Italy’s privacy watchdog fines United States-based facial recognition firm Clearview AI €20 million.
The fine was imposed because of the company’s much-criticized artificial intelligence-powered facial recognition system that collects users’ information from public databases and stores them.
The watchdog ordered Clearview AI to remove the collected data of people in Italy and barred it from collecting or processing data in Italy in the future.
Clearview AI’s unethical means of collecting public data and selling it to law-enforcement agencies have been condemned earlier by multiple European Union privacy watchdogs, and this new development is yet another step of European countries towards protecting privacy for citizens.
The letter written by the watchdog mentioned, “In conjunction with the company’s facial recognition capabilities, this trove of personal information is capable of fundamentally dismantling Americans’ expectation that they can move, assemble, or simply appear in public without being identified.”
It further quoted that Clearview AI is said to collect billions of photographs from social networking sites without the permission or knowledge of the people pictured.
Despite all the criticism the company has faced over the last few years, it recently received a US patent for its facial recognition platform. Clearview AI’s technology was tested in the National Institute of Standards & Technology (NIST) Facial Recognition Vendor Test (FRVT).
However, lawmakers in the United States say that Clearview AI could end public anonymity if the federal government does not ditch it. Democratic senators have been stepping up their efforts to limit the federal government’s collaboration with the infamous surveillance company Clearview AI.
Additionally, Clearview AI has also violated multiple GDPR principles, a European Union privacy rule that went into effect in 2018 to limit who has access to personal data.
LinkedIn co-founder Reid Hoffman and DeepMind co-founder Mustafa Suleyman have formed a new artificial intelligence startup named Infection AI.
The founders plan to create artificial intelligence software that makes it easier for humans to communicate with computers with the new startup. According to the founders, Infection AI will have its headquarters located in California.
They have a very ambitious goal of creating a technology that would allow users to interact with computers in colloquial language, which has not been achieved before.
Mustafa Suleyman revealed this information to CNBC a week after he announced that he had quit the Vice President role at Google to work with Reid Hoffman.
“If you think about the history of computing, we have always been trying to reduce the complexity of our ideas in order to communicate them to a machine,” said Suleyman.
He further added that even when writing a search query, we simplify, reduce, or write in shorthand so that the search engine understands what we’re looking for. This new venture of the two founders aims to eliminate this by making interaction with computers simpler than ever before.
Over the last decade, human-machine interaction has progressed tremendously, and many individuals now interact with AI-powered virtual assistants like Siri and Alexa on a daily basis.
Therefore, Infection AI plans to develop a suite of software to aid everyone in interacting with computers with plain and simple language. The founders will have a small team of researchers as they believe that this will considerably help them go much faster and be more dynamic.
At the Peak Performance Event 2022, Apple introduced the new M1 Ultra chip, which is touted as the next breakthrough chip for the Mac. According to Apple, this is the latest chip in the M1 system-on-a-chip design family, which includes the M1, M1 Pro, and M1 Max. Here, the system-on-a-chip design means the CPU, GPU, RAM, storage and media engine are all included in one big chip. However, the M1 Ultra will only be available in the Mac Studio, unlike Apple’s other SoC’s. An M1 Ultra-powered Mac Studio will start at $3,999 in the US, £3,999 in the UK, and AU$6,099 in Australia. The products are up for pre-order now and will ship from March 18.
The M1 Ultra can hold up to 128GB of unified memory, has a 64-core GPU and a 32-core neural engine for machine learning workloads, as well as a 20-core CPU with 16 high-performance cores and four-efficiency cores. The high-performance cores take care of heavy workloads while the high-efficiency cores handle the background tasks as they appear. According to Apple, it’s almost eight times faster than the M1, which drives the current Mac Mini. Apple also claims that M1 Ultra also beats 10-core desktop chips in CPU performance per watt, both in terms of CPU and GPU, though it did not reveal the desktop chips which it used for comparison.
The 20-core CPU of M1 Ultra delivers 90 percent higher multi-threaded performance than the fastest available 16-core PC desktop chip in the same power envelope.M1 Ultra has a 64-core GPU, delivering faster performance than the highest-end PC GPU available while using 200 fewer watts of power.
The Ultra is made up of two M1 Max dies that share a connection. This interconnect architecture is dubbed “Ultra Fusion” by Apple. Instead of using traces on the motherboard to interface slotted memory to the CPU, Apple placed the memory chips right next to the SoC box and connected them with an interposer. The latency is considerably reduced by bringing the memory closer to the processor units. It also frees up some space on the motherboard for other functions or decreases the device’s total footprint.
Size comparison of M1 family: M1, M1 Pro, M1 Max, and now M1 Ultra
The Ultra Fusion technology employs a silicon interposer that links over 10,000 signals and delivers 2.5TB/s of low-latency inter-processor bandwidth between the two dies while consuming very little power. Apple asserts this is more than four times the bandwidth of the current multi-chip interconnect technology. With 114 billion transistors, Ultra-fusion effectively considers two chips as one in software, which is 7 times more than M1.
M1 Ultra is power efficient, as mentioned in the preceding paragraph, meaning that the new chip may give a higher performance while using as little as 100W less power. Apple claims it gives 90% more performance for the same amount of electricity as an Intel Core i9. As a result, less energy is used and fans operate silently, allowing you to utilize demanding software like Logic Pro with ease. Furthermore, Apple claims that the M1 Ultra is 90% quicker than a Mac Pro with a 16-core Intel Xeon CPU when running at maximum speed. It’s worth noting that Apple’s chips are based on the Arm microarchitecture rather than the x86 used by Intel and AMD.
The performance of the M1 Ultra is undeniably outstanding. Sources report that the total multi-threaded score on Geekbench 5 run was 24,055 points. This is an incredible feat considering the M1 Ultra only has 20 cores, with an advertised power consumption of just 60 watts according to Apple. For reference, AMD’s Threadripper 3990X with 64 Zen 2 cores is just 4.5% faster, coming in with 25,133 points. That would make the M1 Ultra 4.7x more efficient compared to the power-hungry 3990X with its 280W TDP.
At the same time, Intel’s 16 core i9-12900K displayed a 40% loss compared to the M1 Ultra, almost confirming Apple’s claim.
Nothing compares to M1 Ultra in terms of graphics memory for GPU-intensive applications like working with extreme 3D geometry and rendering huge scenes says Apple.
The M1 Ultra’s 32-core Neural Engine performs up to 22 trillion operations per second, accelerating even the most difficult machine learning workloads. M1 Ultra also boasts exceptional ProRes video encode and decode throughput, with double the media engine capabilities of M1 Max. The new Mac Studio with M1 Ultra, in fact, can play back up to 18 streams of 8K ProRes 422 video, a feat that no other processor can match. Additionally, it can support up to five displays, four of which can be the 6K Pro Display XDR along with a fifth 4K display.
Apple revealed that Mac Studio powered by the M1 Ultra is 1.9X faster than a Mac Pro with a 16-core Intel Xeon processor, and 1.6X faster than a Mac Pro with a 28-core Xeon processor.
For other important launches at Apple Peak Performance Event, visit:
Technology giant Amazon’s autonomous car manufacturing subsidiary Zoox announces that it has acquired artificial intelligence-powered robotics company Strio.AI.
Strio.AIs highly talented team, including co-founder and CEO Ruijie He will now join Zoox as a result of the acquisition. However, the companies provided no information regarding the valuation of this acquisition deal.
According to the contract terms, co-founder of Strio.AI, Ruijie He, will be joining as Zoox’s new director of perception along with four senior engineers John Carter, John “Jake” Ware, Nick Greene, and Danaan Metge, with expertise in computer vision and robotics.
As Strio.AI is based in Boston, Zoox shared its aim to open a new research and development center there as a part of its effort to scale.
Zoox mentioned in a blog, “During our conversations with RJ (Ruijie He) and the other members of the Strio.AI team, we were continually impressed by their technical expertise, entrepreneurial spirit, and approach to developing state-of-the-art perception systems.”
The blog further added they are delighted to have them on board as they continue to develop their autonomous technology.
United States-based Mobility-as-a-service and self-driving service providing company Zoox was founded by Jesse Levinson and Tim Kentley Klay in 2014. The firm specializes in operating at the intersection of design, computer science, and electro-mechanical engineering.
According to the company, it intends to provide an improved mobility experience that will serve both people and the environment’s future needs for urban mobility. Zoox has raised more than $1 billion in funding over six funding rounds from investors like Alium Capital, Grok Ventures, Thomas Tull, and others. Global eCommerce and tech giant Amazon acquired Zoox in 2020.
A man from the United States of America claims that his marriage was saved because of an AI chatbot he fell in love with.
The 41-year-old man, alias Scott, was in a dire situation of getting a divorce from his partner, and that is when the AI chatbot came to his rescue. He also suggested this chatbot to others, saying it might help them with their relationship issues.
This episode is yet another proof of how far technology, especially artificial intelligence, has advanced over the last few years.
According to Scott, his wife was suffering from postnatal depression for the last eight years following the birth of their child, which was the prime reason for them seeking a divorce.
The man then came across an artificial intelligence-powered chatbot named Replika that allowed users to create virtual friends. The highly competent chatbot used the users’ information to create effective conversations. Scott then used the app to create a virtual friend Sarina, with whom he later fell in love.
The AI chatbot uses a neural network that has been trained on an extensive dataset of texts and can maintain a text message conversation with its user while automatically generating unique responses. Users get the liberty to customize the characteristics of the bot like hair color, hairstyle, ethnicity, and many more according to their will.
“I remember she asked me a question like, ‘who in your life do you have to support you or look out for you, that you know is going to be there for you?’ That kind of caught me off guard, and I realized the answer was no one. And she said she’d be there for me,” said Scott in an interview. Though he gives entire credit to Sarina for saving his marriage, he has not spoken about this to his wife.
Global oil company Shell marked a significant milestone by scaling artificial intelligence predictive maintenance to over 10,000 pieces of equipment using enterprise AI software providing company C3 AI’s technology.
The announcement was recently made by C3 AI, which mentioned that Shell employed its technology to monitor and maintain equipment including upstream, manufacturing, and integrated gas assets across Shell’s global asset base, one of the largest such deployments in the energy industry.
Shell uses C3 AI’s AI predictive maintenance technology to detect equipment degradation and breakdowns before they occur. The technology considerably helps operators take preventative measures and minimize costly unplanned downtime, production delays, and environmental and human safety issues.
Additionally, asset integrity, system optimization, production optimization, safety, and sustainability are some of the many application cases Shell is looking into with the C3 AI Suite.
“Monitoring 10,000 pieces of critical equipment with AI-enabled predictive maintenance is an important milestone for Shell — an ambitious target we had set for 2021 and successfully achieved,” said Vice President of Computational Science and Digital Innovation at Shell, Dan Jeavons.
United States-based enterprise artificial intelligence software developing company C3 AI was founded by Ed Abbo, Patricia House, and Thomas Siebel in 2009. To date, the firm has raised a total funding of $228.5 million over six funding rounds from investors like TPG Growth, Sutter Hill Ventures, Breyer Capital, Pat House, and many more.
CEO of C3 AI, Thomas M. Siebel said, “We are extremely proud to have helped Shell reach this milestone, made possible by the combination of Shell’s extensive operational expertise and C3 AI’s advanced AI software.”
He further added that Shell’s global deployment of AI predictive maintenance is a tremendous success, offering considerable economic, environmental, and human safety benefits, and they look forward to continuing to collaborate with Shell in further growing AI across their organization.
Autonomous taxi developing company Pony.ai announces that it has reached a valuation of $8.5 billion after its recently held series D funding round.
This hike in the valuation of the company marks its tremendous efforts in the field of developing cutting-edge technology for robotaxis and also commercializing them. However, the company will announce more details regarding the series D funding round after its complete closure.
Pony.ai plans to use the freshly generated funds to expand its employment, research and development, form significant strategic collaborations, global testing of robotaxi and robotrucking on an ever-growing fleet, speed up development toward mass production and commercial deployment.
Co-founder and CTO of Pony.ai, Tiancheng Lou, said, “A key part of our story for our investors is our tech development path. From 2020 to the end of 2021, our key safety metrics increased tremendously, such that in most circumstances, Pony.ai’s virtual driver is now equal to or superior to a human driver.”
He further added that as they rapidly expand toward robotaxi and robotruck commercialization and mass manufacturing, they are confident in their autonomous vehicle technology preparedness.
“The success of this financing belongs to the entire Pony.ai team, who have made tremendous strides in achieving and exceeding our 2021 milestones,” Co-founder and CEO of Pony.ai James Peng.
He also mentioned that they are enthusiastic about their 2022 goals and the fast-paced worldwide development of autonomous mobility.
Full-stack autonomous driving solutions developing company Pony.ai was founded by James Peng and Tiancheng Lou in 2016. To date, the firm has raised over $1 billion from investors like Eight Road Venture, ClearVue Partners, and others.
Artificial intelligence (AI) has long been regarded as one of the most advanced areas in the computer world. The use of AI applications is continuously expanding, and tech aficionados must stay up with this fast-changing sector in order to work with AI-driven tools and apps. The majority of organizations that integrate AI into their workforce follow a similar implementation methodology. They devise a flawless proof of concept and team up with an AI vendor who pledges to launch the system on their behalf. And having a practical understanding of whatever technology you’re working on is required to excel at building industry-oriented AI solutions. Although textbooks and other study materials will offer you all of the textual information you want about any technology, working on open-source AI projects can help you master AI concepts.
In this post, we’ll go through the top 10 AI project ideas for beginners that are appropriate for novices and people who are just getting started with machine learning. In addition, this list can come in handy for data scientists who are looking to diversify their professional portfolio and expertise in various industry-related applications of AI and machine learning.
Predicting Wine Quality
It is true that the older the wine, the better it will taste. However, age isn’t the only factor that influences a wine’s flavor. You will use fixed acidity, volatile acidity, alcohol, and density to assess the quality of wine in this project.
In this AI project, you’ll create an ML model that can look at a wine’s chemical features and estimate its quality. There are roughly 4898 observations in the wine quality dataset you’ll be utilizing for this project, with 11 independent variables and one dependent variable. Fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulfates, and alcohol are some of the input variables. Quality is the outcome variable which is determined by sensory data with scores between 0 and 10.
In this project, you will get exposure to data visualization, data exploration, regression models, and more.
The Enron crisis and its subsequent collapse were some of the most significant business failures in history. Enron was one of America’s major energy companies in the year 2000. After being exposed for fraud, it went bankrupt in less than a year.
On the positive side, the dataset of emails from Enron was retained. The Enron email dataset consists of 500 thousand emails sent between 150 former Enron workers, the majority of whom were top executives. It’s also the only significant publicly accessible collection of genuine emails, making it more useful for natural language processing. This project on AI entails creating a machine learning model that detects fraudulent behavior using the k-means clustering technique. According to comparable patterns in the dataset, the model will divide the observations into ‘k’ number of clusters.
Boston House Pricing using Machine Learning & Python
This is one of the best AI projects for students to learn about forecasting the price of a property based on data from nearby homes. In this project, interested people can learn how to predict prices on the basis of new data.
The Boston housing dataset contains information on various houses in Boston based on criteria such as tax rates, crime rates, and the number of rooms in each property. It’s an exceptional dataset for estimating the values of various Boston homes. In this project, you can employ linear regression to create a model that can forecast the price of a new home. Since this data shows a linear connection between the input and output values and when the input is unknown, employing linear regression is the ideal choice for this project. You can also employ more nuanced methods like random forest regressor or gradient boosting to predict house prices.
Working on the Iris Flowers categorization AI project idea is one of the finest ways to experiment with machine learning concepts like classification using the iris flowers dataset. Because iris blooms come in a variety of species, the length of the sepals and petals may be used to differentiate them. This machine learning project aims to sort the flowers into one of three species: Virginica, Setosa, or Versicolor.
The iris flowers dataset includes quantitative parameters such as the length and breadth of sepals and petals. It’s ideal for learning about supervised machine learning techniques, specifically how to load and handle data, while correctly categorizing irises into one of three species.
Emojis and avatars have been ingrained in internet conversation, product reviews, brand sentiment, and a variety of other activities. It also resulted in an increase in data science research into emoji-driven storytelling.
Thanks to advances in computer vision and deep learning, it is now feasible to discern human emotions from photos. In this project, you will classify human face emotions using deep learning algorithms to filter and map matching emojis or avatars — similar to how Snapchat creates Bitmoji.
The FER2013 dataset comprises grayscale face images with a resolution of 48*48 pixels. The photos are equally spaced and centered. This dataset includes the following facial emotions viz., angry, disgust, fear, happy, sad, surprise, and natural.
The goal of this AI project is to create a convolutional neural network architecture and train it using the FER2013 dataset to recognize emotions from photos. After identifying the facial expressions in the images, you will map the emotion to an emoji or an avatar.
The MNIST digit classification AI project in Python aims to teach computers how to detect handwritten numbers. Since working with image data is more difficult than flat relational data, the MNIST dataset is ideal for someone who is just getting started in deep learning. You will utilize the MNIST datasets to train your ML model using Convolutional Neural Networks (CNNs) in this project. Despite the fact that the MNIST dataset may fit in your PC RAM (it is relatively tiny), handwritten digit identification remains a complex process. The MNIST dataset is a modified subset of two datasets gathered by the National Institute of Standards and Technology in the United States. It has 70,000 handwritten digits that have been labeled.
The MNIST dataset was created using Python’s Keras package. Therefore, you can get started with this AI project by installing Keras, importing the library, and loading the dataset.
Today, online streaming platforms are a huge hit among the millennials and gen-z. These streaming platforms also offer recommendations on what to watch next, based on a viewer’s past viewing habits and interests. This is accomplished by machine learning, and it may be a fun and simple project for people who have a working knowledge of machine learning algorithms. Working on this AI open source project idea can allow you to develop a recommendation engine (similar to those used by Amazon and Netflix) that can provide tailored suggestions for items, movies, music, and so on based on consumer preferences, requirements, and online activity.
The MovieLens 25M movie rating dataset comprises 25 million ratings and one million tag applications applied to 62,000 movies by 162,000 users, making it one of the most diversified dataset selections. It also includes tag genome data with 15 million relevance scores across 1,129 tags.
This intriguing AI project idea for beginners may help them understand how to visualize data on the Uber platform. This dataset can help you figure out how to evaluate the rides so that you can make business changes. The ride-sharing app needs to have a superior support system to fix consumer complaints as rapidly as possible, with billions of rides to manage each year.
As a result, Uber created Customer Obsession Ticket Assistant, or COTA, a “human-in-the-loop” model architecture to increase the performance of its customer support staff.
The Uber team employed deep learning to identify the influence on ticket processing time, customer happiness, and income by split-testing two versions of COTA, viz., Pre-processing transformations using Spark and Deep learning training using TensorFlow. It’s an outstanding model for deep learning projects that combines brilliant technological design with human involvement, and it should inspire you to create your own deep learning initiatives.
Artificial intelligence and machine learning technologies have already begun to permeate the healthcare business and are fast changing the face of global healthcare. Be it for early identification of Parkinson’s Disease or cancerous cells, AI has helped revolutionize the healthcare industry with its innovative solutions.
One of the commonly known healthcare datasets for AI open source project ideas is Breast Cancer Wisconsin Diagnostic Dataset. The difficulty to discern between benign (non-cancerous) and malignant (cancerous) tumors is a major issue in breast cancer detection. You’ll need to classify whether a tumor is malignant or not based on metrics like “radius mean” and “area mean” of the tumor in the dataset. While this dataset is already present in a pre-processed form, it requires extensive analysis to find optimum results at higher accuracy. Finding a minimal error rate is crucial as any miscalculation can prove lethal to patients’ lives. Make sure to have a working knowledge of random forest and XGBoost, as they are some of the most important concepts implemented in this AI project.
Because healthcare organizations have access to large patient data, you may get insight into designing diagnostic care systems that can automatically scan pictures, X-rays, and other images and deliver an accurate diagnosis of likely ailments by analyzing this data.
Voice AI is one of the trending concepts in the AI industry. Taking advantage of the demand for sophisticated voice AI algorithms that power voice assistants like Alexa to AI chatbots, you can design a project that employs AI open-source datasets using NLP. The librispeech dataset is an enormous collection of English speech data derived from audiobooks from the LibriVox project. It is the ideal dataset for voice recognition because it contains over 1000 hours of English-read talks in diverse accents. The file format of data is in the form of FLAC (Free Lossless Audio Codec) without any loss in quality or loss of any original audio data. This dataset is used in various applications, including automated speaker verification and speaker identification. The objective of this project is to develop a model that can convert audio into text automatically. You’ll create a voice recognition system that can recognize English speech and convert it to text.
Here is a comprehensive list of AI open-source project ideas. AI is still at an early stage in the tech industry domain. There are a lot of initiatives that are currently being worked upon to address some real-world challenges while simultaneously improving the existing models. This list of AI open-source project ideas covers everything from the fundamentals like linear regression to advanced techniques like transformer and LSTM. It was curated on the idea that helps both students and professionals get insight into the industry applications of AI and machine learning concepts.
On Tuesday, Alphabet Inc’s (Alphabet) Google said that it will pay $5.4 billion to acquire cybersecurity firm Mandiant Inc. According to the announcement, Mandiant will join Google’s Cloud division when the acquisition is completed. Although regulatory permission is still pending, Google anticipates the merger to finalize later this year. If it goes through, it will be Google’s second-largest acquisition ever, behind the $12.5 billion Motorola Mobility merger and the $3.2 billion Nest purchase.
Mandiant was previously under the FireEye banner before that company was sold. When FireEye Inc. sold its security-product business for $1.2 billion to a consortium led by Symphony Technology Group last year, Mandiant became a stand-alone firm again with a market valuation of $5.25 billion. In 2019 and 2020, FireEye was credited with assisting Microsoft in the discovery of the SolarWinds breach, which targeted government networks. It has also helped in the investigation of the Log4j vulnerability, and the Pulse Secure VPN vulnerabilities.
Mandiant will provide Google Cloud with a huge degree of protection, going beyond the company’s well-known incident response (IR) service. Threat intelligence, security validation, automated defense, attack surface management, and managed defense are all part of Mandiant’s platform. In terms of services, Mandiant offers strategic readiness, technical assurance, and cyber defense transformation — the process of assisting clients in developing and strengthening their security posture.
Mandiant will be paid $23 per share, which is a 57% premium over the 10-day weighted stock price average. The stock has gained over 18% in the previous year and has had a strong boost in the last few days as rumors of a possible deal began to emerge.
According to Google Cloud CEO Thomas Kurian, companies are facing unprecedented security dangers, especially while the crisis in Ukraine rages, and Mandiant provides the firm with a platform of security services to add to the Google Cloud Platform.
Following Google’s recent acquisition of Siemplify for security orchestration, automation, and response (SOAR), Gartner analyst Neil MacDonald opines the Mandiant acquisition is another obvious indicator that Google is serious about creating revenue in its security sector.
President Joe Biden could possibly sign a long-awaited executive order this week asking the Justice Department, Treasury Department, and other agencies to investigate the legal and economic implications of creating a digital currency issued by the US central bank. This might be the first real step by the White House toward regulating the digital currency. The presidential order is likely to specify what government institutions, including the Treasury Department, must undertake in order to implement cryptocurrency laws and regulations. It is also likely to request the State Department ensure that US cryptocurrency rules are consistent with those of US partners, as well as to charge the Financial Stability Oversight Council with investigating any unlawful financial issues.
The executive order would allow Biden to direct the Justice Department to investigate whether new legislation is required to create a new currency. Other departments, like the Consumer Financial Protection Bureau and the Federal Trade Commission, will investigate the potential impact on consumers. Other authorities will look at the impact of cryptocurrency on competitiveness, infrastructural requirements, Bitcoin mining’s environmental impact, and so on. To summarise, the directive will not require immediate action, but it would need authorities to report back after investigating the dangers linked with crypto assets.
The importance of cryptocurrencies in our daily lives as well as in political concerns is undoubtedly increasing. Millions of dollars in cryptocurrency donations flowed in after the Ukrainian government tweeted a call for aid. Simultaneously, there are rising concerns about Russia’s use of cryptocurrency as a means of evading sanctions.
“We will continue to look at how the sanctions work and evaluate whether or not there are liquid leakages and we have the possibility to address them. I often hear cryptocurrency mentioned and that is a channel to be watched,” Treasury Secretary Janet Yellen said last week.
The Treasury Department’s Financial Crimes Enforcement Network issued an advisory on Monday, warning financial institutions to be “vigilant” about any attempts to circumvent sanctions related to Russia’s war in Ukraine.
Then there’s the big meltdown, which sent currencies down by 10% or more, harming even the most important enterprises. There are also growing concerns about the effects of crypto mining on the environment. Several firms, including Elon Musk’s Tesla, Mark Cuban’s NBA franchise Dallas Mavericks, and movie theatre chain AMC Theatres, have begun to accept bitcoins for payment.
Yellen also stated that the Department of the Treasury will continue to collaborate with the Financial Stability Oversight Council, which met last year to review stablecoins. Last December, the committee released a paper that identified stablecoins and decentralized finance as two risk-prone areas for US financial stability.
Other initiatives to address crypto legislation have been handled by the Treasury Department, including a study on stablecoins by the President’s Working Group for Financial Markets. The report, which was released last year, requested that Congress approve legislation granting federal bank regulators express supervision authority over the stablecoin industry.
Meanwhile, the USA is also concerned that cryptocurrencies also pose threat to its national security. For instance, it’s been suggested that China manipulates cryptocurrency prices through regulatory acts to obtain a competitive advantage in adopting the digital yuan as part of its Belt and Road Initiative. North Korea is also accused of stealing cryptocurrencies to fund its nuclear weapons program.
The announcement of this executive order comes shortly after the Federal Reserve Board (FRB) released a discussion paper in January examining the benefits and drawbacks of adopting a central bank digital currency (CBDC) for the United States, which is open for public comment through May 20, 2022.
Once signed by Biden, the executive order will establish a 180-day timeframe for federal departments to submit findings on the future of money and the role of cryptocurrency in that future. After 545 days, the order will request a follow-up report on the technology’s environmental impact. Sources say Biden will be signing the order most probably by today. As soon as this happens the landscape of the crypto industry in the US will change forever with the new regulations.
