Artificial Intelligence (AI) is everywhere now; we see it in Google search, sci-fi movies, in our smartphones as smart assistants, and as a helping hand in managing our smart homes. The term “Artificial Intelligence” has been floating around the world since long. It is a broader concept that involves everything from GOFAI (“Good Old-Fashioned Artificial Intelligence”) to futuristic technologies like Deep Learning. Essentially, it is an approach towards a developing technology – a combination of both, Machine Learning and Deep Learning.
There is a difference between Artificial Intelligence, Machine Learning and Deep Learning as you can see in the image. In layman terms, Machine Learning is a subset of Artificial Intelligence whereas Deep Learning is a subset of Machine Learning.
Artificial Intelligence (AI) is the replication of human intelligence processes by machines, especially, in computer-operated systems. These processes include Machine Learning, reasoning, and self-correction using Deep Learning.
Algorithms that can learn on their own are termed as training algorithms; the set of these algorithms when broadly classified are knowns as Machine Learning (ML).
Deep learning (DL) is a subdivision of Machine Learning that can generate solutions to the problems using neural networks.
“The overall Deep Learning market is estimated to be esteemed at USD 3.18 Billion in 2018 and is expected to be worth USD 18.16 Billion by 2023.” – MarketsandMarkets
Deep learning is the trending topic in the AI world as it has evolved and opened new avenues for enterprises. It is still developing and has a stimulating scope of research in the emerging technology area.
Let’s see how Deep Learning works:
The Deep Machine Learning process has two major phases;
- Training – It is a process of categorizing large amounts of data to regulate their matching characteristic for training.
- Inferring – The system compares the regulated characteristics and memorizes them to make right actions when it comes across similar data the next time.
The Deep Learning training and inferring process includes following steps:
- Artificial neural networks (ANN) request a set of binary true/false questions
- Extract numerical values from data blocks
- Classify data based on the answers received
- Label new unexposed data
- Makes decisions/actions and label new unexposed data using their learning
Deep learning has transformed pattern recognition by introducing a way that now powers a wide range of intelligent technologies, i.e. NLP (Natural Language Processing) and automatic speech recognition.
Here are some of the ways Deep Learning can help businesses.
Customer Loyalty Prediction
Customer loyalty prediction and customer segmentation are some of the major challenges entrepreneurs face today. Enterprises have access to huge amount of data thanks to Internet of Things and Big Data, which can be effectively used to derive significant business insights. Machine learning, data mining and Deep Learning can help businesses predict customer behaviors, their purchase patterns, and support in creating best possible offers to loyal customers based on their browsing and purchase history.
Most of the eCommerce websites today are taking advantage of Machine Learning for generating product recommendations. The unsupervised Machine Learning helps marketers to have product-based recommendation systems for their valuable customers.
ML and DL algorithms use customer’s purchase history and compare it with a large product inventory. They then identify hidden patterns and compare similar types of products together. This is followed by generating a customized product recommendation to the customer, which motivates them to purchase the product.
Manufacturing units regularly need to follow traditional preventive maintenance practices that are expensive and often inefficient. With the advancement of Machine Learning and Deep Learning, manufacturers can make use of ML to discover hidden insights and patterns from their factory data. This is generally known as predictive maintenance. This helps in decreasing the risks associated with unexpected failures and eliminates unnecessary expenses. Deep Learning environment can be developed by monitoring historical data, flexible analysis environment or using workflow visualization tools.
Most businesses go through tough challenges due to duplicate and inaccurate data. Machine learning and predictive modeling algorithms can extensively avoid any errors caused by manual data entry. Deep Learning and Machine Learning make these processes better by evaluating the revealed data so that employees can use their time for other valuable tasks and add value to the organization.
At enterprise level, it is very important to detect spamming due to security purposes and virus threats. Previously, they used to have pre-existing and rule-based techniques to filter out Spam. Now this process is much easier thanks to ML and DL algorithms that creates new rules by using neural networks to detect phishing messages and Spam.
Image recognition has the ability to yield numeric and symbolic information and expression from images and additional high-dimensional data. In image recognition, Machine Learning plays an important role and is used by many organizations in various industries including retail, automobiles, healthcare, etc. It includes Machine Learning, data mining, and pattern recognition.
Fake news detection
“By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust.” – Gartner
When it comes to fake news detection, a neural network architecture model will understand the appearance in the natural language and its automatic tagging will detect a fake news article. The Deep Learning architecture can precisely identify the fake content by comparing headlines, article body and images. Enterprises need to monitor what is being published about their brands along with the context to make sure that fake content is not detrimental to their brand value.
Deep Learning is already being extensively used in enterprises for identifying purchase patterns, automating business procedures, solving customer demands, improving the overall performance and it is still evolving.
Businesses today have now identified that a high-performing intelligent infrastructure is essential to support the advanced analytics and virtual modeling applications of tomorrow. They have realized that these intelligent technologies will lead the world soon. Unless they follow trending technologies, the competition will leave them behind.
It’s imperative that before strategizing their future plan for an organization-wide digital transformation, businesses dive deep into the ocean of Artificial Intelligence. Cygnet Infotech will help you navigate just that and more. To understand the relevance and impact of integrating Deep Learning in your business model, talk to our experts at +1-609-245-0971 or firstname.lastname@example.org.