Max Mozes

AI2

Learning:

“We define learning as the transformative process of taking in information that—when internalized and mixed with what we have experienced—changes what we know and builds on what we do. It’s based on input, process, and reflection. It is what changes us.”–From The New Social Learning by Tony Bingham and Marcia Conner

-http://theelearningcoach.com/learning/10-definitions-learning/

My favorite example of me personally learning is fencing, once you understand the basic movements and counter movements all that’s left to do is learn how your opponent operates. Everyone big or small approaches fencing differently even within the strictest schools there is still at least slight variation between fencers. The goal of any fencer is to take in those differences of how your opponent fences internalize them by making new motions to see how they react. When you feel comfortable you mix in your previous experiences with similar fencers and set yourself up for an opportunity to strike before they strike you, based on the knowledge you have acquired about them.

Machine Learning:

“Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

-https://www.sas.com/en_us/insights/analytics/machine-learning.html

Using natural language processing, as well as machine learning and advanced analytics, Hello Barbie is both able to listen and respond. A small microphone found on Barbie’s necklace can record what was said to it and transmit that information to the servers at ToyTalk. There, the recording is then analyzed to determine the appropriate response for Barbie from 8,000 lines of prerecorded dialogue. The servers then transmit the selected response back to the Barbie doll in under a second so she can respond in real time. The answers to questions like what their favorite food is are also able to be stored so that they can later be used in conversation.

Symbolic Machine learning:

“Symbolic Artificial Intelligence, also known as Good Old Fashioned AI (GOFAI), makes use of strings that represent real-world entities or concepts. These strings are then stored manually or incrementally in a Knowledge Base (any appropriate data structure) and made available to the interfacing human being/machine as and when requested, as well as used to make intelligent

conclusions and decisions based on the memorized facts and rules put together by propositional logic or first-order predicate calculus techniques.”

- https://medium.com/datadriveninvestor/symbolic-ai-v-s-non-symbolic-ai-and-everything-in-between-ffcc2b03bc2e

An example of symbolic machine learning is facial recognition software, by having good code and putting in enough examples of photographs of someone’s face into this algorithm it can spot the patterns, such as eye distance, ear height, nose length, mouth shape and with this information it can make incredibly accurate inferences as to who that photo is of. By giving this program the points to look for and the proper training examples for whatever project you want to have it do with facial recognition it can be incredibly accurate.