NASA Sponsors Inaugural Magnolia Regional FIRST Robotics Competition March 15-18

Organizers are preparing to host 30-plus teams for an inaugural FIRST Robotics Magnolia Regional Competition in Laurel, Mississippi, on March 15-18, thanks, in large part, to NASA’s Stennis Space Center, a lead sponsor for the event and a driving force in its launch.

Through the competition, NASA Stennis is joining with NASA’s Robotics Alliance Project and co-sponsor Mississippi Power to bring to life all aspects of science, technology, engineering, and mathematics (STEM) in the Magnolia state. It particularly hopes to lead students from rural areas to pursue STEM studies and careers as NASA continues in its mission to inspire the world through discovery. The new regional event is a critical milestone to enhance engagement with robotics programs and the K-12 community across the southeast region of the U.S.

“It is important to be able to model and provide examples to students who geographically do not get exposed to STEM activities,” said NASA Stennis Office of STEM Engagement Director Kelly Martin-Rivers. “If it is something that you do not see around you, it is hard to understand how to get there. One importance of having a robotics regional and setting it in central Mississippi is to provide that access, opportunity, and visibility to an area of students that have not had a strong STEM connection.”

The FIRST (For the Inspiration and Recognition of Science and Technology) Robotics Magnolia Regional Competition is scheduled at the Magnolia Center in Laurel, Mississippi. The event is free, open to the public, and offers students the opportunity to use STEM skills through teamwork and the excitement of competition.

The regional competition will feature two teams from Mexico, along with more than 30 high school teams from states, including Alabama, Louisiana, Mississippi, Missouri, Texas, and Tennessee. It also will serve as a championship-qualifying event to send several teams to the world championship competition in Houston, Texas, in April.

The NASA Stennis workforce is helping to facilitate the Laurel event by providing judges and volunteers throughout the four days of activities. Additionally, nine of the teams scheduled to compete are considered NASA Stennis house teams, which means they have an ongoing relationship with NASA Stennis and a NASA engineer as their team mentor.



This includes five teams from Mississippi: Team Fusion 364 from Gulfport; Chahta Warriors from Choctaw Central; Team Chaos from Picayune Memorial and Pearl River Central; Delta Overload from Gentry High School in Indianola; and Alpha Omega from Our Lady Academy in Bay St. Louis.

Other teams scheduled to compete from Mississippi include: Siege Robotics from Vicksburg; Team Tempest from Biloxi; Team Hero from Petal; Team Storm from Gulfport; and JXN United from Jackson.

High school teams sponsored by NASA Stennis from Louisiana include: Team Combustion from Northshore; The S.S. Prometheus from Mandeville; Tiger Robotics from Slidell; and Power Struck Girls from Academy of Our Lady in Marrero.

Other teams scheduled to compete from Louisiana include: Tigerbots from Boutte; FHS Robodawgs from Covington; Wildcat Robotics from Destrehan; MAGNAtech from West Monroe; Team Phenomena 3616 from Lafayette; Bulabots from Baton Rouge; Voodoo Voltage from New Orleans; Ramageddon Robotics from Lafayette; Denham Venom from Denham Springs; Trinity Force from St. Rose; and SWLA Tech Pirates from Lake Charles.

In FIRST Robotics, high school teams receive identical parts kits and competition guidelines. They use the kits to design and build robots to compete in achieving competition goals. For house teams, NASA Stennis engineers help students prepare for the competition. Students learn engineering and problem-solving skills that can be applied to the competition and real-world situations in the future. The teams each create an identity, raise funds to meet goals, and work to promote STEM in their community.

The 2023 season challenge, “Charged Up” challenges teams to reimagine the future of sustainable energy. The theme calls teams to explore ways to unlock the power of engineering to transform renewable energy and power a better future. For the regional competition, teams will build and program industrial-sized robots to play an action-packed game on a themed field.

The goal is to have the Magnolia Regional expose more students in rural areas to STEM and become an annual FIRST Robotics Competition, much like the Bayou Regional event in Louisiana, which NASA Stennis has supported since its inception. The Bayou Regional is scheduled for March 30-April 1 at the Pontchartrain Center in Kenner, Louisiana.

info :https://www.nasa.gov/centers/stennis/news/releases/2023/NASA-Sponsors-Inaugural-Magnolia-Regional-FIRST-Robotics-Competition

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Computer Science Professor Awarded NSF CAREER Grant for Robotics Research

sst. Prof. Reza Ahmadzadeh of the Miner School of Computer and Information Sciences envisions a world where robots can help people live more comfortably and safely, and he’s working to develop the methods to make that happen.

“In my career, I hope to get to a place where we use robots in our everyday lives,” he says.

The National Science Foundation (NSF) recognized Ahmadzadeh’s potential and awarded him a prestigious faculty early-career development CAREER grant. The nearly $500,000 grant will fund Ahmadzadeh’s project on robot learning of complex tasks over the next five years.

“This NSF CAREER award speaks volumes about Asst. Prof. Reza Ahmadzadeh’s ability to generate great ideas in the interdisciplinary field of robotics,” Kennedy College of Sciences Dean Noureddine Melikechi says.

Since his time as a Ph.D. student in robotics, cognition and interaction technologies at the University of Genoa in Italy, Ahmadzadeh has focused his research on robots learning from human demonstration. This can be accomplished through kinesthetic teaching, where a person holds the robot and moves it around, or through the robot “watching” the person via feeds from a virtual reality headset, joystick or camera.

The information gathered from the human demonstration is then sent to algorithms programmed within the robot. Existing algorithms work well for getting robots to replicate simple tasks, such as picking up an object, “but human life is not just made of simple tasks,” Ahmadzadeh says.

For his NSF CAREER project, he will be developing new algorithms for robots to learn complex tasks.

The prospective breakthroughs from this project could help automate difficult and dangerous duties in the workplace, freeing up employees’ time to pursue more creative or higher-value projects. It could also help older adults remain in their homes longer by assisting them with daily chores.

“The results of Reza’s funded research will have a great impact on how people work with robot systems in the future,” says Holly Yanco, chair of the Miner School.

For instance, the algorithms developed could give robots the ability to load a dishwasher or change a light bulb.




“If a robot is sent into a human’s home now, they will not be useful with complex tasks,” Ahmadzadeh says. “The robot could learn to hand a person a light bulb, but it wouldn’t be able to change it.”

Ahmadzadeh plans to review studies in human movement to see if algorithms used for primitive skills in robots can be improved. He then will seek new approaches that allow robots to string together a library of reusable skills to accomplish complex tasks.

Ahmadzadeh will also build algorithms that let a robot discover the new skills needed to finish a job.

“If a human shows a robot how to make coffee, the robot may know how to pick up a mug and place it, but not how to press the button on the machine,” he says. “The robot would be able to grab that skill and put it in its library to be used again later.”

Ahmadzadeh will create methods to refine the robot’s skills so it can complete a task no matter the environment. For example, if a robot is capable of opening a door, but it comes across a door with a different handle, it needs to be able to hone its skills to complete the same task.

“A human would give another demonstration, and the robot would realize that this is the same skill but applied to a new situation, so it would refine what it already knows,” he says.

Undergraduate and graduate students will be assisting Ahmadzadeh with the research, which will lead to a revamping of his Robot Learning course. Students will also get exposure to robot learning during one-day workshops that Ahmadzadeh plans to hold throughout the duration of the project. The workshops will run as part of SoarCS, a summer program for incoming first-year computer science students.

“We will teach the students the basics of robot learning algorithms, then present them with code where they can implement simple functions,” he says. “With me and the graduate students supervising, they will run the algorithm on the actual robots.”

info:https://www.uml.edu/news/stories/2023/reza-ahmadzadeh-nsf-career-award.aspx

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Machine Learning Vs Deep Learning: A Beginner’s Overview of the Two Dominant Approaches to Artificial Intelligence

As technology continues to evolve, artificial intelligence (AI) has become increasingly prominent in our daily lives. Within the field of AI, machine learning and deep learning have emerged as two popular subsets. While the terms may be used interchangeably, they are fundamentally different in their approach and applications. Machine learning involves algorithms that learn patterns and relationships in data to make predictions or decisions, while deep learning involves neural networks modeled after the human brain to process complex data. In this beginner’s guide, we will explore the similarities and differences between machine learning and deep learning, as well as their potential applications and limitations. By the end of this article, you will have a basic understanding of these two important concepts in AI.


Machine learning is a form of AI that allows machines to learn from data, without being explicitly programmed. It involves algorithms that learn patterns and relationships in data, and use these insights to make predictions or decisions. Machine learning can be used for a variety of tasks, including image recognition, speech recognition, and natural language processing.
Unsupervised Learning

Unsupervised learning involves training a machine learning model on unlabeled data, where the desired output is unknown. The model then learns patterns and relationships in the data, which can be used for tasks such as clustering and anomaly detection.
Reinforcement Learning

Reinforcement learning involves training a machine learning model to make decisions based on a reward system. The model learns through trial and error, receiving positive or negative feedback depending on the outcome of its actions.

What is Deep Learning?

Deep learning is a subset of machine learning that involves neural networks. Neural networks are modeled after the human brain and consist of layers of interconnected nodes that process information. Deep learning can be used for tasks such as image recognition, speech recognition, and natural language processing.
Neural Networks

Neural networks are composed of layers of nodes, which are organized into input, hidden, and output layers. Each node receives input from the previous layer, performs a calculation, and passes the output to the next layer. The final output layer produces the prediction or decision.

Types of Deep Learning

There are several types of deep learning:
Convolutional Neural Networks (CNNs)

CNNs are used for image and video recognition. They use convolutional layers to extract features from the input image, and pooling layers to reduce the dimensionality of the feature maps.
Recurrent Neural Networks (RNNs)

RNNs are used for sequential data, such as text and speech. They use recurrent layers to process sequences of input and can retain information from previous inputs.
Generative Adversarial Networks (GANs)

GANs are used for generating new data, such as images and text. They consist of a generator network, which creates new samples, and a discriminator network, which distinguishes between real and fake samples.

Differences Between Machine Learning and Deep Learning

While both machine learning and deep learning involve algorithms that learn from data, there are some key differences between the two:
Complexity

Deep learning is more complex than machine learning, as it involves neural networks with multiple layers. This complexity allows deep learning models to learn more complex patterns and relationships in data.

Data Requirements

Deep learning requires more data than machine learning, as it involves more complex models. This can be a challenge for organizations with limited data resources.
Hardware Requirements

Deep learning requires more powerful hardware than machine learning, as it involves neural networks with many layers. This can be a barrier to entry for organizations without the necessary hardware resources.

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Google Maps uses machine learning to remove fake business and misleading images

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Google Maps uses machine learning to remove fake business and misleading images


Google Maps is one of the most popular navigation service, with over one billion active users every month. Apart from satellite imagery,


one of the ways the tech giant keeps Maps updated is by relying on content contributed by people worldwide.


info:https://indianexpress.com/article/technology/tech-news-technology/google-using-machine-learning-to-remove-fake-reviews-and-images-on-maps-8535459/

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The state of artificial intelligence: Stanford HAI releases its latest AI Index Report

The Stanford Institute for Human-Centered Artificial Intelligence today released the latest edition of its AI Index Report, which explores the past year’s machine learning developments.

Stanford HAI, as the institute is commonly known, launched in early 2019. It researches new AI methods and also studies the technology’s impact on society. It releases its AI Index Report annually.

The latest edition of the study that was published today includes more than 350 pages. It covers a long list of topics, including the cost of AI training, efforts to mitigate bias in language models and the technology’s impact on public policy. In each area that it surveys, the report points out multiple notable milestones that were reached during the past year.
AI advances and challenges

The most advanced neural networks have become more complicated over the past year. Stanford HAI points to Google LLC’s Minerva large language model as one example. The model, which debuted last June, features 540 billion parameters and took nine times more compute capacity to train than OpenAI LP’s GPT-3.

The growing hardware requirements of AI software are reflected in the rising cost of machine learning projects. Stanford HAI estimates that PaLM, another Google model released last year, cost $8 million to develop. That’s 160 times more than GPT-2, a predecessor to GPT-3 that OpenAI released in 2019.

Though AI models can perform significantly more tasks than a few years ago, they continue to have limitations. Those limitations span several different areas.

In today’s report, Stanford HAI highlighted a 2022 research paper that found advanced language models struggle with some reasoning tasks. Tasks that require planning are often particularly challenging for neural networks. Last year, researchers also identified many cases of AI bias in both large language models and neural networks optimized for image generation.

Researchers’ efforts to address those issues came to the fore in 2022. In today’s report, Stanford HAI highlighted how a new model training technique called instruction tuning has shown promise as a method for mitigating AI bias. Introduced by Google in late 2021, instruction training involves rephrasing AI prompts to make them easier to understand for a neural network.
New use cases

Last year, researchers not only developed more capable AI models but also found new applications for the technology. Some of those applications led to scientific discoveries.

In October 2022, Google’s DeepMind machine learning unit detailed a new AI system called AlphaTensor. DeepMind researchers used the system to develop a more efficient way of carrying out matrix multiplications. A matrix multiplication is a mathematical calculation that machine learning models use extensively in the process of turning data into decisions.

Last year also saw scientists apply AI to support research in a range of other areas, Stanford HAI pointed out. One project demonstrated that AI could be used to discover new antibodies. Another project, also led by Google’s DeepMind, led to the development of a neural network that can control the plasma in a nuclear fusion reactor.
The societal impact of AI

Stanford HAI’s new report also dedicates multiple chapters to the impact of AI on society. Though large language models have only entered the public consciousness in recent months, AI is already making an impact across several areas.

In 2021, only 2% of federal AI-related bills proposed by U.S. lawmakers were passed into law. Last year, that number jumped to 10%. At the state level, meanwhile, 35% of all AI-related bills passed in 2022.

The impact of machine learning is also being felt in the education sector. According to Stanford HAI’s research, 11 countries have officially endorsed and implemented a K-12 AI curriculum as of 2021. Meanwhile, the percentage of new computer science Ph.D. graduates from U.S. universities who specialized in AI nearly doubled between 2010 and 2021, to 19.1%.

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From Artificial Intelligence to Artificial Intuitions: CEO, Affordable Robotic & Automation Ltd

 Artificial intelligence (AI) is rapidly transforming various industries and revo ..

Read more at:

https://infra.economictimes.indiatimes.com/news/construction/from-artificial-intelligence-to-artificial-intuitions-ceo-affordable-robotic-automation-ltd/99212712

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