ibm

https://www.ibm.com/think/topics/machine-learning

[2] What is machine learning? - IBM — Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. Supervised learning, also known as supervised machine learning, is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. However, implementing machine learning in businesses has also raised a number of ethical concerns about AI technologies. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders.

mit

https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

[3] Machine learning, explained - MIT Sloan — Machine learning, explained | MIT Sloan Skip to main content MIT Sloan logo academics Ideas Made to Matter Diversity Events Alumni Faculty About Executive Education academics contact facebook instagram linkedin twitter youtube MIT Sloan is the leader in research and teaching in AI. Here’s what you need to know about the potential and limitations of machine learning and how it’s being used. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed. From manufacturing to retail and banking to bakeries, even legacy companies are using machine learning to unlock new value or boost efficiency. Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.

coursera

https://www.coursera.org/articles/what-is-machine-learning

[4] What Is Machine Learning? Definition, Types, and Examples — Definition, Types, and Examples Written by Coursera Staff • Updated on Feb 3, 2025 Machine learning is a common type of artificial intelligence. Machine learning is a subfield of artificial intelligence that uses algorithms trained on data sets to create models capable of performing tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuations. In this article, you’ll learn more about what machine learning is, including how it works, its different types, and how it's actually used in the real world. Machine learning definition Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models capable of predicting outcomes and classifying information without human intervention.

geeksforgeeks

https://www.geeksforgeeks.org/introduction-machine-learning/

[5] Introduction to Machine Learning: What Is and Its Applications — Machine Learning & Data Science It involves feeding data into algorithms to identify patterns and make predictions on new data.* Machine learning is used in various applications, including image and speech recognition, natural language processing, and recommender systems. *Machine Learning* algorithm learns from data, train on patterns, and solve or predict complex problems beyond the scope of traditional programming. Data is the foundation of machine learning (ML). Without quality data, ML models cannot learn, perform, or make accurate predictions. ML | Introduction to Data in Machine Learning ML | Introduction to Data in Machine Learning Data refers to the set of observations or measurements to train a machine learning models. Why is Data Crucial in Machine Learning?

spotintelligence

https://spotintelligence.com/2024/03/12/performance-metrics-in-machine-learning/

[10] Top 9 Performance Metrics In Machine Learning & How To Use Them — Understanding these classification and regression metrics is essential for evaluating and comparing the performance of machine learning models across different tasks and datasets. By carefully considering these factors and selecting the right performance metrics, we can effectively evaluate model performance, drive informed decision-making, and ultimately deliver impactful machine learning solutions that meet stakeholders’ needs and address real-world challenges. Whether it’s classification, regression, or deep learning tasks, understanding the nuances of different evaluation metrics is crucial for effectively evaluating model performance. From accuracy, precision, recall, and F1 score in classification tasks to mean absolute error, mean squared error, and R-squared in regression tasks, each metric offers unique insights into different aspects of model performance.

medium

https://medium.com/analytics-vidhya/complete-guide-to-machine-learning-evaluation-metrics-615c2864d916

[11] Complete Guide to Machine Learning Evaluation Metrics — The points in the ROC curve can be calculated by evaluating a supervised machine learning model like logistic regression with, but this would be inefficient. The AUC ROC Plot is one of the most popular metrics used for determining machine learning model predictive capabilities. There are some concerns over the AUC ROC curve as it accounts for the order of probabilities, not the model’s capability to predict positive data points with higher probability. Root mean squared error is the most popular metrics used in Regression problems.RMSE is defined by the standard deviation of prediction errors. As the name suggest Root mean squared logarithmic error takes the log of actual values and predicted value. https://www.coursera.org/lecture/big-data-machine-learning/metrics-to-evaluate-model-performance-pFTGm

towardsdatascience

https://towardsdatascience.com/measuring-success-ef3aff9c28e4

[13] Measuring Success of Machine Learning Products — Unfortunately, it frequents being part of the machine learning development process far too often. ML projects can be doomed from conception due to a misalignment between product metrics and model metrics. Today, there are many skilled individuals who can create highly accurate models, and poor modelling capabilities is not a common pitfall.

restack

https://www.restack.io/docs/mlflow-knowledge-kpi-machine-learning-mlflow

[14] Understanding KPIs in Machine Learning with MLflow — Restack — Key Performance Indicators (KPIs) are essential in evaluating machine learning (ML) models, providing quantifiable measures of performance and success. Understanding what is KPI in machine learning involves recognizing the metrics that align with business objectives and model goals. Core KPIs in ML Evaluation

makersmuse

https://makersmuse.in/key-benefits-of-hands-on-learning-in-an-ai-lab/

[24] Hands-On Learning in AI Labs: Transforming STEM Education — According to a LinkedIn report, AI and machine learning are among the top skills sought by employers. Hands-on experience in AI labs gives students a competitive edge, equipping them with practical skills for future tech-driven careers. 5. Fosters Teamwork and Collaboration . AI lab projects often require teamwork.

udacity

https://www.udacity.com/blog/2024/04/project-based-learning-in-tech-the-value-of-hands-on-education-in-a-digital-age.html

[25] Project-based Learning In Tech: The Value of Hands-On Education In A ... — Student-centered and inquiry-based learning: Project-based learning is a student-centered approach, where students take an active role in their learning process. They are encouraged to ask questions, explore their curiosities, and engage in self-directed inquiry to find solutions to the problem or challenge.

iabac

https://iabac.org/blog/the-impact-of-machine-learning-in-the-education-sector

[26] The Impact of Machine Learning in the Education Sector — Machine learning applications in education exhibit a practical capability to forecast student performance and pinpoint potential obstacles. Through the examination of historical data, these systems can provide timely interventions for students encountering difficulties, thereby averting academic setbacks.

perplexity

https://www.perplexity.ai/page/the-history-of-backpropagation-LoxpCKvnQmq7nKjA.AWjBA

[46] The History of Backpropagation - perplexity.ai — Backpropagation, a fundamental algorithm in training artificial neural networks, has a rich history spanning several decades. From its early conceptual roots in the 1960s to its formal development in the 1970s and widespread adoption in the 1980s, backpropagation has played a crucial role in advancing machine learning and artificial intelligence.

medium

https://suryansh-raghuvanshi.medium.com/the-evolution-of-backpropagation-a-revolutionary-breakthrough-in-machine-learning-4bcab272239b

[47] The Evolution of Backpropagation | Medium — The Evolution of Backpropagation: A Revolutionary Breakthrough in Machine Learning The landscape of machine learning has been revolutionized by an ingenious technique called backpropagation. In this article, we will explore the fascinating history and evolution of backpropagation, tracing its origins, key milestones, and its impact on modern neural network training. Early Work on Backpropagation and Gradient Descent Backpropagation, although derived multiple times independently, is essentially an efficient application of the chain rule to neural networks. Rumelhart developed the backpropagation technique independently, further solidifying its importance in neural network training. Gradient descent, the underlying optimization algorithm used in backpropagation, faced initial objections. From its origins in the 1960s to its standardization and experimental analysis in the 1980s, backpropagation has transformed the capabilities of neural networks.

medium

https://medium.com/@juanc.olamendy/backpropagation-in-deep-learning-the-key-to-optimizing-neural-networks-7c063a03f677

[48] Backpropagation in Deep Learning: The Key to Optimizing Neural ... - Medium — Backpropagation in Deep Learning: The Key to Optimizing Neural Networks | by Juan C Olamendy | Medium Backpropagation in Deep Learning: The Key to Optimizing Neural Networks This article delves into backpropagation, explaining how it revolutionized machine learning by enabling efficient training of deep neural networks. Backpropagation is a supervised learning algorithm used for training artificial neural networks. Without backpropagation, training deep neural networks would be inefficient and impractical. Backpropagation revolutionized the field of machine learning by enabling the training of deep neural networks. In the context of neural networks, the chain rule helps in computing the gradient of the loss function with respect to each weight. Backpropagation is the backbone of deep learning, enabling neural networks to learn from data and improve their performance.

medium

https://medium.com/@pole.indraneel/the-story-of-backpropagation-how-an-old-idea-transformed-ai-48ba235c60bc

[49] The Story of Backpropagation: How an Old Idea Transformed AI — The Story of Backpropagation: How an Old Idea Transformed AI | by Indraneel Pole | Dec, 2024 | Medium The Story of Backpropagation: How an Old Idea Transformed AI Backpropagation is a fundamental algorithm in artificial intelligence that powers modern neural networks. In the 1970s, neural networks were largely dismissed by the AI research community. The AI community was still skeptical of neural networks, partly because symbolic AI systems were outperforming them on key benchmarks. As computing power grew in the early 2000s, researchers began to see neural networks outperform symbolic AI systems in fields like image recognition and speech processing. Backpropagation became the standard method for training these networks. Backpropagation in Modern AI Backpropagation transformed AI by making neural networks practical.

robotsauthority

https://robotsauthority.com/perceptrons-and-the-birth-of-neural-networks-during-the-1960s/

[61] Perceptrons and the Birth of Neural Networks During the 1960S — Rosenblatt’s model was limited to linearly separable data, but it laid the foundation for future advancements in neural network architectures and sparked significant interest in artificial intelligence research during the 1960s. The perceptron’s learning mechanism, which involved adjusting weights based on misclassified examples, laid the groundwork for how modern neural networks are trained. Rosenblatt’s pioneering work with perceptrons revolutionized AI research, laying the foundation for future neural network advancements. Through these contributions, Rosenblatt’s perceptrons didn’t just change the landscape of AI research; they laid the groundwork for the sophisticated neural networks and deep learning systems we see today. Frank Rosenblatt’s perceptron algorithm is foundational to many advancements in AI, sparking interest in neural networks and leading to today’s sophisticated models.

geeksforgeeks

https://www.geeksforgeeks.org/what-is-perceptron-the-simplest-artificial-neural-network/

[62] What is Perceptron | The Simplest Artificial neural network — Despite being one of the simplest forms of artificial neural networks, the Perceptron model proved to be highly effective in solving specific classification problems, laying the groundwork for advancements in AI and machine learning. *Perceptron* is a type of neural network that performs binary classification that maps input features to an output decision, usually classifying data into one of two categories, such as 0 or 1. This process enables the perceptron to learn from data and improve its prediction accuracy over time. The Perceptron Learning Algorithm is a binary classification algorithm that adjusts weights associated with input features iteratively based on misclassifications, aiming to find a decision boundary that separates classes. The Perceptron is a single-layer neural network used for binary classification, learning linearly separable patterns.

robotsauthority

https://robotsauthority.com/the-early-days-of-machine-learning-techniques-and-challenges/

[76] The Early Days of Machine Learning: Techniques and Challenges — In the early days of machine learning, excitement surrounded neural networks and models like the perceptron. However, researchers faced significant challenges, including limited computational power and the complexities of real-world applications.

machinelearningmodels

https://machinelearningmodels.org/the-evolution-of-machine-learning-a-brief-history-and-timeline/

[77] The Evolution of Machine Learning: A Brief History and Timeline — Rosenblatt's work demonstrated that machines could learn to recognize patterns and make decisions based on input data, paving the way for future developments in neural network research. Rosenblatt's perceptron sparked significant interest in the field of machine learning and led to the development of multilayer perceptrons (MLPs) and backpropagation, techniques that enabled the training of deeper neural networks. With the availability of big data, machine learning models could be trained to recognize more complex patterns and make more accurate predictions. In healthcare, machine learning models are used for medical imaging analysis, drug discovery, and personalized treatment plans. Financial institutions leverage machine learning models to analyze large volumes of transaction data, identify fraudulent activities, and make informed investment decisions.

github

https://maelfabien.github.io/deeplearning/Perceptron/

[78] The Rosenblatt's Perceptron - GitHub Pages — The Rosenblatt's Perceptron (1957) The classic model. The Rosenblatt's Perceptron was designed to overcome most issues of the McCulloch-Pitts neuron : it can process non-boolean inputs; and it can assign different weights to each input automatically; the threshold \( heta\) is computed automatically; A perceptron is a single layer Neural

encord

https://encord.com/blog/reducing-bias-machine-learning/

[84] Mitigating Model Bias in Machine Learning - Encord — Mitigating Model Bias in Machine Learning | Encord Bias in machine learning refers to systematic errors introduced by algorithms or training data that lead to unfair or disproportionate predictions for specific groups or individuals. Pre-processing techniques involve modifying the training data to reduce bias, while post-processing methods adjust model outputs to ensure fairness. Bias in machine learning refers to systematic errors introduced by algorithms or training data that lead to unfair predictions or decisions for specific groups or individuals. Fairness metrics, such as Equal Opportunity Difference and Disparate Misclassification Rate, also help assess bias in machine learning models. Strategies include collecting diverse and representative data, using bias-aware algorithms, enhancing model interpretability, applying pre-processing and post-processing techniques, and regularly auditing and monitoring AI models.

medium

https://medium.com/@drpa/detecting-and-mitigating-bias-in-machine-learning-models-d4b6de6fe85f

[85] Detecting and Mitigating Bias in Machine Learning Models — Detecting and Mitigating Bias in Machine Learning Models | by Dr. Pooja | Medium Detecting and Mitigating Bias in Machine Learning Models To identify data bias, you must evaluate if the protected groups that may be impacted by your model are well-represented in the dataset. By identifying biases in model performance, you can make informed decisions on how to address them and ensure fair outcomes for all individuals or groups. Detecting and mitigating bias in machine learning models is crucial for ensuring fair and accurate outcomes. By utilizing tools like the What-If tool, identifying and mitigating data bias, ensuring fairness in model training, and monitoring AI systems in production, you can address bias and build trustworthy and reliable AI models.

techinfer

https://techinfer.com/understanding-supervised-vs-unsupervised-learning-which-is-right-for-your-project/

[86] Understanding Supervised vs Unsupervised Learning: Which is Right for ... — Selecting between supervised and unsupervised learning depends on your project's goals and available data: Data Availability: In majority of cases, supervised learning is more appropriate if labelled data and a prediction task are present. Unsupervised learning is more applicable when you do not wish to spend any label data.

refontelearning

https://www.refontelearning.com/blog/supervised-vs-unsupervised-machine-learning

[87] Supervised vs Unsupervised Machine Learning — Data Input: Supervised learning uses labeled datasets (each input has a known output), whereas unsupervised learning uses unlabeled data. For example, you might use unsupervised learning to identify segments in your data, then apply supervised learning to make predictions within each segment. In practice, data scientists might use unsupervised learning as a prelude to supervised learning. Refonte Learning’s community of instructors and alumni frequently shares tips on how to approach problems like feature engineering for supervised models or interpreting clusters from unsupervised results. Here's how understanding Supervised vs Unsupervised Machine Learning can benefit your career: By mastering supervised and unsupervised methods through online learning and practical projects, you’re investing in a skill set that’s indispensable in today’s data-driven world.

medium

https://medium.com/@christophe.atten/top-10-use-cases-for-unsupervised-learning-in-finance-and-healthcare-and-a-bonus-fd4b3d8f830d

[109] Unsupervised learning use cases in finance and co. | Medium — Fraud detection in finance: Fraud detection is one of the most common applications of unsupervised learning in finance. Patterns or anomalies in financial data can be detected by unsupervised

medrise-studio

https://www.medrise-studio.com/blog-posts/ai-in-healthcare-unsupervised-learning-techniques-explained

[111] AI in Healthcare: Unsupervised Learning Techniques Explained - Nevo — Here are a few broader ways in which unsupervised learning is driving innovation in the medical field: - Identifying New Diseases: Unsupervised learning can cluster patients with similar symptoms or genetic markers, potentially uncovering new diseases or conditions. This helps in recognizing previously unknown health risks or disease subtypes.

geeksforgeeks

https://www.geeksforgeeks.org/steps-to-build-a-machine-learning-model/

[113] Steps to Build a Machine Learning Model - GeeksforGeeks — By using data-driven insights and sophisticated algorithms, machine learning models help us achieve unparalleled accuracy and efficiency in solving real-world problems. Building a machine learning model involves several steps, from data collection to model deployment. In this phase of machine learning model development, relevant data is gathered from various sources to train the machine learning model and enable it to make accurate predictions. In conclusion, building a machine learning model involves collecting and preparing data, selecting the right algorithm, tuning it, evaluating its performance, and deploying it for real-time decision-making. Machine Learning Tutorial Machine learning is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions without being explicitly programmed.

dataversity

https://www.dataversity.net/ai-and-machine-learning-trends-to-watch-in-2023/

[116] AI and Machine Learning Trends to Watch in 2023 - DATAVERSITY — Case Studies More Data Topics Analytics Database Data Architecture Data Literacy Data Science Data Strategy Data Modeling EIM Governance & Quality Smart Data Advertisement Homepage > Data Education > Smart Data News, Articles, & Education > AI and Machine Learning Trends to Watch in 2023 AI and Machine Learning Trends to Watch in 2023 By Paramita (Guha) Ghosh on January 31, 2023January 30, 2023 This article highlights 10 of the biggest trends triggered by technological advancements in artificial intelligence (AI) and machine learning (ML). The broad AI and machine learning trends include the provisioning of cloud platforms for data activities – accelerating the use of AI and machine learning technologies and tools for business data and analytics. Here are 10 major trends that have been triggered by recent advancements in AI and machine learning technologies: Trend 1: Increased Use of Cloud-Based Software Systems and Cloud Services Thanks largely to the development of AI- and ML-powered, cloud-based software, organizations are now able to monitor and analyze volumes of enterprise data in real time, and make necessary adjustments to their business processes. Trend 3: Tremendous Rise in Automation of Business Processes AI and ML platforms have jointly contributed to the rising importance of automation throughout the business value chain. Trend 4: Augmented Data Analytics Thanks to the many AI-enabled data analytics platforms or solutions available today, “augmented data analytics” is a reality, where many of the critical phases like data collection, data cleansing, and data preparation are handled by smart tools, so that human data scientists or analysts are free to engage in complex data analysis issues.

hackerrank

https://www.hackerrank.com/blog/top-machine-learning-trends/

[117] Top 7 Machine Learning Trends in 2023 - HackerRank Blog — Pricing For candidates Contact Us Contact us Log in For developers Log in Request demo Sign up Blog Tech Roles Artificial Intelligence Cloud Cybersecurity Data Engineering Data Science & Analytics Mobile Development Quality Assurance Software Engineering Web Development Tech Skills Programming Frameworks Programming Languages Technology Deep Dives Hiring Tech Talent Candidate Experience Diversity, Equity and Inclusion Early Talent Hiring Hiring Best Practices Remote Hiring Talent Sourcing Career Growth Leadership Advice Managing Developers Skills Improvement Solutions Set Up Your Skills Strategy Showcase Your Talent Brand Optimize Your Hiring Process Mobilize Your Internal Talent Embrace AI Updates Customer Stories Events Industry Reports Partnerships Product Updates Thought Leadership Artificial Intelligence Top 7 Machine Learning Trends in 2023 Written By April Bohnert | July 26, 2023 Read now From predictive text in our smartphones to recommendation engines on our favorite shopping websites, machine learning (ML) is already embedded in our daily routines. But ML isn’t standing still – the field is in a state of constant evolution. Now, as we enter the second half of 2023, these technological advancements have paved the way for new and exciting trends in machine learning. These trends not only reflect the ongoing advancement in machine learning technology but also highlight its growing accessibility and the increasingly crucial role of ethics in its applications. From no-code machine learning to tinyML, these seven trends are worth watching in 2023.

wiley

https://onlinelibrary.wiley.com/doi/10.1111/exsy.13506

[124] Recent trends and advances in machine learning challenges and ... — The pace of advancement in machine learning and its applications to industrial processes continues to accelerate, opening up unprecedented opportunities for automation and optimisation. These developments have reverberated across multiple domains, ranging from methodological advances (Bertolini et al., 2021 ) to the development of ml-based

digitaldefynd

https://digitaldefynd.com/IQ/machine-learning-case-studies/

[125] Top 30 Machine Learning Case Studies [2025] - DigitalDefynd — These sensors collect data on soil moisture levels, crop density, and health, then are processed by machine learning algorithms to provide real-time insights and recommendations to farmers via a dashboard. Implementation: AT&T utilizes historical and real-time data from its network operations to train machine learning models. Implementation: The machine learning model integrates with Google’s data center management system to provide real-time predictive insights into cooling requirements. Solution: Square developed a machine learning-based credit risk model that leverages the transaction data processed through its platform. *Implementation:* The integrated machine learning models within HSBC’s monitoring systems process real-time and historical data to detect patterns that may indicate fraudulent or money laundering activities. *Implementation:* The machine learning models utilize historical data from past aircraft designs and real-world performance metrics.

knack

https://www.knack.com/no-code-machine-learning-platforms/

[132] Top No-Code Machine Learning Platforms (Guide) | Knack — The Future of No-Code Machine Learning. The future of no-code machine learning is bright - let's take a look at its upward movement in the coming years. Market Growth and Adoption of No-Code ML. Gartner predicts that by 2025, 70% of new applications developed by organizations will use low-code or no-code technologies.

zerocodeinstitute

https://zerocodeinstitute.com/the-future-of-no-code-a-game-changer-for-2025-and-beyond/

[134] The Future of No-Code: A Game-Changer for 2025 and Beyond — In 2025 and beyond, nocode will continue to empower non-technical users, making it possible to build powerful digital ... incorporating AI and machine learning to automate more complex processes without the need for coding skills. This will significantly enhance productivity and efficiency across industries. The Impact of No-Code on the Job

digitaldefynd

https://digitaldefynd.com/IQ/unsupervised-learning-pros-cons/

[139] 10 Pros and Cons of Unsupervised Learning [2025] - DigitalDefynd — Unsupervised learning is highly scalable, making it exceptionally suitable for dealing with large datasets that are becoming increasingly common in the age of big data. Since these algorithms do not require labeled data, they can be applied directly to vast amounts of raw data, sidestepping the manual labeling bottleneck.

weskill

https://blog.weskill.org/2024/12/Unsupervised-LearningDiscovering-Hidden-Patterns.html

[140] Unsupervised Learning: Discovering Hidden Patterns — Unlike supervised learning, where the model is trained on labeled datasets (input-output pairs), unsupervised learning algorithms are tasked with discovering underlying patterns and structures within the data on their own. Unsupervised Learning: Works with unlabeled data, where the model identifies patterns or structures in the data without any predefined labels. As the field of AI continues to evolve, unsupervised learning will play an increasingly important role in enabling machines to discover insights and make data-driven decisions without human intervention. Supervised learning requires labeled data to train a model, while unsupervised learning works with unlabeled data to discover patterns and structures. Unsupervised learning is essential for analyzing big data because it helps identify patterns, trends, and anomalies within vast and unstructured datasets.

sciencedirect

https://www.sciencedirect.com/science/article/pii/S0893395224002667

[166] Ethical and Bias Considerations in Artificial Intelligence/Machine Learning — Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning - ScienceDirect Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny. Ethics and bias are important considerations in our practice settings, especially as increased number of machine learning (ML) systems are being integrated within our various medical domains. Addressing these biases is crucial to ensure that AI-ML systems remain fair, transparent, and beneficial to all. This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. For all open access content, the Creative Commons licensing terms apply.

mljourney

https://mljourney.com/what-are-the-ethical-considerations-in-ai-and-machine-learning/

[167] What Are the Ethical Considerations in AI and Machine Learning? — What Are the Ethical Considerations in AI and Machine Learning? What Are the Ethical Considerations in AI and Machine Learning? This article explores the key ethical challenges of AI and ML, why they matter, and what can be done to build AI that is fair, transparent, and beneficial for everyone. Ensure human oversight in AI-powered decision-making to correct for unintended biases. Risks Associated with AI and Data Privacy AI systems are often developed collaboratively by data scientists, engineers, and organizations, making it unclear who should be held responsible for harmful or incorrect decisions. Addressing bias, ensuring transparency, protecting privacy, defining accountability, and regulating AI applications are essential for building trustworthy and responsible AI systems. What Are the Ethical Considerations in AI and Machine Learning?

data-science-ua

https://data-science-ua.com/blog/what-is-bias-in-machine-learning-types-and-examples/

[171] What is Bias in Machine Learning: A Complete Overview — The sources of machine learning biases range from being statistical to the datasets used for training the models, algorithms, and decision-making processes of the developers. A balance between bias in machine learning and variance enables organizations to build models that shall be both accurate and fair, leading to better results in real-world applications. Working with Data Science UA can help organizations successfully identify and reduce bias in machine learning models. With the sources of this bias and the ways to reduce them, developers will be in a position to build fair and more accurate machine learning models for all users. Data bias in machine learning refers to systematic mistakes in the data used to train models, which leads to unequal and unfair predictions.

medium

https://adabhishekdabas.medium.com/algorithmic-bias-in-real-world-b98808e01586

[172] Algorithmic Bias in Real-world - Medium — While there are many real and potential benefits of using AI, a flawed decision-making process caused by Human bias embedded in AI output makes this a big concern for its real-world implementation. The growth of Artificial Intelligence in sensitive areas such as hiring, criminal justice, and healthcare has sparked debates on bias and fairness.

ibm

https://www.ibm.com/think/topics/shedding-light-on-ai-bias-with-real-world-examples

[173] AI Bias Examples | IBM — AI Bias Examples | IBM Examples of AI bias in the real world show us that when discriminatory data and algorithms are baked into AI models, the models deploy biases at scale and amplify the resulting negative effects. Examples of AI bias from real life provide organizations with useful insights on how to identify and address bias. AI systems learn to make decisions based on training data, so it is essential to assess datasets for the presence of bias. As a result, people may build these biases into AI systems through the selection of data or how the data is weighted. Reducing bias and AI governance Bias, AI and IBM

prolific

https://www.prolific.com/resources/shocking-ai-bias

[174] AI Bias: 8 Shocking Examples and How to Avoid Them | Prolific — AI Bias: 8 Shocking Examples and How to Avoid Them | Prolific AI research and development It suggests that the AI's training data likely lacked sufficient examples of disabled individuals in leadership roles, leading to biased and inaccurate representations. AI learns bias from the data it’s trained on, which means researchers have to be really careful about how they gather and treat that data. Learn how to avoid bias with ethical data collection in The Quick Guide to AI Ethics for Researchers. Articles Ethical considerations in research: Best practices and examples 3 minutes read October 2, 2024 Articles How to avoid bias in research: 15 things marketers need to know 5 mins read October 24, 2023 Articles Where do ethics come into the conversation with AI?

analyticsinsight

https://www.analyticsinsight.net/machine-learning/balancing-innovation-and-privacy-the-future-of-machine-learning-security

[178] Balancing Innovation and Privacy: The Future of Machine Learning Security — Data privacy in machine learning has become a pressing concern in today’s AI-driven world. The rapid expansion of AI applications has led to an exponential rise in data generation, making privacy preservation more critical than ever. Advanced privacy-preserving techniques like federated learning, differential privacy, and homomorphic encryption are emerging as promising solutions. With emergence, the technology will be rapidly accepted, and analysts predict that the global homomorphic encryption market will touch $2.3 billion by 2027, reflecting growing demand for privacy-preserving AI solutions that do not compromise on analytical power or accuracy. Innovations like homomorphic encryption, differential privacy, and federated learning will enable secure AI application creation. ###### Learn AI for Free: 5 Best Online Courses to Take in 2025

nested

https://nested.ai/2024/12/15/data-privacy-in-the-age-of-machine-learning/

[179] Data Privacy in the Age of Machine Learning - Nested — Data privacy in the age of machine learning is a complex but critical issue. Ensuring the protection of personal information requires a multifaceted approach that includes advanced anonymization techniques, robust encryption, ethical AI practices, and regulatory compliance.

logic2020

https://logic2020.com/insight/ai-data-privacy-strategies/

[180] AI and data privacy: Safeguarding sensitive information — Quick summary: By leveraging best practices in AI and privacy, businesses can maintain compliance with data privacy laws and build customer trust while also realizing the benefits of artificial intelligence and machine learning.

linkedin

https://www.linkedin.com/pulse/ethics-ai-machine-learning-healthcare-balancing-innovation-kanu-kkxwf

[181] The Ethics of AI and Machine Learning in Healthcare: Balancing ... — Artificial intelligence (AI) and machine learning (ML) are revolutionizing healthcare, offering groundbreaking advancements in diagnostics, treatment planning, and patient monitoring. AI-powered

urfjournals

https://urfjournals.org/open-access/ethical-considerations-in-ab-testing-examining-the-ethical-implications-of-ab-testing-including-user-consent-data-privacy-and-potential-biases.pdf

[192] PDF — 3. User Content Obtaining informed consent is a fundamental ethical principle in research involving human subjects. In A/B testing, user consent refers to informing users about the experimentation and obtaining explicit permission to participate in the tests. Informed consent ensures that users know the nature, purpose, risks, and

nih

https://pmc.ncbi.nlm.nih.gov/articles/PMC8404542/

[193] AI, big data, and the future of consent - PMC — We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. ... Big data and informed consent. ... How the machine 'thinks:' understanding opacity in machine learning algorithms. Big Data Soc. 2016;3(1):1-12

nih

https://pubmed.ncbi.nlm.nih.gov/34483498/

[194] AI, big data, and the future of consent - PubMed — We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem.

simplilearn

https://www.simplilearn.com/artificial-intelligence-ai-and-machine-learning-trends-article

[207] Top AI and ML Trends Reshaping the World in 2025 - Simplilearn — As we enter 2025, it is evident that AI and ML are at the forefront of technological advancement, and their impact on our world is more profound than ever before. This article will delve into the top AI and ML trends that are currently shaping our global landscape, providing a comprehensive overview of the key developments, applications, and implications of these technologies. Providing mainstream applications in generating text, videos, images, and speech mimicking humans, the generative AI is user-friendly and hence holds maximized acceptance and usage among the general masses. It enhances the performance of applications, making them more aware of context and improving their capabilities. Advancement in this sector is also among the trends in Machine Learning, where real-time identification, raising warnings, predictability, and neutralization of cyber threats are some hot areas of research.

techtarget

https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends

[208] 8 AI and machine learning trends to watch in 2025 | TechTarget — AI agents, multimodal models, an emphasis on real-world results -- learn about the top AI and machine learning trends and what they mean for businesses in 2025. Increasingly, the future of AI looks to center around multimodal models, like OpenAI's text-to-video Sora and ElevenLabs' AI voice generator, which can handle nontext data types, such as audio, video and images. 4. Generative AI models become commodities Lev Craig covers AI and machine learning as site editor for TechTarget's Enterprise AI site. The importance and impact of AI is covered next, followed by information on AI's key benefits and risks, current and potential AI use cases, building a successful AI strategy, steps for implementing AI tools in the enterprise and technological breakthroughs that are driving the field forward.

inoru

https://www.inoru.com/blog/the-role-of-generative-ai-in-modern-healthcare/

[210] The Role of Generative AI in Modern Healthcare - inoru.com — Generative AI may also play a pivotal role in mental healthcare by generating personalized therapeutic content for patients with conditions like anxiety and depression. AI-based systems could develop guided meditation scripts, create personalized self-help content, or even simulate therapeutic conversations to support patients between appointments.

nih

https://pmc.ncbi.nlm.nih.gov/articles/PMC11739231/

[211] Generative Artificial Intelligence Use in Healthcare: Opportunities for ... — Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. Keywords: Generative AI, Artificial intelligence, Healthcare, Large language models, Clinical excellence, Ethics, Health information technology, AI applications, ChatGPT, Medicine Applications such as personalized treatment plans, medical image analysis, and synthetic data generation have demonstrated the transformative capabilities of Gen AI in enhancing diagnostic accuracy, streamlining operations, and facilitating personalized medicine. Available from: https://www.computerworld.com/article/1627101/what-are-large-language-models-and-how-are-they-used-in-generative-ai.html. Available from: https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/generative-ai-in-healthcare.html.

mckinsey

https://www.mckinsey.com/industries/healthcare/our-insights/generative-ai-in-healthcare-adoption-trends-and-whats-next

[214] The future of generative AI in healthcare | McKinsey — In our Q1 2024 survey, more than 70 percent of respondents from healthcare organizations—including payers, providers, and healthcare services and technology (HST) groups—say that they are pursuing or have already implemented gen AI capabilities (see sidebar, “Research methodology”). To better understand how healthcare organizations are thinking about generative AI (gen AI) use, McKinsey launched a research effort to gather insights from leaders in payer, provider, and healthcare services and technology (HST) groups. We surveyed US healthcare stakeholders about a number of topics, including their plans to use gen AI solutions, how they expect to adopt gen AI tools, their ROI measurements, their expectations for areas that will benefit the most from gen AI, and the roadblocks to scaling gen AI.

healthmanagement

https://healthmanagement.org/c/it/pharmacy/generative-ai-in-healthcare-trends-challenges-and-future-directions

[215] Generative AI in Healthcare: Trends, Challenges, and Future Directions — The continued evolution of strategic partnerships will likely shape the future of generative AI in healthcare, advances in AI technology, and the development of robust governance frameworks. As organisations gain more experience with gen AI, there is an expectation that its use will expand beyond clinically adjacent applications to more core

springer

https://link.springer.com/article/10.1007/s10796-022-10279-0

[216] Artificial Intelligence and Blockchain Integration in Business: Trends ... — The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The developments of AI and blockchain has propelled their integration to revolutionize the next digital generation ignited by IR 4.0.

mdpi

https://www.mdpi.com/1999-5903/16/9/324

[217] Machine Learning for Blockchain and IoT Systems in Smart Cities ... - MDPI — The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML's data analytics and predictive capabilities to enhance the intelligence of IoT systems, while blockchain provides a secure, decentralized

sciendo

https://sciendo.com/article/10.2478/ijssis-2025-0002

[218] Convergence of blockchain, IoT, and machine learning:... — It addresses the challenges of scalability, security, and data management posed by the growth of interconnected IoT devices, proposing solutions through advanced algorithms and the integration of blockchain for data security and immutability.

wjarr

https://wjarr.com/content/enhancing-iot-edge-intelligence-machine-learning-driven-visualization-smart-cities-decision

[219] Enhancing IoT edge intelligence: Machine learning-driven visualization ... — Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training.

akira

https://www.akira.ai/blog/multi-modal-in-healthcare

[222] The Future of Healthcare: Multimodal AI for Precision Medicine — Example: AI-Assisted Cancer Diagnosis. Consider a patient suspected of having lung cancer. A traditional diagnostic approach might involve analyzing a CT scan of the lungs and performing a biopsy. ... newer solutions provide a more comprehensive understanding of a patient's health. Multimodal Data Integration: Combining information from

springer

https://link.springer.com/article/10.1007/s41060-025-00715-0

[223] An overview of methods and techniques in multimodal data fusion with ... — Multimodal data fusion in healthcare platforms aligns with the principles of predictive, preventive, and personalized medicine (3PM) by harnessing the power of diverse data sources. The integrated approach enables predictive modeling, preventative interventions, and personalized healthcare strategies which result in better patient outcomes and more effective delivery of healthcare. This paper

restack

https://www.restack.io/p/multimodal-ai-answer-applications-entertainment-cat-ai

[224] Multimodal AI Applications In Entertainment | Restackio — Applications in Entertainment. Multimodal AI applications are making significant strides in the entertainment industry. Here are some key areas where these technologies are being utilized: ... and AI models that translate data from different modalities into a joint semantic space serve as powerful tools for artistic exploration. Here are some

ssrn

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086425

[247] Federated Learning and Data Privacy: A Review of Challenges and ... - SSRN — Federated learning is a distributed machine learning paradigm enabling collaborative model training across decentralized devices without transferring raw data to a central repository. This method reduces privacy risks and aligns with regulatory compliance while unlocking potential in sensitive domains such as healthcare, finance, and IoT.

sciencedirect

https://www.sciencedirect.com/science/article/pii/S0045790625002046

[249] Privacy-Preserving Federated Learning with Differentially Private ... — Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Differential Privacy (DP) techniques are often introduced to mitigate these risks, but simply injecting DP noise into black-box ML models can compromise accuracy, particularly in dynamic IoT contexts, where continuous, lifelong learning leads to excessive noise accumulation. We propose Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework for FL that addresses the privacy challenges in dynamic IoT environments. Finally, Section 7 summarizes the contributions of this work in developing privacy-preserving FL models for IoT and offers suggestions for future research directions. An efficient approach for privacy preserving decentralized deep learning models based on secure multi-party computation

medium

https://medium.com/@nemagan/the-future-of-federated-learning-and-its-privacy-implications-0282d568d16c

[250] The Future of Federated Learning and Its Privacy Implications — Conclusion. Federated learning presents a groundbreaking solution for harnessing data insights while safeguarding privacy. As the technology matures, it will transform industries by enabling data