We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is to identify only whether the given example belongs to a specific class, which can be different in different applications of the classifier. For instance, this is the case in an image search engine. We consider the Single Logit Classification (SLC) task: training the network so that at test-time, it would be possible to accurately identify if the example belongs to a given class, based only on the output logit for this class. We propose a natural principle, the Principle of Logit Separation, as a guideline for choosing and designing losses suitable for the SLC. We show that the cross-entropy loss function is not aligned with the Principle of Logit Separation. In contrast, there are known loss functions, as well as novel batch loss functions that we propose, which are aligned with this principle. In total, we study seven loss functions. Our experiments show that indeed in almost all cases, losses that are aligned with Principle of Logit Separation obtain a 20%-35% relative performance improvement in the SLC task, compared to losses that are not aligned with it. We therefore conclude that the Principle of Logit Separation sheds light on an important property of the most common loss functions used by neural network classifiers.

More and more of the world's computer systems incorporate neural networks - artificial-intelligence driven systems that can "learn" how to do something without anyone - including their creators - understanding exactly how they're doing it. This has caused some concern, especially in fields where safety is critical. These "black box" systems hide their inner workings, so we don't actually know when errors are happening - only when they manifest in the real world. And that can be in very rare situations that don't get caught during a normal testing process. Even when we do catch them, the inscrutable inner workings of deep learning systems mean that these errors can be hard to fix because we don't know exactly what caused them. All we can do is give the system negative feedback and keep an eye on the problem.

Developers of artificial intelligence (AI) systems have been dabbling in the arts for a while now. There's already an AI capable of composing original music -- it even has its own album -- writing film screenplays, and even painting. Now, as an early Halloween treat, the latest AI artist is aspiring to be a horror novelist. Meet Shelley AI, a deep learning algorithm that was developed by researchers at the Massachusetts Institute of Technology (MIT) and named after Victorian-era novelist Mary Shelley who penned Frankenstein. The AI was trained using stories collected from a subreddit dedicated to sharing original eerie tales.

From a developer's standpoint, deep learning is usually a hands-on exercise conducted within a particular modeling framework. Typically, a developer has needed to adapt their own manual coding style to interfaces provided by a specific framework, such as TensorFlow, Apache MXNet, Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Torch and Keras.

In this post, I propose that IoT analytics should be a part of'Smart objects' and discuss the implications of doing so The term'Smart objects' has been around from the times of Ubiquitous Computing. However, as we have started building Smart objects, I believe that the meaning and definition has evolved. Some of these analytics could be performed on the device itself ex computing at the edge concept from Intel, Cisco and others. To manage multiple sensor feeds, we need to understand concepts like sensor fusion (pdf) (source freescale). In addition, the rise of CPU capacity leads to greater intelligence on the device – for example Qualcomm Zeroth platform which enables Deep learning algorithms on the device. So, in a nutshell, its a evolving concept especially if we include IoT analytics in the definition of Smart objects (and that some of these analytics could be performed at the Edge) ..

Hewlett Packard Enterprise (HPE) is tying artificial intelligence (AI) into a handful of new products and services, with a specific focus on enhancing deep learning. The new offerings include hardware, software, reference designs, and physical research locations. On the hardware and software front, HPE launched an integrated product that ties its Apollo 6500 hardware box with software from technology partner Bright Computing. The combination is designed to allow for deep learning application development using pre-configured software frameworks, libraries, automated software updates, and cluster management. The AI Research unit at Hewlett Packard Labs also unveiled a set of tools to help customers in selecting hardware and software environments for different deep learning tasks.

PALO ALTO, Calif., Oct. 25, 2017 (GLOBE NEWSWIRE) -- Hewlett Packard Enterprise (NYSE:HPE) today announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. "We live in a world today where we're generating copious amounts of data, and deep learning can help unleash intelligence from this data," said Pankaj Goyal, vice president, Artificial Intelligence Business, Hewlett Packard Enterprise.

The answer to the question of what makes deep learning different from traditional machine learning for predictive... You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

A new set of products and services unveiled by HPE on Wednesday aim to accelerate enterprise adoption of artificial intelligence (AI), making it easier to take advantage of technologies like deep learning, the firm announced in a press release. Deep learning can be used for processes such as image, face, or voice recognition, along with image classification and more, the release said. However, implementing such technologies often requires a complex compute infrastructure to build the models and make use of the data itself. Additionally, specialized hardware and software, along with deep technical expertise to integrate such tools, often create other barriers to adopting deep learning. According to the HPE release, its recent announcements are meant to help businesses overcome these challenges. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) One of the first solutions mentioned was the HPE Rapid Software Installation for AI, an integrated hardware and software offering geared toward deep learning.

Artificial intelligence has historically over-promised and under-delivered. That routine leads to spurts of what those in the field call "hype"--outsized excitement about the potential of a core technology--followed after a few years and several million (or billion) dollars by crashing disappointment. In the end, we still don't have the flying cars or realistic robot dogs we were promised. But DeepMind's AlphaGo, a star pupil in a time we'll likely look back on as a golden age of AI research, has made a habit of blowing away experts' notions of what's possible. When DeepMind announced that the AI system could play Go on a professional level, masters of the game said it was too complex for any machine.