Autore: martechcubejohn

Generative AI technology like ChatGPT has brought the world one step closer to the futuristic society envisioned by forward-thinking science fiction writers. But will this future be a utopian or dystopian one? Time will tell. In the meantime, businesses must understand and leverage AI’s burgeoning influence over the zeitgeist to build favorable public sentiment about their brand’s reputation. It has become a make-it-or-break-it moment for corporations in the battle against the spread of AI-led misinformation.  

AI Angst

Opinions run hot, cold, and everywhere in between when it comes to AI’s possibilities and ramifications. A recent survey by the Pew Research Center found that 52% of Americans are expressing greater concern rather than excitement regarding the increasing dependence on AI. This sentiment has risen by 14% since 2022. The current era represents a Cognitive Industrial Revolution teeming with potential, including AI’s provocative ability to sway public opinion.

Businesses and their communication teams must fortify their message with an if-you-can’t-beat-them, join-them approach, leveraging AI’s influence over public opinion and using that to their advantage. By being open and transparent, companies can direct the narrative and strengthen their brand’s image by becoming thought leaders in their industry—with more communication, not less.

Better Communication Through Thought Leadership

Businesses are urged to become thought leaders, effectively communicating their brand message through credible third-party channels such as the media and influencers. The influencer marketing sector is booming and is expected to reach a value of $24 billion by year-end. AI is being embraced by 63% of companies for campaign executions, with 55% utilizing it to pinpoint influencers. Moreover, 33% of the total market capitalization of the S&P 500, attributed to goodwill, is impacted by public relations (PR) strategies and tactics.  Brands must proactively shape and manage their narratives to influence their target audiences. Failure to do so relinquishes control of these narratives to others—rendering marketing, and sales, less effective.

However, implementing this shift necessitates moving away from stale approaches in public relations and public perception. Traditional methods in these areas have been neglected, with only a minority understanding the strategic guiding of public opinion. As AI-generated content becomes more prevalent, the importance of compelling storytelling at the beginning of the customer journey or at the start or top of the PR>Marketing>Sales funnel cannot be overstated.

Mastering the Codified Body of Knowledge of Public Opinion

Companies benefit from becoming better acquainted with the established principles and empirical data that mold public perception. Delving into case studies, contemporary theories, rules of engagement, and the evolution of public relations offers valuable insights into the intricate dynamics that shape public opinion. Armed with this knowledge, organizations can develop strategies, and tailor messages that strike a chord with audiences, enhancing the prospects of broader acceptance and dissemination.

To Know More, Read Full Article @ https://ai-techpark.com/opportunities-in-the-cognitive-revolution/ 

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As we have stepped into an era of rapid technological transformation and competition, the business environment needs to prioritize workers’ safety and well-being to safeguard their employees from workplace hazards. According to a recent statement by the International Labour Organization (ILO), it is reported that approximately 2.3 million individuals globally lose their lives to work-related incidents or illnesses annually, and here AI comes as a savior that enhances occupational safety and health (OHS) practices.

The continuous advancement of technology unlocked a new channel of employee welfare, which paved the way for a future where employees feel protected and prosperous. AI ensures workforce measures, identifies potential hazards, and creates a more secure work environment for employees.

The implementation of occupational health and safety software can aid employers in elevating their company’s safety, as AI and data professionals design these digital solutions in collaboration with occupational healthcare practitioners, safety professionals, and paramedics to document, monitor, analyze, and manage employees’ health care.

Today’s exclusive AITech Park article will discuss the top six occupational safety and health software programs that will change workplace dynamics forever.

The Top Five Occupational Health and Safety (OHS) Software of 2024

For a better understanding, let’s have a quick look at some of the best occupational health and safety software that will be a guide for employers to streamline their business processes.

EcoOnline Platform

The EcoOnline Platform is a centralized cloud-based solution for workplace safety, chemical safety, and compliance that includes the guidance of safety managers and chemical managers to understand OHS practices. This software is developed for companies to adopt health and safety procedures so that all workers may take part in them and make safe decisions. It contains numerous modules for SDS management, chemical safety reporting, risk assessments, accident management, work permits, training, and much more.

Intenseye Software

Intenseye is the world’s #1 environmental health and safety (EHS) platform, powered by cutting-edge AI. Using existing cameras within facilities, Intenseye captures safety risks and provides real-time notifications, risk and trend reports, visualizations, and tailored mitigation strategies to modernize overall safety management and ensure the frontline remains injury-free. The software encourages EHS teams to focus on priority tasks, establish robust safety measures, and achieve time and resource savings, ultimately boosting productivity.

To Know More, Read Full Article @ https://ai-techpark.com/occupational-safety-and-health-software-programs/ 

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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:

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.

Towards Zero Waste

In 2024, architects will be more focused on eliminating the challenges of waste management to create resilient and sustainable cities by implementing smart waste management systems that have sensor-driven bins and smart waste collection vehicles that will optimize waste collection routes and reduce fuel consumption. Advanced waste-to-energy technologies are used to convert organic waste into renewable energy sources, minimizing landfill usage and mitigating environmental impacts.

Innovative Solutions for Water Sustainability

According to a report by the U.N. World Water Development Report 2023, water scarcity is one of the biggest crises that the world is facing, as it was revealed that 2 billion people (26% of the population) lack safe drinking water, while 3.6 billion (46%) lack access to safely managed sanitation.

Therefore, to curb these issues and strategize for water conservation and management, architects and IT professionals can implement IoT-enabled water meters to monitor water usage in real-time, enabling residents to optimize water consumption and identify leakages.

Bottom Line

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.

Air Quality Monitoring

The precise real-time air quality assessments are based on data analysis from smart sensors, enabling scientists and engineers to take prompt action in areas with high pollution levels. The ML models also come in handy for forecasting potential pollution levels based on various factors and, thus, taking proactive actions to mitigate air pollution.

Read about The Convergence of Artificial Intelligence and Sustainability in the IT Industry

Industry Leaders’ Perspectives on AI and Environment Sustainability

When it comes to introducing AI-driven sustainability initiatives, leaders should ensure that all stakeholders are on board with the idea and must collaborate and think about this issue as a collective thing.

Having a long-term vision is essential, as companies sometimes focus on immediate benefits that will help increase profit in the next quarter. But when companies start incorporating environmental, societal, and financial variables, it will help C-suites get a clear picture and give thought to the long-term implementation of sustainability and technology.

For any environmental and sustainability initiative, the C-suites must have a strategic vision with robust leadership and stakeholders’ commitment to developing a more resistant and structured plan that will help in creating sustainable business with improved outcomes for the customer and society.

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The role of AI in environmental sustainability will have a wide role in the future, as it will not only involve handling and analyzing more complex datasets but also enabling environmental prediction.

Similarly, the integration of smart technology with the Internet of Things (IoT) will allow organizations to collect data and focus on enhancing environmental monitoring and resource management. To accelerate the development and adoption of AI-based solutions for environmental challenges, enterprises need to collaborate with every government, business, academia, and NGO at both local and global levels, as their expertise and knowledge will help in fostering innovation and investing smartly in tailored environmental applications.

Ultimately, the implementation of AI in addressing environmental challenges is just one part of the effort to transition to a more sustainable society.

 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.

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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.

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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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