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In today’s competitive landscape, Customer Experience (CX) is a key market differentiator. Despite economic uncertainty, companies are adopting digital Customer Experience Management (CXM) to gain a competitive edge and enhance customer retention.

Using Everest Group’s survey of organizations with revenues over $500 million, this webinar explored how companies use Data and Analytics (D&A) and AI to drive CX transformation, leveraging technologies like automation, agent assist tools, and AI bots.

Categories: Artificial Intelligence,

Have you heard about Language Processing Units (LPUs) yet? If you haven’t, prepare to be wowed! LPUs are specialised processors engineered specifically for language-related tasks. They differ from other processors that handle multiple tasks simultaneously. The LPU combines the best of the Central Processing Unit (CPU) — great at sequential tasks, and the Graphic Processing Unit (GPU) — great at concurrent tasks.

Groq is the creator of the world’s first LPU, and in terms of processing, they are the new sheriff in town: 10x faster, 90% less latency, and minimal energy than traditional Graphics Processing Units (GPUs). So, what does this mean for AI in the future?

Imagine you’re at a bustling coffee shop trying to place an order. The barista needs to hear your order, understand it amidst the noise, and get it right — quickly and efficiently. This is not unlike the daily challenges faced in customer service, where clarity and speed are paramount. Enter Language Processing Units or LPUs, the latest buzz in tech circles, especially in customer service. These specialised processors are designed to handle these exact challenges in AI-driven interactions.

Before LPUs entered the scene, CPUs and GPUs did the heavy lifting. Let’s break it down:

The barista is like a CPU (Central Processing Unit). This person is very skilled and can handle various tasks, from making coffee to taking orders and cleaning up. However, because the barista does everything, each task takes a bit of time, and they can only do one thing at a time. If there’s a rush of customers, the barista might get overwhelmed and slow down.

The Team of Baristas (GPU)

Now, imagine you have a team of baristas (GPU — Graphics Processing Unit). Each barista specialises in a specific task. One makes espresso, another steams milk, and another adds flavourings. This team can handle many customers simultaneously, especially if everyone wants the same type of coffee, because they can work in parallel. However, if customers start asking for highly customised orders, the team might not be as efficient since their specialisation is more suited to repetitive tasks.

Finally, picture a super-efficient barista (LPU — Language Processing Unit). This robot is specifically designed to handle complex and varied coffee orders swiftly. It can understand detailed instructions quickly and adapt to each customer’s unique preferences with incredible speed and accuracy. Unlike the single barista or the team of baristas, the robot barista excels at processing these intricate orders without slowing down, no matter how many customers are lined up or how complex the orders are.

LPUs bring this level of personalisation and efficiency to customer service AI, making every interaction smoother and more intuitive. Let’s explore how these new processors are reshaping the landscape of AI communications.

Taking AI Interactions to The Next Level in Contact Centres

As far as contact centre operations go, the speed and accuracy of AI applications are crucial to success. LPUs transform voice AI, most notably enriching real-time speech-to-text and text-to-speech conversions. This improvement is key for developing more natural and efficient customer service interactions, where delays or misunderstandings can negatively impact customer satisfaction.

One of the standout benefits of LPUs is their ability to tackle the latency challenge. In customer service, where every second counts, reducing latency improves the customer experience and boosts the service’s efficiency. LPUs ensure that the dialogue between the customer and the AI is as smooth and seamless as if it were between two humans, with minimal delay.

Tatum Bisley, product lead at contact centres solutions providerCirrus , says: “Language Processing Units are not just changing how we interact with technology in contact centres; they’re setting the stage for a future where real-time processing is seamlessly integrated across various sectors. With LPUs, we’re seeing a dramatic reduction in latency, making interactions with finance or healthcare customers as smooth and natural as face-to-face conversations.

“Much like how modern CGI has made it difficult to distinguish between real and computer-generated imagery, LPUs work behind the scenes to ensure a seamless customer experience. The average person doesn’t talk about the CPU in their laptop or the GPU in their gaming console; similarly, they won’t discuss LPUs. However, they will notice how effortlessly and naturally their interactions unfold.

“The potential applications of this technology extend far beyond our current use cases. Imagine LPUs in autonomous vehicles or real-time language translation services, where split-second processing can make a world of difference. We are just scratching the surface of what’s possible.”

The Impact of LPUs on AI’s Predictive Capabilities

Beyond merely improving real-time interactions, LPUs profoundly impact AI systems’ predictive capabilities. This is because LPUs can rapidly process large datasets that will boost AI’s predictive functions. This enhancement enables AI to react to inputs more swiftly, anticipate user needs and adapt interactions accordingly. By handling sequential predictions with much-improved efficiency, LPUs allow AI to deliver contextually relevant and timely responses, creating more natural and engaging dialogues.

Moreover, LPUs excel at creating AI that can engage in meaningful conversations, predict user intentions, and respond appropriately in real time. This advancement is pivotal for AI applications where understanding and processing human language are crucial, such as customer service or virtual assistance. Adding LPUs redefines AI’s boundaries, promising substantial progress in how machines comprehend, interact with, and serve humans. As LPUs become more integrated into AI frameworks, we can anticipate even more groundbreaking progression in AI capabilities across various industries.

Challenges and Limitations

While the excitement around LPUs is well-founded, it’s essential to recognise the practical considerations of integrating this new technology. One main challenge is ensuring LPUs can work seamlessly with existing systems in contact centres, particularly where GPUs and CPUs are still in use, potentially limiting latency improvements. However, this should not be a major concern for contact centre managers.

Suppliers of these LPUs provide Infrastructure as a Service (IaaS), meaning you pay for what you use rather than bearing the capital expense of the hardware itself—similar to what AWS did for software businesses in the 2000s. The more pressing issues are around misuse or misrepresentation. For instance, using AI to pose as a human can be problematic. While society is still catching up with these advancements, it’s crucial to check with the customer base on what is acceptable and what isn’t.

Additionally, ensuring sufficient handoffs are in place is vital—AI isn’t a silver bullet (yet). Training now focuses on maintaining and fine-tuning the systems, tweaking the models, and adjusting the prompts. So, while there are challenges, they are manageable and should not overshadow the significant benefits LPUs bring to enhancing customer interactions.

Broader Impact Beyond Contact Centres

LPUs aren’t just changing the game in contact centres; they will likely impact operations in most sectors at some point. In healthcare, for instance, real-time language processing could help with everything from scheduling ap
pointments to understanding patient symptoms faster and more accurately. In finance, LPUs could speed up customer service interactions and reduce or even remove wait times for customers seeking advice or needing more complex problem resolution. Retail businesses can leverage LPUs to deliver personalised shopping experiences by enabling customers to find products through voice commands and receive instant information without negatively impacting the shopping experience. Of course, all of these things will take time and investment to come to fruition, but we are clearly on a path to a new kind of customer experience. But are we mere humans ready?

Future Outlook

Looking ahead, the potential for LPUs in AI development is vast. As technology advances, we can expect LPUs to become even more capable of handling more complex language processing tasks more efficiently. They will likely play a crucial role as voice AI continues integrating with emerging technologies like 5G, improving connectivity, and the Internet of Things (IoT), which will broaden the scope of smart devices that can benefit from real-time voice interaction. As LPUs evolve, they will refine how AI understands and processes human language and expand the horizons of what AI-powered systems can achieve across different industries.

Bisley concludes: “As we look toward the future, voice technology in contact centres is not just about understanding words—it’s about understanding intentions and emotions, shaping interactions that feel as natural and nuanced as human conversation. With LPUs, we are stepping into an era where AI doesn’t just mimic human interaction; it enriches it, making every customer interaction more efficient, personal, and insightful. The potential is vast, and as these technologies evolve, they will transform contact centres and redefine the essence of customer service.”

Integrating LPUs into voice AI systems represents a giant leap for contact centres, offering unprecedented improvements in operational efficiency, customer satisfaction, and agent workload. As these technologies mature, their potential to refine the mechanics of voice AI and the very nature of customer interactions is huge. Looking forward, LPUs are set to redefine customer service, making voice AI interactions indistinguishable from human engagements regarding their responsiveness and reliability. The future of AI in customer experiences, powered by LPUs, is not just about maintaining pace with technological advancements but setting new benchmarks for what AI can achieve.

Categories: Applications, Artificial Intelligence, Chatbots, Companies, Development, Education, Energy, Enterprise, Entertainment & Retail, Face Recognition, Finance, Gaming, Healthcare, Industries, Logistics, Manufacturing, Military, Space, Telecoms, Virtual Assistants, Voice Recognition,

Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.

SAS, a specialist in data and AI solutions, has unveiled what it describes as a “game-changing approach” for organisations to tackle business challenges head-on.

Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem.

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

SAS is democratising AI by offering out-of-the-box, lightweight AI models — making AI accessible regardless of skill set — starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

Categories: Artificial Intelligence, Development,

Ryan Daws is a senior editor at TechForge Media with over a decade of experience in crafting compelling narratives and making complex topics accessible. His articles and interviews with industry leaders have earned him recognition as a key influencer by organisations like Onalytica. Under his leadership, publications have been praised by analyst firms such as Forrester for their excellence and performance. Connect with him on X (@gadget_ry) or Mastodon (@gadgetry@techhub.social)

Without visibility into your custom application logic and data flows, major vulnerabilities can be exploited, allowing sensitive information theft, financial fraud, and even operational disruption. While you still need perimeter firewall defenses, exclusively relying on them to safeguard increasingly powerful web properties leaves you playing a risky game of chance (with very real consequences).

By adding specialised web application firewalls (WAFs) designed to analyse requests in the full context of your app environments — and enhanced by AI for even greater accuracy — you can lock things down and confidently build out advanced digital capabilities. With a layered defense-in-depth approach combining network and application-level protections, you can securely deliver the types of seamless, personalised digital experiences that form the foundation of lasting customer relationships and operational excellence in 2024.

Gaps in traditional firewall defences

The chances are you already have traditional firewall protection guarding your overall network (if you run any online services). These firewalls filter incoming traffic based on a set of predefined rules focused primarily around protocol, port number, IP address ranges, and basic connection state.

For example, common firewall rules restrict outside access to private intranet resources, block unwanted traffic types like online gaming protocols, detect large-scale network scans, and mitigate distributed denial of service (DDoS) attacks.

This perimeter protection works well for classic network-focused cyberthreats. But a traditional firewall lacks context about the application logic, user workflows, and data structures unique to custom web apps and APIs. It simply scans network packets as they arrive and attempts to allow or block them accordingly. This leaves it vulnerable to the evolving tactics of AI-powered attackers.

Without insight into application internals, major vulnerabilities can sneak right past traditional firewall defences:

Hackers can also target configuration issues, flawed business logic flows, identity management gaps, and unsafe object level access once inside applications themselves. AI-powered attacks can exploit these vulnerabilities with alarming speed and precision—and your firewall wouldn’t see it coming.

These exploitable application flaws allow attackers to steal sensitive business data and personal information, mine cryptocurrency illicitly on servers, hold systems ransom, take over client accounts, and both deny legitimate access and destroy backend resources. AI has only amplified these risks.

Still, traditional firewalls remain extremely important as the first line of network perimeter defence. But for companies conducting operations online through modern web apps, additional safeguards tuned to application threats — and bolstered by AI’s threat detection capabilities — are essential.

Why WAFs provide critical protection

Web application firewalls address the application layer vulnerabilities and holes in logic that basic network firewalls miss. WAFs are designed specifically to protect web apps, APIs, microservices, and rich internet applications. AI further enhances their ability to identify and respond to these threats.

Categories: Artificial Intelligence, Companies, Development, Meta (Facebook),

Ryan Daws is a senior editor at TechForge Media with over a decade of experience in crafting compelling narratives and making complex topics accessible. His articles and interviews with industry leaders have earned him recognition as a key influencer by organisations like Onalytica. Under his leadership, publications have been praised by analyst firms such as Forrester for their excellence and performance. Connect with him on X (@gadget_ry) or Mastodon (@gadgetry@techhub.social)

Meta has unveiled five major new AI models and research, including multi-modal systems that can process both text and images, next-gen language models, music generation, AI speech detection, and efforts to improve diversity in AI systems.

The releases come from Meta’s Fundamental AI Research (FAIR) team which has focused on advancing AI through open research and collaboration for over a decade. As AI rapidly innovates, Meta believes working with the global community is crucial.

“By publicly sharing this research, we hope to inspire iterations and ultimately help advance AI in a responsible way,” said Meta.

Among the releases are key components of Meta’s ‘Chameleon’ models under a research license. Chameleon is a family of multi-modal models that can understand and generate both text and images simultaneously—unlike most large language models which are typically unimodal.

“Just as humans can process the words and images simultaneously, Chameleon can process and deliver both image and text at the same time,” explained Meta. “Chameleon can take any combination of text and images as input and also output any combination of text and images.”

Potential use cases are virtually limitless from generating creative captions to prompting new scenes with text and images.

Meta has also released pretrained models for code completion that use ‘multi-token prediction’ under a non-commercial research license. Traditional language model training is inefficient by predicting just the next word. Multi-token models can predict multiple future words simultaneously to train faster.

“While [the one-word] approach is simple and scalable, it’s also inefficient. It requires several orders of magnitude more text than what children need to learn the same degree of language fluency,” said Meta.

On the creative side, Meta’s JASCO allows generating music clips from text while affording more control by accepting inputs like chords and beats.

“While existing text-to-music models like MusicGen rely mainly on text inputs for music generation, our new model, JASCO, is capable of accepting various inputs, such as chords or beat, to improve control over generated music outputs,” explained Meta.

Meta claims AudioSeal is the first audio watermarking system designed to detect AI-generated speech. It can pinpoint the specific segments generated by AI within larger audio clips up to 485x faster than previous methods.

“AudioSeal is being released under a commercial license. It’s just one of several lines of responsible research we have shared to help prevent the misuse of generative AI tools,” said Meta.

Categories: Artificial Intelligence, Enterprise, Manufacturing,

Dashveenjit is an experienced tech and business journalist with a determination to find and produce stories for online and print daily. She is also an experienced parliament reporter with occasional pursuits in the lifestyle and art industries.

Taiwan Semiconductor Manufacturing Company (TSMC) has raised its revenue forecast for 2024, citing strong demand for chips in AI applications. The world’s largest contract chipmaker anticipates growth slightly above the mid-20% range in US dollar terms, up from its previous estimate. This adjustment comes as TSMC reports better-than-expected profits for the second quarter of 2024.

TSMC’s chairman and CEO, C.C. Wei, painted a vivid picture of the current landscape: “AI is so hot; right now everybody, all my customers, want to put AI functionality into their devices.”

TSMC’s success is intrinsically linked to the global AI boom, which has helped offset the tapering demand for pandemic-driven electronics. As a critical supplier to tech giants like Apple Inc and Nvidia, TSMC finds itself at the heart of the AI revolution, producing the advanced chips that power everything from smartphones to data centres.

It is reflected in the company’s stellar performance based on TSMC’s recent financial results, with net profit for the April-June quarter soaring to T$247.8 billion ($7.60 billion), surpassing market expectations. Despite global economic uncertainties and geopolitical tensions, this robust growth comes, underscoring TSMC’s resilience and strategic positioning in the semiconductor industry.

The company’s optimism extends into the near future, with CFO Wendell Huang projecting strong demand for TSMC’s leading-edge process technologies, particularly in smartphones and AI-related applications. This positive outlook is backed by concrete plans. TSMC adjusts its capital expenditure for the year to between $30 billion and $32 billion, signalling its commitment to expanding capacity and maintaining its technological edge.

However, TSMC’s journey is not without challenges. The company faces intense pressure to meet the skyrocketing demand for advanced chips, with Wei describing the situation as “very, very tight.” TSMC is working to ramp up capacity to support customer needs through 2026 and beyond.

In the face of these challenges, TSMC remains committed to its global expansion strategy. The company is investing heavily in new facilities, including a $65 billion investment in three plants in Arizona and projects in Japan and potentially in Europe. This global footprint helps TSMC meet worldwide demand and positions the company strategically in an increasingly complex geopolitical landscape.

Interestingly, TSMC has firmly rejected the idea of a joint venture in the US despite recent comments from US political figures about Taiwan’s dominance in the chip industry. Wei reaffirmed the company’s commitment to its expansion plans, emphasizing TSMC’s independence and strategic vision.

As TSMC continues to push the boundaries of semiconductor technology, its impact extends far beyond its balance sheet. The company’s innovations are driving advancements in AI, powering the next generation of smart devices, and shaping the future of global technology.

In conclusion, TSMC’s latest financial results and optimistic forecasts paint a picture of a company at the forefront of the AI revolution. As the world increasingly relies on advanced computing power, TSMC’s role in providing the chips that make it all possible has never been more critical. With its ambitious expansion plans and unwavering focus on innovation, TSMC is not just riding the AI wave — it’s helping to create it.

Categories: Artificial Intelligence,

Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.

Discover how AI is revolutionising digital marketing with success stories and key strategies. Learn about personalisation, predictive analytics, content creation, and more.

The rapid evolution of AI is revolutionising digital marketing, offering unprecedented opportunities for personalisation, efficiency, and customer engagement.

By leveraging advanced algorithms and machine learning techniques, AI is transforming how marketers interact with their audiences, predict customer behaviour, and optimise their strategies for better results. This article delves into the multifaceted impact of AI on digital marketing, highlighting success stories and key strategies that are shaping the future of the industry.

AI’s influence on digital marketing careers and education

AI is reshaping digital marketing careers, requiring new skills and knowledge. As AI continues to integrate into marketing practices, professionals must adapt by acquiring expertise in data analysis, machine learning, and AI tools. Visit DigiPortal to learn about career opportunities and educational resources. Killian Smith, the mind behind DigiPortal, has over a decade of experience in software development and cybersecurity.

AI helps in creating highly personalised marketing campaigns by analysing vast amounts of data to derive customer insights. Machine learning algorithms can identify patterns and preferences, allowing marketers to tailor their messages to individual customers. For instance, Netflix and Amazon use AI to recommend products and content based on user behaviour, resulting in higher engagement and satisfaction.

AI-driven predictive analytics enable marketers to anticipate customer behaviour and make informed decisions. By analysing historical data, AI can forecast future trends, helping businesses to plan their strategies effectively. For example, retailers use predictive analytics to optimise inventory levels and marketing efforts, reducing costs and improving customer satisfaction.

AI-driven content creation and curation

AI tools are revolutionising content creation and curation, allowing marketers to produce high-quality content efficiently.

Tools like GPT-4 are capable of generating high-quality text content, from blog posts to social media updates. These tools can create content that is engaging and relevant, saving time and resources for marketers. For example, The Washington Post uses AI to write news articles, freeing up journalists to focus on in-depth reporting.

AI helps optimise content for search engines by analysing keywords, recommending improvements, and tracking performance. AI-driven SEO tools can identify the most effective keywords, suggest content structure, and monitor rankings. A table comparing traditional vs. AI-driven SEO strategies highlights the efficiency and accuracy of AI in optimising content.

Categories: Artificial Intelligence, Companies, NVIDIA,

Dashveenjit is an experienced tech and business journalist with a determination to find and produce stories for online and print daily. She is also an experienced parliament reporter with occasional pursuits in the lifestyle and art industries.

Nvidia recently overtook Microsoft as the world’s most valuable company and is now in the crosshairs of French antitrust regulators. The French authority is preparing to charge Nvidia with anti-competitive practices as part of the EU’s commitment to maintaining checks and balances within the industry.

The French authorities’ move is part of a broader trend of increasing regulatory scrutiny of tech giants worldwide. Governments and regulatory bodies are increasingly wary of companies like Nvidia’s outsized influence and market power. In Europe, where antitrust laws are particularly stringent, regulators are keen to ensure a level playing field and protect consumer interests.

Potential Implications

If the charges are upheld, Nvidia could face substantial fines and be forced to alter its business practices. Though potentially significant, the financial penalties might not be the most critical aspect of the investigation. The operational changes imposed on Nvidia could be more consequential, impacting its competitive edge and market strategy.

In short, the stakes are high for Nvidia. The company’s leadership in AI and other cutting-edge technologies relies on its ability to innovate and dominate the market. Regulatory constraints could slow its momentum and allow competitors to catch up. Moreover, the scrutiny could extend beyond France, prompting investigations in other jurisdictions and creating a ripple effect across the global tech industry.

Nvidia’s situation is not unique. Tech giants worldwide are facing similar challenges as regulators grapple with the complexities of the digital economy. In recent years, companies like Google, Amazon, and Facebook have also been targets of antitrust investigations and regulatory actions.

It points to a widening consensus on balancing innovation with fair competition. While tech companies drive economic growth and technological progress, their market dominance can threaten competition and consumer choice. Regulators are tasked with finding this balance, ensuring that the benefits of technological advancement are widely shared without stifling innovation.

Nvidia said in a February filing that officials in the US, European Union, China, and the UK are also scrutinizing its operations. “Our position in markets relating to AI has led to increased interest in our business from regulators worldwide,” the chipmaker said.

What is next for Nvidia and the French regulators?

It is more likely than not for Nvidia to mount a robust defence because the AI chip giant has consistently argued that its business practices are competitive and that its innovations benefit consumers and industries alike. Nvidia will likely emphasize its contributions to technological progress and economic growth, positioning itself as a driver of positive change rather than a monopolistic force.

However, public perception and regulatory interpretations can differ. Thus, the challenge for Nvidia is clear: to continue its trajectory of success while addressing the concerns of regulators and stakeholders. Ultimately, Nvidia’s response to this regulatory challenge could define its legacy as the world’s most valuable company, demonstrating whether it can uphold its leadership position while adapting to the evolving demands of a fair and competitive market.