|Automatically read, interpret, and process images of scanned text and handwriting. While we wait for the digital revolution to sweep through all areas of business, we are often tasked with creating novel digital interfaces to analog problems. Scanned and photographed images of forms are easy, yet time consuming for a human to read, transcribe, and redirect. Your intelligent algorithm will replace the human and process the often poorly-scanned images, digitize typeset and handwritten content, check for and flag errors, verify signatures with our database, detect the language, search for embedded features like QR codes, and route the extracted information to the proper internal system.||PostFinance|
|We have an exciting project that automatically analyses news on our customers with an AI in order to help them find the right products. We challenge you to use a new method active learning to fine tune our news analysis models with human user feedback.||NordLB|
|AI based analysis of existing cost calculations for different working steps, identifying and clustering similar work steps from the Excel files and suggesting a historical price range for that work step, including the average price||EDAG|
|Personal running equation machine: Runners who like to enter races with the knowledge of being properly prepared are successful runners. This applies to professional athletes and amateurs alike. The difference is, that professional athletes have a supporting crew of trainers, coaches and more important people, who can do the intelligence, analysis and forecast of future performance. Amateurs simply cannot afford this convenience and are reduced to “”believing”” the in the data presented by current platforms, such as Strava and GarminConnect. These forecasts, however, are less than precise and in reality don’t give the athlete the chance to gain enough insight. The issue becomes much more pronounced in trail and mountain running, where the terrain helps to make a prediction close to impossible, unless the athlete knows his personal running equation. The aim of the project is to design an AI that can use the industry standard .FIT file format, which includes many parameters of a recorded run, such as altitude change, running pace (speed), heart rate and many more, which can be used to find a correlation. Unfortunately, there is no software solution to automatically detect change in slope (how steep of a up/down) in a recorded activity, which is required to be able to forecast the runner’s timing in a future race. The primary goal is thus to automatically detect running speed over xy% slope in a .FIT file. A secondary goal would be a solution to input a .GPX file, which includes all details of a future race course and then automatically calculate the time required to reach predefined places along that race course. Training data is provided in .FIT file format, as are possible simple courses in .GPX file format.||Participant|
|Last year’s challenge was superbly helpful in building the brand new recipe recommendation system on Noonli. We used learnings from the challenge for onboarding and making first recommendations. For this year’s challenge, we want to perform an experiment which is called popular near you. This feature aims to recommend recipes that are popular in a given location. We use the location and recipe information from publicly available information such as Twitter. We define such data like this: |
– explicit data or user input data (e.g., ratings on a scale of 1 to 5 stars, likes or dislikes, reviews, and product comments) and
– implicit data or behavior data (e.g., viewing an item, adding it to a wish list and the time spent on an article, etc.). In our case, previous recommendations. We are also learning more about the problem. We will bring in the data, which will be publicly available data from Twitter-like platforms or food rating website services (like google search results, etc).
|In this challenge, you will use ML techniques to determine the feasibility of identifying hidden patterns or insights associated with social skills & functioning among pre-school children diagnosed with Autism Spectrum Disorder. Data is provided from the EU DREAM trial (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236939) comparing two interventions (Robot vs Therapist), where their Digital Biomarkers (skeletal movements, head orientation & eye gaze) were collected. Performance of their social skills were accessed by therapy tasks consisting of imitation, joint-attention & turn taking.||Participant|