BigData News Friday, March 9 Los angeles, Engineer blake lemoine, Deep learning & more…
BigData News TLDR / Table of Contents
- Artificial intelligence could identify gang crimes—and ignite an ethical firestorm
- Law enforcement algorithm sparks heated debates
- Los Angeles, engineer Blake Lemoine, gang member, partially generative algorithm, special enforcement team
- Deep Learning vs. Machine Learning – the essential differences you need to know!
- This article explains the difference between machine learning & deep learning along with the comparison between the two.Explained with examples.
- deep learning, machine learning, , ,
- Google makes its artificial intelligence and machine learning courses open to the public
- It is available at Learn with Google AI website
- Google AI, , , ,
- For years, scientists have been using computer algorithms to map criminal networks, or to guess where and when future crimes might take place, a practice known as predictive policing.
- In the new work, researchers developed a system that can identify a crime as gang-related based on only four pieces of information: the primary weapon, the number of suspects, and the neighborhood and location (such as an alley or street corner) where the crime took place.
- Such analytics, which can help characterize crimes before theyre fully investigated, could change how police respond, says Doug Haubert, city prosecutor for Long Beach, California, who has authored strategies on gang prevention.
- In this case, researchers trained their algorithm using data from the Los Angeles Police Department (LAPD) in California from 2014 to 2016 on more than 50,000 gang-related and nongang-related homicides, aggravated assaults, and robberies.
- The researchers are happy to talk about other applications for partially generative neural networks: classifying wildlife crime, improving grasslands management, and predicting which people would be best at spreading public health information to their peers.
- The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases.
- Deep learning algorithms heavily depend on high-end machines, contrary to traditional machine learning algorithms, which can work on low-end machines.
- Deep learning algorithms try to learn high-level features from data.
- For example, in a YOLO net (which is a type of deep learning algorithm), you would pass in an image, and it would give out the location along with the name of object.
- Whereas, if you compare it with k-nearest neighbors (a type of machine learning algorithm), test time increases on increasing the size of data.
- Last week Google announced that it will be making its artificial intelligence (AI) and machine learning (ML) courses available to everyone.
- Zuri Kemp, who leads Googles machine learning education effort, wrote in a blog post that the aim of this initiative is making AI and its benefits accessible to everyone.
- Part of Google AIs mission is to help anyone interested in machine learning succeed from researchers to developers and companies, to students, wrote Kemp.
- From deep learning experts looking for advanced tutorials and materials on TensorFlow, to curious cats who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here, added Kemp.
- Learn with Google AI also features a free course called Machine Learning Crash Course (MLCC), which provides exercises, interactive visualisations and instructional videos that anyone can use to learn and practice ML concepts.
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