Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more. AI for Good is a movement in which institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1] The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]

Quantitative asset management company WorldQuant, in partnership with global online learning company Udacity, has launched a new Artificial Intelligence for Trading Nanodegree program. Students enrolled in the programme will analyse real data and build financial models by learning the basics of quantitative trading, as well as how to analyse alternative data and use machine learning to generate trading signals. Udacity and WorldQuant have collaborated with top industry professionals with prior experience at leading financial institutions to ensure students are exposed to the latest AI applications in trading and quantitative finance. By learning from industry experts, students will advance their finance knowledge, build a strong portfolio of real-world projects and learn to generate trading signals using natural language processing, recurrent neural networks and random forests. Graduates will gain the quantitative skills currently in demand across multiple functions and roles at hedge funds, investment banks and fintech startups.

Sessions are subject to change. Day & Time: Wednesday, 11:00 AM – 11:45 AM, Grand Oaks A&B Presenter: Jim Breen, Doug Foster Need to get your team trained on InterSystems products quickly? Attend this session to learn how you can get your employees up to speed and add value to your company – fast! Hear how other InterSystems' clients have created successful teams using Learning Services content as one piece of the puzzle, and how you can too! Takeaway: InterSystems Learning Services can help me quickly onboard new employees and grow the skill sets of existing employees. Day & Time: Monday, 2:00 PM – 2:45 PM, Grand Oaks E&F Tuesday, 2:00 PM – 2:45 PM, Grand Oaks C&D Presenter: Andreas Dieckow This session provides an overview of what it takes to move an existing Caché or Ensemble application to InterSystems IRIS Data Platform. You will learn that migration is not urgent (unless you want to take advantage of new features in InterSystems IRIS) but that it is often less complex than you might expect.

Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application. The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.

Nowadays, mobile devices have enough computing power to run pre-trained models on them. This results in an optimal use of the hardware and an increase in speed, because the data are not sent over the Internet, which also means more privacy. In my presentation I will show different ways to integrate machine learning models into an iOS and Android application. The participants will learn how to integrate a pre-training model into an iOS and Android application with CoreML and Tensorflow Lite, and how to re-train a model for own pictures and use it instead of the pre-trained model. All examples are shown in a small demo.

This talk will cover how to accelerate deep learning with GPUs. GPUs have an architecture that is well-adapted to speeding up the massive parallel array calculations at the heart of deep learning. Today, manufacturers like NVIDIA are releasing GPUs with deep learning-specific features to further speed up model training and improve the throughput of deployed models. Installing and deploying GPU accelerated code can be challenging, so Anaconda has curated popular deep learning frameworks and packed them with GPU acceleration in the Anaconda Distribution. There they can be combined with Python packages like Pandas, Dask, and Jupyter to power data science experiments and production deployments.

An attendee looks at a Tifana.com Co. AI service character displayed on a screen at the Artificial Intelligence Exhibition & Conference in Tokyo, Japan, on Wednesday, April 4, 2018. The AI Expo will run through April 6. It's generally accepted that as technology moves into classrooms, teachers will move, as the saying goes, "from a sage on the stage to a guide on side." That shift has rightly troubled teachers and teaching advocates who fear that educators who instruct, analyze and provide vital context will be diminished or co-opted outright by soulless, algorithm-driven tech.

I recently wrote a brief guide on the Math required for Machine Learning. People liked it, and asked me to write one on how to master ML at a mathematically rigorous, conceptual level. That is the focus of this guide, no bullshit, no easy routes, and real, fundamental understanding. I'll be going through the later part of the curriculum myself. A quick question to ask yourself: Why do I want to learn ML?

Citrine is a company that builds data infrastructure and predictive data analysis software for the materials industry. Machine learning is a key tool in our toolbox. I have had a few professors and students in materials departments ask me (1) how machine learning could help in their research; and (2) how to quickly come up to speed in machine learning without going back to school for a degree in computer science. While a variety of machine learning courses and how-tos exist on the web already (see here, here, or here), none are specific to the field of materials science. I think the best way to master a new concept is by directly applying it, so this tutorial will show you how to build a machine learning-based model of a canonical solid-state materials property: band gap.

Learn how to use Google's Deep Learning Framework - TensorFlow with Python! IMPORTANT NOTE: THIS COURSE IS CURRENTLY IN EARLY BIRD RELEASE! Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand.