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Artificial intelligence is often vilified due to its controversial rep. But there is lots of good stuff it can do for us too.

Deep yearning for deep learning

These fears are understandable. Especially, when we have a pleasure to observe how some of these macabre auguries are materializing right before our eyes. For example, in the words of law professors Bobby Chesney and Danielle Citron, deepfakes form a breeding ground for the so-called liar’s dividend — a dangerous phenomenon when legitimate evidence of inappropriate or even criminal behavior can be labeled ‘fake’ by the suspect and discarded.

But seeing negative aspects is always easier. With all its flaws, artificial intelligence can do a lot of good. Here’s a concise review of some benign purposes that artificial brains are serving today.

1.   Antispoofing

A fun fact: your state of being alive is nearly everything you need to get authorized by a biometric system. (Apart from your biometric profile stored during the enrollment stage.) For that purpose, liveness standards and liveness detection have been designed.

Using AI-powered algorithms — like time-difference-of-arrival analysis implemented in speech verification — a system scans the so-called liveness cues. These cues indicate that the system is dealing with a living breathing human and not some sophisticated digital puppet created with a Generative Adversarial Network (GAN) or a scarily real mask sculpted with a 3D printer.

By analyzing your iris patterns, vocal spectrograms, heart rate, fingerprint friction ridges, facial geometry, and other cues, the system can reach a correct verdict and deny access to an impostor if they claim to be you. Yes, it’s possible to copy and falsify your biometrics with deep learning. But it can also protect them from almost all attack scenarios imaginable.

2.   More accurate prognosis 

Just like Romeo in act 3, we can also call ourselves “fortune’s fools” sometimes. Even though we may never obtain a fate-predicting crystal ball, AI is the best next thing. We are talking about special relativity, initially described in Einstein’s work On the Electrodynamics of Moving Bodies. (Check it here.) And what’s pretty cool, it covers a broad scope of application areas: from weather forecasting to predicting stock price dynamics.

Prediction algorithms are also important for automatics. For instance, a mine-clearing robot — akin to Mesa’s Matilda — can better understand how certain objects or substances may react upon physical interaction, thus reducing the possibility of explosion.

And even when we do mundane stuff, like shopping for groceries, AI can make our lives a bit easier. A clever algorithm can literally time-travel 10-15 seconds into the future by analyzing the number of shoppers, their movement speed and patterns, amount of purchases, and other similar data. As a result, it can predict how soon the checkout queue will grow and call extra cashiers to help. So, thanks to this, we can avoid tediously long lines.

3.   Producing art

It seems, robots can write symphonies and turn digital canvas into masterpieces after all. At least, this checks out for the project DALL-E, which can turn your words into a surprisingly good painting and which is based on Generative Pre-trained Transformer 3 algorithms.

Sometimes, its work is just decent enough and pleasant to look at. Other times, it can qualify to be a masterpiece like in the case of the macabre image series named Last selfie on Earth, which caused a lot of buzz online.

Apart from DALL-E you can also try its junior version Craiyon, Neural Blender, Dream, Kandinsky, and other solutions. Even though neural clusters of a robot would never replace Michelangelo or Cézanne, they at times look mesmerizing. So, if you can’t afford a visual artist for your project just now, consider recruiting a robot with a digital brush. 

Picture: Martian Sunset by Dream, an original painting

4.   Health

Healthcare is already benefiting from partnership with Dr. Robot. As reported by Mayo Clinic, machine learning is capable of detecting heart anomalies and diseases. Among all else, it’s used for screening left ventricular dysfunction, which can be a sudden killer if not detected timely.

Diabetes is another malady that can be kept at bay thanks to AI diagnosis. In this case, a specific model can estimate how a certain food type will affect the glucose levels. This info is indispensable for patients as they won’t have to check their glucose levels manually every time. 

These are just two examples. In the future, AI will be used even more for predicting and diagnosing a multitude of diseases, especially hereditary ones: sickle cell disease, hemophilia and other serious disorders.

5.   Always here to help

Unlike human operators, AI doesn’t need to sleep. You’ve probably had the pleasure to exchange a few words with a robot assistant in your banking app.

Now, we can’t outsource the entire 911 department to AI. But it certainly can help in many other situations: booking tickets, requesting a medicine delivery online, helping you find a parking lot, choosing a safe area to stay in a foreign country during a voyage, and so on.

Police1 reports that an AI assistant can even be a cop partner. Among all else, it can tag video files from body cameras, transcribe audio records, use constellations of surveillance cameras to aid investigation, or remind to complete a boring, but mandatory report. It’s almost like KITT from Knight Rider only without wheels.

Learn more about deepfake detection, biometric security and neural network architectures at Antispoofing Wiki.

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Ai And Machine Learning For Marketing

AI and Machine Learning for marketing Our Artificial Intelligence for marketing guide focusing on Machine Learning and predictive analytics How will this guide to Artificial Intelligence and Machine Learning help me and my business?

It used to be impossible for all but the largest businesses to harness Artificial Intelligence technology to their marketing. Today, now even smaller businesses can apply publicly available algorithms or off the shelf machine learning services to generate useful insights and create prediction models based on their customer’s behaviours.

There are many new AI and Machine Learning services developing which mean that these techniques are now open to every business. The purpose of this guide is to cut through the hype and noise around these powerful technologies and show what you can put in place today to boost your business results. It provides models and strategies to successfully run these projects and gives examples and case studies of how the technology is used in businesses of all sizes, so you can understand how you can use it for your business.

The guide aims to help businesses of all sizes to apply to their marketing, focusing on Artificial Intelligence. All businesses can now use the services we recommend to implement Machine Learning. The guide explains why, how with an actionable and practical approach.

In this report we make recommendations on tools and techniques that you can apply to make more use of AI in marketing by:

Enabling new machine learning features on existing platforms you use such as  Google Ads, your email platforms or content management systems.

Configuring data-mining tools and predictive analytics tools to develop your own analysis and predictive analytics. This requires in-house skills with knowledge of the principles of machine learning which we explain in the last section of the report.

Who is this guide for?

This guide is aimed at anyone interested or responsible for learning about the techniques and technologies using the power of AI to improve results and reduce costs in marketing including:

Marketing and digital marketing managers

Brand innovation managers

How is the guide structured?

The guide is structured into the following sections:

Introduction and definitions

Artificial Intelligence applications for marketing including Chatbots

Examples of using Machine Learning across the RACE customer lifecycle

Machine learning for predictive analytics

The business potential of applying Machine Learning to predictive data analytics

The fundamental principles of Machine Learning – examples of theory in practice

Common mistakes when using Machine Learning

Planning an Artificial Intelligence or Machine Learning project

Resources – open source and paid tools and learning materials

Latest updates

Introduction focuses on the Gartner Hype Cycle to illustrate AI’s many potential applications in marketing

New examples of AI being used to improve marketing results and examples of applying Machine Learning and AI across the customer lifecycle

10 questions marketers should be asking to generate their own use-cases for applying predictive analytics

Resource Details

Author: Rob Allen

Resource format: Online hosted content with mindtools and examples

About the author

Robert Allen

Robert was the Editor of Smart Insights between 2024-2024. He managed the blog and you will find blog articles on a range of subjects- Marketing Technology trends and latest tech developments are a regular focus, as well as exploring key marketing concepts. You can get in touch with him on Twitter and connect on LinkedIn.

Dave Chaffey of Smart Insights worked with Robert on the first edition of this guide and updated the 2023 edition.

Dr. Dave Chaffey

Dave is co-founder of Smart Insights. He is editor of the 100+ templates, ebooks and courses in our digital marketing resource library created by our team of 25+ digital marketing experts. Our resources used by our members in more than 100 countries to Plan, Manage and Optimize their digital marketing.

For my full profile, or to connect on LinkedIn or other social networks, see the About Dave Chaffey profile page on Smart Insights. Dave is author of 5 bestselling books on digital marketing including Digital Marketing Excellence and Digital Marketing: Strategy, Implementation and Practice. In 2004 he was recognised by the Chartered Institute of Marketing as one of 50 marketing ‘gurus’ worldwide who have helped shape the future of marketing.

Roadmap To Study Ai, Machine Learning, And Deep Machine Learning

AI also known as Artificial Intelligence, Machine learning in short written as ML, and deep learning (DL) are a few of the top three fast-emerging, great, and intriguing technological disciplines containing a wide range of implementations i.e. applications like self-driving automobiles and face recognition systems. Because of their complexities, understanding these topics may appear difficult. Yet, success in these domains requires a solid foundation in computer science, mathematics, and statistics. Moreover, familiarity with common libraries and modeling tools is required.

This article outlines a learning route for AI, ML, and DL, outlining key ideas, tools, and methodologies. This roadmap provides a clear path for starting your learning journey and equips you with the abilities needed to flourish in these subjects, without repeating any knowledge from other sources.

Road Map

Here is a roadmap to help you get started −

1. Understand the Basics

Before delving into the more complicated components of AI, it is critical to grasp the fundamentals. Linear algebra, calculus, statistics, and probability theory are all included. You should also be comfortable with programming languages like Python, Java, and C++. A solid foundation in mathematics and programming can help you understand AI topics more readily.

2. Learn the Foundations of AI

You may begin learning the principles of AI once you have a good foundation in mathematics and programming. Understanding the many forms of learning, such as supervised, unsupervised, and reinforcement learning, is essential. You’ll also need to familiarise yourself with decision trees and clustering methods. On these topics, there are several free online courses and tutorials accessible.

3. Study Machine Learning

When you’ve grasped the fundamentals of AI, you may progress to Machine Learning. You’ll need to understand the methods for regression, classification, and clustering. You’ll also need to understand how to preprocess data, do feature engineering, and choose a model. There are also several online courses and tutorials available on these subjects.

4. Understand Deep Learning

Deep Learning is a major Machine Learning (ML) attempt that learns data using neural networks inspired by the human brain. Backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders are all topics that must be understood before diving into Deep Learning. Tensorflow and PyTorch are two popular deep-learning libraries. Understanding deep learning is crucial since it is used in many disciplines, including natural language processing, computer vision, and many more.

5. Learn About Natural Language Processing

It is a branch of AI that can be solved with the help of ML and deep learning. It deals with the understanding by computer systems that what the language is trying to say i.e. understanding it and interpreting words and phrases. Tokenization (splitting sentences into tokens), stemming (turning each word to its basic form), part-of-speech tagging (assigning a part of speech to each dish), and named entity identification are all abilities you’ll need. The NLTK library is a well-known NLP library. Learning NLP may help you design chatbots, sentiment analysis, and other applications.

6. Study Computer Vision

Computer Vision is the study of pictures and movies. You’ll need to learn about picture categorization, feature extraction, and object detection. OpenCV is a well-known computer vision library. Image and video processing has become a crucial ability for AI specialists due to the proliferation of cameras.

7. Practice, Practice, Practice

It is vital to put your newfound knowledge into action. Work on small projects and apply your expertise to real-world problems. Kaggle is an excellent platform for discovering datasets and competing against other data scientists. Participating in hackathons and designing applications might help you enhance your skills.

8. Keep up with the Latest Research

AI is a fast-changing topic, and it is critical to stay up to date on the newest research and breakthroughs in the field. Attend conferences and study research papers to keep current. Keeping up with the newest research might help you develop creative solutions.

9. Build a Portfolio

Creating a portfolio of your work and achievements will help you demonstrate your abilities and stand out to potential employers. You may build a website for your portfolio or upload your creations to GitHub. Possessing a portfolio showcases your practical talents and can help you find a job.

10. Network with Other Conclusion

Learning AI, machine learning, and deep learning can seem overwhelming, but a systematic approach can help. By building a strong foundation in computer science, mathematics, and statistics, and learning to use popular libraries and tools, one can develop the skills needed to excel in these exciting and rapidly evolving fields. Following this roadmap can help you start your learning journey and equip you with the knowledge and expertise to thrive in AI, ML, and DL.

Top Motorbikes That Are Using Machine Learning Models

Motorbikes have come on leaps and bounds in the last five years. At this point, Motorcycle AI is a high contender for the next big innovation for futuristic motorcycles.  Self-learning technology is already a huge part of our lives. Industries such as healthcare and e-commerce greatly benefit from this technology – and the motorcycle industry is no exception. Thanks to machine learning, electric motorcycles can now learn and adapt to each individual rider to improve the riding experience with every journey. That being said, the most influential way in which self-learning technology has revolutionized riding is perhaps through motorcycle safety. While motorcycling is classed as a more dangerous form of transportation compared to the rest, it’s the most common worldwide. So, a logical application where technology can help is augmenting rider awareness, resulting in safer motorcycle riders. Damon is one of the motorcycle manufacturers that are starting to focus more on motorcycle safety. This is evident in its in-house industry-disrupting software. From the 100% electric powertrain, HyperDrive™, to the award-winning CoPilot™, Advanced Warning System for Motorcycles (AWSM), its technology helps to reach the goal of no fatal accidents on any of the HyperSport Motorcycles by 2030. The purpose of the project is to help the Ducati team simply make better decisions when it comes to the bike configurations. Each year, the MotoGP bikes need to be configured for 18 tracks, and each time there are endless possibilities. That is where the machine learning algorithms come in, and according to Ducati’s statements, it has made a difference in making the right decision when it comes to bike setup. To go big on big data, Ducati implemented an AI and IoT project, so they can simulate the behavior and performance of the bike under various conditions. The sensors on the bike, ranging from 40 to 100, collect data such as speed, engine running parameters, revs, tire and brake temperatures, acceleration, oscillation, vibration, and grip. Once the data is collected, AI is applied to figure out the right configuration. According to their statements, around 4,000 sectors of race tracks and 20 different racing scenarios have been analyzed, with a wider roll-out of the solution expected. Moreover, the machine learning techniques can also predict the performance and behavior of the bike after a setting change. More details on silicon.co.uk.

Motorbikes have come on leaps and bounds in the last five years. At this point, Motorcycle AI is a high contender for the next big innovation for futuristic motorcycles. Self-learning technology is already a huge part of our lives. Industries such as healthcare and e-commerce greatly benefit from this technology – and the motorcycle industry is no exception. Thanks to machine learning, electric motorcycles can now learn and adapt to each individual rider to improve the riding experience with every journey. That being said, the most influential way in which self-learning technology has revolutionized riding is perhaps through motorcycle safety. While motorcycling is classed as a more dangerous form of transportation compared to the rest, it’s the most common worldwide. So, a logical application where technology can help is augmenting rider awareness, resulting in safer motorcycle riders. Damon is one of the motorcycle manufacturers that are starting to focus more on motorcycle safety. This is evident in its in-house industry-disrupting software. From the 100% electric powertrain, HyperDrive™, to the award-winning CoPilot™, Advanced Warning System for Motorcycles (AWSM), its technology helps to reach the goal of no fatal accidents on any of the HyperSport Motorcycles by 2030. Gigi Dall’Igna, Ducati course General Manager, who has got two World Superbike titles, among others, was given the challenging task of steering the Ducati racing ship back on course, after its factory racing efforts in both MotoGP and World Superbike began to founder, as stated by chúng tôi As such, he turned to big data besides turning to Lorenzo (not much of a turning for that matter) and implemented the first IoT and AI technologies into the Ducati’s bikes for the MotoGP competition. The purpose of the project is to help the Ducati team simply make better decisions when it comes to the bike configurations. Each year, the MotoGP bikes need to be configured for 18 tracks, and each time there are endless possibilities. That is where the machine learning algorithms come in, and according to Ducati’s statements, it has made a difference in making the right decision when it comes to bike setup. To go big on big data, Ducati implemented an AI and IoT project, so they can simulate the behavior and performance of the bike under various conditions. The sensors on the bike, ranging from 40 to 100, collect data such as speed, engine running parameters, revs, tire and brake temperatures, acceleration, oscillation, vibration, and grip. Once the data is collected, AI is applied to figure out the right configuration. According to their statements, around 4,000 sectors of race tracks and 20 different racing scenarios have been analyzed, with a wider roll-out of the solution expected. Moreover, the machine learning techniques can also predict the performance and behavior of the bike after a setting change. More details on chúng tôi When it comes to bikes, Ducati is not the only manufacturer turning to big data for insights. Yamaha also goes big on AI and ML and created an updated version of its self-driving motorcycle that after 3 years of learning, went on a circuit and competed with Valentino Rossi’s time. Equipped with a humanoid robot, MOTOBOT managed to do a complete lap of the circuit, but without being even close to Rossi’s time. We’re still impressed. And a bit freaked out: Yamaha boldly predicts the bot will outperform Rossi within two years, and that freaks us out even more. However, the purpose of the project is not to build a bike that could compete in MotoGP, but to improve the existing street bikes, making them safer for riders.

Feature Engineering For Machine Learning

Feature engineering is the practice of altering data in order to improve the performance of machine learning models. It is a critical component of the machine learning process because it assures the quality of features that have a significant influence on the machine learning model. Superior models are more likely to be produced by a machine learning expert who is well-versed in feature engineering. This post will go through many techniques to feature engineering on data in machine learning.

Feature Engineering Methods

There are many types of data and depending on the type of data, a feature engineering method is chosen. Below is a list of some feature engineering techniques −

1. Feature scaling

This method entails scaling the feature’s values into a common range. To ensure that it has equal weight in the model, ranges might be like 0 to 1 or -1 to 1.

The following techniques for feature scaling are listed −

Min-Max scaling entails reducing the feature’s values to a range between 0 and 1, as calculated by the formula: X__scaled = (X – X__min) / (X__max – X__min).

Standardization is the process of scaling the values of a feature to have a mean of 0 and a standard deviation of 1, as computed by the formula: (X – X mean) / X std = X scaled

Log transformation − This entails employing a logarithmic function to change the values of the feature, which can assist to lessen the influence of outliers and enhance data distribution.

2. Feature Extraction

It is a process of extracting new features from our older data.

Below are the different methods to extract features from data −

PCA − Full form of PCA is Principal component analysis. It is a process in which we decrease the dimensions of data by capturing important patterns and correlations in the data.

Independent component analysis (ICA) is the process of detecting separate sources of variability in data and dividing them into distinct features that encapsulate different elements of the data.

Wavelet transform − This involves analyzing the data at different scales and frequencies, and extracting new features that capture the patterns and relationships at each scale.

Fourier transform − This involves analyzing the data in the frequency domain and extracting new features that capture the frequency components of the data.

Convolutional neural networks (CNNs) − This involves using deep learning techniques to automatically extract features from high-dimensional and complex data, such as images and audio.

3. Feature Selection

If you select

This entails picking a subset of the most relevant characteristics in order to minimize data dimensionality and enhance model performance.

There are various methods for selecting features, including −

Filter techniques entail rating the characteristics based on some statistical measure, such as correlation or mutual information, and picking the features with the highest ranking.

Wrapper approaches entail employing a machine learning algorithm to assess the performance of several subsets of features and picking the subset with the greatest performance.

Embedded approaches include picking the most relevant characteristics within the machine learning algorithm’s training phase, for as through regularization or decision tree-based algorithms.

Dimensionality reduction approaches entail translating the original characteristics into a lower-dimensional representation, such as principal component analysis (PCA) or singular value decomposition (SVD).

The feature selection approach used is determined by the nature of the data and the model’s needs. In general, filter techniques are quicker and more efficient, but may not capture the entire complexity of the data, whereas wrapper methods and embedding methods are more accurate but can be computationally expensive.

4. One-hot encoding

Converting categorical variables into numerical features entails constructing a binary indicator variable for each category.

One hot encoding approach is used to express categorical variables into numerical data that may be fed into machine learning algorithms. Each category is represented in one hot encoding by a binary vector that is as long as the number of categories and has a value of 1 in the position that corresponds to the category and 0s in all other locations.

Because many machine learning algorithms cannot handle categorical data directly, one hot encoding is required. We may utilize categorical variables as input for algorithms by transforming them into numerical data. Because each category is represented by a binary vector of the same length, one hot encoding assures that each category is equally weighted.

5. Binning

This entails categorizing numerical data into discrete bins in order to lessen the influence of outliers and increase model resilience.

Binning can be done in a variety of methods, including −

Equal-width binning is the process of separating a range of values into bins of equal width. For instance, if we have a feature with values ranging from 0 to 100 and wish to generate 5 bins, each bin would have a 20-unit range (0-20, 21-40, 41-60, 61-80, 81-100).

Equal frequency binning involves dividing the data into bins with roughly the same number of data points in each. This method may be useful when the data distribution is skewed.

The borders of the bins are manually determined based on domain expertise or other criteria in bespoke binning.

Binding may be beneficial when the connection here between the feature and even the target variable is not linear, or when there are too many unique values for a feature to be employed efficiently in a machine-learning technique. Nevertheless, it might cause data loss and does not always enhance performance. Before using binning, it is critical to assess its influence on model performance.

6. Text Processing

Text processing is the alteration and analysis of text material, typically with the goal of extracting useful information. This might cover a wide range of tasks, from basic operations like removing punctuation or converting text to lowercase to more challenging tasks like identifying named things or classifying text based on its content.

Text processing methods that are often utilized include −

Tokenization is the process of separating a piece of text into separate words or tokens.

Stopword reduction is eliminating frequent terms that aren’t beneficial for analysis, such as “the,” “and,” or “in.”

Stemming and lemmatization are strategies for improving analysis that include reducing words to their root form (e.g., “running” becomes “ran”).

Tagging parts of speech is marking each word in a document with its grammatical function, such as “noun” or “verb.”

Named entity recognition is the process of identifying and classifying entities in a text such as individuals, organizations, and locations.

Sentiment analysis is the process of evaluating text in order to discover the overall sentiment or emotional tone.

Conclusion

To summarize, feature engineering is an important phase in machine learning that entails choosing, modifying, and inventing features to improve model performance. Domain expertise, inventiveness, and experimentation are required. While automated feature engineering approaches are being developed, human skill is still required to generate relevant features that capture the underlying patterns in the data.

How To Utilize Machine Learning For Iot Analysis

Machine Learning and the Internet of Things (IoT) have been the buzzwords for the decade. These technologies find application in almost all industries, from enabling artificially intelligent powered digital assistants to the supply chain’s automation.

They have revolutionized not only how we interact on social media but also how we pay the bills. Here is how to use Machine Learning for IoT Analysis.

Taking a glance at the Google tendencies analysis below, one can be sure that these technologies offer a profitable career, so many people are interested in learning about these.

You previously know what Machine Learning and IoT are.

On the other contrary, the Internet of Things refers to a method of internet-connected items that will communicate over wireless networks.

IoT devices create a lot of data, which may seem useless to people, however, that is where the function of Machine Learning comes into the picture.

How Can Machine Learning be useful to IoT?

Talking about data analytics, Predictive and regulatory Analytics both utilize machine learning and find software in the realm of IoT.

Also read: Best Video Editing Tips for Beginners in 2023

Smart Watch utilizing a broad range of detectors is an illustration of Prescriptive Analytics.

Tesla Vehicles have always been in the news broadcast and even more so today. Likely it’s a fantasy car for many people.

Have you ever imagined how these Self Driving Automobiles get the job done? These vehicles have many sensors such as lidars, radars, cameras, IoT devices that communicate with each other and send the information in the kind of images and numerical values to a dedicated host.

Based on the data received, various Deep Learning models like Convolution Neural Network and VGG16 are applied to make the car learn mechanically and enhance over-time with knowledge.

Advantages of Using Machine Learning to IoT Data Evaluation

Machine Learning may be used to recognize patterns in data and create real-time predictions. For illustration, it helps to create a better user experience when combined with appliances like Air Conditioning. The machine learning models can learn from the previous data at what temperatures you’re more comfortable with.

It may routinely optimize the room temperature according to your necessities when returning home from work by using past data and current temperature.

Machine Learning and IoT can automate some industrial actions and ensure employee safety in risky areas by using IoT, and Machine Learning enabled instruments to track and optimize processes.

IoT Analysis helps in taking cost-saving measures in Industrial Applications. We are done with the older school idea of ‘Scheduled Maintenance and we are now looking forward to decreasing the surprise downtime utilizing Predictive Maintenance.

Also read: 11 best ways to Improve Personal Development and Self-Growth and its Benefit on our Life

Sensors

Modern machines use detectors that monitor a broad variety of data, including bandwidth, usage, energy intake, and a log of program disruptions. In case of a problem, the historic data combined with the predictive analysis done by Machine Learning Designs informs the worried individual about the whole life cycle of this part and how the quality of the generation as a result of defective part.

Machine learning can be used to estimate risks by using past data and automate responses for this threat.

You can get process effectiveness by utilizing Machine Learning together with IoT. Machine Learning models can maximize a procedure to maintain the preferred output using data from the past to adjust parameters in real-time. For illustration, In the case of a Smart Traffic Management System, CCTV Cameras fixed onto the top of traffic lights can capture real time pictures and, dependent on the Algorithm it is trained on, can detect if or not a road is overcrowded or not.

Safety

While we have talked a lot about IoT’s reward and how fantastic it is, there is a clear question mark in the form of its safety.

A details published by Thales Group, among the pioneers at Cyber safety, says that 90% of the customers lack self-confidence in IoT Devices’ safety. Additionally, about 63%of the users in the developed world have termed this equipment as ‘creepy.’ With increasing Statistics Breach cases reported today and then, the end-users are more concerned about if their data is misused or not.

IoT Devices hold a great deal of personal info and even the least breach might signify all your information is compromised. Therefore, there is an ever-increasing requirement to make these intelligent devices even more protected.

The first step for any IoT company is to experience a thorough security risk assessment that examines vulnerabilities in devices and network systems and customer and client backend systems.

Also read: 2023’s Top 10 Business Process Management Software

Summary

Rapidly, using IoT and ML, we might predict unlucky events such as train crashes and crimes even before they happen. These technologies are, for positive, opening the door to unlimited opportunities.

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