What is AI? Demystifying artificial intelligence in 2024 - Explained for beginners
Exploring the essence of artificial intelligence in 2024, breaking down its complexities and unveiling its significance.
What exactly is artificial intelligence?
When the term artificial intelligence (AI) comes up, images of self-driving cars, robots, AI chatbots like ChatGPT, and computer-generated images might spring to mind. However, it's essential to delve deeper into AI's workings, understanding its mechanisms and impacts on current and future generations.
Formally introduced in the 1950s, AI refers to a machine's capability to perform tasks that previously required human intelligence. This definition has evolved over decades of research and technological progress.
To assess whether an artificial system truly possesses intelligence, it's crucial to first define the term "intelligence" when attributing it to a machine, such as a computer.
Our unique intelligence distinguishes us from other living creatures and plays a crucial role in the human experience. According to some experts, intelligence encompasses the capacity to adapt, solve problems, plan, improvise in unfamiliar situations, and acquire new knowledge.Given the pivotal role of intelligence in the human experience, it's understandable that we would seek to replicate it artificially through scientific pursuits. Modern AI systems exhibit certain aspects of human intelligence, such as learning, problem-solving, perception, and to a limited extent, creativity and social intelligence.
How can I use AI?
AI manifests in various forms that are now commonplace in daily life. Smart speakers featuring voice assistants like Alexa or Google exemplify this. Similarly, widely-used AI chatbots like ChatGPT, Bing Chat, and Google Bard are prominent examples.
While these systems don't replicate human intelligence or social interaction, they can leverage their training to adapt and acquire new skills for tasks not explicitly programmed.
What types of AI exist?
Artificial intelligence can be categorized into three main subtypes: narrow AI, general AI, and super AI.
What is narrow AI?
Artificial narrow intelligence (ANI) plays a vital role in voice assistants like Siri, Alexa, and Google Assistant. These systems are designed or trained to perform specific tasks or solve particular problems without explicit programming for each task. ANI, sometimes called weak AI, lacks general intelligence but is proficient in tasks like image recognition, basic customer service interactions, and content moderation online.
ChatGPT is an example of ANI because it's programmed to perform a specific task: generating text responses based on the prompts it receives.
What is general AI?
Artificial general intelligence (AGI), often referred to as strong AI, remains a theoretical concept. AGI would entail machines understanding and performing a wide range of tasks based on their accumulated experience, akin to human intellect.
Similar to humans, AGI would have the potential to comprehend intellectual tasks, think abstractly, learn from experiences, and apply knowledge to solve new problems. This concept includes the idea of a system or machine possessing common sense, a feat not currently achievable with existing AI technologies. While developing a system with consciousness remains a distant goal, it represents the pinnacle of AI research.
What are some recent instances of AI?
The most significant advancements in AI include the development and launch of GPT-3.5 and GPT-4. However, there have been numerous other groundbreaking achievements in artificial intelligence, too many to list comprehensively here. One notable example is ChatGPT, an AI chatbot capable of natural language generation, translation, and answering questions. Another groundbreaking development is the creation of GPT-1, GPT-2, and GPT-3 by OpenAI, which have had a profound impact on the field of artificial intelligence.
GPT stands for Generative Pre-trained Transformer. When it was launched in 2020, GPT-3 was the largest language model in existence, with 175 billion parameters. The most recent version, GPT-4, which can be accessed through platforms like ChatGPT Plus or Bing Chat, boasts one trillion parameters.
Self-driving cars While concerns about the safety of self-driving cars remain high among potential users, the technology is continuously advancing and improving through AI breakthroughs. These vehicles utilize machine-learning algorithms to integrate data from sensors and cameras, enabling them to perceive their surroundings and make optimal decisions. Tesla's autopilot feature in its electric vehicles is often the first thing that comes to mind when thinking about self-driving cars. However, Waymo, a company under Google's parent company, Alphabet, offers autonomous rides similar to a taxi service in San Francisco, CA, and Phoenix, AZ. Cruise is another robotaxi service, and major auto manufacturers like Apple, Audi, GM, and Ford are also believed to be developing self-driving vehicle technology.
Robotics Boston Dynamics has made remarkable advancements in AI and robotics. While we're far from creating AI akin to the Terminator movies, observing Boston Dynamics' robots utilize AI to navigate and react to various terrains is truly impressive.
DeepMind DeepMind, a subsidiary of Google, is a trailblazer in AI, aiming for the ultimate goal of artificial general intelligence (AGI). While it hasn't achieved this yet, DeepMind gained prominence in 2016 with AlphaGo, an AI that defeated a human Go champion.
Since then, DeepMind has developed a system for predicting protein folding, which is crucial for understanding diseases, and has created programs that can diagnose eye conditions as accurately as leading global doctors.
What is super AI?
ASI is a theoretical system where a machine's intelligence surpasses all human capabilities, potentially leading to profound impacts on humanity. This concept, reminiscent of science fiction, envisions a scenario where machines outperform humans in every aspect of intelligence, raising significant ethical and existential questions.
While the idea of a self-improving intelligent system remains theoretical, its potential applications, if implemented ethically and efficiently, could drive remarkable advancements in fields like medicine and technology.
What is machine learning?
The key distinction of AI from other computer science fields is its capacity to automate tasks through machine learning. This allows computers to learn from diverse experiences instead of being explicitly programmed for each task. While often synonymous with AI, machine learning is actually a subset of artificial intelligence.
In machine learning, a system is trained on extensive datasets to learn from errors and identify patterns for accurate predictions and decisions, even with data it hasn't encountered before.
Machine learning is evident in various applications like image and speech recognition, fraud protection, and more. For instance, consider Facebook's image recognition system. When users upload photos, the platform can analyze them to recognize faces, prompting suggestions to tag different friends. Over time, the system improves its accuracy through practice and learning.
What constitutes machine learning?
Machine learning, a subset of AI, is typically divided into two primary categories: supervised and unsupervised learning.
Supervised learning
This method involves training AI systems using numerous labeled examples categorized by humans. These systems are provided with vast amounts of annotated data that highlight specific features, effectively teaching through demonstration.
For instance, if you wanted to train a machine-learning model to distinguish between images of circles and squares, you would gather a large dataset containing images of circles and squares in various contexts, such as a drawing of a planet for a circle or a table for a square. Each image would be labeled to indicate its corresponding shape.
The algorithm would then learn from this labeled dataset, identifying the distinguishing characteristics of circles (e.g., no corners) and squares (e.g., four equal sides). Once trained, the system would be able to analyze a new image and determine its shape based on what it learned from the dataset.
Unsupervised learning
Unsupervised learning, on the other hand, takes a different approach. Algorithms in unsupervised learning aim to identify patterns in data, searching for similarities that can help categorize the data.
For instance, one example could involve grouping together fruits that weigh a similar amount or cars with a similar engine size.The algorithm isn't pre-programmed to identify specific types of data; instead, it searches for similarities in the data that can be grouped together. For instance, it might group customers based on their shopping behavior to target them with personalized marketing campaigns.
Reinforcement learning
In reinforcement learning, the system seeks to maximize a reward based on its input data, essentially going through a trial-and-error process to achieve the best outcome possible.
For example, in training a system to play a video game, it could receive a positive reward for achieving a high score and a negative reward for a low score. Through analyzing the game and making moves, the system learns solely from the rewards it receives, eventually becoming capable of playing independently and achieving high scores without human intervention.This approach is also utilized in research, particularly in teaching autonomous robots how to behave optimally in real-world settings.
What are large language models?
One of the most prominent types of AI today is the large language model (LLM). These models utilize unsupervised machine learning and are trained on extensive text datasets to grasp the complexities of human language. These datasets encompass various sources such as articles, books, and websites.
During the training phase, LLMs analyze billions of words and phrases to identify patterns and correlations, enabling them to produce responses that resemble human language.
The most well-known LLM is GPT-3.5, which serves as the foundation for ChatGPT, while the largest LLM is GPT-4. Google's Bard utilizes LaMDA, another significant LLM, which ranks as the second-largest in size.
What is deep learning?
Part of the machine-learning domain, deep learning focuses on training artificial neural networks with three or more layers to execute various tasks. These networks are expanded into complex structures with numerous deep layers, which are trained using vast datasets.
Deep-learning models typically consist of more than three layers, often reaching hundreds of layers. They can employ supervised, unsupervised learning, or a blend of both during training.
Deep-learning technology is frequently employed in natural language processing (NLP), speech recognition, and image recognition because it can learn intricate patterns in data through AI.
What are neural networks?
Machine learning's effectiveness hinges on neural networks, which are mathematical models loosely inspired by the interconnectedness of neurons in the human brain, replicating their signaling process.
Picture a team of robots collaborating to solve a puzzle, each specialized in recognizing a specific shape or color in the puzzle pieces. The robots pool their abilities to solve the puzzle collectively. A neural network operates similarly to this group of robots.
Neural networks can adjust internal parameters to alter their output. They are trained on datasets to learn the appropriate output for specific inputs.
Neural networks consist of layers of algorithms interconnected to process data. They can be trained for specific tasks by adjusting the importance of data between layers. During training, the weights assigned to data passing through layers are adjusted until the output closely matches the desired result. Once trained, neural networks can perform tasks like identifying fruit in images or predicting elevator failures based on sensor data.
What is conversational AI?
Conversational AI refers to systems designed to interact with users through conversation, listening to input and generating responses. These systems utilize natural language processing to understand and reply in a human-like manner.
Examples of conversational AI include chatbots such as Google Bard, voice assistants like Amazon Alexa on smart speakers, and virtual assistants like Siri on smartphones.
What AI services are accessible for use?
Consumers and businesses have access to a wide range of AI services that help streamline tasks and enhance everyday life. These services are found in many households and workplaces, offering both free and paid options. Examples include:
- Chatbots: These virtual assistants can interact with users and engage in human-like conversations, sometimes showing empathy.
- Language translation: Services like Google Translate, Microsoft Translator, Amazon Translate, and ChatGPT use machine learning to translate text.
- Voice assistants: Amazon Alexa, Apple's Siri, and Google Assistant use natural language processing to understand and respond to user commands.
- Productivity tools: Microsoft 365 Copilot, powered by a large language model, automates tasks in applications like Word, PowerPoint, and Excel based on user inputs.
- Software development: AI tools like ChatGPT and GitHub Copilot assist developers in writing and debugging code more efficiently.
- Image and video recognition: AI is used to identify faces, text, and objects in images and videos, with examples like Clarifai and Amazon Rekognition.
- Business solutions: Services such as OpenAI's GPT-4 API and Amazon Bedrock offer AI tools for businesses to build applications and services using large language models.
Which company is at the forefront of the AI competition?
While generative AI is driving many of the major advancements in artificial intelligence in 2023, several other leading companies are also making significant strides in their own AI innovations.
Alphabet
Google's parent company, Alphabet, is deeply involved in various AI systems through its subsidiaries, such as DeepMind, Waymo, and Google itself. DeepMind is particularly focused on advancing artificial general intelligence, as demonstrated by its efforts to solve complex scientific problems using AI. The company has developed machine-learning models for Document AI, enhanced the user experience on YouTube, made AlphaFold accessible to researchers globally, and undertaken other significant initiatives.
While Alphabet's advancements in artificial intelligence may not always make daily headlines, its deep learning projects and broader AI initiatives hold significant potential to shape the future for humanity.
OpenAI
OpenAI's dominance in the current AI landscape is understandable, given its strategic move to provide free access to generative AI tools like ChatGPT and DALL-E 2, an image generator.
Microsoft
In addition to developing Microsoft 365 Copilot for its suite of applications, Microsoft offers a range of AI tools for developers on Azure. These tools include platforms for building machine learning models, data analytics solutions, and conversational AI applications. They also provide customizable APIs that deliver human-like performance in computer vision, speech recognition, and natural language processing.
Microsoft has made significant investments in OpenAI's development, incorporating GPT-4 into the new Bing Chat and utilizing an enhanced version of Dall-E 2 for the Bing Image Creator.
Other companies
These are just a few examples of companies at the forefront of the AI race, but there are many others globally that are also making significant advancements in artificial intelligence, including Baidu, Alibaba, Cruise, Lenovo, Tesla, and others.
How AI will impact the world?
Artificial intelligence has the potential to revolutionize various aspects of our lives, including work, healthcare, media consumption, transportation, and privacy. For example, AI-powered voice assistants can help people hail rides from autonomous cars, leading to more efficient commuting experiences.
Doctors and radiologists could diagnose cancer more efficiently, identify disease-related genetic sequences, and discover molecules for better medications, potentially saving many lives.On the other hand, the rise of neural networks like Dall-E 2, Midjourney, and Bing, which can create realistic images, replicate voices, or produce deepfake videos, poses a threat to the authenticity of photos, videos, and audios.
Another ethical concern regarding AI involves facial recognition and surveillance, raising issues of privacy intrusion. Many experts advocate for banning this technology altogether.
Could AI take over your job?
The potential for AI to replace a significant portion of current jobs is a realistic possibility in the near future. While AI may not replace all jobs, it is certain to change the nature of work, and the only question is how quickly and to what extent automation will transform the workplace.
However, artificial intelligence cannot function independently. While many jobs involving routine, repetitive tasks may be automated, workers in other fields can use tools like generative AI to enhance their productivity and efficiency.
There is a wide range of opinions among AI experts regarding the timeline for AI systems to surpass human capabilities. Fully autonomous self-driving vehicles are not yet a reality. However, according to some predictions, the self-driving trucking industry alone could potentially displace over 500,000 jobs in the US, not including the impact on couriers and taxi drivers.
