Contents
Artificial
Intelligence. 2
Introduction to
Artificial Intelligence. 2
Demonstration. 3
Write a letter to an
old friend. 3
Write a letter to a
company complaining about incorrect work done. 4
Check your
medications. 5
What is artificial
intelligence?. 6
Definition of AI 6
Who Develops AI?. 7
How Are They Paid?. 8
Activity. 9
Objective
Understand what AI is and why it matters.
One of the hallmarks of avoiding
dementia is to have a community. People who you care about, and who care about
you. So, let’s devise an easy way to reconnect to someone you care about but
don’t really keep in touch with. Before next week I would like you to do
something similar and tell us the results.
I will use my cousin Nick, who is 18
months older than me, as my example.
Here are three small anecdotes from our
past
·
You always asked me to get 2 shillings from my
dad, so that we could buy choc wedge ice creams from Susanne’s Cabin opposite
our guesthouse.
·
You used to send me letters from East Malvern to
Healesville full of jokes and riddles, and I would reply. It took maybe 3
weeks for the cycle to complete. I loved the novelty of getting my very own
letters from you.
·
When I was living at Glenn College Wally sent
you with a multi-layer cake German cake for my 18th birthday. It
was such a surprise, and a thrill to show off my big cousin to my friends.
Here are three things about my current
life
·
I teach at the university of the third age in
Brisbane every Wednesday and have done for 4 years now.
·
Where I live is subject to major rain events.
My back garden directly behind my house always gets washed away. I recently
built a rock Japanese garden, and we had a major rain event, and it remained
beautiful.
·
I work in a Gluten Free bakery on the weekend
for some friends, and it is a lot of fun.
Now I am going to ask a few different AI
engines to write me a letter. Here is what I will say
I want to reconnect with my 77-year-old
male cousin again. Write me a letter using the following facts saying that I
would like to reconnect again, as we have drifted apart since his parents’
deaths.
Here are the ones I will try
Perplexity.ai
chatgpt.com
poe.com
deepseek.com
I will ask the different AI engines to
draft me a letter to address a fault in a garden installation
Brookes Blooms
You created me a marvellous Japanese
rock garden just a few weeks ago, and now about 1/2 of the plants a dying.
What should I do?
I
will get AI to check when I should take my medications.
I
take the following medicines every day in the morning. I am a 75-year-old
female. Cn you do an optimal schedule of when I should take these?
Covercyl-Plus
5mg/1.25mg
Zanidip
20 mg
Ezetrol
10 mg
Aspirin
100 mg
Vitamin
D3 1000 IU
Vitamin
B12 1000 mcg
Fish
oil 2000 mg
Red
krill oil 1000 mg
Centrum
Magnesium
Aspartate 500mg Tablet
Curcumin
Fefol
https//youtu.be/KKNCiRWd_j0
Artificial Intelligence (AI) refers to
the development and use of computer systems capable of performing tasks that
typically require human intelligence. These tasks include learning, reasoning,
problem-solving, perception, decision-making, and language understanding.
AI systems are
designed to
·
Learn from data
Using techniques like machine learning and deep learning, AI systems can
improve their performance over time by analysing patterns in data.
·
Simulate human cognition AI mimics cognitive functions such as perception, reasoning, and
decision-making.
·
Perform autonomous actions Some AI systems can act independently to achieve specific goals
without human intervention
1. AI Engineers and Researchers
·
These professionals design, build, and refine AI
models. Their work includes developing machine learning algorithms, conducting
experiments, and optimizing systems for performance.
·
Roles include AI engineers, machine learning
engineers, robotics engineers, and data scientists.
2. Data Labellers[i]
·
Data labelling is essential for training AI
models. Workers annotate datasets
to ensure models learn effectively. This work often involves repetitive tasks
like categorizing images or transcribing text.
·
Many data labellers are employed in developing
countries or through outsourcing firms, sometimes under poor working conditions
with low pay.
3. Content Providers
·
Companies that own large datasets or unique
content often license their material to AI developers for training purposes.
For example, OpenAI pays publishers like DotDash Meredith millions annually to
license content2.
4. Specialized Firms
·
Companies like NVIDIA contribute by creating
hardware (e.g., GPUs[ii]) optimized
for AI training and deployment.
1. Salaries for Professionals
·
AI professionals earn competitive salaries due
to the high demand for their skills.
For instance,
·
AI engineers earn an average of $134,188US
annually.
·
Machine learning engineers make around $123,117US
annually.
·
Salaries increase significantly with experience
and vary by location.
2. Data Labellers
·
Data labellers are often paid low wages,
sometimes as little as a few dollars per hour in developing regions. Some
companies have faced criticism for delayed payments and poor working
conditions.
3. Content Licensing
·
Media companies license their content to AI
firms for model training. Payments can range from one-off deals to recurring
fees based on usage or contribution to AI outputs (e.g., OpenAI's licensing
agreements)2.
4. Revenue Models for Firms
·
AI companies monetize their products through
various strategies
·
Usage-based Pricing Charging clients based on API usage (e.g., OpenAI charges per token
processed by GPT models).
·
Subscriptions
Offering flat rate plans for access to tools like ChatGPT Plus.
·
Outcome-based Pricing Charging based on results achieved by AI systems (e.g., Intercom
charges per successful customer support resolution).
·
Hardware Sales
Companies like NVIDIA profit from selling specialized hardware used in AI
development.
5. Royalties or Revenue Sharing
·
Some firms use algorithms to calculate the
contribution of specific content sources to AI outputs and share revenue
accordingly (e.g., ProRata's attribution percentage model).
AI development
is a collaborative effort involving highly skilled professionals, outsourced
labour, and partnerships with content providers, all compensated through
diverse methods tied to their roles and contributions.
Group discussion “Where have you
encountered AI?”
[i] Data Annotation Market Trends & Statistics for 2025
Machine learning involves computer
systems improving their performance by learning from data, much like humans
learn from experience. Data annotation, or labelling, is crucial in this
process, as it helps train algorithms to recognize patterns and make accurate
predictions.
Effective data management and
annotation services play a vital role in the success of machine learning
projects. In machine learning, neural networks consist of digital neurons
organized in layers. These networks process information similar to the human
brain. Labelled data is vital for supervised learning, a common approach in
machine learning where algorithms learn from labelled examples.
Training and testing datasets with labelled
data enable machine learning models to efficiently interpret and sort incoming
data. We can provide high-quality annotated data to help algorithms learn
autonomously and prioritize results with minimal human intervention. The
importance of data annotation in AI lies in its ability to enhance model
accuracy and performance throughout the AI lifecycle.
Why is Data Annotation Required?
We know for a fact that computers are
capable of delivering ultimate results that are not just precise but relevant
and timely as well. However, how does a machine learn to deliver with such
efficiency?
Without data annotation, every image
would be the same for machines as they don’t have any inherent information or
knowledge about anything in the world.
Data annotation is required to make
systems deliver accurate results, help modules identify elements to train
computer vision models and speech, recognition models. Any model or system that
has a machine-driven decision-making system at the fulcrum, data annotation is
required to ensure the decisions are accurate and relevant.
[ii] AI Overview
A Graphics Processing Unit (GPU)
is a specialized electronic circuit designed to rapidly manipulate and
alter memory to accelerate the building of images in a frame buffer intended
for output to a display. Think of it as a computer's image processing
powerhouse, especially when dealing with complex visuals and video.
Here's a more detailed breakdown:
·
What it does:
GPUs handle tasks related to computer
graphics and image processing, including rendering 3D scenes, playing video,
and accelerating video editing.
·
How it works:
Unlike CPUs (Central Processing Units)
which are designed for general purpose computing and handle one task at a time,
GPUs excel at performing parallel calculations, meaning they can process
multiple tasks simultaneously. This parallel processing ability makes them
ideal for graphics-intensive tasks.
·
Why it's important:
GPUs significantly improve the
performance of tasks involving graphics and video, resulting in smoother
visuals, faster rendering, and overall better user experience.
·
Where you find them:
GPUs can be found in various devices,
including desktop computers (as dedicated graphics cards or integrated on the
motherboard), laptops, game consoles, and even some smartphones.
·
Beyond graphics:
While originally designed for
graphics, GPUs are now also used in other fields like machine learning,
scientific computing, and AI, due to their powerful parallel processing
capabilities.
·
Types:
There are two main types of GPUs:
discrete (separate graphics card) and integrated (built into the CPU or
motherboard).
What’s the difference between GPUs and
CPUs?
A CPU, or central processing unit, is
a hardware component that is the core computational unit in a server. It
handles all types of computing tasks required for the operating system and
applications to run. A graphics processing unit (GPU) is a similar hardware
component but more specialized. It can more efficiently handle complex
mathematical operations that run in parallel than a general CPU. While GPUs
were initially created to handle graphics rendering tasks in gaming and
animation, their uses now extend far beyond that.
Similarities between GPUs and CPUs
Both CPUs and graphics processing
units (GPU) are hardware units that make a computer work. You can think of them
as the brain of a computing device. Both of them have similar internal
components, including cores, memory, and control units.
Core
Both GPU and CPU architecture have
cores that run all computations and logical functions. The core pulls
instructions from memory in the form of digital signals called bits. It decodes
the instructions and runs them through logical gates in a time frame called
an instruction cycle. CPUs initially had a single core, but today
multi-core CPUs and GPUs are common.
Memory
Both CPUs and GPUs complete millions
of calculations every second and use internal memory to improve processing
performance. The cache is the built-in memory that facilitates quick data
access. In CPUs, the labels L1, L2, or L3 indicate
cache arrangement. L1 is the fastest, and L3 is the slowest. A memory
management unit (MMU) controls data movement between the CPU core, cache, and
RAM in every instruction cycle.
Control unit
The control unit synchronizes
processing tasks and determines the frequency of electric pulses that the
processing unit generates. CPUs and GPUs with higher frequency provide better
performance. However, the design and configuration of these components differ
in a CPU and GPU, so the two are useful in different situations.
Key differences: CPUs vs. GPUs
The arrival of computer graphics and
animation resulted in the first compute-intensive workloads that CPUs were
simply not designed to handle. For example, video game animation required
applications to process data to display thousands of pixels—each with their own
individual colour, light intensity, and movement. Geometric mathematical
calculations on CPUs at the time led to performance issues.
Hardware manufacturers began to
recognize that offloading common multimedia-oriented tasks could relieve the
CPU and increase performance. Today, graphics processing unit (GPU) workloads
handle several compute-intensive applications—like machine learning and artificial
intelligence—more efficiently than CPUs.
Function
The main difference between a CPU and
GPU lies in their functions. A server cannot run without a CPU. The CPU handles
all the tasks required for all software on the server to run correctly. A GPU,
on the other hand, supports the CPU to perform concurrent calculations. A GPU
can complete simple and repetitive tasks much faster because it can break the
task down into smaller components and finish them in parallel.
Design
GPUs excel in parallel processing
through several cores or arithmetic logic units (ALU). GPU cores are less
powerful than CPU cores and have less memory. While CPUs can switch between
different instruction sets rapidly, a GPU simply takes a high volume of the
same instructions and pushes them through at high speed. As a result, GPU
functions play an important role in parallel computing.
Example of the differences
To understand better, consider the
following analogy. The CPU is like a head chef in a large restaurant who has to
make sure hundreds of burgers get flipped. Even if the head chef can do it
personally, it’s not the best use of time. All kitchen operations may halt or
slow down while the head chef is completing this simple but time-consuming
task. To avoid this, the head chef can use junior assistants who flip several
burgers in parallel. The GPU is more like a junior assistant with ten hands who
can flip 100 burgers in 10 seconds.
When to use GPUs over CPUs
It's important to note that the choice
between CPUs and graphics processing units (GPU) is not an either-or one. Every
server or server instance in the cloud requires a CPU to run. However, some
servers also include GPUs as additional coprocessors. Specific workloads are
better suited to run on servers with GPUs that perform certain functions more
efficiently. For example, GPUs can be great for floating point number
calculations, graphics processing, or data pattern matching.
Here are some applications where it
may be useful to use GPUs over CPUs.
Deep learning
Deep learning is a method in
artificial intelligence (AI) that teaches computers to process data in a way
inspired by the human brain. For example, deep learning algorithms recognize
complex patterns in pictures, text, sounds, and other data to produce accurate
insights and predictions. GPU-based servers provide high performance for
machine learning, neural networks, and deep learning tasks.
