Supervised Learning

Reinforced Learning

Unsupervised Learning / Predictive learning

Guide machine by providing sample inputs and desired outputs.

Eg: Speech recognition

       Driverless Cars

       Face Recognition

       Classifying emails as spam & non-spam

Learning data gives feedback to the system and evaluates its performance based on it and reacts accordingly.

Eg:Chess master algorithm

There is no data fed for learning with descriptions or labels

Subsets of AI

Machine Learning

Deep Learning

Adaptive Learning

• It learns from data and then applies it to make informed decisions.
• Eg: Recommendations in music list will be based on listeners preference comparing other listeners with similar musical taste.

• Continuously analyze data with a logical structure artificial neural networks.
• Eg : Google's AlphaGo

•  To coordinate interaction with the learners and deliver customize resources and learning activities.
• Eg: Prodigy math helps to build 1st to 8th grade math skills

Education

Healthcare

Environment

• Tutoring customize support and instruction
• Grading using answer key and compile data on performance compared to others
• Immediate feedback to students
• Assistive Technology - helping read out passages for visually impaired student
• School facilities management- Wi-Fi water supply etc.,
• Exam integrity and Plagiarism detection

• DeepMind's AlphaFold has solved the protein-folding problem, Drug discovery especially for rare diseases.
• Robotic Synthesiser can read a research paper and make the compound described in it.
• Better & early decision making.

Eg: Google to predict risk of Heart Attack by scanning retina.

• Checking health through wearables.

Eg: Fitbit, iWatch, can calculate calories burned & sleeping pattern.

• Robot aided surgeon with an automated speech enabling device.

• UNEP's World Environment situation room uses AI to obtain real time sensor data of 140 countries to predict Co2 concentration Glacier mass, sea level rise, biodiversity loss, etc.,
• AI warning systems to detect and notify authorities of algal blooms in freshwater.
• Clean environment - robots picks up waste soda cans in buildings.
• AI-learn and predict weather patterns Eg: IPCC relays on 30 climatological models generated by AI to study large scale climate changes across the planet

Energy

Entertainment

Space

• Smart power consumption by consumers.

Eg: smart electrical meters.

• Help energy companies to discover faults before they lead to energy failures by maintaining Ideal maintenance schedules by analysing data.
• AI helps Oil companies to identify exactly where to drill using IOT sensors.
• Anticipate energy demand reducing required output reserves for the company.

• Large language models like chat GPT is used to create text music and art with excellence.
• Personalisation of content.

Eg: Netflix, spotify

• Movie production-   Automate sound production procedures.
• Subtitles generation.
• Gaming and sports industry along with virtual reality(VR) and augmented reality(AR).
• Metadata tagging- classify items based on the characteristics and tag them→ used by media content creators.

• Helps process satellite data for big data analytics
• Assists Navigation system
• Monitor satellite health- equipment malfunctions/ satellite collisions.
• Assists astronauts

Eg: Feder(Russia) sent to space to assist astronauts in ISS for 10 days.

• Clean debris in space

Eg: Chaser (ESA) to be sent in 2025 to clean orbital debris.