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Machine Learning 101: Decision Tree Algorithm for Classification

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The decision tree Algorithm belongs to the family of supervised machine learning algorithms. It can be used for both a classification problem as well as for regression problem. The goal of this algorithm is to create a model that predicts the value of a target variable, for which the decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree. It will split our data into two branches High and Normal based on cholesterol, as you can see in the above figure. Let's suppose our new patient has high cholesterol by the above split of our data we cannot say whether Drug B or Drug A will be suitable for the patient.


AI in Cybersecurity: Six Considerations for 2021 - insideBIGDATA

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Heading into 2021, the future of artificial intelligence (AI) in technology and cybersecurity will only continue to evolve as more organizations adopt new and innovative techniques. According to one recent survey, two-thirds of organizations are already using the intelligent technology for cybersecurity purposes. Using these tools allows for companies to be more prepared for the innovative attacks that cybercriminals continue to develop – also using AI technologies. For example, just last year, criminals employed AI-based software to replicate a CEO's voice to command a cash transfer of €220,000 (approximately $243,000). For businesses looking to implement more AI into their security stack in 2021, it's important to follow these six steps to ensure the effective use of AI – without compromising security anywhere else down the line.


Tinder sees massive rise in mentions of 'courting' and 'flirting' in bios

Mashable

Tinder has released data showing a dramatic rise in mentions of the words "courting" and "flirting" in dating app bios, spelling a return to good old fashioned wooing. According to Tinder, "courting" has been included in 81 percent more Tinder bios this year, compared to February 2020. Interestingly, that data pertains to users aged between 18 and 25, meaning Gen Z daters seem to be showing an interest in more traditional forms of romancing. The dating app thinks that the popularity of period dramas like Netflix's Bridgerton are the reason for this. The term "flirting" has also seen a massive increase in 2021, with 132 percent more mentions in bios than the previous year.


Feature Stores need an HTAP Database

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A Feature Store is a collection of organized and curated features used for training and serving Machine Learning models. Keeping them up to date, serving feature vectors, and creating training data sets requires a combination of transactional (OLTP) and analytical (OLAP) database processing. This kind of mixed workload database is called HTAP for hybrid transactional analytical processing. The most useful Feature Stores incorporate data pipelines that continuously keep their features up to date through either batch or real-time processing that matches the cadence of the source data. Since these features are always up to date, they provide an ideal source of feature vectors used for inferencing.


Machine learning based predictors for COVID-19 disease severity

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Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with $$\text {AUC} = 0.80$$ for predicting ICU need and $$\text {AUC} = 0.82$$ for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.


CryptoDivine

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CryptoDivine.ai is a deep-learning AI that predicts price movements in Bitcoin and Ethereum, placing the full power of machine learning right where it matters most: the center of your decision-making process. Get in early, this introductory price is a limited offer.


Understanding Artificial Intelligence Marketing: Approaches and Techniques - DATAVERSITY

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Click here to learn more about Gilad David Maayan. What Is Artificial Intelligence Marketing? In marketing, artificial intelligence (AI) is the process of using data models, mathematics, and algorithms to generate insights that marketers can use. Marketers use insights gained from AI to guide future decisions on event spending, strategy, and content topics. AI also makes it possible to measure and optimize marketing activities without human intervention.


Computer-assisted Venus flytrap captures objects on demand

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Exploring new approaches to improve the capabilities and accuracy of robots, a team of researchers in Singapore has turned to an unexpected source: plants. Robots have been dispatched to move cars, lift weighty inventory in warehouses and assist in construction projects. But what if you need to delicately lift a tiny object 1/50th of an inch? To accomplish that task, the Singapore team turned to a Venus flytrap, one of nature's more fascinating plants. The flytrap, a native of North Carolina, contains tiny hairs on two leaf lobes that, when stimulated by an insect, shut tight and slowly devour the prey.


This cute book explains AI to children, without scary Terminators

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Called Many Intelligences ($22), the cute children's book for ages six and up--which Loglio both wrote and illustrated--walks through the gradient of human, animal, and robotic intelligence. It explains how thinking generally comes from brains but still exists in starfish and even plants (both of which lack gray matter). Along the way, the book does make one claim that parents might want to flag on a read-along, that collaboration is a uniquely human trait. In fact, modern science is revealing that there's strong evidence of thoughtful animal collaboration across the planet. Animal thinking is often shoehorned as instinctual, while human thinking is championed as something greater.


Artificial Intelligence-Worshipping Church Officially Shuts Down

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Remember that artificial intelligence-worshipping church, the Way of the Future? Well, first of all: Yes, that existed. But secondly, founder Anthony Levandowski told TechCrunch this week that he has now decided to dissolve the church and donate all of its funds -- just over $175,000 -- to the NAACP Legal Defense and Education Fund. Levandowski still supports the church's mission to responsibly develop and support artificial general intelligence, but he said he was inspired by the Black Lives Matter movement to do something with a more immediate impact. "I wanted to donate to the NAACP Legal Defense and Education Fund because it's doing really important work in criminal justice reform and I know the money will be put to good use," Levandowski told TechCrunch.