In an era where digital security is paramount, the persistent reliance on passwords remains a significant vulnerability for enterprises globally. FIDO 2.0 emerges as a timely solution, reimagining credential authorization using available technologies.
Legacy credential systems, rooted in the Internet 1.0 era, increasingly expose organisations to sophisticated AI-backed cyber threats. The 15% increase in attacks against Indian organisations, now averaging 2,138 attempts per week, can largely be attributed to these poorly secured credentials. As companies and industries continue to thrive throughout India and the region, security teams benefit from implementing new credential approaches, such as FIDO 2.0 stands from the very implementation of their networks.
Despite CISOs and cybersecurity practitioners’ efforts in network security, advanced authentication implementation, and staff training on cyber hygiene, it still only takes a single breach to bring operations to a halt.
Changing the credentials status quo
Despite diverse authentication methods, the prevalent use of alphanumeric codes for logins continues to compromise organisational security.
Recent years have particularly highlighted these faults in the Asia Pacific region. This has resulted in:
31% of global attacks as its digital transformation continues at a rapid clip across sectors.
The most hit sectors were governments, absorbing the brunt of 22% of the attacks
49% of all attacks led to the compromise of sensitive information, with 27% of successful attacks disrupting core organisation operations.
This goes beyond the financial and personal burden put on people as they try to understand if their information is compromised.
In the past, these attacks were successfully conducted by identifying a vulnerability within a system and exploiting it using relevant tactics. However, today companies face two main threats, phishing attacks and device compromise.
Device compromise
Organisations permitting remote work or personal device use face an additional security layer– unfamiliar devices.
IT operators have always struggled to identify and approve all devices on a network– again relying on usernames, passwords, and perhaps some other alphanumeric authentication technique. The danger lies in the possibility that these two-factor authentication methods may also be compromised alongside user credentials.
Adding to the compilation, single sign-on has grown in popularity, but if a user is compromised, so too are their profiles created across all the tools that they have given access to the single point. Even with examples of organisational approved SSO with a secure environment, no matter how secure those APIs and authentications are, if the front door is still secured with a username, password, and alphanumeric authentication then the risk is still ever-present
To Know More, Read Full Article @ https://ai-techpark.com/revolutionizing-security-fido-2-0/
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Today, in the field of technology, product management is rapidly changing because of artificial intelligence (AI) and machine learning (ML). With these quick advancements in technology and the ever-growing reliance on data-driven decision-making, product managers find themselves at odds; they must forget old ways to learn new ones that fit into this digital age.
Rather than simply managing cutting-edge products or services developed by others, a product manager in today’s IT organization should be viewed as someone who can transform everything about them using any new technique or technology available while also engaging stakeholders like never before.
This article gives an overview of what the digital world means for you as a product manager and some popular certifications in this area.
The Role of Product Managers in the Digital World
Product managers should know the different technologies that are currently being used to process data, understand what each one does best, and how they can be applied.They need not only technical skills but also business acumen to identify many areas where innovation is possible within an organization through the use of data-driven strategies. These strategies will then guide them towards coming up with insights that will push for invention around those areas, leading to the successful launch of new products or services under their control.
Data Analysis and Interpretation
Product managers need to analyze large and complex datasets and identify trends, patterns, and insights to make informed decisions on product development optimization. They also need to collaborate with data scientists to develop product models, perform necessary statistical analysis, and conduct A/B testing.
Product Vision and Strategy
The PM needs to work closely with different teams, which include business stakeholders, data scientists, and software engineers, to identify the product vision and roadmap. Along with that, PM needs to develop business cases to create a data-driven presentation and communicate the product vision and strategy to their stakeholders.
User Experience and Design
Collaboration with UI and UX designers to create user-friendly and intuitive interfaces that enable customers to interact with data-driven services and products. The product managers need to conduct user research and usability testing to comprehend the customer’s needs and preferences and develop user personas and journey maps to inform product development and optimize UX. Let’s use an understanding of the top four trending product management certification courses that product managers can consider to build a strong portfolio in the competitive market.
To Know More, Read Full Article @ https://ai-techpark.com/the-power-of-ai-with-product-management-certifications/
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In the 21st century, people are searching for an abode that will provide better public infrastructure and easily accessible resources that will make their lives easier.
Traditional cities often grapple with major issues of inadequate infrastructure, huge population growth, inefficient resource and waste management, and traffic congestion, aiming at a lack of urban development.
However, the introduction of smart cities represents a pivotal shift towards embracing new-age technologies to solve some of the most pressing challenges of urban living and make cities have better infrastructure, public services, and sustainable growth.
The concept of smart cities emerged as a transformative trend in the fields of technology and architecture that will reshape the urban landscape and revolutionize the way people interact with our environment. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics, architects and IT professionals can set new standards for service delivery, sustainability, and livability.
In 2024, IT professionals and architects will be at the forefront of this environmental sustainability movement, leveraging technology and innovative design principles to develop cities that are technologically advanced, sustainable, and efficient to cater to the different needs of each resident.
In today’s exclusive AITech Park article, we will explore the emerging trend of smart cities and how IT professionals and architects can play a pivotal role in the development of these cities.
Powering the City with Renewables
The transition to renewable energy sources is an important aspect of smart city development, with IT professionals and architects leading the charge toward implementing sustainable energy solutions. For instance, the integration of solar panels on rooftops and farms to harness the sun’s energy to power homes, businesses, and public facilities. In many American cities, the strategic use of wind turbines capitalizes on renewable wind energy resources, supplementing the city’s power grid.
Redefining Urban Transport
The traditional model of urban transportation has undergone a shift that is purely based on AI algorithms for making smart cities efficient and sustainable. With the help of IT professionals, architects can take the initiative to transform urban mobility. The first initiative is to prioritize public transportation systems that are well aligned with big data analysis and ML technologies to enhance accessibility and reliability. Urban planners should adopt different electric vehicles and embrace clean energy solutions to reduce emissions and combat air pollution.
As the world’s population continues to grow at an unprecedented rate, the essentiality of smart cities becomes more pronounced, as they provide a blueprint to address the challenges of urbanization and strive to reach the different goals related to improving urban lifestyle, achieving economic growth, and environmental sustainability.
To Know More, Read Full Article @ https://ai-techpark.com/the-emergence-of-smart-cities-in-2024/
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We are well aware that in recent times, climate change has impacted the economic, social, and environmental systems across the planet, and unfortunately, its consequences are expected to continue in the future.
It has been witnessed that cities in the United States, Philippines, China, and Madagascar are facing warmer, drier, and wetter climates, resulting in natural hazards; these extreme weather events have affected 145,000 human fatalities across cities, as they invite seasonal diseases, drought, famine, and even death.
Therefore, with these adversities in mind, meteorological departments and governments across the country have started taking advantage of technologies such as artificial intelligence (AI) and machine learning (ML) that have the potential to protect the environment.
In today’s special edition at AI Tech Park, we will discuss the use of artificial intelligence in monitoring environmental conditions and its potential to save the planet.
AI Applications for Addressing Environmental Issues
AI has always been the best possible solution, as it can perform any task that requires human intelligence. These machines are dependent on large amounts of data that can be easily analyzed, create patterns, and make appropriate decisions based on that data.
Therefore, when AI and environmental sustainability are combined, it can deal with any environmental issues, such as cutting down forests, water crises, and climate change, as AI can accurately make data-driven decisions, letting us watch over the change in ecosystems and focus on planning and protecting nature.
Let’s look at the AI application in environmental solutions:
Predicting Climate Patterns
AI can analyze historical and real-time data that empowers predictive modeling for each day’s climate patterns and any natural disaster. The advanced algorithm can forecast weather events, track slight to massive changes in climate conditions, and anticipate the intensity of natural disasters. The AI-driven predictive capabilities allow meteorologists to prepare people for disasters and develop evacuation plans and resource allocation.
Energy Consumption Optimization
AI-driven technologies help energy engineers and scientists streamline energy consumption by analyzing patterns and demand fluctuations. For instance, smart grids are driven by intelligent algorithms that align with energy supply and demand. These systems are extremely useful as they efficiently integrate renewable energy sources, and their implementation will continue to increase in the long run.
To Know More, Read Full Article @ https://ai-techpark.com/digital-leadership-for-eco-sustainability/
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Digital twins have become an influential technology in recent years, particularly in manufacturing or heavy industries such as transportation or energy. A simple definition of a digital twin is a faithful, detailed digital model of a real-world system or process – anything from a consumer product prototype to an entire factory or telecommunications network.
Digital models make great testing grounds, one significant advantage being that systems can be tested virtually, with any number of ‘what if’ scenarios being run, outcomes examined and changes to the virtual version of the system made instantaneously. It’s a quicker, cheaper, lower-stakes way to test those changes as opposed to making them in the physical version. This parallels software’s move towards agile development, with its smaller, faster feedback loops.
AIOps as a Digital-to-Digital Twin
Interestingly, the concept of digital twins can be a powerful tool within the field of artificial intelligence for IT Operations (AIOps) to develop self-healing closed-loop ecosystems.
To elaborate, a ‘classic’ digital twin is a representation of a piece of physical reality, and very accurate in emulating and predicting the behavior of mechanical components. For example, a jet engine, a manufacturing line, or even a human heart. This digital representation requires a steady flow of data to stay current. It isn’t a closed loop. In addition, any changes that need to be incorporated into the original version of the twin need to be manually added. This creates a delay and the possibility of errors, which can compromise the digital twin’s speed and agility. That in itself limits its value, because the ability to respond quickly to change is a key for success in today’s highly agile business environment.
By contrast, IT production environments exist solely in a digital reality. While they obviously contain physical elements such as computers, mobile devices, servers, cables and so on, those
only come alive when connected by digital components such as software and data flows. Driven by AI algorithms that enable intelligent automation, digital twins work within AIOps for IT operations, representing the entire IT environment, including hardware, software, and their interactions. This translates to the self-management of IT environments, the ability to predict incidents, offer ways to prevent them, and even suggest improvements when permanently resolving a problem requires a change in the IT environment’s design or architecture.
Taking the principles of digital twins and integrating that into AIOps, organizations can move beyond reactive problem-solving and achieve a proactive, self-healing closed-loop ecosystem that can detect and respond to IT issues in real-time. This approach minimizes manual intervention and allows IT teams to proactively address problems before they impact end-users.
Only digital-to-digital can close the loop seamlessly. Of course, all of this does not mean that humans will lose control of IT as it remains a software platform controlled by IT staff. It does, however, free up IT expertise from repetitive tasks to focus on more complex high value tasks.
To Know More, Read Full Article @ https://ai-techpark.com/digital-twins-for-self-healing-aiops/
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As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.
With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills is equally important for budding and seasoned data scientists to earn a handsome salary and also stay on top of the competition.
In this article, we will explore the top 10 data science certifications that are essential for budding or seasoned data scientists to build a strong foundation in this field.
Data Science Council of America (DASCA) Senior Data Scientist (SDS)
The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program is designed for data scientists with five or more years of professional experience in data research and analytics. The program focuses on qualified knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. This data science program has five trackers that will rank the candidates and track their requirements in terms of their educational and professional degree levels.
IBM Data Science Professional Certificate
The IBM Data Science Professional Certificate is an ideal program for data scientists who started their careers in the data science field. This certification consists of a series of nine courses that will help you acquire skills such as data science, open source tools, data science methodology, Python, databases and SQL, data analysis, data visualization, and machine learning (ML). By the end of the program, the candidates will have numerous assignments and projects to showcase their skills and enhance their resumes.
Open Certified Data Scientist (Open CDS)
The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experienced certification program for candidates who are looking for an upgrade in their data science skills. The programs have three main levels: level one is to become a Certified Data Scientist; level two is to acquire a Master’s Certified Data Scientist; and the third level is to become a Distinguished Certified. This course will allow data scientists to earn their certificates and stay updated about new data trends.
Earning a certification in data science courses and programs is an excellent way to kickstart your career in data science and stand out from the competition. However, before selecting the correct course, it is best to consider which certification type is appropriate according to your education and job goals.
To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-science-certifications-to-boost-your-skills/
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In a business world that’s increasingly leaning on hybrid and multi-cloud environments for agility and competitiveness, DH2i’s recent launch of DxOperator couldn’t be more timely. For those managing SQL Server within Kubernetes — especially when dealing with the intricacies of operating across various cloud platforms — it is a true game changer.
DxOperator is the result of a close relationship with the Microsoft SQL Server team, which led to the creation of a tool that is ideally suited to automate SQL Server container deployment in Kubernetes. What makes it truly unique and a stand-out in this space is DxOperator’s ability to take complex setups and make them simple — which ensures that HA and operational efficiency are easily achievable, even across multi-cloud environments.
Of course, another reason that DxOperator is in a league of its own is how it turns your specific requirements into optimized actions. DxOperator handles everything from custom pod naming to node selection with such finesse that managing SQL Server containers becomes a breeze. It’s all about making sure that your deployments are not just efficient but also best practice compliant.
Microsoft’s Rob Horrocks praised DxOperator (see announcement) for its ease-of-use and effectiveness, noting its potential to simplify complex deployments for those who might not be Kubernetes experts. DxOperator’s user-friendly nature, together with its robustness is reshaping how businesses approach database management.
Key Advantages:
Effortless Automation: DxOperator automates complex tasks like custom pod naming and node selection, making SQL Server container management a breeze. DxOperator ensures deployments adhere to best practices, optimizing performance and security.
Unprecedented Efficiency: Previously requiring 30 minutes and vast amounts of code, DxOperator reduces deployment time to 3-5 minutes with minimal coding. This simplifies the transition to Kubernetes for SQL Server experts.
Focus on Availability Groups: Designed by DH2i's CTO, OJ Ngo, DxOperator excels at automating and managing SQL Server availability groups, a critical aspect for high availability.
The rise of hybrid and multi-cloud environments demands agility and cost-efficiency. In this landscape, DH2i's DxOperator emerges as a game-changer for managing SQL Server within Kubernetes. Developed in collaboration with Microsoft, DxOperator automates SQL Server container deployment in Kubernetes, simplifying even the most intricate setups.
To Know More, Read Full Article @ https://ai-techpark.com/sql-server-for-hybrid-multi-cloud/
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In the digital era, spatial computing (SC) is a rapidly evolving field as we have started to interact with humans and machines in three-dimensional spaces. Technologies under this umbrella, including augmented reality (AR) and virtual reality (VR), can redefine the enterprise’s interaction with these gadgets and unlock a new realm of possibilities and opportunities.
Today, spatial computing is no longer a vision but a reality for finding the correct applications in numerous fields, especially in the business world.
In this AI Tech Park article, we will take a closer look at how spatial computing is the new solution for IT professionals who are looking to improve their data analysis and process optimization.
The Technology Behind Spatial Computing
Spatial computing has emerged as an interactive technology that can merge the digital and physical worlds, allowing users to interact with computers in an immersive and seamless manner.
With the help of a wide range of technologies, such as artificial intelligence (AI), camera sensors, computer vision, the Internet of Things (IoT), AR, VR, and mixed reality (MR), IT professionals can develop new technologies, a seamless business process, and better data analysis to optimize the process.
This technology employs numerous devices and hardware components to provide an interactive customer experience. A few well-known devices in the business world are smart glasses such as Apple Vision Pro and Meta Quest 3, which interface virtual objects with the real world.
Another interactive spatial computing technology is the depth camera by Microsoft Azure Kinect and the Intel RealSense D400 series, which captures the depth of the physical world and creates virtual objects that will fit into the real world.
Spatial computing leverages numerous technologies, such as machine learning (ML), advanced sensors, and computer vision, to understand and interact with the physical world.
Computer vision, also a subset of AI, enables computers to process and understand visual information by tracking users’ movements and understanding the environment. This allows IT professionals to create a digital representation of the physical world, which can be further used to overlay digital content onto the real world.
ML is another key technology in spatial computing that IT professionals use to train computers to understand and predict user behavior. For instance, if the user reaches to touch a digital object, the computer needs to understand this information and take action to respond accordingly and further predict the user’s future actions.
Sensors are also an essential component of spatial technology as they provide the data that the computer needs in the physical world, which includes the user’s behavior, environment, and interaction with digital content.
Spatial computing is indeed considered the future of technology, as it has the potential to revolutionize any industry by enabling human interaction with machines and the environment. This innovative blend of the virtual and physical worlds provides immersive experiences and boosts productivity. At its core, spatial computing integrates MR, VR, and AR to bridge the gap between the real world and the digital realm, which helps shape the future of technology.
To Know More, Read Full Article @ https://ai-techpark.com/spatial-computing-in-business/
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Can you tell us a little about your background and what interested you in pursuing a career in the technology industry?
Prior to joining the Zendesk family, I was the founder of Talkdesk and Cleverly, which was acquired by Zendesk in 2021. In between I also became a Venture Capital investor.
Around 2016, Artificial Intelligence (AI) started to gain popularity in the tech industry, especially among startups. Behind the hype training and deploying AI models was a highly manual process, customised per customer which made it hard to scale. To add to that, due to the effort required to implement AI, it would only be accessible to bigger companies.
Having an engineering background, that got me curious and I decided to study AI to understand the possibilities and limitations of the technology. Eventually, matching that to the CX industry I knew, we co-founded Cleverly which was later acquired by Zendesk.
Please tell us a little about your role as the Head of AI, Zendesk.
At Zendesk, I lead the product development of our AI-related features, which means I am responsible for not only delivering value to our customers, but also ensuring leadership and organisational development. I keep a close pulse on Zendesk’s AI capabilities, identify potential gaps in our technology and manpower to maintain an organisational design that can expand, deliver and rapidly react to market dynamics. This includes growing the machine learning (ML) team, as well as enabling other teams at Zendesk to build ML-powered products while building foundational capabilities and the right infrastructure to support them.
I believe that in the near future, no one will buy software that does not have intelligence embedded in it and it is my job to make sure we are building for that future. Additionally, one of our goals is to innovate for our customers and be their primary partner as they implement AI in their operations.
How do you see AI changing the customer experience industry in the next 5-10 years?
Done right, AI can help CX teams be more consistent, better understand customers and glean more actionable insights. Insights derived through AI can also help businesses identify knowledge gaps and pinpoint problems before they snowball into large volume issues.
Looking ahead, we anticipate that almost all customer service will be AI-first by 2025, and in the future, as AI technology continues to advance, we expect there to be many new use cases that will impact the CX industry, not just customer-facing AI such as front line customer interactions, but also on the business operations side for admins, developers and more.
Can you share an example of a successful AI implementation at Zendesk that has improved customer experience?
We’ve worked closely with many of our customers to implement AI-powered CX tools that are flexible, adaptable and customisable to their ever-changing business needs. One such customer is a global video game developer that leverages our AI tools to quickly diagnose and adapt to dramatic growth. Doing so enabled them to swiftly automate and improve their front-line self-service strategy, resulting in $1.3million in cost savings.
To Know More, Read Full Interview @ https://ai-techpark.com/implementing-ai-in-business/
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Knowledge graphs (KGs) are digital maps that capture the relationships between entities in the real world. This structured data provides a foundation for LLMs, powerful AI systems that can process and understand human language. In 2024, the marriage of these technologies is leading to a new generation of AI. By combining the context-awareness of LLMs with the structured knowledge of KGs, AI systems are becoming more adept at tasks like information retrieval, question answering, and even generating creative text formats. This is fundamentally changing how we interact with computers, allowing for more natural and intuitive communication.
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.
Embrace Web 3.0 For a Decentralized Future
The core of Web 3.0 (Web3) lies in three fundamentals: smart contracts, blockchain, and digital assets, which allow platform users to manage their authority and enable them to participate in the growth of the technological ecosystem. Web3 allows IT professionals to create, own, and manage their content and enjoy authority over their assets and data to safeguard them, as this technology eliminates third-party control and enhances the user’s experience by focusing on privacy, transparency, and security.
Revolutionizing Business Operations With Virtual Assistants
The concept of virtual assistance (VA) has transformed the way we work, interact, and communicate with technology, as it offers a myriad of benefits such as maximizing productivity, streamlining daily operations, and offering personalized customer experiences. The introduction of technological advancements such as AI, NLP, and DL has improved the VA’s intelligence and capabilities, allowing the VA application to address specific news and solve real-world issues.
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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