What is AI?

Artificial intelligence (AI), in essence, refers to the simulation of human intelligence processes by machines, particularly computer systems. Examples of AI applications include expert systems, natural language processing (NLP), speech recognition, and machine vision.

As the hype around AI has accelerated, vendors have scrambled to promote how their products and services incorporate it. Often, what they refer to as “AI” is a well-established technology such as machine learning.

AI requires specialized hardware and software for writing and training machine learning algorithms.

Weak AI vs. Strong AI

When discussing artificial intelligence (AI), it is common to distinguish between two broad categories: weak AI and strong AI. Let’s explore the characteristics of each type:

1) Weak AI (Narrow AI)

Weak AI refers to AI systems that are designed to perform specific tasks and are limited to those tasks only. These AI systems excel at their designated functions but lack general intelligence. Examples of weak AI include voice assistants like Siri or Alexa, recommendation algorithms, and image recognition systems. Weak AI operates within predefined boundaries and cannot generalize beyond their specialized domain.

2) Strong AI (General AI)

Strong AI refers to AI systems that possess human-level intelligence or even surpass it across a wide range of tasks. These systems can understand, reason, learn, and apply knowledge to solve complex problems like human cognition.

Types of Artificial Intelligence (AI)

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Below are the various types of AI:

1. Purely Reactive

These machines do not have any memory or data to work with, specializing in just one field of work. For example, in a chess game, the machine observes the moves and makes the best possible decision to win.

2. Limited Memory

These machines collect previous data and continue adding it to their memory. They have enough memory or experience to make proper decisions, but memory is minimal. For example, this machine can suggest a restaurant based on the location data that has been gathered.

3. Theory of Mind

This kind of AI can understand thoughts and emotions, as well as interact socially. However, a machine based on this type is yet to be built.

4. Self-Aware

Self-aware machines are the future generation of these new technologies. They will be intelligent, sentient, and conscious.

Ways of Implementing AI

Let’s explore the following ways that explain how we can implement AI:

1) Machine Learning

Machine learning (ML) is a branch of AI that uses data and algorithms to mimic human learning and improve accuracy over time.

2) Deep Learning

Deep learning (DL), a subset of machine learning, uses deep neural networks to mimic the brain’s decision-making. It powers most AI applications today.

Deep Learning vs. Machine Learning

Let’s explore the contrast between deep learning and machine learning:

Machine Learning:

Machine Learning deals with the creation of methods and techniques by which machines can learn from data and make a decision or prediction by themselves. Here are the key characteristics of machine learning:

Feature Engineering: In feature engineering in machine learning, specialists independently choose features out of the input information set to assist the algorithm in its work.
Supervised and Unsupervised Learning: Machine learning algorithms are of two types: supervised learning in which the learning algorithm learns from the examples that have the output also identified alongside the input and there is unsupervised learning the operation of which is independent of the output variable and it tries to identify the relationships that exist in data set which is up for analysis.
Broad Applicability: Machine learning approach has acceptance in almost all the fields in fields of image and speech recognition, natural language processing, and recommendation systems.

Deep Learning:

Deep Learning is a branch of machine learning that follows the process of developing artificial neural networks based on the patterns of human brains. Here are the key characteristics of deep learning:

Automatic Feature Extraction: A key advantage of deep learning algorithms is the useless of feature extraction where input data is preprocessed to extract important features.
Deep Neural Networks: An extension of machine learning is known as deep learning, where we use neural networks with layers of connected nodes known as neurons and can learn representations at multiple levels.
High Performance: The results from deep learning outdo traditional machine learning techniques in areas like computer vision, NLP, and speech recognition.

How does AI work?

In general, AI systems work by ingesting large amounts of labeled training data, analyzing that data for correlations and patterns, and using these patterns to make predictions about future states.

For example, an AI chatbot that is fed examples of text can learn to generate lifelike exchanges with people, and an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. Generative AI techniques, which have advanced rapidly over the past few years, can create realistic text, images, music, and other media.

Programming AI systems focus on cognitive skills such as the following:

Learning. This aspect of AI programming involves acquiring data and creating rules, known as algorithms, to transform it into actionable information. These algorithms provide computing devices with step-by-step instructions for completing specific tasks.
Reasoning. This aspect involves choosing the right algorithm to reach a desired outcome.
Self-correction. This aspect involves algorithms continuously learning and tuning themselves to provide the most accurate results possible.
Creativity. This aspect uses neural networks, rule-based systems, statistical methods, and other AI techniques to generate new images, text, music, ideas, and so on.

Why is AI important?

AI is important for its potential to change how we live, work, and play. It has been effectively used in business to automate tasks traditionally done by humans, including customer service, lead generation, fraud detection, and quality control.

In several areas, AI can perform tasks more efficiently and accurately than humans. AI’s ability to process massive data sets gives enterprises insights into their operations they might not otherwise have noticed. The rapidly expanding array of generative AI tools is also becoming important in fields ranging from education to marketing to product design.

Advances in AI techniques have not only helped fuel an explosion in efficiency but also opened the door to entirely new business opportunities for some larger enterprises. Before the current wave of AI, for example, it would have been hard to imagine using computer software to connect riders to taxis on demand, yet Uber has become a Fortune 500 company by doing just that.

AI has become central to many of today’s largest and most successful companies, including Alphabet, Apple, Microsoft, and Meta, which use AI to improve their operations and outpace competitors. At Alphabet subsidiary Google, for example, AI is central to its eponymous search engine, and self-driving car company Waymo began as an Alphabet division. The Google Brain research lab also invented the transformer architecture that underpins recent NLP breakthroughs such as OpenAI’s ChatGPT.

Generative AI

Generative AI, sometimes called “gen AI“, refers to deep learning models that can create complex original content—such as long-form text, high-quality images, realistic video or audio, and more—in response to a user’s prompt or request.

At a high level, generative models encode a simplified representation of their training data and then draw from that representation to create new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. However, over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep-learning model types:

Variational autoencoders or VAEs, which were introduced in 2013, enabled models that could generate multiple variations of content in response to a prompt or instruction.
Diffusion models, first seen in 2014, which add “noise” to images until they are unrecognizable, and then remove the noise to generate original images in response to prompts.
Transformers (also called transformer models) are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, frames of a video, or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard, and Midjourney.

How generative AI works

In general, generative AI operates in three phases:

Training, to create a foundation model.
Tuning, to adapt the model to a specific application.
Generation, evaluation, and more tuning, to improve accuracy.

Training

Generative AI begins with a “foundation model“; a deep learning model that serves as the basis for multiple different types of generative AI applications.

The most common foundation models today are large language models (LLMs), created for text generation applications. But there are also foundation models for image, video, sound, or music generation, and multimodal foundation models that support several kinds of content.

This training process is compute-intensive, time-consuming, and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which typically cost millions of dollars. Open-source foundation model projects, such as Meta’s Llama-2, enable gen AI developers to avoid this step and its costs.

Tuning

Next, the model must be tuned to a specific content generation task. This can be done in various ways, including:

Fine-tuning, which involves feeding the model application-specific labeled data—questions or prompts the application is likely to receive, and corresponding correct answers in the wanted format.
Reinforcement learning with human feedback (RLHF), in which human users evaluate the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.

Generation, evaluation, and more tuning

Developers and users regularly assess the outputs of their generative AI apps and further tune the model—even as often as once a week—for greater accuracy or relevance. In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.

Another option for improving a gen AI app’s performance is retrieval augmented generation(RAG), a technique for extending the foundation model to use relevant sources outside of the training data to refine the parameters for greater accuracy or relevance.

Benefits of Artificial Intelligence (AI)

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AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:

Automation of repetitive tasks.
More and faster insight from data.
Enhanced decision-making.
Fewer human errors.
24×7 availability.
Reduced physical risks.

Automation of repetitive tasks

AI can automate routine, repetitive, and often tedious tasks—including digital tasks such as data collection, entering, and preprocessing, and physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees us to work on higher-value, more creative work.

Enhanced decision-making

Whether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, data-driven decisions. Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real-time and without human intervention.

Fewer human errors

AI can reduce human errors in various ways, from guiding people through the proper steps of a process to flagging potential errors before they occur, and fully automating processes without human intervention. This is especially important in industries such as healthcare where, for example, AI-guided surgical robotics enable consistent precision.

Machine learning algorithms can continually improve their accuracy and further reduce errors as they’re exposed to more data and “learn” from experience.

Round-the-clock availability and consistency

AI is always on, available around the clock, and delivers consistent performance every time. Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications—such as materials processing or production lines—AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks.

Reduced physical risk

By automating dangerous work—such as animal control, handling explosives, performing tasks in deep ocean water, high altitudes, or in outer space—AI can eliminate the need to put human workers at risk of injury or worse. While they have yet to be perfected, self-driving cars and other vehicles offer the potential to reduce the risk of injury to passengers.

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