We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles. The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition. We retain the basic design of a fundamental algorithmic substrate called an "AI kernel" for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways. Omega includes eight representation languages and six classes of neural networks, which are briefly introduced. The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks. We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

This is a talk by Ilya Sutskever for course 6.S099: Artificial General Intelligence. He is the Co-Founder of OpenAI. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world.

About this course: This capstone project course will give you a taste of what data scientists go through in real life when working with data. You will learn about why data cleaning and munging is an important part of data science and how it occupies more than 80% of a data scientist's daily work. You will learn about location data and different location data providers, such as Foursquare. You will learn how to make RESTful API calls to the Foursquare API to retrieve data about venues in different neighborhoods around the world. You will also learn how to be creative in situations where data are not readily available by scraping web data and parsing HTML code.

BI-RADS DATA SCIENCE FOR SWIFT/PYTHON HACKERS, is a course designed by an iOS Developer for iOS and Python Developers. You will learn iPython enough to implement algorithms used in Data Science with little effort. As a Swift Programmer you will find the syntax needed to flow through iPython in Jupyter a breeze!!! After grasping a thorough knowledge of supervised learning in the first two sections, you will dive into xCode and write a Logistic Regression Binary Based application. In your final project, you will build BIRADS, a Breast Imaging-Reporting and Data System that takes the output data from a Neural Network and assigns a BI-RADS Category given input from the following features...

South Africa is facing a shortage of data scientists -- a new breed of analytical data experts with the technical skills to solve complex problems. And because they straddle both the business and IT worlds, they're highly sought-after and well paid. The demand for data scientists is being driven by the emergence of big data -- that unwieldy mass of unstructured information that can no longer be ignored and forgotten. It's a potential gold mine for companies -- as long as there's someone who can dig in and unearth the business insights that no one thought to look for before. South African universities like Wits and UCT have introduced data science degrees at the master's level, but this is producing about 40 data scientists a year, far short of the number that the country's banks, insurers, retailers, health companies and telecommunications providers, among others, require.

Python is a powerful, modern programming language that has the capabilities required for experienced programmers, while being easy enough for beginners to learn. Python is a well-developed, stable, and fun programming language that is suitable for complex and simple development projects. Programmers love Python because of how simple and easy it is to use. This course has everything you need to get started with Python. Once we're done with that, we'll learn about functions and files in Python.

Deep Learning is a type of Artificial Intelligence where we give the computer the ability to learn, rather than tell it what to learn. Here at MPCR, we look at Deep Learning as a member of multiple fields, if not every field. AI has its roots in Psychology and Biology, and we strive to remain true to those origins when we consider Deep Learning as a Theory of the Brain. However, it is also highly computational and is an important tool for today's Data Scientists. Deep Learning has already begun to answer questions in fields such as Medicine, Biology, Chemistry, and Engineering, and it is gaining momentum.

Natural Language Processing (NLP) is a hot topic into the Machine Learning field. This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications. In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library.

Reinforcement learning (RL) is hot! It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment. This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge.

If you're looking at this course, you are already interested in Python, and I'm not going to sell you on it. You already know that this popular language is great for solving a huge variety of tasks from REST api development, system scripting, numerical analysis, manipulating data, data analysis to machine learning and AI. But do you want to learn idiomatic Python? Do you want to understand why certain things work the way they do in Python? Do you want to learn best practices in Python, and common pitfalls Python developers can fall into?