A 2018 research survey found that companies see competitors gearing up for big data and AI success -- and that's fueling their investment in similar capabilities. After years of hope and promise, 2018 may be the year when artificial intelligence (AI) gains meaningful traction within Fortune 1000 companies. This is a key finding of NewVantage Partners' annual executive survey, which finds that investment in big data and AI are increasing rapidly, though ROI remains elusive. In this webinar, Randy Bean, CEO of NewVantage Partners and author of "How Big Data and AI Are Driving Business Innovation in 2018," discusses the findings from the 2018 study of Fortune 1000 companies, gives examples, and shares his thoughts on the current and future states of big data and artificial intelligence implementation among leading business organizations. In this webinar, you'll learn: Get periodic email updates on upcoming webinars, panel discussions, and other special events.

Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals. The 30 lectures in the course are embedded below, but may also be viewed in this YouTube playlist.

The IoT is transforming the industrial sector, enabling dramatic gains in efficiency and productivity. But to capture these benefits, you need a way to analyze the high volume of diverse streaming data coming through your machines in real time, and interpret it for actionable insight. Increasingly, this means deploying machine learning, but the question is how to do so. While the cloud has merit as a data modeling and machine learning portal, it cannot always provide the real-time responsiveness needed in applications for the manufacturing, oil and gas, construction, transportation, and smart buildings industries. Thus, there has been a move to augment the cloud with machine learning at the edge.

Apple is expected to remove the headphone adapter with its next iPhone, leaving people unable to use traditional headphones with their new mobiles. The company famously – or infamously – dropped the headphone jack from its handsets with the iPhone 7. But at the time it promised that nothing much would change for people with traditional headphones, since the box would come with an adapter that would allow them to be plugged into the charging port. Now that adapter is being removed, according to various reports. As such, people will be required to stop using their traditional headphones, or get an adapter of their own.

Dez Blanchfield speaks with business leaders about artificial intelligence and deep learning adoption in the "From Here to AI" podcast series from IBM Power Systems. When you start to investigate artificial intelligence (AI), or branch out to buy a couple AI servers to tinker with for your organization, the process of implementing a full AI solution can seem daunting. With the help of four business executives and AI leaders and digital transformation expert and avid podcaster Dez Blanchfield, we set out to outline the natural progression of implementing AI in the data center. No matter what stage of the journey you are on, these podcasts should help you get "from here to AI." Below is a quick overview of each session. We've posted them as a series so you can binge-listen if you have the time, or you can tee them up separately to plug into the ones that interest you most.

NVIDIA is expanding its Deep Learning Institute (DLI) with new partnerships and educational courses. DLI, which trains thousands of students, developers and data scientists with critical skills needed to apply artificial intelligence, has joined hands with Booz Allen Hamilton and deeplearning.ai DLI and Booz Allen Hamilton will provide hands-on training for data scientists to solve challenging problems in healthcare, cybersecurity and defense. NVIDIA is also expanding its reach with the new NVIDIA University Ambassador Program that enables instructors worldwide to teach students critical job skills and practical applications of AI at no cost. The graphics processing designer is already working with professors at several universities, including Arizona State, Harvard, Hong Kong University of Science and Technology and UCLA.

Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.

"Grokking Algorithms: An illustrated guide for programmers and other curious people" br /

You define Machine Learning problems using JavaScript and push them over for fast computation to any GPU using WebGL. Or you train your model on a standard TensorFlow stack using high performance GPUs, convert the model and use it in the browser for prediction or transfer learning. This might not sound like a big deal, but tight integration of Machine Learning with all the things that make the web great opens up a whole new world of applications in the areas of education, deployment, visualization, gaming, development, and overall UX. You will also learn how to do your own experiments. Show that Machine Learning in the Browser is not a toy.