Big Data

Who watches the watchers? CryptoHarlem founder Matt Mitchell explains why surveillance is the enemy

CryptoHarlem founder Matt Mitchell says government and corporate surveillance and citizens’ inability to protect against it are great threats to personal security.

Technology can be one’s best friend or, in some cases, their worst enemy. For example, Meta and TikTok seamlessly connect millions of people with loved ones and strangers, and while the platforms are a great resource for finding information and communicating with others, there are valid concerns about violations of users’ privacy and the monetization and possible outright theft of users’ data.

The same can be said for surveillance and security. There is often a gift-and-a-curse style relationship, wherein the exact surveillance tools meant to keep people safe and deter crime are often used to oppress and control citizens or even ignore the criminal acts of those in power.

To explore this contentious topic in greater depth, show hosts Jonathan DeYoung and Ray Salmond invited renowned hacker and activist Matt Mitchell to the most recent episode of The Agenda podcast.

Who is watching the watchers?

When asked to share some examples of what drives his passion for hacktivism and which threats might be the most immediate for the average person, Mitchell said:

“You exist as a target of surveillance no matter what you do, right? And it might be commercial surveillance, the cookies on your browser, it might be the tracking on your phone. And normally, the incentive is financial gain, right? So, people want to sell your data to an advertiser to learn more about you so they can monetize it. Even the most failed startup is like sell this data, get out of this problem.”

To emphasize the increased danger of the surveillance threat to communities of color in the United States, Mitchell explained:

“Now, if you are a Black person or you’re in a historically Black community or a majority Black community, that surveillance includes law enforcement surveillance. It also includes private surveillance. That’s commercial surveillance. It might include the housing project you live in or the development community surveillance. And when you put it all together, there’s a 4D, like 4K, super-high-res image of your life because you’re under so many layers of surveillance that there’s almost no space that’s actually your private space.”

Mitchell said the very first thing he teaches people is that “surveillance is bad, and we need to stop it.”

When Salmond suggested that security is ultimately designed to keep citizens safe, Mitchell countered with:

“For example, let’s say you work as a tech, you have privileged access. So, only you and three other cybersecurity people or data people have access to all the sensitive keys. In the beginning, it’s used to stop abuse on the platform, but at the end, you’re using it to stalk someone you’re romantically interested, right? Because surveillance corrupts you in an insidious way, kind of like the One Ring.”

According to Mitchell:

“The group that wields the surveillance tool is not wielding it upon itself. They’re not the ones that are being watched. It is the watcher, not the watchers, that is on the empowerment side of this thing.”

To hear more from Mitchell’s conversation with The Agenda — including his backstory and the revolutionary objectives of CryptoHarlem — listen to the full episode on Cointelegraph’s Podcasts page, Apple Podcasts or Spotify. And don’t forget to check out Cointelegraph’s full lineup of other shows!

The views, thoughts and opinions expressed here are the authors’ alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

5 key features of machine learning

Machine learning is based on the idea that a system can learn to perform a task without being explicitly programmed.

Machine learning has a wide range of applications in the finance, healthcare, marketing and transportation industries. It is used to analyze and process large amounts of data, make predictions, and automate decision-making processes, among other tasks.

In this article, learn the five key features of machine learning that make it a powerful tool for solving a broad set of problems, from image and speech recognition to recommendation systems and natural language processing.

What is machine learning?

Machine learning is a subfield of artificial intelligence (AI) that involves the development of algorithms and statistical models, which allow computers to learn from data without being explicitly programmed. Building systems with the ability to continuously improve their performance on a given task based on the experience obtained from the data they are exposed to is the aim of machine learning. This is accomplished by giving algorithms extensive training on huge data sets, which enables the algorithms to find patterns and connections in the data.

Supervised learning: This involves training a model on a labeled data set, where the correct output is provided for each input. The algorithm uses this information to learn the relationship between inputs and outputs and can then make predictions on new, unseen data.
Unsupervised learning: This involves training a model on an unlabeled data set where the correct output is not provided. The algorithm must find the structure in the data on its own and is typically used for clustering, dimensionality reduction and anomaly detection.
Reinforcement learning: This involves training an agent to make decisions in an environment where it receives feedback through rewards or punishments. The algorithm uses this feedback to learn the best strategy for maximizing rewards over time.

5 key features of machine learning

Machine learning has become one of the most important technological advancements in recent years and has significantly impacted a broad range of industries and applications. Its main features are:

Predictive modeling: Data is used by machine learning algorithms to create models that forecast future events. These models can be used to determine the risk of a loan default or the likelihood that a consumer would make a purchase, among other things.
Automation: Machine learning algorithms automate the process of finding patterns in data, requiring less human involvement and enabling more precise and effective analysis.
Scalability: Machine learning techniques are well suited for processing big data because they are made to handle massive amounts of data. As a result, businesses can make decisions based on information gleaned from such data.
Generalization: Algorithms for machine learning are capable of discovering broad patterns in data that can be used to analyze fresh, unexplored data. Even though the data used to train the model may not be immediately applicable to the task at hand, they are useful for forecasting future events.
Adaptiveness: As new data becomes available, machine learning algorithms are built to learn and adapt continuously. As a result, they can enhance their performance over time, becoming more precise and efficient as more data is made available to them.

The integration of machine learning and blockchain technology

The integration of machine learning and blockchain technology holds great promise for the future. Machine learning algorithms can be used to assess the data and generate predictions based on it using a decentralized and secure platform like the blockchain.

One possible area of usage for this integration is in the banking sector, where blockchain technology’s decentralized character and ability to prohibit unauthorized access to sensitive data can help machine learning algorithms detect fraud and money laundering more efficiently.

Machine learning and blockchain technology can also make a significant difference in supply chain management. While blockchain technology can be used to provide openness and accountability in the supply chain, machine learning algorithms can be utilized to optimize supply chain operations and forecast demand.

Blockchain technology can enable the secure and private sharing of medical records, while machine learning algorithms can be used to predict disease outbreaks and enhance patient outcomes.

The future of machine learning

The future of machine learning is expected to be characterized by continued advances in algorithms, computing power and data availability. As machine learning becomes more widely adopted and integrated into various industries, it has the potential to greatly impact society in a number of ways.

Some of the key trends and developments in the future of machine learning include:

Increased automation: As machine learning algorithms progress, they will be able to automate a larger range of jobs, requiring less human input and boosting productivity.
More personalized experiences: Machine learning algorithms will have the capacity to assess and make use of enormous volumes of data to deliver highly individualized experiences, such as personalized suggestions and adverts.
Enhanced judgment: As machine learning algorithms get better at making complicated judgments and predictions, numerous businesses will benefit from more precise and efficient decision-making.
AI ethical advancements: As machine learning becomes more common, there will be a growing emphasis on ensuring that it is developed and utilized ethically and responsibly, with a focus on safeguarding privacy and eliminating biases in decision-making.
Interdisciplinary collaboration: Machine learning will increasingly be used in collaboration with other fields, such as neuroscience and biology, to drive new discoveries and advancements in those areas.

Overall, the future of machine learning holds great promise and is expected to continue transforming a wide range of industries, from finance to healthcare, in the coming years.

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.