Connect with us

AI

Google-led paper pushes back against claims of AI inefficiency

Avatar

Published

on

Join Transform 2021 this July 12-16. Register for the AI event of the year.


Google this week pushed back against claims by earlier research that large AI models can contribute significantly to carbon emissions. In a paper coauthored by Google AI chief scientist Jeff Dean, researchers at the company say that the choice of model, datacenter, and processor can reduce carbon footprint by up to 100 times and that “misunderstandings” about the model lifecycle contributed to “miscalculations” in impact estimates.

Carbon dioxide, methane, and nitrous oxide levels are at the highest they’ve been in the last 800,000 years. Together with other drivers, greenhouse gases likely catalyzed the global warming that’s been observed since the mid-20th century. It’s widely believed that machine learning models, too, have contributed to the adverse environmental trend. That’s because they require a substantial amount of computational resources and energy — models are routinely trained for thousands of hours on specialized hardware accelerators in datacenters estimated to use 200 terawatt-hours per year. The average U.S. home consumes about 10,000 kilowatt-hours per year, a fraction of that total.

In June 2020, researchers at the University of Massachusetts at Amherst released a report estimating that the amount of power required for training and searching a certain model involves the emissions of roughly 626,000 pounds of carbon dioxide, equivalent to nearly 5 times the lifetime emissions of the average U.S. car. Separately, leading AI researcher Timnit Gebru coauthored a paper that spotlights the impact of large language models’ carbon footprint on marginalized communities.

Gebru, who was fired from her position on an AI ethics team at Google in what she claims was retaliation, was told her work didn’t meet Google’s criteria for publication because it lacked reference to recent research. In an email, Dean accused Gebru and the study’s other coauthors of disregarding advances that have shown greater efficiencies in training and might mitigate carbon impact.

This latest Google-led research, which was conducted with University of California, Berkeley researchers and focuses on natural language model training, defines the footprint of a model as a function of several variables. They include the choice of algorithm, the program that implements it, the number of processors that run the program, the speed and power of those processors, a datacenter’s efficiency in delivering power and cooling the processors, and the energy supply mix — for example, renewable, gas, or coal.

The coauthors argue that Google engineers are often improving the quality of existing models rather than starting from scratch, which minimizes the environmental impact of training. For example, the papers suggests that Google’s Evolved Transformer model, an improvement upon the Transformer, uses 1.6 times fewer floating point operations per second (FLOPS) and takes 1.1 to 1.3 times less training time. Another improvement — sparse activation — leads to 55 times less energy usage and reduces net carbon emissions by around 130 times compared with “dense” alternatives, according to the researchers.

The paper also makes the claim that Google’s custom AI processors, called tensor processing units (TPUs), enable energy savings in the cloud far greater than previous research has acknowledged. The average cloud datacenter is roughly twice as energy efficient as an enterprise datacenter, the coauthors posit, pointing to a recent paper in Science that found that global datacenter energy consumption increased by only 6% compared with 2010, despite computing capacity increasing by 550% over the same time period.

Earlier studies, the paper says, made incorrect assumptions about model training approaches like neural architecture search, which automates the design of systems by finding the best model for a particular task. One energy consumption estimate for Evolve Transformers ended up 18.7 times “too high” and 88 times off in emissions, in the Google-led research team’s estimation. And publicly available calculators like ML Emissions and Green Algorithms estimate gross carbon dioxide emissions as opposed to net emissions, which could be up to 10 times lower, the paper says.

“Reviewers of early [research] suggested that … any tasks run in a green datacenter simply shift other work to dirtier datacenters, so there is no net gain,” the coauthors wrote. “It’s not true, but that speculation reveals many seemingly plausible but incorrect fallacies: datacenters are fully utilized, cloud centers can’t grow, renewable energy is fixed and can’t grow, Google … model training competes with other tasks in the datacenter, training must run in all datacenters, [and] there is no business reason to reduce carbon emissions.”

The coauthors evaluated the energy usage and carbon emissions of five recent large natural language processing models, using their own formulas for the calculations. They concluded that:

  • T5, Google’s pretrained language model, used 86 megawatts and produced 47 metric tons of carbon dioxide emissions
  • Meena, Google’s multiturn, open-domain chatbot, used 232 megawatts and produced 96 metric tons of carbon dioxide emissions
  • GShard, a Google-developed language translation framework, used 24 megawatts and produced 4.3 metric tons of carbon dioxide emissions.
  • Switch Transformer, a Google-developed routing algorithm, used 179 megawatts and produced 59 metric tons of carbon dioxide emissions
  • GPT-3, OpenAI’s sophisticated natural language model, used 1,287 megawatts and produced metric 552 metric tons of carbon dioxide emissions

“We believe machine learning papers requiring large computational resources should make energy consumption and carbon dioxide emissions explicit when practical,” the coauthors wrote. “We are working to be more transparent about energy use and carbon dioxide emissions in our future research. To help reduce the carbon footprint of machine learning, we believe energy usage and carbon dioxide emissions should be a key metric in evaluating models.”

Conflict of interest

The thoroughness of the paper belies the conflict of Google’s commercial interests with viewpoints expressed in third-party research. Many of the models the company develops power customer-facing products, including Cloud Translation API and Natural Language API. Revenue from Google Cloud, Google’s cloud division that includes its managed AI services, jumped nearly 46% year-over-year in Q1 2021 to $4.04 billion.

While the Google-led research disputes this, at least one study shows that the amount of compute used to train the largest models for natural language processing and other applications has increased 300,000 times in 6 years — a higher pace than Moore’s law. The coauthors of a recent MIT study say that this suggests that deep learning is approaching its computational limits. “We do not anticipate [meeting] the computational requirements implied by the targets … The hardware, environmental, and monetary costs would be prohibitive,” the MIT coauthors said.

Even if the Google-led paper’s figures are taken at face value, the training of Google’s models produced a total of over 200 metric tons of carbon dioxide emissions. That’s equivalent to average greenhouse gas emissions from roughly 43 cars or 24 homes over the course of the year. Matching the threshold of emissions reached by training OpenAI’s GPT-3 alone would require driving a passenger vehicle just over 1.3 million miles.

It’s been established that impoverished groups are more likely to experience significant environmental-related health issues, with one study out of Yale finding low-income communities and those comprised predominantly of minorities experienced higher exposure to air pollution compared to nearby white neighborhoods. A more recent study from the University of Illinois at Urbana-Champaign shows that Black Americans are subjected to more pollution from every source, including industry, agriculture, all manner of vehicles, construction, residential sources, and even emissions from restaurants.

Gebru’s work notes that while some of the energy supplying datacenters comes from renewable or carbon credit-offset sources, the majority is not sourced from renewable sources, and many sources in the world aren’t carbon neutral. Moreover, renewable energy sources are still costly to the environment, Gebru and coauthors note, and datacenters with increasing computation requirements take away from other potential uses of green energy.

“When we perform a risk/benefit analyses of language technology, we must keep in mind how the risks and benefits are distributed, because they do not accrue to the same people,” Gebru and coauthors wrote. “Is it fair or just to ask, for example, that the residents of the Maldives (likely to be underwater by 2100) or the 800,000 people in Sudan affected by drastic floods pay the environmental price of training and deploying ever-larger English language models, when similar large-scale models aren’t being produced for Dhivehi or Sudanese Arabic?”

The Google-led paper and prior works do align on recommendations to reduce the carbon impact of models, at least on the topic of transparency. As have others, the Google coauthors call on researchers to measure energy usage and carbon dioxide emissions and publish the data in their papers. They also argue that efficiency should be an evaluation criterion for publishing machine learning research on computationally intensive models, as well as accuracy and related metrics. Beyond this, the Google-led paper calls for researchers to publish the amount of accelerator hardware they used and how much time they took to train computationally intensive models.

“When developing a new model, much of the research process involves training many model variants on a training set and performing inference on a small development set. In such a setting, more efficient training procedures can lead to greater savings,” scientists at the Allen Institute for AI, Carnegie Mellon University, and the University of Washington wrote in a recent paper. “[Increasing] the prevalence of ‘green AI’ [can be accomplished] by highlighting its benefits [and] advocating a standard measure of efficiency.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://venturebeat.com/2021/04/29/google-led-paper-pushes-back-against-claims-of-ai-inefficiency/

AI

7 Ways Artificial Intelligence is Improving Healthcare

Avatar

Published

on

Emerging technologies have the potential to completely reshape the healthcare industry and the way people manage their health. In fact, tech innovation in healthcare and the use of artificial intelligence (AI) could provide more convenient, personalized care for patients.

It could also create substantially more value for the industry as a whole—up to $410 billion per year by 2025.

This graphic by RYAH MedTech explores the ways that technology, and more specifically AI, is transforming healthcare.

How is Technology Disrupting the Patient Experience?

Tech innovation is emerging across a wide range of medical applications.

Because of this, AI has the potential to impact every step of a patient’s journey—from early detection, to rehabilitation, and even follow-up appointments.

Here’s a look at each step in the patient journey, and how AI is expected to transform it:

1. Prevention

Wearables and apps track vast amounts of personal data, so in the future, AI could use that information to make health recommendations for patients. For example, AI could track the glucose levels of patients with diabetes to provide personalized, real-time health advice.

2. Early Detection

Devices like smartwatches, biosensors, and fitness trackers can monitor things like heart rate and respiratory patterns. Because of this, health apps could notify users of any abnormalities before conditions become critical.

Wearables could also have a huge impact on fall prevention among seniors. AI-enabled accelerometer bracelets and smart belts could detect early warning signs, such as low grip strength, hydration levels, and muscle mass.

3. Doctors Visits

A variety of smart devices have the potential to provide support for healthcare workers. For instance, voice technology could help transcribe clinical data, which would mean less administrative work for healthcare workers, giving them more time to focus on patient care.

Virtual assistants are expected to take off in the next decade. In fact, the healthcare virtual assistant market is projected to reach USD $2.8 billion by 2027, at a CAGR of 27%.

4. Test Results

Traditionally, test results are analyzed manually, but AI has the potential to automate this process through pattern recognition. This would have a significant impact on infection testing.

5. Surgery / Hospital Visits

Research indicates that the use of robotics in surgery can save lives. In fact, one study found that robot assisted kidney surgeries saw a 52% increase in success rate.

Robotics can also support healthcare workers with repetitive tasks, such as restocking supplies, disinfecting patient rooms, and transporting medical equipment, which gives healthcare workers more time with their patients.

6. Rehabilitation

Personalized apps have significant care management potential. On the patient level, AI-enabled apps could be specifically tailored to individuals to track progress or adjust treatment plans based on real-time patient feedback.

On an industry level, data generated from users may have the potential to reduce costs on research and development, and improve the accuracy of clinical trials.

7. Follow-ups and Remote Monitoring

Virtual nurse apps can help patients stay accountable by consistently monitoring their own progress. This empowers patients by putting the control in their own hands.

This shift in power is already happening—for instance, a recent survey by Deloitte found that more than a third of respondents are willing to use at-home diagnostics, and more than half are comfortable telling their doctor when they disagree with them.

It’s All About the Experience

Through the use of wearables, smart devices, and personalized apps, patients are becoming increasingly more connected, and therefore less dependent on traditional healthcare.

However, as virtual care becomes more common, healthcare workers need to maintain a high quality of care. To do this, virtual training for physicians is critical, along with user-friendly platforms and intentionally designed apps to provide a seamless user experience.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.visualcapitalist.com/7-ways-artificial-intelligence-is-improving-healthcare/

Continue Reading

AI

The ‘Cyber Attacks’ Winter is Coming — straight for small firms in India Inc.

Avatar

Published

on

Cyber intrusions and attacks have increased exponentially over the last decade approximately, exposing sensitive information pertaining to people and businesses, thus disrupting critical operations, and imposing huge liabilities on the economy. 

Cybersecurity is a responsibility that employees and leaders across functions must shoulder simply because it is the gospel truth – you cannot protect what you cannot see. As organizations have shifted to the work-from-home model due to the outbreak of the COVID-19 pandemic, it’s increasingly important to keep your company’s data secure. 

While the pandemic has led to near or complete digitalization of operations amongst financial institutions, it’s also increased the potential for cyberattacks that lead to adverse financial, reputational, and/or regulatory implications for organizations. 

According to Accenture, cybercrime is said to cost businesses $5.2 trillion worldwide within five years. “With 43% of online attacks now aimed at small businesses, a favorite target of high-tech villains, yet only 14% prepared to defend themselves, owners increasingly need to start making high-tech security a top priority,” the report continues.

A recent McAfee study shows global cybercrime costs crossed US$1 trillion dollars in 2020, up almost 50% from 2018.

India too saw an exponential rise in cybersecurity incidents amid the coronavirus pandemic. Information tracked by the Indian Computer Emergency Response Team (CERT-In) showed that cybersecurity attacks saw a four-fold jump in 2018, and recorded an 89 percent growth in 2019.

The government has set up a Cyber Crisis Management Plan for countering cyber-attacks effectively, while also operating the Cyber Swachhta Kendra (Botnet Cleaning and Malware Analysis Centre).

Banks and Financial Institutions (FIs) are some of the highest targeted market sectors. An analysis by Can we hyperlink this: https://www.fitchratings.com/videos/exploring-bank-cybersecurity-risk-13-04-2021?mkt_tok=NzMyLUNLSC03NjcAAAF82rxN_2lbDTsEp4tfBu4tUGP7i6wyb1OGpyNY0Z8lQPhdz9C7KQ-NIriTcJqNSDyb9qfQ_essxS-TdNWMgJesb-RA4yN4t7T-XqXmVfWW4dau36SW6ZE 

“>FitchRatings in collaboration with SecurityScorecard reveals that banks with higher credit ratings exhibited better cybersecurity scores than banks with lower credit ratings. 

Bharti Airtel’s chief executive officer for India, Gopal Vittal, in a letter to the telco’s 307.9 million subscribers, detailed out how Airtel is carrying out home delivery of SIM cards and cautioned subscribers from falling prey to cyber frauds. He cautioned them against the rapid rise in cyber frauds, highly likely via digital payments. “There has been a massive increase in cyber frauds. And as usual, fraudsters are always finding new ways to trick you,” he added in the letter. 

Barcelona-based Glovo, valued at over $1 billion, that delivers everything from food to household supplies to some 10 million users across 20 countries, came under attack recently when the “hacker gained access to a system on April 29 via an old administrator platform but was ejected as soon as the intrusion was detected”, according to the company.

The attack came less than a month after Glovo raised 450 million euros ($541 million) in funding. 

According to Kaspersky’s telemetry, close on the heels of coronavirus-led pandemic and subsequent lockdown in March 2020, saw a total number of meticulously planned attacks against remote desktop protocol (RDP) jumped from 93.1 million worldwide in February 2020 to 277.4 million 2020 in March — a whopping 197 percent increase. In India, the numbers went from 1.3 million in February 2020 to 3.3 million in March 2020. In July 2020, India recorded its highest number of cyberattacks at 4.5 million.

The recent data breach at the payment firm Mobikwik, affected 3.5 million users, exposing Know Your Customer (KYC) documents such as addresses, phone numbers, Aadhaar card details, PAN card numbers, and so on. The company, however, still maintains that there was no such data breach. It was only after the Reserve Bank of India’s intervention that Mobikwik got a forensic audit conducted immediately by a CERT-IN empaneled auditor and submitted the report. 

Security experts have observed a 500% rise in the number of cyber attacks and security breaches and a 3 to 4 times rise in the number of phishing attacks from March until June 2020.

These attacks, however, are not just pertaining to the BFSI sector, but also the healthcare sector, and the education sector.

Image Source: BusinessStandard.com

What motivates hackers to target SMBs? 

Hackers essentially target SMBs because it’s a source of easy money. From inadequate cyber defenses to lower budgets and/or resources, smaller businesses often lack strong security policies, cybersecurity education programs, and more, making them soft targets. 

SMBs can also be a ‘gateway’ to larger organizations. As many SMBs are usually connected electronically to the IT systems of larger partner organizations, it becomes an inroad to the bigger organizations and their data. 

How can companies shield themselves from a potential cyberattack: 

As a response to the rising number of attacks in cyberspace, the Home Ministry of India issued an advisory with suggestions on the prevention of cyber thefts, especially for the large number of people working from home. Organizations and key decision-makers in a company can also create an effective cybersecurity strategy that’s flexible for adaptation in a changing climate too. Here are a few use cases: 

  • CERT-In conducted ‘Black Swan – Cyber Security Breach Tabletop Exercise’, in order to deal with cyber crisis and incidents emerging amid the COVID-19 pandemic, resulting from lowered security controls. 
  • To counter fraudulent behavior in the finance sector, the government is also considering setting up a Computer Emergency Response Team for the Financial Sector or CERT-Fin.
  • Several tech companies have come forth to address cybersecurity threats by building secure systems and software to mitigate issues like these in the foreseeable future. For example, IBM Security has collaborated with HCL Technologies to streamline threat management for clients through a modernized security operation center (SOC) platform called HCL’s Cybersecurity Fusion Centres. 

Some of the ways through which companies can mitigate potential risks include: 

  • Informing users of hacker tactics and possible attacks
  • Establish security rules, create policies, and an incident response plan to cover the entire gamut of their operations
  • Basic security measures such as regularly updating applications and systems
  • Following a two-factor authentication method for accounts and more

While these measures are some of the ways to be on top of your game in the cybersecurity space, they will also help in sound threat detection while helping gain better insights into attacks and prioritizing security alerts so that India is better prepared for an oncoming attack and battling any unforeseen circumstance that might result in huge loss of data, resources and more. 

Coinsmart. Beste Bitcoin-Börse in Europa Source: https://www.mantralabsglobal.com/blog/the-cyber-attacks-winter-is-coming-straight-for-small-firms-in-india-inc/

Continue Reading

AI

Paris-based Shift Technology becomes the latest insurtech unicorn in France after raising €183.2 million

Avatar

Published

on

shift_technology

Shift Technology, the French startup that has created a solution that enables its insurance clients to detect fraudulent claims, is now worth $1 billion after raising its fourth round of funding. The startup, which also operates in the UK and the US, will expand its team of data scientists, particularly in France.

The AI-based insuretech startup recently announced that it had raised $220 million or around €183.2 million in a series D from Advent International, Avenir Growth, Accel, Bessemer Venture Partners, General Catalyst, Iris Capital and Bpifrance. This latest funding should enable it to structure its R&D, while its offer has expanded since its foundation in 2013. Shift Technology initially focused on fraud detection, but the startup now intends to offer a tool capable of managing the entire chain. It also aims to continue its deployment in the UK and the US, strengthened by its recent unicorn status, whereby its valuation now exceeds one billion dollars.

Originally, the startup sought to facilitate the customer compensation process offered by insurers in the event of a claim – water damage, car accident, etc. Described as the number one fear of policyholders by Jeremy Jawish, CEO and co-founder of Shift Technology and, as such, a major issue for their clients. Once this brick was laid, during its first years of existence, the startup decided to go beyond declaration fraud by making its solution a decision-making aid for insurers. They now offer automated closure of claims files and detection of underwriting fraud. These complementary products are already in production with its customers, who are, to date, around one hundred in some 25 countries. This production was made possible thanks to the previous funding round of €53 million in March 2019.

Shift Technology says it has already analysed 2 billion claims on behalf of insurers since its inception. According to CEO Jeremy Jewish, they receive the data provided by insurers, as well as a number of public data about the claimant. Their algorithms read, among other things, the claim declaration before determining whether to file an appeal or carry out a check for money laundering. The Banque Postale has adopted its solution to accelerate the management of claims for its customers. So has the Axa group, which is also a user. For the latter, the aim is to “limit the manual actions that its employees have to carry out. And to satisfy its customers, Shift Technology is counting on its team of data scientists, which it claims to be “the largest in the insurance sector” and which will be further strengthened.

With 350 employees, the company says that recruitment will be the main focus of its investment strategy following its Series D. “We’re going to recruit a lot in France and a little in the US,” says Jérémy Jawish, who also wants to “approach the health insurance sub-sector more aggressively. Shift Technology says it wants to set up “the largest French centre dedicated to artificial intelligence in insurance” with 300 experts by 2023. With an underlying aim, the startup wants to show that “champions are being created in France”. CEO Jérémy Jawish adds that the COVID-19 crisis has had “a big impact” on its activities according, but has not slowed down the pace of its market openings. A pace that should remain fairly steady.

Shift Technology aims to become an international player in its market. To do this, the french company is counting on its ‘unique’ model based on a single vertical – insurance again and again. However, competition, especially in the US, is a key driver for them to stay on top of their game. As a reminder, this Series D round brings the total amount of funds raised by the company since 2013 to $320 million (nearly €267 million).

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.eu-startups.com/2021/05/paris-based-shift-technology-becomes-the-latest-insurtech-unicorn-in-france-after-raising-e183-2-million/

Continue Reading

AI

Paris-based Shift Technology becomes the latest insurtech unicorn in France after raising €183.2 million

Avatar

Published

on

shift_technology

Shift Technology, the French startup that has created a solution that enables its insurance clients to detect fraudulent claims, is now worth $1 billion after raising its fourth round of funding. The startup, which also operates in the UK and the US, will expand its team of data scientists, particularly in France.

The AI-based insuretech startup recently announced that it had raised $220 million or around €183.2 million in a series D from Advent International, Avenir Growth, Accel, Bessemer Venture Partners, General Catalyst, Iris Capital and Bpifrance. This latest funding should enable it to structure its R&D, while its offer has expanded since its foundation in 2013. Shift Technology initially focused on fraud detection, but the startup now intends to offer a tool capable of managing the entire chain. It also aims to continue its deployment in the UK and the US, strengthened by its recent unicorn status, whereby its valuation now exceeds one billion dollars.

Originally, the startup sought to facilitate the customer compensation process offered by insurers in the event of a claim – water damage, car accident, etc. Described as the number one fear of policyholders by Jeremy Jawish, CEO and co-founder of Shift Technology and, as such, a major issue for their clients. Once this brick was laid, during its first years of existence, the startup decided to go beyond declaration fraud by making its solution a decision-making aid for insurers. They now offer automated closure of claims files and detection of underwriting fraud. These complementary products are already in production with its customers, who are, to date, around one hundred in some 25 countries. This production was made possible thanks to the previous funding round of €53 million in March 2019.

Shift Technology says it has already analysed 2 billion claims on behalf of insurers since its inception. According to CEO Jeremy Jewish, they receive the data provided by insurers, as well as a number of public data about the claimant. Their algorithms read, among other things, the claim declaration before determining whether to file an appeal or carry out a check for money laundering. The Banque Postale has adopted its solution to accelerate the management of claims for its customers. So has the Axa group, which is also a user. For the latter, the aim is to “limit the manual actions that its employees have to carry out. And to satisfy its customers, Shift Technology is counting on its team of data scientists, which it claims to be “the largest in the insurance sector” and which will be further strengthened.

With 350 employees, the company says that recruitment will be the main focus of its investment strategy following its Series D. “We’re going to recruit a lot in France and a little in the US,” says Jérémy Jawish, who also wants to “approach the health insurance sub-sector more aggressively. Shift Technology says it wants to set up “the largest French centre dedicated to artificial intelligence in insurance” with 300 experts by 2023. With an underlying aim, the startup wants to show that “champions are being created in France”. CEO Jérémy Jawish adds that the COVID-19 crisis has had “a big impact” on its activities according, but has not slowed down the pace of its market openings. A pace that should remain fairly steady.

Shift Technology aims to become an international player in its market. To do this, the french company is counting on its ‘unique’ model based on a single vertical – insurance again and again. However, competition, especially in the US, is a key driver for them to stay on top of their game. As a reminder, this Series D round brings the total amount of funds raised by the company since 2013 to $320 million (nearly €267 million).

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.eu-startups.com/2021/05/paris-based-shift-technology-becomes-the-latest-insurtech-unicorn-in-france-after-raising-e183-2-million/

Continue Reading
Blockchain3 days ago

The Reason for Ethereum’s Recent Rally to ATH According to Changpeng Zhao

Aviation4 days ago

American Airlines Passenger Arrested After Alleged Crew Attack

Blockchain2 days ago

Chiliz Price Prediction 2021-2025: $1.76 By the End of 2025

Blockchain3 days ago

Mining Bitcoin: How to Mine Bitcoin

Blockchain3 days ago

Mining Bitcoin: How to Mine Bitcoin

Fintech4 days ago

Talking Fintech: Customer Experience and the Productivity Revolution

Fintech5 days ago

CGS-CIMB Issued S$150-Million Commercial Paper in Digital Securities on iSTOX

Blockchain5 days ago

Ruffer Investment Sold Bitcoin Holdings After Elon Musk’s Bullish Tweets

PR Newswire2 days ago

Teamsters Lead Historic Defeat of CEO Pay at Marathon Petroleum

Blockchain4 days ago

Bitcoin Gains Bullish Momentum, Signals Another Major Rally

Blockchain5 days ago

Weekly Wrap-up: Ether Breaks Past $3K – New All Time High (May 3, 2021)

Blockchain5 days ago

Ethereum Market Capital Overtakes Bank of America

Aviation5 days ago

Lufthansa To Equip Entire Boeing 777F Fleet With Sharkskin Technology

Cyber Security5 days ago

Where To Pirate Audio Books ? | 10 Best Audiobook Torrenting Sites 2021

Blockchain3 days ago

Mining Bitcoin: How to Mine Bitcoin

Startups5 days ago

Equity Monday: TechCrunch goes Yahoo while welding robots raise $56M

Cyber Security4 days ago

Alaska Court System Temporarily Disconnected the Internet After a Cybersecurity Threat

Start Ups5 days ago

British events startup FIXR raises €7.4 million and prepares to welcome back nightlife

AR/VR1 day ago

Apple is giving a laser company that builds some of its AR tech $410 million

Blockchain5 days ago

Ripple Releases $1.6 Billion XRP from Escrow Account

Trending