The potential future advancements in generative AI technology are both broad and impactful, encompassing improvements in capabilities, accessibility, and ethical considerations. Here are several key areas where significant advancements may occur:

1. Enhanced Creativity and Complexity:

Future generative AI models could offer more sophisticated and nuanced content generation, producing outputs that are indistinguishable from human-created content. This includes advancements in writing, music, art, and design.

2. Improved Understanding and Context Awareness:

AI systems are likely to develop better contextual understanding, allowing them to generate more relevant and accurate responses or content. This could involve a deeper grasp of subtleties, sarcasm, cultural nuances, and emotional undertones.

3. Multimodal Capabilities:

The integration of multimodal functionalities, where AI can process and generate content across different forms of media (text, image, video, audio) seamlessly, is expected to expand. For instance, an AI could take a story written in text and convert it into a fully animated video.

4. Customization and Personalization:

Generative AI could become highly personalized, adapting to individual user preferences, styles, and needs in real-time. This might include customizing educational content to a student’s learning style or adapting marketing content to align with audience demographics.

5. Interactivity and Real-time Feedback:

AI might become more interactive, providing real-time generation and modification of content based on user feedback. This could be particularly transformative in fields like video gaming, virtual reality, and interactive learning environments.

6. Safety and Ethical Advances:

As concerns about AI ethics and safety grow, future developments are likely to include more robust mechanisms to prevent the generation of harmful, biased, or unethical content. This includes better content filtering systems, fairness audits, and transparency in AI decision-making processes.

7. Energy Efficiency and Scalability:

New techniques could make AI models more energy-efficient and easier to scale, reducing the environmental impact and making powerful AI tools accessible to a broader range of users and developers.

8. Regulatory and Standard Development:

The development of international standards and regulations for AI usage could lead to more consistent and safe deployment of AI technologies across different sectors and countries.

9. Generalization and Few-shot Learning:

Advancements in few-shot learning, where models require significantly less data to learn new tasks, could lead to more robust generalization capabilities. This means AIs could perform well in a wider range of applications with minimal specialized training.

10. Integration into Daily Life and Industry:

Generative AI could become more deeply integrated into everyday tools and professional software, enhancing productivity and creativity in various industries such as healthcare, education, entertainment, and more.

These advancements are contingent on continuous research, ethical oversight, and thoughtful implementation to ensure that the benefits of generative AI are realized while minimizing potential risks and harms.

The post What are the potential future advancements in generative AI technology? appeared first on Artificial Intelligence.

Generative Adversarial Networks (GANs) are a fascinating class of machine learning models used to generate new data that resembles the training data. They were first introduced by Ian Goodfellow and his colleagues in 2014. GANs are particularly popular in the field of image generation but have applications in other areas as well.

Here’s how GANs generally work:

1. Architecture

A GAN consists of two main parts:

• Generator: This component generates new data instances.
• Discriminator: This component evaluates them. It tries to distinguish between real data (from the training dataset) and fake data (created by the generator).

2. Training Process

The training of a GAN involves the following steps:

• The generator takes a random noise vector (random input) and transforms it into a data instance.
• The discriminator receives either a generated data instance or a real data instance and must determine if it is real or fake.

3. Adversarial Relationship

The core idea behind GANs is based on a game-theoretical scenario where the generator and the discriminator are in a constant battle. The generator aims to produce data that is indistinguishable from genuine data, tricking the discriminator. The discriminator, on the other hand, learns to become better at distinguishing fake data from real data. This adversarial process leads to improvements in both models:

• Generator’s Goal: Fool the discriminator by generating realistic data.
• Discriminator’s Goal: Accurately distinguish between real and generated data.

4. Loss Functions

Each component has its loss function that needs to be optimized:

• Discriminator Loss: This aims to correctly classify real data as real and generated data as fake.
• Generator Loss: This encourages the generator to produce data that the discriminator will classify as real.

5. Backpropagation and Optimization

Both the generator and the discriminator are typically neural networks, and they are trained using backpropagation. They are trained simultaneously with the discriminator adjusting its weights to get better at telling real from fake, and the generator adjusting its weights to generate increasingly realistic data.

6. Convergence

The training process is ideally stopped when the generator produces data that the discriminator judges as real about half the time, meaning the discriminator is essentially guessing, unable to distinguish real from fake effectively.

Example Use Cases:

• Image Generation: GANs can generate realistic images that look like they could belong to the training set.
• Super Resolution: Enhancing the resolution of images.
• Style Transfer: Applying the style of one image to the content of another.
• Data Augmentation: Creating new training data for machine learning models.

GANs have been revolutionary due to their ability to generate high-quality, realistic outputs, making them a powerful tool in the AI toolkit. However, training GANs can be challenging due to issues like mode collapse (where the generator produces a limited diversity of samples) and non-convergence.

The post How do generative models like GANs (Generative Adversarial Networks) work? appeared first on Artificial Intelligence.

R is a powerful programming language and software environment for statistical computing and graphics. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now maintained by the R Development Core Team. R provides a wide variety of statistical and graphical techniques, and is widely used by statisticians and data scientists for data analysis, data visualization, and predictive modeling.

Top Use Cases of R

• Data Analysis: R is commonly used for data analysis tasks such as data cleaning, data manipulation, and data visualization. Its extensive library of statistical functions and packages make it a popular choice for analyzing and interpreting data.

• Machine Learning: R has a rich ecosystem of packages for machine learning, including popular libraries like caret, random Forest, and glmnet. These packages provide implementations of various machine learning algorithms, making it easy to build predictive models and perform tasks like classification, regression, and clustering.

• Statistical Modeling: R is widely used for statistical modeling, including linear regression, logistic regression, time series analysis, and more. Its built-in functions and packages make it easy to fit models to data and perform statistical inference.

• Data Visualization: R provides powerful tools for creating visualizations, including bar plots, scatter plots, line plots, and more. Its ggplot2 package is particularly popular for creating publication-quality graphics.

Features of R

• Open Source: R is an open-source language, which means that it is freely available and can be modified and distributed by anyone. This has led to a large and active community of developers who contribute to the language and create new packages.
• Extensive Library: R has a vast library of packages and functions for various purposes, such as data manipulation, statistical analysis, machine learning, and more. These packages can be easily installed and loaded into R, providing additional functionality and making it easy to perform complex tasks.
• Reproducibility: R promotes reproducible research by providing tools for documenting and sharing code and results. This allows others to easily reproduce and verify your analysis, increasing transparency and trust in the research process.

Workflow of R

The workflow of R typically involves several steps:

1. Data Import: The first step is to import your data into R. This can be done using functions like read.csv() or read.table() for reading data from files, or using packages like dplyr or tidyr for importing data from databases or other sources.
2. Data Cleaning and Manipulation: Once the data is imported, you may need to clean and manipulate it to prepare it for analysis. R provides a wide range of functions and packages for performing tasks like removing missing values, transforming variables, and creating new variables.
3. Data Analysis: After the data is cleaned and prepared, you can perform various statistical analyses using R’s built-in functions or packages. This may involve fitting models, performing hypothesis tests, or calculating summary statistics.
4. Data Visualization: R provides powerful tools for creating visualizations to explore and communicate your data. This can be done using functions like plot() or with packages like ggplot2, which allows for more advanced and customizable graphics.
5. Reporting and Sharing: Finally, you can generate reports or presentations of your analysis using R Markdown or other tools. This allows you to combine code, text, and visualizations in a single document, making it easy to share your work with others.

How R Works & Architecture

R is an interpreted language, which means that code is executed line by line without the need for compilation. When you run an R script or command, the R interpreter reads the code, evaluates it, and produces the desired output.

The architecture of R consists of several components:

• R Console: This is the interface where you interact with R. You can type commands directly into the console and see the results immediately.
• R Scripts: R scripts are files that contain a series of R commands. They can be saved and executed as a batch, making it easy to automate repetitive tasks or perform complex analyses.
• R Packages: R packages are collections of functions, data, and documentation that extend the functionality of R. They can be installed and loaded into R using the install.packages() and library() functions, respectively.
• R Environment: The R environment is where objects like data, functions, and variables are stored during an R session. You can create, modify, and manipulate these objects to perform your analysis.

How to Install and Configure R

To install R, you can visit the official R website (https://www.r-project.org/) and download the appropriate version for your operating system. Once downloaded, you can follow the installation instructions provided on the website.

After installing R, you may also want to install an integrated development environment (IDE) for a better coding experience. Some popular IDEs for R include RStudio, Visual Studio Code, and Jupyter Notebook.

Once you have R and an IDE installed, you can start writing and executing R code.

Step by Step Tutorials for R – Hello World Program

To get started with R, you can follow these step-by-step tutorials to write a simple “Hello World” program:

1. Open your preferred IDE and create a new R script.
2. In the script, type the following code:

# Print "Hello, World!" to the console
print("Hello, World!")

3. Save the script with a .R file extension, such as hello_world.R.
4. Run the script by clicking the “Run” or “Execute” button in your IDE. The output “Hello, World!” should be displayed in the console.

Congratulations! You have successfully written and executed your first R program.

Remember, learning R is a journey, and there is always more to explore and discover. Have fun exploring the world of statistical computing and data analysis with R!

The post What is R and How R Works & Architecture ? appeared first on Artificial Intelligence.

IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post Top 10 high paying IT certifications in the world in 2022 appeared first on Artificial Intelligence.

I am going to tell you the Difference between AIOps and Artificial intelligence (AI) on the basis of their Definition and how they work and what are the components of them. So let’s start.

What is AIOps?

AIOps stands for artificial intelligence for operations team promises to improve the events correlation, speed root cause analysis, and drive automation.

In other words, the ability to drive the automated process by using automation, whether the process is around incident management, remediation.

Let’s take an example- If you are getting so much alerts noise at the time of monitoring you could either ignore them or put lots of effort to solve that, but the AIOps is driven to drive the resolution to that issue with the help of automation, that means not much effort, work done in less time or say in a smarter way.

 AIOps is all about delivering a better customer experience, that’s why much more customers are adopting AI machine learning. With AIOps you can predict and fix most common IT problems before they impact customer experience and free up the IT teams to innovate.

AIOps leverages big data and collects data from different platforms like ops tools and devices to automatically spot and react to the issue in real-time.

The goal is to increase the speed of delivery of the services to improve the efficiency of IT services and in other words to provide a superior user experience.

It’s clear that AIOps break down the siloed operations and enable the generation of insights that can be communicated to stakeholders and it can help in driving automation and collaboration.

Need of AIOps

AIOPs offer clarity to performance data and dependencies throughout all environments, examine the data to take out the important events which are associated with outages or slow down, and automatically alert members to problems, the root causes, and recommended solutions.

Components of AIOps

1) Extensive and diverse IT Data

2) Aggregated big data platform

3) Machine learning

4) Observe

5) Engage

6) ACT

7) Automation

AIOPs bridges three different IT disciplines –

1) Service management

2) Performace management

3) Automation

What is artificial intelligence (AI)?

AI refers to the automation of tasks by feeding data or by taking the help of machine learning to learn new things by getting data from the internet, locally saved data, or from the instruction that has been installed to work like as instructed.

Machine learning is a kind of brain to AI that helps it to think or decide like a human brain but not completely because humans are creative. We can do anything by using our brains that can’t do machines.

AI had been thought of in 1955 and introduced in 1956 in a seminar by John McCarthy, that’s why we call him the father of AI as well.

It is said AI is our future but it’s not true AI is present as well as future.

Some examples that we are using currently are Alexa, Siri on iPhone, Google Assistant, Tesla car, Cortana on windows. All these are some examples of present AI that we are using and Google maps are also one of them and many more.

Artificial Intelligence (AI) in the field of computer science.

Stages of AI

1. General AI
2. Narrow AI
3. Artificial super intelligence

General AI means lots of works and activities can be done. Humans can dance, eat and do many more activities, in the same way, AI can also do multiple tasks. But unfortunately, we don’t have that much evolved AI right now. We can make it do any particular task that we want to make it done. In other words, we have only narrow AI’s right now.

Narrow AI means it is focused on any particular task that is assigned to it such as an application is designed to take a photo but a human can do anything with that photo. So this is the difference between AI and humans. (General and narrow AI).

Artificial super-intelligence means the machine which will surpass humans in thinking, behaving, etc, and can do much more which we can’t imagine. But we don’t have such kind of super-intelligence right now but. It is like hypothetical robots that have been shown in movies.

Advantages and Disadvantages of AI

Advantages

• Workloads can be decreased.
• Time can be saved.
• Errors can be reduced
• Automation
• To remember things easily
• We can use robots instead of humans as cops
• Designing and construction without hard work
• Can work without breaks
• Collection of data and many more.
• Solve problems and perform complicated tasks

Disadvantages

• Humans will become lazy.
• If somehow anyone can succeed in manipulating the AI then it can be dangerous to human’s kinds.
• Machines can keep an eye on us all the time by using cameras and many more, which means no privacy.
• It can give unemployment to people
• High cost of maintenance
• Can’t sense like humans
• Lack of creativity

Types of AI

• Reactive machine AI
• Limited memory AI
• Theory of mind AI
• Self-aware AI

Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post Difference between AIOps and Artificial intelligence (AI) appeared first on Artificial Intelligence.

A case of machine learning in healthcare shows that algorithms and liquid metal could lead to the development of prosthetic hands having the ability to feel objects.

By using machine learning in healthcare, researchers from Florida Atlantic University’s College of Engineering and Computer Science and collaborators are creating prosthetic hands that can “feel” by incorporating stretchable tactile sensors using liquid metal on the fingertips.

“Encapsulated within silicone-based elastomers, this technology provides key advantages over traditional sensors, including high conductivity, compliance, flexibility and stretchability. This hierarchical multi-finger tactile sensation integration could provide a higher level of intelligence for artificial hands,” the press release stated.

Each fingertip has more than 3,000 touch receptors that respond to pressure. The sensation felt in the fingertips is what humans rely on to manipulate objects. Individuals with upper limb amputations face a unique challenge without that pressured sense of touch.

Dig Deeper

• EHR Data Boosts Machine Learning Algorithms for Chronic Disease
• Machine Learning Algorithm Brings Predictive Analytics to Cell Study
• Machine Learning Uncovers Link Between Diet, Chronic Disease Risk

Although there are several high-tech, dexterous prosthetics available, the ability to have a sense of touch is still lacking. The absence of sensory feedback often results in objects being dropped or crushed by prosthetic hands.

For the study, researchers used individual fingertips on the prosthetic hand to differentiate between various speeds of a sliding motion along four different textured surfaces. The textures had one variable parameter: the distance between the ridges. In order to detect the textures and speeds, researchers trained four machine learning algorithms.

There were 20 trials conducted on each of the 10 surfaces. These were used to see if the machine learning algorithms could distinguish between the ten different complex surfaces made up of randomly generated permutations of four different textures.

The result revealed that the tactile information from the liquid metal sensors were able to differentiate between the multi-textured surfaces, demonstrating a new form of hierarchical intelligence. Additionally, the machine learning algorithms were able to distinguish between all the speeds with high accuracy.

“Significant research has been done on tactile sensors for artificial hands, but there is still a need for advances in lightweight, low-cost, robust multimodal tactile sensors,” Erik Engeberg, PhD, senior author, an associate professor in the Department of Ocean and Mechanical Engineering said in a press release.

“The tactile information from all the individual fingertips in our study provided the foundation for a higher hand-level of perception enabling the distinction between ten complex, multi-textured surfaces that would not have been possible using purely local information from an individual fingertip,” Engeberg continued.

“We believe that these tactile details could be useful in the future to afford a more realistic experience for prosthetic hand users through an advanced haptic display, which could enrich the amputee-prosthesis interface and prevent amputees from abandoning their prosthetic hand.”

The team of researchers compared the four different machine learning algorithms for their successful classification abilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). 

The time-frequency features of the liquid metal sensors were removed to train and test the machine learning algorithms. The NN seemed to perform the best with 99.2 percent accuracy with speed and texture detection.

“The loss of an upper limb can be a daunting challenge for an individual who is trying to seamlessly engage in regular activities,” Stella Batalama, PhD, dean, College of Engineering and Computer Science said in a press release.

“Although advances in prosthetic limbs have been beneficial and allow amputees to better perform their daily duties, they do not provide them with sensory information such as touch. They also don’t enable them to control the prosthetic limb naturally with their minds,” Batalama continued.

“With this latest technology from our research team, we are one step closer to providing people all over the world with a more natural prosthetic device that can ‘feel’ and respond to its environment.”

Researchers believe that this artificial intelligence technology can improve the control of prosthetic hands and the lives of those who need them.

The post Machine Learning in Healthcare Helps Prosthetic Hands Feel appeared first on Artificial Intelligence.

The Defense Threat Reduction Agency wants information on companies, universities and other organizations working on artificial intelligence, machine learning and data science technologies that could help counter weapons of mass destruction and other emerging threats.

DTRA intends to use AI, ML and data science tools to improve decision-making and situational awareness for countering WMD and supporting deterrence missions, automate the identification of CWMD and deterrence objects and activities and facilitate information delivery to meet warfighter operational needs, according to a request for information posted Friday.

The technology interest areas outlined in the RFI include AI-enhanced modeling and simulation, natural language processing, computer vision, high performance computing and multiagent systems.

The agency is seeking information on data analytics, cloud platforms for data transfer and harmonization, data storage and accessibility, automated data labeling and other data-related capabilities.

DTRA has asked interested stakeholders to share information on other specific interest areas, including the detection of spectral emissions, sensor data integration, human/computer interface and extraction of actionable information from noisy data.

The post DTRA Seeks Info on AI, Machine Learning, Data Science Tech Capabilities appeared first on Artificial Intelligence.

A study reveals the use of machine learning algorithms leveraging EHR data could assist in a patient’s lung cancer prognosis.

By using machine learning algorithms, researchers examined if creating a large-scale electronic health record (EHR) data-based lung cancer cohort could be effective in studying a patient’s prognosis and estimating survival. The cohort study was recently published in JAMA.

Across the world, lung cancer is one of the most diagnosed cancers and is the leading cause of cancer-related deaths behind skin cancer. In the United States, the current five-year survival rate is around 20.6 percent. However, patients with lung cancer will have different outcomes based on a variety of clinical factors.

“A large cohort with adequate clinical information is necessary to identify stable and reliable prognostic variables and the factors associated with improved survival outcomes,” the authors wrote in the study.

Dig Deeper

• Machine Learning Algorithm Brings Predictive Analytics to Cell Study
• Machine Learning Model Helps Predict Clinical Lab Test Results
• Deep Learning Aids Prediction of Lung Cancer Immunotherapy Response

As the accessibility of EHR data continues to grow, researchers are given a timely and low-cost alternative to the traditional cohort study. With EHR data being coding in various ways, implementing machine learning algorithms was an important step for researchers to compare information accurately.

“Our primary goal was to build a large and reliable lung cancer EHR cohort that could be used for studying lung cancer progression with a set of generalizable approaches. To this end, we combined structured data and unstructured data to identify patients with lung cancer and extract clinical variables. We evaluated the completeness and accuracy of the extracted data,” the authors wrote.

“To further illustrate the application of EHR cohort data, we developed and validated a prognostic model to predict 1-year to 5-year overall survival (OS) among individuals with non–small cell lung cancer (NSCLC).,” the study authors continued.

In the cohort study, patients with lung cancer were identified from 76,643 individuals with at least one lung cancer diagnostic coded deposited in an EHR in Mass General Brigham health care system from July 1988 to October 2018.

A machine learning algorithm identified patients and extracted clinical information from structured and unstructured data by using natural language processing tools. Researchers then examined the data’s completeness and accuracy by comparing the Boston Lung Cancer study to the standard EHR review results.

Additionally, a prognostic model for non-small cell lung cancer (NSCLC) overall survival was created for clinical application.

Of the 76,642 patients with at least one lung cancer diagnostic code, 42,069 patients were identified to have lung cancer. The AI tool produced a positive predictive value of 94.4 percent. The study cohort was made up of 35,375 patients after removing those with a history of lung cancer and less than 14 days of follow-up after the initial diagnosis.

“We assembled a large lung cancer cohort from EHRs using a phenotyping algorithm and extraction strategies combining structured and unstructured data. Our findings suggest that a prognostic model based on EHR cohort may be used conveniently to facilitate prediction of NSCLC survival,” the authors concluded.

The post EHR Data Boosts Machine Learning Algorithms for Chronic Disease appeared first on Artificial Intelligence.

As machine learning has advanced throughout time, a multitude of sectors has utilized it to innovate and streamline corporate processes. AI and machine learning have been used to improve client experiences in a variety of industries, including healthcare, commerce, industrial, defense, and academia. Machine learning has revolutionized the way tiny data is processed. It has sped up the processing to seconds. 

Professor Gabriel Gomila’s microscopic bioelectrical classification group at Catalonia’s Institute for Bioengineering has been studying a cell type using a sort of microscope called scanning dielectric force volume microscopy. They created this technique in recent years to construct maps of the dielectric constant, an electrical physical parameter. Researchers used this method to speed up the processing of nanoscale information. In this article, let us explore more on how machine learning is used to reduce data time processing.

What can this study on machine learning provide?

When Hans and Zacharias Janssen — a Dutch father and son — built the world’s first microscope in 1590, our interest in what happens at the tiniest levels has resulted in the development of extremely powerful equipment. In 2021, researchers can create precise maps of a variety of physical and chemical characteristics using non-optical approaches like scanning force microscopes, besides optical microscopy technologies that allow us to view microscopic particles in higher definition than it’s ever been. Here’s what this study can provide.

• Because each of the macromolecules that make up cells—lipids, proteins, and nucleic acids—has distinctive dielectric properties, a mapping of this feature is effectively a representation of cell constitution.
• They created an approach that outperforms the existing conventional optical approach, which entails the use of a fluorescent dye that can disturb the cell investigation.
• Their method eliminates the need for any highly destabilizing external agents.
• However, the implementation of this technique necessitates a lengthy post-processing step to translate the observed data points into physical magnitudes, which takes a long time in eukaryotic cells.
• A workstation computer can take months to process a single image. That is because it uses locally recreated geometrical prototypes and calculates the dielectric constant as pixel by pixel.

The researchers used a novel methodology to speed up the microscopic processing of data in this new work, which was a recent issue of the journal Small Methods. Rather than using traditional computational approaches, they applied machine learning models this time. The outcome was stunning after being instructed; the ML algorithm could generate a composition map of the cells with dielectric biochemical within seconds. No foreign compounds were used in the experiment, which is a long-sought objective in cell biology composition characterization. They were able to accomplish these quick results by employing a complex algorithm known as neural networks, which simulate the way human brain neurons function. The key points to be considered are:

• The investigators employed dried-out cells in their concrete evidence work to avoid the tremendous impact of water in dielectric observables owing to its increased dielectric constant.
• They also focused on fixed cells that are in a fluid state. They could accurately map the biomolecules that resulted in eukaryotic cells by comprehensively comparing the dry and liquid versions.
•  Plants, animals, fungi, and other creatures comprise these multi-structured cells. The approach will be used to electrically responsive live cells, such as neurons, where significant electrical impulses happen as its next phase in this project. 

Biomedical Application

The researchers confirmed their observations by comparing them to well-known aspects of cell architecture, like the lipid-rich structure of the cell membrane and the extensive amount of nucleic acids found in the nucleus. They’ve made it possible to analyze enormous numbers of cells in record time thanks to this effort. This research study provides biologists with a powerful tool for doing fundamental research and also prospective practical diagnostics. 

Variations in the cell’s dielectric properties are being investigated as potential indicators for disorders like cancer and neurological diseases. This is the first experiment to produce a microscopic biological composition model from dielectric measurements of dried eukaryotes, which are notoriously difficult to trace owing to their complicated three-dimensional geometry.

Finally, with such progression in the research and experimentation, it is needless to say we are transforming into the new phases of machine learning, with grace, intelligence, and facts. While the work on this nanoscale dielectric constant has just filled few gaps, the future is more dynamic in the aspects of data processing. What took months is now taking seconds, and that is undeniably a revolution of its own. With such applications in the biomedical industry, who would guess it can turn on a real-time diagnosis of many deadly diseases? 

The post How Machine Learning reduces data time processing appeared first on Artificial Intelligence.

Applications of machine learning and other forms of artificial intelligence have been recognized in robotics and analytics. Now the technology is adding some spice to basic control applications.

Using your noodle to think things through tends to make things go much more smoothly—even if you’re just a high-speed food packaging machine wrapping instant noodles. That’s an important lesson gained from machine learning technology used by systems integrator Tianjin FengYuLingKong of Tianjin, China.

This form of artificial intelligence (AI) allowed the firm’s engineers to develop a multivariable inspection model for one of China’s largest producers of noodles. Relying on this model, the control system for the packaging lines can now deduce whether sachets containing spices and dried vegetables for flavoring were placed correctly on the precooked noodle blocks before each block is individually wrapped.

This ability is an example of how machine learning and other forms of AI are moving beyond applications like robotics and analytics and into control applications.

In Tianjin FengYu’s case, there was no other cost-effective way to check whether an occasional sachet of flavorings might have slipped between two blocks of noodles and been cut open by a cross-cutting tool. Although cutting a sachet generates measurable signals within the machine, other events such as vibration and changes in packaging material, conveyor speed, and cutting tension also affect those signals, making conventional forms of process monitoring unreliable.

For this reason, Tianjin FengYu decided to develop, train, and deploy a mathematical model using TwinCAT Machine Learning from Beckhoff Automation. The integrator’s engineers collected sensor data via EtherCAT terminals and TwinCAT Scope View charting software. Then, the data were correlated into a model using TwinCAT Condition Monitoring, and the model was trained using an open-source framework called Scikit-learn.

After being saved as a description file in a binary format suited for serialization in TwinCAT, the trained model was loaded into a CX5100 series embedded PC, which runs the model in real time. This embedded PC is integrated with the main controller on the packaging line.

The control system can run the model in real time as each packaging line wraps about 500 packages of noodles per minute. “A trained model actually runs fairly quickly,” notes Beckhoff’s Daymon Thompson. “And that’s what’s usually running in the controllers.”

Training the model is a different story, however. Thompson says that training needs a lot of processing power, as much as 30 minutes to a full day, depending on the model and the computer training it. So, the initial training and any subsequent retraining are often done on a server or an offline controller.

Besides in-process inspection, another application for machine learning in controls is the optimization of motion profiles. Consider a conveyor system that carries payloads around corners and coordinates motion with loading and other activities in a demonstration created by Beckhoff using its eXtended Transport System (XTS). “Instead of just running everything around as fast as we can to get in line for the next synchronized event, we want the motion to be optimized to minimize energy consumption and wear and tear on the mechanics,” explains Thompson.

The machine learning algorithm figures out exactly what the motion profile should look like. “Because the motors driving the system need to be coordinated in real time, the motion profile really needs to be built into the machine control,” notes Thompson. “It can’t be done on a server or even an edge device.”

AI benefits closed-loop control 
“Traditionally, PLC programmers would write ladder logic to tune systems with either creative rungs of arithmetic or PID control blocks,” says Kevin McClusky, co-director of sales engineering at Inductive Automation. “Today, closed-loop control with AI allows users to feed data into predictive models that can optimize output based on past performance or cost reduction, allowing far more complex algorithms to be applied to achieve efficiency or productivity goals.”

He reports that the catalogs of several PLC manufacturers now offer AI modules for closed-loop control. Although not every application needs the technology, these modules are another set of tools in the toolbox. McClusky compares them to a simple PID block in ladder logic. “It’s not needed in a lot of applications, but it sure is handy in applications that can benefit from it,” he says.

“Model outputs can be integrated into the control scheme to extend the capabilities of classical control methods,” adds Jennifer Mansfield, marketing manager—analytics at Rockwell Automation. “Challenging problems, like enabling predictive maintenance or dynamic control, are better addressed with machine learning than classical control.”

Illustrating her point is the model predictive control (MPC) that EnWin Utilities Ltd. implemented to mitigate pressure spikes in the water distribution system in Windsor, Ontario. These spikes had been contributing to an increasing number of watermain breaks in the aging system.

The old control scheme had depended upon PID logic that maintained a flow setpoint based upon outlet header pressure. Pressure would vary whenever operators would start and stop pumps at the two pumping stations and an auxiliary booster station to adjust flows to compensate for fluctuating demand.

To even out pressure, EnWin chose an MPC-based system that could handle more variables than just flow and outlet header pressure. Working with engineers from Rockwell Automation, EnWin began by creating 17 remote pressure stations throughout the water distribution system. The team also installed server-based MPC on its existing supervisory control and data acquisition (SCADA) system. The system now maintains the lowest possible pressure for producing adequate flow as demand changes.

To optimize pressure and flow control further, the main campus uses a new ControlLogix controller with onboard MPC. “We knew we could optimize the system by incorporating pump start-stop functionality and flow control valves,” explains Quin Dennis, an application engineer at Rockwell Automation. “But given the existing interval speed, [server-based] MPC would not be able to make system adjustments quickly enough to mitigate the rapid pressure spikes from pump starts or stops.” Onboard MPC, however, reduced the 15 to 16 second interval speed down to 0.5 to 1 second.

The system is now responsive enough to regulate the speed of the pumps and adjust control valves to offset any pressure spikes. By replacing PID logic with MPC at the controller level, as well as at the server level, EnWin was able to reduce watermain breaks by 21%. It also reduced average pressure by 2.8 psi and standard deviation by 29%, saving the company $125,000 in annual energy and leakage costs.

Predictive applications
Another benefit of AI is that it can help users peer deeper into their processes than controllers would otherwise permit. This is especially true in applications that require processing large amounts of data.

“AI is now being implemented on the edge in situations where large volumes of data must be analyzed quickly before being sent to the cloud,” observes Joe Berti, vice president of AI applications atIBM Corp. “As a result, smart technology is broadening engineers and technicians’ understanding of their assets’ health by capturing and interpreting more information faster than any human could.”

Consequently, Berti thinks that the biggest contribution AI and machine learning are making to controls technology is the ability to streamline detection and resolution of developing problems before they have a chance to escalate. “In the past, an asset might have been inspected on an annual basis,” he says. “Now, IoT sensors and enterprise asset management systems can detect patterns in asset data and then translate those findings into potential problems.”

An example of this kind of application can be seen in the use of AI to discover oil degradation on a food packaging line installed by Novate Solutions Inc., an engineering and technology services firm based in West Sacramento, Calif. The clues to the problem came from IBM’s cloud-based AI technology and Maximo Monitor software, which Novate uses to provide a process monitoring service. The AI noticed that the average torque of a servomotor had been increasing over time, so the Novate solution flagged the equipment for inspection.

Upon being alerted by Novate engineers, the production crew at the food producer checked the packaging equipment and found that the oil had not been changed as the maintenance log had suggested. The oil had completely degraded, causing the motor to work increasingly harder over time.

Trained for decision-making
Another application for AI in basic control is the automation of decision-making in continuous processes. “Here, an AI system controls a part of a facility or operation, sending signals to do basic control of different pieces of equipment,” says Inductive Automation’s McClusky.

He points to the way a type of machine learning known as reinforcement learning is being deployed by Andritz Automation, a worldwide systems integrator headquartered in Graz, Austria. In reinforcement learning, models are trained to make a sequence of decisions by means of a trial-and-error method that strives to maximize a cumulative score of rewards and penalties.

In what may have been the first implementation of this AI technology in continuous industrial processes, Andritz engineers in Canada and Germany collaborated on developing prototype software. They then implemented the prototype in a pilot program at Newmont GoldCorp., a Vancouver-based goldmining company.

This prototype uses the integrator’s process simulation software as the training ground for machine-learning algorithms. The AI engine learns by interacting with several simulations as they run. A user can set up batches of training scenarios, such as particular plant malfunctions that the AI engine needs to know. After the training exercises are performed, the algorithms are stored and used for automatic plant control.

A key technology for developing and implementing this AI engine was Inductive Automation’s Ignition development environment for SCADA. Ignition provided a bridge between the AI engine and either the integrator’s process simulation software or the real plant, using scripted HTTP calls on one side and OPC on the other. 

Ignition’s sequential function charts control the dispatch of training scenarios. All scenario configuration and training results are stored in a SQL database. During the training process, the two teams in Canada and Germany were able to work on the project at the same time because the training environment was deployed on a small virtual network on a Microsoft Azure cloud server in Europe. Each team could run Vision clients simultaneously and access the database gateway and simulation machines.

The post When Artificial Intelligence Comes to Control appeared first on Artificial Intelligence.