Week 8 Flashcards
Describe the waves of technological revolution
Roughly every 14 years a technological revolution hits. We are admit a new wave that will change how we work, how we live, and how, hopefully as humanity, we thrive
- 1994
- 2008
- 2022
Currently in the wave of artificial intelligence
Artificial Intelligence
Artificial Intelligence (AI) - computer software that can mimic or improve upon functions that would otherwise require human intelligence
Think of AI as a simulation of human intelligence processes by machines/computer systems that exceed what a human can do alone
Artificial Intelligence can be found in: pattern recognition, medical diagnosis, computer vision, search recognition, self-driving automobiles, natural language processing
What is the ‘fuel’ of Artificial Intelligence
Compute power is the fuel of AI
- AI works more efficiently on specialized chips, designed for rapid computations needed
- Recall: Moore’s Law - compute power doubles approx. every 2 years
- There is a new generation of hardware chips that are tailored to AI use cases (GPU vs CPU)
What are the major types of Artificial Intelligence
Major types of Artificial Intelligence:
(1) Machine Learning
(2) Deep Learning
Now there is a new type called Generative AI
Type of Artificial Intelligence: (1) Machine Learning
Type of Artificial Intelligence: (1) Machine Learning
AI broadly defined as software with the ability to learn or improve without being explicitly programmed
Ex. Netflix uses ML to analyze viewing habits of customers to make prediction around what viewers might enjoy next via the “watch next” feature
There are two SUB-CATEGORIES of machine learning: (1) supervised learning and (2) unsupervised learning
Type of Artificial Intelligence: (2) Deep Learning
Type of Artificial Intelligence: (2) Deep Learning
Sub-category of machine learning and the deep refers to the layers of interconnections of neural networks to arrive at results to process data and make decisions
Ex. Visa/Mastercard uses deep learning to prevent fraud by detecting anomalies in user transactions and creating/modifying user profiles
Supervised Learning
A sub-category of machine learning –> Supervised learning is algorithms trained by specific examples and classifications
Ex. Gmail/Outlook email spam filters learn to classify emails as “spam” or “not spam” by recognizing patterns and features in emails such as certain phrases or sender profiles, which it then uses to classify new, unseen emails
Unsupervised Learning
A sub-category of machine learning –> unsupervised learning is algorithms that are not fed to a pre-determined result
Ex. Facebook’s “People you may know” feature, which uses ‘clustering’ to identify patterns in user data without being told exactly what to look for (i.e., # of connections with people who attend the same school as you)
Generative AI
Generative AI creates new written, visual, and auditory/video content given prompts or existing data
Describe the new generation of hardware chips used for AI
There is a new generation of hardware chips that are tailored to AI use cases (GPU vs CPU)
GPU - graphic processing unit
CPU - central processing unit
Both are essential components in a computer system, however AI tends to favour the parallel processing power of the GPU
- CPU = generalists
- GPU = specialists, designed for specific tasks like handling large blocks of data simultaneously which makes them more efficient for intense computations in AI
- CPUs still required, but handle sequential processing tasks
Consider NVIDIA - one of the biggest winners in this space
Name some popular categories of Software used in AI
(1) Neural Networks
(2) Expert Systems
(3) Algorithms
Neural Networks
Popular categories of software used in AI: Neural Networks - statistical computer model inspired by the human brain
- consist of of interconnected layers of neurons/nodes that process information
- neural networks hunt down and expose patterns, building a multi-layer relationships that humans cant detect on their own
- if a set of interrelationships is strong, they are “approved” in the model
- if a better set of relationships is found, old ones tweaked or discarded
What plays a critical role in Neural Networks
Data plays a critical role and neural networks require a massive amount of data to work
What are some use cases of Neural Networks
Major use case: image recognition and natural language processing
Example: tiktok reccomendations algorithm - by feeding large amo9unts of user data, the algorithm learns and tailors content for each individual user
Expert Systems
Popular Categories of Software used in AI: Expert Systems
Expert Systems - AI systems that leverage set of programmed decision rules or example outcomes to perform a task in a way that mimics applied human expertise
-Take the form of “IF THIS, THEN THAT” decision trees or rules executed by analyzing specific cases against outcomes
-Do X, because Y variable is a certain measure
-Ex. make less product because weather <= 40F
Unlike Neural Networks and other modern machine learning techniques, expert systems do not typically require massive amounts of data to set up.
- However, they do require the ability to extract rules or expertise, which means there is time and expense working with subject matter experts to test and iterate to ensure outcomes are hat is expected
