The 5 AI basics every business executive needs to understand right now 2. NVIDIA and Canon Medical Systems announce partnership to accelerate the use of AI in healthcare 3. Auditors use AI to tackle accounting fraud 4. Risk managers can assess and limit the risk in M&A with AI 5. MIT and Stanford researchers develop AI system that processes sounds as well as humans 5. THE 5 AI BASICS EVERY BUSINESS EXECUTIVE SHOULD KNOW AI is not about technical analysis, it is about leveraging data and machine learning to drive business success. "Without business leadership, AI success in business will only be random and limited – which is why active, proactive involvement of business leadership is critical. READ ARTICLE 6. NVIDIA AND CANON MEDICAL SYSTEMS PARTNER TO ADVANCE HEALTHCARE Imaging providers worldwide may soon have access to a single source of AI- powered continuous updates for their existing install bases in the form of a single, virtual supercomputer. READ ARTICLE 7. AUDITORS USE AI TO TACKLE ACCOUNTING FRAUD Accounting fraud has long eaten into the revenue of some businesses, but auditors are enlisting a new defensive tool: artificial intelligence. A typical organization can lose 5 percent of its annual revenue to fraud, according to an estimate from the Association of Certified Fraud Examiners. Businesses are putting AI on the task of anomaly detection in an effort to staunch losses. READ BLOG 8. RISK MANAGERS CAN ASSESS AND LIMIT RISK IN M&A WITH AI A company that has joined another is almost always saddled with data that's not so easy to organize and categorize. Most obviously, GPUs bring speed to the equation – and speeding up the process translates to identifying -- and mitigating -- risks sooner. "We're just trying to give users a tool to learn about data," said Jonathan Bailey, vice president of analytics at Congruity360. READ BLOG 9. RESEARCHERS DEVELOP AI SYSTEM THAT PROCESSES SOUND LIKE HUMANS The method, which is the first model of its kind, can replicate listening tasks such as identifying a musical genre or identifying words. The researchers built the model to shed light on how the human brain may be performing listening tasks. The hope is that it's learning something general, so if you present a new sound that the model has never heard before, it will do well, and in practice that is often the case. The 5 AI basics every business executive needs to understand right now 2. NVIDIA and Canon Medical Systems announce partnership to accelerate the use of AI in healthcare 3. Auditors use AI to tackle accounting fraud 4. Risk managers can assess and limit the risk in M&A with AI 5. MIT and Stanford researchers develop AI system that processes sounds as well as humans "Without business leadership, AI success in business will only be random and limited – which is why active, proactive involvement of business leadership is critical.

Overview This paper addresses a key NLP task known as sarcasm detection using a combination of model based on convolutional neural networks (CNNs). Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence. Example Sarcasm can be considered as expressing a bitter gibe or taunt. Examples include statements such as "Is it time for your medication or mine?" and "I work 40 hours a week to be this poor". Challenges To understand and detect sarcasm it is important to understand the facts related to an event.

That was the message one young officer gave Lt. General John "Jack" Shanahan -- the Pentagon's director for defense for warfighter support -- who is hustling to put artificial intelligence and machine learning to work for the U.S. Defense Department. Highlighting the growing role AI is playing in security, intelligence and defense, Shanahan spoke Wednesday during a keynote address about his team's use of GPU-driven deep learning at our GPU Technology Conference in Washington. Shanahan leads Project Maven, an effort launched in April to put machine learning and AI to work, starting with efforts to turn the countless hours of aerial video surveillance collected by the U.S. military into actionable intelligence. "We have analysts looking at full-motion video, staring at screens 6, 7, 8, 9, 10, 11 hours at a time. They're doing the same thing photographic interpreters were doing in World War II," Shanahan said.

Researchers from NVIDIA, led by Guilin Liu, introduced a state-of-the-art deep learning method that can edit images or reconstruct a corrupted image, one that has holes or is missing pixels. The method can also be used to edit images by removing content and filling in the resulting holes. The method, which performs a process called "image inpainting", could be implemented in photo editing software to remove unwanted content, while filling it with a realistic computer-generated alternative. "Our model can robustly handle holes of any shape, size location, or distance from the image borders. Previous deep learning approaches have focused on rectangular regions located around the center of the image, and often rely on expensive post-processing," the NVIDIA researchers stated in their research paper.

NVIDIA announced the integration of our TensorRT inference optimization tool with TensorFlow. TensorRT integration will be available for use in the TensorFlow 1.7 branch. TensorFlow remains the most popular deep learning framework today while NVIDIA TensorRT speeds up deep learning inference through optimizations and high-performance runtimes for GPU-based platforms. We wish to give TensorFlow users the highest inference performance possible along with a near transparent workflow using TensorRT. The new integration provides a simple API which applies powerful FP16 and INT8 optimizations using TensorRT from within TensorFlow.

Agenda • "Ready" - Introduction to AI/Machine Learning – April 29 Boaz Ziniman • "Set" - What is ML for developers?

These and many other fascinating findings and insights are from a recent McKinsey Global Institute (MGI) Discussion Paper, Notes from the AI frontier: Applications and value of deep learning. Titled Notes from the AI Frontier: Insights From Hundreds Of Use Cases (36 pp., PDF, no opt-in) the discussion paper draws on MGI research and the firm's applied experience with artificial intelligence (AI) of McKinsey Analytics, assessing the practical applications and the economic potential of advanced AI techniques. The discussion paper's findings are based on intensive MGI analytics collated and integrated with more than 400 use cases across 19 industries and nine business functions.

Recently, I was asked to teach a class on forecasting using Python. I thought my notes would be a good source of information for every one interested in this area and I might learn from my reader's feedback as well. In this part 1 of the article we will talk about basic forecasting methods -- Naive, Average, Moving Average and Weighted Average. In the next parts we will discuss measures of scoring, exponential smoothing techniques, Holt, Holt winters, ARIMA, ARMA and deep learning methods using LSTM. Incidentally Kaggle also released a competition on forecasting which plans to forecast for 145K time series.

Let's say that someone somewhere has been keeping tabs on where I've eaten for the last six months. That person might be able to predict my next meal, but chances are, it won't be all that accurate. After all, there are so many factors that go into how I choose my meal -- from what I ate for breakfast to whether or not I saw an ad for fried chicken that morning on my phone (in which case, Popeye's might be getting some business from me). Even assuming that the all-seeing overseer of my lunch habits has data on what I eat for breakfast and dinner, when and how often I go to the gym, whether I was out late last night, my sleeping habits, the weather, what ads I saw on my phone and so on, there are so many variables that it would take ages for that person to find a pattern -- if one even exists.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)