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Artificial intelligence (AI) has a rich history dating back to ancient myths and legends, but its formal study began in the 20th century. The concept gained scientific traction in the 1950s when computer scientists like Alan Turing and John McCarthy laid the groundwork. Turing's work on the Turing Test and McCarthy's coining of the term "artificial intelligence" in 1956 were pivotal moments.

The 1956 Dartmouth Conference is often considered the birth of AI as a field. Early efforts focused on symbolic AI, which involved programming computers to solve problems through logic and rules. This era saw the development of the first AI programs, such as the Logic Theorist and the General Problem Solver.

The 1970s and 1980s were marked by a period of over-optimism and subsequent "AI winters," where funding and interest waned due to unmet expectations. Despite setbacks, research continued, leading to advances in machine learning, neural networks, and expert systems.

The 1990s and 2000s saw a resurgence in AI, driven by improvements in computational power, data availability, and algorithmic advancements. Techniques like deep learning, which leverages neural networks with many layers, began to show significant promise.

In recent years, AI has experienced exponential growth, impacting various sectors such as healthcare, finance, transportation, and more. Breakthroughs in natural language processing, computer vision, and reinforcement learning have led to the development of sophisticated systems like self-driving cars, virtual assistants, and advanced diagnostic tools.

Overall, the history of AI is a testament to human ingenuity and the persistent quest to create machines that can emulate and extend human capabilities.

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Sure, here is the text translated into English:

The development of artificial intelligence (AI) has gone through several important stages:

**1. Beginnings and Theoretical Foundations (1950s and 1960s):**

- **Alan Turing**: In 1950, he proposed the Turing Test and asked the question, "Can machines think?"

- **John McCarthy**: In 1956, he introduced the term "artificial intelligence" at the Dartmouth Conference. This conference is considered the starting point of AI research.

**2. Early Achievements and Hype (1960s and 1970s):**

- Early AI systems achieved some success, particularly in game playing and problem-solving. For example, IBM's Deep Blue computer defeated world chess champion Garry Kasparov in 1997.

- However, the lack of necessary hardware and data for AI to exhibit human-level intelligence led to a period of stagnation in AI research known as the AI winter.

**3. Expert Systems and Temporary Decline (1980s):**

- Expert systems focused on solving problems using expert knowledge in specific domains. These systems were commercially successful.

- However, the limitations and high costs of these systems led to decreased interest, resulting in a second AI winter.

**4. Machine Learning and Big Data (1990s and 2000s):**

- **Machine learning** algorithms brought new life to AI by enabling systems to learn from data. This became particularly viable with the availability of large datasets and increased computational power.

- Advances in **deep learning** led to significant breakthroughs in image recognition, speech recognition, and natural language processing.

**5. Present and Future (2010s and beyond):**

- Today, AI is used in healthcare, finance, transportation, security, and many other fields. For example, Google's AlphaGo system defeated the world champion in the game of Go in 2016.

- Applications such as **autonomous vehicles**, **personal assistants** (Siri, Alexa), **facial recognition systems**, and **robotic surgery** have become widespread.

- The development of **artificial general intelligence** (AGI) and **artificial superintelligence** (ASI) is still in the research phase. AGI refers to systems that can exhibit a broad range of intelligence at a human level, while ASI refers to systems that surpass human intelligence.

The future of artificial intelligence brings many questions and challenges, including ethical and security concerns. Research is focused on how AI can improve human life and how potential risks can be minimized.

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Artificial intelligence has had a revolutionary impact on industry and technology. It is widely used to optimize production processes, increase efficiency, and reduce costs in manufacturing. AI-powered robots working alongside human workers on production lines enable faster and more precise manufacturing. Additionally, through big data analytics and machine learning, production efficiency is enhanced, and quality control processes are improved. For example, image recognition systems can detect faulty products, reducing waste. Furthermore, predictive analytics enables demand forecasting, optimizing inventory management and production planning. AI also facilitates the advancement of automation to higher levels, allowing humans to focus on more strategic and creative tasks. As a result, artificial intelligence is shaping the future of industry and technology to be more efficient, flexible, and competitive.

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## Important Turning Points and Revolutionary Studies in the Evolution of Artificial Intelligence

Artificial intelligence (AI) has been a field that has attracted the imagination and curiosity of human beings for centuries. The first AI research 20. it began in the middle of the century, and since then this field has made tremendous progress. This progress has been characterized by many important milestones and revolutionary work.

Some important turning points are:

*1950s:** Alan Turing's article "Computing Machines and Intelligence" formed the basis of artificial intelligence and introduced the "Turing Test".

*1960s:** The first artificial intelligence programs were developed, for example, General Problem Solver (GPS) and ELIZA.

*1970s:** There was an increase in artificial intelligence research inspired by neuroscience.

*1980s:** Machine learning algorithms were developed and artificial intelligence applications began to be used in fields such as medicine and finance.

*1990s:** The rise of the Internet has created new opportunities in artificial intelligence research.

*2000s:** The availability of large datasets and the increase in computing power led to a major advance in artificial intelligence.

*2010s:** Deep learning algorithms were developed and great achievements were achieved in areas such as image recognition and natural language processing.

*2020s:** Artificial intelligence is now used in many areas of our lives and is expected to play an even more important role in the coming years.

Some of the revolutionary works are as follows:

*AlphaGo:** In 2016, the AlphaGo program, developed by Google's DeepMind company, managed to defeat world champion Lee Sedol in the game of Go. This made history as the first time a computer defeated a professional Go player.

*Watson:** In 2011, the Watson program developed by IBM, Jeopardy! he defeated two human champions in the knowledge contest named. Watson was noted for his ability to understand natural language and provide human-like responses to questions.

*Alexa:** Alexa, developed by Amazon, is a virtual assistant that can be controlled by voice commands. Alexa can perform a variety of tasks, such as playing music, reading the news, controlling smart home devices, and more.

*Self-driving cars:** Companies such as Google, Waymo and Tesla are making major investments in developing self-driving cars. These cars can sense their surroundings using sensors and artificial intelligence and drive safely on the roads.

Artificial intelligence is a rapidly developing field and is expected to have an even greater impact in the coming years. AI holds promise for solving many problems, but it also raises important questions from an ethical and social point of view. The responsible development and use of AI is very important for it to be for the benefit of all humanity.

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The future of AI holds tremendous potential for both excitement and concern. On one hand, AI promises to revolutionize nearly every aspect of human life, from healthcare and transportation to entertainment and education. We're likely to see increasingly sophisticated AI systems capable of performing tasks that were once thought to be the exclusive domain of human intelligence. This could lead to significant advancements in areas like medical diagnosis, drug discovery, personalized learning, and more efficient resource management.

However, there are also legitimate concerns about the impact of AI on jobs, privacy, security, and even the nature of humanity itself. As AI systems become more capable, there's the possibility of widespread automation leading to job displacement, exacerbating existing inequalities. There are also ethical considerations surrounding issues like bias in AI algorithms, the potential for misuse in surveillance and warfare, and the existential risks associated with creating superintelligent AI.

To navigate this complex landscape, it's crucial for society to engage in thoughtful discussions about the ethical, social, and economic implications of AI. We need robust regulations and safeguards to ensure that AI is developed and deployed in a responsible and equitable manner. Additionally, investing in education and training programs will be essential to help people adapt to the changing job market and to ensure that everyone can benefit from the advances brought about by AI. Ultimately, the future of AI will be shaped by the choices we make today.

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Artificial intelligence (AI) stands as one of the paramount technological advancements of our time, exerting a profound impact on both industry and technology sectors. Its utilization, particularly within the realms of industry and manufacturing, has been pivotal in transforming operational processes and enhancing productivity.

AI finds application across various domains within industry and manufacturing. For instance, AI and machine learning techniques on production lines are employed to optimize manufacturing processes, thereby increasing productivity while concurrently reducing error rates.

Furthermore, AI-based robots play a significant role in industrial facilities by enhancing worker health and safety. These robots undertake hazardous or monotonous tasks, enabling humans to work in safer environments.

Moreover, AI plays a substantial role in forecasting and planning processes within the industry. AI-based systems are utilized to predict production demand, optimize inventory management, and streamline logistics processes, thus augmenting productivity.

This animation video should elucidate the diverse applications of AI in industry and technology sectors, elucidating its impact. It should showcase how techniques like AI and machine learning are employed in industrial processes, underscoring how these technologies have made a difference in industrial productivity, safety, and competitive advantage.

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In the bustling landscape of the digital age, where every click and command is governed by the unseen hand of Artificial Intelligence (AI), lies a rich tapestry of history waiting to be unveiled. "Chronicles of AI: A Journey Through Time" takes audiences on an exhilarating animated adventure, delving into the captivating evolution of AI across the epochs.

Episode 1: Genesis Our journey begins amidst the intellectual fervor of ancient civilizations. From the calculating prowess of the abacus to the philosophical musings on artificial beings in Greek myths, humanity's fascination with automata lays the groundwork for AI's genesis.

Episode 2: The Renaissance of Reason As the Renaissance sweeps across Europe, the quest for knowledge ignites. Visionaries like Leonardo da Vinci envision machines that mimic human motion, while mathematicians develop the rudiments of algorithms. The dawn of computation beckons.

Episode 3: The Industrial Revolution and Beyond With the advent of steam power, the Industrial Revolution propels humanity into a new era of innovation. Charles Babbage's Analytical Engine sparks the dream of mechanical computation, laying the foundation for modern computers.

Episode 4: World Wars and the Birth of Modern Computing Amidst the turmoil of global conflict, the quest for computational supremacy intensifies. Alan Turing's visionary work at Bletchley Park during World War II leads to the birth of the modern computer and the conceptualization of AI.

Episode 5: The Digital Age Dawns As transistors shrink and silicon chips proliferate, the digital age unfolds with unprecedented velocity. The birth of neural networks and machine learning algorithms heralds a new era of AI, where computers learn from data and adapt autonomously.

Episode 6: The AI Revolution In the 21st century, AI permeates every aspect of modern life, from virtual assistants to self-driving cars. Ethical dilemmas arise as AI systems grapple with complex decisions, raising questions about autonomy, bias, and the future of humanity.

"Chronicles of AI: A Journey Through Time" intertwines history, science, and imagination to illuminate the profound impact of AI on society. Through vibrant animation and captivating storytelling, audiences are invited to explore the past, present, and future of artificial intelligence in all its glory and complexity. Join us on this epic voyage through the annals of time, where the dreams of visionaries and the ingenuity of innovators converge to shape the destiny of humankind.

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In the early 1950s, Alan Turing's "Can Machines Think?" The concept of machine learning emerged with the question "Artificial Intelligence" by Computer Scientist and Cognitive Scientist "John McCarthy".

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EceYAL-Hande Akçasu Yeşilköy Anatolian High School

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EceYAL-Hande Akçasu Yeşilköy Anatolian High School

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The term artificial intelligence (AI) refers to intelligence operations performed by machines designed to reproduce the capabilities of the human brain through combinations of algorithms.

More specifically, artificial intelligence is an intelligence that allows certain machines to perceive the environment around them and respond to it in a similar way to the human brain. This includes the ability to perform functions such as reasoning, perceiving, learning, and problem solving.

Computer science, logic, philosophy, and robotics have contributed to the creation and design of machines that can solve problems using the artificial intelligence model. Types of artificial intelligence

1. Expert Systems

It is a type of artificial intelligence that attempts to imitate human expertise on a particular subject. Anything from a technical service person to a receptionist, from a movie buff to an economist can be the subject of expert systems.

2. Machine Learning

Machine learning actually fits perfectly with the theoretical explanation of artificial intelligence. This technique refers to the ability of an artificial intelligence, a software or a robot to learn on its own.

Machine Learning follows the classic steps of artificial intelligence: Learning first involves training that creates experience and an application that tells us whether the task was completed successfully.

Machine Learning has two types: supervised or unsupervised. In the first, there is a human who tells the system what it is doing right and what is wrong. Unsupervised Machine Learning is the process of discovering what the artificial intelligence system does well and what it does wrong based on the rules given to it.

3. Neural networks

Compared to other systems that mimic the behavior of the human brain, neural networks attempt to copy the behavior of neurons, that is, nerve cells in the brain that transmit and process information. This is another way of learning and is therefore considered a form of Machine Learning.

An artificial neuron takes input data, applies mathematical operations to them, a sequence, and an activation function (a mathematical formula) to produce a result. Although it seems like a fundamentally simple mechanism, millions of neurons can be run in parallel to create Artificial Neural Networks.

What distinguishes Artificial Neural Networks from a computer program is that they change their input and output less by commands or rules, but rather by interaction with each other and through learning and error depending on the assigned task.

Neural networks are suitable for tasks where you need to recognize a pattern or relate ideas. They are used in areas such as robot control, text and image recognition, and natural language processing.

4. Deep Learning

Deep Learning is a type of Machine Learning used to process big data. Deep Learning processes data in different layers using artificial neural networks.

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The impact of artificial intelligence (AI) on industry and technology has been transformative, revolutionizing various aspects of production and operations. Here are some key points highlighting its influence:

Automation: AI enables automation of repetitive tasks, increasing efficiency and reducing costs in industries such as manufacturing, logistics, and services. This includes tasks like assembly line operations, quality control, and inventory management.

Predictive Maintenance: AI algorithms can analyze vast amounts of data from sensors and machinery to predict when equipment is likely to fail. This allows companies to schedule maintenance proactively, minimizing downtime and optimizing resources.

Optimization: AI algorithms can optimize processes and workflows by analyzing data and identifying patterns that humans might overlook. This optimization can lead to improved resource utilization, faster production cycles, and better decision-making.

Personalization: In industries like retail and marketing, AI enables personalized recommendations and targeted advertising based on consumer behavior and preferences. This enhances customer experience and increases engagement.

Supply Chain Management: AI improves supply chain management by forecasting demand, optimizing inventory levels, and identifying potential disruptions. This leads to smoother operations, reduced costs, and improved customer satisfaction.

Quality Control: AI-powered systems can inspect products for defects with greater accuracy and speed than human inspectors. This ensures higher quality standards and reduces the likelihood of defective products reaching consumers.

Innovation: AI facilitates innovation by enabling rapid prototyping, simulation, and experimentation. It can also assist in research and development processes by analyzing data and generating insights that lead to breakthroughs in product design and development.

Safety: AI technologies like computer vision and natural language processing enhance workplace safety by identifying hazards, monitoring employee behavior, and providing real-time alerts in hazardous environments.

Energy Efficiency: AI can optimize energy usage in industrial processes by analyzing energy consumption patterns and recommending strategies to minimize waste. This contributes to sustainability efforts and cost savings.

Scalability: AI systems can scale operations more efficiently than traditional methods, allowing companies to handle increased production volumes without proportionally increasing labor costs.

Overall, the integration of AI into industry and production processes has led to increased productivity, cost savings, innovation, and improved competitiveness for businesses across various sectors. However, it also raises ethical and societal concerns, such as job displacement and data privacy, which need to be addressed as AI continues to advance.

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AI may be new in our lives but its foundations start from the 1940s: The Birth of Computing. The 1940s saw the development of early electronic computers such as the Colossus and the ENIAC, which laid the foundation for computational technology. These machines were primarily used for wartime cryptography and calculations.1943: Turing's Vision. Alan Turing introduces the concept of a universal machine capable of performing any computation, now known as the Turing machine. His work laid the theoretical groundwork for artificial intelligence.1949: Cybernetics Emerges. Mathematician Norbert Wiener publishes "Cybernetics: Or Control and Communication in the Animal and the Machine," proposing a new interdisciplinary field studying systems of control and communication.1950: Turing Test. Alan Turing publishes his seminal paper "Computing Machinery and Intelligence," introducing the concept of the Turing Test to determine a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.1956: The Dartmouth Conference. Considered the birth of artificial intelligence as a field, John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organize the Dartmouth Conference, where the term "artificial intelligence" is coined. The conference marks the beginning of focused research into creating machines capable of intelligent behavior.1950s-1960s: Early AI Programs. Researchers develop some of the first AI programs, including the Logic Theorist by Allen Newell and Herbert A. Simon, which could prove mathematical theorems, and the General Problem Solver (GPS), a computer program designed to emulate human problem-solving skills.1970s-1980s: The AI Winter. Optimism waned due to overpromising and underdelivering. Funding dried up as progress stagnated, leading to a period known as the AI Winter. Expert systems and symbolic AI dominated this period.1990s-2000s: The Rise of Machine Learning. Breakthroughs in machine learning, particularly neural networks and statistical methods, rejuvenated interest in AI. The advent of the internet and the availability of large datasets fueled progress.2010s: Deep Learning Revolution. Deep learning, a subset of machine learning, gained prominence with advances in algorithms, computational power, and big data. Applications such as image recognition, natural language processing, and recommendation systems saw significant improvements.2020s: AI in Everyday Life. AI becomes increasingly integrated into everyday life, powering virtual assistants, autonomous vehicles, personalized recommendations, and more. Ethical concerns regarding bias, privacy, and job displacement gain attention as AI adoption accelerates.

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https://prezo.ai/public/Exploring-the-Evolution-of-Artificial-Intelligence--6b583d29-a871-4ed4-9601-bcb52ca30a5a

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Exploring the Evolution of Artificial Intelligence | Built with Prezo

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Current state of artificial intelligence

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Ways AI is helping industry

Automation of processes: AI enables the automation of various processes in the industry, which helps increase efficiency and reduce costs. For example, AI-powered robots can automatically perform complex tasks in manufacturing, logistics, or equipment maintenance.

Predictive analytics: AI can analyze large amounts of data to predict future trends, demand, or equipment failures. This helps companies make better-informed decisions and plan their activities in advance.

Error detection: AI can identify errors or anomalies in production processes or equipment operations, allowing companies to react quickly and prevent potential problems before they occur.

Personalization of products and services: AI enables the personalization of products and services based on customer data, helping companies better meet the needs of their customers and increase loyalty.

Development of new products and innovations: AI can help companies identify new opportunities for innovation and product development through data analysis or using technologies such as machine learning.

More efficient supply chain management: AI can optimize supply chain management by identifying the most efficient routes, managing inventory, and predicting demand.

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Artificial Intelligence and Industry 4.0 in the Factory Plan

The most interesting aspect of Industry 4.0 is that machines in the factory environment can provide composable information. Intelligent machines have the ability to tell who the future brand is and when it was produced. Situations such as the fact that the factory can operate even open, etc., are all called "factory equipped with Artificial Intelligence", although it is not emphasized.

Artificial Intelligence in Logistics

Another branch of Industry 4.0 is logistics. Here we see the impact of Artificial Intelligence again. It is possible to learn where the car containing the product is and at what speed by tracking the findings obtained as a result of the machine products, what is written. There is also the possibility of making logistics more efficient.

Logistics

Internet of Things (Internet of Things) is the presence of electrical devices and structures in the interaction system environment. With its sensitivity and network connectivity features, the software enables collaboration objects to obtain and exchange different information. The internet of things (IoT) has the ability to enable control, management and computer alerting in case network infrastructures are damaged.

Equality between the connections of systems and interactions between Machine and Machine (M2M) are increased.

The internet of things (IoT) is not specific to industry 4.0. To give examples from other areas where IoT is used: It is used in all kinds of projects you can think of, such as home automation (smart home project), automatically turned on lights, automatically heated thermoses, smart cities, smart transportation, robot vacuum cleaners, remotely monitored and controlled ovens and freezers.

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Advancements in AI: It's anticipated that artificial intelligence will continue to advance rapidly, with improvements in areas such as natural language processing, computer vision, and reinforcement learning. This progress will likely lead to AI systems that are more capable, efficient, and adaptable to various tasks.

Automation and Job Displacement: One of the significant impacts of AI on the job market is automation. While AI has the potential to streamline processes and increase productivity, it also raises concerns about job displacement. Many routine tasks currently performed by humans could be automated, leading to potential unemployment or the need for reskilling in new areas.

Economic Transformation: AI is expected to drive significant economic transformations, potentially leading to the creation of new industries and job opportunities. However, it may also exacerbate income inequality if certain segments of the workforce are left behind due to lack of skills or access to AI technologies.

Ethical Considerations: As AI becomes more prevalent in society, there are growing concerns about ethics and responsible use. Issues such as algorithmic bias, data privacy, and the potential for misuse of AI technologies raise important ethical questions that need to be addressed by policymakers, businesses, and researchers.

Human-AI Collaboration: Rather than replacing humans entirely, AI is likely to augment human capabilities and enable new forms of collaboration between humans and machines. This could lead to more efficient decision-making processes, improved healthcare outcomes, and enhanced creativity in various fields.

Regulatory Challenges: Policymakers face the challenge of developing regulations and guidelines to govern the development and deployment of AI technologies. Balancing innovation with safety, privacy, and fairness will be crucial to ensuring that AI benefits society as a whole.

Social Impacts: AI has the potential to impact various aspects of society, including healthcare, education, transportation, and entertainment. While AI-driven innovations could lead to improvements in quality of life, there are concerns about issues such as algorithmic discrimination, job loss, and social isolation.

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Feryal. Mafen, TEAM 5 - Deep Learning Mixed Team Video

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Feryal. Mafen, TEAM 5 - Deep Learning Mixed Team Video

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Click to watch the AI video “TEAM 5- DEEP LEARNING” generated with Synthesia STUDIO.

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The development process of artificial intelligence

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Understanding Artificial Intelligence Techniques

Understanding AI Techniques is important to grasp the inner workings of AI systems, which aim to replicate human-like cognitive functions. These techniques offer several fundamental aspects, each playing a significant role in AI's ability to learn, reason and comprehend human language.

Learning is the bedrock of AI. It is the process by which AI systems accumulate knowledge from data and experiences. Through learning, AI systems discern patterns and relationships within data, enabling them to make predictions and decisions autonomously.

Reasoning is the cognitive process in which AI systems employ logical thinking to draw conclusions, make inferences, and solve complex problems. This aspect allows AI to make informed decisions based on available information, much like human thought processes. Problem-solving is a critical facet of AI Techniques. AI employs algorithms, mathematical procedures, and computational methods to tackle specific challenges efficiently. Whether optimising delivery vehicle routes or solving intricate mathematical equations, AI's problem-solving capabilities are instrumental in many applications.

Perception equips AI systems with the ability to interpret their environment. Through various sensors and data sources, AI perceives the world around it. This sensory input is vital for tasks such as image recognition, where AI processes visual data to identify objects, and speech recognition, where it translates auditory input into meaningful information.

Language understanding, often achieved through Natural Language Processing (NLP) techniques, is pivotal for effective human-computer interaction. AI systems equipped with NLP can comprehend human language, including nuances, context, and sentiment. This enables them to converse seamlessly with humans, making possible chatbots, virtual assistants, and language translation applications.

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Industrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.

Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.

With AI, manufacturers can employ computer vision algorithms to analyze images or videos of products and components. These algorithms can identify defects, anomalies, and deviations from quality standards with exceptional precision, surpassing human capabilitie

ndustrial artificial intelligence, or industrial AI, usually refers to the application of artificial intelligence to industry and business. Unlike general artificial intelligence which is a frontier research discipline to build computerized systems that perform tasks requiring human intelligence, industrial AI is more concerned with the application of such technologies to address industrial pain-points for customer value creation, productivity improvement, cost reduction, site optimization, predictive analysis and insight discovery.

Artificial intelligence and machine learning have become key enablers to leverage data in production in recent years due to a number of different factors: More affordable sensors and the automated process of data acquisition; More powerful computation capability of computers to perform more complex tasks at a faster speed with lower cost; Faster connectivity infrastructure and more accessible cloud services for data management and computing power outsourcing.

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The impact of AI is vast and can be seen across various industries, from healthcare and finance to transportation and retail. AI can improve efficiency, reduce costs, enhance accuracy, and provide personalized experiences to customers.

One of the most significant impacts of AI on the high-tech industry will be automation. By using machine learning algorithms and natural language processing, AI can automate many of the tasks currently performed by humans, from data entry to customer service.

Artificial intelligence transforms business operations by automating tasks, analyzing data, and enhancing decision-making. From automating repetitive processes and optimizing supply chains to predicting maintenance needs and offering personalized customer experiences, AI significantly boosts operational efficiency.

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Automation and Increased Efficiency: Artificial intelligence can automate many industrial processes, reducing human intervention and increasing efficiency. For example, in manufacturing, robots can perform tasks on assembly lines, boosting production speed.

Prediction and Optimization: By analyzing big data, artificial intelligence can predict future trends and help businesses manage their resources more effectively. It can be used in areas such as supply chain management, demand forecasting, and inventory optimization.

Customer Experience and Personalization: Artificial intelligence can provide more personalized experiences by analyzing customer behaviors. For instance, recommendation systems on online shopping platforms can suggest products based on user preferences, enhancing the shopping experience.

Innovation in Healthcare: Artificial intelligence plays a significant role in medical diagnosis and treatment processes. For example, image recognition systems can analyze medical images to assist doctors in making accurate diagnoses.

Security and Risk Management: Artificial intelligence is also used in cybersecurity. AI-based systems can quickly identify threats and take preventive measures in processes like threat detection and attack detection.

Education and Learning: In education, artificial intelligence can be used to identify individual learning needs and provide personalized learning experiences. Analyzing student performance can help optimize teaching methods.

Environment and Sustainability: Artificial intelligence can support sustainability efforts in areas such as energy efficiency, waste management, and environmental monitoring. Smart energy systems and environmental sensor networks can help in the more efficient use of resources.

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In the early 1950s, Alan Turing's "Can Machines Think?" The concept of machine learning emerged with the question, and the term "Artificial Intelligence" was used for the first time by Computer Scientist and Cognitive Scientist "John McCarthy" at the "Dartmouth Conference", the artificial intelligence conference held in 1956.

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Beatriz Holanda / Agrupamento Francisco de Holanda, Portugal

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Beatriz Holanda / Agrupamento Francisco de Holanda, Portugal

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Beatriz Holanda / Agrupamento de Escolas Francisco de Holanda, Portugal

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Beatriz Holanda / Agrupamento de Escolas Francisco de Holanda, Portugal

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Deep learning emerged in the early 2010s due to a convergence of factors: abundant data availability, increased computational power (thanks to GPUs), algorithmic advances, breakthroughs in research (like deep belief networks and successful competitions), industry adoption, and the open-source community's contributions. These elements combined to propel deep learning into the forefront of AI, revolutionizing various fields with its powerful capabilities.

Deep learning, as a subsector, is constantly evolving. As we speak, researches are being conducted to improve this field, focusing on improving training techniques, enhancing interpretability, exploring self-supervised learning, and developing specialized hardware. These efforts aim to make deep learning models not only more efficient, but also more reliable, interpretable, and ethical.

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Artificial intelligence (AI) has become a cornerstone of industry and technology, profoundly impacting production processes and operational efficiency. In manufacturing, AI optimizes production lines through predictive maintenance, which forecasts equipment failures, minimizing downtime and maintenance costs. Robotics, powered by AI, automate tasks, enhancing productivity and safety on factory floors.

Supply chain management benefits from AI's ability to analyze vast datasets, forecasting demand, optimizing inventory levels, and improving logistics operations. These insights enable companies to make data-driven decisions, reducing stockouts and excess inventory while improving overall efficiency.

AI drives innovation in product design and development, leveraging generative design algorithms to explore countless design possibilities and optimize components for performance and cost-effectiveness.

In the broader technology landscape, AI enhances data analysis, cybersecurity, and user experiences. Recommendation systems, virtual assistants, and chatbots streamline customer interactions, while AI algorithms bolster cybersecurity measures by detecting and mitigating threats in real-time.

Overall, AI's integration into industry and technology fosters agility, efficiency, and innovation, transforming traditional processes and driving growth across sectors. As AI continues to evolve, its impact on industry and production will only deepen, unlocking new opportunities for businesses to thrive in a rapidly changing global landscape.

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1. Introduction

- Briefly introduce the topic of artificial intelligence.

- Highlight the significance and impact of AI in today's world.

2. Early Beginnings

- Alan Turing and the Turing Test: Explain Turing's idea of a machine that can imitate human intelligence.

- McCulloch & Pitts Neuron: Introduce the basic concept of artificial neural networks inspired by biological neural networks.

3. The AI Winter

- 1950s-1970s: Discuss the initial enthusiasm followed by periods of reduced funding and interest due to unmet expectations.

4. Rise of Expert Systems

- 1980s: Explain the development of expert systems, rule-based systems designed to mimic human decision-making in specific domains.

5. Machine Learning Revolution

- 1990s-Present: Discuss the shift towards machine learning, algorithms that allow computers to learn from data.

- Key Algorithms: Briefly explain concepts like supervised learning, unsupervised learning, and reinforcement learning.

6. Deep Learning

- 2000s-Present: Introduce deep learning, a subset of machine learning based on artificial neural networks with multiple layers.

- Breakthroughs: Highlight key milestones like ImageNet competition victories and advancements in natural language processing.

7. Current Applications

- Self-driving cars, healthcare, finance: Showcase real-world applications of AI and its transformative impact on various industries.

8. Future Prospects

- Ethical Considerations: Discuss challenges and considerations related to AI ethics, bias, and regulation.

- Technological Advancements: Touch upon emerging technologies like quantum computing and their potential impact on AI.

9. Conclusion

- Summarize the journey of AI from its inception to its current state.

- Emphasize the ongoing evolution and future potential of artificial intelligence.

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Artificial intelligence (AI) holds immense potential for reshaping society, business, and ethics. In the future, AI is likely to revolutionize industries through automation, efficiency improvements, and innovation. It could lead to significant job displacement in certain sectors but also create new opportunities for employment in AI development, maintenance, and oversight.

In society, AI may enhance healthcare with personalized treatments, improve education through adaptive learning systems, and streamline transportation with autonomous vehicles. However, concerns about privacy, algorithmic bias, and the digital divide must be addressed to ensure equitable access and protection of individual rights.

In the business world, AI-powered analytics will enable data-driven decision-making, optimize supply chains, and personalize customer experiences. Yet, there's a need for transparent AI governance to mitigate risks such as data breaches and misuse of algorithms.

Ethically, AI raises questions about accountability, fairness, and the impact on human autonomy. Safeguards like ethical guidelines, regulatory frameworks, and interdisciplinary collaboration are essential to navigate these challenges and ensure that AI benefits humanity responsibly. Ultimately, the future of AI depends on how society chooses to harness its capabilities while prioritizing human values and well-being.

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The evolution of artificial intelligence (AI) has been marked by several pivotal moments that have revolutionized the field.

1. Dartmouth Conference (1956): Considered the birth of AI, this conference brought together leading researchers to discuss the potential of creating intelligent machines.

2. Expert Systems (1970s-1980s): Expert systems, which mimic the decision-making ability of human experts, gained prominence during this period, demonstrating the practical applications of AI.

3. Neural Networks Resurgence (2000s): Advances in computational power and algorithms reignited interest in neural networks, leading to breakthroughs in areas like image recognition and natural language processing.

4. Deep Learning (2010s): Deep learning, a subset of machine learning, propelled AI to new heights by enabling computers to learn from vast amounts of data. This led to significant advancements in areas such as autonomous vehicles, healthcare, and finance.

5. AlphaGo's Victory (2016): Google's AlphaGo defeated world champion Go player Lee Sedol, showcasing the power of AI in mastering complex games and tasks previously thought to require human intuition.

These turning points highlight the continuous evolution and transformative impact of AI, shaping the way we interact with technology and the world around us.

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The development of artificial intelligence (AI) in computer science is a rich tapestry that stretches back several decades. It all began in the 1950s with the birth of neural networks, inspired by the structure and function of the human brain. Early pioneers such as Alan Turing and Marvin Minsky laid the groundwork for AI research, sparking excitement about the potential for machines to mimic human intelligence.

However, progress in AI hasn't been a linear trajectory. The field experienced what became known as "AI winters" during the 1970s and 1980s, marked by disillusionment and decreased funding due to overinflated expectations and underwhelming results. Yet, despite these setbacks, researchers persevered, exploring different approaches and algorithms to tackle the challenges of AI.

The turn of the 21st century witnessed a resurgence of interest in AI, driven by advancements in computing power, the availability of vast amounts of data, and breakthroughs in machine learning algorithms. One pivotal moment was the introduction of deep learning, a subset of machine learning that utilizes neural networks with many layers (hence "deep") to learn intricate patterns from data. This approach revolutionized various AI applications, from image and speech recognition to natural language processing.

Today, AI is ubiquitous, permeating many aspects of our daily lives. Virtual assistants like Siri and Alexa, recommendation systems on streaming platforms, and self-driving cars are just a few examples of how AI technologies have become integral to modern society. Moreover, AI continues to evolve rapidly, with ongoing research pushing the boundaries of what is possible, from reinforcement learning for autonomous agents to the exploration of quantum computing for AI applications.

As we move forward, ethical considerations surrounding AI, such as bias in algorithms and the impact on employment, become increasingly important. Nevertheless, the trajectory of AI development in computer science is one of remarkable progress and promise, shaping the future of technology and society.

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Bruna e Oriana/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bruna e Oriana/Agrupamento de Escolas Francisco de Holanda/Portugal

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https://www.researchgate.net/publication/348642504/figure/fig6/AS:11431281115532926@1674962699806/Major-applications-of-speech-processing.png

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Bruna e Oriana/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bruna e Oriana/Agrupamento de Escolas Francisco de Holanda/Portugal

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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The future of artificial intelligence (AI) is expected to have significant impacts on society, business, and ethics. In the business world, AI is predicted to become increasingly integrated into daily operations, improving operational efficiency and providing predictive analytics.

AI is expected to become increasingly powerful and integrated into daily life by 2030. Interacting with AI systems will become as natural as interacting with other humans, with AI serving as personal assistants, tutors, career counselors, therapists, accountants, lawyers, and even significant others. Additionally, over one hundred thousand humanoid robots are predicted to be deployed in the real world by 2030, transforming the physical world with AI capabilities.

In terms of society, AI has the potential to change the way people live and interact with each other, bringing both positive and negative consequences (such as mass unemployment and the consolidation of financial and political power). To mitigate these risks, it is crucial to have an honest conversation about the potential threats of AI and take proactive measures, such as upskilling and adapting career paths, to ensure a smooth transition.

Companies must consider the ethical implications of AI adoption, including potential biases, transparency, explainability, and the impact on all stakeholders.

In summary, the future of AI will significantly impact society, business, and ethics. Companies must consider the ethical implications of AI adoption, establish and enforce ethical principles for AI, and implement strict guidelines around the use of AI to prevent its misuse and ensure its ethical application.

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Júlia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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https://www.researchgate.net/publication/370345407/figure/fig1/AS:11431281154117536@1682685867891/Overview-of-Artificial-Intelligence-In-modern-day-practice-Deep-Learning-DL-and.png

AI is a rapidly evolving field of computer science that combines various research areas, including machine learning, computer vision, natural language processing, machine listening, and robotics. AI research is conducted in various institutions, including universities and research centers, and is supported by various funding agencies, including the NSF. AI research has the potential to transform various aspects of society, from food production and climate change to healthcare and education, while also posing potential risks, such as the amplification of bias, displacement of workers, or misuse by malicious actors to cause harm.

https://www.perplexity.ai/search/explaining-the-development-uxHtiDEhRB6uhQOE.2JsHA

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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Bia/Agrupamento de Escolas Francisco de Holanda/Portugal

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https://www.chitkara.edu.in/blogs/wp-content/uploads/2023/06/The-impact-of-artificial-intelligence-on-various-industries.jpg

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https://amfg.ai/wp-content/uploads/2018/08/Industry-4.0-timeline.jpg

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Beatriz/Agrupamento de Escolas Francisco de Holanda/Portugal

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Beatriz/Agrupamento de Escolas Francisco de Holanda/Portugal

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https://static.javatpoint.com/tutorial/ai/images/history-of-ai.png

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The developmental years: Progress and setbacks

The 1960s and 1970s witnessed significant advancements, with the development of fundamental algorithms and the conception of machine learning. However, the field also faced “AI winters” — periods of reduced funding and interest in AI research — primarily due to unmet expectations and technological limitations.

KEY EVENTS

1921: Rossum’s Universal Robots, a play by Karel Čapek, opened in London, telling the story of artificial people made in a factory. This was the first time the word “robot” was used in the English language, which led to others applying the word and idea to art and research.

1927: The science fiction movie Metropolis was released; it follows a robot girl named Maria as she wreaks havoc in a 2026 dystopia. This was a significant early portrayal of a robot in cinema, later serving as the inspiration for C-3PO in the Star Wars films.

1939: Looking for ways to solve equations more quickly, inventor and physicist John Vincent Atanasoff constructed the first digital computing machine with graduate student Clifton Berry. The Atanasoff Berry Computer wasn’t programmable, but it could solve up to 29 linear equations concurrently, turning Atanasoff into the Father of the Computer.

1950: Mathematician and logician Alan Turing released “Computing Machinery and Intelligence,” questioning whether or not machines could manifest human intelligence. His proposal came in the form of The Imitation Game (better known today as The Turing Test), which evaluated a machine’s ability to think as humans do. The Turing Test has since become the cornerstone of AI theory and its evolution.

1955: John McCarthy used “artificial intelligence” in a proposal for a summer computing workshop at Dartmouth College. When the workshop took place in 1956, he was officially credited with creating the term.

1959: Arthur Samuel originated the phrase “machine learning,” defining it as “the field of study that gives computers the ability to learn without explicitly being programmed.”

1961: General Motors began using Unimate, the first industrial robot, in its assembly lines. In his original 1954 patent, inventor George Devol described a “programmed article transfer” machine, an autonomous device that could perform systematic digital commands. At General Motors, Unimate was assigned to extract hot metal castings from another machine, a job that was too hazardous for humans.

1968: When director Stanley Kubrick released 2001: A Space Odyssey, he put sci-fi back in mainstream media. The film features HAL (Heuristically programmed Algorithmic computer), a sentient computer that manages the Discovery One spacecraft’s systems and interacts with its crew. A malfunction turns a friendly HAL hostile, kicking off a debate about the relationship mankind has with technology.

1979: Hans Moravec (then a Ph.D. student, later a computer scientist) added a camera to mechanical engineer James L. Adam’s 1961 remote-controlled Standford Cart. This allowed the machine to successfully move around a chair-filled room on its own, becoming one of the earliest examples of an autonomous vehicle.

1997: In a highly publicized six-game match, IBM’s Deep Blue computer defeated world chess champion Gary Kasparov. It was the first program to beat a human.

1997: Dragon Systems built Dragon NaturallySpeaking, the world’s first commercial speech recognition software. Compatible with Microsoft’s Windows 95 and Windows NT, it could understand 100 words per minute, and its framework is still used today in more modern versions.

1998: The famed Furby could be considered the first successful domestic “robot.” The toy initially spoke its own language (Furbish) but then gradually learned English words and phrases. Some may argue Furby is just a toy, though, as it has limited interactive capabilities.

2003: NASA launched two Exploration Rovers — Spirit and Opportunity — to learn more about past water activity on Mars. When the rovers landed on the planet in 2004, they operated autonomously, collecting surface samples and performing scientific experiments. Both far outlived their planned 90-day mission by several years.

2006: Along with computer scientists Michele Banko and Michael Cafarella, computer science professor Oren Etzioni added another term to the AI vernacular. “Machine reading” gives computers the ability to “read, understand, reason, and answer questions about unstructured natural language text.”

2006: Social media companies like Facebook, Twitter, and Netflix incorporated AI into their advertising and user experience algorithms. These algorithms now pilot most, if not all, of the social media channels we use today.

2011: Apple’s launch of Siri on the iPhone 4S sparked a trend in virtual assistants — most notably Amazon’s Alexa and Microsoft’s Cortana, both released in 2014. The rugged 2011 version of Siri has since been finessed and integrated into other Apple products.

2016: Hanson Robotics caused an uproar with its humanoid robot Sophia, whose likeness closely resembles a real human being. Deemed the world’s first “robot citizen,” Sophia describes herself as a “human-crafted science fiction character depicting where AI and robotics are heading.”

2020: OpenAI started experimenting with what we now know as ChatGPT 3, a trained natural language processing model. One of the most advanced chatbots to date, ChatGPT 3 can answer philosophical questions, write code, pen essays, and more. (Its predecessor, ChatGPT 2, trained using data from Reddit posts and was released as an open-source model.) A year later, OpenAI took another giant leap in generative AI with DALL-E, a program that produces realistic art and images based on user prompts.

https://www.bighuman.com/blog/history-of-artificial-intelligence

https://wearetechwomen.com/the-evolution-of-artificial-intelligence-from-origins-to-2024/

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What is Deep Learning? Why is deep learning important?

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EMERGENCE OF THE CONCEPT OF ARTIFICIAL INTELLIGENCE

When observing the emergence of artificial intelligence, the following scientists certainly stand out.

Alan Turing in his influential work from 1950. laid the first stone in the philosophy of artificial intelligence by proposing a test by which we can determine whether a machine is intelligent: if in conversation with it we cannot distinguish it from a human. For a machine to pass the Turing test, it needs to have natural language processing, knowledge representation, inference and learning capabilities.

In the middle of the fifties of the XX century, several scientists developed the hypothesis that the logical basis of the functioning of the human brain is the manipulation of symbols. That idea became more concrete with the development of the first computers, so it encouraged many mathematicians, psychologists, engineers and other scientists to actively start researching the possibility of creating an artificial brain. Research has shown that the brain is a network of interconnected cells, neurons. Neurons pass information to each other using electrical impulses. Marvin Minsky together with Dean Edmonds in 1951. constructed the first artificial neural network.

THE GOLDEN AGE OF ARTIFICIAL INTELLIGENCE

The years following the Dartmouth conference represent an era of great discovery as well as optimism in a new field of science. Programs that have been developed for many people are fascinating.

One of the first paradigms created in the golden age is the observation of intelligent behavior as finding the most efficient sequence of actions - steps that lead to the solution of a problem (reasoning as search). The solution is reached gradually, step by step, with the memorization of previous steps used for corrections if the path taken turns out to be a dead end. The step that reduces the difference to the desired goal the most is chosen.

The first research and the first results in the field of artificial intelligence were based on the application of logical rules and the manipulation of symbols. In the late fifties, John McCarthy proposed a new way of creating intelligent systems based on logic. McCarthy proposed a system in which heuristics are an integral part of the system, and programming is reduced to communicating facts-premises to the computer using a suitable formal language. This paradigm was named logic programming, and in the following decades it was developed through the programming language Prolog (1972).

THE FIRST CRISIS OF ARTIFICIAL INTELLIGENCE

The initial successes in the field of artificial intelligence led to high expectations from the public, governments and investors, which were fueled by leading scientists. And many literary works and films predicted the rapid development of artificial intelligence. However, it turned out that both scientists and artists underestimated the difficulty of creating artificial intelligence.

DEEP BLUE ACCOUNTANT BEAT THE WORLD CHESS CHAMPION

The rapid growth in processing power and memory capacity of computers during the eighties and nineties increasingly narrowed the gap between the top chess players and the best systems. Finally, in May 1997, Deep Blue, a chess computer developed by IBM, defeated the then current world chess champion, Garry Kasparov. The match consisted of 6 games, and the winning ratio was 2:1 for Deep Blue, with 3 draws.

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-Artificial Intelligence (AI) is a subfield of computer science that develops and studies intelligent machines.

- The goal of artificial intelligence research is to develop programs (software), which will enable computers to behave in a way that could be characterized as intelligent.

- Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily humans.

- Artificial intelligence, as a term in a broader sense, means the capacity of an artificial creation to realize functions that are characteristic of human thinking.

-AI has allowed us to automate a wide range of tasks that were previously thought to be too complex for machines to perform. In the field of copywriting and design, artificial intelligence is having a big impact. This has allowed us to produce high-quality content on a scale previously unimaginable, while reducing time and cost.

-Artificial intelligence could transform the payment industry. From automating certain tasks to contributing to a better user experience, artificial intelligence could be the missing link in creating better financial systems.

- Industry experts are already touching on these topics and supporting the implementation of AI technologies in the payments sector.

- Not only that, but AI systems that analyze customers' spending habits can help them develop savings plans and monitor their finances in a more efficient way.

-Global data and analytics company NRG (National Research Group) says journalists are using artificial intelligence to write online travel guides.

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AI in the organization

AI technology improves enterprise performance and productivity by automating processes or activities that once required human intervention. AI can also give meaning to data on a scale that no human ever could. This functionality can bring substantial business benefits. For example, Netflix uses machine learning to provide a level of personalization that has helped the company grow its customer base by over 25%.

Most companies have made data science a priority and are investing heavily in this area. A 2021 McKinsey AI survey found that organizations that have implemented AI in at least one function have grown by 56%, up from 50% a year ago. Additionally, 27% of respondents reported that at least 5% of revenue could be attributed to AI, up from 22% last year.

AI has value for most functions, businesses and domains. It includes general and domain-specific applications such as

Using transactional and demographic data to predict how much specific customers will spend over the course of their relationship with a business (or customer lifecycle value)

Optimizing prices based on customer behavior and preferences

Using image recognition to analyze X-ray images for signs of cancer.

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Francisca/Agrupamento de Escolas Francisco de Holanda

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Francisca/Agrupamento de Escolas Francisco de Holanda

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According to the European Commission's Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs, the impact of Artificial Intelligence (AI) on European industry is expected to reach 200 billion euros by 2023, largely thanks to the application of artificial intelligence in the management of its assets and resources.

According to experts at Keepler Data Tech, these technologies have proved essential for rationalising manufacturing processes, carrying out preventive maintenance and improving customer knowledge. KPMG also estimates that AI can generate a positive impact that increases industrial productivity by 15& as a whole.

Industry is thus taking advantage of technologies to increase its productivity and performance and, specifically, AI opens up innovative possibilities, such as flexible and efficient production, especially in the manufacture of complex and increasingly personalised items, in small series and highly geared to the specific needs of each customer.

According to the experts at Keepler Data Tech, the benefits of AI for industrial companies are threefold. Firstly, there is a reduction in the number of errors, thanks to intelligent algorithms that handle repetitive tasks, for example, with greater precision and less margin for error.

On the other hand, AI facilitates the preventive maintenance of industrial assets, reducing the need for manual and scheduled monitoring, avoiding unnecessary system downtime or reducing the risk of downtime due to wear and tear or machine breakdown. As a result, there is a significant reduction in costs, relieving human labour, which has been freed from repetitive tasks and is now dedicated to much more important and business-critical tasks that require more attention.

What's more, AI provides more accurate, faster and contextualised data, which is a valuable aid in improving results and supporting managers' decisions.

The analysis company Everest Group estimates that manufacturing companies can reduce costs by up to four times thanks to AI. Among the functions that will be improved with intelligent automation are employee productivity, operational efficiency and quality, delivery times and process management, all of which are key to boosting an organisation's overall performance and results. The same study predicts that the company's revenues will double in two and a half years, with a 55 per cent improvement in the customer experience and a 52% improvement in the employee experience.

According to Pablo Ríos, Business Development Manager for the industrial sector at Keepler Data Tech, "AI is one of the enablers that brings the greatest benefit to industrial sectors and is an important catalyst in the field of Industry 4.0, which is a mandatory step for any manufacturing company that wants to increase its competitiveness in today's environment of change and uncertainty. According to Gartner, by 2025, the 10% of companies that use AI will generate at least three times more value for their business than the 90% of companies that don't."

The head of the sector also emphasises that they "really believe that the benefits of AI in industrial environments are proven and the big challenge is to scale these projects and move from a proof of concept or laboratory mode to a production phase to really impact the business".

In order for the impact of AI to be effective in production processes and deliver the results and efficiencies that are sought, it is still necessary to implement some of the aspects mentioned, to unify the different formats and origins of the data, in the same storage that favours its integration into analytical environments, as well as to take into account the correct management of the data and its subsequent exploitation.

Text based on the article published in https://www.itinsight.pt/news/digital/inteligencia-artificial-tem-forte-impacto-na-industria-europeia, on 10/12/2022

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Agrupamento de Escolas Francisco de Holanda/Maria Helena

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Agrupamento de Escolas Francisco de Holanda/Maria Helena

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Narrow AI dominates today's market, excelling in specific tasks like product recommendation and weather prediction. While it competes with human capabilities in certain contexts, its strength lies in controlled environments. To enhance AI skills, consider the Introduction to Artificial Intelligence course by Great Learning Academy.

AGI aims to replicate human cognitive abilities across domains, requiring collaboration among thousands of Narrow AI systems. Despite technological advancements, like Fujitsu's K and IBM's Watson, achieving AGI remains challenging due to the complexity of simulating neuronal activity.

ASI, the progression from AGI, promises to exceed human capabilities in decision-making and creativity. While the gap between AGI and ASI may seem narrow, the journey toward AGI suggests ASI is a futuristic concept.

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Agrupamento de Escolas Francisco de Holanda/Maria Helena

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Agrupamento de Escolas Francisco de Holanda/Maria Helena

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Dobre Bogdan Andrei, Romania: Short intruduction and the current applications of Ai Deep Learning

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Dobre Bogdan Andrei, Romania: Short intruduction and the current applications of Ai Deep Learning

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Deep learning is a subset of artificial intelligence (AI) that mimics the workings of the human brain in processing data and creating patterns for use in decision making. It's based on neural networks, which are interconnected layers of algorithms that learn from vast amounts of data. Deep learning excels in tasks such as image and speech recognition, natural language processing, and autonomous driving.

As for current applications, deep learning is being used in various fields:

1. Computer Vision: Deep learning is revolutionizing computer vision tasks such as object detection, image classification, and facial recognition. Applications range from surveillance systems to medical imaging diagnostics.

2. Natural Language Processing (NLP): Deep learning models like transformers have significantly advanced NLP tasks, including language translation, sentiment analysis, and chatbots. They power virtual assistants like Siri, Alexa, and Google Assistant.

3. Healthcare: Deep learning is being used for medical image analysis, disease diagnosis, personalized treatment plans, and drug discovery. It aids in early detection of diseases like cancer, Alzheimer's, and diabetic retinopathy.

4. Autonomous Vehicles: Deep learning algorithms enable self-driving cars to perceive their surroundings, make decisions, and navigate safely. They process data from sensors like cameras, lidar, and radar to identify objects and anticipate road conditions.

5. Finance: Deep learning is utilized in fraud detection, algorithmic trading, risk assessment, and customer service automation. It helps financial institutions analyze vast amounts of data to make informed decisions and detect anomalies.

6. Manufacturing: Deep learning optimizes manufacturing processes by predicting equipment failures, improving quality control, and streamlining supply chain management. It enables predictive maintenance and enhances product design.

7. Entertainment: Deep learning powers recommendation systems for streaming platforms like Netflix and Spotify, enhancing user experience by suggesting personalized content. It's also used in content creation, such as generating music, art, and writing.

8. Robotics: Deep learning facilitates robotic perception and manipulation tasks, enabling robots to understand their environment, grasp objects, and interact with humans more effectively. It's used in industrial automation, healthcare assistance, and household robotics.

These are just a few examples of how deep learning is transforming various industries, with its versatility and scalability continually expanding its applications.

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The term “artificial intelligence” was coined and came into popular use. Dates of note: 1950: Alan Turing published “Computer Machinery and Intelligence” which proposed a test of machine intelligence called The Imitation Game.

In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the “heartless” Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence

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I want to take the leader.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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!!! Hello guys, your new task has been defined for you.First of all, you will choose a leader from your team.Volunteering is essential.Other students will collect information and images by doing research with the task and add them to the padlet.Finally, your team leader will create a video with this information.You can get support from your team mentor when you need it.

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TASK:To prepare an animated video explaining the predictions for the future of artificial intelligence and the possible effects of this technology on society, business world and ethics.

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TASK:To prepare an animated video explaining the predictions for the future of artificial intelligence and the possible effects of this technology on society, business world and ethics.

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TASK:To prepare an animation video explaining the emergence of deep learning and modern artificial intelligence techniques, innovations in these fields and their current applications.

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TASK:To prepare an animation video explaining the emergence of deep learning and modern artificial intelligence techniques, innovations in these fields and their current applications.

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TASK:To prepare an animated video explaining the development of artificial intelligence in computer science and the main research in this field.

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TASK:To prepare an animated video explaining the development of artificial intelligence in computer science and the main research in this field.

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TASK:To prepare an animation video explaining the impact of artificial intelligence on industry and technology, especially its use in industry and production.

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TASK:To prepare an animation video explaining the impact of artificial intelligence on industry and technology, especially its use in industry and production.

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TASK:To prepare an animation video covering the important turning points in the evolution of artificial intelligence and the revolutionary studies carried out in this field.

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TASK:To prepare an animation video covering the important turning points in the evolution of artificial intelligence and the revolutionary studies carried out in this field.

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TASK:To prepare an animation video explaining the historical development and basic concepts of artificial intelligence starting from the early stages.

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TASK:To prepare an animation video explaining the historical development and basic concepts of artificial intelligence starting from the early stages.