Kimola Newsletter | Cognitive 3# Inspiration Out, AI In!

Oct 07, 2022 - 4 min read

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

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Scrape & Classify Udemy Reviews

Scrape consumer conversations with Kimola Cognitive's browser extension and analyze them by classifying them with the new machine learning model created by Kimola Cognitive for the world of educational technologies! You can sign up from this link to experience Kimola Cognitive, where you can automatically classify consumer opinions with a simple drag and drop.

About Our New Machine Learning Model:

With the digitalization process accelerated by the pandemic, the number of online courses has increased considerably in recent years. In addition, with the development of technology, the world of education, which was gradually separated from desks, classrooms and buildings, has now become a place where everyone can access and learn anything at any time. Naturally, these developments in the education world made it inevitable for online learning platforms to analyze user comments. This is exactly why this model was created to quickly analyze online course feedback. 🙌

Training dataset:

💎 In this model, the content of 5 tags was taught to artificial intelligence to classify user feedback on online education platforms such as Udemy and Coursera. In short, your data is classified with the tags "course duration", "instructor", "content and material", "clarity and informative", and "other" using a simple drag-and-drop method.

💎 Manually tested by our data analysts, the model has an accuracy rate of 85% in English.

Inspiration out, AI in!

With flamboyant performances and costumes, the Eurovision Song Contest is probably one of the weirdest shows to grace our screens. But can machines understand and provide such a unique human experience? The answer is in AI Song Contest, a Eurovision-inspired music competition where all the songs are written by artificial intelligence. This year, 46 groups participated in the competition, which has been held since 2020. The 15 teams that made it to the final met in Liege, Belgium. In addition to musicians, data scientists and software developers from all over the world took part in the teams for the songs prepared by artificial intelligence. Of course, we can't wait for inspiration for a machine to produce these songs. For a good song produced by artificial intelligence, scientists and musicians need to teach the machine the necessary algorithms and musical ideas after months of work.

Generating Sentences for 3D Objects with Google AI

The number of artificial intelligence that transform written expressions into visuals is increasing. It has become increasingly common to paint and create digital images with our words using many different artificial intelligence applications such as Midjourney, GauGAN2, Craiyon, especially DALL-E, which has recently been opened to everyone's access. Finally, Google introduced Dreamfusion, artificial intelligence that transforms written sentences into three-dimensional objects. Last year, Google had announced Dream Fields, artificial intelligence that can make various objects three-dimensional. On the other hand, Dreamfusion emerged with the combination of this artificial intelligence and Imagen, Google's image model artificial intelligence.

Text-to-Video Conversion with Artificial Intelligence

Artificial intelligence technologies appear in many different fields, such as art and cyber security, especially software. While getting used to artificial intelligence systems that create images from text, Meta announced the next step in this field, the artificial intelligence-supported video creation platform “Make-a-Video”. Users will be able to write the scenario they want and print it out on Make-A-Video. Stating that the platform can create short videos, the company announced that Make-A-Video was developed with the machine learning method.

Artificial Intelligence Improves Image Quality.

Digital images consist of a fixed number of pixels in a two-dimensional grid. AI super-resolution uses machine learning (ML) algorithms to infer from an original image way that more pixels may be added to improve that image in some way. Basically, the technology increases the resolution by creating a more pixelated version of the image that can offer more detail. For example, many televisions, for instance, may be able to display a grid of 3840 x 2160 pixels, sometimes called 4K (an approximation of the horizontal number of pixels) or ultra-high definition (UHD). Many TV signals, however, are broadcast only with grids of 1920 x 1080 pixels, also known as 1080p. AI algorithms convert each pixel in the 1080p signal into a grid of four pixels, making the image quality more detailed. These algorithms provide higher resolutions that may be required for engineering, construction, surgery, and other applications that rely on cameras to gather important details.