AI applications and methodologies 2 Flashcards
Semantic Segmentation
Semantic Segmentation is also called the Image classification. Semantic
segmentation is a process in Computer Vision where an image is classified
depending on its visual content. Basically, a set of classes (objects to identify in
images) are defined and a model is trained to recognize them with the help of
labelled example photos. In simple terms it takes an image as an input and
outputs a class i.e. a cat, dog etc. or a probability of classes from which one has
the highest chance of being correct.
Classification and Localization
Once the object classified and labelled, the localization task is evoked
which puts a bounding box around the object in the picture. The term
‘localization’ refers to where the object is in the image. Say we have a
dog in an image, the algorithm predicts the class and creates a
bounding box around the object in the image
Object
Detection
When human beings see a video or
an image, they immediately identify
the objects present in them. This
intelligence can be duplicated using
a computer. If we have multiple
objects in the image, the algorithm
will identify all of them and localise
(put a bounding box around) each
one of them. You will therefore, have
multiple bounding boxes and labels
around the objects.
Instance segmentation
Instance segmentation is that technique of CV which helps in
identifying and outlining distinctly each object of interest appearing in
an image. This process helps to create a pixel-wise mask for each
object in the image and provides us a far more granular
understanding of the object(s) in the image. As you can see in the
image below, objects belonging to the same class are shown in
multiple colours
Advantages of weather forecasting
1)Accurate weather forecasting allows farmers to better plan for
harvesting.
2)It allows airlines to fly their passengers with safety.
3)Electricity departments can make decisions about their capacity
needs during summer and winters.
4)It allows governments to better prepare their responses to natural
disasters that impact the lives of millions
What is weather forecasting?
Weather forecasting deals with gathering the satellites data,
identifying patterns in the observations made, and then computing
the results to get accurate weather predictions. This is done in realtime to prevent disasters. Artificial Intelligence uses computergenerated mathematical programs and computer vision technology to
identify patterns so that relevant weather predictions can be made.
Leading IT companies have been doing their
intensive research by leveraging technologies like
AI, IoT, and Big Data:
IBM Global High-resolution Atmospheric Forecasting System (IBM
GRAF) is a high-precision global weather model that updates hourly
to provide a clearer picture of weather activity around the globe.
2. Panasonic has been working on its weather forecasting model for
years. The company makes TAMDAR, a speciality weather sensor
installed on commercial airplanes.
What is commodity?
In commodity market, production is normally local but consumption is global.
Therefore, price forecast is beneficial for farmers, policymakers or for industries.
A reliable price forecast tool will
allow producer to make informed decision to manage their price
Advantages of AI based commodity price forecasting
system:
- The accuracy level would be much higher than the classical
forecasting model. - AI can work on broad range of data and due to which it can reveal
new insights. - AI model is not rigid like classical model therefore its forecasting is
always based on the most recent input
What is a self driving car?
A self-driving car, also known as an autonomous vehicle (AV), driverless car, robot
car, or robotic car is a vehicle that is capable of sensing its environment and moving
safely with little or no human input.
Self-driving cars combine a variety of sensors to perceive their surroundings, such
as radar, lidar, sonar, GPS, odometry and inertial measurement units. They also use
Computer Vision.
Characteristics of AI
- Artificial Intelligence is autonomous and can make independent decisions
- Has the capacity to predict and adapt
- It is continuously learning – It learns from data patterns.
- AI is reactive – It perceives a problem and acts on perception
- AI is futuristic
Types of AI
Data driven AI: Data driven AI systems, get trained with large datasets, before it
makes predictions, forecasts or decisions.
Autonomous System: Autonomous system is a technology which understands the
environment and reacts without human intervention.
Recommendation systems: A recommendation system recommends or suggests
products, services, information to users based on analysis of data based on a
number of factors such as the history, behaviour, preference, and interest of the
user, etc..
What is Cognitive Computing?
Cognitive Computing can be defined as a technology platform that is built
on AI and signal processing, to mimic the functioning of a human brain
(speech, vision, reasoning etc.) and help humans in decision making.
Applications of Cognitive Computing
- For example, ‘The IBM Watson for Oncology’ has been used at the
‘Memorial Sloan Kettering Cancer Centre’ to provide oncologists with
evidence-based treatment alternatives for patients having cancer.
When medical staff pose questions, Watson generates a list of
hypotheses and offers treatment possibilities for doctors.
the key social benefits of AI
- IBM Watson (An AI Tool by IBM) can predict development of a
particular form of cancer up to 12 months before its onset with almost
a 90% accuracy - To control the outbreak of CORONA virus in China, the country
leaned on Artificial Intelligence (AI), Data Science, to track cases and
fight the pandemic - Transportation
- Disaster Prediction
- Agriculture Farming
