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Ethiopia Set to Pioneer Bitcoin Mining in Africa – Tech in Africa

Last Thursday, February 15, Ethiopian Investment Holdings (EIH), the investment arm of the Ethiopian Government, signed a memorandum of understanding with Hong Kong-based West Data Groups Center Service PLC to initiate bitcoin mining operations.

The collaboration is governed by a comprehensive agreement for a significant $250 million data mining initiative aimed at establishing state-of-the-art infrastructure for data mining and artificial intelligence training operations in Ethiopia, as stated by the EIH. Ethiopia is strategically positioning itself as a frontrunner in the data center sector in Africa, which is projected to reach $5.4 billion by 2027, according to Aritzon Advisory and Intelligence.

According to Kal Kassa, CEO for Ethiopia at Hashlabs Mining, the development is aligned with the Ethiopian Governments goal of boosting economic growth through the strategic use of technology and energy resources to attract foreign investments.

The EIH has not confirmed the specifics of its bitcoin mining operations or provided any comments to other publications. However, as the project develops, we anticipate receiving more detailed information from them regarding the arrangement.

In 2022, despite the ban on crypto trading in the country, there was a significant development with the ratification of favorable data mining laws allowing for high-performance computing and data mining, encompassing bitcoin mining. This shift has attracted a surge of miners due to the comparatively positive reception towards bitcoin mining, abundant hydro-based energy sources, optimal weather conditions, and cost-efficient energy.

In 2023, Ethiopia emerged as the fourth leading destination for Bitcoin mining rigs, trailing only the USA, Hong Kong, and Asia, as reported by Bitcoin mining services company Luxor Technologies. The country has witnessed significant developments in this sector, with Russian company Bitcluster establishing a 120 MW bitcoin mining facility and Hashlabs Mining commencing the construction of bitcoin mines to cater to global clients.

According to a forecast by a senior executive at Bitmain and reported by Bloomberg, Ethiopias energy potential could soon rival Texass generation capacity. Currently, Texas accounts for an impressive 28.5% of the USs 40% global hash rate.

However, bitcoin miners are expressing caution regarding the future of regulation in the country. As demonstrated in other regions, bitcoin mining is not immune to changing regulatory landscapes. It is still premature to anticipate whether Ethiopia will follow the lead of Iran and Kazakhstan, altering its stance on bitcoin mining as they did when faced with competition from domestic energy demand.

In any case, the government is committed to increasing its foreign currency reserves to address its economic challenges. It sees mining as a promising investment opportunity to achieve this objective.

With the energy infrastructure still struggling to meet the demand for electricity in many African countries, bitcoin mining has emerged as an appealing solution to provide power to millions.

In Ethiopia, more than 40% of its population, approximately 120 million people, lack access to electricity. However, the country boasts an installed capacity generation of over 5,000 MW, with plans for an additional capacity generation of approximately 5,150 MW upon the completion of the Grand Ethiopian Renaissance Dam (GERD), the largest hydroelectric project in Africa.

Drawing inspiration from successful mining projects in Africa like Gridless and Trojan Mining, Ethiopia has the opportunity to harness its surplus green energy to power bitcoin mining operations and provide electricity to its citizens. This pioneering initiative could set a precedent for other African nations with similar energy resources, offering a viable solution to common economic challenges.

Moreover, integrating bitcoin mining into the Ethiopian economy has the potential to contribute $2 to $4 billion to its GDP, as per data from Project Mano. This open collective aims to educate the government on the economic benefits of bitcoin for the country.

Ethiopia stands to gain immense economic advantages by strategically harnessing its abundant energy resources for Bitcoin mining. Its only a matter of time before other African nations follow suit, tapping into this lucrative opportunity.

Bitcoin mining has the potential to significantly contribute to addressing the economic challenges faced by several African countries.

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10 Python Libraries for Data Cleaning and Preprocessing – Analytics Insight

In data science, data cleaning and preprocessing are key steps in preparing raw data for analysis and modeling. Pythons vast ecosystem of libraries provides severaltools to assist with these tasks. In this article, well explore the top 10 Python libraries for data cleaning and preprocessing, providing insights into their features, benefits, and recommendations for optimizing your data analysis workflow.

Pandas is a robust data manipulation librarythat offers high-performance, user-friendly data structures and analytical tools in Python. Pandas enables users to import, clean, transform, and analyze structured data efficiently. It offers flexible data structures such as DataFrames and Series, along with a wide range of functions for data cleaning, preprocessing, and exploration. Pandas is a versatile library that is commonly used in data science projects for tasks such asdata cleaning, filtering, grouping, and visualization.

NumPy is a fundamental library for numerical computing in Python, including multidimensional arrays and matrices. It provides a diverse set of mathematical functions and operations for data manipulation, such as array manipulation, linear algebra, statistical analysis, and random number generation. NumPys array-based computing capabilities make it ideal for data preparation tasks including datanormalization, scaling, and transformation. It is a core component of the Python scientific computing ecosystem and is often used in conjunction with other libraries such as Pandas and Matplotlib.

SciPy is an open-source Python library for scientific computing that includes a variety of functions and algorithms for numerical optimization, integration, interpolation, and signal processing. It builds upon NumPy and provides additional functionality for scientific and technical computing tasks.SciPys optimization and interpolation methods are very useful for data preparation tasks like feature engineering, dimension reduction, and data imputation. It is popularly usedin data science and machine learning projects due to its extensive collectionof algorithms and tools.

scikit-learn is a versatile Python machine-learninglibrary that offers simple and efficient tools for data mining and analysis. It provides a wide rangeof algorithms for classification, regression, clustering, dimensionality reduction, and model selection. scikit-learns preprocessing module includes functions for data scaling, normalization, encoding categorical variables, and handling missing values. It is widely used in data preprocessing pipelines for machine learning tasks and provides a consistent interface for building and evaluating predictive models.

TensorFlow Data Validation (TFDV) is a library for exploring and validating datasets for machine learning. It includes tools for assessing the characteristics of datasets, detecting abnormalities, and determining data quality issues. TFDVs features include schema inference, data drift detection, and anomaly detection, making it useful for data cleaning and preprocessing tasks. It is often used in conjunction with TensorFlow Extended (TFX) for building end-to-end machine learning pipelines.

Feature-Engine is a Python library that facilitates feature engineering and selection in machine learning projects. It includes a wide range of transformers for data preprocessing tasks such as handling missing values, encoding category variables, and scaling numerical features. Feature-Engines transformers can be easily integrated into scikit-learn pipelines, making it a a convenient tool for building data preprocessing workflows. It is designed to be fast, flexible, and easy to use, making it suitable for both beginners and experienced data scientists.

Dora is a Python library for data preprocessing and supports exploratory data analysis (EDA). It provides a set of functions and utilities for visualizing and understanding datasets, identifying patterns and trends, and preparing data for analysis. Doras features include data cleaning, transformation, and visualization, making it a versatile tool for data preprocessing. It is developed on top of Pandas and offers an easy-to-use interface for data exploration and manipulation.

Pyjanitor is a Python library for data cleaning and preparation, inspired by the R packagejanitor. It includes a suite of functions and utilities for cleaning messy datasets, handling missing values, and reshaping data. Pyjanitor provides functions for renaming columns, deleting duplicates,converting data types, and performing group-wise operations. It is designed to be simple, expressive, and easy to use, making it a useful tool for data cleaning and preprocessing tasks.

Featuretools is a Python library for automatedfeature engineering and feature selection. IIt provides tools for creating new features from existing data, identifying relevant features for machine learning tasks, and building feature sets for predictive modeling. Featuretools automated featureengineering capabilities can considerably minimize the time and effort needed for data preprocessing tasks. It is particularly useful for handling complex datasets with multiple tables and relationships.

Dask is a flexible parallel computing library for Python that provides scalable data processing capabilities. It enables users to parallelize data processing tasks across numerous cores and nodes, making it ideal for handling enormous datasets that cannot be stored in memory. Dasks DataFrame and Array data structures are compatible with Pandas and NumPy, allowing users to leverage their familiar APIs for data preprocessing tasks. It is particularly useful for distributed data preprocessing tasks in cloud computing environments.

These ten Python libraries provide powerful tools and utilities for data cleaning and preprocessing, allowing data scientists to streamline their data analysis workflow and prepare datasets for machine learning tasks.Data scientists can use these libraries to efficiently manage data cleaning, transformation, and exploration tasks, enabling them to focus on building and deploying predictive models and extracting valuable insights from their data.

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Who is Satoshi Nakamoto, the creator of Bitcoin? It’s not Craig Wright according to a UK judge – PC Gamer

The identity of Satoshi Nakamoto, the pseudonymous creator of Bitcoin, is the biggest mystery in the crypto world. There's been a lot of speculation as to who the person or persons behind the first and most valuable crypto really areand there are a few claimants too. The most publicized of whom is Australian academic Dr Craig Wright. However, after years of litigation, a UK high court judge has ruled that Wright is not Satoshi Nakamoto.

The lawsuit was brought by a conglomerate of cryptocurrency companies called the Crypto Open Patent Alliance (COPA). It sought a judgment preventing Wright from continuing to claim he was Satoshi Nakamoto, and using this recognition to expand his influence over the sector.

Reporting from The Guardian includes quotes from Jonathan Hough KC, a lawyer representing COPA. He argued that Wrights claim that he created Bitcoin and wrote its whitepaper was a brazen lie and elaborate false narrative supported by forgery on an industrial scale. He also added there are elements of Dr Wrights conduct that stray into farce.

The group argued Wright's supporting evidence included provably forged documents, backdated edits and even traces of ChatGPT usage, even though ChatGPT was not released until years later.

The judge overwhelmingly agreed, unusually issuing a verdict immediately after the conclusion of the case. According to court reports, Justice Mellor said Im prepared to say this: Dr Wright is not the inventor of bitcoin" and "Dr Wright is not the author of the bitcoin white paper and he is not the person who adopted the name Satoshi Nakamoto.

Wright has spent years claiming he invented Bitcoin, fighting lawsuits across different jurisdictions. One of the well-known examples involved defending against a fraud lawsuit filed in 2018 by the estate of Dave Kleiman, a US computer scientist. Kleiman died in 2013 and some even suggest he could be Nakamoto himself. Wright lost the lawsuit and was ordered to pay $100 million in damages.

Well over a decade has passed since Satoshi Nakamoto's last publicly known message was posted to the Bitcointalk forums. The message gave no hint that he would be going anywhere. Did he die? Did he wish to step away in order to promote decentralization? Or was "Satoshi Nakamoto" a group that subsequently disbanded?

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Satoshi Nakamoto leaves behind dormant wallets estimated to contain as many as 1.1 million Bitcoins. That amounts to some $75 billion at Bitcoin's current price. If Wright was Satoshi Nakamoto, accessing those early coins would have strengthened his case immensely. He never did.

We may never know who the real person or persons behind Bitcoin are, but if Justice Mellor's judgment is the final word, the saga of "Faketoshi" is done and dusted.

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Consensus Adoption of U.S.-Led Resolution on Artificial Intelligence by the United Nations General Assembly – United … – Department of State

With todays adoption in the UN General Assembly of the U.S.-led resolution on Artificial Intelligence (AI), UN Member States have spoken with one voice to define a global consensus on safe, secure, and trustworthy AI systems for advancing sustainable development. This consensus resolution, developed with direct input from more than 120 countries and cosponsored by more than 120 Member States from every region, is a landmark effort and a first-of-its-kind global approach to the development and use of this powerful emerging technology.

Artificial intelligence has enormous potential to advance sustainable development and the Sustainable Development Goals (SDGs). This resolution helps ensure that the benefits of AI reach countries from all regions and at all levels of development and focuses on capacity building and bridging digital divides, especially for developing countries. It underscores the consensus that AI systems can respect human rights and fundamental freedoms, while delivering on aspirations for sustainable development, as these are fundamentally compatible goals.

Governments must work withthe private sector, civil society, international and regional organizations, academia and research institutions and technical communities, and all other stakeholders to build this approach.Importantly, this resolution will serve as a foundation for multilateral AI efforts and existing and future UN initiatives.

The United States will continue to work with governments and other partners to ensure the design, development, deployment, and use of emerging technologies, including AI, are safe, secure, and trustworthy and are directed to achieving our common goals and solving our most pressing challenges.

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The UN adopts a resolution backing efforts to ensure artificial intelligence is safe – KeysNews.com

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Committee Approves Amended Artificial Intelligence Bill CBIA – CBIA

The state legislatures General Law Committee unanimously approved amended legislation targeting the use and growth of artificial intelligence.

SB 2, championed by committee co-chair Sen. James Maroney, contains many, but not all, consensus items recommended by the AI Working Group earlier this year.

That group was established last year and tasked with making recommendations concerning the ethical and equitable use of AI in state government and regulation of its use in the private sector based on the White Houses Blueprint for an AI Bill of Rights and other similar materials.

The bill favorably reported last week made a number of changes to both the workforce development and regulatory sections originally drafted.

The substitute bill adds both the Department of Consumer Protection and Commission on Human Rights and Opportunities as agencies to enforce the regulatory and reporting requirements imposed on developers and deployers of AI.

The bill originally vested the sole enforcement authority to the Office of the Attorney General.

For example, under Section 11 of the substitute bill, if a business fails to use reasonable care to protect any consumer from any known or reasonably foreseeable risk of algorithmic discrimination, it is a discriminatory practice subject to investigation and enforcement by CHRO.

If a deployer fails to cure a violation within 60 days, CHRO may bring an enforcement action.

Similar to the enforcement measures required by the OAG and DCP, CHRO must issue a notice of violation to the deployer if the commission determined that it is possible to cure such violation.

If the deployer fails to cure the violation within 60 days, CHRO may bring an enforcement action.

Once the action is commenced, Section 11 stipulates that the deployer can assert an affirmative defense that it used reasonable care if they can prove compliance with the reporting and notification requirements laid out in Section 3 of the bill.

Those requirements include (1) creating and continually updating an impact assessment; (2) developing a risk management policy that specifies principles, processes and personnel that the employer will use to maintain the program to identify, document and eliminate any known or reasonably foreseeable risks for discrimination; and (3) notifying consumers and state enforcement agencies when discrimination is discovered.

Sections 6 and 7 also add new requirements for developers and deployers that utilize AI systems that produce synthetic content.

On the developer side, Section 6 requires developers to (1) ensure that the outputs of the AI system are marked in a machine-readable format and detectable as synthetic digital content and such outputs are market and distinguishable; and (2) ensure that technical solutions are effective and interoperable.

The bill adds new requirements for developers and deployers that utilize AI systems that produce synthetic content.

On the deployer side, Section 7 requires deployers to disclose to consumers that the synthetic digital content has been artificially generated and/or manipulated.

No disclosure is necessary if the synthetic content is in the form of text published to inform the public on any matter of public interest.

To meet this exemption, two conditions must be satisfied: (1) the synthetic content has undergone a process of human review or editorial control; and (2) a person holds editorial responsibility for the publication of such synthetic digital content.

Section 26 creates a new competitive grant program housed within the Department of Economic and Community Development to fund pilot studies conducted for the purpose of using AI to reduce health inequities in the state.

Section 30 requires the Department of Public Health to conduct a study of, and make recommendations regarding the adoption of, governance standards concerning the use of AI by healthcare providers.

The study includes an assessment of the extent to which health care providers currently use AI, any means available to increase such use, any risks stemming from such use and any means available to monitor the outcomes produced by AI to ensure that such outcomes are having the desired effect on patient outcomes.

The bill keeps in place the following workforce development initiatives that CBIA supported at the public hearing earlier this month:

The bill awaits action in the Senate.

For more information, contact CBIAsWyatt Bosworth(860.244.1155) |@WyattBosworthCT.

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Which Role for Artificial Intelligence in Electoral Processes? – International IDEA

The International Institute for Democracy and Electoral Assistance (International IDEA) and the Rule of Law Centre of Finland (RoL Centre), in partnership with the Central Election Commission of Bosnia and Herzegovina, will host the regional conference Which Role for Artificial Intelligence in Electoral Processes?, taking place in Sarajevo, Bosnia and Herzegovina on 16 April 2024.

In recent years, the use of Artificial Intelligence (AI) tools, is rapidly expanding amid public enthusiasm, curiosity and concern on their impact on democracy around the globe. While the introduction of AI in democratic processes seems unstoppable in the foreseeable future, institutions and societies are called to reflect and act early on to make it a success for humanitys progress. Elections, in particular, will be impacted by the rapid spread of AI tools. But how should AI be understood within the electoral process and how does it differ from other technologies already used in elections? How can it be deployed by Election Management Bodies (EMBs) preserving, and possibly boosting, the integrity and public trust in elections?

The event seeks to encourage a shared understanding of AI among election stakeholders, notably by analysing global experience with AI tools, and the presence of potential AI elements in digitalization of elections across the Western Balkans. Moreover, experts will explore the implications of potentially expanded use of AI tools for EMB mandates, their capacities and future regulation while they strive to uphold fundamental rights and public trust in elections. The discussions seek to facilitate cross-cutting exchanges among Election Management Bodies (EMBs) from the Western Balkans and EU Member States, alongside esteemed academics, and representatives from civil society organizations. The discussion will also dwell into how EMBs of the Western Balkans should approach AI developments under the prospect of EU accession and the EUs role as a regulator and standard setter in the field.

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Development of an Artificial Intelligence Model for the Classification of Gastric Carcinoma Stages Using Pathology Slides – Cureus

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UN Adopts Resolution Backing Efforts to Ensure Artificial Intelligence is Safe – OODA Loop

The United Nations General Assembly unanimously approved the first-ever resolution on artificial intelligence (AI), co-sponsored by 123 countries including the United States and China, aiming to ensure that AI benefits all nations, respects human rights, and is safe, secure, and trustworthy. U.S. Vice President Kamala Harris hailed the resolution as historic, emphasizing the importance of AI being in the public interest and adopted in a manner that protects everyone from potential harm. The resolution, which came after months of negotiations involving over 120 countries, aims to bridge the digital divide between developed and developing nations and ensure that all countries have access to the benefits of AI technology. While not legally binding, the resolution sets out principles for safe AI use and governance, urging countries and organizations worldwide to develop regulatory frameworks for AI systems while respecting human rights and fundamental freedoms.

Read more: https://www.securityweek.com/un-adopts-resolution-backing-efforts-to-ensure-artificial-intelligence-is-safe/

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OPINION: Artificial intelligence is a bad friend – Maroon

As Director of Composition, I get asked a lot about the possibility that students will use artificial intelligence instead of doing course writing themselvessometimes by faculty and, today, by an intrepid student editor at the Maroon.

My answer starts by pointing out that this is not a new problem: For a long time, students who chose not to do their own coursework mostly got out of doing papers by getting other people to write them.

Whats different about AI?

AI is like a smart friend that specializes in telling you whatever you want to hear.

If the chatbot wants anything, it wants to help you, butas anyone who has ever tried to explain to a chatbot why they want to return a weird shirt can attestAI is only able to give you certain kinds of help.

And what you need help with at college, when you are learning something new, is developing your understanding of the material and connecting new skills or knowledge with what you knew before.

Think about the difference between having a friend write a paper for you and having a friend help you write your own paper. By listening to what you say, your friendespecially if they are an English majorwill ask you questions that can help you get to your ideas, making your paper much betterand also more your own.

In another sense, AI is like having a teacher that marks up all the grammatical errors in your paper without responding to your ideasit wants to fix your commas, your citation style. Arizona State University is experimenting with AI as a tutoring tool.

From where I sit, the problem with that is that the chatbot does not care about the substance of what you say. Faculty do.

When I respond to student work, I am responding as an expert in the subject as well as someone with a history of teaching. I am interested in the evidence, and Im even more interested in the way the student uses it. No two students read the text the same way. Writing a paper isnt only about expressing your ideas, but developing them. So reading student work lets me learn something new about the course material and what a student is getting out of it.

Most college papers ask you to draw evidence from coursework. In my class, that means not only the things we have read together but the discussions we have had: student papers should reflect what you learned from listening to your peers as well as from doing the reading on your own. Most important, it should build on what youand only youknew before you started the class. Integrating new perspectives from coursework across disciplines is the part of your degree that you will take with you.

So, Maroon readers, my argument is that using AI to respond to readings or write papers cheats you out of the most transformative part of a Loyola degree: the hours that you spend engaging with assigned material both in and out of class.

This work not only lets you get more out of your courses, but empowers you to apply ideas from your classes to different contexts and to make funny jokes with your friends.

The time you put in on your coursework is your education. And that education will change what you are able to know and do long after you graduate.

It will change you.

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