Search engines scan the internet to find what you're looking for, or what you don't know you're looking for. Social media platforms surface content you might want to read. And the latest iPhones recognize your face in a split second to unlock your phone. As the artificial intelligence industry grows, the consequences of lacking a diverse workforce can pose major challenges that threaten to ripple into everyday life. Women and minorities are underrepresented in the AI workforce, according to a Stanford report on diversity in AI. More than 83% of AI tenure-track faculty at top universities are male, while over 46% of Ph.D. students in the United States studying AI are white.

Artificial intelligence (AI) is already embedded in a range of digital services. Voice assistants such as Alexa, car routing or content translation all involve machine learning - the most popular form of artificial intelligence technology. There are many warnings these days about AI, such as the ethics behind these machine driven decision systems or threats of automation and the loss of many jobs. Very little is reported about how artificial intelligence can improve public services and can have positive social impact. Smart algorithms combined with cloud computing power allow unprecedented forms of data analysis that would take much longer if humans were doing it.

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills. Meta-reinforcement learning (meta-RL) addresses this challenge by leveraging knowledge learned from training tasks to perform well in previously unseen tasks. However, current meta-RL approaches limit themselves to narrow parametric task distributions, ignoring qualitative differences between tasks that occur in the real world. In this paper, we introduce TIGR, a Task-Inference-based meta-RL algorithm using Gaussian mixture models (GMM) and gated Recurrent units, designed for tasks in non-parametric environments. We employ a generative model involving a GMM to capture the multi-modality of the tasks. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared to state-of-the-art meta-RL approaches in terms of sample efficiency (3-10 times faster), asymptotic performance, and applicability in non-parametric environments with zero-shot adaptation.

The Pulitzer Prize-winning book Gödel, Escher, Bach inspired legions of computer scientists in 1979, but few were as inspired as Melanie Mitchell. After reading the 777-page tome, Mitchell, a high school math teacher in New York, decided she "needed to be" in artificial intelligence. She soon tracked down the book's author, AI researcher Douglas Hofstadter, and talked him into giving her an internship. She had only taken a handful of computer science courses at the time, but he seemed impressed with her chutzpah and unconcerned about her academic credentials. Mitchell prepared a "last-minute" graduate school application and joined Hofstadter's new lab at the University of Michigan in Ann Arbor.

Hillsdale Middle School summer students lined up on opposite sides of the school cafeteria. They were getting into position to have a battle, one they had been preparing for through a summer of math, reading and problem solving activities. The battle would not be between the students but between robots, which the students programmed to turn and move in specific ways by writing code. The science, technology, engineering and mathematics teacher, Jenny Stump, counted down. While the actual battles -- there ended up being multiple rounds -- only lasted a few minutes each, weeks of preparation went into building the skills necessary to execute the activity.

Any new positive coronavirus test among teachers or students could prompt a stay-at-home quarantine in Los Angeles County under a proposed plan.

Robotics is one of the fast-growing areas of technology that is opening doors to a wide range of industries such as security, automation, healthcare, consumer products, customized manufacturing, and interactive entertainment. According to the latest research of the U.S. The Bureau of Labor Statistics, Robotics Engineering is expected to grow 4% by 2028. The area of Robotics is likely to be in demand due to the emergence of new technologies. Since the future is of robots the demand and interest among robotics enthusiasts is also growing day by day. Here are the top online masters in robotics programs for robotic lovers.

Hillsdale Middle School summer students lined up on opposite sides of the school cafeteria. They were getting into position to have a battle, one they had been preparing for through a summer of math, reading and problem solving activities. The battle would not be between the students but between robots, which the students programmed to turn and move in specific ways by writing code. The science, technology, engineering and mathematics teacher, Jenny Stump, counted down. While the actual battles -- there ended up being multiple rounds -- only lasted a few minutes each, weeks of preparation went into building the skills necessary to execute the activity.

ProJo can also help students work together and assess their growth and weaknesses, in both robot form and on a computer screen. It is one of a variety of teaching aids in development, boosted by artificial intelligence, that scientists and educators say could support tomorrow's classrooms. Typically, AI education products serve one function, such as assessing a student's literacy, tailoring tools to individual learners or performing administrative functions such as grading. Next-generation tools may do all of this in a single platform, serving at times as a peer learning partner, a group facilitator and a monitor for educators--a sort of superpowered teacher's assistant personalized for each student. A look at how innovation and technology are transforming the way we live, work and play.