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TYPES OF ARTIFICIAL INTELLIGENCE
Artificial intelligence is not a new concept; it has been around for over fifty years as you now know, but it has received more attention in the last two decades through voice and face recognition applications used in smart phones and social media. Our perception of what it is, how it might be used, and the different types of AI are “fluid concepts”, involving notions of analogy and fluidity.
AI can be classified into different types or categories:
One way of classifying AI is based on its similarities to the human mind and its ability to respond to stimuli. Based on this classification system, there are 4 categories or AI-enabled systems: reactive AI, limited memory, theory of mind-based AI, and self-aware AI.
• Reactive AI – These machines are the simplest machines; they have no memory, they simply react (respond) to stimuli. Consequently, they have no ability to “learn” since they do not use previous experiences (which would be stored in its memory) to predict future actions. A well-known example of this type of AI is IBM’s “Deep Blue” the computer that beat the world chess champion chess in 1997, by researching all the millions of possible future moves without “understanding” the game.
• Limited Memory AI – This type of AI is built to have time constraints. Limited memory AI are machines with limited memory, where data is stored; in other words, they can be “trained” by historical data, past experiences stored in the memory that forms a reference model to predict future situations. Most of the applications we use in our daily lives fall into the category “limited memory AI”. Here is a list with a few examples: self-driving vehicles, virtual assistants, smart maps, chatbots, face or image recognition, translators, voice recognition, etc. This type of AI is heavily dependent on pattern recognition.
• Theory of Mind AI — “Theory of mind” is how scientists refer to the varying ways in which the human mind perceives and thinks. In terms of AI, this concept involves complex systems perceiving both actions and feelings of people (including human emotions), also known as artificial emotional intelligence, then changing actions or behaviours accordingly. Facial expressions or movements of eyes or hands for instance, may be able to be detected and correlated to various emotions. It is the next level of AI in which research is being done alongside advances in other branches of AI and neuroscience data.
• Self-Aware AI – This type of AI would, in theory, learn and internalise emotions and other human traits in a way that is largely indistinguishable from human self-awareness. Whether or not this type of AI is a realistic or desirable reality is very much debateable. This category of AI may be decades or centuries away from coming into fruition and raises a series of ethical questions. The consequences of the evolution of self-aware machines can be positive or extremely negative, culminating with the end of humanity.
There is an alternate system of classification for AI which uses more technical jargon. This system classifies AI into three groups: Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI) (Joshi, 2019).
• Artificial Narrow Intelligence (ANI): This category encompasses all AI currently available. These systems perform a specific task autonomously using human-like capabilities. They cannot do more than what they are programmed to do, that is the reason why they are called artificial narrow intelligence.
• Artificial General Intelligence (AGI): This category represents those machines that can learn, understand and function completely like a human being. They are expected to cut down on time needed for training by creating multiple competencies and form connections and generalisations across domains. AI experts have estimated the development of AGI to be done by 2060.
• Artificial Superintelligence (ASI): This is the type of AI that will be the most intelligent form of intelligence in the planet. It is a type of intelligence that will be capable of replicating the intelligence of human beings, due to an extraordinary memory, faster data processing and analysis, decision-making abilities.
Three Branches of AI
Research into AI can be broken down into three main branches of development:
• Strong AI
• Applied AI
• Cognitive simulation.
Each of these branches reflects a different type of artificial intelligence, and so a different attitude to the idea of intelligence and its meaning.
Strong AI
Strong AI is focused on thought and thought processes. Researchers into strong AI explore how to create an artificial intelligence that can think like a human – to the extent that the person interacting with the AI cannot tell the difference between person and machine.
Although there have been many attempts at building a functioning strong AI, and some success in mimicking humans (e.g., ELIZA), this is arguably the weakest branch of research into artificial intelligence. Replicating intelligence and consciousness, two pieces of the human condition that no-one truly yet understands, is a daunting and potentially impossible task. As such, modern research is generally more focused on the other two branches of development.
Applied AI
Sometimes known as advanced information processing, this type of AI is focussed on developing smart systems, or systems which can apply existing knowledge and make reasonable, evidence-supported suggestions. This type of system collects up all the knowledge that a panel of discipline experts would use and creates one “super expert” AI that can perform high-level analysis. IBM’s Watson Healthcare is an example of this type of system.
Applied AI systems have promise across many applications, but they are heavily affected by the type of knowledge input and can reflect human biases.
Cognitive Simulation
This type of AI research and development is focussed on developing a greater understanding of the human mind. In cognitive simulation, researchers explore how the human mind works by running simulations with advanced computers. This type of research is useful in neuroscience, psychology, and education.
Reasons AI Matters
Artificial intelligence and machine learning systems are growing exponentially around the world. It is estimated that these technologies will generate approximately $22.2 trillion to the global economy by 2030. According to the Australian Government, there will be a need for 32,000 to 160,000 new employees by 2028 in areas of robotics, data science, human language technologies, and other AI related areas.
Aspects of AI have been implemented in a variety of common applications dating back as far as the 1990s. This is because AI is a tool that helps humans derive or create solutions to complex problems, from how to play a perfect chess game through to modelling how to unfold complex proteins to drive medical research. One of the core functions of current AI is recognising patterns – patterns in chess and protein folding, but also patterns in the everyday. You may already interact with AI in a specific discipline, such as:
• Virtual personal assistants, e.g. Google, Alexa, Siri
• Personalised online advertising, which uses AI to recognise usage patterns then re-target content
• ReCaptcha authentication services, which have users help train an AI by verifying the content of images or sounds.
• Facial recognition security systems
This focus on problem solving, along with rapid technological development, means that understanding the basics of AI and what it can do is an essential part of building future-ready job skills.
Remember, though: human planning and programming drive AI. While technology improves, humans must still identify problems and questions in order to drive progress. You don’t need to be a programmer or computer scientists to learn about or work with AI. Many non-computer experts work with “smart machines” every day because they add to our capabilities. Everyone can benefit from an understanding of the basics of AI and how to work with it — this “AI literacy” is an important part of developing future-ready job skills.
Some of the general capabilities AI helps us build upon include:
• Design Thinking
• Creativity
• Critical Thinking
• Computational Thinking.
Design Thinking
This type of thinking is based on the innovative. It uses a strategic approach to identify a problem, identify the problem’s context, and then start exploring potential solutions. Design thinking fits well with AI because it relies on framing a problem then exploring the possible – AI can do that exploration within set parameters quickly and effectively. Once a set of possible solutions are in hand, humans can explore the workability of each individual solution then do further analysis and testing with or without AI help.
Design thinking is a multidisciplinary approach, which means it can be used in business, architecture, science, the creative arts, and more. Investing in design thinking skills can help you develop a foundation for working with AI in the long term.
Creativity
Creativity means using our imaginations to solve problems. This can be as simple as visualising a drawing for a simple rubberband-powered car through to designing a new system for analysing research into how consumers make purchasing decisions.
AI increases our creative options – because it gives us a way to test out ideas quickly and efficiently, we can explore without a high opportunity cost, then identify promising avenues to test out in the next stage of development.
Critical Thinking
Critical thinking involves breaking a problem or idea into its component parts, analysing the parts, then drawing a conclusion or judgement based on facts. It is an essential part of science – and one of the core skills required to effectively work with AI.
Current AI research allows the system to break down problems and ideas into parts and compile lists of facts. Judgement, however, is a complex process requiring a mix of lived experience and expertise – while AI can develop limited expertise, it cannot currently replicate lived experience. This means that an AI can be used for providing the analytical output needed for humans to make appropriate judgements using critical thinking.
Critical thinking depends on questioning and facts – emotionality interferes with the process, though ethics are still a key consideration in most systems.
Computational Thinking
Computational thinking refers to thinking about problems like a computer. This means that computational thinking is an important aspect of framing problems for an AI; it can include learning to break concepts and problems into a set of information processes through to writing simple algorithms. Computational thinking can be used across disciplines – you do not have to be a programmer to implement its principles.
FAQ
FREQUENTLY ASKED QUESTIONS
Here is a list of the most often asked FAQ’s.
General
Q. Why should I enrol with the Academy for Distance Learning?
A. Here at ADL, our students are our priority – we treat everyone as a unique individual.
Q. Do I need to buy text books?
A. No, as each module has been written by highly qualified industry professionals. The content of the material is presented in such a way that text books are not required. However, if you require additional reading your tutor will be able to supply a list.
Q. What happens if I have to stop studying for a while? (eg. become sick, go on holidays, have a baby, move house, etc)
A. It’s OK to take a break and start up your study at a later point in time. Just let us know.
Q. Is there an age limit?
A. There is no maximum age limit. We do however, have a minimum age limit of 18 years. Below that age parental consent would be required.
Q. Are your courses up-to date?
A. Our courses are revised and updated on a rotation system.
Q. Do you have a Cancellation policy?
A. Yes. We have a cancellation policy that is fair and equitable. For further details please click here.
Q. Will I have any opportunity to engage with other students?
A. We have a Student Community group based on facebook! If you don’t have a facebook account already, you could make one just for talking with fellow students on the group.
Enrolment
Q. When can I enrol/start?
A. You may enrol and start at any time of the year – it’s all self- paced.
Q. Can I study from anywhere in the world?
A. Our courses are available to anyone, anywhere in the world from the comfort of your own home. The course content is relevant to any country, culture or economy.
Q. How long do I have to complete the course?
A. You complete the course at any time that is convenient for you.
Q. Completing a 100 hour module – how long will it take?
A. For some students a 100 hour module will take approximately to 3- 6 months to complete. Others take less time and some even longer.
Assessment
Q. Assessment – how does it work?
A. For each 100 hour module you are assessed by assignments (at the end of each lesson) and a final one and a half hour exam (or you may elect to complete a Project, instead of sitting the exam) – the choice is yours – you sit for the exam in your own location.
Q. I don’t cope well with exams – what can I do?
A. You may elect to undertake a Project (set by your tutor) instead of sitting the exam. Projects are completed from your home and can usually take a couple of weeks to complete.
Q. If my assignment is not up to standard is there an opportunity to resubmit my work?
A. Yes –
Q. How many assignments do I need to complete for each module?
A. At the end of each lesson, there is an assignment – so if a course has say, 10 lessons, there would be 10 assignments.
Q. I am having difficulty attending workshops/industry meetings, what can be done?
A. If your course requires attendance at workshops, conferences, or industry meetings; alternative arrangements can be made in your country.
Qualifications
Q. What qualification will I receive?
A. For individual modules, you would be awarded a Certificate endorsed by TQUK (Training Qualifications, UK), providing you complete all assignments and the exam. If you just want to complete only the assignments and not sit for the exam or finish a Project, then a Letter of Achievement would be awarded. For more details on qualifications available please click here.
Q. Can I customize my diploma/higher qualification?
A. Not all educational institution’s certificates /diplomas meet everyone’s needs. The opportunity to Design Your Own Diploma at the Academy (subject to our approval) is an added bonus, not found at other colleges. You choose modules that you think will help you in achieving your goal.
Q. What do I get when I complete the course? Will I receive a transcript?
A. At the completion of all courses and providing all assignments and exam requirements have been met, you will receive your Award and a Transcript.
Tutors
Q. Our tutors – who are they?
A. We appoint Tutors and require that they must be currently active in their industry, with at least 5 years’ experience in their chosen profession.
Q. Can I contact my tutor at any time?
A. Yes – you have unlimited access to your tutor via email through our Online Classroom. You can always leave a message with ADL requesting your tutor to contact you. You decide on how much or how little contact you wish to have.
Q. Practical work – How is this done?
A. To find out more about this part of the course please visit the section on How Our Courses Work here.
