Tag: Machine Learning

With the rapid growth in the digital world, organizations are implementing Automated Machine Learning (AutoML) that helps data scientists and MLOps teams automate the training, tuning, and deployment of machine learning (ML) models. This technology will save time and resources for the data scientists and MLOps teams, which will accelerate research on ML and solve specific problems related to ML models.

For instance, some AutoML tools focus on optimizing ML models for a given dataset, while others focus on finding the best model for specific tasks, such as picking the appropriate ML algorithm for a given situation, preprocessing the data, and optimizing the model’s hyperparameters, aiding different industries to predict customer behavior, detect fraud, and improve supply chain efficiency.

Therefore, AutoML is a powerful mechanism that makes ML models more accessible and efficient; however, to create a model, execute stratified cross-validation, and evaluate classification metrics, data scientists and MLOps teams need the right set of AutoML tools or platforms.

In today’s AI TechPark article, we will introduce you to the top four AutoML tools and platforms that simplify using ML algorithms.

Auto-SKLearn

Auto-SKLearn is an AutoML toolkit that is available as an open-source software library that can automate the process of developing and selecting the correct ML models using the Python programming language. The software package includes attributes that are used in engineering methods such as One-Hot, digital feature standardization, and PCA. It improvises the model and operates SKLearn estimators to process classification and regression problems. Furthermore, Auto-SKLearn builds a pipeline and utilizes Bayes search to optimize that channel, adding two components for hyper-parameter tuning using Bayesian reasoning: The tools also have an inbuilt meta-learning feature that is used to format optimizers using Bayes and assess the auto-collection structure of the arrangement during the optimization process.

Google AutoML Cloud

The Google Cloud AutoML suite is designed to make it easiest for data scientists and MLops teams to apply ML-specific tasks such as image and speech recognition, natural language processing, and language translation in business. The platform accelerates the process of building custom AI solutions with a variety of open-source tools and proprietary technology that Google has evolved over the last decade. AutoML supports homegrown TensorFlow and offers partially pre-trained features for designing custom solutions using smaller data sets.

To Know More, Read Full Article @ https://ai-techpark.com/automl-platforms-for-2024/ 

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Despite remarkable progress in pharmaceuticals, more than 7,000 diseases persist without efficacious treatments. Many medical conditions remain underfunded and overlooked, leading to low success rates in new drug discovery endeavors.

The journey from identifying a potential molecule to developing a market-ready medicine is an extensive, laborious, and expensive process. However, quantum computing (QC) offers the potential to revolutionize this journey by addressing complex challenges within the healthcare supply chain and even creating new medications from scratch. Nevertheless, the integration of QC into drug research remains a gradual process.

Today, we delve into the transformative impact of QC on drug research and its promising prospects in the realm of healthcare.

Enhancing Drug Research Efficiency with Quantum Computing

Drug discovery entails intricate processes that blend computational simulations with laboratory experimentation. QC introduces novel discovery approaches, enabling the selection of candidate molecules with desired properties without the need for exhaustive screening procedures. Leveraging artificial intelligence (AI) and machine learning (ML) alongside QC's unique computational principles accelerates drug development, particularly for diseases such as cancer and Alzheimer's, where traditional methods have fallen short.

Democratizing Drug Development with Quantum Computing

QC not only promises to streamline drug development processes but also democratize access to them. Cloud-based QC services provide researchers, ranging from startups to established pharmaceutical firms, with access to quantum computing resources. This accessibility reduces barriers to entry in the pharmaceutical industry, empowering a wider range of stakeholders to participate in drug development endeavors.

Future Trends of Quantum Computing in Drug Discovery

The future of QC in the pharmaceutical industry is rapidly evolving, especially with the emergence of hybrid quantum-classical systems. These systems combine quantum and classical computing techniques to address complex challenges more efficiently. Collaborative ecosystems between pharmaceutical companies, technology firms, and academic institutions are also on the rise, particularly in the realm of QC-enabled drug discovery. Such collaborations aim to leverage quantum algorithms to enhance ML capabilities in drug design and discovery processes, promising groundbreaking advancements in the field.

In conclusion, QC stands poised to revolutionize drug discovery, offering improvements in accuracy and accelerating the overall process. By harnessing the power of quantum bits and algorithms, researchers can address current challenges in drug development and expedite the delivery of novel treatments. As research and innovation in QC continue to advance, its role in transforming the pharmaceutical industry and improving patient outcomes will undoubtedly become increasingly significant.

To Know More, Read Full Article @ https://ai-techpark.com/the-intersection-of-quantum-computing-and-drug-discovery/ 

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In the past few years, we have witnessed rapid advancements in the field of quantum computing (QC), which triggers the potential revolutionization in various industries, such as healthcare, supply chain, and manufacturing. This technology can perform complex computations at an unimaginable speed when compared to classical computers, even against quantum threats.

According to the National Institute of Standards and Technology (NIST), the post-quantum cryptography (PQC) standards are expected to be completed by 2024, allowing quantum vendors and experts to keep up with the six QC trends that intersect machine learning (ML) and artificial intelligence (AI).

In today’s exclusive AI Tech Park article, we will delve into the top six quantum computing trends for 2024, providing detailed insight for quantum vendors and experts to harness the transformative power of this cutting-edge technology.

Quantum-Sensing Technologies

The implementation of quantum sensing technologies will enable IT organizations, quantum vendors, and experts to achieve unprecedented levels of sensitivity and precision in measuring and detecting applications. In 2024, businesses will leverage quantum sensor tools and applications for environmental monitoring, medical diagnostics, and mineral exploration to gather actionable insights and make informed decisions based on highly accurate data.

Quantum-Safe Cryptography

With the arrival of quantum computers, traditional cryptographic algorithms will become absolute and vulnerable to quantum attacks. Therefore, organizations will adopt quantum-safe cryptography solutions and technology to protect their sensitive data and communications from quantum threats. The implementation of quantum-safe algorithms, such as quantum key distribution or lattice-based cryptography, will become essential tools for securing digital assets and guaranteeing data privacy in a post-quantum world.

Quantum Machine Learning

Quantum computing, when intersected with ML, enables businesses to leverage quantum algorithms for pattern recognition, optimization, and predictive analytics. The quantum machine learning (QML) algorithms will unlock new insights from large data sets, accelerate model training processes, and enable more accurate predictions in numerous domains. The quantum vendors and experts can further explore the possibilities of integrating QML into the data and analysis section to make data-driven decisions to streamline innovation and develop a competitive advantage in this digital world.

To Know More, Read Full Article @ https://ai-techpark.com/the-top-six-quantum-computing-trends-for-2024/ 

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As the trajectory of computing power continues its exponential ascent, quantum computing stands at the forefront, poised to tackle challenges that have long confounded traditional computational methods. In the ever-evolving landscape of the 21st century, quantum computing emerges as a dynamic field brimming with promise, offering a plethora of solutions across diverse domains such as climate modeling, energy optimization, drug discovery, and healthcare.

The allure of quantum computing lies in its ability to conduct simulations and optimizations on a scale previously unimaginable, presenting a paradigm shift that beckons computer engineers, scientists, and developers to delve into the realms of quantum physics. Indeed, the fusion of quantum principles with computational prowess heralds a digital revolution, paving the way for transformative innovations and novel approaches to age-old problems.

To facilitate the journey into this exciting frontier, a curated selection of quantum computing certification courses stands ready to guide aspiring learners:

The Complete Quantum Computing Course by StationX:

Tailored for STEM professionals embarking on their quantum odyssey, this foundational course unravels the mysteries of quantum regulations and their pivotal role in bestowing unparalleled computational supremacy. From quantum computing basics to error correction techniques, quantum algorithms, and states manipulation, participants gain insights into applications spanning cybersecurity, pharmaceuticals, and engineering.

Quantum Computing: The Big Picture by Pluralsight:

Delving into the nuances of quantum mechanics, this professional course offers a panoramic view of key concepts such as superposition, entanglement, and the crafting of quantum algorithms. Designed to empower IT engineers, developers, and computer scientists, it sheds light on the transformative potential of quantum computing across diverse domains including IoT, wireless security, network engineering, and augmented reality.

Applied Quantum Computing III: Algorithm and Software by EdX: 
Catering to the discerning palate of IT engineers and computer scientists, this advanced-level offering delves deep into the intricacies of quantum Fourier transform, search algorithms, and their myriad applications. With a focus on optimization, simulation, quantum chemistry, machine learning, and data science, participants are immersed in live sessions and personalized learning experiences, honing their skills in programming, data science, and algorithmic design.

In conclusion, the imperative of familiarizing oneself with quantum computing in the digital age cannot be overstated. These meticulously curated certification courses offer not merely a gateway, but a pathway to mastery, equipping computer scientists, engineers, and programmers with the requisite knowledge and skills to harness the transformative potential of quantum computing and chart a course towards innovation and excellence.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-quantum-computing-certification-in-2024/ 

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Digital twin (DT) is a rapidly growing concept that has gained traction as it can improve product designs, optimize performance at an industrial level, and create proactive maintenance services. This upgrading technology has started taking shape on an entirely new and different scale as it has become the pillar for futuristic smart cities.

In the scenario of smart cities, digital twins work as virtual replicas of the city’s assets, such as buildings, road lighting systems, energy and grid capabilities, and mobility solutions. However, it is not enough to develop a third-dimensional (3D) model of these sources. Therefore, the digital twin of smart cities pairs the 3D information with spatial modeling (for building the environment), simulations and mathematical models (for workable electric and mechanical systems), and other components that use real-time data feeds from the Internet of Things (IoT) platforms.

In this exclusive AITech Park, we will explore how digital twins will help smart cities evolve in 2024.

Twinning With the New Age Smart Cities

With the introduction of digital twins in the construction field, this technology has the potential to unlock data that was traditionally trapped in silos.

When constructing a new building, the digital twin is developed from the initial phases of the project by the architects, engineers, and construction (AEC) teams to work together to define each other’s performance goals and get the desired outcomes. Now, as the project progresses, the data is continuously collected and fed into the model using any digital twin solution. When the infrastructure is handed over to the owner, the virtual twin collects operations data that will fine-tune performance and manage maintenance in the long term.

As the digital twin mostly revolves around data supplies, it’s the physical twin that helps in performing predictions and simulations in response to real-world conditions. For instance, in the construction industry, the physical twin can be used to align a building’s solar facade that follows the path of the sun and modifies airflow to minimize the spread of germs.

Therefore, it is evident that DT allows the AEC teams to connect better throughout the entire assignment lifecycle, from design to decommissioning. Further, integrating static data aids in specifying the segment and creating maintenance schedules based on the dynamic data of occupancy rates and environmental conditions.

When DT is combined with building information modeling (BIM), the AEC team is well connected to data, which processes dynamic, real-time, bidirectional information management, bringing out the full potential of integrated workflows and information sharing with clients.

As DT is integrated with artificial intelligence (AI) and machine learning (ML), this technology will evolve from being a conceptual tool to becoming more competent and autonomous as software capabilities expand. The application areas for digital twins will continue to reach new heights in the coming years and will change the way AEC teams create, use, and optimize physical spaces and multiple processes.

To Know More, Read Full Article @ https://ai-techpark.com/urbanizing-smart-cities-with-digital-twins/ 

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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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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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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.

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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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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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