Future of computer
What is Artificial Intelligence ?
Artificial intelligence (AI) is the field of computer science dedicated to solving cognitive problems typically associated with human intelligence, such as learning, creativity, and image recognition. Modern organizations collect vast amounts 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 extract meaning from data. AI can then apply that knowledge to solve new problems in human-like ways. For example, AI can respond meaningfully to human conversations, generate authentic images and text, and make decisions based on real-time data inputs. Your organization can integrate AI capabilities into your applications to improve your business processes, enhance customer experiences, and accelerate innovation.
How has AI technology evolved ?In his 1950 paper “Computing Machinery and Intelligence,” Alan Turing explored the possibility of machines thinking. In this paper, Turing first coined the term artificial intelligence and presented it as a theoretical and philosophical concept.
Between 1957 and 1974, advances in computing allowed computers to store more data and process it faster. During this period, scientists developed machine learning (ML) algorithms. Advances in this field led agencies such as the Defense Advanced Research Projects Agency (DARPA) to create an AI research fund. Initially, the main goal of this research paper was to explore whether computers could transcribe and translate spoken language. During the 1980s, development was facilitated by increased funding and an expansion in the set of algorithmic tools that AI scientists used. David Rumelhart and John Hopfield published papers on deep learning techniques, which showed that computers could learn from experience.
Between 1990 and the early 2000s, scientists achieved many of the basic goals of AI, such as beating the world chess champion. With more computing data and increased processing power in the modern era than in previous decades, AI research is now more common and more accessible. It is rapidly evolving into general artificial intelligence so that programs can perform complex tasks. Programs can create, make decisions, and learn on their own, tasks that were previously limited to humans.
What are the benefits of artificial intelligence ?
AI has the potential to offer a range of benefits to different industries.
Overcoming complex problems
AI can use machine learning and deep learning networks to solve complex problems with human-like intelligence. AI can process information at scale, by encountering patterns, identifying information, and providing answers. You can use AI to solve problems in a range of areas such as fraud detection, medical diagnosis, and business analytics.
Increasing business efficiency
Unlike humans, AI can operate 24/7 without performance deterioration. In other words, AI can perform manual tasks without errors. You can let AI focus on repetitive and tedious tasks, so you can use human resources in other areas of the business. AI can reduce the workload of employees while facilitating all business-related tasks.
Make Smarter Decisions
AI can use machine learning to analyze large amounts of data faster than any human. AI platforms can spot trends, analyze data, and provide guidance. By predicting data, AI can help suggest the best course of action for the future.
Automate Business Processes
You can train AI using machine learning so that it can perform tasks accurately and quickly. This can increase operational efficiency by automating parts of the work that employees struggle with or find tedious. Similarly, you can use AI automation to free up employee resources to do more complex and creative work.
What are the practical applications of AI ?
AI has a wide range of uses. While this is not an exhaustive list, here are a few examples that highlight the various use cases for AI.
Intelligent Document Processing
Intelligent Document Processing (IDP) translates unstructured document formats into usable data. For example, it converts business documents such as emails, images, and PDFs into structured information. IDP uses AI techniques such as natural language processing (NLP), deep learning, and computer vision to extract, classify, and validate data.
For example, HM Land Registry processes title deeds for more than 87 percent of England and Wales. HMLR’s caseworkers compare and review complex legal documents related to property transactions. The organization deployed an AI application to automate the document comparison process, reducing review time by 50 percent and increasing approval of property transfers. For more information, read about how HMLR uses Amazon Textract.
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-powered APM tools use historical data to predict problems before they occur. They can also solve problems in real-time by suggesting effective solutions to your developers. This strategy keeps applications running efficiently and addresses bottlenecks.
For example, Atlassian produces products that aim to facilitate teamwork and organization. Atlassian uses AI-powered APM tools to continuously monitor applications, detect potential issues, and prioritize risks. With this functionality, teams can quickly respond to machine learning-powered recommendations and overcome performance declines.
Read about APM »
Predictive Maintenance
AI-enhanced predictive maintenance is the process of using large amounts of data to detect issues that could lead to disruptions in operations, systems, or services. Predictive maintenance allows companies to address potential issues before they occur, reducing downtime and preventing disruptions.
For example, Baxter uses 70 manufacturing sites worldwide that operate 24/7 to deliver medical technology. Baxter uses predictive maintenance to automatically detect abnormal conditions in industrial equipment. Users can implement effective solutions early to reduce downtime and improve operational efficiencies. To learn more, read about how Baxter uses Amazon Monitron.
Medical research
Medical research uses AI to streamline processes, automate repetitive tasks, and process massive amounts of data. You can use AI in medical research to streamline the end-to-end drug discovery and development process, transcribe medical records, and improve time to market for new products.
A real-world example is C2i Genomics using AI to power highly customizable, high-scale genomic pathways and clinical assays. By overlaying algorithms, researchers can focus on clinical performance and method development. Engineering teams also use AI to reduce resource requirements, engineering maintenance, and reduce non-recurring engineering (NRE) costs. For more details, read about how C2i Genomics uses AWS HealthOmics.
Business Analytics
Business analytics uses AI to collect, process, and analyze complex data sets. You can use AI analytics to predict future values, understand the root cause of data, and reduce time-consuming processes.
For example, Foxconn uses AI-enhanced business analytics to improve forecasting accuracy. It achieved an 8 percent increase in forecasting accuracy, resulting in annual savings of $533,000 in its factories. It also uses business analytics to reduce wasted labor and increase customer satisfaction by making data-driven decisions.
What are the key AI technologies?
Deep learning neural networks are at the core of AI technologies. They mimic the processing that occurs in the human brain. The brain contains millions of neurons that work together to process and analyze information. Deep learning neural networks use artificial neurons that process information together. 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 would normally require human intelligence.
You can develop different AI techniques by training deep learning neural networks in different ways. Here are some of the key techniques that are based on neural networks.
Read about deep learning »
Read about neural networks »
Natural language processing
Natural language processing (NLP) uses deep learning algorithms to interpret, understand, and gather meaning from textual data. NLP can process human-generated text, making it useful for summarizing documents, automating chatbots, and performing sentiment analysis.
Read about natural language processing »
Computer vision
Computer vision uses deep learning techniques to extract information and insights from videos and images. Using computer vision, a computer can understand images just as a human would. You can use computer vision to monitor online content for inappropriate images, recognize faces, and classify image details. It is especially important in self-driving cars and trucks, where you can monitor your environment and make split-second decisions. Read about computer vision »
Generative AI
Generative AI refers to AI systems that can generate new content and new elements such as images, videos, text, and audio from simple text commands. Unlike previous AI that was limited to analyzing data, generative AI leverages deep learning and large datasets to produce high-quality, innovative, human-like creative output. While enabling exciting creative applications, there are concerns about bias, harmful content, and intellectual property. Overall, generative AI represents a major advance in the capabilities of AI to generate new content and new elements in a human-like way.
Read about Generative AI »
Speech Recognition
Speech recognition software uses deep learning models to interpret human speech, identify words, and discover meaning. Neural networks can convert speech to text and indicate vocal emotions. You can use speech recognition in technologies such as virtual assistants and call center software to determine meaning and perform related tasks.
Read about speech-to-text »
What are the main components of an AI application architecture?
The AI architecture consists of four basic layers. Each of these layers uses different technologies to perform a specific role. Here’s what happens at each layer.
Layer 1: Data Layer
AI relies on different technologies such as machine learning, natural language processing, and image recognition. Data is at the core of these technologies, and it forms the foundational layer of AI. This layer primarily focuses on preparing data for AI applications. Modern algorithms, especially those based on deep learning, require massive computational resources. Therefore, this layer includes hardware that acts as a sub-layer, providing the underlying infrastructure for training AI models. You can access this layer as a fully managed service from a third-party cloud provider.
Read about Machine Learning »
Layer 2: Machine Learning Frameworks and Algorithm Layer
Engineers, in collaboration with data scientists, create machine learning frameworks 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 such frameworks include TensorFlow, PyTorch, and scikit-learn. These frameworks are vital components of the application architecture and provide essential functionality to easily create and train AI models.
Layer 3: Model Layer
In the model layer, the application developer implements and trains the AI model using the data and algorithms from the previous layer. This layer is pivotal to the decision-making capabilities of the AI system.
Here are some of the key components of this layer.
Model Architecture
This architecture defines the capability of the model, and includes layers, neurons, and activation functions. Depending on the problem and resources, one can choose from feedforward neural networks, convolutional neural networks (CNNs), or other networks.
Model Parameters and Functions
The values acquired during training, such as neural network weights and biases, are essential for predictions. The “loss function” evaluates the performance of the model and aims to minimize the discrepancy between the predicted output and the actual output.
Optimizer
This component tunes the model parameters to minimize the loss function. Different optimization tools such as gradient descent and adaptive gradient algorithm (AdaGrad) serve different purposes.
Layer 4: Application Layer
Layer 4 is the application layer, which is the customer-facing part of the AI architecture. You can ask AI systems to complete specific tasks, generate information, provide information, or make data-driven decisions. The application layer allows end users to interact with AI systems.
What are the challenges of implementing AI?
AI faces a number of challenges that make implementation more difficult. The following are examples of the most common challenges to implementing and using AI.
Data Governance
Data governance policies must adhere to regulatory constraints and privacy laws. To implement AI, you must manage data quality, privacy, and security. You are responsible for customer data and privacy protection. To manage data security, your organization
must have a clear understanding of how AI models are used
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