What is a problem with using incorrect training data to train a machine Quizlet

Investors have pumped millions more dollars into Quizlet, the popular provider of digital study tools, to fund machine learning enhancements and artificial intelligence programs on the online learning platform.

“We’re excited that this new capital will allow us to make key investments in the Quizlet platform, grow our company and work toward our vision of providing an AI-powered tutor to help anyone learn anything,” said Quizlet CEO Matthew Glotzbach in a statement.

The edtech startup — which currently offers more than 220 million study sets — sees over 30 million users each month, claiming that 1 in 2 high schoolers and 1 in 3 college students in the United States uses Quizlet. The company says it is the largest user-generated consumer learning platform in the United States and plans to expand its presence overseas, especially in Western Europe and Asia.

The new $20 million, which was raised through Series B funding, brings Quizlet’s funding totals to $32 million. This recent round of funding was led by investors Icon Ventures, Union Square Ventures, Costanoa Ventures, Owl Ventures and Altos Ventures.

Known for its digital version of traditional paper flashcards, Quizlet adds to the studying and knowledge-retention process through its unique, customized learning activities.

Quizlet Learn, the AI-powered offering that the company plans to develop further with the new funds, creates personalized study plans for any user — all for free. If a student sets a date by which they need to learn the material, Quizlet Learn will generate a sequence of materials to aid in their study, paced appropriately to meet the student’s deadline. Quizlet also launched Quizlet Diagrams, another AI tool that provides a more interactive way for users to learn material.

Both AI programs gather data from anonymous study sessions and combine it with proven cognitive science techniques, the company says. It plans to add between 70 and 120 new employees to improve these existing platforms.

“Quizlet has grown from a tool that helped Andrew Sutherland, its founder, study for his high school French exam, to a scalable and monetizable study platform for people around the globe,” Jeb Miller, general partner at Icon Ventures, said in a statement.

This technology will not seek to replace teachers, but it will be “instrumental in the educational landscape,” Miller added.

Week 1 Quiz - Introduction to deep learning

  1. What does the analogy “AI is the new electricity” refer to?

    • AI is powering personal devices in our homes and offices, similar to electricity.
    • Through the “smart grid”, AI is delivering a new wave of electricity.
    • AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
    • Similar to electricity starting about 100 years ago, AI is transforming multiple industries.

    Note: Andrew illustrated the same idea in the lecture.

  2. Which of these are reasons for Deep Learning recently taking off? (Check the two options that apply.)

    • We have access to a lot more computational power.
    • Neural Networks are a brand new field.
    • We have access to a lot more data.
    • Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
  3. Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)

    • Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
    • Faster computation can help speed up how long a team takes to iterate to a good idea.
    • It is faster to train on a big dataset than a small dataset.
    • Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).

    Note: A bigger dataset generally requires more time to train on a same model.

  4. When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?

    • True
    • False

    Note: Maybe some experience may help, but nobody can always find the best model or hyperparameters without iterations.

  5. Which one of these plots represents a ReLU activation function?

    • Check here.
  6. Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?

    • True
    • False
  7. A demographic dataset with statistics on different cities' population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?

    • True
    • False
  8. Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)

    • It can be trained as a supervised learning problem.
    • It is strictly more powerful than a Convolutional Neural Network (CNN).
    • It is applicable when the input/output is a sequence (e.g., a sequence of words).
    • RNNs represent the recurrent process of Idea->Code->Experiment->Idea->....
  9. In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?

    • x-axis is the amount of data
    • y-axis (vertical axis) is the performance of the algorithm.
  10. Assuming the trends described in the previous question's figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)

    • Increasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
    • Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
    • Decreasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
    • Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.

What is a problem with using incorrect data to train a machine?

Sample bias. This happens when there's a problem with the data used to train the machine learning model. In this type of bias, the data used is either not large enough or representative enough to teach the system.

What is a problem with using incorrect training data to train a machine multiple choice sample bias prejudice bias measurement bias variance bias?

Sample bias is a problem with training data. It occurs when the data used to train your model does not accurately represent the environment that the model will operate in.

What is true about training data in machine learning tasks?

Training data is the initial dataset you use to teach a machine learning application to recognize patterns or perform to your criteria, while testing or validation data is used to evaluate your model's accuracy. You'll need a new dataset to validate the model because it already “knows” the training data.

Which type of bias occurs as a result of training data that is influenced by cultural or other stereotypes?

Prejudicial Bias It occurs when training data content is influenced by stereotypes or prejudice within the population. Data scientists and organizations need to make sure the algorithm doesn't learn and manifest outputs that echo stereotypes or prejudice.