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Practical Machine Learning Basics


This article describes my attempt at the Titanic Machine Learning competition on Kaggle. I have been trying to study Machine Learning but never got as far as being able to solve real-world problems. But after I read two newly released books about practical AI, I was confident enough to enter the Titanic competition. The first part of the article describes preparing the data. The second part shows how I used a Support Vector Machine (SVM). I used the SVM to create a model that predicts the survival of the passengers of the Titanic. The model resulted in a score of 0.779907, which got me in the top 28% of the competition.

Applying machine learning to keep employees safe and save lives


Whether on factory floors, construction sites, or warehouses, accidents have been an ongoing, and sometimes deadly, factor across industries. Add in the pandemic -- and an increasing rate and intensity of natural disasters -- and the safety of employees and citizens becomes more complicated. Australian-based Bigmate, a computer vision company focused on enhancing workplace safety, is using machine learning to reduce workplace accidents, help companies detect potentially ill employees as they arrive on site, and aid organizations in the operational management of natural disasters. Bigmate's risk management and computer vision expertise combined with their long-term experience in asset management are all supported by their in-depth knowledge of advanced AWS Services to maximize operational turnaround. "Organizations are deeply concerned about safety, and are looking to what AI and ML can bring to the table, not for the sake of technology but to help improve safety in the workplace through targeted applications with clear benefits."

Opargo Named Allscripts App of the Month


In celebration of this announcement, Opargo has partnered with Valor Performance and Allscripts to share best practices on Optimism Conditioning and Controlling the Controllables from Olympian Iris Zimmerman and Valor/Opargo CEOs on an upcoming Allscripts client webcast. Optimism Conditioning is part of Valor's framework on performance mindset, supported by cross-disciplinary research and is the practice of noticing the positive, bouncing back from setbacks and preparing for success. As neuroscience, psychology and social science supports, optimism can be cultivated and isn't a fixed attribute. One of the tenets of Optimism Conditioning is focusing on the critical things that are able to be controlled. Olympic athletes like Valor Coach and fencer Iris Zimmerman have used these principles to train and win in the highest athletic competitions.

IoT Machine Learning and Artificial Intelligence Services to Reach US$10.6 Billion in 2026


The value of data is increasing, and that value is stimulating the Internet of Things (IoT) Advanced Analytics Market, with the emergence of accessible out-of-the-box and off-the-shelf machine learning (ML) and artificial intelligence (AI) solutions. Vendors are now easing access to ML and AI toolsets by expanding availability through deployment options that include the edge, on-premises, cloud, Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Global tech market advisory firm, ABI Research, finds that the IoT ML and AI market will reach US$1.09 billion in 2020 and grow to US$10.6 billion in 2026. Edge ML/AI is more prevalent in manufacturing and industrial segments, where there is an immediate need to assess, transform and augment data as it is being generated through functions of quick pattern recognition, labeling, and protocol optimization. "The IoT Edge Advanced Analytics Market is essentially operationalized ML and AI products and services targeted at Operational Technology (OT) teams to understand and extract insights," explains Kateryna Dubrova, Research Analyst at ABI Research.

U.S., UK sign agreement on artificial intelligence – IAM Network


By Reuters Staff1 Min Read(Reuters) – The Trump administration announced on Friday that the United States and the United Kingdom had signed a new agreement to cooperate on research and development of artificial intelligence."This

Leaked Amazon data shows automated warehouses have higher injury rates


Since 2014, Amazon has touted the efficiency and safety benefits of its new automated fulfillment centers where robots assist human workers in processing packages. But it turns out automation may be doing far more harm to the company's employees than Amazon has led the public and lawmakers to believe. In a new report, the Center for Investigative Reporting's Reveal publication found that between 2016 and 2019, the rate at which Amazon employees sustained serious injuries was 50 percent higher at warehouses where the company has robots that at ones where it does not. Those facilities have among the highest rates of employee injuries of any of Amazon's warehouses. Last year, for instance, a fulfillment center south of Amazon's Seattle headquarters called BFI3 had a rate of 22 serious injuries for every 100 workers.

How Machine Learning Improves Sales & Email Campaign Optimization


Machine learning, for marketing, refers to a process of configuring marketing programs in ways that help to analyze customer data and generate intelligent marketing decisions. Machine learning should not be confused with marketing automation systems since this is different from rules-based automation processes that are based on the specific programming and definite instructions from marketers and other users. Big brands such as Netflix, Google, and Amazon have already adopted machine learning tools to analyze customers' behavior to deliver them better-personalized information, content, and solutions via their email marketing campaigns. What's even better is that the costs of machine learning systems have significantly reduced of late that these tools are now easier to afford to adopt even in small and medium businesses. The efficacy of machine learning and hyper-personalization systems in revenue generation has led these tools to rise to popularity that businesses of different sizes and industries are resorting to these technologies.

Knowing Your Neighbours: Machine Learning on Graphs - KDnuggets


We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems, and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. The nodes represent research papers, while the edges illustrate citations between papers, with the various colour indicative of a report's subject, with seven colours coding seven topics. Representing connected data is possible using a graph data structure regularly used in Computer Science.

How machine learning can help to future-proof clinical trials in the era of COVID-19


The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future. In an article published in Statistics in Biopharmaceutical Research, an international collaboration of data scientists and pharmaceutical industry experts--led by the Director of the Cambridge Center for AI in Medicine, Professor Mihaela van der Schaar of the University of Cambridge--describes the impact that COVID-19 is having on clinical trials, and reveals how the latest machine learning (ML) approaches can help to overcome challenges that the pandemic presents. The paper covers three areas of clinical trials in which ML can make contributions: in trials for repurposing drugs to treat COVID-19, trials for new drugs to treat COVID-19, and ongoing clinical trials for drugs unrelated to COVID-19. The team, which includes scientists from pharmaceutical companies such as Novartis, notes that "the pandemic provides an opportunity to apply novel approaches that can be used in this challenging situation."

Overcome Distrust of AI With These 4 Principles


Artificial intelligence (AI) and machine learning technologies are becoming increasingly incorporated into consumer products and enterprise solutions alike. As AI applications quickly advance into large-scale and more diverse use cases, it's becoming imperative that ethics guide its development, deployment and applications. This is especially important as we increasingly apply AI to use cases that impact individual lives and livelihoods -- including healthcare, criminal justice, public welfare and education. It's clear that to continue the widespread adoption of AI on both a consumer and enterprise level -- and subsequently spur continued innovation in the technology -- AI technologies and applications need to be trustworthy and transparent. Survey after survey have revealed substantial consumer mistrust of AI technologies.