Discover how Amazon Web Services (AWS) is revolutionizing businesses through cutting-edge AI technologies, offering scalable solutions for machine learning, natural language processing, and predictive analytics. 

What is artificial intelligence?

Artificial intelligence (AI) is a field of computer science that addresses cognitive problems typically associated with human intelligence, such as learning, creativity, and image recognition. Modern organizations collect a wealth of data from a variety of sources, such as smart sensors, human-generated content, monitoring tools, and system logs. 

The goal of AI is to create self-learning systems that can make sense of the data. AI then applies the knowledge gained to solve new problems in a human-like way. For example, AI technology can respond appropriately to conversations with humans, generate original images and text, and make decisions based on real-time data inputs. 

Your organization can integrate AI capabilities into applications to optimize business processes, enhance customer experiences, and accelerate innovation.

How has artificial intelligence technology developed?

In Alan Turing's seminal paper from 1950, “Computing Machinery and Intelligence,” he considered the question of whether machines can think. In this paper, Turing first introduced the term artificial intelligence and presented it as a theoretical and philosophical concept. 

From 1957 to 1974, advances in computing allowed computers to store more data and process it faster. During this period, scientists further developed machine learning (ML) algorithms. Advances in this field led agencies such as the Defense Advanced Research Projects Agency (DARPA) to create a fund for artificial intelligence research. Initially, the main goal of this research was to explore whether computers could transcribe and translate spoken language.

During the 1980s, there was increased funding and scientists developed an expanded algorithmic toolkit used in developing appropriate AI. David Rumelhart and John Hopfield published papers on deep learning techniques, showing that computers could learn from experience. 

From the 1990s to the early 2000s, scientists achieved many of the core goals of AI, such as defeating the reigning world chess champion. With more computing data and processing power available today than in previous decades, AI research is now more widespread and accessible. It is rapidly evolving into general intelligence , where software can perform complex tasks. Software can create, make decisions, and learn tasks that were previously limited to humans.

Read more: Global IT spending is predicted to increase by 8% by 2024

What are the benefits of artificial intelligence?

Artificial intelligence has the potential to provide a wide range of benefits to different industries.

Solving complex problems

AI technology can use machine learning and deep learning networks to solve complex problems with human-like intelligence. AI can process information at scale—processing patterns, identifying information, and providing answers. You can use AI to solve problems in a wide range of areas, such as fraud detection, medical diagnosis, and business analytics.

Increase business efficiency

Unlike humans, artificial intelligence technology can operate 24/7 without any loss of performance. In other words, AI can perform manual tasks without errors. You can let AI focus on repetitive, tedious tasks, so you can use human resources in other areas of your business. AI can reduce the workload of employees while also adjusting all business-related tasks. 

Make smarter decisions

Artificial intelligence can use machine learning to analyze large volumes of data faster than any human can. AI platforms can detect trends, analyze data, and provide guidance. With data forecasting, AI can help suggest the best course of action in the future.

Business Process Automation

You can train AI with machine learning to perform tasks accurately and quickly. This can increase operational efficiency by automating business operations that employees find difficult or boring. Similarly, you can use AI automation to free up human resources for more complex and creative work. 

What are the practical applications of artificial intelligence?

Artificial intelligence has countless applications. While not an exhaustive list, this is a selection of examples that highlight the diverse use cases of AI.

Intelligent document processing

Intelligent Document Processing (IDP) interprets unstructured document formats into usable data. For example, it converts business documents such as emails, images, and PDFs into structured information. IDP uses AI technologies such as natural language processing (NLP), deep learning, and computer vision to extract, classify, and authenticate data. 

For example, HM Land Registry (HMLR) processes property titles for over 87% of England and Wales. HMLR staff compare and review complex legal documents related to property transactions. The organization deployed an artificial intelligence application to automate document comparisons, cutting review time by 50% and improving the property transfer approval process.

Application performance monitoring

Application Performance Monitoring (APM) is the process of using software tools and telemetry data to monitor the performance of business-critical applications. AI-based APM tools use historical data to predict problems before they occur. They can also resolve problems in real time by suggesting effective solutions to your developers. This strategy keeps applications running efficiently and resolves bottlenecks.

For example, Atlassian creates products to streamline team and organizational collaboration. Atlassian uses AI APM tools to continuously monitor applications, detect potential issues, and prioritize critical issues. With this functionality, teams can quickly respond to ML-generated recommendations and address performance degradation. 

Predictive maintenance

AI-enhanced predictive maintenance is the process of using large volumes of data to identify issues that could lead to downtime in operations, systems, or services. Predictive maintenance allows businesses to address potential issues before they occur, reducing downtime and avoiding disruptions.

For example, Baxter uses 70 manufacturing facilities worldwide that operate 24/7 to deliver medical technology. Baxter uses predictive maintenance to automatically detect abnormal conditions in industrial equipment. Users can deploy effective solutions ahead of time to reduce downtime and improve operational efficiency. To learn more, see how Baxter uses Amazon Monitron.

Medical research

Medical research uses AI to streamline processes, automate repetitive tasks, and process large amounts of data. You can use AI technology in medical research to facilitate end-to-end drug discovery and development, replicate medical records, and improve time to market for new products.

A practical example is C2i Genomics, which uses AI to run high-scale, customizable genomic pipelines and clinical trials. By using computational solutions, researchers can focus on clinical performance and method development. Engineering teams also use artificial intelligence to reduce resource requirements, engineering maintenance, and NRE costs.

Business Analysis

Business analytics uses AI to collect, process, and analyze complex data sets. You can use AI analytics to predict future values, understand the root causes of data, and reduce time-consuming processes. 

For example, Foxconn uses AI-enhanced business analytics to improve forecast accuracy. They achieved an 8% increase in forecast accuracy, saving $533,000 annually in their factories. They also use business analytics to reduce labor waste and increase customer satisfaction through data-driven decision making.

Read: EDUTO’s AI Library

What are the main artificial intelligence technologies?

Deep neural networks form the core of artificial intelligence technology. They mirror the processing that takes place in the human brain. The brain contains millions of neurons that work together to process and analyze information. Deep neural networks use artificial neurons that work together to process information. Each artificial neuron, or node, uses mathematical calculations to process information and solve complex problems. This deep learning approach can solve problems or automate tasks that normally require human intelligence.

You can develop different AI technologies by training deep learning neural networks in different ways. We provide some important neural network-based technologies next.

Natural language processing techniques

NLP uses deep learning algorithms to interpret, understand, and derive meaning from textual data. NLP can process human-generated text, making it useful for summarizing documents, automating chatbots, and conducting sentiment analysis. 

Computer Vision

Computer vision uses deep learning techniques to extract information and insights from videos and images. Using computer vision, computers can understand images just like humans. You can use computer vision to monitor online content for inappropriate images, recognize faces, and classify image details. It is important in self-driving cars and trucks to monitor the environment and make instantaneous decisions.

AI generative

Generative AI refers to artificial intelligence systems that can generate new content and artifacts such as images, videos, text, and audio from simple written prompts. Unlike previous AIs that were limited to analyzing data, generative AI leverages deep learning and massive datasets to produce high-quality, human-like creative output. While enabling exciting creative applications, concerns around bias, harmful content, and intellectual property remain. Overall, generative AI represents a major evolution in AI’s ability to generate new content and artifacts in a human-like manner.

Voice recognition

Speech recognition software uses deep learning models to interpret human speech, identify words, and detect meaning. Neural networks can convert speech into text and represent voice sentiment. You can use speech recognition in technologies such as virtual assistants and call center software to determine meaning and perform related tasks.

What are the main components of an artificial intelligence application architecture?

The AI ​​architecture consists of four core layers. Each layer uses its own technologies to perform a specific role. Here is an explanation of what happens at each layer.

Layer 1: data layer

artificial intelligence is built on various technologies such as machine learning, natural language processing, and image recognition. At the heart of these technologies is data, which forms the foundational layer of AI. This layer is primarily focused on preparing data for AI applications. Modern algorithms, especially deep learning algorithms, require huge computational resources. Therefore, this layer includes hardware that acts as a secondary layer, providing the essential infrastructure for training AI models. You can access this layer as a fully managed service from a third-party cloud service provider.

Layer 2: ML framework and algorithm layer

ML frameworks are created by engineers in collaboration with data scientists to meet the requirements of specific business use cases. Developers can then use pre-built functions and classes to easily build and train models. Examples of these frameworks include TensorFlow, PyTorch, and scikit-learn. These frameworks are a key component of the application architecture and provide essential functionality to easily build and train AI models.

Layer 3: model layer

At the model layer, the application developer deploys the artificial intelligence ​​model and trains it using data and algorithms from the previous layer. This layer is key to the decision-making capabilities of the AI ​​system.

Below are some of the main components of this class.

Model structure

This structure determines the capabilities of a model, including layers, neurons, and activation functions. Depending on the problem and resources, one can choose from feedforward neural networks, convolutional neural networks (CNNs), or others.

Model parameters and functions

The values ​​learned during training, such as the neural network's weights and biases, are important for predictions. The loss function evaluates the model's performance and aims to minimize the difference between the predicted output and the actual output.

Optimizer

This component adjusts the model parameters to reduce the loss function. Different optimizers like gradient descent and Adaptive Gradient Algorithm (Adagrad) have different purposes.

Layer 4 – Application Layer

The fourth layer is the application layer, which is the customer-facing part of the AI ​​architecture. You can ask AI systems to complete tasks, generate information, provide insights, or make decisions based on data. The application layer allows end users to interact with AI systems.

Read more: EDUTO’s Project

What are the challenges of implementing AI?

AI has a number of challenges that make it more difficult to implement. The following barriers are some of the most common challenges to implementing and using artificial intelligence.

Data Management

Data governance policies must comply with regulatory restrictions and privacy laws. To deploy AI, you must manage data quality, privacy, and security. You are responsible for customer data and protecting privacy. To manage data security, your organization needs to understand how AI models use and interact with customer data at each layer.

Technical difficulties

Training artificial intelligence with machine learning is resource intensive. High processing power thresholds are required for deep learning technologies to work. You need a robust computing infrastructure to run AI applications and train your models. Processing power can be expensive and limit the scalability of your AI system.

Data Restrictions

To train fair artificial intelligence systems, you need to ingest massive amounts of data. You need to have enough storage capacity to process the training data. Similarly, you need to have effective data quality and management processes in place to ensure the accuracy of the data you use for training.

EDUTO – Leading Technology Partner in Vietnam

Established in 2020, EDUTO is one of the leading IT companies in Vietnam, specializing in providing digital transformation consulting services and software solutions in the fields of finance, healthcare and retail for domestic and international enterprises. With the desire to contribute to enhancing Vietnam's position on the global IT map, EDUTO aims to bring innovative technology to life by leveraging the technical workforce in Vietnam, and the vision of becoming a leading IT company in the ASEAN region.

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