Recognizing faces is as natural and habitual as can be for human beings. Even with their undeveloped vision, babies can recognize their mother's face within days, while adults typically know some 5,000 faces. But what actually happens inside our brains during the process of recognizing a face? How are different facial features encoded in our brains? And can artificial intelligence learn to recognize faces the way humans do?

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

LEBANON, NH - Recently, deep learning methods have shown promising results for analyzing histological patterns in microscopy images. These approaches, however, require a laborious, high-cost, manual annotation process by pathologists called "region-of-interest annotations." A research team at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center, led by Saeed Hassanpour, PhD, has addressed this shortcoming of current methods by developing a novel attention-based deep learning method that automatically learns clinically important regions on whole-slide images to classify them. The team tested their new approach for identifying cancerous and precancerous esophagus tissue on high-resolution microscopy images without training on region-of-interest annotations. "Our new approach outperformed the current state-of-the-art approach that requires these detailed annotations for its training," concludes Hassanpour.

This is an online demo of the GPT-2 output detector model, based on the /Transformers implementation of RoBERTa. Enter some text in the text box; the predicted probabilities will be displayed below. The results start to get reliable after around 50 tokens.

Our IA A finance solution provides a platform that aims to solve a combination of common challenges arising from managing multiple vendors to complex finance systems with excessive data movements, reconciliations, multiple chart of accounts, lack of policies, procedures leading to highly inconsistent processes and higher operational costs. The finance solution weaves together the new age techniques: Robotics Process Automation (RPA), intelligent cognitive techniques, conversational bots and deep learning that help back offices automate accounting, payment reconciliation, HR including payroll, on-boarding, operations, reporting & compliance and freeing staff to work on value added job.

Adobe Max 2019 wrapped up yesterday, and over the past week the company (and host John Mulaney) revealed a bunch of new automated capabilities, it's currently developing for its various applications--both on desktop and mobile. These demos are always crowd-pleasers and tantalizing teases of how users might soon be able to further streamline their workflows. But in recent years these sneak peeks have also provided a look at how artificial intelligence promises to radically change all the digital tools we use, as more often than not, Adobe's latest and greatest leverage the company's Sensei deep learning platform to pull off their seemingly magical feats. Not to be mistaken with the classic children's toy where plastic pegs were stabbed into a glowing board, Adobe LightRight might be the holy grail for photographers who incessantly tweak and adjust every aspect of their photos in apps like Adobe Lightroom. Using Adobe Sensei, LightRight can be used to radically adjust the lighting in a photo after it was taken, and not just in regards to the overall exposure or brightness.

Machine learning is the process of using algorithms to parse data, learn from it, and then make a decision or prediction.

Artificial Intelligence is here to stay. The development of AI is speeding up on a daily basis. Only recently, Google's DeepMind created the AI AlphaStar that secured a decisive victory against two grandmaster players of the game of StarCraft II. In a series of test matches they played, the algorithm won 5-0. This victory is a decisive moment for artificial intelligence, as the game of StarCraft II is fundamentally more difficult than the other games where Deepmind's algorithm already claimed victory.

The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.