How do you take your artificial intelligence (AI) and deep learning workloads to the next level? We're offering up to 40 percent off on selected NVIDIA GPUs when you order an IBM Cloud bare metal server. If you're already running NVIDIA Tesla K80s, now's the time to give your workload an additional boost by upgrading to the P100. The Tesla P100 provides 4.7 teraFLOPS of double-precision performance to accelerate compute-intensive workloads. The addition of a Tesla P100 accelerator delivers up to 65 percent more machine learning capabilities and 50 times the performance than its predecessor, the Tesla K80.

Gary Marcus has recently published a detailed, rather extensive critique of Deep Learning. While many of Dr. Marcus's points are well-known among those deeply familiar with the field and have been somewhat well-publicized for years, these discussions haven't yet reached many who are newly involved in decision-making in this space. Overall, the discussion the critique has generated seems clarifying and useful. I have decided to write up my thoughts because, while I think Dr. Marcus' critique is thoughtful, necessary and often justified, I disagree with some of the conclusions. To start, Dr. Marcus' assessment that Deep Learning, as originally defined, is merely a statistical technique for classifying patterns is spot on in my opinion.

Machine learning vs. deep learning isn't exactly a boxing knockout – deep learning is a subset of machine learning, and both are subsets of artificial intelligence (AI). However, there is a lot of confusion in the marketplace around the definitions and use cases of machine learning and deep learning, so let's clear up the confusion. Computers identify and act upon data patterns, and over time learn to improve their accuracy without explicit programming. Machine learning is behind analytics like predictive coding, clustering, and visual heat maps. Deep learning computer networks simulate the way a human brain perceives, organizes, and makes decisions from data input.

According to a study from Catalyst, the cost of replacing an employee is around 50% to 75% of the employee's annual salary, on average. Considering a mid-level position with a monthly salary of 20,000 pesos, the total cost of replacing this employee would be around 140,000 pesos. On average, it takes around 50 days to replace an employee, and the costs incurred due to productivity loss will keep adding up. For a big company like everis with over 20,000 employees, considering a turnover rate of 15% and an average salary of $15,000 pesos, the total annual cost of turnover would rise up to at least 270 million pesos. In this article, we provide details about a neural network model that is capable of identifying employee candidates with a high risk of turnover, accomplishing this task with around 96% accuracy.

Artificial Intelligence has existed for a long time. However, it has become a buzzword in recent years due to the huge improvements in this field. AI used to be known as a field for total nerds and geniuses, but due to the development of various libraries and frameworks, it has become a friendlier IT field and has lots of people going into it. In this article, we would be looking at top quality libraries that are used for Artificial Intelligence, their pros, cons and some of their features also. Let's dive in, and explore the world of these AI libraries.

Reminder: RNN is not in any way different or unusual or magical -- just a standard fully connected network. There is no known good way. Somebody recently won a Kaggle competition by doing data augmentation which randomly inserted parts of different rows -- something like that may be useful here. But there has not been any recent state-of-the-art NLP papers that are doing this kind of data augmentation. When using an existing API which expects data to be certain format, you can either change your data to fit that format or you can write your own dataset sub-class to handle the format that your data is already in. Either is fine, but in this case, we will put our data in the format TorchText already support.

Ontario has become a true hub for artificial intelligence, not just in Canada, but worldwide and the province's reputation as a global leader in AI is growing. Whether in fintech, medtech or other fields, some of the most promising AI ventures are located within the province. Here are just a handful of the ones to watch this year. This Kitchener-Waterloo-based company's key cloud service offers a way for auto companies to find malfunctions or predict failures for vehicles coming off the assembly line or being driven – a big, but often overlooked challenge. It does this by using machine learning to find flaws in real time.

Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketch2code. Currently, the largest barrier to automating front-end development is computing power. However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now. In this post, we'll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup. We'll build the neural network in three iterations. In the first version, we'll make a bare minimum version to get a hang of the moving parts. The second version, HTML, will focus on automating all the steps and explaining the neural network layers. In the final version, Bootstrap, we'll create a model that can generalize and explore the LSTM layer.

Microsoft, in collaboration with others, is using algorithms to convert satellite images into information about categories of land cover, such as forests.Credit: Microsoft Earlier this year, I became Microsoft's first chief environment scientist. I've been tasked with deploying the company's deep investments in artificial intelligence (AI) research and technology to help people around the world monitor, model and ultimately manage Earth's natural systems. Most people I meet are surprised that one of the world's leading technology companies has a role such as mine. Yet I believe that in the next few years, every major tech firm will be working on applying AI to sustainability. It is the ethical thing to do. It is good for business.

Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. Most classification models, including deep learning models, are highly vulnerable to adversarial attacks. In this work, we investigate a procedure to improve adversarial robustness of deep neural networks through enforcing representation invariance. The idea is to train the classifier jointly with a discriminator attached to one of its hidden layer and trained to filter the adversarial noise. We perform preliminary experiments to test the viability of the approach and to compare it to other standard adversarial training methods.