What is Terraform and Its Use Cases?

In today’s era of cloud computing and infrastructure as code (IaC), managing and provisioning infrastructure efficiently is critical for organizations. Terraform, developed by HashiCorp, is a popular open-source IaC tool that allows IT teams to define, provision, and manage infrastructure across multiple platforms using a declarative configuration language. Terraform’s ability to automate infrastructure management has made it a cornerstone in DevOps practices.

Terraform simplifies complex workflows by enabling consistent, repeatable, and automated deployments. Its modular structure and robust integrations make it an indispensable tool for managing modern IT environments.

What is Terraform?

Terraform is an open-source infrastructure as code (IaC) tool that allows you to define and provision infrastructure resources such as virtual machines, networks, databases, and more, using a declarative configuration language called HashiCorp Configuration Language (HCL).

Terraform supports multi-cloud environments, including AWS, Azure, Google Cloud, and on-premises solutions, providing flexibility and scalability for diverse infrastructure needs. With its state management capabilities, Terraform ensures that infrastructure remains consistent with the desired state defined in your configuration files.

Top 10 Use Cases of Terraform

1. Multi-Cloud Deployment
   Deploy and manage resources across multiple cloud providers like AWS, Azure, and Google Cloud from a single configuration.
2. Infrastructure Automation
   Automate the provisioning and management of infrastructure, eliminating manual interventions.
3. CI/CD Pipeline Integration
   Integrate Terraform with CI/CD tools to automate infrastructure provisioning during the deployment process.
4. Environment Management
   Manage multiple environments (development, testing, staging, and production) with consistent configurations.
5. Disaster Recovery
   Quickly recreate infrastructure in case of failures by using Terraform’s configuration files as a blueprint.
6. Network Infrastructure Management
   Configure and manage complex network setups, including VPCs, subnets, and firewalls.
7. Compliance Automation
   Enforce compliance by defining infrastructure configurations and ensuring they adhere to organizational policies.
8. Container Orchestration
   Provision Kubernetes clusters and manage containerized environments seamlessly.
9. Resource Scaling
   Dynamically scale resources based on application demand using Terraform’s capabilities.
10. Immutable Infrastructure
    Replace infrastructure components rather than updating them, ensuring consistency and reducing downtime.

What Are the Features of Terraform?

1. Declarative Configuration
   Define the desired state of your infrastructure in code, and Terraform ensures it is achieved.
2. Multi-Provider Support
   Manage infrastructure across cloud providers, on-premises systems, and SaaS platforms.
3. Infrastructure State Management
   Maintains the state of your infrastructure, enabling Terraform to determine the necessary changes to achieve the desired state.
4. Resource Graph
   Visualize dependencies between resources, allowing Terraform to provision infrastructure in the correct order.
5. Modularity
   Use reusable modules to simplify configuration management and promote consistency across environments.
6. Version Control Integration
   Store and manage Terraform configurations in version control systems for collaboration and tracking changes.
7. Drift Detection
   Identify and rectify discrepancies between the desired state and the actual state of infrastructure.
8. Plan and Apply Workflow
   Preview changes before applying them, ensuring controlled and predictable updates to your infrastructure.
9. Community and Ecosystem
   Access a wide range of pre-built modules and plugins from the Terraform Registry.
10. Scalability
    Handle large-scale infrastructure with ease, making it suitable for enterprises.

How Terraform Works and Architecture

How It Works:
Terraform uses a declarative approach where you define your infrastructure’s desired state in configuration files. Terraform reads these files, compares them with the current infrastructure state, and applies the necessary changes to achieve the desired state.

Architecture Overview:

1. Configuration Files:
   Written in HCL, these files define resources, providers, and modules.
2. Terraform CLI:
   Command-line interface to execute commands such as terraform plan and terraform apply.
3. State File:
   Tracks the current state of your infrastructure to identify changes needed to align with the desired state.
4. Providers:
   Integrations that enable Terraform to interact with various platforms and services, such as AWS, Azure, and Kubernetes.
5. Modules:
   Reusable configurations that simplify complex infrastructure setups.

How to Install Terraform

Steps to Install Terraform on Linux:

1. Download Terraform:
Visit the Terraform website and download the appropriate package.

wget https://releases.hashicorp.com/terraform/<version>/terraform_<version>_linux_amd64.zip

2.Unzip the Package:

unzip terraform_<version>_linux_amd64.zip

3. Move to PATH:

sudo mv terraform /usr/local/bin/

4. Verify Installation:

terraform --version

Steps for macOS or Windows:
Follow similar steps using package managers like Homebrew (macOS) or Chocolatey (Windows) for easier installation.

Basic Tutorials of Terraform: Getting Started

1. Create a Configuration File
Define your first resource in a .tf file:

provider "aws" {
  region = "us-east-1"
}

resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
}

2. Initialize Terraform:
Run the following command to initialize your working directory:

terraform init

3. Plan Your Changes:
Preview the changes Terraform will make to your infrastructure:

terraform plan

4. Apply the Configuration:
Provision of the defined infrastructure:

terraform apply

5. Destroy the Infrastructure:
Tear down the resources created by Terraform:

terraform destroy

The post What is Terraform and use cases of Terraform? 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.

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.

Digital giants dominate the cloud and ecommerce markets, and part of the reason for their dominance is artificial intelligence and advanced analytics. The good news is that mainstream enterprises can learn from their experiences and employ cutting-edge technologies. 

That’s the word from R. “Ray” Wang who provides a roadmap for AI success in his latest book, Everybody Wants to Rule the World. Wang calls the capability needed to move forward “AI Smart Services” that help automate precision decisions. “To fine-tune precision decisions at scale-that is, to develop decision velocity, [data-driven enterprises] must automate the process of turning signal intelligence into a decision or action. And the way to do this is by creating AI smart services-automated processes powered by AI.” 

The catch is, of course, “AI smart services are not easy to master,” Wang cautions. “They require more than just great algorithms.” He outlines six requirements for advancing in the AI era:

Computing power. To develop AI smart services, data-driven enterprises “must have access to or own cheap computing power,” Wang says. “The ultimate metric for AI rests in pricing not in terms of computing power, but in terms of potential cost per kilowatt hour. The cheapest rate of computing power may determine the cost structure for AI smart services. The most efficient code for finding signal intelligence will provide a cost advantage for each decision made.”

Time. Time waits for no one, Wang says. “The ability to compress time, or take tasks that would normally take weeks and complete them in minutes, provides [data-driven enterprises] an inherent advantage over their competitors.”  However, “AI smart services need more time to identify new patterns. That’s why early adopters who train their AI smart services to process the massive petabytes of data coming in to them gain an advantage. The earlier and the quicker the AI smart services learns, the more precision they put back into their algorithms.” 

Math talent. Algorithms are only as good as the math talent behind it, Wang says. “Success requires hiring digital artisans — those who can balance right brain and left brain expertise. Digital giants typically have armies of data scientists and a brain trust on hand to fine tune AI smart services for their data-driven enterprises.”

Vertical specific expertise. To make precision decisions, AI smart services “must understand nuances of the various verticals in which they operate — such as size of company, industries, and cultural regions.”

Natural user interfaces and user experiences. Data-driven enterprises must develop AI smart services “that engage users in a variety of human computer interfaces that mimic human interaction in terms of their sensory, visualization, voice, and gesture
capabilities. The interfaces might range from chat bots to virtual assistants, and from augmented reality to brain wave mind readers and computer vision.” 

Contextually relevant recommendations. “Once users are confident about how the system arrives at a recommendation, these AI driven smart services start automating decisions-augmenting humanity, accelerating decision making, and ultimately providing filters that deliver situational awareness (the ability to perceive one’s surroundings, events in a timeline, and the potential future state.”

The post Six essential artificial intelligence capabilities leveraged by the digital giants appeared first on Artificial Intelligence.

Analytics Insight elaborates all essential information you need to know about Artificial Intelligence Engineering

Artificial Intelligence is the hottest disruptive technology in the tech-driven market around the world in the 21^st century.  It is making our lives more productive and smart by integrating machine learning algorithms into different products and services. The global market size of Artificial Intelligence is expected to reach US$93.53 billion in 2021. Multiple industries have realized that the combination of AI machines and human employees can boost productivity and gain massive revenue in the competitive market. Even the governments have started allocating millions of dollars into AI research and Development while the educational institutes are offering specialized degrees in Artificial Intelligence.

The most demanding job opportunity in this vast field is Artificial Intelligence Engineer. Yes, you must have known about the five traditional engineering courses in the education sector. But Artificial Intelligence Engineering is thriving in the market among tech-savvy students. They have realized that Artificial Intelligence is the future of the world and they can pursue AI Engineering to earn lucrative salaries from eminent organizations.

What is Artificial Intelligence Engineering?

Artificial Intelligence Engineering is one of the emergent engineering disciplines solely focused on creating and developing smart tools, machines, and systems to improve the standard of living of the society. AI Engineering covers a wide array of computing power and massive datasets with the integration of machine learning algorithms. This course helps businesses in smart decision-making processes to meet the needs of customers and enhance customer engagement. An engineering background is essential to create, manage and analyze AI functionalities efficiently. Artificial Intelligence Engineering provides a comprehensive framework and tools to design machine learning algorithms in a dynamic environment across the enterprise-to-edge spectrum. There are three pillars of Artificial Intelligence Engineering— Human-centric AI, Scalable AI, and Robust AI.

What are the roles and responsibilities of AI Engineers?

AI Engineers are required for developing, programming, and training the machine learning algorithms to make Artificial Intelligence models function like human brains and bodies. They do not need to write professional code with multiple programming languages but they have to locate enormous volumes of structured and unstructured real-time data from multiple sources. AI Engineering helps to create and manage the Artificial Intelligence development process and infrastructure of smart products and services. Explanable AI makes them explain about whole functionalities of AI models to partners, teams as well as stakeholders.

There is a high demand for AI Engineers from multiple industries and organizations such as retail, manufacturing, healthcare, finance, and many more. The average salary of an AI Engineer is around US$100,000 per year depending on the company. Numerous companies are recruiting AI Engineers with lucrative salaries such as Google, NVIDIA, Wipro, Concentrix, Jio, IBM, TCS, Cognizant, and many more.

Which skills are essential for qualifications?

• Bachelor’s or Master’s degree in Computer Science, Engineering, IT, and other relevant disciplines
• Programming skills in different languages like Python, Java, C++, and R
• Sufficient knowledge of linear algebra, probability, and statistics
• Basic experience in different tools such as Apache Spark, Hadoop, MongoDB, etc.
• Deep understanding of types of neural networks with frameworks related to it like PyTorch, TensorFlow, and many more
• Excellent problem-solving and communication skills

What are the top online certified courses for AI Engineers?

• Executive PG Programme in ML and AI from Udemy in association with IIIT Bangalore
• PG Programme in AI and Machine Learning from Simplilearn in partnership with Purdue and collaboration with IBM
• IBM AI Engineering Professional Certificate from Coursera
• Master’s in Artificial Intelligence from IntelliPaat

That being said, there are multiple educational institutes and professional websites that offer comprehensive details on Artificial Intelligence. This knowledge can drive aspiring AI Engineers towards success in professional career paths. One has to keep in mind that there is a plethora of opportunities in Artificial Intelligence Engineering. One has to keep an eye on the most suitable course or job opportunities according to the understanding and preference.

The post ALL YOU NEED TO KNOW ABOUT ARTIFICIAL INTELLIGENCE ENGINEERING appeared first on Artificial Intelligence.

ABB has signed up to a pilot study with ST Telemedia Global Data Centres (STT GDC) to explore how artificial intelligence (AI), machine learning (ML) and advanced analytics can optimize energy use and reduce a facility’s carbon footprint.

Singapore-headquartered STT GDC, which is one of the fastest growing global data center operators, is leveraging the digital transformation expertise of technology leader ABB as it bids to become net carbon-neutral by 2030.

ABB is conducting the pilot in two phases, beginning with initial data exploration, modelling and validation, studying historical data to establish how digital solutions would impact existing operations and energy use. Once proven, it will be followed by AI control logic testing in a live data center environment. STT GDC aims to achieve at least 10 percent in energy savings from its cooling systems, which is the largest consumption of electrical power in a data center after IT equipment.

“Our group’s AI roadmap will take a big leap forward with this pilot program. The vast amounts of data that can be captured and harnessed in a live data center environment makes for a strong base for AI applications, which can also be applied to other business processes including capacity planning, risk mitigation and predictive maintenance,” said Daniel Pointon, group chief technology officer, ST Telemedia Global Data Centres. “This, and other initiatives around alternative energy sources, water efficiency, construction technology and innovative cooling solutions, are being carried out by our research and development team based in Singapore.”

The ABB team is currently developing AI-based optimization models for the entire data center cooling plant, including the upstream chiller and distribution systems. The AI project is also unlocking new opportunities for efficiency improvement at a granular level within the data center. STT GDC will be able to use AI-generated insights, leveraging cutting-edge ABB Ability™ Genix for industrial analytics and AI, to track and analyze data generated by monitoring systems in the data center, and better facilitate dynamic cooling optimization.

“We look forward to supporting the STT GDC team in their efforts to drive digitalization and energy efficiencies,” said Madhav Kalia, global head of Data Center Automation at ABB. “At ABB, we have a strong track record of supporting data center operators with our best-in-class technology solutions. We are committed to exploring the synergies between our offerings with STT GDC as it embarks on an ambitious plan.”

STT GDC is one of the fastest-growing data center providers, with a global platform of data centers in the world’s major business markets. It has more than 130 facilities across Singapore, UK, India, China, Thailand, South Korea and Indonesia.

ABB

ABB (ABBN: SIX Swiss Ex) is a leading global technology company that energizes the transformation of society and industry to achieve a more productive, sustainable future. By connecting software to its electrification, robotics, automation and motion portfolio, ABB pushes the boundaries of technology to drive performance to new levels. With a history of excellence stretching back more than 130 years, ABB’s success is driven by about 105,000 talented employees in over 100 countries.

The post ABB to Deliver Artificial Intelligence Modelling for Data Center Energy Optimization in Singapore 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.

A team of researchers at USC is helping Artificial Intelligence (AI) imagine the unseen, a technique that could also lead to fairer AI, new medicines and increased autonomous vehicle safety.

Imagine an orange cat. Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world.

In other words, as humans, it’s easy to envision an object with different attributes. But, despite advances in deep neural networks that match or surpass human performance in certain tasks, computers still struggle with the very human skill of “imagination.”

Now, a USC research team comprising computer science Professor and Ph.D. students, has developed an AI that uses human-like capabilities to imagine a never-before-seen object with different attributes. The paper, titled “Zero-Shot Synthesis with Group-Supervised Learning,” was published in the 2021 International Conference on Learning Representations on May 7.

“We were inspired by human visual generalization capabilities to try to simulate human imagination in machines,” said Ge, the study’s lead author.

“Humans can separate their learned knowledge by attributes—for instance, shape, pose, position, color—and then recombine them to imagine a new object. Our paper attempts to simulate this process using neural networks.”

AI’s generalization problem

For instance, say you want to create an Artificial Intelligence (AI) system that generates images of cars. Ideally, you would provide the algorithm with a few images of a car, and it would be able to generate many types of cars—from Porsches to Pontiacs to pick-up trucks—in any color, from multiple angles.

This is one of the long-sought goals of Artificial Intelligence (AI): creating models that can extrapolate. This means that, given a few examples, the model should be able to extract the underlying rules and apply them to a vast range of novel examples it hasn’t seen before. But machines are most commonly trained on sample features, pixels for instance, without taking into account the object’s attributes.

The science of imagination

In this new study, the researchers attempt to overcome this limitation using a concept called disentanglement. Disentanglement can be used to generate deepfakes, for instance, by disentangling human face movements and identity. By doing this, said researcher, “people can synthesize new images and videos that substitute the original person’s identity with another person, but keep the original movement.”

Similarly, the new approach takes a group of sample images—rather than one sample at a time as traditional algorithms have done—and mines the similarity between them to achieve something called “controllable disentangled representation learning.”

Then, it recombines this knowledge to achieve “controllable novel image synthesis,” or what you might call imagination. “For instance, take the Transformer movie as an example” said researcher, “It can take the shape of Megatron car, the color and pose of a yellow Bumblebee car, and the background of New York’s Times Square. The result will be a Bumblebee-colored Megatron car driving in Times Square, even if this sample was not witnessed during the training session.”

This is similar to how we as humans extrapolate: when a human sees a color from one object, we can easily apply it to any other object by substituting the original color with the new one. Using their technique, the group generated a new dataset containing 1.56 million images that could help future research in the field.

Understanding the world

While disentanglement is not a new idea, the researchers say their framework can be compatible with nearly any type of data or knowledge. This widens the opportunity for applications. For instance, disentangling race and gender-related knowledge to make fairer AI by removing sensitive attributes from the equation altogether.

In the field of medicine, it could help doctors and biologists discover more useful drugs by disentangling the medicine function from other properties, and then recombining them to synthesize new medicine. Imbuing machines with imagination could also help create safer AI by, for instance, allowing autonomous vehicles to imagine and avoid dangerous scenarios previously unseen during training.

“Deep learning has already demonstrated unsurpassed performance and promise in many domains, but all too often this has happened through shallow mimicry, and without a deeper understanding of the separate attributes that make each object unique,” said Itti. “This new disentanglement approach, for the first time, truly unleashes a new sense of imagination in A.I. systems, bringing them closer to humans’ understanding of the world.”

The post Enabling the ‘Imagination’ of Artificial Intelligence appeared first on Artificial Intelligence.

How artificial intelligence can be used for potential business value.

The use of the artificial intelligence market is expected to grow to $390.9 billion by 2025, and industries within the space show a similar trend that is automotive AI, for example, is expected to grow by 35% year over year, and manufacturing AI will likely increase by $7.22 billion by 2023. However, according to top industry analysts, most (about 80%) of AI projects stall at the pilot phase or proof-of-concept phase, never reaching production. In many cases, this is due to a lack of high-quality data. Ethical and responsible AI continue to be obstacles for many companies, which often lack the resources or internal talent to build unbiased models in a time where AI is making increasingly impactful decisions. Companies also face an uphill battle with scaling and automation.

Believe in Your Data

The main factor of using artificial intelligence confidently in one’s business is to understand the value of data. People need high-quality training data to launch effective models. So, defining the data strategy upfront, including what the data pipeline will look like, will be crucial to success. Many data scientists and machine learning engineers say that about 80% of their time is spent wrangling data.

• The first step of the process is to collect data. One must start with a clear strategy for data collection. They should think about the use cases they are targeting and ensure that their datasets represent each of them. They must have a clear plan for collecting diverse datasets. Implement the data annotation process would require a diverse crowd of human annotators. The more accurate their labels are, the more precise their model’s predictions will ultimately be. Various perspectives will enable the user to cover a broader selection of use and edge cases. At the data collection and annotation phase, it’s critical to have the right plan for tooling in place. Be sure to integrate quality assurance checks into your processes as well. Given that this step takes up most of the time spent on an AI project, it’s especially helpful to work with a data partner in this area.

• The next step of the process is to train data. Feeding the ML machine with the right data is a vital step. It affects the characteristics of the machines as well as achieving accuracy in the result.

• Once the model reaches the desired accuracy levels, it is ready to launch. Post-deployment, the model will start to encounter real-world data. The user should continue to evaluate the model’s output; if it fails to output the correct data, a loop that data back through the validation phases. It’s helpful to keep a human-in-the-loop to manually check a model’s accuracy and provide corrected feedback in the case of low-confidence predictions or errors.

The Ones Who Tried and Won

In 2017 John Deere acquired Blue River Technologies, and together they’re poised to revolutionize pesticide use. Their AI models use drones and computer vision algorithms to identify weeds on farms. Doing so enables pesticides only to be sprayed on the weeds, rather than all crops in a field. Spending on pesticides was around $20 billion per year, but these efforts, it is expected to lead to a 90% reduction in pesticide costs. The methodology for this AI project is precise image segmentation. This method requires labeling data at the pixel level to determine which component of an image is weed versus crop. As one might imagine, the annotation process is very complex and involved. It requires both a comprehensive tooling interface and human levelers with a deep level of expertise in segmentation.

Use of AI in other Businesses

The manufacturing industry is using AI to automate logistics and supply chains. Nokia, for example, uses machine learning to alert an assembly operator when quality deviates. Specifically, if there are inconsistencies in the production process. AI may also monitor and track packages as part of a smart factory monitoring system, reducing lead time and preventing overstocking, or it can monitor throughput and downtime, highly impactful factors from a cost perspective. There are many automotive AI trends worth highlighting, including automation and safety, voice assistance, and personalization, among others. Self-driving cars are perhaps receiving the most fanfare, as these have the power to most dramatically change our daily lives.

The post USE OF ARTIFICIAL INTELLIGENCE THAT HAS POTENTIAL BUSINESS VALUE appeared first on Artificial Intelligence.

Despite teething problems, artificial intelligence (AI) has become mainstream. In fact, it is more than mainstream. It’s inevitable. That is to say, no matter how enterprises set up their technology infrastructure, it seems unlikely they will remain competitive without AI. A recent report, IBM’s 2021 Global AI Adoption Index, underlines this.

Based on a survey of 5,501 businesses globally, the report shows that one-third of companies are currently using AI in some way, while 43% are exploring it. However, problems remain. While recent advances are making AI more accessible than ever, the survey found that a lack of AI skills and increasing data complexity are top challenges. There are five take-aways from the research:

• Business adoption of AI was basically flat, but companies are planning significant investment.
• COVID-19 accelerated how businesses are using automation.
• Trustworthy and explainable AI is critical to business.
• The ability to access data anywhere is key for increasing AI adoption.
• Natural language processing is at the forefront of recent adoption.

A large majority of investments continue to be focused on the three key capabilities that define AI for business: automating IT and processes, building trust in AI outcomes, and understanding the language of business, the research showed.

Artificial Intelligence in the Mainstream

Other research indicates just how far AI has come in the past few years. Just this week, research conducted by San Mateo, Calif.-based Freshworks looked at the state of IT service management (ITSM) and IT operations management (ITOM) and found that AI technology has hit the mainstream, with 93 percent of businesses currently exploring or deploying some level of AI in ITSM.

That research showed that most organizations expect AI to be deeply integrated within ITSM tools instead of it being an add-on that requires additional effort to engage employees. Among the findings are:

• Virtually all IT managers (93 percent) are currently exploring or deploying some level of AI technology for ITSM/ITOM modernization.
• Nearly 70 percent of IT managers say AI is either critical or very important for upgrading and modernizing service desk capabilities.
• Among six AI use cases, ITSM chatbots are the clear leader in planned or actual AI deployments.

In terms of what organizations hope to achieve, the principal objectives are:

1. Speed of implementation (40 percent).
2. Integration with legacy systems (40 percent).
3. The overall cost of implementation (38 percent).
4. Training AI bots to return the most accurate response (39 percent).

AI Is Already Baked Into Business

Not only has the use of AI in the enterprise reached the mainstream, it also seems that more enterprises are starting to depend on it. Wayne Butterfield, director of ISG Automation, a unit of Stamford, Conn.-based technology research and advisory firm ISG, said it should be no surprise to see organizations adopting or experimenting with AI.

With so many AI components, ranging from machine learning to natural language processing, and lots of AI use cases in between, it’s likely that actual AI usage is even higher than current surveys suggest. AI, in one or more forms, is already built into many of the widely used enterprise platforms, Butterfield said. He cited the example of chatbots using natural language processing in platform tools like SAP, Salesforce and Workday as examples.

“Natural language processing, machine vision and machine learning are just a few of the ways AI is so prevalent in the enterprise today, and why AI will continue to be important moving forward across all industries,” Butterfield said. “Reading and responding to emails, conversing on WhatsApp, extracting a clause from a contract or even assisting in the picking of ripe fruit are all examples of AI currently in use across the enterprise.”

The Role of AIOps in AI Deployment

At the heart of any AI deployment is artificial intelligence for IT operations (AIOps). AIOps uses big data, analytics and machine learning to enhance IT operations and is inevitable for forward-thing organizations. Most modern enterprise IT environments consist of a complex mix of on-premise and cloud environments which run a wide variety of dynamic workloads that require frequent reconfiguration and scaling up and down, said Atul Varshneya, vice president for AI at Santa Clara, Calif.-based Tavant.

These applications and other IT systems generate a massive amount of data, and the data volume keeps increasing as IT environments evolve. Applying analytics and machine learning can help companies extract information from this data to make smarter decisions. For example, enhanced visibility into performance and dependencies across all environments can provide insight into significant events related to slow-downs or outages and automatically alert IT teams about problems and their root causes.

“Through intelligence enabled by analytics and ML techniques, rich information is extracted from the data generated by applications, and systems including various monitoring mechanisms,” Varshneya said. This results in several benefits:

• Proactive management of potential issues: AIOps can predict problems well in advance, enabling IT personnel to resolve them proactively.
• Faster resolution of identified issues: AIOps can also provide rich information about problems through the explainability of its prediction to help identifying root causes and get to resolutions faster.
• Efficient IT operations: With theses capabilities, alerts that require an urgent response can be reduced significantly, leading to more uptime and overall higher performing IT operations.

4 Steps to Make the Most of Artificial Intelligence

So how should enterprises proceed? There are four things enterprises need to consider on their way to AI adoption, said Sam Babic, senior vice president and CIO at Westlake, Ohio-based Hyland.

1. Start small, build momentum: Look for a high value, low complexity problem to solve or decision to make with AI as a starting point. This is also true when tackling projects at the organizational level. Demonstrate small, tangible wins to gain momentum for AI initiatives and then build momentum from there.
2. Create an AI/data center of excellence: In the formative stages of AI adoption, it is good to set up an AI center of excellence where subject matter experts either report directly or through the dotted line. This center of excellence provides focus and dedication to the topic and allows a centralized approach to patterns and practices derived through learning. Likewise, the purchase of tools and other decisions can be consolidated. As it grows, the center can expand into a community of practice with stakeholders throughout the organization.
3. Understand the outcomes you want: Oftentimes, organizations focus on understanding the opportunities AI can unlock and then map them to organizational goals. Instead, start with the organizational goal first and then map how AI can help. This seems like a nuance, but the latter approach enables the organization to more quickly focus on requirements necessary to accomplish the goal vs. getting lost in a sea of possibilities.
4. Be careful of bias: “Garbage in, garbage out” is a long-standing term that is even more important when leveraging AI. Operating with bad data, whether it is stale, incorrect or skewed will yield bad decisions. Training a machine learning algorithm is like training a child. Teach them bad habits and they will execute those bad habits. Closely analyze and clean data to ensure human bias is removed from training.

Proof of Value Replacing Proof of Concept

Artificial intelligence is more than just a nice addition to the technology stack, it’s essential if companies want to survive, said Bruce Orcutt, vice president of product marketing at Milpitas, Calif.-based ABBYY. And it’s getting more sophisticated.

“COVID was definitely an accelerant but also the developer skills shortage is a contributing factor,” he said.

Orcutt pointed to the example of document processing. AI technologies like optical character recognition and machine learning have been used to intelligently capture documents and send content to enterprise applications for years, but they required significant training and IT resources from IT. Now, more advanced AI makes that same legacy document processing technology easy for business analysts to use in the form of cloud-based, no-code platforms that can process any type of content they are working with.

“For AI to see rapid adoption, it needs to be user friendly, deployed quickly and return value immediately,” Orcutt said. “The days of ‘proof of concept’ are gone, enterprise leaders want proof of value now.”

The post Artificial Intelligence Takes Off in the Enterprise appeared first on Artificial Intelligence.