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.
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.
Creating a closed loop system
By integrating digital twin principles within AIOps’ automation capabilities, self-healing closed-loop ecosystems can be established. These ecosystems aim to autonomously detect, diagnose, and resolve IT issues, minimizing downtime and enhancing overall system resilience. AIOps not only accurately represents and predicts conditions in each IT ecosystem, but it can also directly and seamlessly self-heal that environment because both the AIOps predictive model and the IT environment in which it operates in the same digital ecosystem.
For example, an AIOps event management solution can predict a CPU or memory shortage. It can then automatically draw on the instructions it was installed with to increase CPU or memory before they are used up. What’s more, AIOps’ observability capacity allows efficient and effective monitoring of the IT environment, and integrated machine learning (ML) not only predicts how that IT environment will behave but prevents failures from happening.
Digital twin capabilities enable AIOps to simulate various scenarios, including potential failures and system upgrades. This allows for proactive maintenance and optimization of the IT infrastructure before issues arise. AIOps systems can continuously learn from its actions and their outcomes, improving its decision-making over time. By analyzing historical data and applying ML techniques, AIOps can build predictive models that anticipate potential failures or performance bottlenecks. Integrated digital twins serve as testbeds for these models, allowing for validation and refinement before deployment in the live environment.
When incidents occur, digital twin functionality helps in isolating the root cause quickly and accurately. AIOps analyzes the digital twin’s state and compares it to the real-world system to pinpoint the source of the problem. AI algorithms can provide recommendations or decisions on how to address the identified issues. These decisions can range from simple actions, such as restarting a service, to more complex decisions, like reconfiguring network settings.
Gaining a holistic view of the affected components and their interdependencies makes it easier to identify the source of the problem, facilitates faster resolution and reduces the impact of incidents.
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.
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.
SAS Certified Data Scientist
SAS Global Certification Program is an advanced-level certification for data scientists who want to update their knowledge on the latest technological advancements in open-source tools and SAS Data Management. The course further provides an understanding of how to manage and improve data and offers an idea of unstructured and structured data transformations and the importance of data access. This certification program is also divided into three pathways aimed at different professionals in data science: the first course is for Data Curation Professionals, which has 4 courses, whereas the second course is for Advanced Analytics Professionals, which has 9 courses. The last course is for AI and machine learning professionals and has five courses.
Microsoft Certified Azure Data Scientist Associate Certification
The Microsoft Azure Data Scientist Associate certification focuses on data scientists and professionals who have professional knowledge about data science and machine learning projects on the Azure platform. This certificate validates the candidate’s ability to utilize and implement ML into Azure and MLflow for data science tasks. The program focuses on upgrading the candidate’s skills in machine learning, AI solutions, NLP, computer vision, and predictive analytics and deploying an understanding of data governance and storage.
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.
Benefits for Hybrid and Multi-Cloud Strategies:
Seamless Deployment: DxOperator streamlines SQL Server deployment across hybrid and multi-cloud environments, maximizing resource allocation and cost control.
Enhanced Security: DxOperator leverages DxEnterprise's secure tunneling technology, ensuring safe data exchange across any network within a hybrid/multi-cloud setup.
Uninterrupted Operations: DxOperator guarantees smooth operation regardless of where data and applications reside in the cloud.
Tailored for Hybrid and Multi-Cloud Strategies
For organizations embracing hybrid and multi-cloud models, DxOperator is a significant boon. DxOperator streamlines the deployment of SQL Server across various settings, aligning seamlessly with the scalable and adaptable characteristics of hybrid cloud approaches. The result is that businesses have the flexibility to allocate their resources more wisely and keep spending under control. Moreover, digital security is enhanced with our cutting-edge DxEnterprise with secure tunneling technology, ensuring safe and private data exchange across any network. And, at the same time, it ensures everything runs smoothly, no matter where their data and applications are hosted in the cloud.
Highlights:
Efficient Deployment: DxOperator facilitates quick and intelligent setup of SQL Server instances, ideally suiting the complex requirements of hybrid and multi-cloud settings.
High Availability: The tool ensures that your SQL Server environments are always up and running, smoothly integrating into Always On Availability Groups for continuous operation across any cloud setting.
Simplified Management: With DxOperator, the complexity of managing SQL Server environments is significantly reduced, freeing up IT teams to focus on strategic initiatives.
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.
The Area of Applications of Spatial Computing
At the business level, spatial computing technology enables machines to collect information about the physical environment and gather data on the movements and behavior of employees working in their workspace.
Logistics and Supply Chain Optimization
B2B logistics and supply chain companies often use spatial computing technologies to improve the flow of raw materials and goods in their supply chains. The machines have built-in location data sensors, real-time analytics, and sensors to track shipments, efficiently manage inventory, and optimize routes.
Augmented Reality in Manufacturing
In the manufacturing industry, spatial computing is used to employ AR technology for the production process, where employees can use AR glasses to receive digital instruction and real-time visual aids that will guide them. The implementation of AR has proven to be quite efficient and has reduced the error of miscalculation in purchase, distribution, and installation by 62%.
Smart Buildings and Facility Management
The implementation of spatial computing is to create smart buildings that automatically optimize resources such as air conditioners, lights, heating, or any other gadgets that are linked with this technology. The integration of advanced sensors and data analytics allows electronics and technology companies to improve users’ energy consumption and reduce companies’ operational costs as they adhere to environmental responsibility laws and regulations.
Urban Planning and Construction
In the field of architecture and construction, numerous developing and developed cities are using spatial computing to plan and optimize urban spaces. With spatial computing capabilities, especially with mixed reality headsets, architects and civil engineers can design buildings through 3D models. Further, they can analyze the traffic flows, neighboring infrastructure, and demographic data to develop an informed decision to address traffic and water issues and promote sustainable development.
Conclusion
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.
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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.
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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.
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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 Importance of Algorithm Bias Auditing in the Digital Era
As IT companies are using AI and ML, people are questioning the extent of human biases that have made their way into AI models. For instance, in the healthcare sector, the underrepresented data of female or minority groups can skew predictive AI algorithms, which can tamper with computer-aided diagnosis (CAD) systems and provide inaccurate diagnoses that can affect their mental and physical health.
To eliminate the issue of algorithm bias in the digital era, algorithm auditors need to develop a proper technology mix by creating an effective AI and data governance strategy and stressing the key components of modern data architecture and trustworthy AI platforms.
The policy that is orchestrated through data fabric architecture can be an excellent tool as it can simplify complex AI auditing processes. By incorporating AI algorithm audits into the business process, algorithm auditors and data scientists can understand the areas of requirements while inspecting the data.
Algorithm bias is a growing concern in this era, as algorithms are gradually becoming more prevalent in making data-driven decisions. Such bias can often lead to significant repercussions that might lead to protests or anger if no action is taken. Therefore, to eliminate such instances, the intervention of algorithm auditors plays an important role in ensuring that algorithms are fair, accurate, and unbiased without hurting people’s sentiments and values.
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