Conspiracy Theory

"Hidden Truth Show with Jim Breslo" Looks Further into the 2017 Mandalay Bay Massacre

The devastating Mandalay Bay Massacre happened well over a year ago, but the investigations and the questions left in its wake still continue. One such ongoing investigation returned a verdict only recently, in January 2019, when three independent mock juries ruled that the MGM Resorts International-owned Mandalay Bay Hotel was negligent in the mass shooting, for failing to prevent the catastrophe. The mock jury rulings came after MGM Resorts International took unprecedented action in July 2018, and sued over 1,000 victims of the Las Vegas massacre, in an effort to avoid liability.

On a recent episode of the “Hidden Truth Show with Jim Breslo”, host Jim interviewed Las Vegas investigative journalist and retired police officer Doug Poppa about the results of the mock trials, and discussed Poppa’s unique insights into the FBI report about the event. Details of the episode, titled VEGAS: Mock Jury Rules in Favor of Route91 Victims Against MGM, include:

“Doug Poppa, Las Vegas investigative journalist and retired police officer, shares confidential information from inside MGM’s legal defense team that the jury in a mock trial conducted to evaluate the merits of the case ruled in favor of the victims! He also provides unique insight into the FBI report and updates us on the whereabouts of Officer Hendrex who froze on the 31st floor while the shooting continued.”

CLICK HERE to listen to this revealing episode now!

For more listening content relating to the Mandalay Bay massacre, you can also check out The Confessionals Podcast Episode 38: Mandalay Bay - The Untold Eyewitness Stories, Episode 55: Mandalay Bay - What Happens in Vegas Gets Erased in Vegas, and Episode 62: The Room Under Stephen Paddock.

VIDEO: UFO Cover-up Hoax... But the UFOs Are Very Real!

A NEW VIDEO from Tony is available now!

In this video, we revisit the pyramid-shaped UFO that was filmed by at least THREE different people, hovering and rotating over the Pentagon, in December 2018. We’ll show you a new video leak of the event, and also interview one of the people who filmed the UFO sighting firsthand!

Then we dive into the supposed secret UN meeting that took place. This video suggested that the Pentagon made contact with the Pyramid-shaped UFO. However, the video is a complete hoax! Watch now to find out why!

Watch below or click here to view on YouTube.

10 Common Things You May Not Realize Use Artificial Intelligence

10 Common Things You May Not Realize Use Artificial Intelligence

Screen Shot 2019-02-06 at 6.02.04 PM.png

While it was once something only dreamed about in science fiction, artificial intelligence has become a widespread, mainstream reality throughout the world. Nearly every day it seems like a new A.I. advancement is making headlines - for better or for worse. From artificial intelligence creating sellable artwork, to the pros and cons of self-driving cars, to warnings that A.I. will outstrip the abilities of mankind within 50 years, the tangibility of artificially intelligent machines is becoming steadily more prominent in our technological age. For every person who believes that artificial intelligence is the key to a better future, there is someone else who believes that a large-scale A.I. takeover of the human race is imminent. But what some may not be aware of is how artificial intelligence has already integrated itself into our everyday lives in many small-scale ways. While the majority of A.I. systems are not dangerous (so far…), they are everywhere. Although this is by no means a comprehensive list, here are 10 common things you may not realize use artificial intelligence:

Screen Shot 2019-02-06 at 5.01.17 PM.png


Yes, your email is being read by somebody else - an A.I. technology that learns about your email content to help you better navigate your own inbox. The tech uses machine-learning algorithms to constantly learn your patterns and preferences from signals like message metadata, the specific words you use in emails, and what types of messages you consider spam. This is how Gmail knows how to categorize different emails as ‘primary’, ‘promotion,’ and ‘social’ when filtering them to the appropriate inbox. Every time you mark an email as ‘important’, ‘spam,’ or some other label, Gmail is learning and adapting to be one step ahead of you the next time!


For those who listen to Spotify, Pandora, and other internet radio platforms (which is about 300 million people), every time you listen to music you are helping a simple A.I. program learn all about your musical tastes. The A.I. from these radio sites examines all the data it absorbs - keywords, length of tracks, descriptions, artists, key signatures, and so much more - to specially construct playlists based on your personal preferences.

Screen Shot 2019-02-06 at 4.46.05 PM.png

To up the ante, recent speculation says that Spotify is developing groundbreaking technology to use A.I. to actually compose original music, just for you! Last year the company added Francois Pachet to its payroll, one of the world’s foremost experts on the application of artificial intelligence in the world of popular music. Patchet previously oversaw the project that brought us Hello World”, the first musical album to be composed entirely by a computer. It may only be a matter of time before every song on your playlist is custom-crafted by a machine!


If you’ve ever used a transportation service like Uber or Lyft to get from place to place, artificial intelligence has been tracking your every move! Ride-sharing apps are another piece of tech that use machine-learning algorithms to create the most optimal ride for customers. Uber, for example, analyzes the data collected from its 5 billion logged trips to determine the most accurate times of arrival, best pick-up locations, appropriate prices, and traffic patterns. The A.I. used in ride-sharing apps isn’t just filtering data to generate best approximations; it is continuously learning as it goes to improve with each new ride.


Financial institutions are swiftly becoming more and more A.I.-driven as they try to stay relevant in their market and improve their customer service functions. Mobile banking apps provide the perfect platform for them to roll out different types of A.I., including natural language processing, virtual assistance, and robotic process automation. The various artificial intelligences work within the apps to do simple things like send customers reminders, process transactions (faster and with less error than could a human), and use voice technology; or, to perform more advanced functions like providing customer service support, financial planning recommendations, or investment advice.



Google Translate is a valuable tool that breaks language barriers for about half a billion people on a daily basis. But what started in 2006 as just an algorithm has developed into the one of the most widely used artificial intelligence programs in the world. In 2016, Google introduced a “neural machine translation” to its translation app, a system that processes entire sentences at once (instead of word by word) and uses artificial intelligence to improve its translations over time. By utilizing broad context, the translation A.I. determines the most relevant translations, and then adapts them to mimic the way a real person would speak. Google Translate not only learns these speaking patterns and words, but remembers them for its next translation, in order to continually improve its ability to talk like a real human. Even more impressive (and pretty intimidating) is that Google Translate’s artificial intelligence has the ability to perform this function in 103 different languages. 



Those fun little icons on your phone keyboard are more than just modern-day hieroglyphics. Since emoji was first rolled out as a feature, it’s been steadily bolstered by A.I. tech to start learning to ”speak” emoji like a language, and predict what emoji (as well as GIFs and stickers) you may want to use based on your digital conversations. Emoji apps like Dango use a form of artificial intelligence called “deep learning” to understand the nuances of human emotion, and predict emoji based on what you communicate through the words you type. This type of predictive machine learning is meant to make it easier to add emoji into your texts and posts instead of having to scroll for the applicable icons you want to use. Though this A.I. sounds like a simple program that regulates something as innocuous as an emoji, at its core it is a technology that is actually learning, reacting, and adapting to your feelings.


For those who can’t stand spending minutes to hours on the phone waiting for customer service help (and that’s everyone), an increasingly available option is to use “Chat” help on a company’s website to resolve an issue or ask questions. But also increasing with this feature is the probability that you are not communicating with a flesh-and-blood person. Instead, you are more than likely chatting with an artificially intelligent chat bot. These chat bots are not just automatic responders that parrot out pre-programmed dialogue. They actually extract information from your conversation, and use a “natural language processing” program to learn how to 1) respond appropriately to your request, and 2) do so like they are actually a real live person. The highly sophisticated A.I. these chat bots utilize means they are learning to understand and respond to natural language, and they’re getting better at it every day.


It may be no big surprise that Netflix utilizes artificial intelligence. How else would they know which shows we shamelessly binge like no one is watching us, and offer suggestions about what to view next? But it may be surprising to find out just how deep their artificial intelligence goes.

Netflix does indeed use machine learning A.I. to analyze customers’ viewing choices and make the most applicable movie and show recommendations to enhance user experience. In 2013, Netflix claimed “there are 33 million different versions of Netflix” - meaning that each and every viewer (there were 33 million at the time; now there are over 130 million) was receiving a completely unique, personalized viewing experience based on their own preferences. Netflix determined those preferences by using A.I. to learn from millions of ratings, searches and plays each day, as well as the history of billions of hours of content streamed every month.

Screen Shot 2019-02-06 at 5.13.15 PM.png

The streaming service juggernaut is also now working with A.I. tech to crack down on paying users who are sharing their accounts with non-paying individuals. Using a machine learning system developed by Synamedia, Netflix has started analyzing account activity to recognize unusual patterns, such as account details being used in two locations within similar time periods, to pinpoint these offending account sharers. This artificial intelligence should, allegedly, be able to learn so much about customer activity that it will even be able to determine if a customer is at home or away on a vacation when they access their Netflix account.

Finally, Netflix is prepared to use artificial intelligence not only to curate entertainment, but to create it. By using algorithms and studying six years of user content consumption, Netflix has already engineered the creation of the once wildly popular show “House of Cards.” Since then, Netflix has increasingly used this formula to make new shows and movies, achieving success rates of 80% compared to 30% - 40% success rates of traditional TV shows. Given time, Netflix may also be using sophisticated A.I. programs to collaborate on scripts, CGI, and video editing. Already they are so advanced in their A.I. systems that The New York Times has claimed, “Netflix is commissioning original content because it knows what people want before they do.”

It’s a very Netflix-and-chilling thought.


Not all facets of healthcare utilize artificial intelligence, nor is A.I.-based healthcare widely available for public use. But using artificial intelligence in medicine may become a more accepted practice as it continues to learn and develop. Hanover, a machine learning program created by Microsoft, is already assisting doctors to accurately diagnose forms of cancer and recommend various cancer treatments. Project Hanover is currently being developed to:

  • Use natural language processing to build genome-scale knowledge bases by automatically reading millions of biomedical articles.

  • Offer cancer decision support by developing A.I. technology for cancer precision treatment and personalized drug combinations.

  • Provide chronic disease management by developing predictive and preventive A.I. technology for more personalized medicine.

These advancements using artificial intelligence could make it possible to treat or prevent cancer more accurately and efficiently than by human intervention alone. But conspiracy theorists and all those leery of A.I. may wonder what kind of slippery slope we are treading if, or when, humans eventually come to rely on intelligent machines to weigh their healthcare options.


Screen Shot 2019-02-06 at 5.52.13 PM.png

It sounds almost impossible and a bit far fetched, but it’s becoming increasingly real: artificial intelligence is writing the news. Although the practice is still not as widely used as other forms of A.I., there are several ways in which the news has been utilizing machine intelligence to write stories.

Automated routine reporting is a process that uses A.I. to generate reports on topics like sports statistics or corporate earnings, and also summarize long articles into short info pieces to share on social media. A.I. news-writing is also being used to generate and distribute information faster, because it has the ability to instantly react to real-time data with the outlines of a story. For example, a quarterly report released by a large mutual fund may take a small team of portfolio managers weeks to draft. Using machine learning, that same report can now be prepared by A.I. in seconds. Reuters, one of the largest news providers, has already partnered with Graphiq, a service that uses A.I. to build and update data visualizations. That tool enables faster access to data, and once it is embedded in a news story, the visualizations are updated in real time. Other artificial intelligence programs, like WordSmith (used by Twitter) turn structured data into compelling text that is almost indistinguishable from one written by a human author.

In limited capacities, A.I. algorithms have proven they are fully able to generate fact-based articles and news stories. Whether they will become intelligent enough to match  actual journalists in both creativity and accuracy - or whether they will become congested with too much information and start churning out infamous “fake news” stories - still remains to be seen. 

From apps to customer service to healthcare decisions, machine learning is at once a pervasive and pragmatic part of how we’ve all come to function. That we as humans rely so much on thinking machines to live our daily lives is something that spells a technological victory to some and an impending downfall to others (with just a touch of irony all around). Although artificial intelligence is already all around us, its constant advancement means it is going to continue to shape our present and our future - probably both for better and for worse.


Did any of these common A.I. items surprise you? What other forms of everyday artificial intelligence could be added to the list? Comment below!

Making It Rain: How Scientists in the '50s May Have Inadvertently Killed 35 People

In August 1952, the village of Lynmouth in Devon, Britain was struck by one of the worst floods the country has ever seen. Thirty-five people lost their lives when 90 million tons of water and thousands of tons of rocks battered the town. At the time, the disaster was classified as "the hand of God," and locals were left to clean up the very messy aftermath.

However, in the early 2000s - nearly 50 years after the devastating flood - intel emerged that suggests the disaster may have occurred at the hands of scientists rather than the hand of God. Declassified documents allege that the Royal Air Force staged cloud-seeding experiments in the Lynmouth area between 1949 and 1955, resulting in torrents of rain - and in 1952, 35 deaths. 

Although the RAF originally denied conducting any such weather-controlling experiments prior to 1954, Alan Yates, a senior lecturer at Cranfield College of Aeronautics and a glider pilot, claims the scientists asked him to assist them in a cloud-seeding project named Operation Cumulus. According to Yates, the project was successful in producing artificial rain, creating heavy downpours some 50 miles away from Devon. On the night of August 15, 1952, nine inches of rain fell in Lynmouth, swelling two rivers until they broke their banks and flooded the village.  And the rest, as they say, is history.

Or is it? While the Ministry of Defence claims there is no evidence that RAF scientists were responsible for initiating the Lynmouth floods, this is not the first time that "weather control" conspiracies have been theorized over. Governments have been implementing cloud-seeding for decades since the artificial rain-making method was discovered in 1946, and conspiracy theories about weather control have abounded for nearly as long. Although manipulating the weather sounds like a science fiction experiment, the declassified documents about Lynmouth's 1952 disaster suggest that weather manipulation is more science than fiction, and steadily continues to become a reality. As recently as May 2018, Forbes reported that China was launching a weather control initiative that would cover a land mass the size of Alaska.

With technological advances progressing on practically a daily basis, how far-fetched is it to believe that governments and other higher powers may have the ability, after more than 50 years of supposed experimentation, to alter weather? With recent meteorological disasters like Hurricanes Harvey, Irma, and Maria laying waste to multiple countries in quick succession, at what point do we question whether such events are the results of the hand of God, or of the hands of government scientists playing God? 

Read about the Lynmouth flood disaster and RAF rainmaking experiment HERE.