Perform classifications using a network already created and trained. Perform classifications using a network already created and trained. Import folders of images and make them usable with a given network. Import folders of images and make them usable with a given network. Modify a pretrained network to classify images into specified classes.

If you shop at Westfield, you've probably been scanned and recorded by dozens of hidden cameras built into the centres' digital advertising billboards. The semi-camouflaged cameras can determine not only your age and gender but your mood, cueing up tailored advertisements within seconds, thanks to facial detection technology. Westfield's Smartscreen network was developed by the French software firm Quividi back in 2015. Their discreet cameras capture blurry images of shoppers and apply statistical analysis to identify audience demographics. And once the billboards have your attention they hit record, sharing your reaction with advertisers.

Artificial Intelligence (AI) is a collective term for computer systems that can sense their environment, think, learn, and respond to what they are sensing. Forms of AI in use today include digital assistants, chatbots and machine learning among others. Saudi Arabia is seeking to be a global leader in the application of technology related to AI. In a 2017 global study, consulting firm PWC estimated that AI could contribute $135 billion or 12.4 percent to Saudi Arabia's gross domestic product (GDP) by 2030 -- the second-highest share in the region after the UAE. The study saw retail and the public sector, including health care and education, as particularly ripe for transformation by AI.

We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transition matrix as well as the neural network parameters in an alternating manner until convergence. While we do not make any probabilistic assumption on the underlying clean data, our ICE resembles Expectation-Maximization (EM) with variational approximation, and it takes advantage of the property of N-DUDE being locally robust around the true channel. With extensive experiments on several radically different types of data, we show that the ICE equipped N-DUDE (dubbed as ICE-N-DUDE) can perform \emph{universally} well regardless of the uncertainties in both the channel and the clean source. Moreover, we show ICE-N-DUDE becomes extremely robust to its hyperparameters and significantly outperforms the strong baseline that can deal with the channel uncertainties for denoising, the widely used Baum-Welch (BW) algorithm for hidden Markov models (HMM).

A preferential domain is a collection of sets of preferences which are linear orders over a set of alternatives. These domains have been studied extensively in social choice theory due to both its practical importance and theoretical elegance. Examples of some extensively studied preferential domains include single peaked, single crossing, Euclidean, etc. In this paper, we study the sample complexity of testing whether a given preference profile is close to some specific domain. We consider two notions of closeness: (a) closeness via preferences, and (b) closeness via alternatives. We further explore the effect of assuming that the {\em outlier} preferences/alternatives to be random (instead of arbitrary) on the sample complexity of the testing problem. In most cases, we show that the above testing problem can be solved with high probability for all commonly used domains by observing only a small number of samples (independent of the number of preferences, $n$, and often the number of alternatives, $m$). In the remaining few cases, we prove either impossibility results or $\Omega(n)$ lower bound on the sample complexity. We complement our theoretical findings with extensive simulations to figure out the actual constant factors of our asymptotic sample complexity bounds.

The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn from CSAs in an online fashion. In addition to discussing the ML aspects, we also bring out important insights we gleaned from early feedback from CSAs. These insights motivate our future work and also might be of wider interest to ML practitioners.

Training generative adversarial networks (GANs) often suffers from cyclic behaviors of iterates. Based on a simple intuition that the direction of centripetal acceleration of an object moving in uniform circular motion is toward the center of the circle, we present the Simultaneous Centripetal Acceleration (SCA) method and the Alternating Centripetal Acceleration (ACA) method to alleviate the cyclic behaviors. Under suitable conditions, gradient descent methods with either SCA or ACA are shown to be linearly convergent for bilinear games. Numerical experiments are conducted by applying ACA to existing gradient-based algorithms in a GAN setup scenario, which demonstrate the superiority of ACA.

Most of the machine learning algorithms are parametric. What do we mean by parametric? Let's say if we are trying to model an linear regression model with one dependent variable and one independent variable. The best fit we are looking is the line equations with optimized parameters. The parameters could be the intercept and coefficient. For any classification algorithm, we will try to get a boundary.

SAN FRANCISCO – IBM's bold push of its Watson AI technology to the masses may produce product and services opportunities, but the democratization of AI won't happen overnight. Perhaps the strongest message at the IBM Think 2019 event here was the company's "Watson anywhere" strategy to allow customers to run Watson AI services on any public cloud or in any hybrid cloud environment. "You might call this the democratization of Watson and the effect should find a growing number of organizations leveraging IBM AI technologies across various business processes," said Charles King, an analyst at Pund-IT in Hayward, Calif. This "democratization" comes from IBM's services heritage to meet customers where they are technologically. To gain an advantage in the quickening AI race, IBM is giving folks the opportunity to use Watson on any cloud they want.

At the opposite end of the country, in Pembroke Pines, Fla., 87-year-old Marilyn Sumkin uses an app called Join Papa to summon what the company calls "grandchildren on demand." College students show up for shopping, chores and chit-chat. Studies have found that loneliness is worse for health than obesity or inactivity, and is as lethal as smoking 15 cigarettes a day. It's also an epidemic: A recent study from Cigna Corp. found that about half of Americans are lonely. According to a recent Harvard University study, the cost of loneliness for Medicare is $6.7 billion a year.