News, Views & Insights
On June 7, the FBI seized 63.7 bitcoin (BTC), approximately $2.3 million USD, from one of the addresses to which DarkSide’s cluster, described in my earlier post, sent their ransom funds. Normally, this should have been inaccessible to anyone without a private key for that address. The FBI apparently managed, however, to obtain one.
Let me tell you a story about how to trace Russian hackers’ cryptocurrency funds using only public knowledge, some educated guesses and the Wolfram Language.
But first, a little background information.In 2020, Melbourne, Australia, had a 112-day lockdown of the entire city to help stop the spread of COVID-19. The wearing of masks was mandatory and we were limited to one hour a day of outside activity. Otherwise, we were stuck in our homes. This gave me lots of time to look into interesting problems I’d been putting off for years.
I was inspired by a YouTube video by David Oranchak, which looked at the Zodiac Killer’s 340-character cipher (Z340), which is pictured below. This cipher is considered one of the holy grails of cryptography, as at the time the cipher had resisted attacks for 50 years, so any attempts to find a solution were truly a moonshot.
Sound classification can be a hard task, especially when sound samples have small variations that can be imperceptible to the human ear. The use of machines, and recently machine learning models, has been shown to be an effective approach to solving the problem of classifying sounds. These applications can help improve diagnoses and have been a topic of research in areas such as cardiology and pulmonology. Recent innovations such as a convolutional neural network identifying COVID-19 coughs and the MIT AI model detecting asymptomatic COVID-19 infections using cough recordings show some promising results for identifying COVID-19 patients just by the sound of their coughs. Looking at these references, this task may look quite challenging and like something that can be done only by top-notch researchers. In this post, we will discuss how you can get very promising results using the machine learning and audio functionalities in the Wolfram Language.
On December 21, 2020, a visual astronomical spectacle will occur. The planets Jupiter and Saturn will pass so close to each other in the sky that, to the unaided eye, they will be difficult to separate. This is the closest the two planets have come in 397 years; the last time they were this close was July 16, 1623. When Jupiter and Saturn come close to each other in the sky as seen from Earth, this is known as a “great conjunction” and happens about every 20 years or so. But not all great conjunctions are as close as this one. The next great conjunction will be on November 5, 2040, and again on April 10, 2060, but the planets will be a bit over a degree apart, so not as close as the 2020 event. The next comparable event will be on March 15, 2080.
This year has been Wolfram Media’s most productive yet, with five new titles and another published in partnership. While 2020's state and global shutdowns created unprecedented logistical challenges for the Wolfram Media team, I'm really proud of how we pulled together this year's list, with several more books already in production for release next year.
Halloween this year had a surprise up its sleeve. In rare celestial serendipity, the night of costume metamorphosis also featured a full moon, which helped to conjure the spooky mood. Because it might have been the first and last full-moon Halloween that some people witnessed in their lifetime (cue ominous music), I think it was significantly underrated. Moreover, it was the day of a blue moon (the second full moon within a month), but that is not a surprise, as any Halloween’s full moon is always a blue moon. The Moon’s color did not change, though, at least for those away from the smoke of volcanos and forest fires that are capable of turning it visibly blue. To appreciate the science and uniqueness of a full moon this Halloween, I built this visualization that tells the whole story in one picture. This is how I did it.
A few months before I accepted a Wolfram Research internship—around March—I was very fearful, and so was the majority of the world. We knew very little about the novel coronavirus, and the data was just not robust. In addition to the limited data we had, the scientific process necessarily takes time, so even that was not used to its full extent. In a world where not enough data can quickly become data overload, the question didn’t seem to be finding more data, but rather how can one extract useful and meaningful information from the available data?
A worldwide pandemic is definitely stressful, but a worldwide pandemic without accessible and computable information is much more so. Using Wolfram technologies in coordination with several internal teams, I created a Wolfram U course called COVID-19 Data Analysis and Visualization to try and cut through the informational fog and find some clarity. I saw this course as one that gives power to everyone to be able to look at data and gain insight. After all, data is knowledge, and knowledge is power.