Every advancement in technology uncovers lots of opportunities to expand the business landscape wonderfully. But, you must know which technology best matches your business requirements if you are looking for continuous growth.
Companies need to be updated to maximize the benefits of the massive growth of technology in business advancement. That’s why various IT companies are revamping their business strategies and setting new budget limits to outgrow their success curve.
Due to the COVID pandemic, rapid transformations and the sudden influx of new technologies rule the business world. As per the Accenture insights on technology trends 2021, there is 3x spending in cloud computing in the first quarter of 2020. Furthermore, 77% of employees feel their technology architecture is the most significant factor in organizational success.
Artificial Intelligence Vs. Machine Learning Vs. Deep Learning
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Artificial intelligence, machine learning, and deep learning are some advanced technologies, and their constant development is making our survival more comfortable and progressive. We all have heard about these bespoke technologies somewhere and use their based products without even knowing them.
To make a good foundation on technologies, let’s explore them one by one:
Artificial Intelligence: Advance Version of Human Intelligence
Artificial intelligence (AI) is the buzzing word in today’s advanced era that enables computers to think, act, comprehend, and sense. It seems fortunately impossible at first glance, but it is possible with machine learning, computational intelligence, processing of natural languages, and more if you go in-depth.
Image resource: Gartner.Inc
Not a surprise, AI Augmentation will create $2.9 trillion of business value in 2021 as per Gartner, Inc. Their forecast project decision support/augmentation is the most vital type of artificial intelligence that adds value in business growth. The combination of artificial intelligence and human capabilities will deliver significant benefits to companies as well as customers.
Artificial intelligence describes the intelligence that is artificial or that mimics human brain capabilities. Through AI, technology experts allow machines to think, act, and understand the scenarios. The ultimate goal of AI is to develop intelligent computer systems for solving complex problems.
AI comes under the fourth intelligence revolution and focuses on three cognitive attributes: reasoning, learning, and self-correction. Chatbots, facial detection & recognition, Google maps, and digital assistants are some examples of AI. Moreover, the broad categories of AI are:
- Artificial Narrow Intelligence
- AI General Intelligence
- Artificial Super Intelligence
How does AI work?
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AI development companies focus on developing applications using reverse-engineering human traits and capabilities in a machine. The working of AI depends on the fast and iterative processing of large amounts of data with intelligent algorithms.
It allows the software to comprehend all necessary information from patterns and features in the data. While discussing the process of AI, developers need to pay attention to its subfields:
- Machine learning
- Deep learning
- Cognitive learning
- Natural language processing
- Neural networking
- Computer vision
Benefits of AI
Image resource: Accenture
Machine learning: Let Machine Think Smartly
Machine learning is the type of artificial intelligence that typically focuses on data and algorithms to intimate human learning. Now, everything is countable under the data system. So, our dependency on data is enormous, and we are under pressure to create algorithms using statistical methods to uncover critical insights within data mining projects for effective decision-making.
Speech recognition, customer service, recommendation engines, and computer vision are significant real-time machine learning examples. According to a GlobeNewswire report, the global machine learning market will reach 117 billion by the end of 2027, which was $1 billion in 2016 with a pace of 39% CAGR. So, choose to go with machine learning solutions if you focus on the “I” of ROI in business.
As mentioned above, machine learning is the subset of AI. Through statistical methods, developers allow machines to learn from the data and experiences and change their actions accordingly. It works on decision trees, support vector machines, K Means clustering, random forests, and more.
Over-the-top platforms (OTT), Amazon Prime, and Netflix are the best examples of machine learning that works on a user’s browsing history and data. It tries to improve the user experience from past experiences. If you want to get more details on machine learning, connect with a machine learning expert and buy machine learning services.
How does machine learning work?
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The primary logic of machine learning is to teach computers how to think like humans based on past experiences. The ultimate effort is to reduce human intervention by exploring data and identifying patterns.
If you are thinking of completing a task based on some set of rules or data-defined patterns, you can proceed with machine learning. It allows companies to convert processes into an automatic mode, previously done by humans, like customer service calls, reviewing resumes, and bookkeeping. For more in-depth knowledge of the ML, you need two main techniques:
- Supervised learning: In this type of learning, the output is produced through data collection, which is previously deployed by machine learning.
- Unsupervised learning: Through unsupervised learning, you will get through all kinds of unknown patterns in data.
Also Read: IoT vs AI: The Difference Between the Internet of Things (IoT) and Artificial Intelligence (AI)
Benefits of ML
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Deep Learning: New Face of Advance Technology
Deep learning, a subset of machine learning, is a neural network with three or more layers. The basic principle of these neural layers is to simulate human brain behavior, allowing it to learn from a bunch of data. If you seek products or technology based on deep learning, look around, from digital assistants to voice-enabled TV remotes or self-driving cars.
According to the Statista 2019 report, $80 million would be the estimated size of the US deep learning software market by 2025. Many companies look for deep learning to learn about data management to make intelligent decisions.
Deep learning is the field of machine learning that examines computer algorithms to improve results. In machine learning, simple concepts are used, whereas deep learning uses artificial neural networks to intimate how humans think and learn.
Before the advancement in Big Data, neural networks were limited by computing power and less effective for complex problems. But, the advancement makes deep learning perform complex operations easily like abstraction and representation to sense sound, text, and images.
How does deep learning work?
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Like neurons, artificial neural networks are also layers of nodes in which an individual layer is connected to adjacent layers. The more layers you have in a network, the deeper you can go. As in our brain, a single neuron receives signals from thousands of other neurons; likewise, signals travel between nodes and assign corresponding weights in the artificial neural network.
In deep learning, a tremendous amount of data is needed to provide more accurate results. Artificial neural networks can classify the data with the answers from a bunch of binary true or false questions through data processing. Moreover, the process involves highly complex mathematical calculations.
Benefits of Deep learning
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Developing the right application for smooth business operation leads you to search for more information on technologies. Before contacting the artificial intelligence software development company, dive into more knowledge processing.
Machine learning Vs. Deep Learning
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For a clear understanding, refer to the above table to distinguish between machine learning and deep learning. Deep learning works on interconnected layers of software-based calculators called neurons that form neural networks. The primary thought behind this concept is to predict how the human mind would think in a particular situation and learn from the surroundings and sensory details.
For example: let’s take a case of a pot filled with hot water. When you were a child and saw that pot, you wouldn’t understand whether it was hot or cold unless someone said so. If you choose to touch the pot, sensory touch will tell your brain that the pot is hot. It is possible when your neurons transfer the information from the brain to the fingers. Furthermore, it also makes you prepare for the next scenario whenever you see any pot.
This is what our brain learns from inputs, surroundings, and past experiences to make effective decision making. The same concept applies to deep learning. Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks are used in this learning technology.
The Future of AI: Leading the technology from experimental to exponential
Unfolding the future of AI reflects the numbers to depict the insights of the technology advancement in upcoming years that will lead our future.
Image resource: Accenture Infographics
According to Accenture Report, “Build to Scale,” 84% of C-suite executives acknowledge that their companies wouldn’t grow without the involvement of AI. 76% of employees don’t know how to scale AI across the business, and 75% of executives think their business would be risky if they don’t scale AI.
Image resource: Accenture report
The report also briefs three distinct groups of organizations with increasing abilities to scale AI: Proof of Concept factory, Strategically Scaling, and Industrialized for Growth. Companies may struggle to raise AI in their business, but they are widening their ways to its successful implementation as this technology touches each type of service.
Image resource: Accenture
If we talk about the investment in AI, this report tells us that US$306 billion was spent on AI applications in the last few years. And, the ROI gap between companies in the Proof of Concept stage and Strategic Scalers is about US$110 million. So, you need to think hard if you want to develop your business as a successful artificial intelligence software development company.
Being in business, scaling the true exponential power of AI with various digital platforms is not a one-time job. However, constant efforts will lead you to bring your business to its peak where you can easily infuse your strategies with data analytics, leverage a reusable data foundation, and scale through platforms.
Have a look at the video below before any conclusion:
Artificial intelligence, machine learning, and deep learning may be similar but not in reality. If you go in-depth into the concept of technologies, you will find a significant difference between them.
It would be best to comprehend everything about the technologies before integrating them into business. If you are keen to hire AI developers or machine learning experts, ValueCoders must be on your list: a leading IT consulting company offering the assistance of a dedicated development team.
After Stepping into the technology era in 2004, ValueCoders technology experts’ constant effort is to provide the best services and lead the AI development companies’ market. The company’s vision is to help enterprises receive the best outcomes to scale AI in their business with the most competent digital platforms. While maintaining a 97% client retention rate, we are ready to thrive on agile and enhanced digital performance.