Autore: martechcubejohn

What challenges have you faced in implementing AI at Zendesk and how have you overcome them?

I believe that across the industry, businesses have made AI hard to make, understand and use. Up until OpenAI released ChatGPT it was accepted that AI was a highly technical field that required long implementation processes and specialised skills to maintain. But AI should be easy to understand, train and use – that’s something we’re very passionate about at Zendesk, and we absolutely need to have that into account when we develop new features.

AI is a shiny, new tool but those looking to implement it must remember that it should be used to solve real problems for customers, especially now with the advent of generative AI. We also need to remind ourselves that the problems we are solving today have not changed drastically in the last few years.

As AI becomes a foundational tool in building the future of software, companies will have to develop the AI/ML muscle and enable everyone to build ML-powered features which requires a lot of collaboration and tools. An AI strategy built upon a Large Language Model (LLM) is not a strategy. LLMs are very powerful tools, but not always the right one to use for every single use case. That’s why we need to assess that carefully as we build and launch ML-powered features.

How do you ensure that the use of AI is ethical and aligned with customer needs and expectations?

As beneficial as AI is, there are some valid concerns. At Zendesk, we’re committed to providing businesses with the most secure, trusted products and solutions possible. We have outlined a set of design principles that sets a clear foundation for our use of generative AI for CX across all components, from design to deployment. Some examples of how we do this include ensuring that training data is anonymised, restricting the use of live chat data, respecting data locality, providing opt-outs for customers, and reducing the risk of bias by having a diverse set of developers working on projects.

What advice do you have for companies looking to incorporate AI into their customer experience strategy?

At Zendesk, we believe that AI will drive each and every customer touchpoint in the next five years. Even with the significant progress ChatGPT has made in making AI accessible, we are still in the early stages and must remain grounded in the fact that LLMs today still have some limitations that may actually detract from the customer experience. When companies use AI strategically to improve CX, it can be a powerful tool for managing costs as well as maintaining a customer connection. Having said that, there is no replacement for human touch. AI’s core function is to better support teams by managing simpler tasks, allowing humans to take on more complex tasks.

While it’s important to move with speed, companies seeking to deploy AI as part of their CX strategy should be thoughtful in the way it’s implemented.

To Know More, Read Full Interview @ https://ai-techpark.com/implementing-ai-in-business/ 

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As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure.

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management.

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

Large language models (LLMs) and semantic technologies are turbocharging the world of AI. Take ChatGPT for example, it's revolutionized communication and made significant strides in language translation.

But this is just the beginning. As AI advances, LLMs will become even more powerful, and knowledge graphs will emerge as the go-to platform for data experts. Imagine search engines and research fueled by these innovations, all while Web3 ushers in a new era for the internet.

To Know More, Read Full Article @ https://ai-techpark.com/top-four-semantic-technology-trends-of-2024/ 

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Another day, another AI headline. Meta has introduced new AI chatbots, embodied by celebrities, in a bid to mix information with entertainment. Amazon has invested up to $4B in its rival, Anthropic; and Google has launched Gemini, to compete with GPT-4. That’s just some of the AI stories within the last quarter involving three of the most influential companies in the technology sector.

Artificial Intelligence is booming. Its rapid development in 2023 has unlocked a wave of new possibilities and opportunities for the AI and machine learning ecosystem. But one of its beneficiaries isn’t. While AI stock has never been higher, we’ve not seen this optimism translate into the autonomous vehicle (AV) sector. This makes little sense. The development of AI and the future of autonomous vehicles is inextricably linked – the former quite literally powers the latter. So why is there this disparity in market confidence between the two sectors? And what does the surge in artificial intelligence mean for the AV sector as a whole?

The field of autonomous vehicles (AVs) has captured our imagination for decades. While self-driving cars are still a work in progress, the recent boom in artificial intelligence (AI) has the potential to be a game-changer. Let's explore how advancements in AI could transform the landscape of autonomous vehicles.

One of the most significant impacts of AI will be on the decision-making capabilities of AVs. AI algorithms, trained on vast amounts of driving data, can potentially react to complex situations faster and more consistently than human drivers.

The AV crystal ball

The challenges of AV at present are those of AI’s future. One of these big challenges revolves around data. An advanced driver assistance system (ADAS) or autonomous driving (AD) system relies on sensors (such as cameras and radar) to ‘see’ the world around them. The data these sensors collect is processed by machine learning to train an AI algorithm, which then makes decisions to control the car. However, handling, curating, annotating and refining the vast amounts of data needed to train and apply these algorithms is immensely difficult. As such, autonomous vehicles are currently pretty limited in their use cases.

AI developers outside the AV world are similarly drowning in data and how they collate and curate data sets for training is equally crucial. The issue of encoded bias resulting from skewed, low quality data is a big problem across sectors: bias against minorities has been found in hiring and loans, where in 2019 Apple’s credit card was investigated over claims its algorithm offered different credit limits for men and women. As applications of AI only continue to increase and reshape the world around us, it’s critical that the data feeding algorithms are correctly tagged and managed.

In other sectors, errors are more readily tolerated, even while bias harms. Consumers may not mind the odd mistake here and there when they enlist the help of ChatGPT, and even find these lapses amusing, but this leniency won’t last long. As reliance on new AI tools increases, and concern over its power grows, ensuring applications meet consumer expectations will be increasingly important. The pressure to close the gap between promise and performance is getting bigger as AI moves from science fiction to reality.

To Know More, Read Full Article @ https://ai-techpark.com/how-will-the-ai-boom-affect-autonomous-vehicles/ 

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In our increasingly data-driven world, algorithms play a significant role in shaping our lives. From loan approvals to social media feeds, these complex programs make decisions that can have a profound impact. However, algorithms are not infallible, and their development can be susceptible to biases. This is where algorithm auditors step in, acting as crucial watchdogs to ensure fairness and mitigate potential harm.

Algorithm auditors possess a unique skillset. They understand the intricacies of artificial intelligence (AI) and machine learning (ML), the technologies that power algorithms. But their expertise extends beyond technical knowledge. Auditors are also well-versed in ethics and fairness principles, allowing them to identify biases that might creep into the data or the algorithms themselves.

With the use of algorithms becoming widespread, the potential for algorithm bias has also impacted numerous decision-making processes, which is a growing concern in the IT sector.

The phenomenon of algorithm bias starts when the algorithms generate results that are systematically and unfairly skewed towards or against certain groups of people. This can have serious consequences, such as race discrimination, gender inequality, and the development of unfair disadvantages or advantages among citizens.

Therefore, to address this concern, the role of algorithm bias auditors has emerged, who are responsible for evaluating algorithms and their outputs to detect any biases that could impact decision-making.

In this exclusive AI TechPark article, we will comprehend the concept of algorithm bias and acknowledge the role of algorithm bias auditors in detecting algorithm bias.

The Role of Algorithm Auditors to Detect Algorithm Bias

According to a global survey, it has been witnessed that more than 56% of CIOs face issues related to the black box, algorithm bias, and privacy protection that create an adverse effect on citizens. Looking at these concerns, along with data privacy issues, IT organizations acknowledge the need for the role of an algorithm auditor.

Algorithm auditors play an essential role in ensuring that algorithms are unbiased and fair; therefore, they have to have a good understanding of ethics and fairness in artificial intelligence (AI) and machine learning (ML), along with practical knowledge of how algorithms can impact the lives of common people. They need to collaborate with developers and data scientists to review algorithms and ensure that they are fair, transparent, and explainable.

Algorithm auditors also use numerous tools to identify the factors associated with AI and ML algorithms’ results and understand the underlying data that has inherent algorithm bias. They can further execute periodical reviews to determine the fairness of the model after it is deployed in the real world. In addition to recognizing the problems, algorithm auditors also provide recommendations on how to make the model more ethical and explainable by implementing ethical frameworks.

To Know More, Read Full Article @ https://ai-techpark.com/the-crucial-role-of-algorithm-auditors-in-detection-and-mitigation/ 

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As President and CEO at Axiado, please share your background in AI-enabled hardware security.

As the President and CEO of Axiado, my journey in AI-enabled hardware security has been both challenging and exhilarating. Over the past four years, I’ve leveraged my extensive experience from IDT, Marvell and Qualcomm to drive innovation in this field. My background in the wired and wireless networking industry has been crucial in understanding and advancing these technologies.

I’m often referred to as a ‘thrill-seeking CEO,’ a title that reflects my love for extreme sports like skydiving and bungee jumping, as well as other active sports like basketball and cricket. These activities are more than hobbies for me; they symbolize my approach to business—taking calculated risks, embracing challenges, pushing my limits and constantly striving for excellence.

One of the most exciting technology developments I’ve witnessed in my career is the advent of generative AI. I believe it’s the most significant innovation since the smartphone, with the potential to revolutionize various sectors.

What inspired you to lead Axiado in addressing security challenges in cloud data centres and 5G networks?

In this rapidly evolving threat landscape, Axiado saw an opportunity to provide a new approach to cybersecurity and embarked on a mission to conceive a solution that would fortify existing security frameworks. This solution is designed to be reliable, self-learning, self-defending, AI-driven, and fundamentally anchored within hardware. This ambitious vision ultimately gave birth to the concept of trusted compute/control units (TCUs), a meticulously crafted solution designed from inception to deliver comprehensive security for data center control and management ports.

Can you provide an overview of AI-enabled hardware security against ransomware, supply chain, side-channel attacks, and other threats in cloud data centres and 5G networks?

According to IBM Security’s most recent annual Cost of a Data Breach Report, the average cost of a data breach reached a record high of $4.45 million in 2023. The report concluded that AI technology had the greatest impact on accelerating the speed of breach identification and containment. In fact, organizations that fully deployed AI cybersecurity approaches typically experienced 108-day shorter data breach lifecycles and significantly lower incident costs (on average, nearly $1.8 million lower) compared to organizations without AI these technologies.

The ability of a hardware-anchored, AI-driven security platform to continuously monitor and perform run-time attestation of cloud containers, platform operating systems, and firmware creates efficiencies that help reduce time spent investigating potential threats. A hardware solution that integrates AI into a chip can analyze behaviors and CPU usage. This enables it to immediately investigate anomalies in user activity. With this approach, networks can no longer be infiltrated because of software vulnerabilities or porous firmware. AI technology enables heterogeneous platforms that include root-of-trust (RoT) and baseboard management controllers (BMCs) to offer hierarchy and security manageability. By deterring cybercrime at the hardware level, the industry can finally address the long-standing shortfalls of online security.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-gopi-sirineni/ 

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As we have entered the digital era, data and analytics strategies (D&A) have become important, as these technologies can transform any business during a massive data spike. According to global research, it was observed that around 2.5 quintillion bytes of data are produced by IT companies every day; therefore, to understand the importance of data, every employee must be data literate.

For a better understanding of data, the Chief Data Officers (CDOs) play an important role in making every employee data literate, i.e., able to understand, share, and have meaningful insight into data.  

With this mindset, organizations can seamlessly adopt emerging and existing technologies and transform their business outcomes across all departments while fostering quality decision-making, innovation, and a better customer experience. The CDOs

In this exclusive AI TechPark article, we will discuss the evolution of data literacy and how it can transform any organization into a data-literate one.

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The Evolution of Data Literacy in the Technological Era

In the past few decades, data literacy has experienced a significant transformation with the introduction of new technologies and the explosion of data. This shift has ignited from traditional data analysis to a modern era of big data that has redefined the way organizations can make data-driven decisions.

To analyze data, data scientists and analysts were confined to basic statistics and simple datasets. Even to analyze the data, data professionals needed more tools, narrow, small-scale datasets, and internal data sources. However, in the late 20th century, there were a lot of technological advancements, such as the introduction of data storage, big data, and cloud computing. This helped data scientists collect and process massive amounts of data from complex, unstructured datasets that could be further analyzed for deeper insight.

Read more about Navigating the Future With the Integration of Deep Learning in Big Data Analytics

As a result of these technological advancements, the power of data has become a center point for developing strategic planning and seamlessly operating business efficiency in the IT industry. Thus, data literacy becomes equally important to developing a data-literate workforce and ensuring that professionals harness the full potential of data for competitive advantage in the data-driven landscape.

Data is necessary, empowering at both individual and organizational levels by creating a pathway to harness real-world data-driven decision-making and data-driven organizational strategy.

In an era where artificial intelligence, data analysis, machine learning, and big data are driving critical business decisions and the ability to steer through complex datasets and extract business insights, data literacy is the epitome of enhancing employability, making informed business decisions, driving innovation, and gaining a competitive edge.

To Know More, Read Full Article @ https://ai-techpark.com/understanding-data-literacy-in-the-digital-age/ 

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Murali, Could you begin by providing us with an introduction and detailing your career trajectory as the Senior Vice President, Engineering at Skillsoft?

I joined Skillsoft in 2016 as the VP of engineering after a career spanning over two decades at IBM, where I led the build out of large-scale enterprise solutions and innovative software products. 2016 was an exciting time to join Skillsoft as the learning industry was undergoing major disruption. To stay competitive, Skillsoft was in the process of building an innovative, AI-driven learning platform called Percipio. With the support of a new leadership team, we were able to build the platform from the ground up and bring it to market within a year.  

The project involved not only building a new product but changing the culture and operations of our technology team, including the launch of a new tech stack built on the AWS public cloud infrastructure. Over the past years, we have grown the product family and organization to include new products and services, and in the process, took ownership to transform the cloud operations organization.

We managed to modernize how we build, deploy, and support our products in the cloud through continuous integration and deployment to deliver new capabilities to the market at lightning speed while maintaining a highly secure, resilient, and performant learning platform that serves millions of learners.

Over the years, we built a strong culture of innovation within our engineering team, which is one of the most exciting parts of my job today. Every quarter, we do an innovation sprint, where team members organically produce ideas to advance platform capabilities. Our philosophy is to establish a grassroots mindset to produce innovative ideas that solve our customers’ business problems and improve experiences for our learners. Many of our AI and machine learning innovations have come out of this process, helping to make our platform smarter and our learning experiences more personalized.  

Can you provide a brief introduction to CAISY (Conversation AI Simulator) and its role in Skillsoft’s offerings?

CAISY, which is an AI-based conversation simulator that helps learners build business and leadership skills, was born out of one of our innovation sprints. The original idea was implemented on a simple terminal text-based interface using GPT 3.5, though we saw the power of the concept and decided to progress it to be customer-facing. Skillsoft launched CAISY out of beta in September using generative AI and GPT 4, to help learners practice and role model various business conversations. While Skillsoft has extensive learning content on how business, management, and leadership conversations should be handled, learners can now practice and apply these skills in real time. Developments in generative AI allow us to leverage our knowledge and expertise in this area while providing a hands-on environment for our learners, so that they can practice conversational skills in a safe and secure zone before implementing them in the real world.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-murali-sastry/ 

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The amalgamation of artificial intelligence (AI) and wearable technology has transformed how healthcare providers monitor and manage patients’s health through emergency responses, early-stage diagnostics, and medical research.

Therefore, AI-powered wearables are a boon to the digital era as they lower the cost of care delivery, eliminate healthcare providers’ friction, and optimize insurance segmentations. According to research by MIT and Google, these portable medical devices are equipped with large language models (LLMs), machine learning (ML), deep learning (DL), and neural networks that provide personalized digital healthcare solutions catering to each patient’s needs, based on user demographics, health knowledge, and physiological data.

In today’s article, let’s explore the influence of these powerful technologies that have reshaped personalized healthcare solutions.

Integration of AI in Wearable Health Technology

AI has been a transforming force for developing digital health solutions for patients, especially when implemented in wearables. However, 21st-century wearables are not just limited to AI but employ advanced technologies such as deep learning, machine learning, and neural networking to get precise user data and make quick decisions on behalf of medical professionals.

This section will focus on how ML and DL are essential technologies in developing next-generation wearables.

Machine Learning Algorithms to Analyze Data

Machine learning (ML) algorithms are one of the most valuable technologies that analyze the extensive data gathered from AI wearable devices and empower healthcare professionals to identify patterns, predict necessary outcomes, and make suitable decisions on patient care.

For instance, certain wearables use ML algorithms, especially for chronic diseases such as mental health issues, cardiovascular issues, and diabetes, by measuring heart rate, oxygen rate, and blood glucose meters. By detecting these data patterns, physicians can provide early intervention, take a closer look at patients’s vitals, and make decisions.

Recognizing Human Activity with Deep Learning Algorithms

Deep learning (DL) algorithms are implemented in wearables as multi-layered artificial neural networks (ANN) to identify intricate patterns and find relationships within massive datasets. To develop a high-performance computing platform for wearables, numerous DL frameworks are created to recognize human activities such as ECG data, muscle and bone movement, symptoms of epilepsy, and early signs of sleep apnea. The DL framework in the wearables learns the symptoms and signs automatically to provide quick solutions.

However, the only limitation of the DL algorithms in wearable technology is the need for constant training and standardized data collection and analysis to ensure high-quality data.

To Know More, Read Full Article @ https://ai-techpark.com/ai-powered-wearables-in-healthcare/

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Social media has transformed marketing. Platforms like Instagram with its 2 billion subscribers allow businesses to connect directly with customers and build their brands through compelling visual storytelling. However, the highly competitive and fast-paced nature of social media also presents challenges. This is where artificial intelligence (AI) comes in. AI technologies are revolutionizing social media marketing, providing data-driven insights and automation that help brands cut through the noise and thrive on social media.

How Artificial Intelligence Helps in Social Media Marketing

Artificial Intelligence is the next big thing in the world of technology and is poised to set forth the course of digital environments in the coming decades. Here below we will see how artificial intelligence is paving the way ahead:

Understanding Your Audience With AI

One of the foundational principles of marketing is understanding your target audience intimately so you can create relevant and engaging content. AI makes discovering audience interests and behaviors easy. Tools like Facebook Analytics, Sprout Social, and Rafflekey utilize machine learning algorithms to reveal demographic data, top-performing content, post timings, picking up winners, and more. These AI-powered insights help you fine-tune Instagram content to match what your followers respond to. Instagram influencers have massively benefited leveraging AI to create instagram giveaway ideas that helps them in boosting their persona and brand.

AI takes audience analysis even further with sentiment analysis and predictive analytics. Sentiment analysis uses natural language processing to determine how audiences feel about your brand by analyzing emotions like joy, surprise, anger, etc. in user-generated content. Predictive analytics examines past performance data to forecast future outcomes. This helps you stay ahead of trends and optimize social media initiatives for maximum impact.

Generating High-Quality Visual Content With AI

Visual storytelling is central to success on Instagram. But constantly producing fresh, eye-catching photos and videos can be challenging. AI creativity tools expand what’s humanly possible by autonomously generating unique visual content.

For example, tools like Canva, Over, and Recite leverage AI to transform text prompts into stunning social media graphics in just seconds. Adobe’s Sensei AI identifies aesthetically pleasing image compositions and automatically adjusts parameters like lighting, color balance, and cropping. For video, generative AI can craft natural voiceovers for explainer videos based on your script.

These AI creativity enhancements remove friction from design and allow you to produce loads of on-brand, high-quality visual content to feed Instagram’s voracious appetite.

To Know More, Read Full Article @ https://ai-techpark.com/the-role-of-ai-in-social-media-marketing/ 

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Data has always been at the core of a business, which explains the importance of data and analytics as core business functions that often need to be addressed due to a lack of strategic decisions. This factor gives rise to a new technology of stitching data using data fabrics and data mesh, enabling reuse and augmenting data integration services and data pipelines to deliver integration data.

Further, data fabric can be combined with data management, integration, and core services staged across multiple deployments and technologies.

This article will comprehend the value of data fabric architecture in the modern business environment and some key pillars that data and analytics leaders must know before developing modern data management practices.

The Evolution of Modern Data Fabric Architecture

Data management agility has become a vital priority for IT organizations in this increasingly complex environment. Therefore, to reduce human errors and overall expenses, data and analytics (D&A) leaders need to shift their focus from traditional data management practices and move towards modern and innovative AI-driven data integration solutions.

In the modern world, data fabric is not just a combination of traditional and contemporary technologies but an innovative design concept to ease the human workload. With new and upcoming technologies such as embedded machine learning (ML), semantic knowledge graphs, deep learning, and metadata management, D&A leaders can develop data fabric designs that will optimize data management by automating repetitive tasks.

Key Pillars of a Data Fabric Architecture

Implementing an efficient data fabric architecture needs various technological components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working on the key pillars below, D&A leaders can create an efficient data fabric design to optimize data management platforms.

Collect and Analyze All Forms of Metadata

To develop a dynamic data fabric design, D&A leaders need to ensure that the contextual information is well connected to the metadata, enabling the data fabric to identify, analyze, and connect to all kinds of business mechanisms, such as operational, business processes, social, and technical.

Convert Passive Metadata to Active Metadata

IT enterprises need to activate metadata to share data without any challenges. Therefore, the data fabric must continuously analyze available metadata for the KPIs and statistics and build a graph model. When graphically depicted, D&A leaders can easily understand their unique challenges and work on making relevant solutions.

To Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/ 

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