Machine Learning

Microsoft is developing its own AI chip to power ChatGPT: Report

The software giant is reportedly developing its own machine learning chips to power AI projects for OpenAI and its own internal teams.

Microsoft has secretly been developing its own artificial intelligence (AI) chips to deal with the rising costs of development for in-house and OpenAI projects, per a report from The Information.

Reportedly in the works since 2019, Microsoft’s recently revealed hardware venture appears to be designed to reduce the Redmond, Washington company’s reliance on Nvidia’s GPUs.

A Google search reveals that the Nvidia H100, one of the more popular GPUs for training machine learning systems, costs as much as $40,000 on reseller services such as eBay amid increasing market scarcity.

These high costs have pushed several Big Tech companies to develop their hardware, with Meta, Google and Amazon all developing machine-learning chips over the past few years.

Details remain scarce as Microsoft hasn’t officially commented yet, but The Information’s report claims that the chips are being developed under the code name “Athena” — perhaps a nod to the Greek goddess of war, as the generative AI arms race continues to heat up.

Related: Italy ChatGPT ban: Data watchdog demands transparency to lift restriction

The report also mentions that the new chips are already being tested by team members from Microsoft’s internal machine-learning staff and OpenAI’s developers.

While one can only speculate at this time as to how OpenAI intends to use Microsoft’s AI chips, the company’s co-founder and CEO, Sam Altman, recently told a crowd at the Massachusetts Institute of Technology that the infrastructure and design that got the company from GPT-1 to GPT-4 is “played out” and will need to be rethought:

“I think we’re at the end of the era where it’s going to be these, like, giant, giant models. We’ll make them better in other ways.”

This comes on the heels of a busy news cycle for the AI sector, with Amazon recently entering the arena as a (somewhat) new challenger with its first self-developed models leaping onto the scene as part of its Bedrock AI infrastructure rollout.

And, on April 17, tech mogul and world’s richest person Elon Musk announced the impending launch of TruthGPT, a supposed “truth-seeking” large language model designed to take on ChatGPT’s alleged left-wing bias, during an interview with Fox News’ Tucker Carlson.

9 Tech YouTube channels to follow

Discover nine tech-focused YouTube channels covering topics such as programming, machine learning, cybersecurity, blockchain and Web3.

Learning tech via YouTube channels can be a great way to supplement traditional learning methods, as it provides a more interactive and engaging experience. Many YouTube channels dedicated to tech provide in-depth tutorials and explanations of complex concepts in a way that is easy to understand, making it accessible to learners of all skill levels.

Additionally, YouTube channels often provide access to industry experts, giving learners the opportunity to learn from individuals with real-world experience and knowledge. For instance, Cointelegraph’s YouTube channel provides news, interviews and analysis on the latest developments in the cryptocurrency and blockchain industries. The channel’s content is well-produced and features engaging visuals, making it an accessible and entertaining way to learn about these topics.

Here are nine other YouTube channels to follow and learn beyond cryptocurrencies.

Ivan on Tech 

Ivan on Tech is a popular YouTube channel focused on blockchain technology, cryptocurrencies and decentralized applications (DApps). The channel is hosted by Ivan Liljeqvist, a software developer and blockchain expert.

Liljeqvist offers educational material on his YouTube channel on a range of subjects relating to blockchain technology, such as crypto trading, the creation of smart contracts, decentralized finance (DeFi) and more. Also, he offers updates on the most recent events and trends in the sector.

Liljeqvist also maintains an online school called Ivan on Tech Academy in addition to his YouTube channel. This school includes classes on blockchain development, cryptocurrency trading and other relevant subjects.

Andreas Antonopoulos

Andreas Antonopoulos’ YouTube channel is an invaluable resource for anyone seeking in-depth knowledge and insights into Bitcoin (BTC) and cryptocurrencies, featuring a wealth of informative talks, interviews and Q&A sessions.

Antonopoulos is a renowned advocate, speaker and author in the field of Bitcoin and cryptocurrencies. He is widely regarded as a leading expert on blockchain technology and has written several books on the subject, including Mastering Bitcoin and The Internet of Money.

He is renowned for his fervent defense of decentralized systems and his capacity to concisely and clearly convey difficult ideas. Since the beginning of cryptocurrencies and blockchain technology, Antonopoulos has been a vocal proponent of their development and use.

Crypto Daily 

Crypto Daily is a popular YouTube channel dedicated to providing daily news, analysis and commentary on the world of cryptocurrencies. With over 500,000 subscribers, the channel covers a broad range of topics, from the latest developments in cryptocurrencies to initial coin offerings and blockchain technology.

James, the host of the channel, makes his insights interesting for both inexperienced and seasoned crypto aficionados by combining wit, humor and intellect in his delivery. The channel also offers interviews with industry leaders, product reviews and educational content, making it a well-rounded resource for anybody interested in the world of cryptocurrency.

Cybersecurity Ventures 

Cybersecurity Ventures is a YouTube channel focused on providing educational content on cybersecurity, cybercrime and cyberwarfare. The channel offers in-depth analyses of new trends and technology, news updates on the most recent cyber threats and assaults, and interviews with top industry experts.

The channel, which has over 20,000 members, offers guidance and best practices for people and businesses wishing to safeguard themselves against online risks, making it a useful tool for both inexperienced and seasoned cybersecurity professionals.

Related: Top 10 most famous computer programmers of all time

Machine Learning Mastery

Machine Learning Mastery also has a YouTube channel that complements its website by providing video tutorials on machine learning topics. The channel, which is hosted by Jason Brownlee, provides a range of content, including lessons, interviews with business leaders, and discussions of the most recent developments and difficulties in the field of machine learning.

The videos are well-made and very educational, covering everything from the fundamentals of machine learning to more complex subjects, such as neural networks and computer vision. The channel, which complements the substantial materials already offered on the Machine Learning Masters website, has a growing subscriber base and is a great resource for anybody wishing to learn about machine learning in a visual format.

Two Minute Papers 

Two Minute Papers is a popular YouTube channel that summarizes and explains complex research papers in the fields of artificial intelligence, machine learning and computer graphics in two minutes or less. 

The channel, hosted by Károly Zsolnai-Fehér, provides an easy way to stay up-to-date on the most recent developments and discoveries in these areas. The professionally made videos include simple visual explanations and can help viewers understand even the most challenging studies.

In order to personalize the information, Two Minute Papers also includes interviews with researchers and subject-matter experts. Two Minute Papers, a popular and useful resource for people interested in cutting-edge research and advancements in AI and related subjects, has more than 1.5 million subscribers.

 Web3 Foundation

The Web3 Foundation is a nonprofit organization dedicated to supporting and building the decentralized web, also known as Web3. Its YouTube channel provides educational content and updates on the latest developments in Web3 technology, including blockchain, distributed systems and peer-to-peer networks.

Related: What are peer-to-peer (P2P) blockchain networks, and how do they work?

The channel offers talks by prominent authorities in the field, including programmers, researchers and businesspeople, as well as discussions and interviews on subjects pertaining to Web3 technology. Also, it provides updates on the progress of the Polkadot network, an open-source platform for constructing interoperable blockchain networks. Overall, the Web3 Foundation YouTube channel is a great resource for anyone interested in the decentralized web’s future because it has over 20,000 followers.

Dapp University 

Dapp University’s YouTube channel complements its educational platform by providing video tutorials on blockchain development, smart contracts and decentralized application (DApp) development. Hosted by developer and entrepreneur Gregory McCubbin, the channel features clear and concise explanations of complex topics in blockchain technology, making it accessible to beginners and experts alike.

The videos cover a wide range of topics, including Ethereum, Solidity and other blockchain tools and technologies. With over 300,000 subscribers, the Dapp University YouTube channel is a valuable resource for individuals looking to learn how to develop decentralized applications on the blockchain.

Tech With Tim

Tech With Tim is a popular YouTube channel dedicated to teaching programming and computer science concepts to beginners and intermediate learners. The channel offers tutorials on a range of programming languages, including Python, Java and C++, as well as web development, game development and machine learning.

It is hosted by Tim Ruscica, a software engineer and seasoned tutor. The well-produced videos have straightforward explanations and examples of programming topics, making them understandable to a variety of students. Tech With Tim is a great resource for anybody wishing to learn programming and computer science skills, with more than 800,000 members.

Elon Musk reaffirms AI’s potential to destroy civilization

Speaking about artificial intelligence’s potential for civilizational destruction, Musk said, “Anyone who thinks this risk is 0% is an idiot.”

While tech giants worldwide work on materializing the idea of having a generative artificial intelligence (AI) to aid humans in their daily lives, some believe the risk of the nascent technology going rogue remains imminent. Considering this possibility, Tesla and Twitter chief Elon Musk reminded people of AI’s potential to destroy civilization.

On March 15, Musk’s plan to create a new AI startup surfaced after the entrepreneur reportedly assembled a team of AI researchers and engineers. However, Musk continues to highlight the destructive potential of AI — just like any other technology — if it falls into the wrong hands or is developed with ill intentions.

According to Musk, AI can be dangerous. In a FOX interview, he said AI could be more dangerous than mismanaged aircraft design or production maintenance, for example. While acknowledging the low probability, he stated:

“However small one may regard that probability, but it is non-trivial — it has the potential of civilizational destruction.”

As Crypto Twitter picked up on the discussion, Musk followed up with strong support for his statement:

“Anyone who thinks this risk is 0% is an idiot.”

On the other hand, tech entrepreneurs like Bill Gates remain more optimistic about AI and the positive impacts it can bring to humanity.

Related: Elon Musk reportedly buys thousands of GPUs for Twitter AI project

On April 13, Amazon became the latest tech giant to join the race of creating AI services. Amazon Bedrock allows users to build and scale generative AI apps.

According to a blog post announcing the service, Bedrock allows users to “privately customize foundation models with their own data, and easily integrate and deploy them into their applications.”

A brief history of artificial intelligence

AI has evolved from the Turing machine to modern deep learning and natural language processing applications.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor. 

Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data.

Moreover, societal and cultural issues have influenced AI. For instance, discussions concerning the ethics and the ramifications of AI have arisen in response to worries about job losses and automation.

Concerns have also been raised about the possibility of AI being employed for evil intent, such as malicious cyberattacks or disinformation campaigns. As a result, many researchers and decision-makers are attempting to ensure that AI is created and applied ethically and responsibly.

AI has come a long way since its inception in the mid-20th century. Here’s a brief history of artificial intelligence.

Mid-20th century

The origins of artificial intelligence may be dated to the middle of the 20th century, when computer scientists started to create algorithms and software that could carry out tasks that ordinarily need human intelligence, like problem-solving, pattern recognition and judgment.

One of the earliest pioneers of AI was Alan Turing, who proposed the concept of a machine that could simulate any human intelligence task, which is now known as the Turing Test. 

Related: Top 10 most famous computer programmers of all time

1956 Dartmouth conference

The 1956 Dartmouth conference gathered academics from various professions to examine the prospect of constructing robots that can “think.” The conference officially introduced the field of artificial intelligence. During this time, rule-based systems and symbolic thinking were the main topics of AI study.

1960s and 1970s

In the 1960s and 1970s, the focus of AI research shifted to developing expert systems designed to mimic the decisions made by human specialists in specific fields. These methods were frequently employed in industries such as engineering, finance and medicine.

1980s

However, when the drawbacks of rule-based systems became evident in the 1980s, AI research began to focus on machine learning, which is a branch of the discipline that employs statistical methods to let computers learn from data. As a result, neural networks were created and modeled after the human brain’s structure and operation.

1990s and 2000s

AI research made substantial strides in the 1990s in robotics, computer vision and natural language processing. In the early 2000s, advances in speech recognition, image recognition and natural language processing were made possible by the advent of deep learning — a branch of machine learning that uses deep neural networks.

Modern-day AI

Virtual assistants, self-driving cars, medical diagnostics and financial analysis are just a few of the modern-day uses for AI. Artificial intelligence is developing quickly, with researchers looking at novel ideas like reinforcement learning, quantum computing and neuromorphic computing.

Another important trend in modern-day AI is the shift toward more human-like interactions, with voice assistants like Siri and Alexa leading the way. Natural language processing has also made significant progress, enabling machines to understand and respond to human speech with increasing accuracy. ChatGPT — a large language model trained by OpenAI, based on the GPT-3.5 architecture — is an example of the “talk of the town” AI that can understand natural language and generate human-like responses to a wide range of queries and prompts.

Related: Biased, deceptive’: Center for AI accuses ChatGPT creator of violating trade laws

The future of AI

Looking to the future, AI is likely to play an increasingly important role in solving some of the biggest challenges facing society, such as climate change, healthcare and cybersecurity. However, there are concerns about AI’s ethical and social implications, particularly as the technology becomes more advanced and autonomous.

Moreover, as AI continues to evolve, it will likely profoundly impact virtually every aspect of our lives, from how we work and communicate, to how we learn and make decisions.

OKX launches AI integration to monitor market volatility

Cryptocurrency exchange OKX announced a new integration aimed at helping users monitor market volatility in real time via advanced AI algorithms.

After the latest update of the infamous artificial intelligence (AI) chatbot ChatGPT-4, the technology has been a buzzword inside and outside the crypto industry. While opinions on the technology may be mixed, companies continue integrating AI to enhance their users’ experience.

On March 31, the cryptocurrency exchange and Web3 technology company OKX announced that it would launch a new integration from EndoTech, which utilizes AI algorithms to capture crypto market volatility.

The algorithms incorporate both machine learning and “other advanced techniques” in an effort to conduct real-time analyses of data and trading opportunities.

According to Dmitry Gooshchin, chief operating officer of EndoTech, understanding market volatility is “essential for successful trading in the crypto space.“

OKX also jumped on the AI bandwagon on March 30 when it posted an AI-generated poem from ChatGPT-4 about the company’s wallet.

This new platform update comes only a few days after the company announced its intention to expand its services to Australia while beginning to shut down its operations in Canada.

AI is finding various use cases in the crypto industry, not just for identifying real-time market volatility. It’s also used to track blockchain transactions, deploy autonomous economic agents for trading and more.

Related: OKX latest proof of reserves reveals $8.9B in assets

In everyday life, it’s now used for personal assistant-like tasks, social media and customer service needs, among other use cases.

While some have a more positive outlook on the impact of AI technology in scenarios like the metaverse, a recently emerged letter signed by 2,600 researchers and leaders in fintech calls for a pause in AI development.

The primary concern voiced by industry professionals was that “human-competitive intelligence can pose profound risks to society and humanity.” 

Magazine: Can you trust crypto exchanges after the collapse of FTX?

7 free learning resources to land top data science jobs

Discover seven free resources to learn data science and land top jobs.

Data science is an exciting and rapidly growing field that involves extracting insights and knowledge from data. To land a top data science job, it is important to have a solid foundation in key data science skills, including programming, statistics, data manipulation and machine learning.

Fortunately, there are many free online learning resources available that can help you develop these skills and prepare for a career in data science. These resources include online learning platforms such as Coursera, edX and DataCamp, which offer a wide range of courses in data science and related fields.

Coursera

Data science and related subjects are covered in a variety of courses on the online learning platform Coursera. These courses frequently involve subjects such as machine learning, data analysis and statistics and are instructed by academics from prestigious universities.

Here are some examples of data science courses on Coursera:

  • Applied Data Science with Python Specialization: This specialization, offered by the University of Michigan, consists of five courses that cover the basics of data manipulation, analysis and visualization using Python.
  • Machine Learning by Andrew Ng: This course, offered by Stanford University, provides an introduction to machine learning, including topics such as linear regression, logistic regression, neural networks and clustering.
  • Data Science Methodology: This course, offered by IBM, covers the basics of data science, including data preparation, data cleaning and data exploration.
  • Statistics with R Specialization: This specialization, offered by Duke University, consists of four courses that cover statistical inference, regression modeling and machine learning using the R programming language.

One can apply for financial aid to earn these certifications for free. However, doing a course just for certification may not land a dream job in data science.

Kaggle

Kaggle is a platform for data science competitions that provides a wealth of resources for learning and practicing data science skills. One can refine their skills in data analysis, machine learning and other branches of data science by participating in the platform’s challenges and host of datasets.

Here are some examples of free courses available on Kaggle:

  • Python: This course covers the basics of Python programming, including data types, control structures, functions and modules.
  • Pandas: This course covers the basics of data manipulation using Pandas, including data cleaning, data merging and data reshaping.
  • Data Visualization: This course covers the basics of data visualization using Matplotlib and Seaborn, including scatter plots, line plots and bar plots.
  • Intro to Machine Learning: This course covers the basics of machine learning, including classification, regression and clustering.
  • Intermediate Machine Learning: This course covers more advanced topics in machine learning, including feature engineering, model selection and hyperparameter tuning.
  • SQL: This course covers the basics of SQL, including data querying, data filtering and data aggregation.
  • Deep Learning: This course covers the basics of deep learning, including neural networks, convolutional neural networks and recurrent neural networks.

Related: 9 data science project ideas for beginners

edX

EdX is another online learning platform that offers courses in data science and related fields. Many of the courses on edX are taught by professors from top universities, and the platform offers both free and paid options for learning.

Some of the free courses on data science available on edX include:

  • Data Science Essentials: This course, offered by Microsoft, covers the basics of data science, including data exploration, data preparation and data visualization. It also covers key topics in machine learning, such as regression, classification and clustering.
  • Introduction to Python for Data Science: This course, offered by Microsoft, covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as Pandas, NumPy and Matplotlib.
  • Introduction to R for Data Science: This course, offered by Microsoft, covers the basics of R programming, including data types, control structures, functions and packages. It also covers key data science libraries in R, such as dplyr, ggplot2 and tidyr.

All of these courses are free to audit, meaning that you can access all the course materials and lectures without paying a fee. Nevertheless, there will be a cost if you wish to access further course features or receive a certificate of completion. A comprehensive selection of paid courses and programs in data science, machine learning and related topics are also available on edX in addition to these courses.

DataCamp

DataCamp is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers interactive coding challenges and projects that can help you build real-world skills in data science.

The following courses are available for free on DataCamp:

  • Introduction to Python: This course covers the basics of Python programming, including data types, control structures, functions and modules.
  • Introduction to R: This course covers the basics of R programming, including data types, control structures, functions and packages.
  • Introduction to SQL: This course covers the basics of SQL, including data querying, data filtering and data aggregation.
  • Data Manipulation with Pandas: This course covers the basics of data manipulation using Pandas, including data cleaning, data merging and data reshaping.
  • Importing Data in Python: This course covers the basics of importing data into Python, including reading files, connecting to databases and working with web APIs.

All of these courses are free and can be accessed through DataCamp’s online learning platform. In addition to these courses, DataCamp also offers a wide range of paid courses and projects that cover topics such as data visualization, machine learning and data engineering.

Udacity

Udacity is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers both free and paid courses, and many of the courses are taught by industry professionals.

Here are some examples of free courses on data science available on Udacity:

  • Introduction to Python Programming: This course covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as NumPy and Pandas.
  • SQL for Data Analysis: This course covers the basics of SQL, including data querying, data filtering and data aggregation. It also covers more advanced topics in SQL, such as joins and subqueries.
  • Intro to Data Science: This course covers the basics of data science, including data wrangling, exploratory data analysis and statistical inference. It also covers key machine-learning techniques, such as regression, classification and clustering.

Related: 5 high-paying careers in data science

MIT OpenCourseWare

MIT OpenCourseWare is an online repository of course materials from courses taught at the Massachusetts Institute of Technology. The platform offers a variety of courses in data science and related fields, and all of the materials are available for free.

Here are some of the free courses on data science available on MIT OpenCourseWare:

  1. Introduction to Computer Science and Programming in Python: This course covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as NumPy, Pandas and Matplotlib.
  2. Introduction to Probability and Statistics: This course covers the basics of probability theory and statistical inference, including probability distributions, hypothesis testing and confidence intervals.
  3. Machine Learning with Large Datasets: This course covers the basics of machine learning, including linear regression, logistic regression and k-means clustering. It also covers techniques for working with large data sets, such as map-reduce and Hadoop.

GitHub

GitHub is a platform for sharing and collaborating on code, and it can be a valuable resource for learning data science skills. However, GitHub itself does not offer free courses. Instead, one can explore the many open-source data science projects that are hosted on GitHub to find out more about how data science is used in practical situations.

Scikit-learn is a popular Python library for machine learning, which provides a range of algorithms for tasks such as classification, regression and clustering, along with tools for data preprocessing, model selection and evaluation. The project is open-source and available on GitHub.

Jupyter is an open-source web application for creating and sharing interactive notebooks. Jupyter notebooks provide a way to combine code, text and multimedia content in a single document, making it easy to explore and communicate data science results. 

These are just a few examples of the many open-source data science projects available on GitHub. By exploring these projects and contributing to them, one can gain valuable experience with data science tools and techniques, while also building their portfolio and demonstrating their skills to potential employers.

5 high-paying careers in data science

Data science careers tend to have high salaries — often over six figures — as the demand for skilled professionals in this field continues to grow.

Data science plays a critical role in supporting decision-making processes by providing insights and recommendations based on data analysis. In order to create new products, services and procedures, businesses can use data science to gain a deeper understanding of consumer behavior, market trends and corporate performance.

By giving businesses a competitive edge in the market through better decision-making, increased consumer involvement and more efficient corporate processes, it enables companies to achieve a competitive advantage. The demand for data science experts is rising quickly, opening up new possibilities for development on both a personal and professional level.

Here are five high-paying careers in data science.

Data scientist

A data scientist is a specialist who draws conclusions and knowledge from both structured and unstructured data using scientific methods, processes, algorithms and systems. They create models and algorithms to categorize data, make predictions and find hidden patterns. Additionally, they clearly and effectively communicate their findings and outcomes to all relevant parties.

Data scientists have solid backgrounds in statistics, mathematics and computer science, as well as a practical understanding of the Python and R programming languages and expertise in dealing with sizable data sets. The position calls for a blend of technical and analytical abilities, as well as the capacity to explain complicated results to non-technical audiences.

A data scientist in the United States can expect to earn $121,169 per year, according to Glassdoor. Additionally, advantages like stock options, bonuses and profit-sharing are frequently included in remuneration packages for data scientists. However, a data scientist’s pay might vary significantly depending on a number of variables, including geography, industry, years of experience and educational background.

Machine learning engineer

A machine learning engineer is responsible for designing, building and deploying scalable machine learning models for real-world applications. They create and use algorithms to decipher complex data, interpret it and make predictions. In order to incorporate these models into a finished product, they also work with software engineers.

Typically, a machine learning engineer has a solid foundation in programming, computer science and mathematics. In the U.S., the average income for a machine learning engineer is $136,150, while top earners in big cities or those with substantial expertise may make considerably more.

Big data engineer

The architecture of a company’s big data infrastructure is created, built and maintained by big data engineers. They use a variety of big data technologies, including Hadoop, Spark and NoSQL databases, to design, build and manage the storage, processing and analysis of huge and complex data sets.

They also work along with data scientists, data analysts and software engineers to develop and implement big data solutions that satisfy an organization’s business needs. In the U.S., a data engineer can expect to make an average annual salary of $114,501.

Business intelligence manager

An organization’s decision-making processes are supported by data-driven solutions, which are developed and implemented under the direction of a business intelligence (BI) manager. They coordinate the implementation of BI tools and systems, create and prioritize business intelligence initiatives, and work in close collaboration with data analysts, data scientists and IT teams.

The data used in these solutions must be of a high standard, and BI managers must convey the findings and insights to senior leaders and stakeholders in order to inform business strategy. They are essential in creating and maintaining data governance and security rules that safeguard confidential corporate data. The salary range for a business intelligence manager in the U.S. normally ranges from $122,740 to $157,551. And the average compensation is $140,988 per annum.

Data analyst manager

A data analyst manager is responsible for leading a team of data analysts and overseeing the collection, analysis and interpretation of large and complex data sets. They develop and implement data analysis strategies, using various tools and technologies, to support decision-making processes and inform business strategy.

To make sure that data analysis initiatives are in line with company goals and objectives, data analyst managers closely collaborate with data scientists, business intelligence teams and senior management. They also play a crucial part in guaranteeing the accuracy and quality of the data used in analytic initiatives, as well as in conveying findings and suggestions to stakeholders. They could also be in charge of overseeing the allocation of resources and managing the budget for projects involving data analysis. In the U.S., a data analyst makes an average base salary of $66,859.

Human Protocol introduces new blockchain coordination layer for data contribution

Users receive rewards for contributing data on the Human Protocol, which can be used as an initial point of learning for algorithms.

On Thursday, decentralized infrastructure project Human Protocol said it was launching a new blockchain coordination layer to handle routing functionality among third-party vendors to power data contribution on the network. The feature, known as the Routing Protocol, sits atop Human to enable the discovery of network generators, fee agreements, consensus job standards, proof of balance and governance support for network upgrades.

Human Protocol started via an on-chain bot blocker called Captcha, which rewards users for solving CAPTCHAs and gradually became a broader solution for tokenizing contribution. Human Protocol expects the community-developed, open-source Routing Protocol to simplify the steps of operating a network entity such as an Exchange Oracle. This stems from Routing Protocol’s ability to coordinate oracles, job exchanges, layer-1 integrations for job listings and work pool operators.

As an end goal, the Human Protocol network seeks to leverage the peer-to-peer consensus mechanism inherent in blockchain design to resolve automation tasks that cannot be performed without initial human assistance. One example of such a value proposition is in the realm of AI e-commerce marketing. Without an initial “training” data set, a machine-learning algorithm cannot effectively suggest ads relevant to each web user’s particulshopping behaviors.

But by using the Human Protocol, network clients can post smart bounties for consumer reviews and reward users for their input via the HMT token. The development team’s vision is to create a decentralized platform that rewards those who supply their data to the clients who require it. It seeks to meet the objective of facilitating direct, globally-mapped connections at the intersection of workers, companies and machine learning, all at scale.