"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
ResNet50 is a convolutional neural network which has a depth of 50 layers. It was build and trained by Microsoft in 2015 and you can access the model performance results on their paper, titled Deep Residual Learning for Image Recognition. This model is also trained on more than 1 million images from the ImageNet database. Just like VGG-19, it can classify up to 1000 objects and the network was trained on 224x224 pixels colored images.
There is a constant ask from the customer on how to optimize the overall QA (Quality assurance) activities in terms of reducing cycle time, improving quality by reducing production defects, focused testing to get maximum defects in early development phases. Apart from this, most of the customers are adopting digital platforms such as PaaS (Platform as Services) & SaaS (Software as Services) solutions for faster delivery, so how can the QA Team keep pace with development and subsequent validation activities, by automating test case generation. Can we get insights into what areas to automate? Will there be any prediction on what will be the number of defects found, test cases need to be written based on the release magnitude. To get these answers, let's explore the solutions available which we can leverage.
Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. These methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labeled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Cell-based screens have substantially advanced our ability to find new drugs (1). However, most screens are unable to predict the mechanism of action (MoA) of identified hits, necessitating years of follow-up after discovery. In addition, even the most complex screens frequently find hits against cellular processes that are already targeted (2).
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy. Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets.
As living organisms process images with their visual cortex, many researchers have taken the architecture of the mammalian visual cortex as a model for neural networks structured to perform image recognition. Over the past 20 years, progress in computer vision has been remarkable. Some computer vision systems achieve 99% accuracy, and some run decently on mobile devices. Today's best image classification models can detect diverse catalogues of objects at high definition resolution in colour. Additionally, people sometimes use hybrid vision models that combine deep learning with classical machine-learning algorithms and perform specific sub-tasks.
The sudoku game is something almost everyone plays either on a daily basis or at least once in a while. The game consists of a 9 9 board with numbers and blanks on it. The goal is to fill the blank spaces with suitable numbers. These numbers can be filled keeping in mind some rules. The rule for filling these empty spaces is that the number should not appear in the same row, same column or in the same 3 3 grid.
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For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.