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.As part of Wolfram’s core goal of a unified blockchain interface, Wolfram Blockchain Labs (WBL) works to give developers direct Wolfram Language access to a range of blockchains and decentralized technologies. Today, we’re excited to announce a collaboration with IPFS and Filecoin, some of the core building blocks of Web3 (or the “decentralized” web). In addition to Wolfram Language integration with IPFS and the Filecoin blockchain, this unique collaboration lets developers leverage storage, peer-to-peer networking and other protocols to complement their existing applications or new decentralized applications, all from Wolfram technologies such as our Wolfram Language, the Wolfram Cloud and Wolfram Notebooks.
Wolfram Application Server is a new platform developed by Wolfram Research enabling customers to deploy Wolfram Language–powered APIs and webpages into a scalable, highly available enterprise cluster.
Wolfram Application Server lets you:
manage data exchange in your deployments with a robust external services framework. create applications using the Wolfram Natural Language Understanding (NLU) System, the key semantic interpretation technology behind Wolfram|Alpha and Wolfram Language. generate content based on time and location, assign custom endpoints and integrate curated content from the Wolfram Knowledgebase.We have designed Wolfram Application Server for customers who for regulatory, security or business reasons may not wish to deploy onto the Wolfram Cloud but prefer to host their Wolfram Language applications on clusters they control.
As CEO of Wolfram Blockchain Labs (WBL), I think one of the most exciting parts of my job is collaborating with other leaders in the blockchain space to expand tools for developers and business use cases. For several years now, we’ve been adding a steady stream of blockchain functionality into the Wolfram Language to enable development of knowledge-based distributed applications and computational contracts. You may have noticed the growing number of popular blockchains (ARK, Bitcoin, bloxberg, Cardano, Ethereum, MultiChain...) partnering with us and integrating into our platform. It’s already led to some cool explorations, and we have a lot more in the pipeline.
Today, WBL is happy to announce its latest such collaboration, a partnership with TQ Tezos. That includes Tezos blockchain integration in the Wolfram Language, which is great news for smart contract developers and enthusiasts. But that’s just the beginning. Our long-term plans include a lot of big ideas that we think everyone will be excited about!
I enjoy turning mathematical concepts into wearable pieces of art. That’s the idea behind my business, Hanusa Design. I make unique products that feature striking designs inspired by the beauty and precision of mathematics. These pieces are created using the range of functionality in the Wolfram Language. Just in time for Valentine’s Day, we recently launched Spikey earrings in the Wolfram Store, which are available in rose gold–plated brass and red nylon. In this blog, I’ll give a look under the hood and discuss how an idea becomes a product through the Wolfram Language.
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.
心臓病学のような医療分野でも，Wolfram言語は新たな知見獲得や臨床現場における予測に役立っています．最近，金沢大学先進予防医学研究科の中嶋憲一教授がリードする，Wolfram言語の機械学習機能を用いた心不全による死亡リスク予測の研究に参加し，論文が出版されましたのでその概略を紹介します．そこでは，末期心不全(HFD)と重度の不整脈/突然死(ArE)によって引き起こされる心臓死の確率を区別できる分類器を構築することを目指しました.