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AI From Our Editors: The following writing is created using artificial intelligence and has been edited for quality assurance.
1940s-1950s
The concept of AI was influenced by earlier work in mathematics, computer science, and philosophy. Alan Turing's work, particularly the Turing Test proposed in his 1950 paper "Computing Machinery and Intelligence," laid foundational ideas for AI.
1956
The term "artificial intelligence" was coined by John McCarthy at the Dartmouth Conference. This event is often considered the birth of AI as a field of study. Attendees included influential figures like Marvin Minsky, Allen Newell, and Herbert A. Simon.
1950s-1960s
Early AI research focused on symbolic AI and problem-solving. Notable projects included Newell and Simon's Logic Theorist and GPS (General Problem Solver), and McCarthy's development of the LISP programming language.
1970s
AI faced significant challenges due to limited computational power and unrealistic expectations, leading to reduced funding and interest. This period is known as the first AI winter.
1980s
AI experienced a revival with the development of expert systems, which used rule-based systems to emulate human expertise in specific domains. Companies and industries began adopting these systems, leading to renewed interest and investment.
Late 1980s-1990s
Despite the initial success of expert systems, they faced scalability and maintenance issues. Combined with economic factors, this led to another decline in AI research and funding.
1990s-2000s
Advances in computational power, the availability of large datasets, and improvements in algorithms (particularly in machine learning) revitalized AI research. Key developments included the success of statistical methods, support vector machines, and the emergence of early neural networks.
2010s
Deep learning, a subset of machine learning involving neural networks with many layers, began to achieve remarkable success in various tasks such as image and speech recognition. The development of frameworks like TensorFlow and PyTorch facilitated deep learning research and application.
2020s
AI continues to advance rapidly, with significant progress in natural language processing, generative models, reinforcement learning, and more. AI technologies are increasingly integrated into everyday applications, from virtual assistants to autonomous vehicles.
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New York City local Airbnb hosts do a great job of highlighting New York City and the surrounding area.
New York City guidebooks on Airbnb include things like local food scene, essentials, entertainment & activities, shopping, drinks & nightlife, sightseeing and parks & nature.
One nice feature, there are many, on any Airbnb guidebook is the capability to see how many locals recommend any specific place mentioned in the Airbnb host's guidebook.
Look for the host's guidebook link in "The neighborhood" section.
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Buy Local
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Reasons To Buy Local
AI From Our Editors: The following writing is created using artificial intelligence and has been edited for quality assurance.
"Buy Local" is a sentiment that emphasizes the importance of patronizing local businesses and products. It's a movement that encourages people to prioritize buying from local shops, farmers' markets, artisans, and small businesses rather than solely relying on big-box stores and big online retailers.
Reasons to Buy Local:
Overall, "Buy Local" is a call to action for people to be mindful of where they spend their money and to prioritize supporting local businesses whenever possible.
It is worth noting, buying from chain businesses or global businesses with a location in the community can still bring other benefits to you and the community such as access to a wider range of products, technology, and employment opportunities.
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