Webex Assistant can listen for trigger words such as "take a note" and act on them, highlight comments in transcripts, and more. The big players in the space are undoubtably exploring such solutions, too. Video conferencing tool Headroom has some of this with the added ability to figure out and share action points. You can then search through the transcript for mentions of those keywords, or any other word in the search tool. Otter.ai can also identify topics discussed during a meeting, generating around ten keywords that it thinks are key to the session. I make a lot of use of Otter.ai for this, as I find the transcripts it delivers are becoming increasingly accurate and its ability to distinguish between different speakers useful. Based on the training data, the SVM separates the "space" of all possible fish into two parts, which correspond to the classes we are trying to learn (such as "blue" or "not blue").Some products help turn a meeting itself into data. One interesting example is Calendar, which tracks your meetings, spots attendance records, and tries to give you valuable insights, such as how long you spent in meetings in the last few weeks.Īfter a meeting, AI can help summarize the key topics, deliver schedules or work plans, and more. We look at each component of the fish (such as eyes, mouth, body) and assemble all of the metadata for the components (such as number of teeth, body shape) into a vector of numbers for each fish. Levels 6-8 use a Support-Vector Machine (SVM). We classify the new image with the same label (such as "fish" or "not fish") as the images from the training set with the most similar results. Then, for a new image, we feed it to MobileNet and compare its resulting list of annotations to those from the training dataset. Each image in the training dataset is fed to MobileNet, as pixels, to obtain a list of annotations that are most likely to apply to it. In order to customize this model with the labeled training data the student generates in this activity, we use a technique called Transfer Learning. A MobileNet model is a convolutional neural network that has been trained on ImageNet, a dataset of over 14 million images hand-annotated with words such as "balloon" or "strawberry". Levels 2-4 use a pretrained model provided by the TensorFlow MobileNet project.
Includes links to projects and activities, as well as teacher professional development.Ī collection of resources, activities, lesson plans, and professional development for implementing AI in your classroom.Ī list of resources from the AI4K12 Working Group for teachers interested in bringing AI into their classroom.
Students learn the foundations of AI and earn a badge at the end of the course.Ī series of AI-related activities that can be completed at-home or as part of an after-school program.Ī collection of resources for teaching AI in the classroom for all grade levels. All projects are completed in Scratch using new AI blocks to detect body and facial movements.Ī beginner-friendly self-paced introduction to AI designed for high school students. Also includes the ‘Bytes of AI’ series, which are smaller lessons that can be incorporated in any classroom.Ī middle-school, project-based curriculum that explores issues of ethics and societal impact of AI.Ī project-based curriculum about making interactive, movement-focused AI projects.
Train a machine learning model with text, numbers, or images, and use it to make games in Scratch.Ī new alternate curriculum unit for the Exploring Computer Science (ECS) curriculum.Ī series of free online courses created by Reaktor and the University of Helsinki.Īn AI education platform for building games, programming robots and training.Īn interdisciplinary, adaptable curriculum to support high school teachers and students exploring AI. Spark curiosity with free STEM and coding workshops. In the process, you uncover how bias can creep into AI applications and see the impact it has on the people involved.Īn interactive demo of several common machine learning algorithms, with links to additional resources to keep exploring.Īccess free resources including a lesson plan, videos, computer science curriculum, and teacher trainings. Think of a person, even from a book or movie, and this app will guess who you’re thinking of by asking questions.Īn online game where you use machine learning to help screen candidates for job interviews. Help protect the endangered Cape Mountain Zebra by identifying the different animals in the images. Start exploring machine learning through pictures, drawings, language, music, and more. Train a computer to recognize your own images, sounds, and poses. A free app that narrates the world around you in a variety of languages.Ĭan a neural network learn to recognize doodling?