A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks together and update the clusters of the data. We call this model Mixture of Networks. The provided model is a platform that can be used for any deep structure and be trained by any conventional objective function for distribution modeling. As the components of the model are neural networks, it has high capability in characterizing complicated data distributions as well as clustering data. We apply the algorithm on MNIST hand-written digits and Yale face datasets. We also demonstrate the clustering ability of the model using some real-world and toy examples.

Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet. Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than single-step attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a "label leaking" effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera.

Machine Learning (ML) algorithms can learn from data and improve themselves. In a way, that learning process is akin to the way the humans learn from daily experience and improve their own skill sets. "So, together AI and ML are capable of delivering smart robots who can learn from daily experience and keep refining their abilities. Businesses typically have about 65 to 70 percent of their programming tasks that the staff workers conduct by themselves. When all these tasks become automated through Machine Learning powered Artificial Intelligence, about three quarters of the employed manpower may potentially lose their jobs, or their jobs will have to be restructured in different ways.

Connected hospitals with intelligent messaging In today's hospitals, pacemakers, defibrillators and oximeters are all connected to the internet and share vitals immediately with doctors, in turn speeding response times. Hospitals have technicians, nurses, staff, billing departments, insurance providers, patients and patients' families as stakeholders, each with different requirements of information about the care given to patient. Unified Inbox offers an AI-based unified cloud IoT messaging platform for internet of things devices to connect various stakeholders, giving them the freedom to receive different messages at different frequency, with different senses of urgency in different mediums of their choice. Unified Inbox launched this at Nanyang Polytechnic in Singapore as "CUBE," the IoT-secured messaging gateway for healthcare. The artificial intelligence makes the hospitals connected, giving peace of mind to patients and their loved ones while improving efficiency in the overall hospital management and interaction with all stakeholders.

Artificial intelligence changes the way it behaves based on the environment it is in, much like humans do, according to the latest research from DeepMind . Computer scientists from the Google-owned firm have studied how their AI behaves in social situations by using principles from game theory and social sciences. During the work, they found it is possible for AI to act in an "aggressive manner" when it feels it is going to lose out, but agents will work as a team when there is more to be gained. For the research, the AI was tested on two games: a fruit gathering game and a Wolfpack hunting game. These are both basic, 2D games that used AI characters (known as agents) similar to those used in DeepMind's original work with Atari.

Cloud computing has revolutionized industries by democratizing the data center for completely changing the way businesses operate. Your most important assets are now in the cloud with your preferred provider. GPU is the computing platform that transforms big data into super-human intelligence. It is the computational engine for the new era of AI. Now, you can bring all the power of GPU-accelerated deep learning and AI to your data in the cloud--opening up a world of possibility.

As our ability to create AI grows, it's important that we assess how it behaves in different situations. DeepMind, Google's AI division in London, has been concerned with one aspect in particular: what happens when two or more AI have similar or conflicting goals. The team wanted a test similar to the "Prisoner's Dilemma," a popular game that pits two suspects against one another. In this scenario, you're given a choice: testify against the other person and you'll go free, while they have to serve three years. If you both say yes independently, however, you'll serve two years in jail.

To test its AI agents, DeepMind developed two new games, called Gathering and Wolfpack. In Gathering, two colored squares are tasked with picking up "apples" in the middle of the screen. They can also fire a laser which, if accurate, removes the other character from the game temporarily. How co-operative or combative would they be? Unsurprisingly, the pair was quite peaceful at the start, collecting apples at a steady pace.