There’s no shame in thinking about super intelligent robots that will dominate human beings when you hear the term ” artificial intelligence”. Actually, it’s even safe to blame movies like The Matrix, I. Robot, Terminator and even series like The 100 for deeply rooting those thoughts in our subconscious. So, what really is artificial intelligence? Why is it called artificial and what’s with the robot myth? Too many questions spinning at lightning speed, so let’s set some good foundations.
I promise you nothing too big, but after this article you sure will know how to navigate the fascinating and ever-perplexing realm of artificial intelligence. Everything is simply explained and split into three sections: The Basics, A Deep Dive, and An Overview.
PART 1 – The Basics of Ai :
1- What Is Artificial Intelligence?
To truly understand or define the scientific term “artificial intelligence”, let’s decipher each word on its own:
Artificial: not created naturally → man-made “thing” (usually a machine) that mimics the natural.
Intelligence: The single most important trait in humans.
Artificial + Intelligence: Machines that simulate natural intelligence → artificial intelligence is programmed to learn and mimic the actions of humans.
John McCarthy (father of Ai) Definition (mid 1950s):
“It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
Oxford Dictionary Definition:
“The study and development of computer systems that can copy intelligent human behaviour.”
In the world of technology, Artificial intelligence (AI) is a wide-ranging branch of computer science, more specifically an interdisciplinary science with multiple approaches. It refers to the capability of a computer/machine to imitate the characteristics of the human brain by replicating its intelligence.
What is Intelligence?
What is so special about human intelligence that scientists are trying to replicate it with machines?
Our level of intelligence sets us apart from other living beings. It is basically the foundation of human experience. Some experts define intelligence as the ability to adapt, solve problems, plan, improvise in new situations, and learn new things.
To better understand Ai, you should know that at its core, it tries to replicate every trait in human intelligence, and these become Ai traits and goals , such as:
2- Goals of Ai
What was the goal of inventing the wheel or smartphones? The ultimate goal of any scientific or technological advancement is to better human life and experience. Artificial intelligence is based on the principle that:
Human intelligence can be defined in a specific scientific way for a machine to easily mimic it. Based on that mimesis execute any given task → replicate human intelligence.
The ultimate goals of Ai become:
- To mimic human cognitive activity.
- To reach a system with its own consciousness. (NOTE: This is arguably the ultimate goal in AI research, but is still VERY far-fetched.)
3- Why Is Ai Important?
AI augments human intelligence with rich analytics and pattern prediction capabilities to improve the quality, effectiveness, and creativity of human decisions.
- AI automates repetitive learning and discovery through data.
- AI works at superhuman speeds.
- AI adapts through progressive learning algorithms to let the data do the programming.
- AI analyzes more and deeper data using neural networks that have many hidden layers.
- AI achieves incredible accuracy through deep neural networks.
What Makes AI Technology So Useful?
Artificial intelligence offers several critical benefits that make it an excellent tool, but the most important are:
1- Automation – AI can automate tedious processes/tasks, without any fatigue.
2- Analysis and Accuracy– AI analysis is much faster and more accurate than humans. AI can use its ability to interpret data with better decisions.
3- Speed – AI can perform repetitive , detail-oriented tasks at superhuman speeds and with relatively few to no errors.
4- What Are The Different Types of Ai?
Artificial Intelligence can be broadly divided into two categories: AI based on capability and AI based on functionality.
TYPE 1: AI Based on Capabilities
1- Weak AI or Narrow AI:
- Narrow AI is a type of AI which is able to perform a dedicated task with intelligence.
- Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI.
- Narrow AI can fail in unpredictable ways if it goes beyond its limits.
- The most common and currently available AI is Narrow AI.
- Examples include playing chess, purchasing suggestions on e-commerce site, self-driving cars, Siri, speech recognition, and image recognition.
- Despite its impressive abilities, ChatGPT is still a limited memory AI system.
- It is unique from other chatbots because it can call on past answers to update its current output.
- Unfortunately, it’s limited to a single medium: text-based chat.
That makes it a form of narrow or “weak” AI.
2- General AI:
- General AI is a type of intelligence which could perform any intellectual task with efficiency like a human.
- The idea behind the general AI to make such a system that can be smarter and think like a human by its own.
- Currently, there is no example of a system that comes under General AI. However, Researchers worldwide are focused on developing machines with General AI.
3- Super AI:
Here we jump into fiction and theory.
- Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task.
- Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence.
- It can perform any task better than humans with cognitive properties.
- Some key characteristics of strong AI include capability include the ability to think, to reason,solve the puzzle, make judgments, plan, learn, and communicate by its own.
TYPE 2: AI Based on Functionalities:
Arend Hintze, an assistant professor of integrative biology, computer science, and engineering at Michigan State University, explained that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and sentient systems, which do not yet exist. The categories are as follows:
1- Reactive Machines:
These AI systems have no memory and are task-specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on a chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
2- Limited memory:
These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
3- Theory of Mind:
Theory of mind is a psychological term. When applied to AI, it means the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams. But don’t worry, it does not exist yet.
In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI sits more on the theoretic science fiction side.
Part 2 – A Deep Dive Into Artificial Intelligence:
1- How Does AI Work?
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Without going into much detail, generally , AI systems work by:
- Ingesting large amounts of labeled training data
- Analyzing the data for correlations and patterns
- Using these patterns to make predictions about future states
In this way, a chatbot that is fed examples of text can learn to generate lifelike exchanges with people. Similarly, an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
How Does AI Programming Work?
Briefly, AI programming focuses on cognitive skills that include the following:
- Learning: acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
- Reasoning: choosing the right algorithm to reach a desired outcome.
- Self-correction: designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
- Creativity: uses neural networks, rule-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.
2- What Are the Subfields of Artificial Intelligence?
AI is not a single technology. Instead, it is an umbrella term that includes many subfields. Actually, even within the subfields of Ai exist other subfields. Artificial Intelligence is a broad and interdisciplinary field that encompasses various specialized areas. Here are some of the major subfields of AI:
1- Machine Learning (ML): Machine learning focuses on developing algorithms that enable computers to learn from data. Therefore, make predictions or decisions without being explicitly programmed.
2- Natural Language Processing: NLP focuses on enabling computers to understand, interpret, and generate human language.
3- Computer Vision: Computer vision involves teaching machines to interpret and understand visual information from the world, basically to mimic human vision.
4- Robotics: Robotics combines AI and engineering to create autonomous machines that can perform tasks in the physical world.
5- Deep Learning and Neural Networks: Neural networks are a computational model inspired by the structure and function of the human brain. Deep neural networks, or Deep learning (which is a subset of machine learning) have multiple hidden layers and have shown exceptional success in tasks like image and speech recognition.
3- What Are Examples of Ai ?
There are several use cases and applications for Ai that range from AI tools to services. To give you a starting point, here are some examples you might be familiar with:
ChatGPT is an artificial intelligence chatbot capable of producing written content in a range of formats, from essays to code and answers to simple questions. Launched in November 2022 by OpenAI, ChatGPT is powered by a large language model that allows it to closely emulate human writing.
2- Smart Assistants
Personal assistants like Siri, Alexa and Cortana use natural language processing, or NLP, to receive instructions from users to set reminders, search for online information and control the lights in people’s homes.
3- Snapchat & Instagram Filters
These filters use ML algorithms to distinguish between an image’s subject and the background, track facial movements, and adjust the image on the screen based on what the user is doing.
4- Self-Driving Cars
Self-driving cars are a recognizable example of deep learning. They use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signals, and much more.
PART 3 – Artificial Intelligence Overview:
Now let’s go over artificial intelligence in an evaluative way.
1- Advantages & Disadvantages of Artificial Intelligence:
Advantages of AI
- Automation: AI can automate repetitive and mundane tasks, freeing up human resources for more creative and complex tasks.
- Accuracy and Precision: AI systems can perform tasks with a high level of accuracy and consistency, minimizing errors caused by human fatigue or oversight.
- Quick Data Analysis: AI can quickly analyze and process large volumes of data, extracting valuable insights and patterns that would be challenging for humans to identify in a short period of time.
- 24/7 Availability: AI programs do not need to sleep or take breaks, For example chatbots can provide support 24/7, enhancing customer experiences.
- Handling Dangerous Tasks: AI can be used in situations that are hazardous to humans, such as exploring dangerous environments, diffusing bombs, or handling toxic substances.
- Medical Applications: AI can assist in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans based on patient data.
- Efficiency and Productivity: AI can optimize processes, workflows, and supply chains, leading to increased efficiency and reduced costs.
- Personalization: AI-driven recommendations, like those in streaming platforms or online shopping, can provide personalized experiences based on user preferences and behaviors.
Disadvantages of AI
- Initial Implementation Costs: Developing and implementing AI systems can be very expensive, and organizations might need to invest in specialized infrastructure and personnel.
- Bias and Fairness: AI systems can inherit biases from training data, leading to unfair or discriminatory outcomes, particularly in areas like hiring and lending.
- Privacy Concerns: AI can process and analyze vast amounts of personal data, raising concerns about the privacy and security of individuals’ information.
- Dependency and Reliability: Overreliance on AI systems can be problematic if they fail or make incorrect decisions. It’s important to have backup plans and human oversight.
- Ethical Dilemmas: AI poses ethical challenges, such as determining responsibility when AI systems make mistakes or decisions with significant consequences.
- Loss of Human Touch: In certain industries, like customer service, the use of AI might lead to a loss of human interaction and a less personal experience.
- Skill Gap: As AI evolves, there might be a gap between the skills required by the job market and the skills possessed by the workforce.
- Job Displacement: Automation by AI may lead to job loss in certain industries, as machines replace tasks that were previously performed by humans.
2- Challenges and Limitations of Ai:
Based on the following disadvantages, artificial intelligence faces the following main challenges:
- Bias, due to improperly trained algorithms and human bias which leads to unreliable results.
- Misuse, due to deepfakes and phishing.
- Legal concerns, including AI libel and copyright issues.
- Elimination of human jobs.
- Data privacy concerns, particularly in the banking, healthcare and legal fields.
- Ai Processing requirements are way above budget.
- Lack of general public understanding of Ai which leads to lack of trust and unclear goals.
3- What is the Future of Ai?
Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception – To the extent that these can be concretely defined.
Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is intertwined with value judgments that are subject to human experience.
The next step in artificial intelligence research is achieving General AI. It’s the concept where machines can perform any intellectual task a human can. However, this remains a long-term goal that raises philosophical, ethical, and technical questions. Researchers are also exploring fields like quantum computing and neuromorphic computing to advance AI capabilities.
Despite the wonders Ai technology is doing right now, we are still at the tip of the iceberg. You may want to read up on the 7 stages of artificial intelligence.
Conclusion: Ai Is The Inevitable Future
Long story short, Artificial intelligence allows machines to model, or even improve upon, the capabilities of the human mind. And from the development of self-driving cars to the proliferation of generative AI tools like ChatGPT and Google’s Bard, AI is increasingly becoming part of our everyday life. There is no future without Ai in it, and we need to buckle up and enjoy the ride, whether it leads to our doom or salvation. However, rest assured that super world-apocalyptic robots are still a myth (for now).