6th Global summit on artificial intelligence and neural networks

Artificial intelligence:

Artificial intelligence (AI) is the area of computer science that deals with the creation of intelligent machines that are able to carry out tasks that would typically require human intelligence, like understanding natural language, recognising images, making decisions, and picking up new skills over time.

AI systems can be built using a variety of methods, but some of the most well-liked ones are as follows:

Systems using a rule-based approach: These systems base their judgements or actions on the incoming data and a set of established rules.

Using massive datasets to train a model, machine learning enables a system to recognise patterns in data, infer conclusions, and act on them.

Deep learning is a subset of machine learning that uses neural networks—which are built to resemble the structure of the human brain—to tackle challenging issues like speech and picture recognition.

Healthcare, banking, transportation, and entertainment are just a few of the industries where AI is used extensively. Automated driving, chatbots, picture recognition, and natural language processing are some of the current applications of AI.

Although AI has the power to completely transform a variety of industries and vastly enhance our quality of life, there are concerns about the technology's ethics and potential abuse. Thus, in order to ensure that the benefits of AI development are maximised and its risks are reduced, responsible and ethical procedures must be used in tandem with it.

Neural Networks:

Adapted from the design and operation of the human brain, neural networks are a particular class of machine learning model. They are made of interconnected nodes, also known as neurons, which process information and transmit it to the layer of nodes below. Weights are assigned to the connections between neurons, and weights are changed as learning proceeds to improve how well the network does a particular job.

Natural language processing, speech recognition, image and audio recognition, and predictive analytics are just a few of the activities that neural networks are best suited for since they can understand complex patterns and relationships in data.

Many neural network types exist, including:

Feedforward neural networks: The most fundamental kind of neural network, in which data only moves from the input layer to the output layer in one direction.

Recurrent neural networks: These networks are capable of feedback loops, which makes them useful for tasks like time series prediction, speech recognition, and language modelling.

Convolutional neural networks: Created especially for image recognition tasks in which images serve as the input data.

Networks that are used to create new data samples that resemble a specific set of input data are known as "generative adversarial networks."

Applications like audio and image identification, natural language processing, and autonomous cars have all shown how well neural networks perform in these areas. It is a resource-intensive process, though, because training neural networks needs a lot of data and computational power. Furthermore, because neural networks can be challenging to comprehend and debug, there are concerns about their interpretability and transparency.

The purpose of Neural Networks 2018 is to spread knowledge and new ideas among experts, businesspeople, academics, and students working in the artificial intelligence field. By contributing your thoughts and ideas to the scientific conference, you are certain of enabling and securing the theme of "Harnessing the Power of Artificial Intelligence."

Importance and Scope:

The applications of artificial intelligence and neural networks are numerous, and they are crucial in many different industries. Some of the justifications are as follows:

Automation: AI and neural networks are able to automate a variety of processes, which reduces the need for human labour and boosts production. This covers activities including data entry, analysis, and processing.

Personalization: Artificial intelligence (AI) and neural networks (NNs) are able to learn from user behaviour and preferences to personalise services, such as making product recommendations or displaying relevant advertisements.

Predictive analytics: Artificial intelligence (AI) and neural networks can analyse vast volumes of data to find patterns and generate predictions, which can assist organisations in making wise decisions and enhancing their operations.

Health Care: Artificial intelligence (AI) and neural networks are being utilised to enhance healthcare results, including diagnostics, drug discovery, and individualised treatment programmes.

Autonomous vehicles: Neural networks play a key role in the development of autonomous vehicles, which are anticipated to revolutionise transportation and reduce the number of accidents caused by human error.

Artificial intelligence (AI) and neural networks are used for natural language processing, which enables chatbots and virtual assistants.

AI and neural networks are predicted to become much more common in the future as their significance and applications continue to grow. They have the potential to alter many sectors by increasing production and efficiency while lowering costs because of their capacity to automate and optimise operations.

To ensure that AI and neural networks are developed and used properly, there are other issues that must be addressed, such as employment displacement, privacy, and ethical usage of the technology.

Cognitive Computing:

In order to absorb and comprehend complicated data, cognitive computing refers to a category of computing systems that are created to closely resemble how the human brain functions. It involves the use of machine learning algorithms, natural language processing, and other cutting-edge technology to analyse massive volumes of data and produce insights that can assist humans in making better decisions.

When processing enormous amounts of data rapidly and accurately is required, cognitive computing systems are frequently utilised in industries including healthcare, banking, and customer service. A cognitive computing system might be used, for instance, to analyse medical imaging and assist clinicians in the diagnosis of diseases or to study consumer data and forecast which goods or services they are likely to be interested in.

The capacity to interpret natural language, learn from data, and reason and make judgements based on complex knowledge are some of the main characteristics of cognitive computing systems. In addition, by utilising tools like speech recognition and natural language processing, these systems may communicate with people in a more organic way.

Self organizing neural network:

Unsupervised and without explicit guidance from a human operator, a self-organising neural network is a sort of artificial neural network that can learn and organise itself. The network is trained to identify patterns in the input data and to group together data points that share characteristics.

The Kohonen self-organising map is a sort of self-organising neural network that is quite popular (SOM). There are three layers in this network: the input layer, the layer of nodes, and the output layer. Each node is connected to a weight vector that represents a particular pattern or feature of the input data, and the nodes are arranged in a two-dimensional grid.

In order to associate nodes with comparable weight vectors on the output layer during training, the network modifies the nodes' weight vectors to match the input data. The act of grouping related data points together is referred to as clustering, and it enables the network to recognise patterns in the data and classify incoming data points according to how closely they resemble pre-existing patterns.

Image identification, data mining, and pattern recognition are just a few of the many applications that self-organising neural networks are used for. They are especially helpful in situations where the data is complex or challenging to categorise using conventional approaches and where it is necessary to find hidden patterns or links in the data.

Back propagation:

Artificial neural networks are frequently trained using the backpropagation algorithm. It is a supervised learning algorithm, which means it needs a collection of labelled training data to use as its learning foundation.

The neural network generates an output by receiving input data first and then feeding that output into the backpropagation algorithm. The difference in output between the two is then calculated by comparing the output to the desired output. In order to decrease the error, the algorithm then goes backwards through the network, changing the weights of the connections between neurons.

The gradient of the error function is determined with respect to each network weight by the backpropagation algorithm. The weights of the network are updated using this gradient in a way that lowers the error. Throughout numerous epochs, the procedure is repeated numerous times, with the weights changing each time.

Although the backpropagation algorithm is an effective method for training neural networks, it can be expensive to run on computers and has the potential to have problems like overfitting or disappearing gradients. Many changes and improvements to the algorithm have been put forth to solve these problems, including variations like stochastic gradient descent, mini-batch gradient descent, and adjustable learning rate techniques like Adam and RMSprop.

Computational Creativity:

The branch of study known as computational creativity focuses on creating computer systems and algorithms that can generate results that are thought to be creative, unique, or unusual. The arts, music, writing, and other forms of expression are included in this.

Artificial intelligence, machine learning, and cognitive psychology methods are combined in computational creativity to create computer programmes that can simulate the creative process in people. Creating algorithms that can produce original and intriguing ideas, assessing the calibre and originality of the produced outputs, and comprehending how people perceive creativity are some of the important issues in this discipline.

Uses of computational creativity include developing original musical compositions, producing works of art and visual designs, coming up with fresh product concepts, and supporting the development of ideas for diverse sectors such as marketing and advertising.

A few instances of computational innovation are as follows:
The production of art utilising deep neural networks or generative adversarial networks (GANs).
making use of machine learning techniques to create algorithmic musical compositions.

the production of poetry or other forms of creative writing using tools of natural language processing.
the creation of AI helpers to aid many sectors' ideation processes.
Although the study of computational creativity is still in its infancy, it has the potential to lead to the creation of a number of innovative tools and technologies that will help in the creative process and push the limits of what humans can communicate creatively.

Artificial Neural Network:

The structure and operation of biological neural networks in the human brain served as the basis for the development of artificial neural networks (ANNs), a form of machine learning algorithm. ANNs are composed of interconnected nodes, often known as "neurons," arranged in layers. Each layer processes and transforms the data before passing it on to the subsequent layer.

The perceptron is the fundamental component of a synthetic neuron. It accepts input values, multiplies them by weights, and then sends the resulting value through an activation function to produce an output value. The weights are learned throughout the training procedure, which entails modifying the weights to reduce the error between the projected output and the actual output.

ANNs are capable of performing a wide range of tasks, including prediction, natural language processing, speech recognition, and image and speech recognition. One benefit of ANNs is their capacity to learn from vast volumes of data and recognise intricate patterns, which can be challenging for conventional machine learning algorithms to do.

ANNs can be classified into a number of different categories, such as feedforward neural networks, recurrent neural networks, convolutional neural networks, and deep neural networks. Each kind has a unique structure and is tailored for a particular kind of task.

Even though ANNs have shown to be an effective tool for handling complicated issues, they can be computationally demanding and demand a lot of training data. In order to overcome these difficulties, researchers have created a number of methods, including dropout regularisation, batch normalisation, and transfer learning, to enhance the training process and increase the effectiveness of ANNs.

Deep Learning:

Artificial neural networks (ANNs) with numerous layers are used in deep learning to model and resolve complicated issues. The network can include tens or hundreds of layers, which allows it to learn progressively complicated representations of the data. This depth of the network is what is meant when something is said to be "deep".

Computer vision, natural language processing, speech recognition, and robotics are just a few of the industries that deep learning has completely transformed. In a variety of tasks, including speech recognition, object detection, image classification, and language translation, it has demonstrated cutting-edge performance.

The capacity of deep learning to automatically learn features from raw data without the need for manual feature engineering is one of its main advantages. As a result, it may pick up on intricate and abstract patterns that are challenging for conventional machine learning algorithms to recognise.

Convolutional neural networks (CNNs) for image and video analysis, recurrent neural networks (RNNs) for sequence data analysis, and generative adversarial networks (GANs) for image and video generation are some of the well-liked deep learning models.

Deep learning has been used in a variety of industries, including recommender systems, self-driving automobiles, and medical diagnosis. Yet it also has significant drawbacks, like the need for a lot of labelled training data, high computational requirements, and complexity in understanding the inner workings of the model.

In general, deep learning has allowed for considerable advancement in a variety of domains and has the potential to do so in a number of others in the future.

Ambient Intelligence:

The concept of a setting in which digital technology is effortlessly incorporated into daily life is known as ambient intelligence (AmI). It is a paradigm in which computing systems are incorporated into the real environment and have the ability to perceive, identify, and respond to user requirements and preferences.

Ambient intelligence aims to develop a smart environment that is proactive, cognizant of context, and sensitive to human requirements. In order to develop an intelligent environment that can foresee and respond to its inhabitants' demands, technologies for sensors, wireless communication, artificial intelligence, and human-computer interaction must be integrated.

Applications for ambient intelligence include those that can monitor patients in real-time in healthcare facilities, smart homes, smart cities, and intelligent transportation systems. Retail, education, and entertainment are some additional fields where the technology can be used.

In spite of the fact that ambient intelligence has the ability to make our lives better by making our surroundings more effective and individualised, there are also concerns regarding privacy and security. In order to protect user data and ensure that the systems are built with security in mind, it is crucial to ensure that technology is used as widely as possible.

Perceptrons:

An artificial neural network that is based on the structure and operation of the human brain uses perceptrons as its unit of computation. A perceptron is made up of one or more input units, one output unit, and a number of weights that control how strongly the connections between the input and output units are made.

The perceptron can only learn to categorise data that can be divided into distinct classes using a straight line because it is a linear classifier. A collection of labelled training data must be given to the perceptron in order to be trained, and the weights of the connections between the input and output units must be changed using an algorithm known as the perceptron learning rule.

To reduce the discrepancy between the perceptron's predictions and the actual labels of the training data, the learning rule seeks to minimise this discrepancy.

One of the earliest artificial neural networks to be created was the perceptron, which was developed in the 1950s. Since then, more potent and adaptable neural network types have mostly replaced them, including feedforward neural networks and convolutional neural networks, which can be trained to classify more complex input.

Nevertheless, perceptrons are still of theoretical and historical importance, and they are occasionally used in real-world settings when linear categorization is enough.

Cloud Computing:

The distribution of computer services, such as servers, storage, databases, software, and more, via the internet is referred to as cloud computing. Instead of building and maintaining their own physical IT infrastructure, as was the case with previous computing models, organisations can now obtain computing resources on demand from a third-party provider thanks to cloud computing.

A few advantages of cloud computing are its adaptability, scalability, affordability, and dependability. Businesses don't have to spend money on and maintain their own hardware because cloud computing allows them to rapidly and effectively scale up or down their computer capacity as needed. Also, a lot of cloud computing services are pay-per-use, so businesses only pay for what they really use.

Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service are the three primary categories of cloud computing services (SaaS). In contrast to PaaS, which offers a platform for programmers to create and deploy applications, SaaS offers software that can be accessed online. IaaS gives businesses access to computing resources like servers and storage.

Autonomous Robots:

Robots that can work alone and make judgements on their own are called autonomous robots. Artificial intelligence, machine learning, and sensor technologies are used by these robots to navigate their surroundings and carry out tasks that ordinarily require human assistance. Robots that operate autonomously can be programmed to carry out a wide range of jobs, including production, storage, delivery, exploration, and more.

Mobile robots, humanoid robots, and drones are a few examples of autonomous robots. Humanoid robots are made to resemble people in both appearance and movement, whereas mobile robots are made to move about and navigate their surroundings. Drones are aerial robots used for delivery, inspection, and surveillance duties.

In addition to improved efficiency, precision, and safety, autonomous robots have a number of advantages. They are capable of carrying out activities more quickly and precisely than people, and they can work in potentially dangerous areas where it might not be safe for people to do so. Furthermore, by working nonstop without requiring rest or breaks, autonomous robots can lower labour expenses and increase productivity.

Yet, there are other difficulties and worries associated with autonomous robots as well, such as the possibility for employment displacement, privacy and security issues, and the requirement for moral and responsible usage of this technology. The pros and downsides of utilising autonomous robots must be carefully weighed, and the right safeguards and laws must be put in place to ensure their responsible and safe operation.

Support vector machines:

The popular machine learning algorithm known as Support Vector Machines (SVM) is utilised for classification and regression analysis. It is a member of the family of supervised learning techniques, where a model is trained on labelled data before being used to generate predictions about fresh, unobserved data.

The goal of SVM is to identify the hyperplane that best divides the data points into their corresponding classes. The hyperplane is the line that optimises the distance between the nearest data points in each class. Support Vector Machines get their name from the fact that support vectors are the data points that are closest to the hyperplane.

Classification and regression issues can be solved with SVM in both linear and non-linear ways. SVM employs kernel functions to transform the data into a higher-dimensional space, which makes it possible to identify a linear boundary in the case of non-linear situations.

The following are a few benefits of SVM:

• High-dimensional spaces where it works
• Able to withstand overfitting
• Adaptable, suitable for both classification and regression.

Among the SVM's drawbacks are:

• Huge datasets require computationally intensive methods.
• The findings are challenging to interpret.
• The selection of the kernel function and the hyperparameters.

Parallel processing:

The ability to do many activities simultaneously while employing multiple processors or cores is known as parallel processing. It is used to speed up the processing of enormous amounts of data or complicated calculations. Modern computing systems, including desktop PCs and huge data centres, frequently use parallel processing.

In parallel processing, the data is divided into smaller pieces, and each chunk is processed independently by a different processor or core. The final product is then created by combining the results. When compared to a single processor processing the data sequentially, this method provides for substantially faster processing.

A number of methods exist to implement parallel processing, including:

Shared Memory: Each CPU can access any portion of the memory, which is shared by all processors. This strategy works well for issues when it is simple to separate the data into manageable chunks and process it in parallel.

Distributed Memory: Each CPU has its own memory, and connections between them are made across a network. This method works well in situations where it is difficult to break the data into manageable chunks and when each processor must focus on a different segment of the data.

Hybrid: Using both shared and distributed memory, this strategy It is appropriate for issues that call for the computing of intricate calculations and the processing of massive amounts of data.

For many applications, including machine learning, big data analytics, scientific simulations, image and signal processing, and image and signal processing, parallel processing can significantly boost performance.

Yet, it calls for unique software and hardware architectures as well as non-sequential programming-specific programming methods.

Bio Informatics:

In order to analyse and understand biological data, notably in genomics and proteomics, bioinformatics is an interdisciplinary field that merges biology, computer science, and statistics.

Large-scale biological data is processed, examined, and interpreted in bioinformatics using computer tools and methods. DNA and protein sequences, information on gene expression, and information on protein structures are some examples of biological data that are frequently examined in bioinformatics.

The use of bioinformatics is widespread, including in the following areas:

In genomics, genes are identified, DNA and RNA sequences are analysed, and the genomes of various species are compared using bioinformatics techniques.

Proteomics: To identify protein functions and pathways, protein sequences, structures, and interactions are analysed using bioinformatics techniques.

Drug discovery: To find novel therapeutic targets, create new medications, and forecast drug interactions and adverse effects, bioinformatics technologies are used.

Disease diagnosis and treatment: Genetic and molecular data are analysed using bioinformatics technologies to identify diseases and create individualised remedies.
Typical bioinformatics instruments and methods include:

Sequence alignment: Bioinformatics methods are used to compare and contrast DNA and protein sequences in order to determine their similarities and differences and to infer their evolutionary links.

Database search: To find DNA and protein sequences, bioinformatics methods are used to search biological databases like GenBank.

Phylogenetic analysis: Based on DNA and protein sequence information, evolutionary trees are reconstructed using bioinformatics methods.

Machine learning: To categorise and forecast biological data, machine learning models are trained using bioinformatics tools.

Modern biology and medicine are placing more and more importance on bioinformatics, a discipline that is expanding quickly thanks to improvements in genomic and proteomic technologies.

Ubiquitous Computing:

Ubiquitous computing, also referred to as pervasive computing, is a computer paradigm in which computing devices are smoothly incorporated into the environment and become an inseparable component of daily life, all without the user being aware of their presence.

In other words, it's a computing vision in which technology is ingrained in the environment and incorporated into people's and organisations' everyday routines.

The availability of cheap, compact, wireless devices that can communicate over the internet and with one another is a prerequisite for ubiquitous computing. These units may gather and share data across a network because they are connected to one, which enables them to work together to provide beneficial services to users.

Smartphones, smart homes, wearable technology, smart cities, and the Internet of Things are some examples of ubiquitous computing technologies (IoT). These gadgets are capable of detecting their surroundings, reacting to them, and interacting with humans in a smooth and natural way.

Healthcare, transportation, and education are just a few of the sectors that ubiquitous computing has the potential to revolutionise. Personalised and timely care can be given to patients, for instance, by using ubiquitous computing in the healthcare industry to monitor patients remotely.

When it comes to transportation, ubiquitous computing can be utilised to improve traffic flow, ease congestion, and give consumers real-time information about available public transportation options.

Yet, privacy, security, and data ownership are further issues that are brought up by ubiquitous computing. With the increased usage of ubiquitous computing devices, there is a need for strong security and privacy methods to safeguard sensitive data and guarantee that users have control over their data.

Natural language processing:

A branch of artificial intelligence (AI) called "natural language processing" (NLP) aims to make it possible for machines to comprehend, analyse, and produce human language. By giving machines the ability to read, write, and speak human language, NLP strives to close the communication and processing gap between humans and machines.

Machine learning algorithms, statistical models, and linguistic analysis are just a few of the many tools and approaches used in natural language processing (NLP). Typical NLP assignments include:

Text classification is the process of giving a text document a category or label, such as sentiment analysis or spam identification.

Identifying and extracting named entities from a text, such as individuals, organisations, and locations, is known as named entity recognition.

Provide a concise summary of a lengthy text's content using the process of text summarization.

Text is translated from one language to another using a computer programme.

Answering queries in natural language is a task that chatbots and other automated systems perform.

Voice assistants, chatbots, sentiment analysis, and machine translation are just a few of the numerous applications that use NLP. To handle and analyse massive amounts of text data, NLP is also employed in sectors including healthcare, finance, and education.

However, there are a number of obstacles in the way of NLP, including ambiguity, sarcasm, and context sensitivity, which can make it challenging for computers to comprehend and interpret human language correctly.

Moreover, idioms and slang, which can range greatly between different communities and locations, must be taken into account by NLP in order to properly understand the cultural and social components of language.