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AI is becoming increasingly prevalent in our daily lives, impacting every aspect of modern life from smartphones to cars, i.e. all segments of industry.
MInD
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MInD
We, the founders of Machine Intelligence, believe that Humans can reach their full potential if they learn to work together with Machines.
By giving Machines intelligence, we enable them to better serve Humans. Creating Machine Intelligence is rewarding, and at the same time, it is a serious responsibility. We need to understand how Machines perceive our world, and what errors they can make under what conditions. We must be aware of their potential as well as their limitations.
AI is becoming increasingly prevalent in our daily lives, impacting every aspect of modern life from smartphones to cars, i.e. all segments of industry.
Over the past three years since our establishment, we have been privileged to serve a diverse range of clients on almost every continent of the world. This achievement has been made possible by the fact that our team members possess over a decade of experience in the industrial environment, which is underpinned by two decades of expertise in data analysis and processing in fields such as machine vision, voice recognition, and astronomy. You can read more in pdf here and here.
We understand that, first, you want a quick and convincing pilot solution. To get there, we take your data and show you what can be achieved in a few days. This pilot allows us to speak the same language and to see how the final solution fits your internal systems. Based on the results, we define the project schedule and give an offer.
Our team’s extensive industrial experience is further bolstered by many years of collective software development experience in producing numerous industrial equipment, which has strengthened our cohesion and unity as a team. Additionally, we possess a profound understanding of industrial hardware, industrial computers, the manufacturing process, and the production line, including their unique requirements.
The performance of deep learning models can deteriorate over time due to changes in the production process: e.g. new input materials, different image acquisition, new type of error mechanisms, change in packaging, etc. The performance can be maintained in two ways:
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