Applied Artificial Intelligence

Lesson 1: What is AI?

 

In this lesson, students will be introduced to Artificial Intelligence (AI) by “dissecting” technologies they are already familiar with and use day to day (Google, Youtube, Siri … etc) and learn about AI technologies in other fields (healthcare, research, manufacturing, advertising, & education). After the introductory video & content powerpoint is shown, students will form teams and play an AI Jeopardy game, where they will be applying the knowledge they have learned.

By the end of the class, students will have a good understanding how data, algorithms, and predictions are used within AI applications.

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, fundamentals of AI will be introduced from a conceptual level.

Objectives:

  • Understand the definition of Artificial Intelligence (AI).
  • Understand how AI differs from traditional programming.
  • Be able to differentiate AI technologies from other non-AI innovations.
  • Be able to outline how data is used & generated by AI.
  • Understand the importance and relevance of AI.

Time to Complete:

1 hr (with possible extension)
Groups: whole class broken up into smaller teams

Lesson 2: Smart Machines: Sensors and Data

Overview:

In this lesson, students will learn about how computers and AI products use sensors to gather data about their environment. 

Humans (and other living organisms) respond to stimuli and make sense of their environment through their senses. Sensory systems provide us with real time data about our environment constantly. 

Computers can only “see” if they have sensors to collect the data. 

In addition to sensors that are analogs to human sensory organs like cameras & microphones — we can use additional sensors in our AI products to bring additional intelligence beyond that which is possible with basic human senses. 

The activity in this lesson will be a project where students will design their own Sensor based AI product that uses Supervised Machine Learning to process collected data. 

They will present their data in the form of a Data Flow Diagram & Interactive PPT or Prezi, which walks the rest of the class/audience through each process step in the functional design of their Sensor-based AI Product. 

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, fundamentals of AI will be introduced from a conceptual level.

Objectives:

  • Understand that computers need sensors to experience and make sense of their environment. 
  • Understand that sensors interact with the environment and the computer will record and use the data to make decisions. 
  • Students will design their own AI product that uses sensors and Supervised Machine Learning. 
  • Students will create & present a slideshow with a  Data Flow Diagram to describe the flow of data throughout the system. 
  • Students explore the value of good documentation. 
  • Students have a class discussion on potential problems or challenges with using or designing an AI product. 

 

Time to Complete:

50 – 60 min (with possible extension) 

Groups:

Can be individual or a 2 person partner project 

Lesson 3: Speech2Text Chatbot

Overview:

In this lesson, students will make their own Speech to Text interactive chatbots to take a food order, or process other kinds of orders or customizations they are interested in programming. They will be using speech recognition in their programs, to recognize the customer’s or user’s vocalizations and then use the speech recognition result to decide the next direction in the program. 

In Lesson 2: Smart Machines: Sensors & Data, students worked on computational thinking and outlining their prototypes in Data Flow Diagrams. 

In this lesson, Lesson 3: Speech2Text Chatbot, students will work on implementing Data Flow Diagrams , they will be writing and testing their own programs that involve Speech Recognition AI. Students should try to make their Sprite chatbot character as interactive & capable as possible. This involves mapping out different types of responses a user would provide, writing logical flow diagrams, then writing & testing the code. 

Provided in the lesson folder and linked in the lesson guide are all the resources to do the activities. One version of a final project code, FoodApp, is provided, as well a written walkthrough of the program flow and programming logic. 

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, fundamentals of AI will be introduced from a conceptual level.

Objectives:

  • Design & write code for their own programs that involve an interactive chatbot character (mBlock Sprite) & Speech Recognition AI. 
  • Debug & test their own programs that involve Speech Recognition AI. 
  • Practice good programming, with descriptive function names, helpful comments & program documentation. 

Time to Complete:

50 – 60 min (with possible extension) 

Groups:

Individual Work

Lesson 4: Introduction to Supervised Machine Learning

Overview: 

In this lesson, students will be introduced to different types of Machine Learning and will focus on studying Supervised Machine Learning (ML). Supervised Machine Learning involves getting the computer to categorize known datatypes into the correct bins, in an automated fashion. Essentially, the purpose of Supervised ML is to solve classification problems. 

After being introduced to the goal of Supervised Machine Learning and some real life products and applications that use Supervised ML, students will be challenged to train their own Supervised ML model. They will be able to pick the type of classification problem they want to solve and will have to curate their own training and test datasets to optimally evaluate the accuracy of their model. 

Unit Overview: 

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, fundamentals of AI will be introduced from a conceptual level. 

Objectives: 

  • Understand that machine learning algorithms need to be trained.
  •  Understand that the diversity or completeness of training data will impact the algorithm’s prediction accuracy. 
  • Understand that supervised machine learning is used to solve classification problems. 
  • Solve classification problems & train their own Supervised Machine Learning Model with Teachable Machine. 

Time to Complete:

50 – 60 min (with possible extension) 

Groups:

Individual Work & Class Data Comparisons

Lesson 5: Societal Impact of AI

Overview: 

In this lesson, students will be evaluating their Teachable Machine projects for objectivity & accuracy.

  • Does their Supervised Machine Learning Model have an easier time recognizing specific features, colors, or shapes? 
  • Are some forms of a class easier to recognize than others? 
  • Is one class better defined than the other? 

These are the questions students will ask themselves as they test & redesign their Supervised Machine Learning Models from Lesson 4: Introduction to Supervised Machine Learning. The focus of this lesson will be on introducing different forms of technical system bias and the impacts of these biases on individuals & society at large. 

The focus of the activity will be getting students to reveal & uncover bias that exists in their own projects, and to develop methods to improve the accuracy & objectivity of their Supervised Machine Learning Models. The lesson slides will introduce students to the possibilities and instances of bias in Artificial Intelligence systems (and engineering design), and what impacts these biases can have, beyond the mere data applications & processing activities, some of which include: 

  • Physical Safety Concerns 
  • Medical Misdiagnosis or Medical Errors 
  • Identity Bias 

Unit Overview: 

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level. 

Objectives: 

  • Students will be introduced to & understand the different kinds of bias, the impacts of these biases & the meaning of ethics. 
  • Students examine & test their own previous projects for bias.
  • Students as a class & throughout the activity will follow a protocol & develop tests to uncover bias in their algorithms (Supervised Machine Learning Models). 
  • Students will document their findings in a Lab Notebook & use their experimental data to redesign & redevelop their Supervised Machine Learning Models. 
  • Students describe the improvements & setbacks to their Supervised Machine Learning Model after retraining their model with different training datasets. 
  • Students consider how their designs would impact society if implemented at large. 

Time to Complete:

1 hr (with possible extension) 

Groups:

Individual Work

Lesson 6: Careers in AI

Overview:

In this lesson, students will be encouraged to explore paths in AI, both STEM & business related, and map the connections between their own projects and real world applications. The activity in this lesson will be AI career exploration, with the profile quiz as a launching point & student self-guided web research following after. 

Students will use class time to answer questions on the worksheet and make note of interesting & engaging products and career fields they would like to learn more about or be involved in. An extension of this lesson could be a brief in class discussion or a more in-depth project, a career exploration powerpoint presentation, where students can really synthesize their findings and present to the class some of their research findings. 

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, fundamentals of AI will be introduced from a conceptual level.

Objectives:

  • Students will be able to better understand their strengths and weaknesses with regards to the field of Artificial Intelligence. 
  • Students will take the profile quiz & ask themselves guided questions, to better understand their own personality & work preferences. 
  • Students will explore AI related careers in STEM & Business, and learn how project work they are doing now is related to real world applications.
  • Students will present & share their career research notes with their peers. 

Time to Complete:

1 hr (with possible extension) 

Groups:

Individual Work

Lesson 7: Neuroethics

Overview:

The focus in this lesson will be for students to practice using an ethical lens to examine & assess various Brain Computer Interface (BCI) technologies.

The activity for this lesson will be a Neuroethics Debate, where groups of students in their Neuroethics Committees will develop team statements about the value & risks of different Brain Computer Interface technologies. Students can think of the role on a Neuroethics Committee as being a decision maker.

In the Extension Activity students will draft an Ethics Matrix for a specific Brain Computer Interface product and have a discussion with classmates about how ethical considerations in  design or development of that specific technology could improve outcomes for more individuals, lower costs, increase accessibility, improve efficacy, and decrease risks. 

The Instructor will open the lesson with the Lesson Slides and the class will review the material together. During the Brain Computer Interface (BCI) Technology overview portion, students will independently fill in their worksheets with information to use as evidence in their arguments & statements. 

 

After the Brain Computer Interface (BCI) Technology Overview portion, students will work in their small groups, Neuroethics Committees to have a group discussion and share their ideas & notes about the value & concerns related to each Brain Computer Interface (BCI) application. 

The activity will end in a discussion where teams can debate ideas and everyone will work together to really process what is meaningful. The Ethics Matrix extension activity encourages students to fit their statements & observations in a more structured frame. Students will be introduced to the moral or philosophical definition of value & the definition of stakeholders. Through this exercise students can see how purposeful technology & product development can better ensure that people & society are unharmed.

Students will draft their own Ethics Matrices for a specific BCI application of their choice and share their Ethics Matrices with members in their team. 

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives:

By the end of this lesson students will:

  • Be introduced to the definition & field of neuroethics. 
  • Review foundational concepts in Artificial Intelligence & Machine Learning.
  • Use an ethical lens to examine & assess different Brain Computer Interface technologies. 
  • Consider the potential benefits & harms that any given Brain Computer Interface technology could have for the individual user or society at large. 
  • Work in groups to discuss & debate important ethical considerations related to each Brain Computer Interface application. 
  • Understand the definitions for ethics & stakeholder
  • Be able to fill out an ethics matrix & use their matrices to assess different Brain Computer Interface technologies. 
  • Students will be able to have a debate about their ideas and use details in their ethics matrix as discussion material. 

Time to Complete:

1 hr (with possible extension) 

Groups:

Individual Work then group discussion

Lesson 8: Hand Gesture Recognition

Overview:

In this lesson, students will be challenge to create their own hand gesture recognition programs. These programs will recognize the hand gestures presented to the computer camera connected to the student device and then mimic those gestures on the NeuroMaker Hand hardware. 

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives

  • Understand the concept of computer vision and it’s limitations and applications for real time control.
  • Gain hands on experience testing different word choices and programmable motions for a prosthetic
  • Design new possibilities using AI for the control of a mechanical hand

Time to Complete

1 Hour with potential extensions

Groups

Students working in groups of 2-4 students per NeuroMaker Hand

Lesson 9: Text Recognition

Overview:

In this lesson, students will be challenged to create their own Text Recognition systems. Using provided code, walkthrough documentation and ready to use cards, students will complete their own computer vision techniques which will read text to trigger different movements.

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives

  • Understand the concept of computer vision and it’s limitations and applications for text recognition
  • Gain hands on experience testing different word choices and programmable motions for a prosthetic
  • Design new possibilities using AI for the control of a mechanical hand

Time to Complete

1 Hour with potential extensions

Groups

Students working in groups of 2-4 students per NeuroMaker Hand

Lesson 10: Speech Recognition

Overview:

In this lesson, students will be challenged to create their own Speech Recognition systems. Using provided code, walkthrough documentation and sample commands, students will complete their own audio detection which will detect audio commands to trigger different movements.

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives

  • Understand the concept of audio detection and it’s limitations and applications for speech controlled AI technology
  • Gain hands on experience testing different word commands and programmable motions for a prosthetic
  • Design new possibilities using AI for the control of a mechanical hand

Time to Complete

1 Hour with potential extensions

Groups

Students working in groups of 2-4 students per NeuroMaker Hand

Lesson 11: Autonomous Hand

Overview:

In this lesson, students will be challenged with creating their own machine learning systems with the NeuroMaker Hand. Students will train sample data from their camera and use these pre built models to control whatever movements of the hand that they wish. Students will be challenged with integrating knowledge built up from previous units in order to achieve a combination of control factors to move their hands.

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives

  • Apply training data sets to controlled hardware movements
  • Gain hands on experience testing different word commands and programmable motions for a prosthetic
  • Integrated multiple levels of training and control to their NeuroMaker hardware
  • Design new possibilities using AI for the control of a mechanical hand

Time to Complete

1 Hour with potential extensions

Groups

Students working in groups of 2-4 students per NeuroMaker Hand

Lesson 12: Name in American Sign Language

Overview:

In this lesson students will combine all of the skills they have learned in order to build different communication systems for American Sign Language. Students will begin spelling their name using programming tools and then progressing to using any of the above skills to assist communication for those with prosthetics. Students may build a training algorithm with which to converse, may use voice recognition to provide designated ASL words or detect hand gestures in sign language to translate into text!

Unit Overview:

If this is your first time teaching this class, welcome to learning AI with the NeuroMaker HAND! Within the course, the fundamentals of AI will be introduced from a conceptual level

Objectives

  • Combine all new AI skills with a real world problem in assistive technology
  • Empathize with those in the assistive technology community
  • Integrated multiple levels of training and control to their NeuroMaker hardware
  • Design new possibilities using AI for the control of a mechanical hand

Time to Complete

1 Hour with potential extensions

Groups

Students working in groups of 2-4 students per NeuroMaker Hand