Artificial Intelligence (A.I.), which has been described as intelligence exhibited by machines, has application potential in nearly all areas of our lives, society and business – for example in Healthcare, Energy, Traffic, Education and Public Safety. Even though one might think that Opentech AI is only relevant for creating common good via publicly funded research and academia this is not quite the case.
Opentech AI R&D is highly relevant for companies as well, as the momentum on open source platforms, data sets and community development has grown to a point, where an individual organisation will struggle to match the speed of opentech AI development with a pure “in-house” AI technology stack and R&D practices. In other words – it seems that, like software R&D, also mainstream AI R&D is gradually moving towards opentech community development, where commercial competition and value is in the solutions and services of AI applications and in the end-to-end systems needed to host and operate those.
Opentech or not, AI is not a silver bullet or magic that can just be taken out of the box to solve any problem that one might have or imagine. Instead AI could be characterized as one tool (in addition to many other technologies) to solve real problems in real world. Exploiting AI to develop applications that meet industry standards on variety of different sectors, is an endeavor requiring wide and deep technological and business domain expertise, as well as capability to execute highly complex development and change projects in companies.
The impact that AI will ultimately have to our life, society and business is on a scale analogous with transferring from agricultural society to industrial society. A good article covering applications and impact of AI has been published by Harvard Business Review. This page collects links to further materials on AI applications and challenges on different sectors of industry and society as examples of applying AI.
- Competition on lung cancer detection, challenging teams to improve lung cancer detection algorithms based on high-resolution lung scan data.
- IEEE info-graphic on AI vs. Doctors including examples of AI applications in the field of healthcare.
- Restaurant visitor challenge, where based on history data on reservations and visitations to a restaurant challenge is to predict number of future visitors to the restaurant for future dates.
- Price suggestion challenge, where given textual product description challenge is to suggest a price for the product.
- Energy disaggregation competition, where electromagnetic interference data is used to disaggregate total power consumption into consumption of household appliances.
- Forecasting future energy consumption in a city based on history consumption time series data.
- Predicting future stock returns given the historical stock performance and some additional data. Challenge used in evaluating suitability of candidates in recruitment.
- Passenger screening algorithm challenge, to develop threat prediction algorithms based on scanning images.