Kimola Newsletter 7# Homework is for losers.

Author: Yasemin Ozturk - Data Analyst, Kimola
Yasemin Ozturk Data Analyst, Kimola
Sep 21, 2022 - 4 min read
Kimola Newsletter 7# Homework is for losers.

We are gathering news on artificial intelligencemachine learning and Kimola Cognitive's product updates on this newsletter.

Let us know what you think about our newsletter on anything by mentioning us on Twitter @kimolacognitive

Updates From Kimola Cognitive!

 

🤖 Scrape and Analyze Mobile Game Reviews!

Mobile Game Conversations Classifier

Developments in the game world reveal that it is inevitable for game companies to analyze player comments. "But how do we obtain and analyze thousands of comments?" we hear you say.

🎉 It's Easy to Catch Google Play Store Reviews with Airset Generator!

With Kimola's web browser extension compatible with Google Chrome and Opera, you can get consumer reviews from the Google Play Store in seconds. This data from the Google Play Store is stored in the cloud to be analyzed or exported as an Airset in your Kimola account. If you wish, you can watch the YouTube video where we explain how to get data from Google Play Store with Airset Generator.

🎮 You Can Classify Mobile Game Reviews with Kimola Cognitive!

It is possible to analyze the conversations about mobile games you have obtained with Airset Generator with the new machine learning model we have created for the mobile game world. You can sign up from this link to experience Kimola Cognitive, where you can automatically classify consumer opinions by just drag and drop.

Training dataset:

💎In this model, In order to classify the player feedback in the Google Play Store, the content of 4 labels was taught to artificial intelligence: "Game Praise", "In-Game Advertising", "Feature & Design" and "Technical Issues". So you can quickly find out if people are talking about game designs, bugs in games, or in-game ads.

💎 Manually tested by our data analysts, the model has an accuracy rate of 87% in English.
 
 
This Week in Kimola Newsletter
 

Students use artificial intelligence to be successful.

Technology-savvy students, on the other hand, see this as an opportunity and use it to their advantage. With the development of artificial intelligence, it has been discussing whether homework will be done entirely with the help of artificial intelligence in the near future. However, many students interested in technology have already started using artificial intelligence to do their homework. According to students who use artificial intelligence in their homework, the "system" works like this: They transfer the assignment given as an idea to artificial intelligence, usually GPT-3, and the artificial intelligence then create a text for them. As such, it seems inevitable to take new actions in the education world.

 

Algorithms predict the movements of sports teams with 80% accuracy.

Algorithms developed in Cornell's Intelligent Systems and Controls Lab can predict volleyball players' in-game movements with more than 80% accuracy. Now the lab is collaborating with the Big Red hockey team to see the situation in other sports. These algorithms are unique because they bring a holistic approach to actions by combining visual data, such as an athlete's position on the field, with more implicit information, such as the athlete's specific role in the team. By watching volleyball matches, researchers train algorithms to reveal hidden variables in the same way people gain sports knowledge. The algorithms use machine learning to extract data from videos of volleyball games and then use that data to help make predictions when a new set of games is shown. The results show that the algorithms can infer the roles of players (for example, distinguishing a defender from a blocker) with an average accuracy of 85%

 

Artificial intelligence can determine the risk of heart disease in less than 60 seconds.

The new research published in the British Journal of Ophthalmology provides a machine learning-based method for developing quick and cheap cardiovascular screenings. If the findings from this study are validated in future clinical trials, it could mean that individuals could find out their risk of stroke and heart attack without needing blood tests or blood pressure measurements. This would be a huge breakthrough in cardiovascular health and could help save lives by catching risk factors early. The study shows that the predictions made by the AI tool are as accurate as the predictions made by current tests.

 

The use of machine learning in financial services is becoming more common.

Research by the Bank of England and the Financial Conduct Authority shows an increasing number of UK financial services firms using machine learning (ML). Overall, 72% of firms report using or developing machine learning applications. This trend is likely to continue, and companies expect average machine learning applications to increase 3.5 times over the next three years. In addition, companies think that machine learning provides a number of benefits. The most commonly identified benefits at this time are enhanced data analytics capabilities, increased operational efficiency, and better detection of fraud and money laundering.

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