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4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning

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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.


Applications of Artificial Intelligence in Canadian Industries

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Artificial intelligence (AI) involves the simulation of human intelligence through programming machines or creating software to think similar to humans and mimic their actions. In other words, AI research seeks to develop technology that is capable of learning and problem solving the same way that a human would. Though the idea itself can be traced back to antiquity, AI has become increasingly popular in recent years, with ever-evolving applications across many Canadian industries. To this end, read on for IBISWorld's evaluation of how two up-and-coming ventures have the potential to affect the operations of different industries in Canada. In London, ON, a new AI tool called the Chronic Homelessness Artificial Intelligence model (CHAI) analyzes points, such as age, gender, family and shelter history, to assess the chance that a particular individual will become chronically homeless over the next six months.


AI-Assisted Testing

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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.


A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens

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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).


The Future of AI Part 1

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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".


4 Perceived Problems With Self-Driving Vehicles

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The development of autonomous vehicles has been the strongest driver of auto tech investment in the past couple of years. According to the infographic about the future of cars from carsurance.net, more than $9 billion was funneled into the R&D of self-driving vehicles between 2014 and 2018 in 215 deals. The collective efforts of traditional automakers and tech giants, such as Google, Amazon, and Apple, are fast-tracking the maturity of autonomous driving technology. By 2030, about 70% of motor vehicles are projected to have some self-driving features. Furthermore, by the year 2035, it is expected that there will be 4.5 million self-driving cars roaming around the US streets.


Standing Ai (Artificial intelligence) - A brief history - Diginixai

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Thirty years ago, everybody was thinking about flying cars. Do we have flying cars now?? of course not! But we have something better. AI, wheel of our times, it will change the world as the invention of wheel did in the stone age. The term'artificial intelligence' was given by John Mccarthy way back in the 50's, but the journey of understanding the process took more than half of a century.


Better outcomes with AI: Frost & Sullivan names Microsoft the leading AI platform for healthcare IT

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In early 2020, Frost & Sullivan recognized Microsoft as the "undisputed leader" in global Artificial Intelligence (AI) platforms for the Healthcare IT (HCIT) sector on the Frost Radar . In a field of more than 200 global industry participants, Frost & Sullivan independently plotted the top 20 companies across various parameters indicative of growth and innovation, available for consumption here. According to Frost & Sullivan, the global AI HCIT market is on a rapid growth trajectory, with sales of AI-enabled HCIT products expected to generate more than $34.83 billion globally by 2025. Government agencies will contribute almost 50.7 percent of the revenue (including public payers), followed by hospital providers (36.3 percent) and physician practices (13 percent). Clinical AI solutions will drive 40 percent of the market revenue, with financial AI solutions contributing the same, and the remaining 20 percent coming from sales of operational AI solutions. Globally, Microsoft earned the top spot because of its industry-leading effort to incorporate next-generation AI infrastructure to drive precision medicine workflows, aid population health analytics, propel evidence-based clinical research, and expedite drug and treatment discovery.


Top Technologies To Achieve Security And Privacy Of Sensitive Data

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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.