Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, and machine learning.
Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that help map wildlife and scale conservation efforts around the world.
Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go.
Fiaz Mohamed explains how Intel AI solves today’s business problems.
Fiaz Mohammed and Justin Herz discuss how artificial intelligence can improve content discovery and monetization
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
Solving the challenges of efficiency, automation, and safety will require cooperation between researchers and engineers spanning both academia and industry.
A few ways to think differently and integrate innovation and AI into your company's altruistic pursuits.
Innovations that increase detection of, and response to, criminal attacks of financial systems.
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
The AI Conference in NY will feature tutorials, conference sessions, and executive briefings to help business leaders understand and plan for AI technologies.
Why we're taking the AI Conference to Beijing.
The top 5 ways to immerse yourself in deep learning and MXNet.
Leveraging the potential of AI to gain maximum ROI.
A look at the parallels between human and machine knowledge acquisition.
Opportunities and challenges companies will face integrating and implementing deep learning frameworks.
A step-by-step tutorial to develop an RNN that predicts the probability of a word or character given the previous word or character.
A step-by-step tutorial to build generative models through generative adversarial networks (GANs) to generate a new image from existing images.
A step-by-step tutorial on how to use TensorFlow to build a multi-layered convolutional network.
Deep learning’s effectiveness is often attributed to the ability of neural networks to learn rich representations of data.
How CapsNets can overcome some shortcomings of CNNs, including requiring less training data, preserving image details, and handling ambiguity.
Image recognition and machine learning for mar tech and ad tech.
Using machine learning to understand and leverage text.
Finding anomalies in time series using neural networks.
TensorFlow Lite enriches the mobile experience.
We need a new model for how AI systems and humans interact.
RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning.
Though they are typically applied to vision problems, convolution neural networks can be very effective for some language tasks.
How to use AI as a tool in your business.
Use cases and tips to help businesses take full advantage of AI technology.
How to build a multilayered LSTM network to infer stock market sentiment from social conversation using TensorFlow.
From methods to tools to ethics, Ben Lorica looks at what's in store for artificial intelligence.
Experts weigh in on what we can expect from AI in 2018.
Solving problems with gradient ascent, and training an agent in Doom.
Lessons from FizzBuzz for Apache MXNet.
GANs, one of the biggest breakthroughs in unsupervised learning in recent years, will bring us one step closer to general artificial intelligence.
A look at why the U.S. and China are investing heavily in this new computing stack.
Reduce both experimentation time and training time for neural networks by using many GPU servers.
A glimpse behind the scenes of a high-level deep learning framework.
While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.
An overview of adoption, and suggestions to companies interested in AI technologies.
An overview of commercial and industrial applications of reinforcement learning.
A unified methodology for scheduling workflows, managing data, and offloading to GPUs.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
Using the keras TensorFlow abstraction library, the method is simple, easy to implement, and often produces surprisingly good results.
Analytical frameworks come with an entire ecosystem.
The IBM team encourages developers to ask tough questions, be patient, and be ready to fail gracefully.
How to build and train a DCGAN to generate images of faces, using a Jupyter Notebook and TensorFlow.
How to create your own custom object detection model.
What you need know before committing to AI.
A tutorial on how to use machine learning to build recommender systems.
With rich data sources already in place, investments in both technology and organizational change pay off.
Using advanced neural networks to tackle challenging natural language tasks.
Using deep neural networks to make sense of unstructured text.
MXNet’s origins show through in its power and flexibility.
Tim O'Reilly says the algorithms that shape our economy must be rewritten if we want to create a more human-centered future.
Michael Jordan discusses recent results in gradient-based optimization for large-scale data analysis.
Lili Cheng shares two examples of AI that were inspired by nature.
Philippe Poutonnet discusses how you can harness the power of machine learning, whether you have a machine learning team of your own or you just want to use machine learning as a service.
Jia Li explains why a democratized approach to AI ensures that the components behind these technologies reach the widest possible audience.