Inteligencia artificial, open source e IBM Call for Code

Editor's Notes

• #6: IBM has a long history around AI leadership. From “2001: A Space Odyssey”, to winning the world chess champion Garry Kasparov, and recently in 2011 winning Jeopardy against legendary champions Brad Rutter and Ken Jennings. And this brings us to CODAIT, where IBM is concentrating some of the efforts around Open AI leadership.Deep Blue versus Garry Kasparov was a pair of six-game chess matches between world chess champion Garry Kasparov and an IBM supercomputer called Deep Blue. The first match was played in Philadelphia in 1996 and won by Kasparov. The second was played in New York City in 1997 and won by Deep Blue. The 1997 match was the first defeat of a reigning world chess champion by a computer under tournament conditions. Watson is a question-answering computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci.[3] Watson was named after IBM's first CEO, industrialist Thomas J. Watson.[4][5] The computer system was initially developed to answer questions on the quiz show Jeopardy![6] and, in 2011, the Watson computer system competed on Jeopardy!against legendary champions Brad Rutter and Ken Jennings[4][7] winning the first place prize of $1 million.[8]
• #7: Lower the barrier of entrance
• #32: Training Deep Neural Networks is a highly complex and computer intensive task and in addition you need to have the right environment with the right combination of frameworks and resources. We have seen how hard is to actually build a deep learning model, and adding this additional requirement to the Ai engineer or Data Scientist will be a burden that they will not even try. From the operator perspective, each framework requires a different set of environment, dependencies, etc For the data scientist, FfDL brings a consistent way to train their models independent of what framework is being used (e.g. tensorflow, PyTorch, Keras, etc) As the operator, FfDL provides a consistent way to manage these environments that is used for training the deep neural network models i, also, as FfDL runs on top of kubernetes, and leverages various capabilities that kubernetes provide such as scalability, fault tolerance, resilience, Bases for Watson Studio Deep Learning as a Service - Start developing on premise, and move to the cloud when you need scalability - Go directly to cloud, etc We have
• #40: Jupyter Notebooks are an evolution from the good old way of doing interactive development with the Python console.
• #58: O call for code é uma iiciativa para reunir desenvolvedores e inspira-los a resolver uma das questões sociais mais urgentes no nosso tempo: previnir, responder e recuperar desastres naturais.