Artificial Intelligence 101: A Snapshot
By Team Acumentica
What is Artificial Intelligence(AI)?
In plain and simple terms, Artificial Intelligence (AI), also called Machine Intelligence (MI), leverages computers and machines to emulate human problem-solving and decision-making processes.
What is an Artificial Intelligence (AI) Model?
An AI system is built by developing AI models. An AI model consists of stochastic mathematical and statistical algorithms that collect, process, analyze, and convert data into predictive and prescriptive decision support systems to solve specific real-world problems.
What are the Different Types of AI?
There are two types of AI:
- Weak AI or Narrow AI
This is also called Artificial Narrow Intelligence (ANI). It is AI trained and focused on performing specific tasks. Nowadays, weak AI is ubiquitous, found in smartphones, Amazon Alexa, Google Siri, Acumentica Frida, and autonomous vehicles.
- Strong AI or AGI (Artificial General Intelligence)
Artificial General Intelligence (AGI) theoretically refers to a machine with intelligence comparable to a human being. It would be consciously aware of its surroundings and capable of learning, planning, and solving problems independently. While AGI is still in its theoretical phase, researchers and enthusiasts are actively exploring its development.
Difference Between Machine Learning (ML) and Deep Learning (DL)
Machine Learning (ML) and Deep Learning (DL) are often used interchangeably, but they are distinct. Both are subfields of AI, with deep learning being a subset of machine learning.
Machine Learning
Machine Learning (ML) refers to technologies and algorithms that enable machines to recognize patterns, perform decisions, provide recommendation support functions, and self-learn and improve over time. The three types of machine learning are:
- Unsupervised Learning
Uses unlabeled data, allowing the system to identify patterns and associations. A common use is clustering, which groups similar data points together. Examples include e-commerce recommendation systems like those on Netflix.
- Supervised Learning
Requires human intervention. The system is fed labeled training data to learn and make predictions. For example, teaching a system to recognize images of apples involves classification problems, where the system learns to identify apples from labeled images.
- Reinforcement Learning
Involves rewarding the system for correct actions and penalizing it for incorrect ones. Over time, the system learns through trial and error, reinforcing good actions. This is similar to how humans learn.
Where is Machine Learning Used Today?
Machine learning is used in various domains, such as:
- Healthcare: Implementing dynamic treatment regimens for patients with long-term illnesses.
- Autonomous Cars: Tesla and Waymo.
- Traffic Light Control.
- Sales: Acumentica AI Customer Generating System.
- Stock Predictions: Acumentica AI Stock Predictive System
Additionally, machine learning is used in everyday applications like spam filtering in Google, fraud detection in banks, and voice recognition in Amazon Alexa.
What is Deep Learning?
Deep Learning is a branch of machine learning that uses neural networks comprised of many layers. Unlike machine learning, where the agent is given processed data, deep learning uses raw data and autonomously determines relevant data. This reduces human intervention and allows the use of vast datasets, both structured and unstructured. Deep learning systems become more intelligent over time with more data.
Where is Deep Learning Used Today?
Deep learning is used in various applications, such as:
- Fraud Detection.
- Natural Language Processing (NLP): Acumentica AI Growth System.
- Customer Relationship Management: Acumentica AI Customer Generating System.
- Stock Predictions: Acumentica AI Stock Predictive System
- Computer Vision.
- Agriculture.
- AI Voice Recognition System: Acumentica.
- Virtual Assistants.
- E-commerce.
- Manufacturing.
How Does AI Work?
AI systems work similarly to refining petroleum for vehicle fuel. Data, whether structured or unstructured, is collected and processed to remove outliers. The clean data is then fed into an AI system with intelligent learning algorithms that use the data to self-learn and solve problems with minimal human intervention. Big data plays a crucial role in the effectiveness of AI inferences.
Why Acumentica?
Your growth is our success. This is our DNA. We focus on building relationships and growing companies by increasing revenue, ROI, while reducing costs.
At Acumentica, we are dedicated to pioneering advancements in Artificial General Intelligence (AGI) specifically tailored for growth-focused solutions across diverse business landscapes. Harness the full potential of our bespoke AI Growth Solutions to propel your business into new realms of success and market dominance.
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Each of these systems is built on the foundation of advanced AI technologies, designed to navigate the complexities of modern business environments with data-driven confidence and strategic acumen. Experience the future of business growth and innovation today. Contact us. to discover how our AI Growth Solutions can transform your organization.
