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Artificial Intelligence (AI)

What is Deep Learning and How Does it Work?

The deep learning model is based on unsupervised learning, which separates deep understanding from classic machine learning (also known as shallow learning).




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Many a sci-fi writer has predicted that robots would take over the world someday. If by robots, though, you mean algorithms, then today is the day!. However, the artificial intelligence we see in sci-fi is slightly different from the artificial intelligence we see in reality, an artificial neural network. The most advanced artificial neural network to date is the deep neural network. Therefore, it is only normal to ask, “What is deep learning?” Read on for the answer.

1. What is deep learning anyway?

Deep learning is a form of artificial intelligence. It is a subset of machine learning based on an artificial neural network modelled on the biological neural network of the human brain. At the instant, it is the most advanced form of artificial intelligence that we have.

The deep learning model is based on unsupervised learning, which separates deep understanding from classic machine learning (also known as shallow learning).

2. How does deep learning work?

Deep learning algorithms are based on a training process and a learning process. The training data is fed to the algorithms, and they are commanded to perform a new task. This is the training process.

The algorithms are evaluated. Those algorithms that do not succeed at a certain level are eliminated. Those that do grow are used as the model for creating a new “class” of algorithms. This cycle is called a layer, and the cycle is repeated repeatedly until the algorithm has gained a “skill”. The process of actually gaining skills is the learning process.

The number of layers may vary depending on how deep or how shallow the neural network is intended to be. The types of layers can also vary. An input layer, output layer, first layer, second layer, hidden layer, first hidden layer, previous layer, the final layer, and more are possible input layer options.

Neural Networks

These layers are used to form nodes, and these nodes are used to create a deep neural network. The type of neural network can vary, including a simple neural network, traditional neural network, recurrent neural network (RNN) and convolutional neural network (CNN). A convolutional neural network, for example, is used for deep learning applications related to computer vision: image recognition, image extraction, facial recognition and increased pixels.

The breakthrough that separates deep learning from shallow learning is unsupervised learning. Previously, programmers had to administer the entire training process and learning process personally. They had to input all of the training data. They had to evaluate all of the algorithms and eliminate underperformers. Then they had to repeat the cycle again and again. That is shallow learning.

Thanks to new advances in artificial intelligence, however, this is no longer necessary with a deep learning model. Now data scientists can create algorithms capable of performing all of the administrative tasks within the training process and the learning process. Algorithms can train other algorithms!

3. Deep Learning Applications

Breakthroughs in recent years have led to a whole host of deep learning applications, Smart homes with features like Amazon’s Alexa, digital assistants like Siri, intelligent security systems like Ring cameras with their facial recognition technology and driverless cars like those made by Tesla.

Google has been a leader in the field of artificial intelligence. Their Google translates program uses speech recognition technology, made possible by a deep learning algorithm with deep neural networks, to prepare large amounts of data, trillions of elements within datasets, to aggregate all of the possible words and phrases within a natural language for language translation.

Finally, the ability to process big data and large amounts of data has led to the increased development of new sciences within the field of artificial intelligence, such as data analytics, business analytics, predictive analytics and data virtualization. A deeper understanding of data information will continue to lead to more possibilities.

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Artificial Intelligence (AI)

Construction Project Management & Other Construction Trends to Look Out For in 2021

As the construction industry is getting more competitive, you have to look out for the following construction industry trends that will prevail in 2021 and beyond.




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After a turbulent year of adjusting to the pandemic’s changes and the changing online landscape, many businesses, including the construction business industry, are thriving to re-emerge and grow in 2021.

With the increased labour shortage and construction expenses, the construction industry is forced to integrate innovative tech solutions to stand out in the competitive industry niche and reduce the potential risks of error and construction-material waste. While more construction businesses are integrating construction project management software in their business model, they are also focusing on other aspects to improve their projects and meet the expectations of their clients.

With the latest tech-related adjustments, innovative technology trends continuously affect construction sites, improve construction projects, and boost sales and revenue. Certain construction trends are working at the core of construction businesses and frontline construction workers.

As the construction industry is getting more competitive, you have to look out for the following construction industry trends that will prevail in 2021 and beyond. Read on to learn more!

1. Safety Equipment

Safety comes first, particularly when it comes to dangerous occupations, such as working at construction sites. With innovative software and machinery, construction sites improve their safety measurements as the machinery can now detect safety issues before accidents can occur.

A strong internet connection and Wi-Fi are being made available that covers all areas of the construction site, which assists with the quick and immediate reporting of accidents if a worker falls or gets injured. Innovative machinery that is integrated with AI (artificial intelligence) has made its way to construction sites.

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This machinery helps with the transportation and moving of hazardous materials., in addition, you are also more likely to expect AI (artificial intelligence) working at the core of the construction sites that also lay bricks and assist with construction.

When it comes to safety equipment, you will find headsets as an essential element on construction sites. The headsets keep the workers protected from noise pollution, which is common to see on construction sites. While watching workers from noise, the headsets also help the workers stay in tune with their work surroundings.

It is necessary to note here that contrary to the myth that AI (artificial intelligence) in the form of robots will soon be replacing humans, it is the opposite case with construction. The robots that you will find on the construction sites won’t be there to replace humans. On the contrary, the use of AI on construction is only to benefit the workers, reduce their loads, and keep them safe. That said, AI and robots are making room for higher-level jobs and new talent.

2. Efficient Use of Technology

As we mentioned before, we are moving into a different era dominated by technology. The biggest differences that you will find in today’s construction projects and the previous construction projects are the use of technology and how it is integrated into construction sites. Besides the use of construction project management software, you will also find the inclusion of smart contracts. In other words, the pandemic has shifted construction projects to technology construction.

The pandemic has performed an essential role in causing increased reliance on technology. The construction project management software, for instance, helps in keeping all employees on the same page as the milestones are marked. It also allows managers to manage all steps of the construction project remotely and effortlessly.

You can also expect drones to fly on construction sites. One essential benefit of construction drones is that they can cover areas that can effectively report and analyze the construction sites.

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