Neter is a one person company specializing in machine learning and modern web technologies.
Contact us for details.
Neter is currently researching industrial event log anomaly detection (), and doing advisory consultancy about machine intelligence.
The machine intelligence revolution is just beginning, and we will continue to see disruptions across all fields. Robotic cars can replace about a third of all work, as about a third of all work relates to logistics. This trend will not take pauses, but it will simply continue in an accelerating fashion, disrupting new fields as it progresses.
For consultation about the topic, feel free to contact Neter.
About the owner:
Tero Keski-Valkama (MSc) is a technologist and a generalist. He is currently researching using deep neural networks for the purpose of anomaly detection in process traces in Flexible Assembly Systems.
He has a long experience in artificial intelligence and machine learning. He believes that the traditional data science approach of training a model against a batch of offline data, and then shipping this frozen model has significant issues of being unable to deliver true learning systems. Instead of shipping smart, pre-trained models, we need systems capable of true online learning in the field. This requires a different skillset from the traditional data science, requiring strong full-stack software production skills also.
Programming computers from early age, his interest in artificial intelligence and machine learning began when he was ten years old (in 1992) and followed instructions in a book for programming a simple inference engine for Commodore 64 BASIC capable of answering questions like: "IS CAT ANIMAL" ("YES", for previously inputted facts of "CAT IS MAMMAL" and "MAMMAL IS ANIMAL") (pg. 77).
In high school he experimented with simple perceptrons and Kohonen Self-Organizing Maps. He set up neural networks to learn how to play a simple game of Pong. He also made a simple reinforced learning "pet" game where the pet could be conditioned to promote specific behavior.
In Tampere University of Technology he joined Roboteam and became the artificial intelligence and machine vision specialist. Taking part in several Eurobot competitions for autonomous mobile robotics he programmed planning and vision algorithms, optimizing different policies against each other in simulations. He also implemented an application which takes a photograph of a pile of decorative stones and separates the graphics as a hundred or so separate clipped images of single stones for use as graphical content in games and such. He also worked with data clustering and classification algorithms in the topic of signal processing. He has done everything from Sudoku-solvers to game AIs, from fuzzy logic to industrial process optimization.
Working in Elektrobit, he introduced a concept for car domain context dependent services based on semantic web.
Networking in social AI circles in different forums in the internet, he discovered Coursera and the leading edge knowledge about deep neural networks presented by the definitive people in the field: Geoffrey Hinton and Andrew Ng. To update his expertise he took all the other courses related to modern machine learning as well, including but not limited to deep neural networks, statistical analysis, regression, discrete optimization, computational neurobiology, GPU programming, and medical human brain physiology.
Working as a software architect in Cybercom mainly in web and industrial solutions, he took part in the first #Industryhack #HackTheFactory and his team won the competition with a machine learning concept with Markov Chains for anomaly detection. He also worked in the background group for most of the subsequent Industryhack competitions, and he helped creating the concept of social IoT forum (Cybercom Machinebook), and was in a central role in its development into a working concept later. He won his second #IndustryHack #HackTheShip competition in Germany in 2016.
He started researching practical applications for deep neural networks and other modern machine learning technologies aiming towards a doctoral degree. The topic of research is applying deep neural networks for interleaved process traces from Flexible Assembly Systems. He has also countless other open source and hobby projects related to the field, and he actively follows the latest developments in relevant technologies.
He worked for four years in HERE Switzerland GmbH pushing forward the state of the art machine learning in the fields of automatic mapping, logistics and location data. He has authored numerous patents.
Please contact us for a review of what modern machine learning solutions can do for your business.
If you need someone who knows what's a bipartite graph and a mixture density network and is not afraid to use them, don't hesitate to contact!
Business ID (Y-tunnus): 2259502-5