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Identifying light sources using machine learning

AIHub

The identification of light sources is very important for the development of photonic technologies such as light detection and ranging (LiDAR), and microscopy. Typically, a large number of measurements are needed to classify light sources such as sunlight, laser radiation, and molecule fluorescence. The identification has required collection of photon statistics or quantum state tomography. In recently published work, researchers have used a neural network to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. In their paper, authors from Louisiana State University, Universidad Nacional Autónoma de México and Max-Born-Institut describe their experimental and theoretical techniques.


Seven Key Dimensions to Understand Any Machine Learning Problem

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Tackling a machine learning problem might feel overwhelming at first. What model to choose?, which architecture might work best? In a process that is mostly driven by trial and error experimentation, those decisions result incredibly important. One aspect that really helps to navigate that universe of decisions is to clearly understand the nature of the problem. In machine learning scenarios, an important part of understanding the problem is based on understanding its environment.


How Neural Network Can Be Trained To Play The Snake Game

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At the present scenario, video games portray a crucial role when it comes to AI and ML model development and evaluation. This methodology has been around the corner for a few decades now. The custom-built Nimrod digital computer by Ferranti introduced in 1951 is the first known example of AI in gaming that used the game nim and was used to demonstrate its mathematical capabilities. Currently, the gaming environments have been actively utilised for benchmarking AI agents due to their efficiency in the results. In one of our articles, we discussed how Japanese researchers used Mega Man 2 game to assess AI agents.


Why the AI revolution now? Because of 6 key factors.

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About: Data-Driven Science (DDS) provides training for people building a career in Artificial Intelligence (AI). In recent years, AI has been taking off and became a topic that is frequently making it into the news. But why is that actually? AI research has started in the mid-twentieth century when mathematician Alan Turing asked the question "Can Machines Think?" in a famous paper in 1950. However, it's been not until the 21st century that Artificial Intelligence has shaped real-world applications that are impacting billions of people and most industries across the globe.


Using machine learning to identify different types of brain injuries

AIHub

Researchers have developed an algorithm that can detect and identify different types of brain injuries. The team, from the University of Cambridge, Imperial College London and CONICET, have clinically validated and tested their method on large sets of CT scans and found that it was successfully able to detect, segment, quantify and differentiate different types of brain lesions. Their results, reported in The Lancet Digital Health, could be useful in large-scale research studies, for developing more personalised treatments for head injuries and, with further validation, could be useful in certain clinical scenarios, such as those where radiological expertise is at a premium. Head injury is a huge public health burden around the world and affects up to 60 million people each year. It is the leading cause of mortality in young adults.


How to Prevent Overfitting in Machine Learning Models

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Very deep neural networks with a huge number of parameters are very robust machine learning systems. But, in this type of massive networks, overfitting is a common serious problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning), whereas generalization refers to how well the trained model performs on the data that it has never seen before (test set).


Neuro-Symbolic Artificial Intelligence and Potential Impact on Conversational Commerce

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In a joint research effort forged in 2017, the MIT-IBM Watson AI Lab has put significant resources into a new approach to AI that could provide CX and digital transformation specialists with more accurate intent recognition. Known as "neuro-symbolic artificial intelligence," this approach could allow companies to do more with less data and provide for greater transparency and privacy. Employing the approach to Conversational AI could give brands the ability to "add common sense" to their chatbots, intelligent virtual agents and to the prompts provided to live agents. The science combines the probabilistic pattern recognition capabilities of today's Deep Neural Networks (DNNs) and "deep understanding" with an approach to AI that is based on representations of problems, logic and search that are considered more "human-readable." In a new report, Dan Miller, lead analyst and founder with Opus Research, presents the possibility for enterprises to improve automated conversational systems with significant implications for customer care, digital commerce and employee productivity.


Automated screening of sickle cells using a smartphone-based microscope and deep learning

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Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks.


How to Turn Your Business into a Cognitive Enterprise with AI Technologies? Hacker Noon

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Artificial Intelligence is everywhere, opportunities are in abundance for cognitive enterprises. What do we mean by cognitive enterprises? Millions of ideas and think pieces are waiting to grow luxuriantly and cognitive AI technologies will play a bigger role in turning your ideas into a live piece of work. It is expected that AI will bring simplicity to complex business issues and deliver more useful, engaging, intuitive, and profitable solutions, and this is what we say a cognitive approach for enterprises. According to a report published by IDC a market research firm states that global spending on cognitive AI systems will reach $57.6 billion by 2021.


Building A Simple Convolution Layer From Scratch

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A convolution layer provides a method of producing a feature map from a two-dimensional input. This is accomplished by running a filter over the input data. The filter is just a set of weights that must be trained to identify a feature in regions of the input data. These features can be things like edges, points, or more complex information. The filter will have dimensional constraints that indicate width and height, and it will scan over the input data.