Explore the world of Artificial Intelligence
- the concept of artificial intelligence (AI)
- related concepts of machine learning and deep learning
- applications across a range of industries
- a foundation you can build on and apply not just today, but into the future as AI develops and changes.
Generative A.I. can be controversial in terms of intellectual property rights, legalities, ethics and quality.
It is important to keep clarity on what AI does:
It shortens the journey taken to achieve a final result.
It has a place where the result is all that matters; but what about when the journey is as important or more important than the end outcome?
Education for instance is all about changing what is inside someone's head; and it is the journey that achieves that.
Living a full life should be about the journey; not just about your state of existence when you take your last breath.
Using AI well involves seeing it for what it is - a tool. Just as a car gets us to a destination faster than walking; AI can get us to a destination faster than taking the long journey. Be careful though: if we stop walking, fitness declines, and quality of life suffers!
Course Content
This course is divided into eight lessons as follows:
1. Introduction to Artificial Intelligence
- A Brief History of AI
- Theory of Mind
- TYPES OF ARTIFICIAL INTELLIGENCE
- Three Branches of AI
- Strong AI
- Applied AI
- Cognitive Simulation
- Reasons AI Matters
- Introduction to Natural Language Processing
2. Ethical, Economic, and other Concerns
- ETHICS AND ARTIFICIAL INTELLIGENCE
- AI Ethics Principles and Guidelines
- ETHICAL PERSPECTIVES
- Human Rights
- Privacy Breach
3, Neural Networks
- Structure of the Human Nervous System
- Cells of the Nervous Tissue
- Main anatomical features of Neurons
- Neural Circuits
- INTELLIGENCE AND ADAPTABILITY
- Artificial Neurons
- Input
- Weight
- Bias
- Summation Function
- Activation Function
- ARTIFICIAL NEURAL NETWORKS
- APPLYING ARTIFICIAL NEURAL NETWORKS
- Digital Worm Brains
- Control system
- Cybernetics
- Artificial brain cells
- Enhanced mobility
4. Deep Learning
- Size
- Width
- Depth
- Capacity
- Architecture
- UNIVERSAL APPROXIMATORS
- Width Case
- Depth Case
- ABSTRACTION AND FUNCTION
- TRAINING A DEEP NEURAL NETWORK
- ReCaptcha
- Pitfalls of Deep Neural Networks
5, Machine Learning
- ALGORITHMS
- Structured Data
- Practical Applications
- Unstructured Data
- Semi-Structured Data
- Applying Algorithms
- THREE TYPES OF MACHINE LEARNING
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Machine Learning
6. Business Applications
- AI APPLICATIONS
- Marketing
- Customer Relationship Management
- SOCIAL MEDIA
- Machine Learning and Social Media Ads
- Customer Service
7. Application in Environmental & Primary Industries
- Agriculture and Horticulture
- Pest Control
- PRECISION AGRICULTURE
- Harvesting robots
- Robot Tractors
- ENVIRONMENTAL INDUSTRIES AND CLIMATE CHANGE
- Poaching
- Air Quality
- Problem Based Learning (PBL) Project
8. Industrial and Other Applications
- INTRODUCTION
- TRANSPORT
- Travel and Transportation
- Self-driving cars or automated driving systems (ADS)
- Transport, Freight, and Logistics
- FACTORIES
- HEALTH SECTOR
- Individual Health and Personalised Health Care
- General Healthcare
- Modelling
- EDUCATION SECTOR
- Training and education
- Tutoring and Coaching
- Plagiarism Detection and Authorship
- LEISURE INDUSTRY
- Entertainment
- ENVIRONMENT MONITORING AND MANAGEMENT
- Predicting Fire and Analysis (emergency management)
Course Duration - 100 hours
Learn about the Nature and Applications for Artificial Intelligence, Deep Learning and Machine Learning
AI, machine learning, and deep learning are related but they are not identical.
Machine Learning is a science concerned with developing computer programs that automatically improve with experience, in other words it is the science of learning, where different techniques can be applied. With machine learning, it is possible to systematically understand what model performs better and under what circumstances.
Deep learning is a technique or a subset of machine learning derived from artificial neural networks. It is a technique that allows computational models of multiple processing layers to learn representations of data with multiple levels of abstraction. This computational "intelligence" has enabled applications in the health industry for example, that are as efficient as those conducted by a trained physician. It is similarly applied in agriculture, medicine, laboratory, physics, and so on. There are two pre-requisites for deep learning:
- High throughput data (large amount of data, aka Big Data)
- Massive Computing Power and memory (more specifically computer graphics processing unit (GPU), central processing unit (CPU), Random-access memory (RAM)).
Artificial Intelligence is the capacity of a computer or computer operated equipment to carry out tasks that would normally or otherwise be carried out by a human.
USING AI STARTS WITH UNDERSTANDING IT
AI can be very controversial. Some people can only see problems associated with it, and present arguments such as: "What's wrong with human intelligence - if it' flawed and man created AI, doesn't it stand to reason that AI is flawed?"
AI has and will continue to develop many uses; some which have advantages without disadvantages; but others which might have more disadvantages than advantages.
AI must be looked at as a tool, and like any other tool, it needs to be used in full awareness of it's capabilities and limitations; and well informed decisions need to be made about where and how it is used.
This proper application of AI starts with learning the fundamentals -and that is what this course is all about.
Advantages of AI
AI can also offer advantages in terms of some regular or mundane tasks and job roles.
For example -
- Recruitment - AI can be used to help organisations hire new staff. AI can be used to analyse data on job candidates, such as their work experience, skills and education. AI-based analysis might help to create a short list of the best candidate(s) for the job. This potentially reduces the risk of bias and discrimination. If AI is used to screen initial applicants and shortlist those who will be interviewed for a job, the applicants that are shortlisted may not always be the best candidates. They will be the ones who know how to create a covering letter and curriculum vitae/resume which contains the search terms identified as critical by the AI.
- Efficiency - AI can increase efficiency. UPS is a shipping and logistics company that operates globally. They have used AI to save fuel, analyse the weather, the volume of packages, traffic and other factors to reduce the time and distance their deliveries are travelling. This has saved UPS money and reduced their carbon footprint. AI can be used to make tasks and jobs more efficient and well organised.
- A Tool – AI is a tool. As many workers and people today use email, mobile phones and the internet as part of their work, many will come to use AI as part of their daily work.
- Doing the boring jobs - AI tends to be used to do jobs that are mundane, repetitive and boring, such as data processing and data entry. Jobs that require these skills are more likely to be replaced by AI in the future. Businesses that have used staff to complete mundane and data processing tasks may find that they can replace these staff with AI.
- Human Skills - However, this means that staff can be trained and developed in other areas requiring human skills and ingenuity. For example, tasks that require creativity, problem solving and analysis skills, enabling them to look at other opportunities. All of which are useful in the marketing situation. For example, a business might focus its training and staff development on more creative thinking to create new business opportunities. These statistics and this information can seem worrying. Jobs will be replaced by AI, but by carrying out mundane and repetitive tasks, this may allow human workers to become more creative, more analytical, more involved in problem solving and other human skills.
- Virtual assistants may also become more common as AI becomes more complex. Virtual assistants can be used to prioritise tasks, schedule meetings, manage email inboxes, help employees or customers with routine enquiries, etc. This automation of mundane, time-consuming tasks can free up employees to focus more on jobs that require creativity and human skills.
- Cobots are collaborative robots. They work alongside humans doing tasks that are dangerous or difficult for humans. For example, lifting heavy objects or working in hazardous locations. Using Cobots means that the human staff members can focus on tasks that require human skills. Cobots also make the work environment safer and more efficient for the human staff.
AI may have limitations with –
o Empathic tasks.
o Dealing with the unknown or unstructured
o Using precise hand-eye coordination
o Complex, strategic planning
This is not to say that AI might not improve in these areas.
Why Study this Course?
Artificial intelligence is increasingly being used in the world - at work, home and play. Whether you are developing it or using it, and in whatever context, you are likely to be increasingly encountering A.I.
This course provides a fundamental understanding of A.I., it's potential and it's application. It will develop a deeper and more realistic understanding, and in doing so, provide a foundation tor being better at using or working with A.I.
Some may study it to improve efficiencies at work, others as professional development to broaden or deepen their learning of technology.
