Kelley's cat decided Christmas was over. Chris is scared to return to Vana'diel and hates Norrath, but is strangely interested in hanging out with river hobbits in Middle Earth. Meanwhile, Phil tries to recruit everyone into his multilevel Monster Hunter scheme.

The post RPG Cast – Episode 705: “Trip the Gold Digger” appeared first on RPGamer.

Kelley makes a Morgen Wonk. Chris loves the smell of Gundam in the morning. Phil plays some Mathfinder while listening to his favorite new band, Creepy Lego Waifus. The cake has arrived.

The post RPG Cast – Episode 728: “Cats With Jiggle Physics” appeared first on RPGamer.

I've spent quite a lot of today trying to figure out why, exactly, some of the monsters in the Monster Hunter Wilds beta looked like bundles of copulating pyramids slathered in crocodile gravy. Nic clued me in on this reddit thread earlier, which cites unnamed Chinese players who've allegedly data-mined the beta's monster models, and learned that they are extremely large, encompassing hundreds of thousands of polygons.

If every monster in Monster Hunter Wilds were that fancy all of the time, your computer would become a volcano. As such, the game resorts to loading-on-demand systems to ensure that you only see those gorgeous details when the monsters are close by and, as the case may be, angrily sitting on you. When they're further afield, the flourishes fall away to free up memory and processing power. The popular Redditor explanation for the presence of monsters that look like Henry Moore sculpture is basically that the LOD systems are being forgetful, and neglecting to load the additional polygons at proximity.

Read more

A 61-year-old San Antonio man pleaded guilty to two federal charges in the human smuggling incident that led to the deaths of 10 undocumented immigrants this summer.

James Matthew Bradley Jr., who appeared before a U.S. magistrate judge Monday, pleaded guilty to “one count of conspiracy to transport aliens resulting in death and one count of transporting aliens resulting in death,” according to a statement from the U.S. Attorney’s Office for the Western District of Texas.

The office added that Bradley’s “admission of guilt” meant he packed dozens of unauthorized immigrants into a tractor-trailer for financial gain, adding that the suspect confirmed that details from court documents were “factually correct.”

On July 23, San Antonio Police Department officers responded to a call from a Walmart employee shortly past midnight. Once officers arrived, they found 39 immigrants at the scene. Of those carried in the tractor-trailer, eight were found dead in the rear of the trailer, while two died later at nearby hospitals, the statement said.

Survivors of the incident said there was no air conditioning in the overheated trailer and had to take turns to breath through a hole in the back of the truck for air. Bradley also initially told investigators that he was unaware of the immigrants in the trailer until he had stopped at the Walmart in San Antonio for bathroom break.

The attorney’s office also said Bradley faces up to life in prison with the charges and that he is scheduled to be sentenced in January 2018. Immigrants said there were up to 200 people transported on the trailer and that different fees were quoted to them for the ride north from the U.S.-Mexico border, the statement added.

Jason Buch of San Antonio Express-News told the NewsHour earlier this year that Border Patrol agents in Laredo, Texas, reported an uptick of immigrants using tractor-trailers to get pass checkpoints at the border.

“People are usually going on to major metropolitan areas or regions of the country that employ a lot of immigrant laborers, so, areas with large agriculture industries or construction booms,” Buch said.

The NewsHour’s John Yang learned more about the July human smuggling case and immigration politics from Jason Buch of San Antonio Express News.

Shane M. Folden, special agent in charge of homeland security investigations in San Antonio, said in the statement that the proceeding “helps to close the door on one of the conspirators responsible for causing the tragic loss of life and wreaking havoc on those who survived this horrific incident.”

“This case is a glaring reminder that alien smugglers are driven by greed and have little regard for the health and well-being of their human cargo, which can prove to be a deadly combination,” he added.

Bradley’s co-defendant Pedro Silva Segura was also indicted last month with faces two counts of conspiracy and two counts of transporting undocumented immigrants resulting in serious bodily injury and placing lives in jeopardy.

Segura, 47, is an undocumented immigrant who resides in Laredo, Texas.

The post San Antonio truck driver pleads guilty in fatal human smuggling case appeared first on PBS NewsHour.

Planets that orbit close to their parent stars are blasted with radiation and contorted by gravity – and the exoplanet TOI-6255b might be the most extreme example yet

AI companies hope that feeding ever more data to their models will continue to boost performance, eventually leading to human-level intelligence. Behind this hope is the "transformer", a key breakthrough in AI, but what happens if it fails to deliver?

Using more training data and computational power is meant to make AIs more reliable, but tests suggest large language models actually get less reliable as they grow

Eliminating this pernicious policy should be on the Trump administration's first week to-do list.

A new study suggests there could be water miles under the dusty surface of Mars, with enough water to fill a global-sized ocean a mile deep.

A bold new study in Physical Review suggests that a wobble detected in Mars' orbit could be the result of dark matter made up of primordial black holes.

And the Epic Games Achievements system will start to roll out next week.

When NASA’s Voyager 2 spacecraft flew by Uranus in 1986, it provided scientists’ first close glimpse of this ice-giant planet.

The post Voyager 2’s Flyby of Uranus in 1986 was During Anomalous Solar Event, New Study Suggests appeared first on Sci.News: Breaking Science News.

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A.

Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias.

Andrew Ng on...

• What’s next for really big models
• The career advice he didn’t listen to
• Defining the data-centric AI movement
• Synthetic data
• Why Landing AI asks its customers to do the work

The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way?

Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions.

When you say you want a foundation model for computer vision, what do you mean by that?

Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them.

What needs to happen for someone to build a foundation model for video?

Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision.

Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries.

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It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users.

Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation.

“In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.”
—Andrew Ng, CEO & Founder, Landing AI

I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince.

I expect they’re both convinced now.

Ng: I think so, yes.

Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.”

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How do you define data-centric AI, and why do you consider it a movement?

Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.

When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline.

The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up.

You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them?

Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.

When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set?

Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system.

“Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.”
—Andrew Ng

For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance.

Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training?

Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle.

One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way.

When you talk about engineering the data, what do you mean exactly?

Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.

For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow.

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What about using synthetic data, is that often a good solution?

Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development.

Do you mean that synthetic data would allow you to try the model on more data sets?

Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category.

“In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.”
—Andrew Ng

Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data.

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To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment?

Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data.

One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory.

How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up?

Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations.

In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists?

So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work.

Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains.

Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement?

Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it.

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This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

This is a sponsored article brought to you by BESydney.

In July 2024, Sydney woman Katherine Bennell-Pegg made history as the first astronaut to graduate under the Australian flag and the first female astronaut in Australia. Her journey, marked by determination and discipline, showcases Australia’s growing prominence in space exploration and research.

From her academic achievements at the University of Sydney (USYD) to her rigorous training at the European Space Agency (ESA), Bennell-Pegg’s success has paved a path forward for aspiring space and aerospace professionals in Australia and globally.

A journey to the stars begins in Sydney

Katherine Bennell-Pegg was born in Sydney, New South Wales, and grew up in the Northern Beaches area. Her fascination with space began at an early age.

“I always dreamed of being an astronaut,” Bennell-Pegg shared in her “Insights from an Australian Astronaut” Space Forum Speech in July 2024. “When I was young, it was for the adventure, but after more than a decade working in space, it’s now because I know the role it plays in tackling real-world problems and developing new knowledge that can benefit our society, environment and science.”

Sydney: A Hub for Space Innovation

Sydney, the vibrant heart of the state of New South Wales (NSW), stands at the forefront of aerospace innovation in Australia. With its world-class research facilities, leading academic institutions and strategic geographic positioning, Sydney is not only Australia’s gateway to the Indo-Pacific but also a burgeoning hub for international aerospace endeavours.

NSW is home to more than 40 per cent of Australia’s aerospace industry. Substantial investments from both the state and federal governments support this concentration of capabilities, underpinning Sydney’s role as a leader in aerospace. From advanced manufacturing and cybersecurity to quantum technologies and space exploration, this progressive city is truly thriving.

Sydney’s appeal as a desirable location for hosting aerospace conferences and business events is bolstered by its comprehensive infrastructure, vibrant startup community and strategic position as a transport hub.

Sydney’s track record of successfully hosting events highlights the city’s ability to organise impactful international gatherings, including:

• Australian Space Summit
• New Horizons Summit
• CubeSatPlus2024 - NEW SPACE: Unbounded Skies

Sydney will also host the 76th International Astronautical Congress from 29 September to 3 October 2025 and the 34th Congress of the International Council for the Aeronautical Sciences (ICAS) to be held 13 to 17 September 2026. Both will take place at ICC Sydney, further solidifying Sydney’s status as a central hub for aerospace events.

Would you like to know more about Sydney’s credentials in Aerospace? Download our Aerospace eBook or visit besydney.com.au

Sydney proved to be the ideal location for Bennell-Pegg’s journey to begin. She studied at the University of Sydney, where she earned a Bachelor of Engineering (Honors) in Aeronautical Engineering (Space) and a Bachelor of Science (Advanced) in Physics.

Sydney’s universities are at the forefront of aerospace education and research. Institutions such as the University of Sydney (USYD), the University of New South Wales (UNSW Sydney) and the University of Technology Sydney (UTS) attract students from around the world. UNSW Sydney, with its School of Aerospace, Mechanical, and Mechatronic Engineering, is renowned for its innovative research in space technology and satellite systems, while UTS provides cutting-edge programs in aerospace engineering and physics, emphasizing practical applications and industry partnerships. USYD excels in aeronautical engineering and space science, supported by advanced facilities and strong ties to major aerospace organisations. Together, these universities offer comprehensive programs that integrate theoretical knowledge with hands-on experience, preparing students for dynamic careers in the rapidly evolving aerospace and space sectors.

Having excelled in her studies at USYD, Bennell-Pegg was awarded the Charles Kuller Graduation Prize for her top-placed undergraduate thesis. Subsequently, her quest for knowledge took her to Europe, where she earned two Master of Science degrees: one in Astronautics and Space Engineering from Cranfield University and another in Space Technology from Luleå University of Technology.

Reflecting on her educational path, Bennell-Pegg stated, “With the encouragement of my parents, I researched what it would take to become an astronaut and worked hard at school, participating in everything from aerobatic flying lessons to amateur astronomy.”

Inside the rigorous training regimen of an astronaut

Bennell-Pegg’s professional career began with roles at Airbus UK, where she contributed to numerous space missions and concept studies, such as Martian in-situ resource utilisation and space debris removal. Her expertise led her to the Australian Space Agency, where she became the Director of Space Technology.

In 2021, Bennell-Pegg was invited by the European Space Agency (ESA) to undertake Basic Astronaut Training at the European Astronaut Centre in Germany. When the ESA application opened in 2021, it was the first opening in 15 years. Bennell-Pegg jumped at the opportunity to apply alongside over 22,000 others from 22 countries. She endured six knock-out rounds, including medical, psychometrics, psychology and technical tests and made it to the group of 25 who passed.

This historic invitation marked the first time an international astronaut candidate was offered training by the ESA.

“The training was demanding, but it was also an incredible opportunity to learn from some of the best minds in the field and to be part of a team that is pushing the boundaries of human exploration.”—Katherine Bennell-Pegg

Bennell-Pegg’s training regimen was intense, encompassing physical conditioning, complex simulations, and theoretical classes designed to prepare candidates for long-duration missions to the International Space Station (ISS) and beyond. This included:

• Studies in biology, astronomy, earth sciences, meteorology, materials, medical and fluids, both in theory and in labs.
• Radiation research – an area of expertise for Australia. This will increase as humans travel back to the Moon.
• Medical operations: Astronauts need to be able to perform medical procedures on themselves and others.
• Training for expeditions: This included honing team dynamics through behavioral training, ocean and winter survival training, rescue and firefighting.

Sharing her thoughts on this transformative experience, Bennell-Pegg said, “The training was demanding, but it was also an incredible opportunity to learn from some of the best minds in the field and to be part of a team that is pushing the boundaries of human exploration.”

In April 2024, Bennell-Pegg completed her training, graduating with her ESA classmates from “The Hoppers” group. Upon graduation, she became fully qualified for assignments on long-duration missions to the ISS, making her the first Australian female astronaut and the first person to train as an astronaut under the Australian flag.

“I want to use this experience to open doors for Australian scientists and engineers to utilize space for their discoveries,” Bennell-Pegg said. “I hope to inspire the pursuit of STEM careers and show all Australians that they too can reach for the stars.”

Elevating Australia’s role in space exploration

Katherine Bennell-Pegg’s achievements represent a significant milestone. Her journey from the University of Sydney to the rigorous training programs at the European Astronaut Centre showcases the potential of Australian talent in the global space community.

“Being the first astronaut trained under the Australian flag is an incredible honor,” Bennell-Pegg said. “I’m grateful for the support that has fueled me through intense training and opened doors for more Australians in space exploration. Whether I fly or not, there is much to accomplish here on Earth. I’m excited to leverage this experience to inspire future generations in STEM and elevate Australia’s presence in the global space community. Becoming an astronaut is just the beginning.”

Bennell-Pegg’s dream to become an Australian astronaut is more than just a personal triumph; it is a win for anyone who aspires to a career in space or aerospace. Sydney, with its world-class educational institutions, advanced manufacturing facilities scheduled for the Western Sydney Aerotropolis and expanding opportunities in aerospace and defence, is an ideal starting point for anyone looking to make their mark in these sectors.

Would you like to know more about Sydney’s credentials in Aerospace? Download our Aerospace eBook or visit besydney.com.au

1. Swiggy’s reclusive CEO Sriharsha Majety makes rare public appearance at IPO listing ceremony  Hindustan Times
2. Zomato gives ‘Jai and Veeru’ vibes with special meme for rival Swiggy on IPO day  Hindustan Times
3. JM Financial gives Swiggy its first 'buy' tag, sees 20% upside from IPO price  Moneycontrol
4. Swiggy IPO: 9 execs who will become richer by Rs 50 crore or more  The Times of India
5. ‘You and I…’: Zomato welcomes Swiggy on Dalal Street post stock market listing  India Today

For $700, I was hoping for a much larger leap in visual impact.

Tickets for the event will go on sale on November 10, 2024, via BookMyShow

‘Jai Hanuman’, a sequel to ‘HanuMan’, will have Rishab Shetty in the lead and the movie will be produced by Mythri Movie Makers

Hello gentle readers, and welcome to the SwitchArcade Round-Up for August 27th, 2024. In today’s article, we kick things off …

Continue reading "SwitchArcade Round-Up: Today’s Nintendo Direct, Full Review of ‘EGGCONSOLE Star Trader’, Plus New Releases and Sales"

Trump wants to return to the use of mental institutions and proposes tent cities to deal with people who are unhoused and have mental illness. Experts say it's beyond the scope of federal authority.

Company expects to be cash flow positive in the next two quarters.

The United States is home to some truly spectacular lakes. Whether considering the massive Great Lakes themselves or deep alpine gems like Lake Tahoe, with its crystal-clear waters, America is well-stocked with many sizable bodies of water.

New Delhi (AFP) Nov 5, 2024
The toxic smog season in India's capital has just begun, but those unable to escape cancer-causing poisonous fumes say the hazardous impact on health is already taking its toll. New Delhi regularly ranks among the world's most polluted capitals, with a melange of factory and vehicle emissions exacerbated by agricultural fires blanketing the city each winter, stretching from mid-October until

Lahore, Pakistan (AFP) Nov 13, 2024
On the streets of Pakistan's second biggest city, smog stings eyes and burns throats. Inside homes, few people can afford air purifiers to limit the damage of toxic particles that seep through doors and windows. Lahore - a city of 14 million people stuffed with factories on the border with India - regularly ranks among the world's most polluted cities, but it has hit record levels this mon

As people gather to remember those who fought and died to protect this country in past wars, a new poll suggests many Canadians know little about their country's history.

La ville de Québec pourra compter sur des prestations du groupe La Bottine Souriante et de la DJ belge MATTN pour l’amener en 2025.

APPLE could be set to make its biggest new product announcement since the iPhone, with the company believed to be working on a game changer.

Many parents opt for more expensive baby milk, equating higher costs with better quality, the watchdog found.

Four workers red-flagged in a 2014 review of SA’s troubled child protection agency remain suspended on full pay.

Raising deep concern over the partnership between Swiggy Instamart and PharmEasy for a rapid drug delivery model, the All India Organization of Chemists and Druggists (AIOCD) has apprised the Drug Controller

The type 2 diabetes mellitus drug semaglutide is effective for weight loss in non-diabetic overweight or obese adults, when taken alongside a reduced-calorie diet and exercise, researchers have found.

A new type of drug that targets chemotherapy directly to cancer cells reduces the risk of death from the most common type of bladder cancer by 30%, a phase III trial in the New England Journal of Medicine has suggested.

Dalam dunia perjudian online yang terus berkembang, pencarian para pemain untuk menemukan peluang terbaik dalam meraih kemenangan mengarah pada fenomena populer: kumpulan game slot gacor dengan persentase RTP tertinggi hari…

The post Kumpulan Game Slot Gacor Dengan Persentase RTP Tertinggi Hari Ini appeared first on Biosimilarnews.

The quest for realism in Thai drama The Empress of Ayodhaya went too far when a cat was reportedly drugged in a poisoning scene. In episode five of the show, the character Indravedi (Fern Nopjira Lerkkajornnamkul) suspects her drink has been drugged, so she asks nanny Thongdee (Ja Molywon Phantara) to test it out on the black feline. The cat can be seen convulsing and retching, and the camera moves to show Indravedi looking concerned, while Thongdee declares that it is dead. The scene caused public outrage with fears that the cat had actually been killed, and calls to ban the period drama were trending on X. On Nov 7, a now-deleted X account reportedly belonging to Ja posted: "The cat didn't actually die. We put it under anaesthesia, but while filming, the cat retched and seized." She and Fern initially thought the cat had actually died while filming and their faces "turned pale", she added.

While the BJP-led NDA won resoundingly in the 2019 Lok Sabha election, it has lost power in five major State assemblies since December 2018.

Mar 6, 2024

Iranian-backed militants in Yemen are clashing with the United States and British naval forces in the Red Sea over Israel’s operations in Gaza, all in a complex dance for geopolitical leverage in the Middle East. Yet, there is another region with a stake in the conflict brewing in the Bab al-Mandab strait, one seemingly beyond the world’s purview – the Horn of Africa.

Get mortgage help. This is why. MakingHomeAffordable.gov :60

Rob Riggle Stars alongside Brice Williams in the newest Easter Seals Dixon Center PSA, directed by Jim Fabio with support from Judd Apatow

Algiz-RT7 rugged tablet product presentation

Actor/comedian Rob Riggle announces his next breakfast move as the Holiday Inn Express brand�s Creative Director � BREAKFA-mojis!

Denny�s extends food quality commitment with cage-free eggs.