ARMY AL&T
FIGURE 1
MACHINE LEARNING 101 Tere are many different applications of AI, including machine learning, a subspecialty of AI that uses probability and statistics to train computer programs. Te computer
“learning” is usually performed offline using a training dataset to build a mathe- matical model to reflect the real world. Te closer the model reflects reality, the more accurate the computer predictions. Once the program is fielded, it can continue to “learn” to improve its effectiveness.
EXAMPLE: SPAM VS. HAM Early spam email filters were not very effective at identifying spam. Programs used “if-then” rules to identify spam. For instance, if a word like “Viagra” appeared in an email, then the email was automati- cally labeled as spam. Employees at those companies continually updated their word lists to adapt, while spammers only needed to slightly modify words in an email to create new scams and get around spam filters.
Machine learning automates that process by building a statistical model of spam email to classify emails as spam versus
“ham” (good email). Companies gathered a large dataset of spam and ham emails. Using probabilistic and statistics algo- rithms in combination with spam and ham emails, the computer “learned” the probability of an email being either spam or ham. Te machine could then auto- matically classify new emails based on the probability of being spam or ham, given the words in the email.
FACTORS FOR EFFECTIVE MACHINE LEARNING It’s all about the model and the data used to build it. Te more data used to train the model, the better it can reflect the world that is being modeled. Te data, however, must be good data. Erroneous input, whether accidental or deliberate, will skew
TROUBLE AT-THE-HALT
No human being would mistake either of these altered signs for anything but stop signs. National Science Foundation researchers, using an algorithm to defeat AI driving systems, found the systems mistook both for 45 mph limit signs. (Photo courtesy of Cornell University)
the model. Data also must be tagged or labeled with descriptions to train and test algorithms (e.g., emails classified as spam or ham, or pictures tagged as “helicopter”). Without tags, the data is less useful and informative than it could be—a computer learns more from a picture of a helicopter tagged with the word “helicopter” than it does from just the picture without a tag. Depending on the type of data, tagging or classifying it can be a time-intensive, manual process.
Rigorous testing measures how a model performs with a test dataset that does not contain the data used to train the AI model, to give a true representation of the model’s performance. Models tested against training data will have inflated performance scores, as the model has seen the data before and knows how to clas- sify it. Precision, recall and f-scores better judge an algorithm’s performance than the traditional accuracy metric.
Precision measures how many of the predicted items were classified correctly (e.g., how many of the emails labeled as spam were really spam).
Recall measures how many in the total dataset were correctly identified (e.g., did the program find all the spam?). Having high recall is not meaningful if precision is low; conversely, high precision does not necessarily entail high recall.
F-score, the weighted average of precision and recall, overcomes the accuracy para- dox because it takes into account false positives and false negatives and balances recall and precision.
Computational power also affects perfor- mance quality. Te more parameters and the greater the complexity of an algorithm, the more computing power needed. Insuf- ficient processing power prevents a timely and, therefore, useful result.
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