The Beginner’s Guide to AI
The Beginner’s Guide to AI
Explore a beginner's guide to Artificial Intelligence and look at what it is, why it matters, and the evolution of it.
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Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Read how Alegion's Chief Data Scientist discusses the source of most headlines about AI failures here.
What Is Artificial Intelligence?
Artificial Intelligence is the simulation of the human behavior with regard to intelligence processes involved in problem-solving. These processes involve learning, analysis, planning, perception, reasoning, correction, speech recognition, linguistics, and other streams of human cognitive science.
Why Artificial Intelligence Matters
AI simplifies tasks by making machines learn from their past experiences, map efforts and actions to results, identify errors, correct them, adjust to new and random input values, and perform human-like tasks effortlessly by deeply analyzing scenarios. For this, AI uses Natural Language Processing in order to understand natural human communication and translates it internally to a code that machines comprehend. AI also depends on Deep Learning to accomplish this task. Through the application of these technologies, AI trains computers to perform specific tasks with the least amount of human intervention by processing large data volumes and recognition of familiar or new patterns in data.
Objectives of AI
- Creation of Expert Systems: It involves the creation of automated systems that exhibit intelligent behavior and advice humans on the right course of action.
- Implementation of Human Intelligence in Computers: It will help create identical cognitive patterns in computers which will help them behave like humans and take appropriate actions to solve complex problems. This will enable automated processes and reduced human workload through the application of algorithms.
- Multi-Domain Application: AI will help in multiple domains of implementation like Computer Science, Cognitive Science, Statistics, Psychology, Medical Science, Engineering, Ethics, Natural Sciences, Healthcare, Space Technology, Logic, Linguistics, E-commerce, and more.
- Applications in Computer Science: AI helps in developing a number of mechanisms to solve many difficult problems in the field of computer science like Search and Optimization, Logic, Control Theory, Language Analysis, Neural Networks, Classifiers, and Statistical Learning Methods, and Probabilistic Methods for uncertain reasoning.
Evolution of AI
Coined in 1956 by a scientist called John McCarthy, the term Artificial Intelligence has gained its importance today because of larger data volumes, advanced algorithms and programming, and advancements in computer storage and power.
The Implementation and Building of AI Systems Depend on the Following Techniques:
- Natural Language Processing: It is the study of how computers interact with a natural human language and derive an understanding to take appropriate actions. Speech recognition and synthesis are two NLP applications. Voice assistants like Siri, Google Assistant, Amazon’s Alexa, etc are examples of NLP implementation. Other mechanisms used are parsing, text recognition, part-of-speech tagging, text mining, machine translation, and information retrieval.
- Vector Machines: These machines help with information classification based on a parameter value. The use of distinction for division plays an important role in implementing vector machines. Image recognition, face recognition, and text recognition systems are implementations of Vector Machines.
- Heuristics: The fundamental human behavior in learning processes of understanding mistakes and avoiding them is adopted by Heuristics, that works on trial-and-error. This involves the mapping of errors and corrections similar to human brains.
- Artificial Neural Networks: ANN technique is modeled based on the human brain wiring and behavior, by identifying patterns in data inputs. ANN processes large volumes of data to recognize patterns and map them to make decisions similar to that made by a human brain. For this, it uses methods like supervised learning, unsupervised learning, pattern recognition, reinforced learning, machine learning, and deep learning to ape human learning behavior.
Types of AI
The various types of Artificial Intelligence are classified under two categories: Type 1 (Basic) and Type 2 (Advance). Type 1 AI systems are intelligent machines that work based on an input and the programmed output based on variable parameters. The Type 2 AI systems consider real-time events, scenarios, and entities present at the time of decision-making and act accordingly. Observations and dynamic situations influence Type 2 AI systems which are also called sentient systems because they recognize, analyze, and react like humans with emotions.
- Weak or Narrow Artificial Intelligence: This method focuses on one narrow task rather than an array of tasks through generic automation of a series of related tasks. This is the mechanism that not-so-intelligent machines with limited capacities follow without compromising on their smart processing capabilities. An example would be the game of Solitaire, where all machine instructions, rules, and valid moves are fed into the machine when it plays against a human.
- Strong Artificial Intelligence: In this system, the computer analyses and reasons like the human mind through Artificial General Intelligence on weak AI, and behaves similarly in solving more complex problems. Machines seem more sentient because of the way they respond to queries using complex internal algorithms. Voice Assistants like Siri & Google Assistant, that answer random questions in a more realistic way are examples of strong AI.
- Reactive Machines: These are in the moment machines that do not act on past information for future actions. They are the most basic type that can predict a certain outcome based on the current values of various parameters without depending on past data or memory stored. Gaming software for Chess is an example of reactive machines that responds based on a move and does not manipulate it with prior knowledge.
- Limited Memory: These AI systems can use information stored based on past experiences to influence future decisions. Driving assistants are examples that take on-spot decisions based on random and dynamic parameter values. They use memories of a previously visited place to show the best traversal route.
- Theory of Mind: These AI machines react to responses based on emotions, beliefs, thoughts, scenarios, etc. They are useful for social interactions and human demeanor observation.
- Self-Aware: These systems use their own capacitive thinking called super intelligence based on self-awareness of internal traits, states, opportunities, and conditions. This is just a probability that has scope for future implementation.
Major Benefits of AI
The five major benefits of Artificial Intelligence can be associated with its implementation in the modern-day business and personal applications.
- Automation of Customer Interactions: Most customer interactions require human intervention. AI can be used to automate emails, calls, online chats, query responses, purchase advice, social media conversations, etc. Previous customer interaction patterns are stored internally for analysis and provision of better customer experience.
- Real-Time Assistance: This is useful for businesses that need to constantly cater to the needs of a large number of customers in real-time with strict time constraints. Examples include the airline ticket management system, where real-time weather and flight status information needs to be shared with customers in a personalized way.
- Data Mining Capabilities: Using cloud-based AI will quickly discover relevant information after the processing of big data. This could provide better insights into how to improve business performance and gain a competitive edge.
- Power of Prediction: AI systems are based on data analysis of the past and help make predictions with the dynamic information they possess. Many inventory management systems and online marketplaces use this benefit of Artificial intelligence to leverage their sales and determine which products need to be stocked up for sales.
- Operational Automation: AI systems provide cross-functional intelligence for operational automation. They easily collaborate with other technologies to do so in various fields. Examples include temperature control and Intelligent Heating/Cooling for home ventilation systems and freezer storage for consumer food industries. Inventory tracking has been made easy by collaborating with RFID and Cloud technology.
Differences Between ML and AI
Machine learning is the branch of AI where the goal is defined and the steps to achieve this goal are fed into the system along with the rules and alternative actions to be considered. This information is automatically learned by the system that improves itself based on experience. The program that generates the algorithm integrates the input and the output for efficient performance.
- The main aim is to produce accurate results than to produce the desired ones
- It does not involve randomization based on changing parameters, rather it is based on fixed values
- The goal of ML is to learn from a predetermined set of data and produce mechanical solutions for better machine performance, no decision-making is involved
- It is completely algorithm-based with structured data formats for inputs and outputs
- It involves knowledge building without the discretion of what is right or wrong
- The important functionality of ML involves pattern recognition in huge data sets and acting on them
Artificial Intelligence is based on the ability of a machine to acquire knowledge and apply it skillfully based on various real-life scenarios and real-time data. In AI the goal is to act instead of a human in a human-like manner in order to execute specific independent or interdependent tasks. AI may incorporate multiple programming integrations, validations, and pattern recognition methods to behave in an expected way.
- The main aim of AI is to produce a result through intelligent data analysis, mining, and deeper understanding
- It involves producing results based on smart real-time processing and automation where the parameters are dynamic
- The goal of AI is to simulate human intelligence in producing realistic solutions to complex problems through better decision-making
- It is based on the machine’s response to circumstances and complex problems without a fixed algorithm in place. It involves multiple levels and forms of analysis to seek an optimal solution.
- It leads to the wisdom of learning using intelligence and self-imposed discretion of right and wrong.
- AI involves smart learning by a computer through awareness of past iterations and application of alternative information processing and cognitive analysis that demand distinct capabilities
The Pros and Cons of AI
- Simplifies work by analyzing, filtering, sorting, predicting, scoping, and determining large data volumes to follow the best implementation procedures for producing an optimal solution
- Performing mundane tasks is faster and effective with reduced errors when performed efficiently by AI systems
- Accurate results can be obtained by the implementation of highly-responsive AI systems in technology-driven complex tasks and difficult/uncertain explorations
- They function in all environments without being bound physically, environmentally, or emotionally
- Simulations are worked on in real-time by AI systems as if they are real-life scenarios for better and realistic results
- The Security and Protection of data and crucial information is maintained by AI systems and loopholes are automatically fixed or notified about
- High-Quality analysis and results are delivered by AI systems that are well-integrated with various technology systems
- The associated costs are high due to the complex programming capabilities of AI systems
- Repair and maintenance are not only expensive but also complex and demand high levels of expertise
- AI systems lack human judgment during various critical scenarios like target picking during fighter plane routines or investment calls during stock value changes
- AI systems will lead to loss of jobs for humans in the future since they will take over especially in fields like banking and retail
- These systems have the scope of being misused or altered either due to internal glitches or external system breach
- AI systems are not capable of improvement in functionality or infrastructure until these are developed by humans which may leave them obsolete at certain situations
Examples of AI Applications
- Cloud Computing, APIs, and Open-source technologies are Artificial Intelligence examples
- AI Robotics in Surgical Equipment and Healthcare Technologies
- Image Processing for Medical Diagnosis
- Traffic Management Systems
- Climate Change Detection
- Natural Disaster Warning Systems
- Autonomous Vehicles including cars, buses, two-wheel drives, submarines, autopilot flights, and drones
- Grading Systems in the educational field
- Fraud and SPAM detection on servers
- Language translation software like Google Translate
- Recommendation software for products and services like Netflix
- Ticket Reservation Systems
- Chatbots and personalized learning
Future of AI
- Companies like Google and Uber are already using AI capabilities to power self-driven cars. AI will have a great bearing on the automated transportation field by aiding handicapped drivers and preventing accidents.
- More evolved AI systems will support in hazardous factory-based jobs and may replace humans as well.
- Climatic change predictions can be made by AI systems using data sciences and environmental technologies.
- Around 80 percent of customer service operations will be handled by effective and timely AI systems.
- Personalized health management will be made easier through AI systems symptom-identification and medical data processing abilities.
- Cyborg technology can help patients utilize artificial prosthetics for a better living by communicating with a robotic system.
- In space technology, AI can study orbital paths during successful launches and suggest actions based on its observations
Published at DZone with permission of Sourabh Nagar . See the original article here.
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