3 answers
Madison
Student
South Euclid, Ohio
1
Question
Asked
761 views
can you tell me more about ai and programming ?
can you tell me more about ai and programming ?
3 answers
Venkat Narahari
Software Engineer
7
Answers
Austin, Texas
Updated
Venkat’s Answer
That is a very trending question you have asked. So you can find a majority of info on them online but to put it in simple context.
AI: Any intelligence that is man-made or artificial. We currently tend to use computers to currently for creating the AI. AI has been there from the very beginning of computing research and has had breakthroughs since the 1960s.
AI in general is a mix of mathematics, statistics, and a bit of complex logic that utilizes the computer's power to compute these mathematical models.
A lot of trends around AI in 2024 are around GenAI in a sense a computer that can generate text or images or videos based on what it has been trained or taught. The next frontier of AI with be on Artificial General intelligence which can think on its own and develop judgements, infusing AI with robotics once it achieves AGI and then humanoid where humans and machines live hand in hand though this is very much a far away future which everyone needs to be aware of.
To make any of these computers work and in turn to create AI, we use programming. Programming is a set of instructions given to a computer to calculate or do something and since computers only get 0 and 1, we understand only general human languages, we have created an intermediate language so humans can arrange with those binary digits that can achieve something we want.
I learn more about these have a look at below next steps:
https://en.wikipedia.org/wiki/Computer_programming
https://www.youtube.com/watch?v=cwtpLIWylAw
https://www.coursera.org/learn/intro-programming
https://en.wikipedia.org/wiki/Artificial_intelligence
https://www.coursera.org/learn/ai-for-everyone
AI: Any intelligence that is man-made or artificial. We currently tend to use computers to currently for creating the AI. AI has been there from the very beginning of computing research and has had breakthroughs since the 1960s.
AI in general is a mix of mathematics, statistics, and a bit of complex logic that utilizes the computer's power to compute these mathematical models.
A lot of trends around AI in 2024 are around GenAI in a sense a computer that can generate text or images or videos based on what it has been trained or taught. The next frontier of AI with be on Artificial General intelligence which can think on its own and develop judgements, infusing AI with robotics once it achieves AGI and then humanoid where humans and machines live hand in hand though this is very much a far away future which everyone needs to be aware of.
To make any of these computers work and in turn to create AI, we use programming. Programming is a set of instructions given to a computer to calculate or do something and since computers only get 0 and 1, we understand only general human languages, we have created an intermediate language so humans can arrange with those binary digits that can achieve something we want.
I learn more about these have a look at below next steps:
Venkat recommends the following next steps:
Martha Kramer
Corporate Training, Communications
356
Answers
Stamford, Connecticut
Updated
Martha’s Answer
This is an excellent question, Madison, since AI (artificial intelligence) has been in the news a lot. Speaking broadly, AI can be used to analyze existing knowledge or to generate new knowledge. In the first case, AI can help people in call centers find information or help other types researchers faster, leading to better outcomes. In the second case (generative AI), the AI learns from the data it is fed. So that data has to be comprehensive and as free of bias as possible.
Programmers can help AI and AI can help programmers. In the first case, programmers can improve the software and the data AI learns from. In the second case, AI can help programmers work faster by embedding commonly-used coding. Programmers and Quality Assurance professionals still need to be involved to make sure the AI functioning properly with no unintended effects.
There are many courses available to gain a background in AI. I put links to two below because they are developed by credible sources (one by IBM and the other by a Stanford University professor, on a credible site - Coursera - and are free.
Good luck!
Take this course and earn a badge - https://www.coursera.org/learn/introduction-to-ai?utm_source=IBM&utm_medium=institutions&utm_campaign=IBMBadge
Another course choice - https://www.coursera.org/learn/generative-ai-for-everyone?utm_medium=sem&utm_source=gg&utm_campaign=B2C_NAMER_generative-ai-for-everyone_deeplearning-ai_FTCOF_learn_country-US-country-CA&campaignid=20730862737&adgroupid=158782890201&device=c&keyword=generative%20ai&matchtype=b&network=g&devicemodel=&adposition=&creativeid=679234260688&hide_mobile_promo&gad_source=1&gclid=EAIaIQobChMI6vaf9pvvhAMV7mBHAR2k3AT2EAAYAyAAEgIrjvD_BwE
Programmers can help AI and AI can help programmers. In the first case, programmers can improve the software and the data AI learns from. In the second case, AI can help programmers work faster by embedding commonly-used coding. Programmers and Quality Assurance professionals still need to be involved to make sure the AI functioning properly with no unintended effects.
There are many courses available to gain a background in AI. I put links to two below because they are developed by credible sources (one by IBM and the other by a Stanford University professor, on a credible site - Coursera - and are free.
Good luck!
Martha recommends the following next steps:
Patrick Meyer
Executive - Data Analytics and AI
780
Answers
Naperville, Illinois
Updated
Patrick’s Answer
The exciting and fast-paced world of artificial intelligence (AI) and programming is a hotbed for innovation and progress. Programming is the backbone of AI development, as it involves creating algorithms and software systems that can mimic human intelligence to carry out tasks such as learning, reasoning, and making decisions.
Machine learning is a key component of AI programming. It includes a range of techniques that allow computers to learn from data and get better over time without needing specific programming. Python is a top choice for implementing machine learning algorithms because of its simplicity, flexibility, and wide-ranging libraries like TensorFlow, PyTorch, and scikit-learn. These libraries offer ready-to-use tools and functions for tasks like data preprocessing, model training, and evaluation, simplifying the process for developers to build and launch AI systems.
Deep learning is another vital part of AI programming. It's a type of machine learning that focuses on neural networks with multiple layers of interconnected nodes, modeled after the human brain's structure and function. Deep learning has driven significant progress in AI applications such as image recognition, natural language processing, and self-driving cars. Frameworks like TensorFlow and PyTorch provide robust tools for building and training deep neural networks, enabling developers to create advanced AI models capable of complex tasks.
Besides machine learning and deep learning, AI programming covers a variety of other techniques and methodologies, including natural language processing (NLP), reinforcement learning, computer vision, and evolutionary algorithms. Each approach has its own unique programming techniques and tools designed for specific AI applications and areas.
To excel in AI programming, developers need a strong base in computer science, mathematics, and statistics, along with proficiency in programming languages like Python, Java, or C++. Staying current on the latest AI research and technology is also crucial, as new algorithms, frameworks, and methodologies are constantly emerging.
For those interested in AI programming, here are some practical steps:
1. Begin by building a strong foundation in programming basics, data structures, and algorithms. Online courses, tutorials, and textbooks are great resources for beginners.
2. Python is the go-to language for AI programming because of its simplicity and flexibility. Spend time mastering Python programming, including its libraries and frameworks for AI development.
3. Start exploring machine learning by studying concepts like supervised learning, unsupervised learning, and reinforcement learning. Try out popular machine learning libraries like scikit-learn to build predictive models and analyze data.
4. Dig deeper into deep learning techniques, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Get comfortable with deep learning frameworks like TensorFlow and PyTorch for implementing advanced AI models.
5. Put your knowledge into practice by working on AI projects and real-world problems. Building projects not only reinforces your learning but also showcases your skills to potential employers or collaborators.
6. Keep up with AI research publications, attend conferences, and join online forums and communities to stay updated on the latest in AI programming. Networking with other AI enthusiasts and professionals can offer valuable insights and collaboration opportunities.
By following these practical steps and constantly improving your skills in AI programming, you can embark on a fulfilling journey in this dynamic and transformative field, contributing to groundbreaking innovations and shaping the future of artificial intelligence.
Machine learning is a key component of AI programming. It includes a range of techniques that allow computers to learn from data and get better over time without needing specific programming. Python is a top choice for implementing machine learning algorithms because of its simplicity, flexibility, and wide-ranging libraries like TensorFlow, PyTorch, and scikit-learn. These libraries offer ready-to-use tools and functions for tasks like data preprocessing, model training, and evaluation, simplifying the process for developers to build and launch AI systems.
Deep learning is another vital part of AI programming. It's a type of machine learning that focuses on neural networks with multiple layers of interconnected nodes, modeled after the human brain's structure and function. Deep learning has driven significant progress in AI applications such as image recognition, natural language processing, and self-driving cars. Frameworks like TensorFlow and PyTorch provide robust tools for building and training deep neural networks, enabling developers to create advanced AI models capable of complex tasks.
Besides machine learning and deep learning, AI programming covers a variety of other techniques and methodologies, including natural language processing (NLP), reinforcement learning, computer vision, and evolutionary algorithms. Each approach has its own unique programming techniques and tools designed for specific AI applications and areas.
To excel in AI programming, developers need a strong base in computer science, mathematics, and statistics, along with proficiency in programming languages like Python, Java, or C++. Staying current on the latest AI research and technology is also crucial, as new algorithms, frameworks, and methodologies are constantly emerging.
For those interested in AI programming, here are some practical steps:
1. Begin by building a strong foundation in programming basics, data structures, and algorithms. Online courses, tutorials, and textbooks are great resources for beginners.
2. Python is the go-to language for AI programming because of its simplicity and flexibility. Spend time mastering Python programming, including its libraries and frameworks for AI development.
3. Start exploring machine learning by studying concepts like supervised learning, unsupervised learning, and reinforcement learning. Try out popular machine learning libraries like scikit-learn to build predictive models and analyze data.
4. Dig deeper into deep learning techniques, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Get comfortable with deep learning frameworks like TensorFlow and PyTorch for implementing advanced AI models.
5. Put your knowledge into practice by working on AI projects and real-world problems. Building projects not only reinforces your learning but also showcases your skills to potential employers or collaborators.
6. Keep up with AI research publications, attend conferences, and join online forums and communities to stay updated on the latest in AI programming. Networking with other AI enthusiasts and professionals can offer valuable insights and collaboration opportunities.
By following these practical steps and constantly improving your skills in AI programming, you can embark on a fulfilling journey in this dynamic and transformative field, contributing to groundbreaking innovations and shaping the future of artificial intelligence.
Delete Comment
Flag Comment