In this work we propose a new neural network architecture that efficiently implements and learns general purpose set-equivariant functions. Such a function $f$ maps a set of entities $x=\left\{ x_{1},\ldots,x_{n}\right\} $ from one domain to a set of same cardinality $y=f\left(x\right)=\left\{ y_{1},\ldots,y_{n}\right\} $ in another domain regardless of the ordering of the entities. The architecture is based on a gated recurrent network which is iteratively applied to all entities individually and at the same time syncs with the progression of the whole population. In reminiscence to this pattern, which can be frequently observable in nature, we call our approach SWARM mapping. Set-equivariant and generally permutation invariant functions are important building blocks for many state of the art machine learning approaches. Even in application where the permutation invariance is not of primary interest, as to be seen in the recent success of attention based transformer models (Vaswani et. al. 2017). Accordingly, we demonstrate the power and usefulness of SWARM mappings in different applications. We compare the performance of our approach with another recently proposed set-equivariant function, the SetTransformer (Lee et.al. 2018) and we demonstrate that transformer solely based on SWARM layers gives state of the art results.

An artificial intelligence (AI) algorithm can transform low-dose CT (LDCT) scans into high-quality exams that radiologists may even prefer over LDCT studies produced via commercial iterative reconstruction techniques (Nature Machine Intelligence 10.1038/s42256-019-0057-9). A team of researchers from Rensselaer Polytechnic Institute (RPI) in Troy, NY, and Massachusetts General Hospital (MGH) in Boston developed a deep-learning model called a modularized adaptive processing neural network (MAP-NN), which progressively reduces noise on LDCT images with guidance from the radiologist until the optimal level of image quality is achieved. Testing on images from three different vendors, three radiologists found the algorithm produced images that were either better or comparable to images processed with iterative reconstruction. The deep-learning method also processed images much faster. "The deep-learning approach can thus already effectively compete with iterative reconstruction solutions and potentially replace the iterative reconstruction approach," wrote the group, led by Hongming Shan of RPI.

Try the Loan Repayment Prediction machine learning demo to see it in action. Speak to a deep learning model to see if it understands you. Try out an easy-to-understand classification algorithm. See how a deep learning model can identify a thousand different objects in an image. Handwrite a number and let the ML model guess what it is.

At the end of January 2019, it suddenly dawned on me that my understanding of artificial intelligence was insufficient. It is increasingly impacting our every day. AI defends our inboxes from spam, it powers weather updates from Alexa, it enables Amazon to recommend a purchase or Netflix to suggest a movie. Every time we open Twitter or Facebook, it's human versus an AI that knows us better than we know ourselves. Yet here I was -- a professed technologist with so little awareness of what AI actually is.

I have explained the intuition of ADAM optimizer which is one of the most efficient optimizer in deep learning .

The main story of this edition of the Tech Diaries is the Deepfakes issue that has gotten the U.S Congress freaking out. It represents the class of synthetic media generated by AI and represents another dark side of technology -- ringing alarm bells about what the implications of a sudden digital transformation can have on the society as a whole. The disruption caused by deepfakes can have serious consequences on how we differentiate right from wrong -- as if the "fake news" issue on the social media & other platforms isn't enough headache already. U.S lawmakers have started hearings on the issue, fearing the disruptive & deceptive technology may unfairly affect the upcoming U.S Presidential elections in 2020 -- as we saw, how just a simple low tech manipulation of videos of the U.S President & the House Speaker by rival groups earlier this year created headlines. The real problem starts when advanced Deep Learning algorithms are employed to create real-life images.

Deep learning is a machine learning methodology where a system discovers the patterns in data by automatically learning a hierarchical layer of features and then draw conclusions from them. At first, the computer program is provided with training data, for example images that have been labeled with meta tags. The algorithm uses this information to build a progressively more accurate predictive capability. Just like you look for shapes, colors and pictures to solve puzzles – deep learning can gather insights across several inputs to draw a larger conclusion and make better decisions.

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there's a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for artificial intelligence developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child's play for primates such as humans. In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Both AI and cybersecurity are broad and poorly understood fields. This book helps give you an overview of the various technologies that make up AI, where they have come from, and what AI has evolved into today. Cybersecurity is another field that has evolved over the last few decades. Dive into the world of cybersecurity and then learn how AI is being applied to the battle. When you're done reading this book, you will be spouting terms like cognitive computing, machine learning, and deep learning, and know how they apply to the cybersecurity space.

End-to-end reinforcement learning agents learn a state representation and a policy at the same time. Recurrent neural networks (RNNs) have been trained successfully as reinforcement learning agents in settings like dialogue that require structured prediction. In this paper, we investigate the representations learned by RNN-based agents when trained with both policy gradient and value-based methods. We show through extensive experiments and analysis that, when trained with policy gradient, recurrent neural networks often fail to learn a state representation that leads to an optimal policy in settings where the same action should be taken at different states. To explain this failure, we highlight the problem of state aliasing, which entails conflating two or more distinct states in the representation space. We demonstrate that state aliasing occurs when several states share the same optimal action and the agent is trained via policy gradient. We characterize this phenomenon through experiments on a simple maze setting and a more complex text-based game, and make recommendations for training RNNs with reinforcement learning.