Basics of AI

Understanding the Basics of AI?

AI is a subfield of computer science that aims to develop systems that can perform tasks that typically require human intelligence. These include learning, reasoning, problem-solving, perception, and language understanding.

At the heart of AI are machine learning and deep learning. Machine learning uses algorithms to parse data, learn from it, and make informed decisions based on what it has learned. Deep learning, a subset of machine learning, uses structures called neural networks that mimic the human brain’s operations.

The latest AI advancements, like OpenAI’s GPT series and Google’s BERT, have significantly pushed the boundaries in natural language processing, enhancing how machines understand human language.

Beyond language, AI’s capabilities are also impacting various sectors, particularly healthcare. AI-driven tools are transforming patient care through more accurate diagnostics, personalized medicine, and robotic surgeries.?

We can also see the impact of AI in public transportation through the use of self-driven vehicles and drones.

In creative industries, artists are harnessing AI to compose music, create artwork, and even write scripts.?

Beyond typical voice assistants like Siri and Alexa, AI also plays a significant role in powering a variety of smart home devices, improving home automation and security through the Internet of Things (IoT).

Increasingly, building AI systems is becoming less complex and cheaper. The principle behind making a good AI is collecting relevant data to train the AI model. AI models are programs or algorithms that enable the AI to recognize specific patterns in large datasets.

The better you make AI technology, the more wisely it can analyze vast amounts of data to learn how to perform a particular task.

The process of analyzing data and performing tasks is called machine learning (ML). For example, Natural language processing (NLP) gives machines the ability to read, understand human languages, and mimic that behavior. The most promising AI apps rely on ML and deep learning. The latter operates based on neural networks built similarly to those in the human brain.

Real-world applications of AI systems are wide-ranging. Below, you can find the most common examples of AI in daily life:

• Speech Recognition

Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that uses NLP to process human speech into a written format. For example, Siri utilizes speech recognition to conduct voice searches.

• Customer Service

Increasingly, more companies are turning to online virtual agents for customer service, thus replacing human agents. According to Servion Global Solutions, 95% of all customer interactions will involve artificial intelligence by 2025.

• Computer Vision

In this case, AI technology allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs. You can see its application in photo tagging on social media.

• Discovery of Data Trends

AI algorithms can use consumers’ behavior to discover data trends, allowing companies to build effective cross-selling strategies. As a result, companies can offer relevant add-on recommendations during the checkout process. That’s where predictive analytics software steps in.

Such software allows real-time decision-making with your data. For instance, the software can generate risk assessment models, such as fraud and risk detection, targeted advertising, and product recommendations.

• Fraud Prevention

One of the primary problems that artificial intelligence tackles is payment and sensitive information fraud. Companies utilize AI-based systems to detect and prevent this type of fraud effectively.

• Automated Stock Trading

AI-based high-frequency trading platforms make thousands or, sometimes, millions of trades each day. As of 2020, half of stock market trades in America were automated. According to Allied Market Research, the global algorithmic market size is forecast to account for $31.2 million by 2028.

Different Types of AI

Before building your AI, make sure you understand the three types of AI:

1. Artificial Narrow Intelligence (ANI): We encounter this type every day. ANI excels in handling specific tasks, such as driving a car using autonomous driving technologies or managing your schedule like Apple’s Siri. ANI is often referred to as “Weak AI.” However, despite its label, there’s nothing weak about its capabilities within its specialized domain. It performs with remarkable precision and efficiency.?
2. Artificial General Intelligence (AGI): A step beyond ANI, AGI represents a hypothetical AI with human-like cognitive abilities. This type of AI can learn, understand, and apply intelligence across a broad spectrum of tasks, matching the intellectual capabilities of humans. While AGI is still in the developmental stages, it is seen as a potential bridge to more advanced forms of AI. Enthusiasts even suggest that glimpses of AGI capabilities might be observed in advanced systems like OpenAI’s GPT-4, sparking exciting discussions in technology circles.
3. Artificial Superintelligence (ASI): ASI is the pinnacle of AI research. In this envisioned future, AI surpasses human intelligence in all aspects, from creativity and social skills to problem-solving and scientific innovation. This level of AI is expected to pioneer new discoveries and innovations at an unprecedented pace.