AI - PAST, PRESENT, FUTURE.pptx
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A brief look into the history of AI and some future contemplations.
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AI - PAST, PRESENT, FUTURE.pptx
- 1. ARTIFICIAL INTELLIGENCE WHERE WE CAME FROM AND WHERE WE ARE GOING
- 2. PAST
- 3. 1950s The birth of AI as a research field began in the 1950s with the work of computer scientists such as John McCarthy, Marvin Minsky, and Claude Shannon. The Dartmouth Conference in 1956 is often cited as the birthplace of AI.
- 4. THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE WAS A 1956 SUMMER WORKSHOP WIDELY CONSIDERED TO BE THE FOUNDING EVENT OF ARTIFICIAL INTELLIGENCE AS A FIELD. THE PROJECT LASTED APPROXIMATELY SIX TO EIGHT WEEKS AND WAS ESSENTIALLY AN EXTENDED BRAINSTORMING SESSION.
- 5. 1960s – 1970s In the 1960s and 1970s, AI research focused on symbolic reasoning and expert systems. The first AI programs were developed during this time, including the famous ELIZA program, which simulated conversation.
- 6. ELIZA IS AN EARLY NATURAL LANGUAGE PROCESSING COMPUTER PROGRAM CREATED FROM 1964 TO 1966 AT MIT BY JOSEPH WEIZENBAUM
- 7. 1980s - 1990s The 1980s and 1990s saw the rise of connectionism and neural networks, as well as the development of machine learning algorithms. Expert systems continued to be developed, but the hype around AI waned due to a lack of practical applications.
- 8. 2000s – 2010s In the 2000s and 2010s, the availability of large amounts of data and advances in computing power led to a resurgence of interest in AI. Machine learning and deep learning algorithms were developed, leading to breakthroughs in areas such as computer vision, speech recognition, and natural language processing.
- 9. PRESENT
- 10. 2020s In recent years, AI has become ubiquitous, with applications in areas such as autonomous vehicles, healthcare, and finance. Ethical concerns and debates over the impact of AI on society have also become more prominent. The field continues to evolve rapidly, with ongoing research and development in areas such as reinforcement
- 12. Chat GPT is typically integrated into other applications, platforms, or services that require natural language processing or text completion capabilities. More recently exploded in mainstream society. Easy to use API. Huge growth in applications and plug- ins. The GPT model has been used in a variety of applications, from chatbots to language translation. CHAT GPT
- 13. Artificial Intelligence (AI) and blockchain are two rapidly evolving technologies that have the potential to transform various industries. When used together, they can enhance the security, efficiency, and functionality of blockchain applications AI AND BLOCKCHAIN
- 14. Intelligent Supply Chain Management: AI can be used to optimize supply chain processes and make predictions about future demand, while blockchain technology can be used to create a transparent and secure record of all transactions in the supply chain. SUPPLY CHAIN USE CASE
- 15. FUTURE
- 16. 1. Advanced Natural Language Processing (NLP): NLP future advances may enable machines to process human language at a much deeper level. 2. Reinforcement Learning: Reinforcement learning is a subset of machine learning that involves training AI agents through trial and error. 3. Explainable AI: Making it transparent and explainable to humans. 4. Cognitive Computing: Cognitive computing is a field of AI that aims to mimic the human brain's ability to learn and process information. 5. AI Hardware: Future advances in hardware, could enable more efficient and powerful AI systems to run larger and more complex FUTURE ADVANCES
- 17. 1. Decentralized Autonomous Organizations (DAOs): AI could be used to enhance the decision-making process of DAOs, as discussed earlier, by analyzing data and providing insights that can inform decisions. 2. Personalized Web: With the help of AI, the Web3 could be more personalized, catering to individual preferences and needs. AI algorithms could analyze user behavior, preferences, and interactions to provide personalized recommendations, services, and experiences. 3. Intelligent Contracts: AI could enable more intelligent contracts on the Web3, making it possible to execute more complex transactions and agreements. For example, AI could be used to WEB3 AND AI
- 19. As AI continues to develop and become more advanced, there are several potential future concerns that need to be addressed. FUTURE CONCERNS
- 20. 1.Superintelligence: There is a possibility that AI could eventually surpass human intelligence and become superintelligent. 2.Weaponization: AI could be used to develop more advanced and lethal weapons, leading to an arms race between countries and non-state actors. 3.Job Displacement: As AI becomes more advanced and capable of performing a wider range of tasks, it is likely to lead to job displacement and potentially widespread unemployment. 4.Autonomous Weapons: AI-powered autonomous weapons, such as drones, could be used to carry out lethal actions without human intervention. 5.Social and Economic Inequality: AI could exacerbate existing social and economic inequalities, as those with access to the technology and the skills to use it may benefit
- 21. Addressing these ethical concerns will require a multi-faceted approach, involving the development of ethical guidelines and principles, technical innovations to mitigate bias and promote transparency, and policies and regulations to ensure accountability and protect individual rights. ETHICAL CONCERNS
- 22. THANK YOU AIMEE MAREE FORSSTROM Written by assistance from ChatGPT AFICIONADO.TECH
Editor's Notes
- First lets start our look into the past
- AI research in the 1950s was characterized by an optimistic outlook on the possibility of creating machines that could simulate human intelligence. At the time, the idea of AI was still relatively new, and researchers were just beginning to explore the field. During the 1950s, researchers focused primarily on developing symbolic AI, which involves the use of logical and mathematical rules to represent knowledge and make decisions. They believed that if they could develop a set of rules that could mimic human reasoning, they could create machines that could think and learn like humans. Some of the most important figures in AI research during this period included John McCarthy, Marvin Minsky, and Claude Shannon. McCarthy is credited with coining the term "artificial intelligence," and he played a key role in the development of Lisp, a programming language that became widely used in AI research. Overall, AI research in the 1950s was characterized by a sense of optimism and a belief that machines could be created that would be capable of human-like intelligence. While some of the early hopes for AI were not realized, the research that was done during this period laid the foundation for later advances in the field. In 1950 Alan Turing invents the turing test.
- One of the most notable events in AI research during this period was the Dartmouth Conference, which took place in 1956. The conference brought together a group of researchers who were interested in the possibility of creating intelligent machines, and it is considered by many to be the birthplace of AI as a field of study.
- AI research in the 1960s to 1970s saw continued progress in the development of machine learning and expert systems, as well as the emergence of new areas of focus such as robotics and computer vision. One of the key developments during this period was the creation of the first general-purpose robots, which were capable of carrying out a range of tasks in a variety of environments. These robots relied on a combination of AI and engineering expertise, and laid the groundwork for the development of modern robotics technology. Another important area of focus during this time was computer vision, which involves the use of machines to interpret and analyze visual information. Researchers worked to develop algorithms and techniques that could enable machines to recognize objects and patterns in images, which would be useful in a variety of applications, including surveillance, medical imaging, and autonomous driving. The 1970s also saw the emergence of knowledge-based systems, which were designed to incorporate both expert knowledge and data-driven approaches to problem-solving. These systems were used in a range of applications, from medical diagnosis to financial planning. Some of the most important figures in AI research during this period included Geoffrey Hinton, who helped develop backpropagation, a key machine learning technique, and Hans Moravec, who made important contributions to the development of robotics technology. Overall, AI research in the 1960s to 1970s saw continued progress in the development of key technologies and the emergence of new areas of focus. These developments set the stage for later advances in the field and continue to influence AI research today. 1974 first autonomous vehicle was created
- Some of the most important figures in AI research during the 1960s included Arthur Samuel, who developed the first machine learning algorithm, and Joseph Weizenbaum, who created the first NLP program, ELIZA.
- AI research in the 1980s to 1990s was characterized by a shift away from rule-based expert systems and towards more data-driven approaches to machine learning. In the 1980s, researchers began to explore the potential of neural networks, a type of machine learning model inspired by the structure and function of the human brain. These networks were able to learn from large amounts of data and were used in a range of applications, from speech recognition to image analysis. Another important development during this time was the emergence of fuzzy logic, a mathematical framework that allowed for more nuanced and flexible decision-making in AI systems. Fuzzy logic was used in a variety of applications, including control systems for industrial processes and predictive maintenance for mechanical equipment. The 1990s saw the development of support vector machines, a type of machine learning algorithm that was highly effective at classifying data and identifying patterns in complex datasets. These algorithms were used in a range of applications, from image recognition to financial forecasting. During this period, AI research also began to focus more on the development of intelligent agents, which were capable of autonomously carrying out tasks in complex environments. Researchers developed new algorithms and techniques for reasoning, decision-making, and planning, which were used in applications ranging from robotic navigation to scheduling. Overall, AI research in the 1980s to 1990s saw significant progress in the development of new machine learning techniques and the emergence of new areas of focus, such as neural networks and intelligent agents. These developments set the stage for later advances in the field, including the deep learning revolution of the 2010s. In 1995 the worlds first autonomus car was driven across Germany 1997 a Computer AI IBM Big Blue beat an expert chess player .
- AI research in the 2000s to 2010s was marked by significant advances in machine learning, as well as the emergence of new applications and industries. One of the most significant developments during this period was the rise of deep learning, a type of machine learning that uses artificial neural networks with many layers to learn from large amounts of data. Deep learning has been highly effective in a range of applications, including speech recognition, image classification, natural language processing, and autonomous driving. Another important development during this period was the growth of big data, which provided vast amounts of data for machine learning algorithms to learn from. The availability of big data, combined with advances in computing power and deep learning techniques, led to significant breakthroughs in AI research. In the 2010s, there was also a growing interest in applying AI to new industries and areas of society. AI was used in healthcare to assist with diagnosis and treatment, in finance to assist with fraud detection and trading, and in transportation to assist with route optimization and safety. However, there were also concerns around the ethical implications of AI, including issues around bias, privacy, and safety. As a result, there was a growing focus on responsible AI, which involves developing AI systems that are transparent, fair, and accountable. Overall, AI research in the 2000s to 2010s saw significant progress in the development of new machine learning techniques, as well as the emergence of new applications and industries. The advances made during this period set the stage for further progress and innovation in the field of AI. 2011 IBM Watson wins Jepoadry 2016 Google Deep Mind defeats Korean Alpha Go champion > weak AI > single task focused > Alexa is aswell why? Because she can only do what she is trained to do
- As of 2021, AI research in the 2020s is still ongoing, but there are several key areas of focus and development. One of the primary areas of focus is the development of more advanced AI models that are capable of handling more complex tasks and data. This includes the continued development of deep learning models, as well as the exploration of new AI architectures and algorithms. Another area of focus is the integration of AI with other emerging technologies, such as blockchain, the Internet of Things (IoT), and augmented reality (AR). AI can help to enhance the capabilities of these technologies, and vice versa, leading to more powerful and versatile systems. There is also a growing focus on ethical and responsible AI, as concerns around the impact of AI on society continue to grow. This includes issues such as bias, transparency, privacy, and safety, and there is an increasing need for AI systems that are fair, transparent, and accountable. Finally, there is also a focus on the development of AI applications that can help to address major societal challenges, such as climate change, healthcare, and education. AI has the potential to make significant contributions in these areas, and researchers are working to develop AI systems that can have a positive impact on society as a whole. Overall, AI research in the 2020s is expected to continue to drive innovation and advance the capabilities of AI systems, while also addressing the ethical and societal implications of these technologies. We start to see Strong AI come to focus
- Yes, I can explain what I am. I am ChatGPT, a large language model developed by OpenAI, based on the GPT-3.5 architecture. As an AI language model, my main function is to understand and generate human-like language, which allows me to engage in conversations with people and answer their questions on a wide range of topics. I have been trained on a vast corpus of text data, which includes books, articles, and other written materials, and this has enabled me to develop a deep understanding of human language and knowledge across a wide range of subjects. My goal is to be helpful and informative to users, and to provide accurate and useful information to the best of my abilities.
- Artificial Intelligence (AI) and blockchain are two distinct technologies that have the potential to be combined to create innovative solutions to various problems. AI is a technology that enables machines to learn from data and make intelligent decisions, while blockchain is a distributed digital ledger that allows for secure and transparent transactions without the need for intermediaries. When AI is combined with blockchain, it can create a more decentralized and autonomous system that is capable of performing tasks without human intervention. For example, AI algorithms can be used to make decisions and execute transactions on a blockchain network, without the need for a central authority. Overall, the combination of AI and blockchain has the potential to create more efficient, transparent, and secure systems that can help to solve various problems in different domains.
- Another use case for AI and blockchain is in supply chain management. By leveraging blockchain technology, companies can create a transparent and immutable record of the movement of goods and services through the supply chain. AI algorithms can be used to analyze this data and provide insights into areas where efficiencies can be gained, such as identifying bottlenecks in the supply chain or predicting demand for certain products. In addition, AI and blockchain can be used together to create decentralized marketplaces, where buyers and sellers can transact directly with each other without the need for intermediaries. AI algorithms can be used to match buyers and sellers based on their preferences, while blockchain technology can ensure that transactions are secure and transparent.
- One of the key challenges in advanced NLP is dealing with the nuances and complexities of human language, such as sarcasm, ambiguity, and context. reinforcement learning (RL) is a rapidly evolving field of machine learning, and there are several exciting developments and advances that are likely to shape its future such as Transfer learning: Transfer learning is the idea that knowledge learned in one task can be transferred to another task. Transfer learning in RL is still in its early stages, but future advances may involve developing more effective techniques for transferring knowledge across tasks. Interpretable models: One of the key challenges in XAI is developing models that are inherently interpretable to humans, such as decision trees or rule-based systems. Future advances may involve developing new types of interpretable models or improving the interpretability of existing models. Knowledge representation and reasoning: One of the key challenges in cognitive computing is representing and reasoning about complex knowledge. Future advances may involve developing more effective techniques for knowledge representation, such as graph-based or logic-based systems, and more efficient algorithms for reasoning. Future advances AI hardware such as in neuromorphic computing may involve developing more advanced neural network architectures and optimizing the hardware to achieve even greater energy efficiency. Quantum computing is still in its early stages, but it has the potential to greatly improve the performance of AI algorithms, particularly for tasks such as optimization and machine learning. Future advances in chiplet technology may involve developing more specialized chiplets for AI tasks, such as dedicated chips for training deep neural networks or for processing natural language. possible future advances in AI and robotics Swarms of robots: Advances in robotics and AI could enable the development of swarms of robots that can work together to achieve a common goal. Personalized robotics: Advances in AI and robotics could enable the creation of personalized robots that can adapt to individual users' needs and preferences. For example, a home assistant robot could learn a person's daily routine and adapt to their specific needs, such as preparing meals or providing reminders.
- Decentralized AI: One of the most exciting possibilities for Web3 and AI is the development of decentralized AI. This would involve creating AI systems that run on decentralized networks, such as blockchain-based platforms, allowing for greater privacy, security, and transparency. Decentralized AI could enable new applications, such as autonomous organizations and decentralized finance, that are more efficient and accessible than traditional models. Personalized web experiences: As AI algorithms become more sophisticated, they will be able to provide personalized web experiences for users. This could include customized recommendations for products and services, tailored content and advertising, and even personalized user interfaces that adapt to individual preferences and needs. 3. Integration with AI: Smart contracts could be integrated with AI algorithms to automate the execution of complex contracts. For example, an AI-powered smart contract could automatically adjust payment terms based on changes in market conditions.
- Overall, ethical concerns and others highlight the need for careful consideration and planning in the development and deployment of AI, and for collaboration between industry, government, and civil society to address these concerns and ensure that AI benefits society as a whole.
- Superintelligence refers to the hypothetical situation in which an AI system becomes vastly more intelligent than any human being, potentially resulting in a major shift in the balance of power between humans and machines. This could have significant implications for society, as a superintelligent AI system could potentially solve many of the world's problems, but it could also pose an existential threat if its goals and values are not aligned with those of humanity. Weaponization of AI refers to the development and use of AI technology for military purposes, including the development of more advanced and lethal weapons. This could lead to an arms race between countries and non-state actors, with potentially catastrophic consequences for global security. Job displacement refers to the potential for AI to automate many jobs, leading to widespread unemployment and potentially significant economic disruption. While AI has the potential to create new jobs and industries, it is likely to have a significant impact on the labor market, requiring new approaches to education, training, and social safety nets. Autonomous weapons refer to AI-powered weapons systems that can make decisions and take actions without human intervention. This raises significant ethical concerns, as the use of such weapons could result in civilian casualties, war crimes, and violations of human rights. Social and economic inequality refers to the potential for AI to exacerbate existing social and economic inequalities, as those with access to the technology and the skills to use it may benefit disproportionately. This could further entrench existing power imbalances and exacerbate social and political tensions.
- There are several ethical concerns for AI, including: Bias and discrimination: AI systems can inherit the biases of their developers and training data, leading to discrimination against certain groups. This can perpetuate existing societal inequalities. Privacy: AI systems can collect, store, and analyze large amounts of personal data, raising concerns about privacy and data protection. Autonomy and accountability: As AI systems become more autonomous, it can be difficult to determine who is responsible for their actions and decisions. Transparency and explainability: Some AI systems operate as black boxes, making it difficult to understand how they make decisions. This lack of transparency can erode trust in the technology. Job displacement: As AI systems become more advanced, there are concerns that they will replace human workers, leading to job displacement and economic disruption. Security: AI systems can be vulnerable to cyberattacks and hacking, leading to security risks and potential harm to individuals and organizations. Overall, these are important issues that need to be carefully considered as AI continues to advance and become more ubiquitous. It is important to ensure that AI is developed and deployed in an ethical and responsible manner, taking into account the potential risks and benefits for society as a whole.