Company, Education, Technology

Unveiling the Future: Navigating the Landscape of Artificial Intelligence (AI)

AI

The field of Artificial Intelligence (AI) encompasses the development and study of intelligent machines and software, distinguishing itself from human or animal intelligence. AI has become pervasive across various sectors, including industry, government, and science, with notable applications such as advanced web search engines, recommendation systems, speech understanding technologies, self-driving cars, generative and creative tools, and superhuman performance in strategy games like chess and Go.

Alan Turing conducted groundbreaking research in what he termed “Machine Intelligence,” marking the inception of AI as an academic discipline in 1956. The history of AI has witnessed cycles of optimism and disappointment, with fluctuations in funding and interest. However, significant momentum was gained after 2012 with the emergence of deep learning, surpassing previous AI techniques. The transformer architecture further fueled advancements in 2017, leading to the AI spring of the 2020s. During this period, the United States emerged as a hub for pioneering AI developments by companies, universities, and laboratories.

AI research is characterized by various sub-fields focusing on specific goals and utilizing distinct tools. Traditional objectives include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. The ultimate ambition is to achieve general intelligence, enabling machines to perform any task that a human can. AI researchers employ a diverse range of problem-solving techniques, such as search and mathematical optimization, formal logic, artificial neural networks, and statistical, operational, and economic methods. Interdisciplinary collaboration with fields like psychology, linguistics, philosophy, neuroscience, and others further enriches the multifaceted nature of AI research.

 

In the pursuit of simulating intelligence, researchers in Artificial Intelligence (AI) have dissected the overarching problem into several sub-problems. These encompass specific traits or capabilities that are anticipated in intelligent systems, forming the foundation of AI research:

Reasoning and Problem-Solving:

Early AI research focused on developing algorithms that replicated the step-by-step reasoning employed by humans in puzzle-solving and logical deduction. Advances in the late 1980s and 1990s incorporated dealing with uncertain or incomplete information using concepts from probability and economics. However, the challenge persists as these algorithms face a “combinatorial explosion” in larger problems, and achieving accurate and efficient reasoning remains an unsolved problem.

Knowledge Representation:

Knowledge representation and engineering enable AI programs to intelligently answer questions and deduce real-world facts. Ontologies, representing relationships between concepts within a domain, play a crucial role. Challenges include the breadth of commonsense knowledge and the sub-symbolic nature of much of this knowledge. Knowledge acquisition, especially for modern AI applications like ChatGPT, involves scraping the internet, leading to reliability and accuracy issues.

Planning and Decision Making:

In automated planning, agents pursue specific goals, while in decision making, agents have preferences and assign utilities to situations. Dealing with uncertainties in real-world problems, where the agent may not know the situation or the outcomes of actions, presents a challenge. Techniques such as Markov decision processes and game theory are employed to model and address these complexities.

Learning:

Machine learning, a core aspect of AI, involves programs improving their performance on tasks automatically. Various types of machine learning include unsupervised learning, supervised learning (classification and regression), reinforcement learning, transfer learning, and deep learning. Challenges include assessing learners based on computational complexity and sample complexity.

Natural Language Processing (NLP):

NLP enables programs to read, write, and communicate in human languages. Modern techniques like word embedding and transformers have significantly advanced NLP. Generative pre-trained transformer (GPT) language models have achieved human-level scores on various real-world applications.

Perception:

Machine perception involves using input from sensors to deduce aspects of the world. Computer vision, encompassing tasks like image classification and object recognition, is a crucial component of perception.

Social Intelligence:

Affective computing, an interdisciplinary field, involves recognizing, interpreting, processing, or simulating human feelings, emotions, and moods. Virtual assistants with conversational abilities fall under this category, but challenges exist in managing user expectations regarding the true intelligence of computer agents.

General Intelligence:

The ultimate goal is to achieve artificial general intelligence, where machines can solve a wide variety of problems with breadth and versatility akin to human intelligence.

Despite significant progress, each of these areas presents ongoing challenges and opportunities for further exploration and innovation in the field of AI.

AI research employs a diverse array of tools to address its goals. These tools encompass various methodologies and techniques, each tailored to tackle specific challenges within the field:

Search and Optimization:

  • State Space Search: Involves navigating through a tree of possible states to find a goal state. Planning algorithms use means-ends analysis to explore trees of goals and subgoals.
  • Local Search: Utilizes mathematical optimization to incrementally refine a guess until no further improvements can be made. Techniques include stochastic gradient descent, evolutionary computation, and swarm intelligence algorithms like particle swarm optimization and ant colony optimization.

Logic:

  • Formal Logic: Used for reasoning and knowledge representation, including propositional and predicate logic. Logical inference involves proving new statements from known premises, often through search-based methods.

Probabilistic Methods:

  • Bayesian Networks: A versatile tool for reasoning, learning, planning, and perception, using Bayesian inference and expectation-maximization algorithms.
  • Probabilistic Algorithms: Employed for filtering, prediction, smoothing, and finding explanations in the presence of incomplete or uncertain information. Techniques include hidden Markov models and Kalman filters.

Classifiers and Statistical Learning:

  • Classifiers: Functions that use pattern matching to determine the closest match. Decision trees, k-nearest neighbor algorithms, support vector machines, and naive Bayes classifiers are examples.
  • Neural Networks: Modeled after the human brain, these interconnected networks learn complex relationships between inputs and outputs. Techniques like backpropagation enable learning complex patterns.

Deep Learning:

  • Artificial Neural Networks: Hierarchical structures inspired by the human brain, with multiple layers progressively extracting higher-level features. Deep learning has significantly improved performance in computer vision, speech recognition, natural language processing, and image classification.

Specialized Hardware and Software:

  • GPUs: Graphics processing units, especially designed with AI enhancements, have become dominant for large-scale machine learning model training. Specialized software, like TensorFlow, complements these hardware advancements.

Programming Languages:

  • Historical and Modern Languages: AI has historically used specialized languages like Lisp and Prolog. More recently, languages like Python have become prevalent in AI development.

The synergy of these tools, coupled with advances in hardware and access to vast datasets, has propelled AI research forward, resulting in significant breakthroughs across various subfields.

Applications of Artificial Intelligence (AI):

General Applications:

  1. Search Engines: AI, particularly machine learning, powers search engines like Google Search.
  2. Online Advertisements: Targeted advertising on platforms such as AdSense and Facebook utilizes AI for efficient targeting.
  3. Recommendation Systems: Services like Netflix, YouTube, and Amazon leverage AI for personalized content recommendations.
  4. Virtual Assistants: Siri, Alexa, and similar virtual assistants use AI for natural language processing and interaction.
  5. Autonomous Vehicles: AI plays a crucial role in autonomous vehicles, including drones, ADAS, and self-driving cars.
  6. Language Translation: AI, as seen in Microsoft Translator and Google Translate, aids in automatic language translation.
  7. Facial Recognition and Image Labeling: Technologies like Apple’s Face ID, Microsoft’s DeepFace, and Facebook’s image labeling use AI.

Health and Medicine:

  1. Medical Diagnostics: AI applications in healthcare aim to improve accuracy in diagnosing and treating patients.
  2. Big Data Processing: AI serves as a valuable tool in processing and integrating large datasets, particularly in organoid and tissue engineering development.
  3. Pathway Understanding: AI tools, such as AlphaFold 2, enhance the understanding of biomedically relevant pathways by approximating 3D protein structures.

Games:

  1. Game Playing Programs: AI has been used in games since the 1950s, with notable instances like Deep Blue defeating a world chess champion and IBM’s Watson winning a Jeopardy! quiz show exhibition match.
  2. AlphaGo: Demonstrated AI’s prowess in playing Go, defeating professional players like Lee Sedol and Ke Jie.

Military:

  1. AI in Military Applications: Countries deploy AI for command and control, communications, intelligence collection, logistics, and autonomous vehicles in military operations.

Generative AI:

  1. ChatGPT and Large Language Models: Generative AI, represented by models like ChatGPT and GPT-3, gained widespread attention for their realistic text generation capabilities.

Industry Specific Tasks:

  1. AI in Agriculture: Used for irrigation, fertilization, pesticide treatments, yield optimization, and various tasks to enhance agricultural practices.
  2. Medical Diagnosis: AI is employed for medical diagnosis, improving accuracy and efficiency in healthcare.
  3. Military Logistics: AI aids in logistics, intelligence analysis, and coordination in military operations.
  4. Predictive Analytics: AI applications predict outcomes in judicial decisions, foreign policy, and supply chain management.
  5. Energy Storage: AI is used in energy storage systems for optimization and efficiency.
  6. Astronomy: AI assists in analyzing data for discovering exoplanets, forecasting solar activity, and classifying signals in gravitational wave astronomy.
  7. Space Exploration: AI applications support space exploration, including analyzing data from space missions, autonomous spacecraft decisions, and space debris avoidance.

The applications of AI are diverse, impacting various aspects of our daily lives, industries, and scientific endeavors.

Ethical Considerations in Artificial Intelligence (AI):

General Ethical Concerns:

  1. Potential Benefits and Risks: AI has the potential to solve complex problems and advance science, but its widespread use raises ethical concerns and unintended consequences.
  2. Ethics in AI Training: Developers using machine learning in real-world systems must prioritize ethics, especially in inherently unexplainable deep learning algorithms, to prevent bias.

Risks and Harm:

  1. Privacy and Copyright Concerns:
    • Large Data Requirements: Machine learning algorithms need substantial data, raising issues of privacy, surveillance, and copyright.
    • Data Collection Practices: Technology companies collecting user data, including audio and video, for AI applications can raise ethical concerns.
  2. Generative AI and Copyright Issues:
    • Unlicensed Training Data: Generative AI often uses unlicensed copyrighted works, leading to legal and ethical questions.
    • Lawsuits: Leading authors have sued AI companies for using their work to train generative AI, highlighting copyright concerns.
  3. Misinformation:
    • Recommender Systems and Misinformation: AI-driven recommender systems on platforms like YouTube and Facebook unintentionally promoted misinformation, leading to societal harm.
    • Deepfake Technology: Generative AI creating indistinguishable images, audio, and text poses risks of misinformation and propaganda.

Algorithmic Bias and Fairness:

  1. Bias in Machine Learning:
    • Learning from Biased Data: Machine learning applications can inherit biases from training data, leading to biased decisions.
    • Discrimination: Biased algorithms in critical areas like medicine, finance, and policing can result in discrimination.
  2. Real-world Examples:
    • Google Photos Misidentification: Google Photos labeled individuals as “gorillas” due to biased training data, highlighting issues of sample size disparity.
    • COMPAS Racial Bias: COMPAS, a recidivism prediction tool, exhibited racial bias, impacting decision-making in U.S. courts.
  3. Challenges in Defining Fairness:
    • Defining Fairness: Researchers acknowledge challenges in defining “fairness” that satisfies all stakeholders in machine learning applications.

Lack of Transparency:

  1. Complex AI Systems:
    • Inability to Explain Decisions: Many AI systems, especially deep neural networks, are so complex that designers struggle to explain their decision-making processes.
    • Unintended Outcomes: AI programs may produce unexpected outcomes despite passing rigorous tests, emphasizing the need for transparency.
  2. Explanability Techniques:
    • Addressing Transparency: Various techniques like SHAP, LIME, multitask learning, and deconvolution aim to address transparency issues in AI systems.

Weaponized AI and Bad Actors:

  1. Lethal Autonomous Weapons:
    • Concerns: Lethal autonomous weapons without human supervision pose dangers, including accountability for unintended casualties and potential mass destruction.
  2. Use by Authoritarian Governments:
    • Surveillance and Propaganda: AI tools, including facial recognition and recommendation systems, aid authoritarian governments in surveillance, propaganda, and centralized decision-making.

Technological Unemployment:

  1. Impact on Employment:
    • Concerns: AI’s potential to automate tasks raises concerns about job redundancies and unemployment.
    • Varied Estimates: Economists differ in their estimates of the impact, ranging from substantial job loss to potential net benefits.

Existential Risks:

  1. Loss of Control:
    • Uncontrolled AI Power: Concerns that AI may become so powerful that humanity loses control, posing existential risks.
    • Ethical Alignment: The need for AI to align with human values to avoid unintended harmful consequences.

Ethical Machines and Alignment:

  1. Friendly AI and Ethical Decisions:
    • Designing for Ethical Choices: Friendly AI emphasizes designing machines from the start to minimize risks and make choices that benefit humans.
  2. Machine Ethics and Morality:
    • Machine Ethics: Developing ethical principles for machines and resolving ethical dilemmas through the field of machine ethics.

Ethical Frameworks:

  1. AI Frameworks and Testing:
    • Care and Act Framework: Testing AI projects based on values like respect, connection, care, and protection.
    • Other Initiatives: Asilomar Conference, Montreal Declaration, IEEE’s Ethics of Autonomous Systems, among others, contribute to ethical frameworks.

Regulation:

  1. Global Regulatory Landscape:
    • Emerging Regulatory Issues: The global landscape for AI regulation is evolving, with increasing attention from countries and organizations.
    • International Cooperation: Calls for global cooperation to manage challenges and risks of artificial intelligence.
  2. Public Attitudes and Regulation:
    • Varying Public Attitudes: Public attitudes toward AI vary globally, with considerations of benefits and drawbacks.
    • Calls for Regulation: Growing support for AI regulation, with discussions on the importance of government intervention.
  3. Global AI Safety Summit:
    • Summit Initiatives: The first global AI Safety Summit discussed near and far-term risks, exploring mandatory and voluntary regulatory frameworks.
    • United Nations Advisory Body: The UN established an advisory body in 2023 for AI governance, consisting of industry experts, government officials, and academics.

In addressing the ethical challenges posed by AI, ongoing efforts focus on transparency, fairness, regulation, and the alignment of AI systems with human values. The multidimensional nature of these concerns necessitates collaboration among diverse stakeholders to ensure responsible development and deployment of AI technologies.

History of Artificial Intelligence (AI):

Early Foundations:

  1. Philosophical Roots:
    • Antiquity: The study of formal reasoning originated with ancient philosophers and mathematicians.
    • Alan Turing’s Theory: Alan Turing’s theory of computation proposed that machines, using simple symbols like “0” and “1,” could simulate mathematical deduction and formal reasoning, known as the Church–Turing thesis.
  2. Early AI Concepts:
    • Turing’s Early Work: Turing’s 1941 paper on machine intelligence, possibly the earliest AI paper, was followed by McCullouch and Pitts’ 1943 design for artificial neurons.
    • ‘Machine Intelligence’ Term: Turing used the term ‘Machine Intelligence,’ later evolving into ‘Artificial Intelligence’ (AI) after his death in 1954.

Emergence of AI (1950s-1960s):

  1. Turing Test and Radio Broadcasts:
    • Turing Test: Alan Turing’s 1950 paper introduced the famous Turing test, evaluating a machine’s ability to exhibit human-like intelligence.
    • Radio Broadcasts: Turing’s radio broadcasts discussed ‘Intelligent Machinery,’ ‘Can Digital Computers Think?,’ and a panel discussion on automatic calculating machines.
  2. AI Workshop and Early Programs:
    • Dartmouth Workshop: The field of American AI research was founded at Dartmouth College in 1956 during a workshop.
    • Astonishing Progress: AI pioneers and their students produced programs that showcased astonishing progress, including learning checkers, solving algebra problems, proving theorems, and natural language processing.
  3. AI Winter (1970s-1980s):
    • Underestimated Challenges: Early optimism underestimated the difficulty of achieving AI goals, leading to the “AI winter.”
    • Funding Cuts: Criticism from Sir James Lighthill and government pressure resulted in funding cuts, and the perception of AI’s limitations grew.

AI Revival (1980s):

  1. Expert Systems and Funding Restoration:
    • Expert Systems: The commercial success of expert systems in the early 1980s revived interest in AI.
    • Funding Restoration: Inspired by Japan’s fifth-generation computer project, the U.S. and British governments restored funding for academic AI research.
  2. AI Winter (Late 1980s):
    • Lisp Machine Collapse: The collapse of the Lisp Machine market in 1987 marked the beginning of a second, longer-lasting AI winter.
    • Doubts and Sub-symbolic Approaches: Researchers questioned AI’s ability to replicate human cognition and explored “sub-symbolic” approaches.

Neural Networks and Connectionism:

  1. Revival of Connectionism (1990s):
    • Geoffrey Hinton’s Contributions: Connectionism, including neural network research, was revived in the 1990s, with Geoffrey Hinton’s successful application of convolutional neural networks.
  2. Late 1990s – Early 21st Century:
    • Formal Mathematical Methods: AI regained reputation by leveraging formal mathematical methods and solving specific problems.
    • Collaboration and Verification: Focusing on “narrow” and “formal” approaches allowed collaboration with other fields and produced verifiable results.

Artificial General Intelligence (AGI) and Recent Developments:

  1. Concerns and AGI (2000s):
    • AGI Subfield: Concerns about AI deviating from its original goals led to the establishment of the subfield of artificial general intelligence (AGI) around 2002.
  2. Deep Learning Dominance (2012 Onward):
    • Industry Dominance: Deep learning became dominant in industry benchmarks from 2012 onward and saw widespread adoption.
    • Factors for Success: Hardware improvements, access to large datasets, and faster computers contributed to deep learning’s success.

Current Landscape:

  1. AI Resurgence (Late 1990s Onward):
    • Formal Methods: AI regained prominence by employing formal methods and addressing specific challenges.
    • Increased Funding and Interest: Deep learning’s success fueled a surge in interest and funding, with AI-related investments reaching billions annually.
  2. Global Leadership and Academic Focus:
    • U.S. Dominance: The United States led AI research, with companies, universities, and research labs playing a pivotal role.
    • Academic Concerns: By 2002, academic researchers became concerned about AI’s focus, leading to the establishment of AGI.
  3. Recent Developments (2015 Onward):
    • Increased Research: Machine learning research witnessed a 50% increase in total publications from 2015 to 2019.
    • AI Impact and Investments: Around 2022, approximately $50 billion was annually invested in AI in the U.S., with AI-related job openings reaching 800,000.
  4. Focus on Ethical Issues (2016 Onward):
    • Ethical Concerns: Issues of fairness and technology misuse gained prominence in 2016, leading to increased research, funding, and career focus on these aspects.
  5. AI Impacts and Future Concerns:
    • AI’s Role: AI played a crucial role in various domains, with emerging technologies leading in patent applications and granted patents.
    • Alignment Problem: The alignment problem became a significant academic field of study, reflecting concerns about the ethical implications of AI.

The history of AI reflects a journey marked by initial optimism, challenges, periods of skepticism (AI winters), and subsequent revivals. Recent developments underscore the importance of ethical considerations as AI continues to advance.

Philosophy of Artificial Intelligence:

Defining Artificial Intelligence:

  1. Turing’s Inquiry (1950):
    • Alan Turing’s Question: In 1950, Alan Turing posed the question, “Can machines think?” He suggested shifting the focus to whether machinery can exhibit intelligent behavior.
    • Turing Test: Turing introduced the Turing test, evaluating a machine’s ability to simulate human conversation, regardless of whether it “thinks” or possesses a “mind.”
    • Polite Convention: Turing highlighted the human difficulty in determining others’ thoughts, emphasizing a polite convention that assumes everyone thinks.
  2. AI Defined by Action (Russell, Norvig, McCarthy):
    • Action over Thought: Russell, Norvig, and McCarthy emphasized defining AI based on actions rather than thoughts.
    • McCarthy’s Definition: McCarthy defined intelligence as “the computational part of the ability to achieve goals in the world.”
  3. Google’s Synthesis Definition:
    • Information Synthesis: Google’s definition emphasizes the manifestation of intelligence as the ability of systems to synthesize information, resembling biological intelligence.

Evaluating Approaches to AI:

  1. Absence of Unifying Theory:
    • Lack of Unified Theory: AI research lacked a unifying theory for most of its history.
    • Dominance of Machine Learning: Statistical machine learning’s unprecedented success in the 2010s overshadowed other approaches, leading to the term “artificial intelligence” often being synonymous with “machine learning with neural networks.”

Symbolic AI and Its Limits:

  1. Symbolic AI (GOFAI):
    • Conscious Reasoning: Symbolic AI simulated conscious reasoning used in problem-solving, legal reasoning, and mathematics.
    • Successes and Failures: Successful in tasks like algebra or IQ tests, but failed in areas such as learning, object recognition, and commonsense reasoning.
    • Moravec’s Paradox: High-level tasks were easy, but low-level instinctive tasks were challenging.
  2. Neuro-Symbolic AI:
    • Bridging Approaches: Neuro-symbolic AI attempts to bridge symbolic and sub-symbolic approaches.
    • Insufficient Resolved: Critics argue unresolved issues, such as algorithmic bias, still persist in sub-symbolic reasoning.

Neat vs. Scruffy:

  1. Neats and Scruffies Debate:
    • Neats vs. Scruffies: Neats seek elegant principles (logic, optimization), while scruffies believe in solving numerous unrelated problems.
    • Relevance in Modern AI: The debate, active in the 70s and 80s, is seen as less relevant in modern AI, which incorporates elements of both approaches.

Soft vs. Hard Computing:

  1. Soft Computing Introduction:
    • Intractable Problems: Soft computing, introduced in the late 80s, addresses problems where finding provably correct or optimal solutions is intractable.
    • Techniques: Genetic algorithms, fuzzy logic, and neural networks are examples of soft computing techniques.

Narrow vs. General AI:

  1. Divergent AI Goals:
    • AI Research Divergence: AI researchers are divided on whether to pursue artificial general intelligence (AGI) and superintelligence directly or focus on solving specific problems (narrow AI).
    • Verifiable Success: Modern AI has achieved more verifiable successes by addressing specific problems.

Machine Consciousness, Sentience, and Mind:

  1. Philosophy of Mind:
    • Internal Experiences: The philosophy of mind debates whether machines can have a mind, consciousness, and mental states, focusing on internal experiences.
    • AI Research Perspective: Mainstream AI considers this issue irrelevant to the field’s goals, emphasizing problem-solving capabilities.

Consciousness:

  1. Chalmers’ Problems:
    • Hard and Easy Problems: Philosopher David Chalmers identified the “hard” and “easy” problems of consciousness, differentiating between brain processing and subjective experience.
    • Computationalism: Computationalism sees the mind as an information processing system, addressing the mind–body problem.

Robot Rights:

  1. Sentience and Rights:
    • Sentience Entitlement: If machines possess sentience, there is a discussion on whether they should be entitled to certain rights, akin to animal and human rights.
    • Fictional and Real Considerations: The issue, explored in fiction for centuries, is now a topic of real-world consideration, with critics deeming the discussion premature.

Future of Artificial Intelligence:

Superintelligence and the Singularity:

  1. Definition of Superintelligence:
    • Hypothetical Agent: Superintelligence refers to a theoretical agent with intelligence surpassing even the most brilliant human minds.
  2. Intelligence Explosion and Singularity:
    • Self-Improvement: If research on artificial general intelligence achieves highly intelligent software, it could potentially reprogram and enhance itself.
    • Intelligence Explosion: I. J. Good coined the term “intelligence explosion,” and Vernor Vinge referred to the resulting scenario as a “singularity.”
    • Self-Improvement Loop: Improved software continuously enhances itself, leading to an accelerating cycle of improvement.
  3. Limitations and S-Shaped Curve:
    • Technology Limits: Despite the potential for exponential improvement, technologies often encounter physical limits, resulting in an S-shaped curve of growth.
    • Eventual Slowdown: Exponential progress tends to slow down as technologies approach their inherent constraints.

Transhumanism:

  1. Human-Machine Merging:
    • Cyborg Evolution: Predictions by robot designer Hans Moravec, cyberneticist Kevin Warwick, and inventor Ray Kurzweil envision a future where humans and machines merge into advanced cyborgs.
    • Transhumanism Roots: The concept, known as transhumanism, traces its origins to thinkers like Aldous Huxley and Robert Ettinger.
  2. AI as Evolution’s Next Stage:
    • Evolutionary Perspective: Edward Fredkin asserts that “artificial intelligence is the next stage in evolution,” echoing Samuel Butler’s 1863 idea in “Darwin among the Machines.”
    • Dyson’s Exploration: George Dyson expanded on this concept in his 1998 book of the same name, delving into the notion of AI as an evolutionary milestone.

Conclusion:

The future of artificial intelligence holds speculative yet intriguing possibilities, from the potential emergence of superintelligence and the singularity to the convergence of humans and machines in the realm of transhumanism. As these ideas continue to captivate the imaginations of scientists, inventors, and philosophers, the trajectory of AI’s evolution remains a subject of ongoing exploration and debate.

stsspecial

<br /> <meta charset="utf-8" /><br /> <meta content="ie=edge" http-equiv="x-ua-compatible" /><br /> <meta content="width=device-width, initial-scale=1" name="viewport" /><br /> <title>stsspecial






Tagged ,

Leave a Reply

Your email address will not be published. Required fields are marked *