In the middle of 2017, we are beginning to glimpse uses of artificial intelligence that years ago would have seemed like science fiction. Machines are beginning to learn on their own, without the need to program rules for infinite cases. All this is possible thanks to Machine Learning and Deep Learning, which, as I explained in this other post, it will be a technology driver for Industry 4.0 in virtually all sectors.
I know it is difficult to understand and imagine the concrete uses that Machine Learning already has and will have in the coming years. That is why, in this article I present examples of applications that are already a reality in different sectors.
Machine Learning in various sectors
Industry 4.0
- Failure prediction and maintenance proactive maintenance of machinery and production equipment. This will allow to better plan maintenance and anticipate breakdowns before they occur.
- Optimization of agricultural plantations and production methods: improvement and enhancement of production and quality control processes.
- Intelligent and autonomous cars: A whole range of options, from driver fatigue detection to voice recognition. The goal is to improve driver safety and enable autonomous intelligent transportation.
Energy
- Electricity demand forecasting: Ability to foresee consumption peaks and act upon them to ensure under-supply.
- Resource optimization and failure prediction in electrical systems: real-time improvement of all systems that supply electrical energy.
- Daily price forecast of the electric pool.
- Optimizing energy necessary for a business center or other buildings where electricity consumption is very uneven.
Finance
- Customize products according to the customer's profile.
- Detection of fraudulent transactions.
- Scoring of customers to speed up and improve the granting of credit.
- Secure authentication in mobile applications.
Retail and Consumer
- Optimal product sourcing according to customer profiles.
- Automatic and optimal pricing and bidding, according to the type of customer, their previous purchases and the type of products they consume.
- Customer satisfaction and impact on social networks: we can instantly know if our product is being badmouthed and what is being said about our competitors.
Health
- Optimization of clinical trials including patient selection and probability of success: improvement in the selection of subjects for clinical trials.
- Helps in the prediction and diagnosis of diseases in order to design a more effective treatment.
- Optimization of medical resources in hospital centers: e.g., discharge forecasting and management of free beds.
Gaming
- Machine recommendation to the player, when it comes to giving in-game tips or making in-game purchases.
- Estimate the propensity to churn: predict when you are going to stop playing and the reasons (e.g., the difficulty of the game).
- Preventive maintenance of machines or servers that guarantee the operation of the video game or application.
The options offered by Machine Learning are practically infinite... Do you want to discover them?