Sentences handed down by artificial intelligence would be fairer, more efficient, transparent and accurate than those of sitting judges, according to Swinburne researchers. Dean of Swinburne Law School, Professor Dan Hunter, and Swinburne researcher Professor Mirko Bagaric say artificial intelligence (AI) could improve sentencing procedures by removing emotional bias and human error. In a paper for the Criminal Law Journal, Professors Bagaric and Hunter argue that AI sentencing would better identify, sort and calibrate all the variables associated with sentencing, including criminal history, education, drug/alcohol use, emotional motivations and employment. The pair argue that sentencing decisions are often influenced by more than 200 considerations, many of which are variables which have been established prior to court hearings. Professor Bagaric says subconscious bias plays a large part in sentencing in which judges or magistrates hand down harder penalties to offenders of a particular race or background.

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. As the representation learned captures the variation in the environment generated by agents, this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.

We study and empirically optimize contextual bandit learning, exploration, and problem encodings across 500+ datasets, creating a reference for practitioners and discovering or reinforcing a number of natural open problems for researchers. Across these experiments we show that minimizing the amount of exploration is a key design goal for practical performance. Remarkably, many problems can be solved purely via the implicit exploration imposed by the diversity of contexts. For practitioners, we introduce a number of practical improvements to common exploration algorithms including Bootstrap Thompson sampling, Online Cover, and $\epsilon$-greedy. We also detail a new form of reduction to regression for learning from exploration data. Overall, this is a thorough study and review of contextual bandit methodology.

With this article, we, OpenDataScience, launch an open Machine Learning course. This is not aimed at developing another comprehensive introductory course on machine learning or data analysis (so this is not a substitute for fundamental education or online/offline courses/specializations and books). The purpose of this series of articles is to quickly refresh your knowledge and help you find topics for further advancement. Our approach is similar to that of the authors of Deep Learning book, which starts off with a review of mathematics and basics of machine learning -- short, concise, and with many references to other resources. The course is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course.

Lisa Roberson The Chronicle-Telegram ELYRIA -- This is how one educator described the fierce competition taking place at Elyria High School on Saturday: An erector set meets shuffleboard. In a room usually reserved for student-athletes, student engineers showed there is more than one way to compete for first place. Nineteen teams from seven middle schools in Northeast Ohio made up the field of competitors for Elyria's first Vex Middle School Qualifier. At the Elyria Pioneer Classic, students brought their robots, tools and drive to win while parents cheered from the stands as each custom-built robot dropped cones into scoring zones.

At present, there's an ongoing dialogue about Artificial Intelligence (AI) and what it could mean for the human race. Many believe that AI is an opportunity for growth and major improvement, but others worry that there may be repercussions. This debate is top of mind in the education industry, as AI begins to find its way into the classroom. In fact, it is predicted that the use of classroom AI may increase by 47.5% from 2017 to 2021. But it also poses some fascinating questions such as: Could AI replace teachers?

Dimensionality Reduction is a category of unsupervised machine learning techniques which is used to reduce the number of features or variables of columns in a dataset. Lot of variables often enhances the noise signal in the data which is bad for modelling but Dimensionality Reduction techniques can help in this. One of the Dimensionality Reduction Technique is Principal component Analysis which creates a new feature set which are uncorrelated or orthogonal .The newly created features are called Principal components.First principal component explains the most of the variance in the data and then the next principal component explains the remaining. Principal Component analysis is helpful for any dataset which has many variables or variables which are anonymous. Principal component analysis can help in explaining the structure of the dataset or creating the groups in the data or doing the predictive analytics .

If you are looking for that one course that includes everything about data analysis with R, this is it. Let's get on this data analysis journey together. This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of solving data analysis problems with R. The R language is a powerful open source functional programming language.

Stanford University is one of the world's leading teaching and research universities. Since its opening in 1891, Stanford has been dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world. The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Algerian student Fouzia Adjailia is the first Arab Muslim woman to ever enter the world of robotics, according to Al Arabiya. The young designer has built a robot that can listen, talk, walk and even dance. "Gardenia the robot has the ability to recognize individual persons' voices, speak like a real person, conversate with humans and dance to music - after putting it on by itself - all without the need for a remote control," Fouzia said. Fouzia has managed to grab media attention after building the robot in just under four months. "Building the robot was my university graduation project," explained Fawzieh, who will be graduating with a Master's degree in Wireless Application Protocol and Artificial Intelligence.