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Machine Learning and Artificial Intelligence are the “Buzz topics” in every trending article of 2023, and rightfully so. It is much like how the internet emerged as a game-changer in everyone’s lifestyle, Artificial Intelligence and Machine Learning are poised to transform our lives which were unimaginable years ago.

What are Artificial Intelligence and Machine Learning?

Artificial Intelligence (A.I.) is a simplified problem-solving process for humans. It empowers software to do jobs without being explicitly programmed. Also, it has neural networks and profound learning. It’s the larger notion of machines having the ability to do jobs how we’d think about.

And, Machine Learning is the app of Artificial Intelligence (AI) that enables machines to get data and allows them to learn how to execute these jobs. It uses algorithms and enables systems to discover concealed insights without being programmed.

Why are A.I. and ML important?

Considering that the growing volumes and types of information readily available, the demand for computational processing is becoming crucial to supply deep-rooted information that is economical and readily available. With the support of both A.I. and Machine Learning, it is possible to automate versions that may analyze larger, complicated data to return faster and precise results.

Organizations are discovering profitable opportunities to cultivate their company by identifying the exact models to steer clear of unknown dangers. Using algorithms to construct a version is assisting businesses to bridge the difference between their products and consumers with greater choices and human intervention. Most businesses with enormous quantities of information have recognized that the significance of Machine Learning.

By gaining insights from this information, frequently in real-time, organizations are becoming more effective in their livelihood and gaining an edge over other competitors.

The Biggies such as Google, Facebook, and Twitter banks on Artificial Intelligence and Machine Learning to their potential expansion.

Sundar Pichai

Who is using these technologies?

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The major industries where Machine Learning and Artificial Intelligence are used are:

6. Online Search

In Healthcare

Gradually, human practitioners and machines will work in tandem to deliver improved outcomes


AI And Machine Learning are the New Future Technology Trends discuss how the latest technologies like blockchain are impacting India’s capital markets.

Real Estate

The repetitive tasks in an average DBA system provide an opportunity for AI technologies to automate processes and tasks.

The Future of AI

In the post-industrialization era, people have worked to create a machine that behaves like a human. The thinking machine is AI’s biggest gift to humankind the grand entrance of the self-propelled machine has abruptly changed the surgical principles of business.

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The Future of Machine Learning

Here are some predictions about Machine Learning, based on current technology trends and ML’s systematic progression toward maturity:

ML will be an integral part of all AI systems, large or small.

As ML assumes increased importance in business applications, there is a strong possibility of this technology being offered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).

Connected AI systems will enable ML algorithms to “continuously learn,” based on newly emerging information on the internet.

There’ll be a huge rush among hardware vendors to improve CPU power to adapt ML information processing. More correctly, hardware vendors will likely be forced to redesign their machines to do justice to the forces of ML.

Machine Learning will help machines to make better sense of the context and meaning of data.

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Applications Of Artificial Intelligence And Machine Learning In 5G

Applications of artificial intelligence and machine learning in 5G

As 5G standards mature more quickly and its pre-commercial tests are carried out around the globe, the pace of 5G deployment is speeding up and more innovative applications are made possible through 5G networks. In the era of 5G, telecom carriers are also faced with the challenges of network complexity, diverse services and personalized user experience.

Network complexity refers to complex site planning due to densely distributed 5G networks, complex configuration of large-scale antenna arrays, and complex global scheduling brought by SDN/NFV and cloud networks. Diverse services range from original mobile internet services such as voice and data to known and unknown services developed in IoT, industrial internet, and remote medical care. Personalized user experience means to offer differentiated and personal services to users and build user experience model in terms of full-life cycle, full-business process, and full-business scenario that are associated with service experience and marketing activities for smart operations. These challenges require networks to be maintained and operated in a smarter and more agile manner.

Artificial intelligence (AI) represented by machine learning and deep learning has done a remarkable job in the industries of internet and security protection. we believes that AI can also greatly help telecom carriers optimize their investment, reduce costs and improve O&M efficiency, involving precision 5G network planning, capacity expansion forecast, coverage auto-optimization, smart MIMO, dynamic cloud network resource scheduling, and 5G smart slicing (Fig. 1).

In smart network planning and construction, machine learning and AI algorithms can be used to analyze multidimensional data, especially the cross-domain data. For example, the 0-domain data, B-domain data, geographical information, engineering parameters, history KPI, and history complaints in a region, if analyzed by using AI algorithms, can help make reasonable forecast on business growth, peak traffic, and resource utilization in this region. Also multi-mode coverage and interference can be measured for optimization and parameter configuration can then be recommended to guide coordinated network planning, capacity expansion, and blind spot coverage in 4G/5G networks. In this way, operators make their regional network planning close to theoretical optimum and can significantly reduce labor cost in network planning and deployment.

AI technology can be used to identify the law of change in user distribution and forecast the distribution by analyzing and digging up historical user data. In addition, by learning the historical data, the correspondence between radio quality and optimal weights can be worked out. Based on the AI technology, when the scenario or user distribution changes or migrates, the system can automatically guide the MM site to optimize its weights. To achieve optimal combination and best coverage in a multi-cell scenario, interference among multiple MM sites should also be considered besides the intra-cell optimization. For example, when a stadium is used in different scenarios such as a sports event and a concert, its user distribution is quite different. In this case, MM sites in the stadium can automatically identify a different scenario and make adaptive optimization of the weights for the scenario so as to obtain best user coverage.

The application of AI in the telecom field is still in the early stage. The coming 5–10 years will be a critical period for smart transformation of carriers’ networks. With its gradual maturity, AI will be introduced in various telecom scenarios to help carriers transit from the current human management model to the self-driven automatic management mode and truly achieve smart transformation in network operation and maintenance.

The Future Of Artificial Intelligence In Manufacturing

Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.

Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency. 

With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to: 

Detect defects

Predict failures

Optimize processes

Make devices smarter

Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.

Detect Defects

AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.

Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.

Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time. 

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Predict Failures

Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.

Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.

General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.

High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.

Optimize Processes

The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions. 

Make Devices Smarter

By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.

The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.

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What’s Next?

The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes. 

The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.

There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.

The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.

Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.

The Future Of Machine Learning: Automl

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated Machine Learning (AutoML). AutoML simplifies the process of building and tuning machine learning models for organizations to harness the power of these technologies. Figure 1 gives a visual AutoML. In this blog, we’ll explore a look at some of its key benefits and limitations. Get ready to be amazed by the power of AutoML.

Learning Objectives

Understand the basics of AutoML and its methods

Explore the key benefits of using AutoML

Understand the limitations of AutoML

Understand the practical impact of AutoML

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is AutoML?

Methods of AutoML: A Comprehensive Overview

Effortless ML: The Merits of AutoML

AutoML: A Closer Look at the Drawbacks

AutoML in Practice: How Companies are Automating Machine Learning?


What is AutoML? The Future of Machine Learning

AutoML is a game-changer in the field of machine learning. It is a training of machine learning models to automate the process of selecting and tuning algorithms. This includes everything from data preprocessing to selecting the most suitable model for the given task. AutoML tools handle hyperparameter tuning and model selection tasks, which typically require time and expertise. With AutoML, users without experience in machine learning can train high-performing models with minimal effort. Whether you’re a small business owner, a researcher, or a data scientist, AutoML helps to achieve your goals with less time and effort. Examples of popular AutoML platforms include Google Cloud AutoML, chúng tôi and DataRobot.

AutoML provides explainable AI to improve the interpretability of the model. This allows data scientists to understand how the model makes predictions, which is particularly helpful in healthcare, finance, and autonomous systems. This can be used to identify bias in data and prevent wrong predictions. For example, AutoML can be used in healthcare fo gnosis by analyzing medical images, in finance for fraud detection, in retail for product recommendations, and in transportation for self-driving cars. Figure 2 shows the AutoML process.

ethods: A Comprehensive Overview

AutoML automates the use of machine learning for real-world problems. This includes tasks such as algorithm selection, hyperparameter optimization, and f rent methods are being developed to tackle the various aspects of the problem. Some popular approaches are given below

Neural Architecture Search (NAS):

This method uses a search algorithm to automatically find the best neural network architecture for a given task and dataset.

Bayesian Optimization: This method uses a probabilistic model to guide the search for the best set of hyperparameters for a given model and dataset.

Evolutionary Algorithms: This method uses evolutionary algorithms such as genetic algorithms or particle swarm optimization to search for the best set of model hyperparameters.

Gradient-based methods: This method uses gradient-based optimization techniques like gradient descent, Adam, etc., to optimize the model hyperparameters.

Transfer Learning: This method uses a pre-trained model on a similar task or dataset as a starting point and then fine-tunes it on the target task and dataset.

Ensemble methods: This method combines multiple models to create a more robust and accurate final model.

Multi-modal methods: This method uses multiple data modalities such as image, text, and audio to train models and improve performance.

Meta-learning: This method uses a model to learn how to learn from data, which can improve the efficiency of the model selection process.

One-shot or few-shot learning: This method can learn to recognize new classes from only one or a few examples.

AutoML is broadly classified into a model selection and hyperparameter tuning, as shown in Fig 3. Many differen integrated into existing workflows.

Effortless Machine Learning: The Merits of AutoML in Machine Learning

AutoML simplifies the machine learning process and brings many benefits, some of which are given below:

Time-saving: Automating the process of model selection and hyperparameter tuning can save a significant amount of time for data scientists and machine learning engineers.

Accessibility: AutoML allows users with little or no experience with machine learning to train high-performing models.

Improved performance: AutoML methods can often find better model architectures and hyperparameter settings than manual methods, resulting in improved model performance.

Handling large amounts of data: AutoML can handle large amounts of data and find the best model even with more features.

Scalability: AutoML can scale to large datasets and complex models, making it well-suited to big data and high-performance computing environments.

Versatility: AutoML can be used in various industries and applications, including healthcare, finance, retail, and transportation.

Cost-effective: AutoML can save resources and money in the long run by reducing the need for manual labor and expertise.

Reduced risk of human error: Automating the model selection and hyperparameter tuning process can reduce the risk of human error and improve the reproducibility of results.

Increased Efficiency: AutoML can be integrated with other tools and processes to increase efficiency in the data pipeline.

Handling multiple data modalities: AutoML can handle multiple data modalities such as image, text, and audio to train models and improve performance.

AutoML offers several benefits for data scientists and engineers that save time and resources by automating tedious and time-consuming tasks. This also improves the interpretability of the model by providing explainable AI. These combined benefits make AutoML a valuable tool in many industries and applications.

AutoML: A the Drawbacks

AutoML has become a popular tool for data scientists and analysts. However, it has limitations. There are following limitations are given below

Limited control over the model selection and hyperparameter tuning process: AutoML methods operate based on predefined algorithms and settings, and users may have limited control over the final model.

Limited interpretability of the resulting model: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions.

Higher costs than manually designing and training a model: AutoML tools and infrastructure can be costly to implement and maintain.

Difficulty in incorporating domain-specific knowledge into the model: AutoML relies on data and pre-defined algorithms, which can be less effective when incorporating domain-specific knowledge.

Potential for poor performance on edge cases or unusual data distributions: AutoML methods may not perform well on data that is significantly different from the training data.

Limited support for certain models or tasks: AutoML methods may not be well-suited to all models or tasks.

Dependence on large amounts of labeled data: AutoML methods typically require large amounts of labeled data to train models effectively.

Limited ability to handle data with missing values or errors: AutoML methods may not perform well on data with missing values or errors.

Limited ability to explain the model’s predictions and decisions: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions, which can be an issue for certain applications and industries.

Overfitting: AutoML methods may lead to overfitting on the training data if not properly monitored, which can result in poor performance on new unseen data.

AutoML is a powerful tool for automating the machine-learning process, but it is with its limitations. It is important to consider these limitations in the presence of expert supervision to validate the results.

AutoML in Practice: How Companies are Automating Machine Learning?

A few practical examples of AutoML are given below:

Google’s AutoML Vision allows users to train custom machine-learning models for image recognition using th mage datasets’s AutoML enables data scientists and analysts to automatically train and optimize machine learning models without having to write code

DataRobot provides an AutoML platform that can automatically build, evaluate and deploy machine learning models for a wide range of use cases, including fraud detection, customer churn prediction, and predictive maintenance

Amazon SageMaker is a fully managed service that enables data scientists and developers to quickly and , train, and deploy machine learning models at scale

IBM Watson AutoAI is a platform that automates the process of building, training, and deploying machine learning models and provides interpretability and explainability features that help users understand the models’ decision-making processes

Microsoft Azure ML is a cloud-based platform that provides a wide range of tools and services for building, deploying, and managing machine learning models, including AutoML capabilities.

These are a few examples of how companies leverage AutoML in different industries to automate model building and hyperparameter tuning, allowing data scientists to focus on model selection and evaluation.


AutoML automates the process of building and tuning machine-learning models. This method uses algorithms to search the best model and hyperparameters rather than relying on human expertise. AutoML includes increased efficiency and the ability to handle large amounts of data. It can be useful in the shortage of experienced machine learning practitioners. However, there are also limitations to AutoML. It can be computationally expensive and difficult to interpret the results of the automated search process. Additionally, the practical use of AutoML is limited by the data’s quality and computational resources’ availability. In practice, AutoML is mainly used in an indus prove productivity and model performance in scenarios like image, speech, text, and other forms of data.

Key Takeaways:

Simplify the process of building and training models.

AutoML suffers limitations such as a lack of control over the model selection process, huge data requirements, computationally expensive, and overfitting issues.

Expert supervision is important to validate the results of AutoML to counter available limitations.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


The Combination Of Humans And Artificial Intelligence In Cyber Security

Indeed, even as AI innovation changes some aspects of cybersecurity, the crossing point of the two remains significantly human. In spite of the fact that it’s maybe unreasonable, humans are upfront in all pieces of the cybersecurity triad: the terrible actors who look to do hurt, the gullible soft targets, and the great on-screen characters who retaliate. Indeed, even without the approaching phantom of AI, the cybersecurity war zone is frequently hazy to average users and the technologically savvy alike. Including a layer of AI, which contains various innovations that can likewise feel unexplainable to many people, may appear to be doubly unmanageable as well as indifferent. That is on the grounds that in spite of the fact that the cybersecurity battle is once in a while profoundly personal, it’s once in a while pursued face to face. With an expected 3.5 million cybersecurity positions expected to go unfilled by 2023 and with security ruptures increasing some 80% every year, infusing human knowledge with AI and machine learning tools gets critical to shutting the talent availability gap. That is one of the recommendations of a report called Trust at Scale, as of late released by cybersecurity organization Synack and citing job and breach data from Cybersecurity Ventures and Verizon reports, individually. Indeed, when ethical human hackers were upheld by AI and machine learning, they became 73% increasingly proficient at identifying and evaluating IT risks and threats. In any case, while the conceivable outcomes with AI appear to be unfathomable, the possibility that they could wipe out the role of people in cybersecurity divisions is about as unrealistic as the possibility of a phalanx of Baymaxes supplanting the nation’s doctors. While the ultimate objective of AI is to simulate human functions, for example, problem-solving, learning, planning, and intuition, there will consistently be things that AI can’t deal with (yet), as well as things AI should not handle. The principal classification incorporates things like creativity, which can’t be viably instructed or customized, and therefore will require the guiding hand of a human. Anticipating that AI should viably and reliably decide the context of an attack may likewise be an unconquerable ask, at any rate for the time being, just like the idea that AI could make new solutions for security issues. At the end of the day, while AI can unquestionably add speed and exactness to tasks generally handled by people, it is poor at extending the scope of such tasks. As it were, AI’s impact on the field of cybersecurity is the same as its effect on different disciplines, in that individuals frequently terribly overestimate what AI can do. They don’t comprehend that AI often works best when it has a restricted application, similar to anomaly detection, versus a broader one, like engineering a solution to a threat. In contrast to people, AI needs inventiveness. It isn’t inventive. It isn’t cunning. It regularly neglects to consider context and memory, leaving it incapable to decipher occasions like a human mind does. In a meeting with VentureBeat, LogicHub CEO and cofounder Kumar Saurabh showed the requirement for human analysts with a kind of John Henry test for automated threat detection. “A few years ago, we did an examination,” he said. This included arranging a specific amount of information, a trifling sum for an AI model to filter through, yet a sensibly huge sum for a human analyst to perceive how teams utilizing automated frameworks would pass against people in threat detection.

What Are The Advantages And Disadvantages Of Machine Learning?

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Advantages #1 Automation

Machine learning algorithms automate analyzing and interpretation of data and can be used to build predictive models. It eliminates manual data analysis and allows organizations to make data-driven decisions quickly and accurately.

#2 Improved Accuracy

Machine learning algorithms employ pattern recognition techniques to analyze and extract meaningful insights from data, subsequently utilizing these insights to make more accurate predictions. It can be beneficial when dealing with large datasets or constantly changing data.

#3 Cost Reduction

Machine learning algorithms can automate specific processes, reducing labor costs and allowing organizations to focus on more value-adding activities. Additionally, machine learning algorithms often require fewer data and resources to operate, reducing costs.

#4 Scalability

Machine learning algorithms can often be scaled up to handle larger datasets, making them suitable for large-scale applications. It allows organizations to utilize machine learning algorithms to gain insights from their data without needing additional resources.

#5 Increased Efficiency

Machine learning algorithms can automate specific processes, reducing the time required to process and analyze data. It can improve overall efficiency and allow organizations to make more informed decisions.

#1 Data Dependency

Machine learning algorithms are heavily reliant on data for performing any task. These algorithms require large amounts of data to learn and make accurate predictions. With the correct data, the results of a machine-learning model can be balanced and accurate.

#2 Computational Resources

Machine learning algorithms are computationally intensive and require a lot of resources to run. These algorithms can be expensive to train and require a significant upfront investment in hardware and software.

#3 Sampling

Creating a representative sample of the data is essential when using machine learning algorithms. If the sample is as different as expected, the model’s results can be biased accurately.

#4 Privacy and Security

Machine learning algorithms can also help uncover sensitive information from datasets. It means that there are potential privacy and security risks associated with using these algorithms.

#5 Overfitting #6 Time Consumption

Training a machine learning algorithm can be a time-consuming process. Depending on the complexity of the given problem and the amount of data available, training can take anywhere from a few hours to several days.

#7 Black Box Problem

When using machine learning algorithms, it can be challenging to understand how the algorithm reached its decisions and predictions. It can make it difficult to debug and improve the model’s performance.


Improved Accuracy and Efficiency: Machine learning algorithms can process large amounts of data and identify patterns that humans may not be able to detect. It can lead to more accurate predictions and improved efficiency in decision-making.

Automation of Repetitive Tasks: Machine learning systems can automate repetitive and time-consuming tasks, freeing human resources for more complex and creative work.

Data Quality: Machine learning models are only as good as the data they are working upon. The model’s predictions will also be excellent or narrow if the data is of good quality or biased.

Limited Understanding: Machine learning systems can identify patterns and make predictions, but they may not be able to explain how or why they came to a particular decision on a given problem.

High computational cost: Machine learning requires a lot of data and computational power, which can be costly and time-consuming.

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