Stack’ on the AI & Big Data stage from 9:20am-9:50am, and ‘Leveraging AI in Cybersecurity for business transformation’ on the Cybersecurity stage, 10:20am – 10:50am.

Learn more about Deloitte’s solutions and service offerings for AI in business and cybersecurity or email the team at uscyberai@deloitte.com.

(Image source: “Symposium Cisco Ecole Polytechnique 9-10 April 2018 Artificial Intelligence & Cybersecurity” by Ecole polytechnique / Paris / France is licensed under CC BY-SA 2.0.)

[truebusiness]

Breaking Tech News: Musk's Grok Creates Deepfakes, Cisco Announces 7% Workforce Cut, and Google Expands Retail Presence

In the ever-evolving world of technology, today’s headlines are particularly noteworthy, reflecting major developments from prominent tech leaders and companies. Let’s dive into the key stories making waves in the tech industry today: 1. Musk’s AI Chatbot Grok Generates Deepfakes Elon Musk’s latest venture, the AI chatbot Grok, has stirred considerable controversy with its ability to generate…

[mariacallous]

Ever since OpenAI released ChatGPT at the end of 2022, hackers and security researchers have tried to find holes in large language models (LLMs) to get around their guardrails and trick them into spewing out hate speech, bomb-making instructions, propaganda, and other harmful content. In response, OpenAI and other generative AI developers have refined their system defenses to make it more difficult to carry out these attacks. But as the Chinese AI platform DeepSeek rockets to prominence with its new, cheaper R1 reasoning model, its safety protections appear to be far behind those of its established competitors.

Today, security researchers from Cisco and the University of Pennsylvania are publishing findings showing that, when tested with 50 malicious prompts designed to elicit toxic content, DeepSeek’s model did not detect or block a single one. In other words, the researchers say they were shocked to achieve a “100 percent attack success rate.”

The findings are part of a growing body of evidence that DeepSeek’s safety and security measures may not match those of other tech companies developing LLMs. DeepSeek’s censorship of subjects deemed sensitive by China’s government has also been easily bypassed.

“A hundred percent of the attacks succeeded, which tells you that there’s a trade-off,” DJ Sampath, the VP of product, AI software and platform at Cisco, tells WIRED. “Yes, it might have been cheaper to build something here, but the investment has perhaps not gone into thinking through what types of safety and security things you need to put inside of the model.”

Other researchers have had similar findings. Separate analysis published today by the AI security company Adversa AI and shared with WIRED also suggests that DeepSeek is vulnerable to a wide range of jailbreaking tactics, from simple language tricks to complex AI-generated prompts.

DeepSeek, which has been dealing with an avalanche of attention this week and has not spoken publicly about a range of questions, did not respond to WIRED’s request for comment about its model’s safety setup.

Generative AI models, like any technological system, can contain a host of weaknesses or vulnerabilities that, if exploited or set up poorly, can allow malicious actors to conduct attacks against them. For the current wave of AI systems, indirect prompt injection attacks are considered one of the biggest security flaws. These attacks involve an AI system taking in data from an outside source—perhaps hidden instructions of a website the LLM summarizes—and taking actions based on the information.

Jailbreaks, which are one kind of prompt-injection attack, allow people to get around the safety systems put in place to restrict what an LLM can generate. Tech companies don’t want people creating guides to making explosives or using their AI to create reams of disinformation, for example.

Jailbreaks started out simple, with people essentially crafting clever sentences to tell an LLM to ignore content filters—the most popular of which was called “Do Anything Now” or DAN for short. However, as AI companies have put in place more robust protections, some jailbreaks have become more sophisticated, often being generated using AI or using special and obfuscated characters. While all LLMs are susceptible to jailbreaks, and much of the information could be found through simple online searches, chatbots can still be used maliciously.

“Jailbreaks persist simply because eliminating them entirely is nearly impossible—just like buffer overflow vulnerabilities in software (which have existed for over 40 years) or SQL injection flaws in web applications (which have plagued security teams for more than two decades),” Alex Polyakov, the CEO of security firm Adversa AI, told WIRED in an email.

Cisco’s Sampath argues that as companies use more types of AI in their applications, the risks are amplified. “It starts to become a big deal when you start putting these models into important complex systems and those jailbreaks suddenly result in downstream things that increases liability, increases business risk, increases all kinds of issues for enterprises,” Sampath says.

The Cisco researchers drew their 50 randomly selected prompts to test DeepSeek’s R1 from a well-known library of standardized evaluation prompts known as HarmBench. They tested prompts from six HarmBench categories, including general harm, cybercrime, misinformation, and illegal activities. They probed the model running locally on machines rather than through DeepSeek’s website or app, which send data to China.

Beyond this, the researchers say they have also seen some potentially concerning results from testing R1 with more involved, non-linguistic attacks using things like Cyrillic characters and tailored scripts to attempt to achieve code execution. But for their initial tests, Sampath says, his team wanted to focus on findings that stemmed from a generally recognized benchmark.

Cisco also included comparisons of R1’s performance against HarmBench prompts with the performance of other models. And some, like Meta’s Llama 3.1, faltered almost as severely as DeepSeek’s R1. But Sampath emphasizes that DeepSeek’s R1 is a specific reasoning model, which takes longer to generate answers but pulls upon more complex processes to try to produce better results. Therefore, Sampath argues, the best comparison is with OpenAI’s o1 reasoning model, which fared the best of all models tested. (Meta did not immediately respond to a request for comment).

Polyakov, from Adversa AI, explains that DeepSeek appears to detect and reject some well-known jailbreak attacks, saying that “it seems that these responses are often just copied from OpenAI’s dataset.” However, Polyakov says that in his company’s tests of four different types of jailbreaks—from linguistic ones to code-based tricks—DeepSeek’s restrictions could easily be bypassed.

“Every single method worked flawlessly,” Polyakov says. “What’s even more alarming is that these aren’t novel ‘zero-day’ jailbreaks—many have been publicly known for years,” he says, claiming he saw the model go into more depth with some instructions around psychedelics than he had seen any other model create.

“DeepSeek is just another example of how every model can be broken—it’s just a matter of how much effort you put in. Some attacks might get patched, but the attack surface is infinite,” Polyakov adds. “If you’re not continuously red-teaming your AI, you’re already compromised.”

[manamediamarketing]

The Future of Digital Marketing in 2025 – Trends Every Business Must Adopt

Introduction

As we step into 2025, digital marketing is evolving at an unprecedented pace. Businesses that stay ahead of trends will increase brand visibility, attract more leads, and boost conversions. From AI-driven SEO to hyper-personalized marketing, the digital landscape is more competitive than ever.

Whether you’re a small business owner, entrepreneur, or marketing professional, understanding these trends will help you craft a winning digital marketing strategy. Let’s explore the top digital marketing trends for 2025 that will shape the future of online success.

1. AI-Powered SEO is the Future

Search engines are becoming smarter and more intuitive. With AI-powered algorithms like Google’s MUM (Multitask Unified Model) and BERT (Bidirectional Encoder Representations from Transformers), traditional SEO tactics are no longer enough.

How AI is Transforming SEO in 2025?

✔ AI-driven content creation: Advanced AI tools analyze search intent to create highly relevant, optimized content. ✔ Predictive analytics: AI predicts user behavior, helping businesses optimize content for better engagement. ✔ Voice and visual search optimization: As voice assistants like Siri, Alexa, and Google Assistant become more popular, brands must adapt their SEO strategy to long-tail conversational queries.

Actionable Tip: Optimize for natural language searches, use structured data markup, and ensure website accessibility to improve rankings in 2025.

2. Video Marketing Continues to Dominate

With platforms like TikTok, Instagram Reels, and YouTube Shorts, video marketing is becoming the most powerful form of content in 2025.

Why is Video Marketing Essential?

📌 80% of internet traffic will be video content by 2025 (Cisco Report). 📌 Short-form videos increase engagement and hold attention longer than static content. 📌 Live streaming and interactive videos help brands connect with audiences in real-time.

Actionable Tip: Focus on storytelling, behind-the-scenes content, product demonstrations, and influencer collaborations to boost engagement.

3. Hyper-Personalization with AI & Data Analytics

Consumers expect highly personalized experiences, and AI-powered marketing automation makes it possible.

How Does Hyper-Personalization Work?

✔ AI analyzes customer behavior and past interactions to create tailored marketing messages. ✔ Email marketing campaigns are dynamically personalized based on user interests. ✔ Chatbots and voice assistants provide real-time, customized support.

Actionable Tip: Leverage tools like HubSpot, Salesforce, and Marketo to automate personalized marketing campaigns.

4. Influencer Marketing Becomes More Authentic

The influencer marketing industry is projected to reach $21.1 billion by 2025. However, brands are shifting from celebrity influencers to micro and nano-influencers for better authenticity and engagement.

Why Micro-Influencers Matter?

🎯 Higher engagement rates than macro-influencers. 🎯 More trust & relatability with niche audiences. 🎯 Cost-effective collaborations for brands with limited budgets.

Actionable Tip: Partner with influencers in your niche and use user-generated content (UGC) to enhance brand credibility.

5. Voice & Visual Search Optimization is a Must

By 2025, 50% of all searches will be voice or image-based, making traditional text-based SEO insufficient.

How to Optimize for Voice & Visual Search?

✔ Use long-tail keywords & conversational phrases. ✔ Optimize images with alt text & structured data. ✔ Ensure your site is mobile-friendly and fast-loading.

Actionable Tip: Implement Google Lens-friendly content to appear in image-based search results.

Conclusion

The future of digital marketing in 2025 is driven by AI, personalization, and immersive experiences. If you’re not adapting, you’re falling behind!

 Looking for expert digital marketing strategies? Mana Media Marketing can help you grow and dominate your niche. Contact us today!

[cherictech]

Digital Marketing Trends 2023

Digital Marketing Trends 2023: What to Expect and How to Get Ahead

As the year 2023 approaches, significant changes are expected in the world of digital marketing. From artificial intelligence (AI) to automation and voice search, these changes will have a significant impact on businesses of all sizes and shapes. In this post, we explore some of the top digital marketing trends in 2023 that businesses need to be aware of to stay ahead of the curve.

Artificial Intelligence (AI) AI is poised to change the world of digital marketing. It has the ability to optimize ad targeting, personalize user experiences and improve customer service. According to a study by the Boston Consulting Group, artificial intelligence will make up about 60% of marketing functions by 2023. This means that companies using artificial intelligence have an advantage over their competitors in terms of efficiency and the ability to offer personalized experiences to customers.

Automation and chatbots Automation and chatbots are becoming increasingly popular in the digital marketing world. Companies use chatbots to provide 24/7 customer support and automate certain tasks such as scheduling appointments and tracking orders. According to a Gartner report, by 2023 more than 25% of customer service operations will use chatbots across multiple channels. This trend significantly improves customer experience, reduces workload and costs.

Optimizing voice search Voice search is becoming more common thanks to the rise of voice assistants like Amazon's Alexa and Google Home. Businesses need to optimize their content for voice search queries so that their websites and products can be easily found by voice search. ComScore research predicts that by 2023, 50% of searches will be voice-based.

Video marketing Video marketing is an integral part of digital marketing and is predicted to grow further in 2023. According to a Cisco report, 82 percent of internet traffic will be video-based by 2023. This means that businesses that invest in video marketing can reach a wider audience, increase engagement and increase sales.

In conclusion, the world of digital marketing is evolving rapidly and companies must stay ahead of the curve to remain competitive. By embracing these digital marketing trends, businesses can improve efficiency, deliver personalized experiences, reduce workload, reduce costs and increase sales.

For More Details Click here

[globalnodes]

Top 10 Companies Leading the AI Conversational Bot Revolution in 2025

The AI conversational bot landscape is evolving at breakneck speed, transforming how businesses interact with customers, automate workflows, and deliver personalized experiences. As we step into 2025, these intelligent systems are no longer limited to scripted responses—they leverage advanced natural language processing (NLP), generative AI, and emotional intelligence to mimic human-like interactions. Here are the top 10 companies pioneering this revolution and shaping the future of conversational AI.

1. GlobalNodes

GlobalNodes specializes in blockchain-powered AI solutions, but its foray into AI conversational bot has turned heads. Their bots integrate decentralized data security with multilingual support, making them ideal for industries like fintech and supply chain. GlobalNodes’ bots excel in automating complex transactions while ensuring compliance and transparency.

Use Case: Secure customer onboarding and cross-border payment assistance.

2. Cisco Meraki

Known for cloud-managed IT, Cisco Meraki now embeds AI conversational bots into its networking ecosystems. These bots troubleshoot network issues in real-time, guide users through setup processes, and predict hardware failures. Their strength lies in merging IoT data with conversational interfaces for seamless IT management.

Use Case: Proactive enterprise IT support and system diagnostics.

3. Sendbird

Sendbird dominates the in-app chat space, but its AI bots elevate customer engagement by blending chat APIs with generative AI. Brands use Sendbird’s bots for 24/7 product recommendations, cart recovery, and personalized user journeys. Their chatbots are highly customizable, catering to industries like e-commerce and telehealth.

Use Case: In-app customer retention and sales automation.

4. Smartcat

Smartcat isn’t just a translation platform—its AI conversational bots bridge language gaps in real-time. Designed for global teams and customer service, these bots translate conversations instantly while preserving context and tone. Smartcat’s NLP models support over 200 languages, making it a go-to for multilingual enterprises.

Use Case: Cross-language customer support and international collaboration.

5. Kata.ai

A leader in Southeast Asia’s AI scene, Kata.ai offers bots tailored for Bahasa Indonesia and regional dialects. Their solutions focus on hyper-localized marketing, from handling FAQs to driving sales via WhatsApp and Facebook. Kata.ai shines in combining cultural nuance with AI-driven interactions.

Use Case: Regional customer engagement and social commerce.

6. Bobble AI

Bobble AI revolutionizes mobile keyboards with AI-driven content, but its chatbots are equally innovative. Integrated into messaging apps, these bots analyze typing patterns to suggest stickers, GIFs, and quick replies. Bobble’s edge lies in enhancing informal, real-time conversations with humor and personalization.

Use Case: Boosting user engagement in social messaging platforms.

7. Nuacem AI

Nuacem AI focuses on voice-enabled AI bots for healthcare and education. Their systems handle everything from patient symptom checks to virtual classroom interactions. Nuacem’s proprietary speech recognition tech ensures accuracy even in noisy environments, setting it apart in voice-first AI.

Use Case: Telehealth triage and remote learning support.

8. Rasa

Rasa remains a favorite for developers seeking open-source flexibility. Its 2025 upgrades include enhanced dialogue management and LLM integrations, enabling bots to handle ambiguous queries. Rasa’s on-premise deployment appeals to industries like banking and defense, where data privacy is critical.

Use Case: Highly secure, customizable enterprise bots.

9. Boost.ai

Boost.ai combines no-code bot-building with deep learning for enterprises. Its 2025 platform introduces “emotional analytics,” letting bots adjust responses based on user sentiment. Boost.ai dominates Scandinavian markets, helping banks and governments automate services without losing the human touch.

Use Case: Public sector query resolution and financial advisories.

10. Botsonic & NICE

Botsonic by Writesonic offers GPT-4-powered bots that draft content, answer queries, and even generate code snippets—all through a no-code interface. Meanwhile, NICE focuses on AI-driven customer service, using bots to analyze call center data and predict customer needs.

Use Cases:

Botsonic: Marketing and developer support.

NICE: Omnichannel customer experience optimization.

Why These Companies?

The 2025 leaders were chosen based on:

Innovation: Unique features like emotional analytics (Boost.ai) or blockchain integration (GlobalNodes).

Industry Impact: Solving niche challenges, such as Nuacem’s healthcare focus or Kata.ai’s regional expertise.

Scalability: Cloud-based solutions (Sendbird) vs. secure on-premise options (Rasa).

The Future of AI Conversational Bots

In 2025, expect bots to become more proactive—anticipating needs via predictive analytics and integrating with AR/VR for immersive experiences. Ethical AI and multilingual capabilities will also take center stage.

Conclusion

Whether you’re a startup or an enterprise, there’s an AI conversational bot tailored to your needs. From GlobalNodes’ secure transactions to NICE’s customer insights, these companies are redefining engagement in 2025. Ready to join the revolution?

[home-inspiration-blog]

Why was Elon Musk’s AI chatbot Grok preoccupied with South Africa’s racial politics?

By MATT O’BRIEN Much like its creator, Elon Musk’s artificial intelligence chatbot Grok was preoccupied with South African racial politics on social media this week, posting unsolicited claims about the persecution and “genocide” of white people. Related Articles Coinbase says bribed workers leaked data to hacker seeking $20 million in ransom Cisco shares gain on positive sales forecast as AI…

[differenttimemachinecrusade]

AI in Telecommunication Market Research Report 2032: Size, Share, Scope, Forecast, and Growth Overview

The AI In Telecommunication Market was valued at USD 2.6 Billion in 2023 and is expected to reach USD 65.9 Billion by 2032, growing at a CAGR of 42.94% from 2024-2032.

Artificial Intelligence (AI) is revolutionizing the telecommunications industry by enhancing operational efficiency, automating network functions, and improving customer experiences. With the exponential rise in data consumption and demand for high-speed connectivity, telecom providers are increasingly adopting AI-driven technologies to manage complex network infrastructures, detect anomalies, and personalize services. The combination of AI with 5G, edge computing, and cloud-native infrastructure is creating new opportunities for intelligent automation and digital transformation across the telecom value chain.

AI in Telecommunication Market Size, Share, Scope, Analysis, Forecast, Growth, and Industry Report 2032 indicates that the global market is on a trajectory of significant expansion. With AI being integrated into core telecom operations—such as predictive maintenance, fraud detection, dynamic bandwidth allocation, and network optimization—the market is expected to witness substantial growth in the coming years. Service providers are leveraging AI not just to reduce costs but also to introduce smarter, more responsive networks that cater to evolving consumer and enterprise needs.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/5494 

Market Keyplayers:

AT&T - AI-based Network Optimization

Verizon Communications - Virtual Assistant for Customer Service

Huawei Technologies - AI-powered Cloud Computing Solutions

Nokia - Nokia AVA Cognitive Services

Ericsson - Ericsson AI Operations Engine

Cisco Systems - Cisco Cognitive Collaboration

Qualcomm - AI-powered 5G Chipsets

IBM - Watson AI for Telecom

Intel Corporation - Intel AI for Network Optimization

ZTE Corporation - ZTE AI-Driven Network Solutions

T-Mobile - T-Mobile’s AI Chatbot for Customer Support

Orange S.A. - Orange AI-Powered Customer Insights

Vodafone Group - Vodafone’s AI for Predictive Maintenance

Trends Shaping the Market

AI-Driven Network Automation: One of the most impactful trends is the use of AI for automating network management and operations. This includes self-optimizing networks (SON), which adjust parameters in real-time for optimal performance, and AI-powered traffic management that dynamically routes data based on usage patterns.

Predictive Maintenance and Fault Detection: Telecom operators are using AI to predict equipment failures before they occur, minimizing downtime and reducing operational expenses. AI models analyze historical and real-time data to proactively manage infrastructure health.

AI-Powered Customer Service: AI chatbots, voice assistants, and virtual agents are transforming customer engagement. These tools offer round-the-clock support, reduce resolution time, and improve customer satisfaction. Natural language processing (NLP) and sentiment analysis are further enhancing user interactions.

Fraud Detection and Cybersecurity: AI and machine learning algorithms are being deployed to detect suspicious activities in real-time, helping telecom providers combat fraudulent behavior and strengthen data security.

Integration with 5G and Edge Computing: As 5G networks roll out, AI is playing a crucial role in optimizing spectrum allocation, improving low-latency performance, and managing edge devices. AI helps prioritize traffic and maintain network reliability in ultra-connected environments.

Enquiry of This Report: https://www.snsinsider.com/enquiry/5494 

Market Segmentation:

By Technology

Machine Learning

Natural Language Processing

Big Data

Others

By Deployment

Cloud

On-Premises

By Application

Network/IT Operations Management

Customer Service and Marketing VDAS

CRM Management

Radio Access Network

Customer Experience Management

Predictive Maintenance

Market Analysis

North America currently leads the market due to early adoption of advanced technologies and the presence of major tech firms. However, Asia-Pacific is expected to witness the fastest growth, propelled by rapid digitalization, growing mobile user bases, and government initiatives supporting AI development.

Key market segments include solutions (such as network optimization, AI analytics, and intelligent virtual assistants) and services (including professional and managed services). Among these, network optimization is currently the largest revenue-generating segment, with telecoms heavily investing in intelligent network infrastructure to accommodate growing traffic and user demands.

Major players such as Nokia, Huawei, IBM, Ericsson, Google, and Microsoft are shaping the competitive landscape by launching AI-powered platforms and solutions tailored to telecom use cases. Strategic collaborations between telecom companies and AI startups are also playing a vital role in enhancing product innovation and market reach.

Future Prospects

The future of AI in telecommunications is marked by increasing convergence between AI, Internet of Things (IoT), and next-generation connectivity. AI algorithms will play a central role in real-time analytics, enabling smarter decision-making and seamless user experiences. Telecom operators will also expand AI applications beyond operations into areas like personalized marketing, digital onboarding, and value-added services.

As telecom networks become more complex, AI’s role will shift from reactive to predictive and autonomous. Self-healing networks and AI-powered orchestration platforms will allow operators to manage vast ecosystems of devices and services with minimal human intervention. Moreover, as quantum computing matures, AI models will gain new levels of processing power, opening up advanced use cases in optimization and signal processing.

Regulatory developments will also influence the pace of AI adoption. Ensuring ethical use of AI, transparency in automated decision-making, and data privacy will be crucial as telecom companies deepen AI integration. Governments and regulatory bodies are expected to establish frameworks to balance innovation with consumer protection.

Access Complete Report: https://www.snsinsider.com/reports/ai-in-telecommunication-market-5494 

Conclusion

The integration of AI into the telecommunications sector marks a pivotal shift toward more agile, intelligent, and customer-centric operations. As digital ecosystems expand and user expectations evolve, AI is proving to be indispensable in enabling telecom providers to scale services, improve quality, and stay competitive in an increasingly connected world. With significant investments, technological innovation, and rising adoption across regions, the AI in telecommunication market is set to experience robust growth through 2032, redefining the future of global connectivity.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

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[codingnectars]

Personalization in Marketing: Creating Deeper Connections That Go Beyond a Name

In a world drowning in generic ads and robotic interactions, addressing customers by their first name is the marketing equivalent of a limp handshake—polite but meaningless. Today’s consumers crave more than superficial recognition; they want brands to understand them. True personalization isn’t about inserting a name into an email—it’s about anticipating needs, reflecting values, and crafting experiences that feel uniquely tailored. Here’s how to move beyond the basics and build relationships that matter.

Why “Hi [Name]” Isn’t Enough

Using a customer’s name is the lowest bar for engagement. Research shows 66% of consumers expect brands to grasp their unique needs, and over half will abandon companies that fail to personalize meaningfully. The message is clear: Generic won’t cut it.

1. Behavioral Insights: Let Actions Speak Louder Than Demographics

Forget age or location—focus on what customers do. Track browsing habits, purchase history, and content engagement to uncover hidden patterns.

Spotify’s Genius Move: Their Discover Weekly playlists analyze listening behavior to serve up new favorites, making users feel seen.

Your Playbook: Tools like Mixpanel or Hotjar can map user journeys. Segment audiences by behavior (e.g., “cart abandoners” or “weekly blog bingers”) and tailor messaging.

2. Predictive Power: Stay One Step Ahead

AI and machine learning predict future behaviors by analyzing past actions. Think of it as a crystal ball for customer needs.

Amazon’s Secret Sauce: “Frequently Bought Together” recommendations account for 35% of sales by guessing what shoppers want next.

Your Playbook: Use platforms like Optimizely to test predictive campaigns. Send replenishment reminders for subscription products before they run out.

3. Dynamic Content: Real-Time Relevance

Why show everyone the same homepage? Dynamic content adapts based on user data, creating a chameleon-like experience.

Sephora’s Win: Emails update in real time to show available shades of a previously viewed lipstick.

Your Playbook: Tools like Dynamic Yield or HubSpot let you auto-adjust website banners, emails, and ads based on user behavior.

4. Micro-Segmentation: Niche Down to Stand Out

Ditch broad categories like “millennials” and dive into hyper-specific groups.

Fitness Brand Hack: Target “yoga enthusiasts who bought eco-friendly mats and follow mindfulness influencers” instead of “women aged 25–40.”

Your Playbook: Use AI clustering tools in your CRM to identify micro-segments. Craft campaigns that speak directly to their pain points.

5. AI Chatbots: Context Is King

Modern chatbots do more than answer FAQs—they remember past interactions to deliver smarter support.

Bank of America’s Erica: This chatbot analyzes spending habits to offer personalized budgeting tips.

Your Playbook: Platforms like Intercom let you train chatbots to recognize user intent. Use them to suggest products based on browsing history.

6. Privacy-First Personalization: Trust > Tricks

With 86% of consumers wary of data misuse (Cisco), transparency is non-negotiable.

Do This:

Explain data use plainly (“We track your clicks to improve recommendations”).

Let users control their data through preference centers.

Your Playbook: Collect zero-party data (info users willingly share) via quizzes or surveys.

7. Emotional Alignment: Values Over Vanity

Personalization now includes ethical resonance. Customers stick with brands that mirror their beliefs.

Patagonia’s Masterclass: Their anti-consumerist “Don’t Buy This Jacket” campaign won loyalty by prioritizing planet over profit.

Your Playbook: Use social listening tools like Brandwatch to identify causes your audience cares about. Weave these into storytelling.

Avoid These Pitfalls

Creepy Over-Personalization: Don’t retarget ads for that blender they already bought.

Data Chaos: Siloed systems lead to disjointed experiences. Integrate CRM, email, and social data.

Assumption Trap: Validate insights with A/B tests—not all data predicts intent.

The Bottom Line: Personalization is a Mindset

It’s not about ticking a box with a name field. It’s about leveraging data, tech, and empathy to create moments that feel meant for the customer.

Your Action Plan:

Audit your current strategy. Are you stuck in “Hello [Name]” mode?

Invest in AI tools to unify data and predict needs.

Prioritize transparency—trust is the currency of loyalty.

At its core, personalization is respect. Respect for your customer’s time, preferences, and boundaries. Nail this, and you’ll turn casual buyers into lifelong advocates.

Why This Works:

Bold, Conversational Tone: Relatable and jargon-free.

Fresh Examples: Updated brands and tactics.

Actionable Frameworks: Clear “Your Playbook” sections.

Ethical Focus: Balances innovation with privacy.

Ready to ditch the shallow end of personalization? Dive deeper, and watch loyalty soar. 🚀

[datapeakbyfactr]

AI Agents: The Future of Customer Service for Small Businesses 

Artificial intelligence (AI) has transformed industries by automating repetitive tasks, analyzing data, and improving decision-making processes. For small businesses, one of the most exciting applications of AI lies in customer service. AI agents, powered by advanced algorithms and machine learning, are reshaping how businesses engage with their customers.

What Are AI Agents? 

AI agents are software programs designed to perform specific tasks autonomously. In customer service, these agents can interact with customers, answer queries, resolve issues, and even process transactions. They operate through various channels, including chatbots, voice assistants, and email automation tools. Unlike traditional customer support systems, AI agents learn and improve over time, becoming more efficient and effective with every interaction. 

Benefits of AI Agents for Small Businesses 

1. 24/7 Availability 

One of the most significant advantages of AI agents is their ability to provide round-the-clock support. Small businesses often lack the resources to maintain a 24/7 customer service team. AI agents ensure that customers can get assistance anytime, improving satisfaction and fostering loyalty. 

2. Cost Efficiency 

Hiring, training, and maintaining a customer service team can be expensive. AI agents reduce operational costs by automating routine tasks, allowing human employees to focus on more complex issues. This balance helps small businesses optimize their resources. 

3. Faster Response Times 

AI agents can instantly respond to customer inquiries, minimizing wait times and enhancing the overall experience. Immediate responses are particularly valuable in today’s fast-paced world, where customers expect quick resolutions. 

4. Personalization at Scale 

Using data analytics, AI agents can tailor their interactions to individual customers. They can recommend products, offer personalized solutions, and even remember previous interactions, creating a seamless and customized experience for each customer. 

5. Scalability 

As a small business grows, so does the volume of customer inquiries. AI agents can handle increased demand without requiring additional resources. This scalability ensures that customer service remains consistent and effective during periods of growth. 

““AI allows us to move away from reactive service and deliver more proactive solutions, helping customers resolve simpler issues automatically and enabling human agents to focus on the high-value interactions where empathy and problem-solving are essential.””

— Anurag Dhingra, SVP and GM of Cisco Collaboration

AI Agents at Work for Small Businesses 

1. Chatbots on Websites 

AI-powered chatbots can assist website visitors by answering common questions, guiding them through the purchasing process, or providing support. For example, a chatbot on a small e-commerce site can help customers find products, check order statuses, and process returns. 

2. Social Media Support 

AI agents can monitor and respond to customer inquiries on social media platforms. They can address complaints, answer questions, and even engage with customers to build brand loyalty. 

3. Voice Assistants for Phone Support 

Voice-based AI agents can handle phone inquiries, providing automated support for common issues. They can route complex calls to human agents, ensuring that customers always receive the assistance they need. 

4. Email Automation 

AI agents can automate email responses, categorize inquiries, and prioritize urgent matters. This feature streamlines communication and ensures timely resolutions. 

Addressing Challenges and Concerns 

While AI agents offer numerous benefits, small businesses may face challenges when implementing them. Concerns about data privacy, setup costs, and the potential for impersonal interactions are common. However, these issues can be mitigated: 

Data Privacy: Choose AI solutions that prioritize data security and comply with regulations like the General Data Protection Regulation (GDPR). 

Initial Investment: Many affordable AI tools are tailored for small businesses, making it easier to start without a significant financial burden. 

Human Touch: Use a hybrid approach where AI agents handle routine tasks, and human agents manage complex or sensitive interactions. 

The Future of AI in Customer Service 

As AI technology continues to advance its capabilities in customer service will only grow. Future AI agents may incorporate emotional intelligence, enabling them to understand and respond to customers’ emotions. They will also integrate seamlessly with other business tools, providing a unified platform for managing customer relationships. 

For small businesses, adopting AI agents isn’t just about keeping up with trends—it’s about staying ahead in a competitive market. By leveraging AI-driven customer service solutions, small businesses can enhance customer satisfaction, streamline operations, and build a strong foundation for growth. 

AI agents are revolutionizing customer service offering small businesses an opportunity to deliver exceptional support at a fraction of the cost. From chatbots to voice assistants, these tools are versatile, scalable, and efficient. By embracing AI in customer service, small businesses can not only meet but exceed customer expectations, ensuring long-term success in an ever-evolving marketplace. 

Learn more about DataPeak:

[global-research-report]

AI in Telecom: Unlocking New Opportunities for Smart Networks and Services

The global AI in telecommunication market size is expected to reach USD 11.29 billion by 2030, according to a new report by Grand View Research, Inc. The market is anticipated to register a CAGR of 28.2% from 2023 to 2030.

Communication Service Providers (CSPs) need to bring the intelligence in their system optimization, planning, and operations to address the increasing complexities in communication networks caused due to the deployment of new technology paradigms, such as Network Function Virtualization (NFV) and Software-Defined Wide-Area Networking (SD-WAN). Therefore, the telecommunications industry is exploring and introducing AI to improve network efficiency and customer experience.

The telecommunication industry has leveraged technologies, such as cloud computing, big data analytics, and deep learning, to fulfill consumer demands of multimedia services and network security. Also, the intellectualization of communication networks has become possible with the invention of technologies of service-aware network systems and deep packet inspection. Researchers in the industry are tapping into artificial intelligence-based techniques to optimize network architecture & management, and to enable more autonomous operations.

Furthermore, the next-generation wireless networks are anticipated to evolve into more complex system architectures due to the diversified service requirements and heterogeneity in devices, system architectures, and applications. Artificial intelligence has renewed interest in the telecom industry due to the rising complexity of network technology. Potential AI-based use-cases in communication networks include network operation monitoring & management, fraud mitigation, predictive maintenance, cybersecurity, and virtual assistants for marketing and customer service. However, network operation monitoring & management remains the top use-case in the telecom industry as several communications service providers have adopted AI approaches to address the need for communication automation and agility.

AI In Telecommunication Market Report Highlights

Improving customer experience is one of the major factors driving the growth of the market since chatbots deployed for customer service have fueled the business earnings adequately

Machine learning approaches are beginning to emerge in the telecommunication domain to address the challenges of virtualization

AI-supported network-centric applications include anomaly detection for maintenance and provisioning, performance monitoring, alert suppression, automated resolution of a trouble ticket, network faults prediction, and network capacity planning or congestion prediction

Asia Pacific is expected to grow at the fastest CAGR of 32.9% during the forecast period. This growth is attributed to the rapid technological advancements in emerging economies, such as China and India.

AI In Telecommunication Market Segmentation

Grand View Research has segmented the global AI in telecommunication market based on application, and region:

AI In Telecommunication Application Outlook (Revenue, USD Million, 2017 - 2030)

Network Security

Network Optimization

Customer Analytics

Virtual Assistance

Self-Diagnostics

Others

AI In Telecommunication Regional Outlook (Revenue, USD Million, 2017 - 2030)

North America

US

Canada

Europe

UK

Germany

France

Asia Pacific

China

Japan

India

Australia

South Korea

Latin America

Brazil

Mexico

Middle East and Africa

Saudi Arabia

South Africa

UAE

List of Key Players

IBM Corporation

Microsoft

Intel Corporation

Google LLC

AT&T Intellectual Property

Cisco Systems, Inc.

Nuance Communications, Inc.

Evolv Technologies Holdings Inc.

ai.

Infosys Limited

Salesforce, Inc.

NVIDIA Corporation

Order a free sample PDF of the AI In Telecommunication Market Intelligence Study, published by Grand View Research.

[mohammedalothman]

Mohammad Alothman on How AI Usage Challenges Modern Networks

Progress in artificial intelligence is transforming industries and daily life, but at an unmet heavy price: overhauling the "plumbing" on which AI systems depend. Ratcheting up AI chatbots, agents, and communications between machines strains data centers and underpinning networking infrastructure to their limits. 

A new challenge demands both innovative solutions and strategic investment in network capacity.

Mohammad Alothman, the founder and CEO of AI Tech Solutions, shares his expert opinion on this topic, breaking it down and making it easier to understand. 

The Burden on Networking Infrastructure

As AI usage speeds up, it is bound to produce gigantic amounts of data traffic. Industry expert Mohammad Alothman stresses that the data explosion results not just from the transactions between humans and AI but also from the astronomical growth in AI-to-AI communications. Machine-to-machine communication, although vital for the efficiency of AI, causes a tremendous strain on network infrastructure.

Networking, often viewed as the "plumbing" of data systems, enables data to move around both within and between data centers and internet-connected devices. Still, none of this infrastructure has been built with scale or complexity in mind for AI-powered workloads. According to Chris Sharp, CTO at Digital Realty, AI traffic is about to be not just unprecedented but grossly fundamental enough to demand changes in networking systems.

The Need for Improved Networking Solutions

Mohammad Alothman explains that AI workloads are unlike other applications in the level of demands they require. Unlike typical applications, AI systems need low-latency and high-bandwidth networking to process large amounts of data in real-time. AI Tech Solutions is a company that is intimately involved in monitoring AI trend engagements and observes that the move to AI-first in such industries as finance and healthcare has further accelerated the demand for innovative networking solutions.

Market trends reflect this urgency. The global data center networking market, which stands at $34.61 billion today, is estimated to grow to as high as $118.94 billion in 2033, according to Straits Research. 

Such specific technologies, such as data center switches, which do routing of traffic, and back-end switches, which connect AI chips, will probably see superhuman growth. BNP Paribas predicts that sales of back-end switches could quadruple in the next few years, underscoring the scale of the opportunity.

Innovations in Networking Technology

Industry leaders like Nvidia and Cisco are at the forefront of addressing these hurdles. Nvidia has introduced special data center switches, which are meant to handle unique demands in AI workloads. Infrastructure demand is credited to Cisco's steadiness despite its drop in quarterly revenue.

According to Mohammad Alothman, this technology advancement is not only about increasing its capacity but also about increasing its efficiency. "AI workloads require precision and speed," he explains. "The industry must focus on solutions reducing bottlenecks and ensuring seamless data flow."

AI Tech Solutions also boasts that their research shows an increasing interest in software-defined networking (SDN) and AI-driven network management tools. These technologies enable networks to adapt dynamically to changing workloads, optimizing performance and reducing latency.

The Economic Implications

The investment in upgraded AI networking infrastructure is not just a technological necessity but rather an economic opportunity. According to International Data Corp., spending on AI data center switches worldwide will surge from $127.2 million this year to $1 billion by 2027. This is indicative of a growing understanding of networking as a vital enabler of AI innovation.

Mohammad Alothman highlights that this shift will have ripple effects across industries. Enhanced networking capabilities will enable faster deployment of AI solutions, improving productivity and driving cost savings. However, he also cautions that the cost of these upgrades could be a barrier for smaller organizations, underscoring the need for scalable and affordable solutions.

Case Studies: Industries Adopting AI-First Networking

Upgraded networking has become one example of a transformation the financial sector could potentially undergo. Teachers Insurance and Annuity Association of America (TIAA) recently upgraded its networks to support its AI-first strategy. According to Sastry Durvasula, Chief Operating, Information, and Digital Officer at TIAA, such upgrades are needed because the nature of AI workloads requires them.

AI Tech Solutions witnessed similar trends in healthcare, where ultra-reliable networks are needed for AI-driven diagnostics and treatment planning. The improvement in patient outcomes and reduced costs on operations do demonstrate the further benefits of robust networking infrastructure.

Challenges and the Road Ahead

Where the opportunities are significant, overcoming the challenges of upgrading networking infrastructure is no small feat. One major impediment cited by Mohammad Alothman is that organization budgets are typically limited. Partnerships and collaborative investments could help mitigate such costs and allow more users to adopt advanced networking technologies.

Another challenge involves the difficulty of integrating new technologies with an existing platform. AI Tech Solutions points out that most organizations find it difficult to achieve compatibility and attract requisite skills for managing transitions. The inability to address the skills gap will undoubtedly become a critical success factor for networking upgrades.

The Role of Policy and Regulation

Policy and regulation are key influencers of the near future regarding AI networking. Governments and regulatory bodies have to develop a framework that promotes innovation while staying secure about data security and privacy. Mohammad Alothman recognizes the urgent need for a balanced approach that does not compromise between scientific progress and ideological considerations.

Echoing the same view, AI Tech Solutions advocates strong cooperation between key players in the industry as well as policymakers; they can take steps to develop rules that foster sustainable development and resolve issues that are unique to AI-driven networking.

Conclusion

The rise of AI is revolutionizing industries, but it also exposes the limitations of existing networking infrastructure. As Mohammad Alothman aptly puts it, "AI’s potential can only be fully realized if its plumbing is robust enough to support the flow."

AI workloads require significant investments in upgraded networking technologies. Players such as Nvidia, Cisco, and AI Tech Solutions are revolutionizing technologies to ensure that data is transmitted and processed with innovative delivery speed, promise, and difference.

Of course, though the journey will be challenging. There are challenges such as cost, integration, and regulatory issues that need an all-round concerted effort on multiple fronts. With these steps, we might create a platform for the future of more disruptive potential from AI, underpinned by a resilient and efficient network infrastructure.

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Is AI Capable Of Thinking On Its Own? A Discussion With Mohammad Alothman

[drmikewatts]

Weekly Review 1 November 2024

Some interesting links that I Tweeted about in the last week (I also post these on Mastodon, Threads, Newsmast, and Bluesky):

I think this is the biggest reason to not use AI to generate important code or material-it's too easy for bad actors to inject malicious code into the model used: https://arstechnica.com/tech-policy/2024/10/bytedance-intern-fired-for-planting-malicious-code-in-ai-models/

Google's AI mediator, that helps guide people to agree: https://arstechnica.com/ai/2024/10/googles-deepmind-is-building-an-ai-to-keep-us-from-hating-each-other/

The quality of data being used to train AI is declining. Garbage in, garbage out: https://www.bigdatawire.com/2024/10/23/ai-has-a-data-problem-appen-report-says/

Like many other AI, this transcription tool hallucinates: https://techcrunch.com/2024/10/26/openais-whisper-transcription-tool-has-hallucination-issues-researchers-say/

It is going to take some time to sort out the legal issues around the scraping of content to train AI: https://www.theguardian.com/technology/2024/oct/25/unjust-threat-murdoch-and-artists-align-in-fight-over-ai-content-scraping

More ways AI will keep lawyers happy-who's responsible when an AI controlled vehicle crashes? https://dataconomy.com/2024/10/23/the-ethical-dilemmas-of-autonomous-cars-whos-responsible-in-a-crash/

Ten Python libraries you should be familiar with for working with data: https://www.kdnuggets.com/10-essential-python-libraries-for-data-science-in-2024

The last time I got a scam call I told them to talk to my d*ck and put the phone down the front of my trousers. I don't think that would work if it were an AI calling: https://www.theregister.com/2024/10/24/openai_realtime_api_phone_scam/

An AI that can write, and verify, code: https://techcrunch.com/2024/10/24/anthropics-ai-can-now-run-and-write-code/

Biased data produces biased AI models. This is as true for cybersecurity applications of AI as it is for anything else: https://www.datasciencecentral.com/why-ai-bias-is-a-cybersecurity-risk-and-how-to-address-it/

Do we really want an AI to be able to control the mouse on our computers? Maybe useful for people who have motor impairments or tremors: https://arstechnica.com/ai/2024/10/anthropic-publicly-releases-ai-tool-that-can-take-over-the-users-mouse-cursor/

AI company fires back at lawsuits over its scraping of content for training data: https://techcrunch.com/2024/10/24/they-wish-this-technology-didnt-exist-perplexity-responds-to-news-corps-lawsuit/

Did a chatbot AI really encourage a teenager to kill themselves? Time for guardrails: https://www.theguardian.com/technology/2024/oct/23/character-ai-chatbot-sewell-setzer-death

Using AI to enable a garden to talk back: https://www.theguardian.com/lifeandstyle/2024/oct/25/ai-powered-garden-chelsea-flower-show

The idea of multi-agent architectures has been around for decades. Will generative AI be able to coordinate different agents to perform useful tasks? https://www.informationweek.com/machine-learning-ai/10-reasons-why-multi-agent-architectures-will-supercharge-ai

It looks pretty obvious to me that some companies will try to use Microsoft's AI to replace workers, not augment them: https://dataconomy.com/2024/10/23/microsoft-rolls-out-virtual-employee-ai-agents-for-enterprises/

The US wants to use more AI, especially in national security: https://www.computerworld.com/article/3587124/white-house-tells-intelligence-agencies-use-more-ai.html

The AI Cisco is using for customer support: https://www.computerworld.com/article/3578806/ciscos-new-ai-agents-and-assistants-aim-to-ease-customer-service-headaches.html

If the AI chips only last three years, what happens to them after that? Can they be recycled, or is this another way AI can negatively impact the environment? https://www.extremetech.com/computing/data-center-ai-gpus-may-have-extremely-short-lifespans

AI generated material is a threat to us, especially its use in election interference: https://www.informationweek.com/cyber-resilience/ai-manipulation-threatens-the-bonds-of-our-digital-world

An approach to watermarking AI generated text: https://spectrum.ieee.org/watermark

Replacing journalists with AI is not a popular move: https://www.stuff.co.nz/world-news/360462671/polish-radio-station-replaces-journalists-ai-presenters

40 years later, Terminator continues to influence people's opinions of AI: https://arstechnica.com/ai/2024/10/40-years-later-the-terminator-still-shapes-our-view-of-ai/

Who needs AI safety? Not OpenAI: https://www.theregister.com/2024/10/25/open_ai_readiness_advisor_leaves/

[govindhtech]

AMD EPYC Processors Widely Supported By Red Hat OpenShift

EPYC processors

AMD fundamentally altered the rules when it returned to the server market in 2017 with the EPYC chip. Record-breaking performance, robust ecosystem support, and platforms tailored for contemporary workflows allowed EPYC to seize market share fast. AMD EPYC began the year with a meagre 2% of the market, but according to estimates, it now commands more than 30% of the market. All of the main OEMs, including Dell, HPE, Cisco, Lenovo, and Supermicro, offer EPYC CPUs on a variety of platforms.

Best EPYC Processor

Given AMD EPYC’s extensive presence in the public cloud and enterprise server markets, along with its numerous performance and efficiency world records, it is evident that EPYC processors is more than capable of supporting Red Hat OpenShift, the container orchestration platform. EPYC is the finest option for enabling application modernization since it forms the basis of contemporary enterprise architecture and state-of-the-art cloud functionalities. Making EPYC processors argument and demonstrating why AMD EPYC should be taken into consideration for an OpenShift implementation at Red Hat Summit was a compelling opportunity.

Gaining market share while delivering top-notch results

Over the course of four generations, EPYC’s performance has raised the standard. The fastest data centre  CPU in the world is the AMD EPYC 4th Generation. For general purpose applications (SP5-175A), the 128-core EPYC provides 73% better performance at 1.53 times the performance per projected system watt than the 64-core Intel Xeon Platinum 8592+.

In addition, EPYC provides the leadership inference performance needed to manage the increasing ubiquity of  AI. For example, utilising the industry standard end-to-end AI benchmark TPCx-AI SF30, an Intel Xeon Platinum 8592+ (SP5-051A) server has almost 1.5 times the aggregate throughput compared to an AMD EPYC 9654 powered server.

A comprehensive array of data centres and cloud presence

You may be certain that the infrastructure you’re now employing is either AMD-ready or currently operates on AMD while you work to maximise the performance of your applications.

Red Hat OpenShift-certified servers are the best-selling and most suitable for the OpenShift market among all the main providers. Take a time to look through the Red Hat partner catalogue, if you’re intrigued, to see just how many AMD-powered choices are compatible with OpenShift.

On the cloud front, OpenShift certified AMD-powered instances are available on AWS and Microsoft Azure. For instance, the EPYC-powered EC2 instances on AWS are T3a, C5a, C5ad, C6a, M5a, M5ad, M6a, M7a, R5a, and R6a.

Supplying the energy for future tasks

The benefit AMD’s rising prominence in the server market offers enterprises is the assurance that their EPYC infrastructure will perform optimally whether workloads are executed on-site or in the cloud. This is made even more clear by the fact that an increasing number of businesses are looking to jump to the cloud when performance counts, such during Black Friday sales in the retail industry.

Modern applications increasingly incorporate or produce  AI elements for rich user benefits in addition to native scalability flexibility. Another benefit of using AMD EPYC CPUs is their shown ability to provide quick large language model inference responsiveness. A crucial factor in any AI implementation is the latency of LLM inference. At Red Hat Summit, AMD seized the chance to demonstrate exactly that.

AMD performed Llama 2-7B-Chat-HF at bf16 precision​over Red Hat OpenShift on Red Hat Enterprise Linux CoreOS in order to showcase the performance of the 4th Gen AMD EPYC. AMD showcased the potential of EPYC on several distinct use cases, one of which was a chatbot for customer service. The Time to First Token in this instance was 219 milliseconds, easily satisfying the patience of a human user who probably anticipates a response in under a second.

The maximum performance needed by the majority of English readers is approximately 6.5 tokens per second, or 5 English words per second, but the throughput of tokens was 8 tokens per second. The model’s performance can readily produce words quicker than a fast reader can usually keep up, as evidenced by the 127 millisecond latency per token.

Meeting developers, partners, and customers at conferences like Red Hat Summit is always a pleasure, as is getting to hear directly from customers. AMD has worked hard to demonstrate that it provides infrastructure that is more than competitive for the development and deployment of contemporary applications. EPYC processors, EPYC-based commercial servers, and the Red Hat Enterprise Linux and OpenShift ecosystem surrounding them are reliable resources for OpenShift developers.

It was wonderful to interact with the community at the Summit, and it’s always positive to highlight AMD’s partnerships with industry titans like Red Hat. EPYC processors will return this autumn with an update, coinciding with Kubecon.

Red Hat OpenShift’s extensive use of AMD EPYC-based servers is evidence of their potent blend of affordability, effectiveness, and performance. As technology advances, they might expect a number of fascinating breakthroughs in this field:

Improved Efficiency and Performance

EPYC processors of the upcoming generation

AMD is renowned for its quick innovation cycle. It’s expected that upcoming EPYC processors would offer even more cores, faster clock rates, and cutting-edge capabilities like  AI acceleration. Better performance will result from these developments for demanding OpenShift workloads.

Better hardware-software integration

AMD, Red Hat, and hardware partners working together more closely will produce more refined optimizations that will maximize the potential of EPYC-based systems for OpenShift. This entails optimizing virtualization capabilities, I/O performance, and memory subsystems.

Increased Support for Workloads

Acceleration of AI and machine learning

EPYC-based servers equipped with dedicated AI accelerators will proliferate as AI and ML become more widespread. As a result, OpenShift environments will be better equipped to manage challenging AI workloads.

Data analytics and high-performance computing (HPC)

EPYC’s robust performance profile makes it appropriate for these types of applications. Platforms that are tailored for these workloads should be available soon, allowing for OpenShift simulations and sophisticated analytics.

Integration of Edge Computing and IoT

Reduced power consumption

EPYC processors of the future might concentrate on power efficiency, which would make them perfect for edge computing situations where power limitations are an issue. By doing this, OpenShift deployments can be made closer to data sources, which will lower latency and boost responsiveness.

IoT device management

EPYC-based servers have the potential to function as central hubs for the management and processing of data from Internet of Things devices. On these servers, OpenShift can offer a stable foundation for creating and implementing IoT applications.

Environments with Hybrid and Multiple Clouds

Uniform performance across clouds

major cloud providers will probably offer EPYC-based servers, which will guarantee uniform performance for hybrid and multi-cloud OpenShift setups.

Cloud-native apps that are optimised

EPYC-based platforms are designed to run cloud-native applications effectively by utilising microservices and containerisation.

Read more on govindhtech.com

[aparnanukala-blog]

Emerging Trends in Digital Marketing: Skills You Need to Stay Relevant in 2024

The constant change means there is no chance for survival without keeping up with the trends. Thus, similar to other professional fields, a host of emerging trends that have shaped up in 2024 are dictating the future of digital marketing. For marketers to remain relevant, it's time to upskill and reskill. In this blog, we'll cover these trends and highlight the digital marketing skills you need to stay relevant.

Artificial Intelligence and Machine Learning Artificial intelligence and machine learning are changing the face of digital marketing today, with applications ranging from predictive analytics to personalization of customer experience for improving marketing efficiency and effectiveness.

Skills to Master:

Data Analysis: Knowing how to understand and interpret data to make informed decisions

AI Tools: Knowing AI-driven tools such as Google AI, IBM Watson, and chatbots

Basics of Machine Learning Algorithms: Machine learning algorithms and their applications in marketing. Fact: According to Salesforce, 84% of marketers are using AI in some form or another, while 47% are looking at ramping up the same in a period of three years.

2. Video Marketing

Video content has by far ruled digital marketing, and platforms such as YouTube, Instagram, and TikTok will provide vast opportunities for engagement. Video marketing is not only great for capturing attention but also showcases a boost in conversion rate.

Skills to Master:

Video Production: Basic skills on how to shoot and edit videos.

Storytelling: Producing interesting stories that would be able to move the audience.

Video SEO: Optimizing video content so that it obtains better search rankings and visibility.

Stat: By the year 2024, IP video is going to make up 82% of all internet traffic. This surge in the popularity of video streaming and downloads is estimated by Cisco.

Voice Search Optimization More and more people are using voice search. With the recent interest in smart speakers and voice assistants like Alexa, Google Assistant, and Siri, marketers must optimize their content for voice search to stay in business.

Skills to Master:

Conversational Keywords: Using natural language and long-tail keywords

Local SEO: Optimizing for local search results; many voice searches are location-based

Structured Data Markup: Add schema markup to your site so that search engines can better understand your content.

Fact: As per Gartner, 2024 will have 30% of all web browsing sessions being conducted without a screen, placing voice search at centre stage.

Influencer Marketing Influencer marketing is on the rise, and through the influencing process, they have an influential impact on consumer behaviour. By partnering with influencers, one can easily spread brand messages to reach out to a larger mass of people.

Skills to Master:

Influencer Identification: It involves identifying the right influencers whose audiences are your brand prospects.

Negotiation: Clear communication and negotiation to create effective collaborations.

Campaign Management: Plan, execute, and analyse influencer marketing campaigns.

Stat: According to Statista, the influencer marketing industry is forecasted to reach a value of $22.3 billion by 2024. Interpolator:

Interactive Content There is a fast-rising demand for interactive content in the form of polls, quizzes, and interactive videos, just to mention a few, since they make users very engaged and involved. This kind of content may dramatically improve the engagement and increase any website's dwell time.

Skills to Master:

Content Creation Tools: Canva, Type form, Adobe Spark User Experience Design: Ease and aesthetic of designing interacting content.

Data Collection: Interactive content helps collect hard data and valuable insights from users.

Fact: According to Demand Metric, interactive content generates engagement at almost twice that of static content.

Privacy and Data Protection With growing concern regarding data privacy, marketers have to be even more careful when it comes to user data and protection thereof, in adherence to regulations such as GDPR and India's Personal Data Protection Bill.

Skills to Master:

Data Security Practices: It involves understanding and implementing best practices on data security.

Compliance knowledge: Staying up to date on data protection regulations and maintaining compliance with them.

Transparency: Building trust by allowing transparency in data collection and usage practices.

Stat: In a survey conducted by Cisco, 84% of consumers sincerely care about data privacy, while 48% have indeed changed companies over the data policies.

Conclusion:

A fast-moving digital marketing environment calls for a lot of learning and adapting. By taking control of these emerging trends and the associated digital marketing skills, you can ensure that your strategies keep driving effective results and are always one step ahead in 2024.