Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by observing their design state sequences without inducing problem-specific modelling bias or extra information about the problem. Furthermore, an end-to-end agent is developed that uses this deep learning framework as its core in conjunction with image processing to map pixel-to-design moves as a mechanism to generate designs. Finally, the designs generated by a computational team of these agents are then compared to actual human data for teams solving a truss design problem. Results demonstrates that these agents are able to create feasible and efficient truss designs without guidance, showing that this methodology allows agents to learn effective design strategies.

Welcome toA Gentle Introduction to Deep Learning Using Keras. Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.

In 2017, DeepMind started trialling a new app with the Royal Free hospital in London. Called Streams, it was intended to help clinicians identify and monitor acute kidney injury (AKI) – a condition linked to 100,000 deaths in the UK every year. But unlike most of DeepMind's headline-grabbing work, Streams doesn't contain a jot of artificial intelligence. Instead, the app brings together medical information, such as blood test results and vital signs, and notifies clinicians when a patient's kidney health deteriorates, using a well-established formula for evaluating kidney function. Now DeepMind has provided the first hints that using artificial intelligence might be a much better way of assessing whether someone is at risk of AKI.

Machine learning is a field that has emerged out of numerous innovations in computational sciences, spanning centuries. So, can a machine learning enthusiast skip linear algebra and flourish? The short answer is -- NO. Linear Algebra is a branch of mathematics that is widely used throughout science and engineering. Good understanding of linear algebra is essential for understanding and working with many ML algorithms, especially deep learning algorithms.

Five years after Google acquired DeepMind, the health and artificial intelligence group is unveiling its biggest breakthrough yet in health care. Its technology is able to predict if a patient has potentially fatal kidney injuries 48 hours before many symptoms can be recognized by doctors. In a paper published on Wednesday in the journal Nature, DeepMind researchers said their algorithms correctly predicted 90 percent of acute kidney injuries that would end up requiring dialysis. The work was the result of a project with the U.S. Department of Veteran Affairs to help doctors get a head start on treatment. "We've been really excited for the potential of using AI to support clinicians moving care from reactive to proactive and preventative," said Dominic King, DeepMind's co-founder and clinical lead, in an interview.

Indian software solutions provider Mphasis, which specializes in cloud and cognitive services, has launched its new Deep Learning algorithms. The new algorithms, which will be made available on Amazon Web Services (AWS) Marketplace for Machine Learning, are on-demand solutions targeting practical enterprise use cases such as influence analytics, insurance claims analysis, payment card fraud, and image analytics for supply chain and logistics. The solutions, available for a free trial and download on AWS Marketplace for Machine Learning website, will help users simplify data experimentation, formulate deeper insights from disparate sources across their data estate, and foster new levels of productivity and efficiency for a wide variety of use cases. Some of the algorithms are DeepInsights Card Fraud Analysis that is a Deep-Learning powered classification solution that provides valuable insights from any data that is highly skewed and HyperGraf Auto Claims Prediction which provides occurrence and claim amount predictions for policyholders among others, as per the company statement. Dr Jai Ganesh -Senior Vice President & Head, Mphasis NEXT Labs said "Our solutions target practical, high-value use cases that can deliver immediate impact and ROI in critical enterprise business processes and operations. And users can deploy them with the speed and security provided by AWS." Mphasis is an advanced consulting partner in the AWS Partner Network (APN) and leverages AWS with customers across its business.

These days, the latest developments in artificial intelligence (AI) research always get plenty of attention, but an AI researcher at the U.S. Naval Research Laboratory believes one AI technique might be getting a little too much. Ranjeev Mittu heads NRL's Information Management and Decision Architectures Branch and has been working in the AI field for more than two decades. "I think people have focused on an area of machine learning--deep learning (aka deep networks)--and less so on the variety of other artificial intelligence techniques," Mittu said. "The biggest limitation of deep networks is that a complete understanding of how these networks arrive at a solution is still far from reality." Deep learning is a machine learning technique that can be used to recognize patterns, such as identifying a collection of pixels as an image of a dog.

Everything comes with a price, and artificial intelligence is no exception. The last decade has witnessed AI breakthroughs in object recognition, game playing, machine translation and many other areas. But these massive improvements required massive amounts of compute. For example, 2017 deep learning model AlphaZero consumed 300,000 times more computational power during training than 2012's revolutionary AlexNet. However, with global concerns growing regarding climate change and other environmental threats, recent mainstream media stories have pointed fingers at the massive carbon footprint left by the training of today's resource-hungry machine learning models.

Imagine you're completing a mission in a computer game. Maybe you're going through a military depot to find a secret weapon. You get points for the right actions (killing an enemy) and lose them for the wrong ones (falling into a pit or getting hit). If you're playing on high difficulty, you might not conclude this task in just one attempt. Try after try, you learn which consecutive actions are needed to get out of a location safe, armed, and equipped with bonuses like extra health points or small artifacts in your bag.

Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of quantization systems acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by properly processing the analog signals prior to quantization. However, the design of such hybrid quantizers is quite complex, and their implementation requires complete knowledge of the statistical model of the analog signal, which may not be available in practice. In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools. These representations are designed to facilitate the task of recovering underlying information from the quantized signals, which can be a set of parameters to estimate, or alternatively, a classification task. By utilizing deep learning, we circumvent the need to explicitly recover the system model and to find the proper quantization rule for it. Our main target application is multiple-input multiple-output (MIMO) communication receivers, which simultaneously acquire a set of analog signals, and are commonly subject to constraints on the number of bits. Our results indicate that, in a MIMO channel estimation setup, the proposed deep task-bask quantizer is capable of approaching the optimal performance limits dictated by indirect rate-distortion theory, achievable using vector quantizers and requiring complete knowledge of the underlying statistical model. Furthermore, for a symbol detection scenario, it is demonstrated that the proposed approach can realize reliable bit-efficient hybrid MIMO receivers capable of setting their quantization rule in light of the task, e.g., to minimize the bit error rate.