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State of AI and Ethical Issues

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State of AI and Ethical Issues

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How to Regulate Artificial Intelligence the Right Way: State of AI and Ethical Issues

The current artificial intelligence (AI) systems are regulated by other existing regulations such as data protection, consumer protection and market competition laws.

It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning.

Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions.

Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa and Apple’s Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.

Deep learning (DL) is a subset of machine learning that uses advanced algorithms to enable an AI system to train itself to perform tasks by exposing multilayered neural networks to vast amounts of data. It then uses what it learns to recognize new patterns contained in the data. Learning can be human-supervised learningunsupervised learning, and/or reinforcement learning, like Google used with DeepMind to learn how to beat humans at the game Go.

State of Artificial Intelligence in the Pandemic Era

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Artificial intelligence (AI) is stepping up in more concrete ways in blockchain, education, internet of things, quantum computing, arm race and vaccine development.

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During the Covid-19 pandemic, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery to mission critical infrastructure like electricity grids.

AI-first approaches have taken biology by storm with faster simulations of humans’ cellular machinery (proteins and RNA). This has the potential to transform drug discovery and healthcare.

Transformers have emerged as a general purpose architecture for machine learning, beating the state of the art in many domains including natural language planning (NLP), computer vision, and even protein structure prediction.

AI is now an actual arms race rather than a figurative one. 

Organizations must learn from the mistakes made with the internet, and prepare for a safer AI.

Artificial intelligence deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment.

There are 3 stages of artificial intelligence:

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1. Artificial Narrow Intelligence (ANI), which has a limited range of capabilities. As an example: AlphaGo, IBM’s Watson, virtual assistants like Siri, disease mapping and prediction tools, self-driving cars, machine learning models like recommendation systems and deep learning translation.

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2. Artificial General Intelligence (AGI), which has attributes that are on par with human capabilities. This level hasn’t been achieved yet. 

3. Artificial Super Intelligence (ASI), which has skills that surpass humans and can make them obsolete. This level hasn’t been achieved yet. 

Why Governments Need to Regulate Artificial Intelligence?

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We need to regulate artificial intelligence for two reasons.

  • First, because governments and companies use AI to make decisions that can have a significant impact on our lives. For example, algorithms that calculate school performance can have a devastating effect. 

  • Second, because whenever someone takes a decision that affects us, they have to be accountable to us. Human rights law sets out minimum standards of treatment that everyone can expect. It gives everyone the right to a remedy where those standards are not met, and you suffer harm.

Is There An International Artificial Intelligence Law?

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As of today, there is no international artificial intelligence law nor specific legislation designed to regulate its use. However, progress has been made as bills have been passed to regulate certain specific AI systems and frameworks.

Artificial intelligence has changed rapidly over the last few decades. It has made our lives so much easier and saves us valuable time to complete other tasks.

AI must be regulated to protect the positive progress of the technology. Legislators across the globe have to this day failed to design laws that specifically regulate the use of artificial intelligence. This allows profit-oriented companies to develop systems that may cause harm to individuals and to the broader society. 

National and International Artificial Intelligence Regulations

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National and local governments have started adopting strategies and working on new laws for a number of years, but no legislation has been passed yet.

China for example has developed in 2017 a strategy to become the world’s leader in AI in 2030. In the US, the White House issued ten principles for the regulation of AI. They include the promotion of “reliable, robust and trustworthy AI applications”, public participation and scientific integrity. International bodies that give advice to governments, such as the OECD or the World Economic Forum, have developed ethical guidelines. 

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The Council of Europe created a Committee dedicated to help develop a legal framework on AI. The most ambitious proposal yet comes from the EU. On 21 April 2021, the EU Commission put forward a proposal for a new AI Act. 

Ethical Concerns of Artificial Intelligence

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Police forces across the EU deploy facial recognition technologies and predictive policing systems. These systems are inevitably biased and thus perpetuate discrimination and inequality.

Crime prediction and recidivism risk are a second AI application fraught with legal problems. A ProPublica investigation into an algorithm-based criminal risk assessment tool found the formula more likely to flag black defendants as future criminals, labelling them at twice the rate as white defendants, and white defendants were mislabeled as low-risk more often than black defendants. We need to think about the way we are mass producing decisions and processing people, particularly low income and low-status individuals, through automation and their consequences for society.

How to Regulate Artificial Intelligence the Right Way

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An effective, rights-protecting AI regulation must, at a minimum, contain the following safeguards. First, artificial intelligence regulation must prohibit use cases, which violate fundamental rights, such as biometric mass surveillance or predictive policing systems. The prohibition should not contain exceptions that allow corporations or public authorities to use them “under certain conditions”.

Second, there must be clear rules setting out exactly what organizations have to make public about their products and services. Companies must provide a detailed description of the AI system itself. This includes information on the data it uses, the development process, the systems’ purpose and where and by whom it is used. It is also key that individuals exposed to AI are informed about it, for example in the case of hiring algorithms. Systems that can have a significant impact on people’s lives should face extra scrutiny and feature in a publicly accessible database. This would make it easier for researchers and journalists to make sure companies and governments are protecting our freedoms properly.

Third, individuals and organisations protecting consumers need to be able to hold governments and corporations responsible when there are problems. Existing rules on accountability must be adapted to recognise that decisions are made by an algorithm and not by the user. This could mean putting the company that developed the algorithm under an obligation to check the data with which algorithms are trained and the decisions algorithms make so they can correct problems.

Fourth, new regulations must make sure that there is a regulator that can make companies and the authorities accountable and that they are following the rules properly. This watchdog should be independent and have the resources and powers it needs to do its job.

Finally, AI regulation should also contain safeguards to protect the most vulnerable. It should set up a system that allows people who have been harmed by AI systems to make a complaint and get compensation. Workers should have the right to take action against invasive AI systems used by their employer without fear of retaliation.

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Conclusion

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A trustworthy artificial intelligence should respect all applicable laws and regulations, 
as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:

  • Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.

  • Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.

  • Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.

  • Transparency: The traceability of AI systems should be ensured.

  • Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.

  • Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.

  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.

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TECHNOLOGY

The Best Programming Languages for Web Services and Their Advantages In 2022

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The world is becoming more digital, and the need to learn digital skills is higher now than ever before. One such digital skill that has gained more prominence since the last decade is programming languages. We now live in a world revolving around technology, and basic knowledge of programming languages is becoming a necessity for many career options and job profiles. 

There are many reasons to learn about the different programming languages and their advantages, and web services or web development is a big part of it. Web development is an ever-growing field and has plenty of opportunities. To grab these opportunities in web development, you need to equip yourself with the correct programming language.  However, the plethora of language options available to you can be confusing, especially if you are a beginner. 

Javascript

This high-level language, often used by most websites as a client-side language, is a core technology in the World Wide Web. Initially, it was used only for developing web browsers; however, they now have new usages in non-web browser applications and server-side website deployments. 

Since its creation in 1995, it has evolved into one of the world’s most popular programming languages and is highly sought after by various organizations. 

Pros

  • Learning and implementing javascript is easy.
  • It is very popular and is everywhere on the net.
  • It can run immediately in the client-side browser.
  • It doesn’t demand much from the website server. 

Cons

  • Different browsers may interpret it differently sometimes, so writing cross-browser code is difficult.

Python

This is a powerful programming language and is one of the best used in web development for multiple purposes. Furthermore, this open-source language is very easy to learn and deploy and is among the best languages for learning web development since it develops scalable applications and web pages. 

Python has a unique selling point: the simple, productive, and elegant design of this language. It is the best for backend development with first-class integration with other programming languages such as C++ and C. 

Python has numerous usages, especially in developing web and desktop applications and GUI-based applications such as machine learning, data science, and network servers. It also offers multiple toolsets for mathematics, statistics, and computational science with numerous libraries and frameworks like NumPy, Pandas, SciPy, and Scikit-Learn. 

Pros 

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  • It increases the productivity of programmers and developers. 
  • It supports several systems and platforms.
  • It follows object-oriented programming. 
  • It ensures easy scalability, even with complex web applications.
  • Offers numerous support libraries. 
  • It focuses on code readability. 

Cons

  • The database is primitive and slightly underdeveloped.
  • Not ideal for mobile computing.

HTML

HTML stands for HyperText Markup Language. This language is the foundation upon which web development is built, and every developer must familiarize themselves with it. It is a very popular language and is currently one of the most used programming languages. While it cannot be described as a full-fledged programming language, HTML is the standard language for creating web pages. 

HTML gives web pages structure and ensures that images and texts are correctly formatted. In addition, it is a fast language and helps developers design web pages much quickly because it uses templates. 

Pros 

  • Learning and implementing is easy. 
  • It is free and accessible.
  • All browsers support it. 

Cons

  • This language is static, so creating dynamic pages is impossible. 
  • It requires lots of code to develop a simple webpage. 

PHP

This is an ideal language for web development. Even though many other programming languages like python and javascript are also suitable for web development, the need for professional developers who work with PHP remains very high in the market. It is a general-purpose language that can be used for developing server-side applications. In addition, PHP is dynamic and maintained by open-source platforms working across Windows. UNIX, and Mac systems. 

PHP is highly productive in web development and is used vastly within this industry to build and deploy applications quickly. 

Pros

  • It has plenty of frameworks to offer. 
  • It can be used to develop web pages with ease. 
  • It supports object-oriented and functional programming paradigms.
  • It also supports multiple automation tools used for testing and deploying applications.
  • It uses the Xdebug extension, which allows for great debugging. 
  • It has a large ecosystem with incredible community support. 

Cons 

  • It doesn’t have features for handling errors and security. 
  • It is slower to build a web page using this language alone than other programming languages. 

CSS

CSS is similar to HTML because it is also a basic language that all developers should know. CSS is the acronym for Cascading Style Sheets. This language is mainly used along with HTML to govern a web page’s style. Combining CSS and HTML languages is often the mother of programming languages. For example, CSS determines the color, positions, and size of elements within a webpage. 

CSS uses a cascading style, which means an applied style cascades down from the parent to the child throughout the web page. This cascading nature allows it to uphold consistency. 

Pro

  • It is a beginner-friendly language that’s easy to learn.
  • CSS supports responsive website design. 
  • It simplified web page customization
  • It increases the webpage loading speed. 

Cons 

  • It is possible to suffer cross-browser issues.
  • It has multiple levels, such as CSS2 and CSS3, which may lead to confusion.

C++

This middle-level programming language was created in 1980 as an Objective C extension. C++ language is simple and easy to learn for beginners, and it has lots of usefulness. It offers everything that Objective C offers and more. It supports features such as object-oriented programming, except type checking and handling. 

C++ supports OOP concepts such as encapsulation, inheritance, and abstraction. The compiling mechanism is quick and efficient, and it has a massive standard library. 

Pros 

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  • It is highly scalable. 
  • It is portable and easy to handle.
  • It has extensive community support. 

Cons

  • Garbage collection is not automatic.
  • The pointers have associated complexities. 

Conclusion 

There are numerous options for programming languages to learn for web development in 2022. However, you could get spoiled for choice and miss your way if you’re a beginner and new to the web development scene. Therefore, this article discusses some of the best and most popular programming languages you can kick start your web service journey in 2022. 

Author Bio

Eun Rockwell is a professional journalist with extensive experience, four years. She also works as a freelancer and writes a lot of articles. In addition, she has had writing stints at My-assignment.help and Superiorpapers. Eliza is always focused on doing quality work to achieve her goals and objectives. In addition, Eliza is fascinated by creating original works that meet high standards. Feel free to connect with her by email. 

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