Advanced Artificial intelligence

The application of Artificial Intelligence  in organizations at this time will be decisive for the improvement of productivity.

The ability to equip computers with algorithms capable of performing human tasks is revolutionising business optimisation. Artificial intelligence allows us to carry out a simulation of human intelligence processes by machines. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

AI techniques applied to business models

• Probabilistic models 

One of the key benefits of probabilistic models is that they give an idea about the uncertainty linked with predictions  by taking into account  the impact of random events or actions in predicting the potential occurrence of future outcomes.

We may get an idea of how confident a machine learning model is on its prediction. For example, if the probabilistic classifier allocates a probability of 0.9 for the ‘Dog’ class in its place of 0.6, it means the classifier is extra confident that the animal in the image is a dog. These concepts connected to uncertainty and confidence are very valuable when we perform probabilistic inventory modelling for companies. This makes use of a probability distribution to specify the value of the demand or other unknown variable.

• Classic Machine Learning, Deep learning

Machine learning allows us software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and Predictive maintenance.

Deep learning eliminates some of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing some of the dependency on human experts. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”, et cetera. Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another.

• Prediction models, decision making, planning

Process used to predict future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior and trends.

• Time series / spatial trajectory analysis

Such data has numerous applications across various industries. Examples of time series analysis: Electrical activity in the brain, Rainfall measurements, Stock prices, Number of sunspots. Annual retail sales, Monthly subscribers or Heartbeats per minute. 

There are also a multitude of applications of AI. We apply AI in Industry 4.0 / 5.0, robotics, autonomous cars, people’s behaviour, social interactions, health, care, food, sustainability, trustworthy / fair AI, social impacts, ethics, privacy, …