Ethical Hacking

Ethical hacking is an authorized practice of detecting vulnerabilities in an application, system, or organization’s infrastructure and bypassing system security to identify potential data breaches and threats in a network. Ethical hackers aim to investigate the system or network for weak points that malicious hackers can exploit or destroy. They can improve the security footprint to withstand attacks better or divert them.

The company that owns the system or network allows Cyber Security engineers to perform such activities in order to test the system’s defenses. Thus, unlike malicious hacking, this process is planned, approved, and more importantly, legal.

Ethical hackers aim to investigate the system or network for weak points that malicious hackers can exploit or destroy. They collect and analyze the information to figure out ways to strengthen the security of the system/network/applications. By doing so,  they can improve the security footprint so that it can better withstand attacks or divert them.

Ethical hackers are hired by organizations to look into the vulnerabilities of their systems and networks and develop solutions to prevent data breaches. Consider it a high-tech permutation of the old saying “It takes a thief to catch a thief.”

They check for key vulnerabilities include but are not limited to:

• Injection attacks
• Changes in security settings
• Exposure of sensitive data
• Breach in authentication protocols
• Components used in the system or network that may be used as access points

Ethical hackers' code of ethics

Ethical hackers follow a strict code of ethics to make sure their actions help rather than harm companies. Many organizations that train or certify ethical hackers, such as the International Council of E-Commerce Consultants (EC Council), publish their own formal written code of ethics. While stated ethics can vary among hackers or organizations,  the general guidelines are:

• Ethical hackers get permission from the companies they hack: Ethical hackers are employed by or partnered with the organizations they hack. They work with companies to define a scope for their activities including hacking timelines, methods used and systems and assets tested. 
• Ethical hackers don't cause any harm: Ethical hackers don't do any actual damage to the systems they hack, nor do they steal any sensitive data they find. When white hats hack a network, they're only doing it to demonstrate what real cybercriminals might do. 
• Ethical hackers keep their findings confidential: Ethical hackers share the information they gather on vulnerabilities and security systems with the company—and only the company. They also assist the company in using these findings to improve network defenses.
• Ethical hackers work within the confines of the law: Ethical hackers use only legal methods to assess information security. They don't associate with black hats or participate in malicious hacks.

What are the key concepts of ethical hacking?

Hacking experts follow four key protocol concepts.

• Stay legal. Obtain proper approval before accessing and performing a security assessment.
• Define the scope. Determine the scope of the assessment so that the ethical hacker’s work remains legal and within the organization’s approved boundaries.
• Disclose the findings. Notify the organization of all vulnerabilities discovered during the assessment, and provide remediation advice for resolving these vulnerabilities.
• Respect data sensitivity. Depending on the data sensitivity, ethical hackers may have to agree to a nondisclosure agreement, in addition to other terms and conditions required by the assessed organization. 

Ethical hacking skills and certificates

Ethical hacking is a legitimate career path. Most ethical hackers have a bachelor's degree in computer science, information security, or a related field. They tend to know common programming and scripting languages like python and SQL.

They’re skilled—and continue to build their skills—in the same hacking tools and methodologies as malicious hackers, including network scanning tools like Nmap, penetration testing platforms like Metasploit and specialized hacking operating systems like Kali Linux.

Like other cybersecurity professionals, ethical hackers typically earn credentials to demonstrate their skills and their commitment to ethics. Many take ethical hacking courses or enroll in certification programs specific to the field. Some of the most common ethical hacking certifications include:

• Certified Ethical Hacker (CEH): Offered by EC-Council, an international cybersecurity certification body, CEH is one of the most widely recognized ethical hacking certifications.
• CompTIA PenTest+: This certification focuses on penetration testing and vulnerability assessment.
• SANS GIAC Penetration Tester (GPEN): Like PenTest+, the SANS Institute's GPEN certification validates an ethical hacker's pen testing skills.

Roles and Responsibilities of an Ethical Hacker

Ethical Hackers must follow certain guidelines to perform hacking legally. A good hacker knows his or her responsibility and adheres to all of the ethical guidelines. Here are the most important rules of Ethical Hacking:

• An ethical hacker must seek authorization from the organization that owns the system. Hackers should obtain complete approval before performing any security assessment on the system or network.
• Determine the scope of their assessment and make known their plan to the organization.
• Report any security breaches and vulnerabilities found in the system or network.
• Keep their discoveries confidential. As their purpose is to secure the system or network, ethical hackers should agree to and respect their non-disclosure agreement.
• Erase all traces of the hack after checking the system for any vulnerability. It prevents malicious hackers from entering the system through the identified loopholes.

What are some limitations of ethical hacking?

• Scope. Ethical hackers cannot progress beyond a defined scope to make an attack successful. However, it’s not unreasonable to discuss out-of-scope attack potential with the organization.  
• Resources. Malicious hackers don’t have time constraints that ethical hackers often face. Computing power and budget are additional constraints of ethical hackers.
• Methods. Some organizations ask experts to avoid test cases that lead the servers to crash (e.g., denial-of-service attacks).

Quantum Cryptography

Quantum cryptography (also known as quantum encryption) refers to various cybersecurity methods for encrypting and transmitting secure data based on the naturally occurring and immutable laws of quantum mechanics.

While still in its early stages, quantum encryption has the potential to be far more secure than previous types of cryptographic algorithms and is even theoretically unhackable.

Unlike traditional cryptography, which is built on mathematics, quantum cryptography is built on the laws of physics. Specifically, quantum cryptography relies on the unique principles of quantum mechanics:

• Particles are inherently uncertain: On a quantum level, particles can simultaneously exist in more than one place or in more than one state of being at the same time. And it is impossible to predict their exact quantum state. 
• Photons can be measured randomly in binary positions: Photons, the smallest particles of light, can be set to have specific polarities, or spins, which can serve as a binary counterpart for the ones and zeros of classical computational systems. 
• A quantum system cannot be measured without being altered: According to the laws of quantum physics, the basic act of measuring or even observing a quantum system will always have a measurable effect on that system. 
• Particles can be partially, but not totally cloned: While the properties of some particles can be cloned, a 100% clone is believed to be impossible. 

How does Quantum Cryptography work?

Quantum Cryptography works on the principle of quantum entanglement, which is a phenomenon where two particles are correlated in a way that the state of one particle affects the state of the other particle, even when they are separated by a large distance. In quantum cryptography, the two parties, Alice and Bob, use a pair of entangled particles to establish a secure communication channel.

The process involves the following steps:

1. Alice sends a stream of photons (particles of light) to Bob.
2. Bob randomly selects a subset of photons and measures their polarization (direction of oscillation).
3. Bob sends the result of his measurements to Alice through a classical communication channel.
4. Alice and Bob compare a subset of their measurements to detect any eavesdropping.
5. If no eavesdropping is detected, they use the remaining photons to encode their message.
6. The encoded message is then sent over a classical communication channel.

Benefits of quantum cryptography

• Provides secure communication. Instead of difficult-to-crack numbers, quantum cryptography is based on the laws of physics, which is a more sophisticated and secure method of encryption.
• Detects eavesdropping. If a third party attempts to read the encoded data, then the quantum state changes, modifying the expected outcome for the users.
• Offers multiple methods for security. There are numerous quantum cryptography protocols used. Some, like QKD, for example, can combine with classical encryption methods to increase security.

Limitations of quantum cryptography

• Changes in polarization and error rates. Photons may change polarization in transit, which potentially increases error rates.
• Range. The maximum range of quantum cryptography has typically been around 400 to 500 km, with the exception of Terra Quantum, as noted below.
• Expense. Quantum cryptography typically requires its own infrastructure, using fiber optic lines and repeaters.
• Number of destinations. It is not possible to send keys to two or more locations in a quantum channel.

Applications of Quantum Cryptography

Quantum Cryptography has the potential to revolutionize the way we communicate by providing a secure communication channel that is immune to cyber-attacks. Some of the applications of Quantum Cryptography include:

• Financial transactions: Quantum Cryptography can provide a secure communication channel for financial transactions, making it impossible for cybercriminals to intercept and steal sensitive financial information.
• Military and government communication: Quantum Cryptography can be used by military and government agencies to securely communicate sensitive information without the fear of interception.
• Healthcare: Quantum Cryptography can be used to secure healthcare data, including patient records and medical research.
• Internet of Things (IoT): Quantum Cryptography can be used to secure the communication channels of IoT devices, which are vulnerable to cyber-attacks due to their low computing power.

Challenges of Quantum Cryptography

While Quantum Cryptography is a promising technology, it is not without its challenges. Some of the challenges include:

• Cost: Quantum Cryptography is an expensive technology that requires specialized equipment and infrastructure, making it difficult to implement on a large scale.
• Distance limitations: The distance between the two parties is limited by the attenuation of the photons during transmission, which can affect the quality of the communication channel.
• Practical implementation: The implementation of Quantum Cryptography in real-world scenarios is still in its early stages, and there is a need for more research and development to make it more practical and scalable.

Reinforcement Learning

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience. 

Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that we would find in supervised or unsupervised machine learning.

Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm receives feedback that helps it determine whether the choice it made was correct, neutral or incorrect. It is a good technique to use for automated systems that have to make a lot of small decisions without human guidance.

Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes.

Examples of Reinforcement Learning

• Robotics. Robots with pre-programmed behavior are useful in structured environments, such as the assembly line of an automobile manufacturing plant, where the task is repetitive in nature. In the real world, where the response of the environment to the behavior of the robot is uncertain, pre-programming accurate actions is nearly impossible. In such scenarios, RL provides an efficient way to build general-purpose robots. It has been successfully applied to robotic path planning, where a robot must find a short, smooth, and navigable path between two locations, void of collisions and compatible with the dynamics of the robot.
• AlphaGo. One of the most complex strategic games is a 3,000-year-old Chinese board game called Go. Its complexity stems from the fact that there are 10^270 possible board combinations, several orders of magnitude more than the game of chess. In 2016, an RL-based Go agent called AlphaGo defeated the greatest human Go player. Much like a human player, it learned by experience, playing thousands of games with professional players. The latest RL-based Go agent has the capability to learn by playing against itself, an advantage that the human player doesn’t have.
• Autonomous Driving. An autonomous driving system must perform multiple perception and planning tasks in an uncertain environment. Some specific tasks where RL finds application include vehicle path planning and motion prediction. Vehicle path planning requires several low and high-level policies to make decisions over varying temporal and spatial scales. Motion prediction is the task of predicting the movement of pedestrians and other vehicles, to understand how the situation might develop based on the current state of the environment.

Benefits of Reinforcement Learning

Reinforcement learning is applicable to a wide range of complex problems that cannot be tackled with other machine learning algorithms. RL is closer to artificial general intelligence (AGI), as it possesses the ability to seek a long-term goal while exploring various possibilities autonomously. Some of the benefits of RL include:

• Focuses on the problem as a whole. Conventional machine learning algorithms are designed to excel at specific subtasks, without a notion of the big picture. RL, on the other hand, doesn’t divide the problem into subproblems; it directly works to maximize the long-term reward. It has an obvious purpose, understands the goal, and is capable of trading off short-term rewards for long-term benefits.
• Does not need a separate data collection step. In RL, training data is obtained via the direct interaction of the agent with the environment. Training data is the learning agent’s experience, not a separate collection of data that has to be fed to the algorithm. This significantly reduces the burden on the supervisor in charge of the training process.
• Works in dynamic, uncertain environments. RL algorithms are inherently adaptive and built to respond to changes in the environment. In RL, time matters and the experience that the agent collects is not independently and identically distributed (i.i.d.), unlike conventional machine learning algorithms. Since the dimension of time is deeply buried in the mechanics of RL, the learning is inherently adaptive.

Challenges with Reinforcement Learning

While RL algorithms have been successful in solving complex problems in diverse simulated environments, their adoption in the real world has been slow. Here are some of the challenges that have made their uptake difficult:

• RL agent needs extensive experience. RL methods autonomously generate training data by interacting with the environment. Thus, the rate of data collection is limited by the dynamics of the environment. Environments with high latency slow down the learning curve. Furthermore, in complex environments with high-dimensional state spaces, extensive exploration is needed before a good solution can be found.
• Delayed rewards. The learning agent can trade off short-term rewards for long-term gains. While this foundational principle makes RL useful, it also makes it difficult for the agent to discover the optimal policy. This is especially true in environments where the outcome is unknown until a large number of sequential actions are taken. In this scenario, assigning credit to a previous action for the final outcome is challenging and can introduce large variance during training. The game of chess is a relevant example here, where the outcome of the game is unknown until both players have made all their moves.
• Lack of interpretability. Once an RL agent has learned the optimal policy and is deployed in the environment, it takes actions based on its experience. To an external observer, the reason for these actions might not be obvious. This lack of interpretability interferes with the development of trust between the agent and the observer. If an observer could explain the actions that the RL agent tasks, it would help him in understanding the problem better and discovering limitations of the model, especially in high-risk environments.

The future of reinforcement learning

Reinforcement learning is projected to play a bigger role in the future of AI. The other approaches to training machine learning algorithms require large amounts of preexisting training data. Reinforcement learning agents, on the other hand, require the time to gradually learn how to operate via interactions with their environments. Despite the challenges, various industries are expected to continue exploring reinforcement learning's potential.

Reinforcement learning has already demonstrated promise in various areas. For example, marketing and advertising firms are using algorithms trained this way for recommendation engines. Manufacturers are using reinforcement learning to train their next-generation robotic systems.

Scientists at Alphabet's AI subsidiary, Google DeepMind, have proposed that reinforcement learning could bring the current state of AI -- often called narrow AI -- to its theoretical final form of artificial general intelligence. They believe machines that learn through reinforcement learning will eventually become sentient and operate independently of human supervision.

Computer Vision

Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.

If AI enables computers to think, computer vision enables them to see, observe and understand. 

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving or something is wrong with an image.

Computer vision trains machines to perform these functions, but it must do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

Computer vision is used in industries that range from energy and utilities to manufacturing and automotive—and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.

Key Aspects of Computer Vision

• Image Recognition: This is the most common application, where the system identifies a specific object, person, or action in an image.
• Object Detection: This involves recognizing multiple objects within an image and identifying their location with a bounding box. This is widely used in applications such as self-driving cars, where it’s necessary to recognize all relevant objects around the vehicle.
• Image Segmentation: This process partitions an image into multiple segments to simplify or change the representation of an image into something more meaningful and easier to analyze. It is commonly used in medical imaging.
• Facial Recognition: This is a specialized application of image processing where the system identifies or verifies a person from a digital image or video frame.
• Motion Analysis: This involves understanding the trajectory of moving objects in a video, commonly used in security, surveillance, and sports analytics.
• Machine Vision: This combines computer vision with robotics to process visual data and control hardware movements in applications such as automated factory assembly lines.

How does computer vision work?

Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

Challenges of Computer Vision

Computer vision, despite its advances, faces several challenges that researchers and practitioners continue to address:

• Variability in Lighting Conditions: Changes in lighting can dramatically affect the visibility and appearance of objects in images.
• Occlusions: Objects can be partially or fully blocked by other objects, making detection and recognition difficult.
• Scale Variation: Objects can appear in different sizes and distances, complicating detection.
• Background Clutter: Complex backgrounds can make it hard to distinguish and segment objects properly.
• Intra-class Variation: Objects of the same category can look very different (e.g., different breeds of dogs).
• Viewpoint Variation: Objects can appear different when viewed from different angles.
• Deformations: Flexible or soft objects can change shape, and it is challenging to maintain consistent detection and tracking.
• Adverse Weather Conditions: Fog, rain, and snow can obscure vision and degrade image quality.
• Limited Data and Annotation: Training advanced models requires large datasets with accurate labeling, which can be costly and time-consuming.
• Ethical and Privacy Concerns: Facial recognition and other tracking technologies raise significant privacy and ethical questions.
• Integration with Other Sensors and Systems: Combining computer vision data with other sensor data can be challenging but is often necessary for applications like autonomous driving.

Computer Vision Benefits

Computer vision offers numerous benefits across various industries, transforming how organizations operate and deliver services. Here are some of the key benefits:

• Automation of Visual Tasks: Computer vision automates tasks that require visual cognition, significantly speeding up processes and reducing human error, such as in manufacturing quality control or sorting systems.
• Enhanced Accuracy: In many applications, such as medical imaging analysis, computer vision can detect anomalies more accurately and consistently than human observers.
• Real-Time Processing: Computer vision enables real-time processing and interpretation of visual data, crucial for applications like autonomous driving and security surveillance, where immediate response is essential.
• Scalability: Once developed, computer vision systems can be scaled across multiple locations and devices, making expanding operations easier without a proportional labor increase.
• Cost Reduction: By automating routine and labor-intensive tasks, computer vision reduces the need for manual labor, thereby cutting operational costs over time.
• Enhanced Safety: In industrial environments, computer vision can monitor workplace safety, detect unsafe behaviors, and ensure compliance with safety protocols, reducing the risk of accidents.
• Improved User Experience: In retail and entertainment, computer vision enhances customer interaction through personalized recommendations and immersive experiences like augmented reality.
• Data Insights: By analyzing visual data, businesses can gain insights into consumer behavior, operational bottlenecks, and other critical metrics, aiding in informed decision-making.
• Accessibility: Computer vision enhances accessibility by helping to create assistive technologies for the visually impaired, such as real-time text-to-speech systems or navigation aids.
• Innovation: As a frontier technology, computer vision drives innovation in many fields, from developing advanced healthcare diagnostic tools to creating interactive gaming systems.

Computer Vision Disadvantages

• Complexity and Cost: Developing and deploying computer vision systems can be complex and costly, requiring specialized expertise in machine learning, significant computational resources, and substantial investment in data collection and annotation.
• Privacy Concerns: Computer vision, particularly in applications like facial recognition and surveillance, raises significant privacy concerns regarding data collection, surveillance, and potential misuse of personal information.
• Ethical Implications: Computer vision algorithms may inadvertently perpetuate biases in the training data, leading to unfair or discriminatory outcomes, such as facial recognition systems that disproportionately misidentify certain demographic groups.
• Reliance on Data Quality: The precision and efficiency of computer vision systems rely greatly on the caliber and variety of the training data. Biased or inadequate data may result in erroneous outcomes and compromise the system's dependability.
• Vulnerability to Adversarial Attacks: Computer vision systems are susceptible to adversarial attacks, where minor perturbations or modifications to input data can cause the system to make incorrect predictions or classifications, potentially leading to security vulnerabilities.

Neural Networks

A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.

Every neural network consists of layers of nodes, or artificial neurons—an input layer, one or more hidden layers, and an output layer. Each node connects to others, and has its own associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the best-known examples of a neural network is Google’s search algorithm.

Neural networks are sometimes called artificial neural networks (ANNs) or simulated neural networks (SNNs). They are a subset of machine learning, and at the heart of deep learning models.

How does Neural Networks work?

Consider a neural network for email classification. The input layer takes features like email content, sender information, and subject. These inputs, multiplied by adjusted weights, pass through hidden layers. The network, through training, learns to recognize patterns indicating whether an email is spam or not. The output layer, with a binary activation function, predicts whether the email is spam (1) or not (0). As the network iteratively refines its weights through backpropagation, it becomes adept at distinguishing between spam and legitimate emails, showcasing the practicality of neural networks in real-world applications like email filtering.

Learning of a Neural Network

1. Learning with supervised learning

In supervised learning, the neural network is guided by a teacher who has access to both input-output pairs. The network creates outputs based on inputs without taking into account the surroundings. By comparing these outputs to the teacher-known desired outputs, an error signal is generated. In order to reduce errors, the network’s parameters are changed iteratively and stop when performance is at an acceptable level.

2. Learning with Unsupervised learning

Equivalent output variables are absent in unsupervised learning. Its main goal is to comprehend incoming data’s (X) underlying structure. No instructor is present to offer advice. Modeling data patterns and relationships is the intended outcome instead. Words like regression and classification are related to supervised learning, whereas unsupervised learning is associated with clustering and association.

3. Learning with Reinforcement Learning

Through interaction with the environment and feedback in the form of rewards or penalties, the network gains knowledge. Finding a policy or strategy that optimizes cumulative rewards over time is the goal for the network. This kind is frequently utilized in gaming and decision-making applications.

Types of Neural Networks

There are seven types of neural networks that can be used.

• Feedforward Neteworks: A feedforward neural network is a simple artificial neural network architecture in which data moves from input to output in a single direction. It has input, hidden, and output layers; feedback loops are absent. Its straightforward architecture makes it appropriate for a number of applications, such as regression and pattern recognition.
• Multilayer Perceptron (MLP): MLP is a type of feedforward neural network with three or more layers, including an input layer, one or more hidden layers, and an output layer. It uses nonlinear activation functions.
• Convolutional Neural Network (CNN): A Convolutional Neural Network (CNN) is a specialized artificial neural network designed for image processing. It employs convolutional layers to automatically learn hierarchical features from input images, enabling effective image recognition and classification. CNNs have revolutionized computer vision and are pivotal in tasks like object detection and image analysis.
• Recurrent Neural Network (RNN): An artificial neural network type intended for sequential data processing is called a Recurrent Neural Network (RNN). It is appropriate for applications where contextual dependencies are critical, such as time series prediction and natural language processing, since it makes use of feedback loops, which enable information to survive within the network.
• Long Short-Term Memory (LSTM): LSTM is a type of RNN that is designed to overcome the vanishing gradient problem in training RNNs. It uses memory cells and gates to selectively read, write, and erase information.

Advantages of Neural Networks

Neural networks are widely used in many different applications because of their many benefits:

• Adaptability: Neural networks are useful for activities where the link between inputs and outputs is complex or not well defined because they can adapt to new situations and learn from data.
• Pattern Recognition: Their proficiency in pattern recognition renders them efficacious in tasks like as audio and image identification, natural language processing, and other intricate data patterns.
• Parallel Processing: Because neural networks are capable of parallel processing by nature, they can process numerous jobs at once, which speeds up and improves the efficiency of computations.
• Non-Linearity: Neural networks are able to model and comprehend complicated relationships in data by virtue of the non-linear activation functions found in neurons, which overcome the drawbacks of linear models.

Disadvantages of Neural Networks

Neural networks, while powerful, are not without drawbacks and difficulties:

• Computational Intensity: Large neural network training can be a laborious and computationally demanding process that demands a lot of computing power.
• Black box Nature: As “black box” models, neural networks pose a problem in important applications since it is difficult to understand how they make decisions.
• Overfitting: Overfitting is a phenomenon in which neural networks commit training material to memory rather than identifying patterns in the data. Although regularization approaches help to alleviate this, the problem still exists.
• Need for Large datasets: For efficient training, neural networks frequently need sizable, labeled datasets; otherwise, their performance may suffer from incomplete or skewed data.

Deep Learning

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

The chief difference between deep learning and machine learning is the structure of the underlying neural network architecture. “Nondeep,” traditional machine learning models use simple neural networks with one or two computational layers. Deep learning models use three or more layers—but typically hundreds or thousands of layers—to train the models.

While supervised learning models require structured, labeled input data to make accurate outputs, deep learning models can use unsupervised learning. With unsupervised learning, deep learning models can extract the characteristics, features and relationships they need to make accurate outputs from raw, unstructured data. Additionally, these models can even evaluate and refine their outputs for increased precision.

Deep learning is an aspect of data science that drives many applications and services that improve automation, performing analytical and physical tasks without human intervention. This enables many everyday products and services—such as digital assistants, voice-enabled TV remotes, credit card fraud detection, self-driving cars and generative AI.

Deep learning AI can be used for supervised, unsupervised as well as reinforcement machine learning. it uses a variety of ways to process these.

• Supervised Machine Learning: Supervised machine learning is the machine learning technique in which the neural network learns to make predictions or classify data based on the labeled datasets. Here we input both input features along with the target variables. the neural network learns to make predictions based on the cost or error that comes from the difference between the predicted and the actual target, this process is known as backpropagation.  Deep learning algorithms like Convolutional neural networks, Recurrent neural networks are used for many supervised tasks like image classifications and recognization, sentiment analysis, language translations, etc.
• Unsupervised Machine Learning: Unsupervised machine learning is the machine learning technique in which the neural network learns to discover the patterns or to cluster the dataset based on unlabeled datasets. Here there are no target variables. while the machine has to self-determined the hidden patterns or relationships within the datasets. Deep learning algorithms like autoencoders and generative models are used for unsupervised tasks like clustering, dimensionality reduction, and anomaly detection.
• Reinforcement  Machine Learning: Reinforcement  Machine Learning is the machine learning technique in which an agent learns to make decisions in an environment to maximize a reward signal. The agent interacts with the environment by taking action and observing the resulting rewards. Deep learning can be used to learn policies, or a set of actions, that maximizes the cumulative reward over time. Deep reinforcement learning algorithms like Deep Q networks and Deep Deterministic Policy Gradient (DDPG) are used to reinforce tasks like robotics and game playing etc.

Deep Learning Applications:

The main applications of deep learning AI can be divided into computer vision, natural language processing (NLP), and reinforcement learning. 

1. Computer vision

The first Deep Learning applications is Computer vision. In computer vision, Deep learning AI models can enable machines to identify and understand visual data. Some of the main applications of deep learning in computer vision include:

• Object detection and recognition: Deep learning model can be used to identify and locate objects within images and videos, making it possible for machines to perform tasks such as self-driving cars, surveillance, and robotics. 
• Image classification: Deep learning models can be used to classify images into categories such as animals, plants, and buildings. This is used in applications such as medical imaging, quality control, and image retrieval. 
• Image segmentation: Deep learning models can be used for image segmentation into different regions, making it possible to identify specific features within images.

2. Natural language processing (NLP): 

In Deep learning applications, second application is NLP. NLP, the  Deep learning model can enable machines to understand and generate human language. Some of the main applications of deep learning in NLP include: 

• Automatic Text Generation – Deep learning model can learn the corpus of text and new text like summaries, essays can be automatically generated using these trained models.
• Language translation: Deep learning models can translate text from one language to another, making it possible to communicate with people from different linguistic backgrounds. 
• Sentiment analysis: Deep learning models can analyze the sentiment of a piece of text, making it possible to determine whether the text is positive, negative, or neutral. This is used in applications such as customer service, social media monitoring, and political analysis. 
• Speech recognition: Deep learning models can recognize and transcribe spoken words, making it possible to perform tasks such as speech-to-text conversion, voice search, and voice-controlled devices. 

3. Reinforcement learning: 

In reinforcement learning, deep learning works as training agents to take action in an environment to maximize a reward. Some of the main applications of deep learning in reinforcement learning include: 

• Game playing: Deep reinforcement learning models have been able to beat human experts at games such as Go, Chess, and Atari. 
• Robotics: Deep reinforcement learning models can be used to train robots to perform complex tasks such as grasping objects, navigation, and manipulation. 
• Control systems: Deep reinforcement learning models can be used to control complex systems such as power grids, traffic management, and supply chain optimization. 

Challenges in Deep Learning

Deep learning has made significant advancements in various fields, but there are still some challenges that need to be addressed. Here are some of the main challenges in deep learning:

• Data availability: It requires large amounts of data to learn from. For using deep learning it’s a big concern to gather as much data for training.
• Computational Resources: For training the deep learning model, it is computationally expensive because it requires specialized hardware like GPUs and TPUs.
• Time-consuming: While working on sequential data depending on the computational resource it can take very large even in days or months. 
• Interpretability: Deep learning models are complex, it works like a black box. it is very difficult to interpret the result.
• Overfitting: when the model is trained again and again, it becomes too specialized for the training data, leading to overfitting and poor performance on new data.

Advantages of Deep Learning

• High accuracy: Deep Learning algorithms can achieve state-of-the-art performance in various tasks, such as image recognition and natural language processing.
• Automated feature engineering: Deep Learning algorithms can automatically discover and learn relevant features from data without the need for manual feature engineering.
• Scalability: Deep Learning models can scale to handle large and complex datasets, and can learn from massive amounts of data.
• Flexibility: Deep Learning models can be applied to a wide range of tasks and can handle various types of data, such as images, text, and speech.
• Continual improvement: Deep Learning models can continually improve their performance as more data becomes available.

Disadvantages of Deep Learning

• High computational requirements: Deep Learning AI models require large amounts of data and computational resources to train and optimize.
• Requires large amounts of labeled data: Deep Learning models often require a large amount of labeled data for training, which can be expensive and time- consuming to acquire.
• Interpretability: Deep Learning models can be challenging to interpret, making it difficult to understand how they make decisions.
• Overfitting: Deep Learning models can sometimes overfit to the training data, resulting in poor performance on new and unseen data.
• Black-box nature: Deep Learning models are often treated as black boxes, making it difficult to understand how they work and how they arrived at their predictions.

Hyperautomation

Hyperautomation consists of increasing the automation of business processes (production chains, work flows, marketing processes, etc.) by introducing Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA). To a point where almost any repetitive task can be automated and it is even possible to find out which processes can be automated and to create bots to perform them.

In addition, hyperautomation is a key factor in the digital transformation as it eliminates human involvement in low-value processes and provides data that offers a level of business intelligence that was not available before. It can become a key factor in building fluid organisations capable of adapting rapidly to change.

Why is hyperautomation important? 

Hyperautomation refers to a superpower automation process combining several key elements: the power of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and optical character recognition (OCR). At its core, hyperautomation begins with RPA and adds a range of advanced technologies to achieve end-to-end automation through advanced tools and analytics like AI, machine learning, and business process management systems (BPMS). 

In other words, hyperautomation scales on automation and amplifies its capabilities, building a process that is constantly advancing and improving through data. By adding intelligence to automation, this combination offers the horsepower and flexibility to automate the toughest processes – including undocumented operations that depend on unstructured information.

Independent of an organization’s infrastructure and repetitive labor, automation robots backed by AI and ML can still maneuver unstructured data inputs and make nuanced decisions. This enables enterprises to address customer expectations quickly, fulfill business goals, improve productivity, and boost efficiency. 

For example, pure RPA bots are limited to reading standardized and digitized invoices and documents. On the other hand, when OCR and NLP are added to RPA,  hyperautomated robots can perform tedious yet intuitive tasks such as sales reports, contracts, and reading invoices, emails, or official documents in various formats from different vendors. They can also listen, read, and engage in conversations to help respond and identify opportunities at record speed. 

The key points of hyperautomation

Hyperautomation is not based on one single technology but on integrating a number of them, including:

• Robotic Process Automation - Robotic process automation makes it possible to configure software that allows robots to perform repetitive, structured tasks in digital systems.
• Machine Learning - Machine Learning is the technology that uses algorithms to teach computers to perform complex tasks by themselves without the need for additional programming by human beings.
• Artificial Intelligence - The purpose of Artificial Intelligence is to create machines that are capable of making decisions and solving problems by emulating human logical thinking.
• Big Data - Big Data is a set of technologies that make it possible to store, analyse and manage huge amounts of data produced by devices in order to identify patterns and create optimal solutions.
• Cobots - Cobots are the prime example of collaborative robotics, in other words, robots that share tasks with human workers and are revolutionising production processes.
• Chatbots - Chatbots are systems based on AI, ML and Natural Language Processing (NLP) that can hold a conversation in real time with a human being using text or speech.

Advantages of hyperautomation

Hyperautomation has numerous advantages, both for the performance of a company as well as for the well-being of its workers. These include:

• The integration of disruptive technologies, such as AI, ML, RPA and NLP, into the day-to-day workings of the company, allowing it to perform processes more quickly and efficiently and reducing errors.
• Increased employee satisfaction, as they are operating in a smart working environment and do not have to waste their time on tedious tasks that add no value, and it enhances the ability of the workforce to increase productivity and competitiveness.
• Organisations can transform digitally, aligning their business processes and their investment in technology.
• Reduction in the operating costs of organisations. According to Gartner, by 2024, combining hyperautomation technologies with redesigned operating processes will cut costs by 30 %.
• Big Data and AI technology mean business information can be extracted from data and decisions made more effectively.

What are some hyperautomation use cases?

Hyperautomation's enhanced robotic intelligence capabilities enable organizations to amplify the automation of key business processes. A few use cases include:

• Healthcare: Machine learning, AI, NLP, and RPA provide immense value in improving processes in the healthcare industry. Using these tools together, hyperautomation enables organizations to save time, standardize processes, and reduce errors by automating repetitive tasks related to patient testing, medication reconciliation, patient registration, insurance verification, and more. 
• Financial services: The rise of alternative lending methods, fintechs, and challenger banks has made the financial services industry even more competitive. With hyperautomation, financial institutions can transform their operations and remain competitive by improving customer onboarding, streamlining compliance processes, and improving accuracy and speed.
• Customer service: As customer expectations and demands change, a business must find ways to adapt its operations to address customer concerns and enhance the customer experience. Integrating hyperautomation into customer service processes and systems can reduce manual tasks, sort queries, provide fast solutions, and streamline workflows.

Intelligent Composable Business

Intelligent composable business transforms decision-making by accessing and reacting on data in a better, more flexible method. Intelligent composable business will allow redesigned digital business moments, new business models, autonomous operations and new products, services and channels to exist. 

A composable business model is an acceleration of the digital era and offers both the customer and employee an agile experience; you are ensuring real-time adaptability. Architecting your business processes around this idea, forces businesses to be resilient in the ever-changing times and creates a culture of stability within evolving environments. 

You can think of the general concept of composability like building blocks that form a greater whole. The form of that whole could begin as a house, then evolve into a tower. Composable architectures can change shape and add capabilities relatively easily, because they’re made of blocks of different shapes, sizes, and functionality that can be evolved to fit a new need. 

In contrast, monolithic systems are solid wholes. They’re the prefab house that comes assembled and it’s not easy to change what they do and how they do it. When you want to expand a monolithic system or add to or change functionality, you’ve either got to start from scratch or plan for a long development period in order to change it.

When it comes to tech stacks, this more modular approach to business platform architecture allows for greater flexibility and extensibility. Retailers and the developers they work with can pick their preferred third party services or apps and compose them into an ecommerce site. In theory at least (we’ll go into the realities below) this can be done at a speed that’s more in line with the speed of business in contrast to a traditional, clunky monolithic platform developed in a lengthy waterfall development cycle. 

The three building blocks of composable business

• Composable thinking: when you combine the principles of modularity, autonomy, orchestration and discovery with composable thinking, it can guide your approach to conceptualizing when to compose, and what.
• Composable business architecture ensures that a business is built to be flexible and resilient. It’s about structure and purpose. These are structural capabilities — giving you mechanisms to use in architecting your business.
• Composable technologies are the tools for today and tomorrow. They are the pieces and parts, and what connects them all. The four principles are product design goals driving the features of technology that support the notions of composability. 

What are the benefits of a composable business?

Composable businesses come with a number of benefits, which mirror that of composable architecture.

Easier scaling

Composable businesses are easier to scale than businesses that are more monolithic in shape. With new customers comes the need for new infrastructure, new products, and new processes, whether we’re talking about additional back-end fulfillment capabilities or a new approach to more seamless checkouts. In a composable business structure, existing capabilities can be drawn from, reintegrated, and added to as new capabilities to meet demand. Rather than building entirely new systems that take months if not years to implement, business building blocks are simply added and built upon to expand.

Individual empowerment

Composable businesses empower the individual user or unit to fix problems and add new capabilities as needed. This local authority can increase efficiency and provide a better sense of ownership than in monolithic systems where roles are obscured.

Resilience and adaptability

Two of the biggest benefits of composable business are the organization’s ability to quickly react and adapt to market changes. While still a challenge, an unexpected and unprecedented event like the COVID-19 pandemic becomes a new challenge to pivot around with new processes, services, and products. For instance, a clothing manufacturer that viewed their processes in a composable manner might have pivoted quickly to produce masks using the same processes they used to make shirts. Composability allows businesses to plan for the current reality, and adapt quickly to the future.

What are the disadvantages of a composable business?

Organizational complexity and overhead

Fully composable organizations increase organizational complexity. Unlike with a clear hierarchical structure, distributed teams can be difficult to navigate when it comes time to coordinate. Who owns what, and who should any given person go to for feedback and development? It can also increase redundancy if the business units are operating too independently without enough cross-communication. This can in turn increase overhead and costs. After all, some problems are best solved at a more global level, rather than at individual, smaller scales. 

Performance and reliability issues

Fragmentation can lead to performance issues, which can in turn slow business processes and technical development. The absence of shared processes can lead to different business units operating in wildly different ways—and this can make it difficult to design for. On the technical side, it can be challenging to debug a distributed system, and the lack of a shared code base can slow developer productivity.

Increased costs

From maintenance to operational costs, and everything in between, increased complexity leads to increased costs. The same goes for the security vulnerabilities created when there are disparate business packages rather than hardened units. Fully composable business structures and platforms can require a great deal of technical overhead and support, each of which have their costs.

Distributed Cloud

Distributed cloud is a public cloud computing service that lets you run public cloud infrastructure in multiple locations—your own cloud provider's data centers, other cloud providers' data centers, third-party data centers or colocation centers, and on-premises—manage everything from a single control plane.

With this targeted, centrally managed distribution of public cloud services, your business can deploy and run applications or individual application components in a mix of cloud locations and environments that best meets your requirements for performance, regulatory compliance, and more. Distributed cloud resolves the operational and management inconsistencies that can occur in hybrid cloud or multicloud environments.

Most important, distributed cloud provides the ideal foundation for edge computing—running servers and applications closer to where data is created.

The demand for distributed cloud and edge computing is driven primarily by Internet of Things (IoT), artificial intelligence (AI), telecommunications (telco) and other applications that need to process huge amounts of data in real time. But distributed cloud is also helping companies surmount the challenges of complying with country- or industry-specific data privacy regulations—and, more recently, providing IT services to employees and users redistributed by the COVID-19 pandemic.

How distributed cloud works

You might have heard of distributed computing, in which application components are spread across different networked computers, and communicate with one another through messaging or APIs, with the goal of improving overall application performance or maximizing computing efficiency.

Distributed cloud goes a giant step further by distributing a public cloud provider's entire compute stack to wherever a customer might need it—on-premises in the customer's own data center or private cloud, or off-premises in one or more public cloud data centers that might or might not belong to the cloud provider. 

In effect, distributed cloud extends the provider's centralized cloud with geographically distributed micro-cloud satellites. The cloud provider retains central control over the operations, updates, governance, security and reliability of all distributed infrastructure. 

The customer accesses everything—the centralized cloud services, and the satellites wherever they are located—as a single cloud and manages it all from a single control plane. In this way, as industry analyst Gartner puts it, distributed cloud fixes with hybrid cloud and hybrid multicloud breaks. 

Advantages of a distributed cloud

• Less latency. By moving processing tasks closer to end users, distributed cloud services can minimize latency and increase the responsiveness of applications.
• Greater scalability. Distributed cloud architecture makes it easier for organizations to quickly expand to edge locations without building out new data centers.
• Increase visibility. Organizations can use a single console to manage and monitor activity within hybrid cloud and multicloud infrastructure that forms a distributed cloud.
• Improved reliability. Distributed systems are inherently more fault-tolerant and offer greater redundancy. If cloud services in one location go offline, organizations can continue to access cloud services from other distributed locations.

Limitations of a distributed cloud

• Security issues. With data and infrastructure distributed throughout the world, managing data and cloud network security can be more challenging.
• Backups. Backing up and recovering data from a distributed architecture can be more complicated, as many regulations require data to stay in specific locations.
• Availability. The various locations in a distributed cloud environment may have different connectivity models and capacities, limiting bandwidth and requiring upgrades to slower connections.
• Complexity. Distributed computing systems are more difficult to deploy, maintain, and troubleshoot than centralized cloud computing implementations.
• Cost. Distributed cloud computing systems require a larger investment up front, and adding capacity for increased processing may add to the initial expense.

Use cases for distributed cloud

• Improved hybrid cloud or multicloud visibility and manageability: Distributed cloud can help any organization gain greater control over its hybrid multicloud infrastructure by providing visibility and management from one console, with a single set of tools.

• Efficient, cost-effective scalability and agility: It's expensive and time-consuming to expand a dedicated data center or to build out new data center locations in different geographies. With distributed cloud, an organization can expand to existing infrastructure or edge locations without physical buildout, and can develop and deploy anywhere in the environment quickly, by using the same tools and personnel.

• Easier industry or localized regulatory compliance:  Many data privacy regulations specify that a user's personal information (PI) cannot travel outside the user's country. Distributed cloud infrastructure makes it much easier for an organization to process PI in each user's country of residence. Processing data at its source can also simplify compliance with data privacy regulations in healthcare, telecommunications and other industries.

• Faster content delivery: A content delivery network (CDN) deployed on a distributed cloud can improve streaming video content performance—and the user experience—by storing and delivering video content from locations closer to users.

• IoT, (AI) and machine-learning applications: Video surveillance, manufacturing automation, self-driving cars, healthcare applications, smart buildings and other applications rely on real-time data analysis that can't wait for data to travel to a central cloud data center and back. Distributed cloud and edge computing deliver the low latency these applications demand.

Internet of Behaviors (IOB)

Internet of Behaviors (IOB) is an emerging technology that will influence the tech market for the next few years. But what does IoB mean? IoB or Internet of Behaviors is based on understanding, predicting, and affecting human behavior through data analysis.

This term is related to behavioral science. In other words, the IoB concept is to use the result of data analysis to create UX design, get a new approach to search experience optimization, or change the way of product marketing. Another term related to IoB is the Internet of Things (IoT), as technically, all the data gathered from IoT and other sources are used to influence consumer behavior.

IoB has expanded from the IoT. As you may know, the Internet of Things is a network of physical devices that helps to collect and share a wide variety of data. For example, your phone can track your real-life geo position. IoT technology connects your phone with a laptop, voice assistance, or smart house and gets a lot of information about your interests and how you use products. Organizations can use the data for different reasons, for example:

• to measure the effectiveness of their campaigns,
• to measure the patient’s activity (health providers can use these features),
• to personalize content.

How does IOB work?

The idea of IoB is to use this data to change behavior. The implementation of IoB in different industries can vary. For example, there is a solution for the logistic market well-known as telematic. Cprime has broad expertise in building telematics solutions for commercial vehicle tracking. Telematics can analyze real-time data on a vehicle’s location, speed, fuel consumption, route, or driving behavior to improve the logistics.

For instance, the data can be shared with insurance companies to get accurate information on breakdown or incident reasons. It also helps to manage the workload and delivery schedule in real-time. Telematic solutions for fleet management are an example of implementing IoB for this industry.

It is also important to mention that IoB has ethical implications depending on the goals of using it. That is why privacy laws have a significant impact on the adoption and scale of the IoB.

Different applications of IoB

Due to IoB’s ability to generate insights for individuals, it can be used in many applications that provide very specific and personalized support to users. A few of them are discussed below.

Digital Marketing and Advertising/ Social Media

Based on customer interactions with specific products, marketing agencies and organizations can personalize advertisements so that every individual sees what piques their interests the most. For example, if a sensor or a device understands that a person spends more time at the gym, then he would get advertisements about brands that sell protein supplements, gym trainers, equipment, etc. If a person focussed on training a specific muscle all the time, the wearable could essentially advise him to shift his focus and train other body parts as well. Noticed how Google or YouTube advertises products you had searched for within the last hour or discussed with your friends? Yup, connected devices making use of IoB.

Healthcare

A large number of the world’s population currently suffer from chronic illnesses. Healthcare providers can monitor their patients’ behavior in real-time. From understanding how to react to certain medications to keep a tab on their regimens, physicians can now do everything with the help of Internet of behaviors. What’s more, these devices can be trained to give out insights based on user activities so that the healthcare providers can easily form diagnoses.

Government/Policymaking

The government can use the data generated by IoB devices to track the activities of persons of interest and avoid mishaps from taking place. The government can also undertake surveys to understand what the citizens are collectively interested in and track behavioral patterns of large groups to maintain law and order. Of course, there is an element of over-regulation but a committee to monitor such activities could also be set up to uphold the privacy of citizens.

Insurance

In sectors like vehicle insurance, the insurance companies could monitor the activities of drivers using IoB to gauge their roles in accidents so that insurance companies can correctly identify whose fault the mishap was.  These devices could also play a role in preventing driving under influence or even identify medical emergencies.

These are only a few of the areas where IoB’s use has been prevalent. Other areas include defense, facial recognition, geolocation-based activity reminders, and predictions, finance management, efficiency and productivity, cutting costs, and industrial automation among others.

The Benefits of IoB

Market products more effectively to customers

Many digital marketing agencies are already using analytics tools to uncover insights into common consumer behaviors. Marketers can use the IoB to analyze customer purchasing habits across platforms, gain access to previously unobtainable data, redefine the value chain, and even provide real-time point-of-sale notifications and targeted ads.

Improve user experience

ux design services is a crucial part of sales. Organizations can have a better understanding of people’s attitudes toward specific products or services thanks to the knowledge provided by IoB, making it even easier to resolve customer concerns.

Enhance public health

Companies in the manufacturing industry are already using sensors and RFID tags to determine whether or not on-site employees wash their hands regularly. Furthermore, computer vision can determine whether or not employees are following mask protocol or social distancing directives. In the health industry, providers can track patients’ activation and engagement efforts.

Improve public safety

Monitoring public safety is opening up exciting new opportunities in a variety of industries. Vehicle telematics is used in one application to track driver behavior and flag erratic or dangerous behavior.

What does the future hold for IoB? 

However, the collection of behavioral events data can be problematic. The IoB raises concerns about how businesses gather, navigate, and use data, particularly as more of it is collected. Whatever perspectives are on IoT and IoB, experts predict that they will continue to grow and influence in the near future.

According to Gartner, by the end of 2025, more than half of the world’s population will be subject to at least one IoB program, whether from a commercial or governmental source. IoB, like other technology trends such as AI and machine learning, is likely to spark significant debate about the ethics vs. positive applications of this technology. According to these experts, by 2023, the individual activities of 40% of the global population will be tracked digitally to influence their behavior through the IoB concept. In 2023, that percentage will represent more than 3 billion people worldwide (Gartner 2020).

Low-Code and No-Code AI

Low-code and no-code is a development paradigm that professional and citizen developers alike use to build mobile or web applications quickly and easily. Platforms that offer low-code or no-code development are adopted, thanks to their drag-and-drop functionality and simple UIs.

Coding is the writing of programming. It is the script of instructions that a computer follows to achieve a certain goal. However, not all computers use the same scripts, otherwise known as programming languages.

Examples of these languages are Python, Javascript, and Java. Expertise in these languages easily equates to a full-time job.

Complex programs and applications still require coding, but low-code and no-code systems allow users to:

• build their own websites,
• design their own applications,
• deploy AI and machine learning without code.

Difference Between Low-Code and No-Code

As the name suggests, low-code refers to systems that require less coding, while no-code are systems without the need for code at all. Both approaches are popular and targeted towards professionals and business people. Furthermore, they are used in artificial intelligence and machine learning models, outside of regular computing.

Additionally, both approaches are typically used to serve specific business purposes, like data classification, or the definition of a workflow. Low-code goes hand in hand with no-code, but these platforms are also frequently used by developers themselves.

Often, these experienced programmers take advantage of these tools to make their jobs easier, by avoiding writing extra code. While no-code is most often used by managers in different industries and departments.

What Are The Advantages? 

Ease Of Use

Development is simplified. Without the need to write code, users can quickly learn how to bring their creations to life. As such, it gives the users more time to actually focus on determining what they want their development or algorithm to do.

Fast Development

Programming is a meticulous process that requires attention and persistence to achieve good results. However, using platforms that allow for low-code and no-code development means that users can easily switch around pre-made components.

This means that users can go through the process of trial and error more quickly, which in turn speeds up overall development.

Lower Costs

For the bottom line of any professional or business, the reduction of cost in any way can be truly beneficial. Using a no-code or low-code system for development can reduce time requirements and the necessity of heavy maintenance.

Moreover, it can allow businesses to try out new ideas inexpensively, thereby increasing productivity.

What Are The Disadvantages?

Security

Given that the program or algorithm isn’t built by an outside AI engineer or consultant, the data is handled by the professional or business itself. In this case, any sensitive data is kept within the walls of the ultimate user. Following on from this, using no-code and low-code platforms is secure as it keeps third-party developers out.

However, there is the issue of platform security. Some platforms may fail to design secure access protocols. Therefore this can mean that users should take care to research the terms and conditions.

Lack Of Customisation

Despite the speed of low-code and no-code platforms, their use cases and functionalities are often limited. Given that most platforms are designed to address specific problems, it’s difficult to use them for creating more complex solutions.

Requires Training

Even if the creation of models, algorithms, or applications is made easy by low-code and no-code AI, the deployment still requires a certain degree of understanding.

Such understanding needs to be taught to the management team, or the people that will mainly use the finished product.

Top Low-Code & No-Code Platforms

Google AutoML

A Google-backed platform that allows developers with limited ML expertise to train high-quality models specific to their business needs. The platform focuses on image and video annotation and labelling. As well as this, it also has the ability to complete semantic text analysis and classification.

Create ML

An Apple-backed no-code platform that uses a Mac OS framework. It allows users to easily build ML models with an easy-to-use app interface and no code. The platform can train a variety of models, from image recognition to sentiment and regression analysis.

Levity

A no-code platform that allows users to train and build AI models. The platform focuses on image, text, and document classification. It enables users to train custom models on their use-case-specific data.

Custom models also have a human-in-the-loop option, which means the model asks for input where it is unsure. Some use cases are automated data iteration, and classification of images, texts, and documents.

Obviously AI

A no-code AI platform that builds ML algorithms for data prediction. Users can take a bird’s-eye view of existing data, understand it and draw conclusions.

The platform also suggests ready-made datasets, so you can test them out and get predictions right away. Some business-case usage would be personalisation of marketing campaigns, forecasting of company revenue, and supply chain optimisation.

SuperAnnotate

A leading platform designed to build the highest quality training datasets available for computer vision and natural language processing. This platform has advanced tools, like automation features, data curation, offline access, and integrated annotation services. It allows ML teams to build incredibly accurate datasets 3-5x faster.