The use of virtual reality in construction can improve safety, precision and collaboration, saving contractors time and money.
Virtual reality (VR) is finally here and it is not just for gaming.
In fact, VR has begun to spread into almost every industry you can think of as more and more possible applications become known or invented, and the construction industry is certainly no exception. In the coming decades, the way that construction is done will change completely to make way for the time-saving and money-saving benefits offered by VR. Virtual reality is now capable of offering a really immersive experience that can be really helpful in industries like construction. In fact, construction may be the industry that is set to be changed the most by the usage of virtual and augmented reality.
Building Information Modelling (BIM) is one of the reasons why construction and virtual reality go so well together. BIM is being used more and more often for construction because of consumer demand. People want builders to provide them with a three-dimensional model of the project before the first nail is driven.
There are current methods of 3D modelling that are being used, but virtual reality has the potential to make these 3D models something that you could actually walk in and explore, giving developers the opportunity to interact with the design, see it up close and make changes that they can check in real-time. Being able to see a build in 3D through the lens of virtual reality means that builders can create better construction projects and spot problems before the building actually begins. This will save both time and money for builders and the potential customers asking for the build.
Obviously, there are some pretty big benefits to virtual reality as it stands now, but things are going to change in a huge way in the coming decades. What is possible now will seem like a drop in the bucket compared to what is coming with the pairing of virtual reality and the construction industry. This article will discuss this pairing now and in the future, and some of the exciting things that will be coming as a result of construction projects using virtual reality to model projects before the build begins.
3D Modelling and Virtual Reality
Architects and engineers use BIM to represent structures so that they will have a plan for construction that shows them where the flaws are in the architectural plan and any changes that need to be made. In the beginning stages of a project, 3D modelling is an important step because it saves the architect from having to go back and redesign something that didn’t work in the real world, and it saves the builder and person paying for the construction project money on wasted supplies and time that was used to build something that didn’t work.
But that is not the only way the current 3D modelling is used in construction. 3D modelling can demonstrate to the customer, the builder or anyone else who needs to know that the project has been built to specifications. It can be difficult to verify a building project from the architectural 2D plans, but a 3D model can demonstrate that the plans have been followed and that the design is accurate. But virtual reality can make this so much more accurate and easy to view.
Virtual reality gives builders and architects the opportunity to work with full-size structures in the VR environment instead of using the old method of scale models on computer screens or physical scale models.
Collaboration with Virtual Reality
One of the most exciting things in the construction field that can be done with virtual reality in a collaboration with other people. Collaboration is important because it is not just the architect or engineer that comes up with the initial design, it is also the client paying for the build. In addition, the builder should be able to check out what is being asked for as well to find out if it is possible, to give accurate quotes and to suggest improvements based on their own experience.
But it is not just the client, designer and builder that can add value to the construction project by being able to see it in virtual space. Today, hospitals are asking doctors and nurses to take a look at virtual reality constructs of new buildings to give valuable input as to how the room should be designed and what the layout should be. This is set to create transformative change for the healthcare industry and it starts at the ground floor with the construction of the project.
Virtual reality has so much potential when it comes to collaboration because as VR improves, people will be able to walk in and see exactly what their build is going to look like, and even do things like change paint colours, adjust options and basically have total freedom over what they want to be built using virtual reality.
Improving the Customer Experience with VR
Another thing the VR does is improve the customer experience. Developers that are selling houses off of plans are using virtual reality to show customers exactly what homes will be like once they are built and create a buying experience that they cannot get anywhere else. Customers are able to take a test drive of sorts in their home before they decide to build it. They may be able to look at dozens of different plans in order to figure out what they want, something that is relatively difficult to do when it comes to 3D modelling. Customers want this experience. They want to be able to see what they are getting before they buy.
In addition, as we previously discussed the customer is going to have an old great deal of freedom in the virtual reality space as well. With the standard 3D modelling mockup, it can be difficult to see yourself in that home. But when customers start adding things like coats of paint, curtains, furniture and all of the other things that go into a home, they are able to see themselves living there much more easily and end up getting the perfect home for themselves. The customer experience is one of the areas where VR is going to be the biggest enhancement. The finished product can be viewed long before it is completed and changed if necessary. Customization will definitely improve the customer experience.
Professional Training with Virtual Reality
Professional training in construction can also be done with virtual reality. There are lots of fields where professional training is going to be instrumental in training people to do the job, but construction is definitely one of them. It is a lot safer and less expensive to train someone on heavy equipment – and especially equipment like cranes – with modern virtual reality rather than in the real world. Right now, lots of people graduate school with degrees in engineering or architecture despite never having actually worked on a construction site. Many of them have never even seen one up close. But virtual reality is about to change all that.
Imagine how much more understanding and experience an engineer or architect will be able to bring to a job if they have actual experience working with their designs in the virtual world before they even graduate. In addition, people will learn to do dangerous jobs and operate extremely dangerous equipment in a safe environment where mistakes do not have the potential to cost a life. Virtual reality is almost vital when it comes to professional training in a variety of fields and that definitely includes construction. In a few decades, virtual reality will be almost indistinguishable from the real thing and that will mean some major advancements when it comes to the construction industry.
The Future of VR and Construction
The future of virtual reality is uncertain but only from the perspective that it is impossible to predict just how far virtual reality will bring the construction industry as well as all of the other industries that it can improve. There is no doubt that virtual reality is going to make a huge difference when it comes to construction; it is just that no one knows how far the technology will take us. It is very likely that in the near future, virtual reality will be the standard by which all construction projects are conceived and built. No construction project will be started or finalized until it has been built in virtual space first. But there is a lot more to it than that.
Right now, not that many companies are using virtual reality in order to improve their construction business, at least in meaningful ways. Not that many architects and engineers are using this technology yet either. But that is expected to change very soon. Firms are just barely starting to see the value of this technology, which is one of the reasons why the response has been slow. Another reason that companies have been slow to adopt virtual reality is that technology isn’t perfect yet. But that’s all going to change in the next 5 years as the technology fidelity catches up with mainstream renderings. In the future, it will be nearly perfect and everyone will be using it for an unforeseen number of applications.
3 Best Practices For Predictive Data Modeling
Predictive modeling is used to develop models that use past occurrences as reference points for organizations to forecast future business-related events and make clever decisions.
It is heavily involved in the strategy-making processes of companies in industries such as healthcare, law enforcement, pharmaceuticals and many more. The practices that can be used to make predictive data modeling error-free can be of great importance to everybody.
Predictive data modeling involves the creation, testing and validation of data models that will be used for predictive analysis in businesses. The lifecycle management of such models is a part of predictive data modeling. Such models, which use data captured by AI systems, machine learning tools, and other sources, can be used in advanced predictive analysis software systems used by organizations. The predictive data modeling process can be broken down into four steps:
Developing a model
Testing the model
Validating the model
Evaluating the model
There are a significant number of application areas for predictive analysis, such as financial risk management, international trade, clinical trials, cancer detection and many others. As we can see, each application area specified above is sensitive to mistakes or prediction inaccuracies. An inaccurate prediction could lead to incorrect diagnoses, potential patient deaths or financial turmoil in such industries. Therefore, organizations must implement certain practices to optimize the process of predictive data modeling. They must also continuously monitor the performance of the models.
1) Keeping the First Model Simple
As a process, predictive data modeling uses plenty of resources before organizations can expect it to bear fruit for them. Therefore, the competence of IT infrastructure present in the organization to carry out predictive data modeling is vital for streamlining the process without lag or inefficiencies. Accordingly, businesses must invest time and money to firstly make sure that their IT infrastructure is able to handle the process. This can be made sure with actions such as checking network connectivity, checking internet speeds, cybersecurity-related elements, and other factors before your business can use predictive data modeling. Additionally, your business needs to make sure that all your IT tools are aligned perfectly to make the model development process smoother.
More importantly, the first model created by an organization need not be overly complex or fancy. The first model will not be used for hardcore endpoint applications. A simplistic model provides the metrics and behaviors that can be used as a yardstick to test bigger and more complicated data models in the future. During the initial phases, businesses need to answer a few queries related to carrying out the predictive data modeling process. Some of such questions are related to the number of features needed to test a specific hypotheses, whether features that are useful are practical to make for the future, where they can store a model for maximum data security and threat protection, and finally, whether every significant decision-maker believes that the architecture and tools present currently in the organization are good enough to carry out the process.
Having an advanced hardware and software infrastructure conducive to predictive data modeling is vital for the process to be a success. Maintaining the simplicity of the first data model is valuable to train other, more complex models easily in the future.
2) Validating Models Consistently
Result validation involves organizations running their model and evaluating its results with visualization tools. To carry out the validation process, organizations need to understand how business data is generated, and how it flows through organizational data networks. As we know, today, data analytics is highly integrated into nearly every business aspect. Individuals at every level in an organization use company resources and the web to make calculated business decisions. Information is gathered for predictive model training purposes too. Accordingly, getting the datasets that can be used to train predictive models also requires a lot of effort. The level of effort involved in data collection means that predictive models are quite highly valued, and each model may have the power to influence organizational data compliance (in a good or a bad way), financial bottom lines, as well as the creation of legal risks for the organization. As a result, such high-value assets need to be validated consistently.
Additionally, businesses may be under the impression that model validation is a one-off process and does not need to be carried out in the future. However, as an expert in the field of model training will tell you, that is a misconception. Predictive models need to constantly evolve with time to become more adept at making accurate forecasts, and so, the validation process needs to take place on a consistent basis. Here are some of the tasks that must certainly be carried out in the validation process:
The Thorough Validation of ‘Predictor’ Variables
A model is made up of several variables. Some of those variables may have strong predictive abilities. Such variables are labeled ‘predictors’ due to those capabilities. While predictors are useful for regular business work, they may, in some cases, also cause unwanted risk exposure for their organization when they are used for predictive analysis. For example, the absence of ultra-personal details of users in models may be a conscious effort taken by network administrators to not fall into legal troubles regarding privacy violations of users.
The Validation of Data Distribution
This type of validation is carried out by organizations to get an understanding of the distribution of predictor and target variables. Over time, there may be distribution shifts in such variables and models. If such shifts are detected in variables within data models, such models will have to be retrained with new data as they wouldn’t be able to provide predictive analysis with accuracy.
The Validation of Algorithms
As we know, analytical algorithms are used to train models. Validation must be done for algorithms that train models, which go on to carry out predictive analysis in businesses. Also, only certain types of models can provide clear, interpretable predictions. For example, there are multiple types of models, such as decision trees and neural networks. Decision trees provide more open and interpretable—albeit less accurate—results whereas neural networks do not—with more accurate results. So, decision trees must be validated more frequently as they participate more in predictive analysis. Data administrators need to choose between interpretability and prediction accuracy when carrying out the validation of such algorithms.
Compare Model-Prediction Accuracy Tests
To know the actual competence of a model, it must be compared with other models for accuracy. The most accurate models must be used in predictive analysis systems. This is also a validation task and must be carried out regularly if newer, more accurate models enter the fray with time. After all, the improvements in predictive analysis performance carry on perpetually.
Additionally, tasks such as auditing of models, and keeping track of every validation log entry are included under the umbrella of validation. Finally, the performance of models is monitored before and after deployment. Before deployment, businesses must test them for operational glitches that may impact their decision-making and predictive capabilities. Pre-deployment checking is essential because most models chosen for predictive analytics are used in real-world environments.
After a model is deployed, it needs to be monitored for wear, as, generally, models tend to degrade over time. So, validation helps with phasing such models out from a predictive analytics system and replacing them with new, useful ones. With constant validation, models could become less error-prone and more time-efficient. Constant validation is a potent practice as it improves the predictive data modeling process in several ways.
3) Recognizing Data Imbalances
Imbalanced data is a classification issue where the number of observations per class is not equally distributed. As a result, there may be a higher number of observations for a given class—known as a majority class—and much fewer observations for one or other classes—known as minority classes. Data imbalances cause inaccuracies in predictive analysis.
A data imbalance in a model can cause it to be erratic, and not very useful. For example, let’s talk about a fraud forecasting system proposed for a bank. Now, the bank may have a record of 95%— meaning that 95% of its transactions turn out to be non-fraudulent. In an imbalanced system, a system may state that the bank is 100% safe. Now, while the system may be right, and the bank will face fraud only 5% of the time, the forecast system will be in trouble whenever any fraud takes place because the system had clearly stated that the safety quotient was at a 100%.
Predictive data modeling is a tough task in the current digital world due to certain potential weaknesses that may creep into its functioning. By following the best practices, businesses can be sure of avoiding poor forecasting.
To Leverage Deep Learning, You Must Know This First!
Before implementing deep learning for business, it is vital that business leaders understand the capabilities and features of this path-breaking technology.
What is Deep Learning?
Deep learning is a type of machine learning in AI that gathers huge datasets to make machines act like humans. Due to the use of neural networks, deep learning produces optimized results. You must have observed how Facebook automatically finds your friend in an image and suggests you tag her. Here, Facebook uses deep learning to recognize your friend. We were amazed to read what Gartner had to predict for about deep learning. It said, “Deep learning will soon provide best-in-class performance for demand, fraud and failure predictions.” Such a prediction encourages business leaders to implement deep learning for business and drive their business to greater success. While most business leaders are aware of the term, deep learning, they have very little to no understanding of the technology. Before leveraging deep learning for business, leaders should take a look at what deep learning offers and what the future of deep learning will look like.
Deep Learning for Business
For any business, the ultimate goal is to make profits. Organizations can make profits only if customers buy their products or service. Hence, every company aims at keeping their customers happy and fulfilling their demands. In any business, leaders must ask questions like:
What are the challenges faced with the current model?
How can deep learning help overcome the challenges?
How will the technology help to keep customers happy and attract new customers?
What are the areas in their business where they can implement deep learning to make higher gains?
Shared below are few deep learning applications that every business leader must leverage:
Image recognition – Deep learning algorithm recognizes various elements that are located in an image. One of the most common examples of image recognition is Google Images. Based on the content that we are searching for, Google offers us a set of relevant images. Another example is the self-driving vehicles that use image recognition to recognize obstacle son road and act in time. Also, the healthcare industry is using image recognition to understand the human anatomy better.
Sequence learning – Using predictive analysis, business leaders can predict the future outcomes in their business. An example is the product recommendation, when we shop online.
Machine translation – Google’s translate engine helps translate the language you enter to any other language of your choice.
The Future of Deep Learning
The future of deep learning is bright. Soon we will see self-driving vehicles becoming a part of our daily lives, thereby reducing accidents and air pollution. Amazon is already using drones for delivering products in 30 minutes or less. There are enormous opportunities for various industries to benefit with the deep learning technology. Business leaders should find these hidden opportunities and drive their business ahead of the curve.
Now that you have a comprehensive idea about the capabilities of deep learning, you should start planning to implement deep learning in your business.
5 Things You Didn’t Know About Artificial Intelligence
Artificial intelligence (AI) has been a hot topic for a while now.
While it might appear to be a fairly new concept, artificial intelligence has actually been around since the first computer was built back in the 1930s.
Source: Decan Herald
Based on capabilities, artificial intelligence can be classified into three types:
- Narrow AI
- General AI
- Super AI
Computers could learn and act on data sets without any human programming. Essentially, AI is mimicking the human brain, but it will soon surpass our mind. AI learns and adapts to information and scenarios, and it gets smarter in the process. Over time, it can begin to react differently to achieve better results.
While AI has been around for quite some time now, there are still several things that many people don’t know about it. Let’s take a closer look. Here are 5 things you didn’t know about artificial intelligence.
1. Artificial Intelligence is Already Present In Our Everyday Lives
Without even realizing it, you’re likely interacting with artificial intelligence every day. If you’ve searched for information on your smartphone, asked your digital assistant for the weather forecast, or plugged in directions in your car’s navigation system, you’ve used machine learning, which is a subset of AI. It might not seem all that significant, but these little things help us live our daily lives.
2. Artificial Intelligence Can Assist with Weather Forecasting
Artificial intelligence has the ability to take in massive volumes of data and uncover patterns that would take humans days, weeks, or even months. In such a capacity, it can assist weather forecasting. It can predict severe weather conditions, such as hail or hurricanes, that can help utility companies make smarter decisions. It can also be beneficial for solar power system companies. In recognizing specific patterns, it can also predict weather patterns that might impact solar production. This can be helpful for power producers, enabling them to adjust accordingly.
3. Artificial Intelligence Can Restore Vintage and Damaged Photographs
Vintage photographs provide us with a glimpse into how things used to be in the past. The thing is, pictures don’t last forever. Old photos fade or get damaged. Since these images were created before computers were really a thing, they’re likely to disappear forever once they’re gone.
Thanks to artificial intelligence, we can save vintage and damaged pictures. NVIDIA developed an AI that uses what’s known as image painting to successfully restore images with rips, holes, or missing spots. After being fed thousands of images, the AI can analyze any picture and automatically fill in the missing areas, effectively repairing them.
4. Artificial Intelligence Can Help Spot “Deepfakes”
Deepfakes are AI-generated videos that are becoming increasingly more common and realistic-looking. These images take faces of people and put them on the bodies of others. They can also create some rather convincing audio. Well-done videos and pictures can make it appear as though someone said or did something they didn’t actually do.
Not only can artificial intelligence make deepfakes, but it can help to spot them, too. As convincing as many deepfakes are, there are some tell-tale signs that they’re not real. The voice might sound a bit too robotic, or there’s something a little off with the image (such as hair not quite matching up). AI can help to spot these inconsistencies, which can protect people. In some cases, it may even help to prove a person’s innocence.
5. Artificial Intelligence is Helping Medical Professionals Fight Cancer
Computers are an integral tool in the field of medicine. One way artificial intelligence is helping is by assisting medical professionals in the fight against cancer. AIs developed by the University of Surrey and the University of California were fed thousands of accounts of people affected by cancer. Now, they can predict which people are likely to develop cancer. The technology may also be able to detect symptoms of cancer in its earliest stages.
These are just a few of the incredible things artificial intelligence can do. AI is smart, and it’s getting better. It’s beating humans at their own games, such as solving Rubix cube puzzles and topping the high score charts in video games. While it can be a strange thought that machines are becoming smarter than humans, AI can go a long way in helping us achieve things we never thought possible.
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