For this week, we still continue doing some research and discussions. After continue doing a few researches, our team has found some algorithms, but we are interested most with the monte carlo algorithm. According to our team, the monte carlo algorithm is likely to be efficient and suitable for our final project. We decided to pursue using the monte carlo algorithm. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values for any factor that has inherent uncertainty.

We are asked to teach computer vision for this week.

Computer vision is one of the subfields of Artificial Intelligence that deals with how computers can be made to gain high-level understanding from digital images or videos. Computer vision seeks to automate tasks that the human visual system can do. Automation basically is making machines do the job of humanbeings. Computer vision allows machines, semi-controlled vehicles, drones, factories and farm equipment to work effectively and safely.

Computer vision has been implemented in several aspects including finance, automotive, healthcare, agriculture, industry, etc. I will be specifically discuss on the aspect of finance. One of the application that has implemented computer vision in the financial area is Cortexia. Cortexia relies on visual Search.

Everyone has probably faced the challenge of liking something but not knowing where to buy it. All we need to do is to take a photo of a magazine page or drag an image from social media, and find a similar or the exact item to buy.

For this week, our team has decided to resume making our final project. We did not really made any significant changes and progress. Last week, we learnt about a new algorithm called Naive Bayes in class. Initially we thought maybe Naive Bayes is one of the few algorithms that we should consider to use for our final project. Naive Bayes is not really difficult to be implemented. Searching for another efficient algorithm is also what we plan to do. In the end, we realized that Naive Bayes algorithm is not suitable for our final project.

Naive Bayes classifiers are a collection of classification algorithms based on Bayesâ€™ Theorem. Naive Bayes classifier is a family of algorithms where all of the algorithms share a common principle. Every classified feature is independent of each other.

Bayes’ Theorem is a mathematical formula created by Thomas Bayes. Bayes’ Theorem is a mathematical formula for determining conditional probability. The theorem provides a way to revise existing predictions or theories (update probabilities) given new or additional evidence.

Now let us practice

The first step is to divide the classes into mammals and non-mammals. Then, we determine how many animals in each class.

The Mammals Class

The Non-mammals Class

The second step is to count the prior probability.

In total, there are 7 mammals and 13 non-mammals.

Divide 7 by 20 to get the prior probability of mammals.

Divide 13 by 20 to get the prior probability of non-mammals.

The third step is to count the probability.

Now, let us test the model if the instance is:

Give birth = yes, can fly = no, live in water = yes, have legs = no

Based on the equation:

P(mammals|give birth, can fly, live in water, have legs) = 0.020991254

P(non-mammals|give birth, can fly, live in water, have legs) = 0.002730997

The class that has the highest probability is: Mammals.

For this week, our team believed that we still need to do some more researches before making any significant progress to our final project. We had one time a thought of changing the idea for our final project. We spend most of our time doing several researches and discussions. We discussed on which algorithm is suitable and efficient to be implemented to the game called “cangkul”. We have not really distribute the task of each team member. We realized that since mid-exam is approaching, our team decided to pause on doing several research.

Artificial Intelligence is the ability of a computer to act, think, and perform like a human-being.

There are 2 categories of AI:

Narrow AI

Artificial General Intelligence (AGI)

Narrow AI

Sometime narrow AI is referred as a “Weak AI”. Narrow AI operates within a limited context and is a simulation of human intelligence. The focus of Narrow AI is performing a specific task extremely well.

Several examples of Narrow AI include:

Google search

Image recognition software

Siri, Alexa and other personal assistants

Self-driving cars

IBM’s Watson

Artificial General Intelligence (AGI)

Artificial General Intelligence sometimes is referred as “Strong AI”. AGI is the type of artificial intelligence that we see in movies and games. The robots in the movie, I Robot, can be considered as AGI. A movie character, Data, from Star Trek is also an example of AGI.

Our group consists of three members: Galastu Chandra Utama, Arden Djaja, and myself. During the first week, we spend most of our time on doing several researches and discussion on what we are going to create for our final project. At first, we wanted to create chess with machine learning for our final project. After doing several researches, we realized that other people have already created it. Finally, We decided to make a traditional Indonesian card game called “cangkul” using Python for our final project.