00:34 AI is defined as exceeding or matching the capabilities of a human, including the ability to discover, infer, and reason. 01:30 Machine learning involves predictions or decisions based on data and learns from the data rather than being programmed. 02:29 There are two types of machine learning: supervised and unsupervised, with supervised having more human oversight. 03:03 Deep learning is a subfield of machine learning that involves neural networks with multiple layers, but the system may not always show its work fully. 04:09 AI is a superset of machine learning, deep learning, and other capabilities such as natural language processing, vision, text-to-speech, and robotics. 05:37 Machine learning and other capabilities are subsets of AI, and all of them are important parts of AI.
no calculator is not AI because a calculator doesnt thinks .It just replies to data fed to it. AI calculator means say calculator calculated the total expense for chritmas and campared with your budget and then came with some decision of How much money should be appropriate to expend taking into account other expenses like house rent ,kids college fees and other miscellaneous expenses. @@gustavozaniboni6312
@@gustavozaniboni6312 it has predefined algo. It can't process NLP. The 2 main core parts of are intelligence are taking input (hear, smell, see, touch, and taste) and then computing the input with previous data and coming up with an output.
What are the go-tos for English speaking fluency then 😢😢😢. I'm non-native struggling English speaker. How to know about what phrase or different types of words to on different scenarios and for the exact thought expression.😢😢😢
Big shout out to the creator for explaining everything so clearly and interestingly, BUT MOST IMPRESSIVE TO ME, WRITING EVERYTHING BACKWARDS. WELL DONE!!!!
@@cnydo you have a camera in front and in back. think of the pen and the point it touches the clear glass screen. if you take the front camera and the back camera they both touch at that same point? all you need to do is sync the "front view" with the contact point of the "back view" also i have no idea what i am talking about and made this up. no clue how they do it.
by far the most concise explanation of the difference between AI, DL and ML. they seem to be interchangeable in many contexts though... like 'Lemon AI' isn't called 'Lemon DL' although it's based on deep learning models 🤷♂
🎯 Key Takeaways for quick navigation: 00:00 *🧠 Introduction to AI and ML* - Exploring the difference and the relationship between Artificial Intelligence (AI) and Machine Learning (ML). - Framing the discussion around whether AI equals ML or if they are distinctly different concepts. 01:30 *🔍 Deep Dive into Machine Learning* - Defining Machine Learning as a capability that involves making predictions or decisions based on data. - Introduction to supervised and unsupervised machine learning, emphasizing their differences and how ML learns from data. 03:03 *🧠💻 Introduction to Deep Learning* - Describing Deep Learning as a subfield of ML that uses neural networks to model complex patterns. - Discussing the opaqueness of deep learning processes and their impact on the interpretability of its insights. 03:35 *🌐 AI as a Superset of ML and DL* - Positioning AI as the overarching set that includes ML, Deep Learning (DL), and other technologies. - Exploring domains like natural language processing, vision, hearing, and robotics as parts of AI, illustrating AI's broader scope beyond ML and DL. Made with HARPA AI
I love how simply it's clarified, and I believe people should know the difference. I went for the interview for “AI Intern” at a startup, and later found they were doing ML, but referring to it as AI.
BY FAR the best explanation I have seen of the concepts and great use of visuals. I have watched over 100 videos on the topics and this is the most concise and clear explanation. Great definitions and visuals. Subscribed.
This actially is very timely since we are seeing a lot of tools just immediately being tagged as an AI. A good example of machine learning are image generators such as Bluewillow which uses datasets.
🎯 Key Takeaways for quick navigation: 00:00 Artificial Intelligence (AI) aims to match or exceed human capabilities, involving tasks like discovering new information, inferring from various sources, and reasoning. 01:30 Machine Learning (ML) is a subset of AI, focusing on predictions and decisions based on data, utilizing sophisticated statistical analysis. It learns from data rather than being explicitly programmed. 03:03 Deep Learning (DL) is a subset of ML, involving neural networks with multiple layers. It can provide valuable insights, but the process may not always be transparent. 04:09 AI encompasses ML, DL, and more, including natural language processing, vision, robotics, text-to-speech, and motion. It is a superset that covers a broadrange of human-like capabilities. 05:37 The correct perspective is that machine learning is a subset of AI, emphasizing that when engaging in machine learning or other AI-related activities, one is inherently involved in AI. Made with HARPA AI
Excelent video! Had much difficulties to learn the difference between concepts, with this is sums very clearly and comprehends the difference between those two. The Venn diagram helped a lot to understand and comprehend better thank you very much! Nice content!
The most essential fact, in my opinion, is that AI and ML are literally subsets of " Computer Science. " If you can acknowledge all the abilities of critiquing & becoming a computer scientist then every other subject follows as they all coincide. Correct me if I'm wrong, but CS is basically the Godfather of learning any type of machine learning or artificial intelligence in the technology field. Anyways thanks for this brilliant video, Mr. Crume! Oh yeah, all of these subjects are subsets of us humans, it's funny because AI and ML learn from us just like we learn from each other on a daily basis.
Certainly AI is a field of study within Comp Sci. Traditionally, the latter has been focused on programming whereas the former is more about making a system that can learn
This was great! As an instructor, I value plain spoken, logical and imformative presentations. This was all of these!. I was surprised that he didn't expound on neural networks, perhaps that a bit too deep into the weeds, but the context of AI, machine learning, and deep learning was very well presented.
Thanks so much for the great feedback! Yes, I would have liked to delve deeper but we try to keep these videos relatively short on the channel but certainly an area I’ll consider for the future as a separate video
Simple and concise. One of the best and easy to understand explanations on ML and AI. Got me to pause several times as the explanations sounded profound at a time where confusing ideas are being shared across.
Everything outside the ML box is domain of AI. ML is what is used to make models in each of the domains. Not sure if this classification makes sense. It's like placing gradient descent in the bucket of ML. It's an algorithm which could be used for training an ML model.
This seems like a IBM marketing video and completely wrong. He defines AI as capability to discover, infer and reason. Then his diagram completely leaves those out! NLP, vision, hearing, speech...you can lose all those and still be human and intelligent. As for ML producing a desired output? Well a bowl of rocks is great system that will give you the most stable configuration ...of a bowl of rocks. Seems like next video is gonna be hey we've created a system with all these capabilities ...ergo we have created IBM AI. And that AI would be as intelligent as a bowl of rocks!
@@alibaba-ng7vm Could be. Coulde be we are witnessing a period of tentative AI bubbles, where all of them are trying to jump on the bandwagon of GPT parrots presented as AI.
Thank you for presenting a way to think about AI and ML. I have been thinking that the vast number of people talking about AI today are really meaning ML.
That's right. Equating AI to ML only was a global PR campaign involving really huge money. A few distinct features in addition: A common program executes a ready algorithm. AI can generate new algorithms. This is called problem solving. ML can't be equated to neural nets either. Training modifies synapses and creates some structure, but you can do it manually yourself. There are very useful solutions of this kind.
«AI can generate new algorithms. » -- Could be sometime in the future. Maybe after 5000 years. So this one, of yours, indirectly proves that there's no AI today.
Very good explanation video! Though I personally would not say that all of ML is contained in AI. I'd say that ML is one way of building/creating AI, but not everything done with/for ML would be considered AI.
Somehow, what’s impressing me most about this video is the innovation of having the ‘chalk-board’ as a see-through object in front of the instructor. Nice little 101-01, thank you. I was not aware AI was conceptually focussed on ‘becoming Human’, though perhaps it can be elevated to ‘super-human’ once we figure out what restrictions are necessary to approach a workable intelligence and which can be relieved (like moving to change places, speaking to exchange information, or observing through senses)?
thanks for bringing that out into the open. i was suggesting to my former boss that we could some form of ml or deep learning to get a machine make parts and self adjust, either threw feedback or vision of the part it was suppose to produce. instead of them taking 4 days to get a machine working. he no clue what i was taking about. i would still like to create a business that implements ml or deep learning on machines that have no pid. Using a raspberry pi and some sensors to measure timing and measure point that can be adjusted.
Love how that was explained thoroughly, but I'm also expecting in the future, for touch to be a part of the AI, but that might be for future interactions. I believe that AI integration will be a crucial part, and sending this message to the right audience would greatly impact this through platforms like Cleo that pushes the "Marketing for Good" concept.
This method used to draw on the board is very successful. Most of the old fashioned board is not efficient because while u are writing something on the table you lose the interaction with listener. For example, You need to turn your back to listener. If you are talking when you are using the board, then the listener's attention is distracted between what you wrote and what you said. If you don't saying anything when you are using board, then there will be weird silence. So, i really liked this style of using board.
@@brendawilliams8062 «What?I am glad for you. I still can’t separate it from the whole apple.» -- Nicely spotted! Nothing else but ML repackaged to seem beyond ML.
Notes on Artificial Intelligence and Machine Learning 1. Introduction The lecture discusses the relationship between Artificial Intelligence (AI) and Machine Learning (ML) It addresses common questions about whether AI and ML are the same, different, or if one includes the other 1 2. Defining AI Definition: AI is about matching or exceeding human capabilities and intelligence Key Aspects: Ability to discover Natural language processing Vision (seeing and distinguishing objects) Hearing and audio processing Text-to-speech conversion Motion and robotics 2 3 4 3. Understanding Machine Learning (ML) Definition: ML involves making predictions or decisions based on data Characteristics: Sophisticated form of statistical analysis Improves with more data input Focuses on finding patterns and making predictions 5 4. Deep Learning (DL) Definition: A subfield of Machine Learning Key Features: Involves neural networks Models the way human minds work Uses multiple layers of neural networks (hence "deep") Can provide interesting insights May not always provide clear explanations for its decisions 6 5. Relationship between AI, ML, and DL AI is presented as the superset ML is a subset of AI DL is a subset of ML Other AI capabilities exist beyond ML and DL 3 6. Additional AI Capabilities Natural Language Processing: Understanding and generating human language Computer Vision: Ability to see and interpret visual information Speech Recognition: Understanding spoken language Text-to-Speech: Converting written text to spoken words Robotics: Enabling motion and physical interaction with the environment 3 4 Key Takeaways AI is the broadest category, encompassing various technologies that aim to replicate or exceed human intelligence. ML is a subset of AI, focused on making predictions and decisions based on data analysis. DL is a specialized form of ML, using complex neural networks to process information. AI includes additional capabilities beyond ML and DL, such as natural language processing, computer vision, and robotics.
🎯 Key Takeaways for quick navigation: 00:00 🤖 AI is about matching or exceeding human intelligence and capabilities, including discovering new information, inferring from implicit data, and reasoning. 01:30 🛠️ Machine learning involves making predictions and decisions based on data, learning from the data rather than being explicitly programmed, and can be supervised or unsupervised. 03:03 🧠 Deep learning is a subset of machine learning using neural networks with multiple layers, capable of producing valuable insights but often lacks complete transparency in how it derives its results. 04:09 🔍 AI encompasses machine learning, deep learning, natural language processing, vision, text-to-speech, and robotics. It aims to mimic human capabilities like seeing, hearing, and motion. 05:37 🧩 Machine learning is a subset of AI, and AI comprises various technologies and techniques, each contributing essential aspects, but none encompasses the entirety of AI. Made with HARPA AI
Yes, there are indeed a lot of unclear things and people often confuse similar and not quite the same things, and I think it would be appropriate for you to make a video about the ethical side and how generative intelligence is still different from artificial intelligence.
Indeed, there are many interesting ethical questions surrounding all of this. I did one video on the 5 pillars/principles of trustworthy AI that gets into this to some degree
This guy is a legend! Delivers clean and concise knowledge in lay-man terms without compromising on quality of the information being presented. Even takes the time to reply to some of the comments here, wow.
When considering notes on AI, it's essential to recognize that Machine Learning is a subset of AI. The extent of this subset is quite extensive, as ML can be seen as the brain of AI, while other components play sensory roles. It's reasonable to view ML as a crucial element within the broader field of Artificial Intelligence
True but the early expert systems in the 80’s and 90’s typically did not leverage ML but simulated thinking. ML is just a newer technology that is more effective
How is Artifical Intelligence achieved - through machines learning? In that case, does this Venn Diagram hold true? Looks like this is a comparison between a goal and the tools used to reach a goal. Would like to understand more.
The issue with that defintion is that human intelligence varies greatly. So you're trying to compare it to something that inherently is vastly different in each person.
True. The definition isn’t perfect but neither is our understanding of intelligence in the first place. Here I wanted to present a simplified explanation to reach a broader audience for whom the existing materials have been inaccessible
Generative AI applications and AGI (Artificial General Intelligence) are distinct concepts within the field of artificial intelligence. Generative AI Generative AI refers to AI systems that can create content, such as text, images, music, and more. These systems use machine learning models, often trained on large datasets, to generate new data that resembles the training data. Examples of generative AI applications include: Language Models: GPT-4 (developed by OpenAI) can generate human-like text, answer questions, and assist in creative writing. Image Generation: Tools like DALL-E (also by OpenAI) can create images from textual descriptions. Music and Art: Systems that can compose music or create visual art based on learned patterns from existing works. Generative AI is an example of advanced ANI (Artificial Narrow Intelligence), as it is designed to perform specific tasks within a defined domain. Artificial General Intelligence (AGI) AGI, on the other hand, is a theoretical concept referring to AI that possesses the ability to understand, learn, and apply intelligence across a wide range of tasks at a human-like level. AGI would not be limited to specific tasks or domains; it would have the capacity for general cognitive abilities, reasoning, and problem-solving across diverse situations. AGI remains a long-term goal in AI research and has not yet been achieved. Key Differences Scope and Capability: Generative AI is specialized and excels in specific tasks such as generating text or images. AGI would have broad, human-like cognitive abilities applicable to a wide range of tasks. Current Status: Generative AI applications are widely available and used in various industries today. AGI is still a theoretical concept and has not been realized. Focus: Generative AI focuses on creating content based on learned patterns. AGI would focus on general understanding and problem-solving across diverse contexts. In summary, while generative AI applications represent significant advancements in specific areas of artificial intelligence, they are not the same as AGI. The development of AGI involves overcoming substantial scientific and technical challenges and remains a long-term objective in the field of AI.
I would argue that the purpose of AI is to automate some decision making criteria. Automated decision making criteria, and thus AI, can be categorized into one of two forms: First is criteria that is not based on historical data, like rule-based systems. For example, non-player characters in basic video games may appear to be making intelligent decisions, but they are likely just following a series of instructions written by the developer. Second, is criteria based on historical data. The techniques needed to build decision making criteria based on historical data are collectively treated as their own discipline, called "Data Science". One of these techniques, is to build "models" that allow us to estimate unknown values of some entity, using its known values. This process uses historical data to build a model that gives us the best possible estimate (maximum likelihood estimate) given the available data. Building models in this fashion is called "Machine Learning". So, in summary, Machine Learning is a subset of Data Science. Data Science is a subset of AI.
Thanks for the kind complements! That video was recorded all in one continuous shot. Nearly all of the ones I’ve done to date have been done that way but we are starting to experiment with segments and transitions, but those will be obvious. Not trying to trick anyone
but, NLP, computer vision, and robotics it could be a part of ML and/or DL? i mean, it involves using neural networks like CNN, DNN, and RNN in most cases, so i always struggle when try to explain the application of AI except ML and DL?
Yes, NLP *uses* ML, which is why I put it where I did in outer layer of the Venn diagram, which was not meant to be a precise definition.Just to get the general notion across about the relationship of ML to AI
00:34 AI is defined as exceeding or matching the capabilities of a human, including the ability to discover, infer, and reason.
01:30 Machine learning involves predictions or decisions based on data and learns from the data rather than being programmed.
02:29 There are two types of machine learning: supervised and unsupervised, with supervised having more human oversight.
03:03 Deep learning is a subfield of machine learning that involves neural networks with multiple layers, but the system may not always show its work fully.
04:09 AI is a superset of machine learning, deep learning, and other capabilities such as natural language processing, vision, text-to-speech, and robotics.
05:37 Machine learning and other capabilities are subsets of AI, and all of them are important parts of AI.
a calculator is AI? "AI is defined as exceeding or matching the capabilities of a human,"????
calculator cannot discover, infer and reason @@gustavozaniboni6312
no calculator is not AI because a calculator doesnt thinks .It just replies to data fed to it. AI calculator means say calculator calculated the total expense for chritmas and campared with your budget and then came with some decision of How much money should be appropriate to expend taking into account other expenses like house rent ,kids college fees and other miscellaneous expenses. @@gustavozaniboni6312
@@gustavozaniboni6312 it has predefined algo. It can't process NLP. The 2 main core parts of are intelligence are taking input (hear, smell, see, touch, and taste) and then computing the input with previous data and coming up with an output.
And you used AI to generate this summary. Thank you.
The simplest , shortest and clearest explanation I have heard so far.
Thank you.
I’m glad you liked it!
These IBM shorts have become my go-to to get up to speed on technical concepts quickly. I hope you continue to produce these. Thanks a lot!
ChatGPT 4 for me - 1/3 of my Gooogling now is ChatGPT 4. I TH-cam just for visual entertainment - but give it a few years... .
What are the go-tos for English speaking fluency then 😢😢😢. I'm non-native struggling English speaker. How to know about what phrase or different types of words to on different scenarios and for the exact thought expression.😢😢😢
@@SarFirraEdits not my problem
@@SarFirraEdits just use AI
Big shout out to the creator for explaining everything so clearly and interestingly, BUT MOST IMPRESSIVE TO ME, WRITING EVERYTHING BACKWARDS. WELL DONE!!!!
I wish I possessed that backward writing skill but, alas, we just flip the video in post edit. :-)
the watch betrayed you ;) but cool :)
@@jeffcrume wait how 😳
@@cnydo you have a camera in front and in back. think of the pen and the point it touches the clear glass screen. if you take the front camera and the back camera they both touch at that same point? all you need to do is sync the "front view" with the contact point of the "back view" also i have no idea what i am talking about and made this up. no clue how they do it.
He wrote it normally and then they mirrored the video so the text appears the correct direction.
I love when someone can help clearly define something like here, with precise and not dumbed down descriptions.
Thanks so much for saying so!
by far the most concise explanation of the difference between AI, DL and ML. they seem to be interchangeable in many contexts though... like 'Lemon AI' isn't called 'Lemon DL' although it's based on deep learning models 🤷♂
Thanks for the great feedback. I know what you mean as I hear people using these terms interchangeably all the time.
🎯 Key Takeaways for quick navigation:
00:00 *🧠 Introduction to AI and ML*
- Exploring the difference and the relationship between Artificial Intelligence (AI) and Machine Learning (ML).
- Framing the discussion around whether AI equals ML or if they are distinctly different concepts.
01:30 *🔍 Deep Dive into Machine Learning*
- Defining Machine Learning as a capability that involves making predictions or decisions based on data.
- Introduction to supervised and unsupervised machine learning, emphasizing their differences and how ML learns from data.
03:03 *🧠💻 Introduction to Deep Learning*
- Describing Deep Learning as a subfield of ML that uses neural networks to model complex patterns.
- Discussing the opaqueness of deep learning processes and their impact on the interpretability of its insights.
03:35 *🌐 AI as a Superset of ML and DL*
- Positioning AI as the overarching set that includes ML, Deep Learning (DL), and other technologies.
- Exploring domains like natural language processing, vision, hearing, and robotics as parts of AI, illustrating AI's broader scope beyond ML and DL.
Made with HARPA AI
The first clear and concise explanation of this I've found. Awesome thank you
EXCELLENT clarity. Finally, someone who truly understands and can explain the components and relationships between these elements of AI.
So glad you liked it!
I love how simply it's clarified, and I believe people should know the difference.
I went for the interview for “AI Intern” at a startup, and later found they were doing ML, but referring to it as AI.
Which is not wrong, but simply unspecific/vague.
An AI intern might spend their whole internship on ML and that would fit the definitions described by this IBM guy
But ML IS AI, its just that ML is a subset of AI
@@beaverbuoy3011 and is it even a subset? Computer Vision applies ML models to images. NLP is ML for text.
@@olemew exactly, seems that ML is AI in my opinion. At least from the examples given in this video
I really need a full course with this amazing professor. It was an outstanding master class. 🎉
Simple, concise and well explained. Best explanation I've come across so far on ML vs AI
The best and clearest video of explaining the differences
To sum up, ML is integral part of AI!
Yesh 😅
That's like saying spaghetti is an integral part of food
@@i_am_acai ahahaha
@@i_am_acai why would you say this is wrong, just trying to understand?
Thanks lol
BY FAR the best explanation I have seen of the concepts and great use of visuals. I have watched over 100 videos on the topics and this is the most concise and clear explanation. Great definitions and visuals. Subscribed.
Thanks so much for the great feedback @brandonfuller246! Always good to hear if we are hitting the mark with these
This actially is very timely since we are seeing a lot of tools just immediately being tagged as an AI. A good example of machine learning are image generators such as Bluewillow which uses datasets.
The average person has no idea what ML is. Everyone knows the word AI. I call it AI all the time because of this reason.
Another great video from Jeff and the IBM team.
Trust me ! You are only guy in the whole wide world that got the layman question and answered it properly
You’re very kind to say so! It seems to be an area of great confusion. I hope this helps …
Clearly explained.
🎯 Key Takeaways for quick navigation:
00:00 Artificial Intelligence (AI) aims to match or exceed human capabilities, involving tasks like discovering new information, inferring from various sources, and reasoning.
01:30 Machine Learning (ML) is a subset of AI, focusing on predictions and decisions based on data, utilizing sophisticated statistical analysis. It learns from data rather than being explicitly programmed.
03:03 Deep Learning (DL) is a subset of ML, involving neural networks with multiple layers. It can provide valuable insights, but the process may not always be transparent.
04:09 AI encompasses ML, DL, and more, including natural language processing, vision, robotics, text-to-speech, and motion. It is a superset that covers a broadrange of human-like capabilities.
05:37 The correct perspective is that machine learning is a subset of AI, emphasizing that when engaging in machine learning or other AI-related activities, one is inherently involved in AI.
Made with HARPA AI
Clear Articulation of the AI and ML and how it related. Hats Off.
Excelent video! Had much difficulties to learn the difference between concepts, with this is sums very clearly and comprehends the difference between those two. The Venn diagram helped a lot to understand and comprehend better thank you very much! Nice content!
The most essential fact, in my opinion, is that AI and ML are literally subsets of " Computer Science. " If you can acknowledge all the abilities of critiquing & becoming a computer scientist then every other subject follows as they all coincide. Correct me if I'm wrong, but CS is basically the Godfather of learning any type of machine learning or artificial intelligence in the technology field. Anyways thanks for this brilliant video, Mr. Crume! Oh yeah, all of these subjects are subsets of us humans, it's funny because AI and ML learn from us just like we learn from each other on a daily basis.
Certainly AI is a field of study within Comp Sci. Traditionally, the latter has been focused on programming whereas the former is more about making a system that can learn
Brilliant simple introductory explanation on AI vs ML. Cheers.
An amazing video and explanation. So easy to understand for those with no background to AI.
This was great! As an instructor, I value plain spoken, logical and imformative presentations. This was all of these!. I was surprised that he didn't expound on neural networks, perhaps that a bit too deep into the weeds, but the context of AI, machine learning, and deep learning was very well presented.
Thanks so much for the great feedback! Yes, I would have liked to delve deeper but we try to keep these videos relatively short on the channel but certainly an area I’ll consider for the future as a separate video
Simple and concise. One of the best and easy to understand explanations on ML and AI. Got me to pause several times as the explanations sounded profound at a time where confusing ideas are being shared across.
So glad you liked it @amit!
Everything outside the ML box is domain of AI. ML is what is used to make models in each of the domains. Not sure if this classification makes sense. It's like placing gradient descent in the bucket of ML. It's an algorithm which could be used for training an ML model.
The way you train the thing you train... There is everything that can make sense
This seems like a IBM marketing video and completely wrong. He defines AI as capability to discover, infer and reason. Then his diagram completely leaves those out! NLP, vision, hearing, speech...you can lose all those and still be human and intelligent. As for ML producing a desired output? Well a bowl of rocks is great system that will give you the most stable configuration ...of a bowl of rocks.
Seems like next video is gonna be hey we've created a system with all these capabilities ...ergo we have created IBM AI. And that AI would be as intelligent as a bowl of rocks!
@@alibaba-ng7vm Could be. Coulde be we are witnessing a period of tentative AI bubbles, where all of them are trying to jump on the bandwagon of GPT parrots presented as AI.
This is BRILLIANT. Great micro-learning - THANK YOU!
Thank you for saying so!
Entirely thanks to this amazing introduction and expression. Very useful
This was great! Wonderful visual to explain MI, AI, and DL. Excellent!
Thank you for presenting a way to think about AI and ML. I have been thinking that the vast number of people talking about AI today are really meaning ML.
Indeed, they are. Since ML is a subset of AI, they aren’t wrong, just not complete as AI encompasses a whole series of technologies
Enlightening, thanks a lot for the nice AI, ML, and DL intro video!
Thank you, crystal clear now for the three concepts of AL/ML/DL.
They are only 1 concept
Excellent description. Cleared things up for me. Thank you!
That's right. Equating AI to ML only was a global PR campaign involving really huge money.
A few distinct features in addition:
A common program executes a ready algorithm. AI can generate new algorithms. This is called problem solving.
ML can't be equated to neural nets either. Training modifies synapses and creates some structure, but you can do it manually yourself. There are very useful solutions of this kind.
«AI can generate new algorithms. »
--
Could be sometime in the future. Maybe after 5000 years. So this one, of yours, indirectly proves that there's no AI today.
Love it how simply and concisely he explained.
Thanks for simplicity and yet short compressive explaination of inter related concepts
You’re very welcome! Thanks for watching!
perfect explanation of diff between AI and ML, I have seen so far. 👏🏻
Thank you for explaining these concepts succinctly ❤
You’re very welcome and thanks for watching!
Now I understood AI & ML
Great explanation
Very good explanation video! Though I personally would not say that all of ML is contained in AI. I'd say that ML is one way of building/creating AI, but not everything done with/for ML would be considered AI.
That is an excellent and straightforward explanation. Loved it!! Thank you!
Clear and concise big W 👍
Machine learning, natural language processing, reactive machines, computer vision are all subsets of AI. Thank you for this very helpful video.
Thank you for watching!
Somehow, what’s impressing me most about this video is the innovation of having the ‘chalk-board’ as a see-through object in front of the instructor.
Nice little 101-01, thank you. I was not aware AI was conceptually focussed on ‘becoming Human’, though perhaps it can be elevated to ‘super-human’ once we figure out what restrictions are necessary to approach a workable intelligence and which can be relieved (like moving to change places, speaking to exchange information, or observing through senses)?
Glad you liked it. Here's how the lightboard works ... th-cam.com/video/LdnJoT5IWPM/w-d-xo.html
Absolutely fantastic. You're a great teacher.
Thanks so much for the kind words!
Very well explained. This is a common confusion and it has been tackled brilliantly. Thanks!
The examples of AI outside of ML are vision and NLP. But arent Vision and NLP using Machine Learning too??
Superbly explained! Thank you
Clearing explications. Thanks à lot.
thanks for bringing that out into the open. i was suggesting to my former boss that we could some form of ml or deep learning to get a machine make parts and self adjust, either threw feedback or vision of the part it was suppose to produce. instead of them taking 4 days to get a machine working. he no clue what i was taking about. i would still like to create a business that implements ml or deep learning on machines that have no pid. Using a raspberry pi and some sensors to measure timing and measure point that can be adjusted.
Very clearly and precisely articulated!
Such a good explanation! Keep it up, editor/producer/director/etc.......!!!
simple, easy, clear explanation
Love how that was explained thoroughly, but I'm also expecting in the future, for touch to be a part of the AI, but that might be for future interactions. I believe that AI integration will be a crucial part, and sending this message to the right audience would greatly impact this through platforms like Cleo that pushes the "Marketing for Good" concept.
You might have missed the "motion" part of what hes saying.
This method used to draw on the board is very successful. Most of the old fashioned board is not efficient because while u are writing something on the table you lose the interaction with listener. For example, You need to turn your back to listener. If you are talking when you are using the board, then the listener's attention is distracted between what you wrote and what you said. If you don't saying anything when you are using board, then there will be weird silence. So, i really liked this style of using board.
Thanks! I’m glad you liked it!
Beautifully explained
Thank you!
One of the few channels that can refill my motivation to be a better Engineer
@darshansrinivas6883 I’m very glad to hear that!
Thank you so much Sir for this clarification 👌👍! I'm really impressed with the simplicity in your explanation.
I’m very glad to know that you liked it!
Well explained. It tackles the confusion mathematically. Thanks for sharing.
What?I am glad for you. I still can’t separate it from the whole apple.
@@brendawilliams8062 «What?I am glad for you. I still can’t separate it from the whole apple.»
--
Nicely spotted! Nothing else but ML repackaged to seem beyond ML.
thank you very much for the simplistic explanation
Notes on Artificial Intelligence and Machine Learning
1. Introduction
The lecture discusses the relationship between Artificial Intelligence (AI) and Machine Learning (ML)
It addresses common questions about whether AI and ML are the same, different, or if one includes the other 1
2. Defining AI
Definition: AI is about matching or exceeding human capabilities and intelligence
Key Aspects:
Ability to discover
Natural language processing
Vision (seeing and distinguishing objects)
Hearing and audio processing
Text-to-speech conversion
Motion and robotics 2 3 4
3. Understanding Machine Learning (ML)
Definition: ML involves making predictions or decisions based on data
Characteristics:
Sophisticated form of statistical analysis
Improves with more data input
Focuses on finding patterns and making predictions 5
4. Deep Learning (DL)
Definition: A subfield of Machine Learning
Key Features:
Involves neural networks
Models the way human minds work
Uses multiple layers of neural networks (hence "deep")
Can provide interesting insights
May not always provide clear explanations for its decisions 6
5. Relationship between AI, ML, and DL
AI is presented as the superset
ML is a subset of AI
DL is a subset of ML
Other AI capabilities exist beyond ML and DL 3
6. Additional AI Capabilities
Natural Language Processing: Understanding and generating human language
Computer Vision: Ability to see and interpret visual information
Speech Recognition: Understanding spoken language
Text-to-Speech: Converting written text to spoken words
Robotics: Enabling motion and physical interaction with the environment 3 4
Key Takeaways
AI is the broadest category, encompassing various technologies that aim to replicate or exceed human intelligence.
ML is a subset of AI, focused on making predictions and decisions based on data analysis.
DL is a specialized form of ML, using complex neural networks to process information.
AI includes additional capabilities beyond ML and DL, such as natural language processing, computer vision, and robotics.
Very Easy Explanation , Well Done !!!
Amazing teaching technique. Made is super simple to understand
Awseome and simple overview. Thanks 😊
Thanks for saying so!
Absolutely wonderful explanation, thank you!
You are very welcome!
Excellent video! Especially works great to make sales people to understand the concept of AI and its subsets!
Simple and clear explanation. Thank you.
Great clarity
Excellent precise and to the point. Thank you
Thanks for watching!
Awesome short videos -excellent for quick learning !
Danke für die Klärung der Begriffe.
🎯 Key Takeaways for quick navigation:
00:00 🤖 AI is about matching or exceeding human intelligence and capabilities, including discovering new information, inferring from implicit data, and reasoning.
01:30 🛠️ Machine learning involves making predictions and decisions based on data, learning from the data rather than being explicitly programmed, and can be supervised or unsupervised.
03:03 🧠 Deep learning is a subset of machine learning using neural networks with multiple layers, capable of producing valuable insights but often lacks complete transparency in how it derives its results.
04:09 🔍 AI encompasses machine learning, deep learning, natural language processing, vision, text-to-speech, and robotics. It aims to mimic human capabilities like seeing, hearing, and motion.
05:37 🧩 Machine learning is a subset of AI, and AI comprises various technologies and techniques, each contributing essential aspects, but none encompasses the entirety of AI.
Made with HARPA AI
Great summary!
This was very helpful, thank you!
Cette vidéo est géniale, synthétique et structurante , bravo à l'interlocuteur pour cette explication simple et condensée.
The best explanation 👍
Yes, there are indeed a lot of unclear things and people often confuse similar and not quite the same things, and I think it would be appropriate for you to make a video about the ethical side and how generative intelligence is still different from artificial intelligence.
Indeed, there are many interesting ethical questions surrounding all of this. I did one video on the 5 pillars/principles of trustworthy AI that gets into this to some degree
@insights_universe - and this is why I read the comments. I've never heard of generative intelligence.
This guy is a legend! Delivers clean and concise knowledge in lay-man terms without compromising on quality of the information being presented. Even takes the time to reply to some of the comments here, wow.
Thanks so much for the kind words! I truly enjoy doing these and knowing that someone else finds value in them makes it all worthwhile!
Brilliant explanation! Well explained, yet very precise!
Awesome video!! Wondering how this video is created? Is it done by Surface Pro with some special app?
Glad you liked it. Search for “how we make them” on this channel and you’ll see
When considering notes on AI, it's essential to recognize that Machine Learning is a subset of AI. The extent of this subset is quite extensive, as ML can be seen as the brain of AI, while other components play sensory roles. It's reasonable to view ML as a crucial element within the broader field of Artificial Intelligence
True but the early expert systems in the 80’s and 90’s typically did not leverage ML but simulated thinking. ML is just a newer technology that is more effective
Good stuff, well explained. Thanks a lot!
How is Artifical Intelligence achieved - through machines learning?
In that case, does this Venn Diagram hold true? Looks like this is a comparison between a goal and the tools used to reach a goal. Would like to understand more.
ML is one form of AI but not the only one
@@jeffcrume Hmm, interesting. Looks like an if conditional alone is sufficient to build an AI system.
Explain very well, great
The issue with that defintion is that human intelligence varies greatly. So you're trying to compare it to something that inherently is vastly different in each person.
True. The definition isn’t perfect but neither is our understanding of intelligence in the first place. Here I wanted to present a simplified explanation to reach a broader audience for whom the existing materials have been inaccessible
Beautiful. Refined my mental model 10 fold.
Awesome!
Great quick explanation
Generative AI applications and AGI (Artificial General Intelligence) are distinct concepts within the field of artificial intelligence.
Generative AI
Generative AI refers to AI systems that can create content, such as text, images, music, and more. These systems use machine learning models, often trained on large datasets, to generate new data that resembles the training data. Examples of generative AI applications include:
Language Models: GPT-4 (developed by OpenAI) can generate human-like text, answer questions, and assist in creative writing.
Image Generation: Tools like DALL-E (also by OpenAI) can create images from textual descriptions.
Music and Art: Systems that can compose music or create visual art based on learned patterns from existing works.
Generative AI is an example of advanced ANI (Artificial Narrow Intelligence), as it is designed to perform specific tasks within a defined domain.
Artificial General Intelligence (AGI)
AGI, on the other hand, is a theoretical concept referring to AI that possesses the ability to understand, learn, and apply intelligence across a wide range of tasks at a human-like level. AGI would not be limited to specific tasks or domains; it would have the capacity for general cognitive abilities, reasoning, and problem-solving across diverse situations. AGI remains a long-term goal in AI research and has not yet been achieved.
Key Differences
Scope and Capability: Generative AI is specialized and excels in specific tasks such as generating text or images. AGI would have broad, human-like cognitive abilities applicable to a wide range of tasks.
Current Status: Generative AI applications are widely available and used in various industries today. AGI is still a theoretical concept and has not been realized.
Focus: Generative AI focuses on creating content based on learned patterns. AGI would focus on general understanding and problem-solving across diverse contexts.
In summary, while generative AI applications represent significant advancements in specific areas of artificial intelligence, they are not the same as AGI. The development of AGI involves overcoming substantial scientific and technical challenges and remains a long-term objective in the field of AI.
Sir explanation was very clear. The content was really easy to understand. Please talk about reinforcement learning.
RL is basically ML without datasets.. it learns from experience.
I would argue that the purpose of AI is to automate some decision making criteria. Automated decision making criteria, and thus AI, can be categorized into one of two forms:
First is criteria that is not based on historical data, like rule-based systems. For example, non-player characters in basic video games may appear to be making intelligent decisions, but they are likely just following a series of instructions written by the developer.
Second, is criteria based on historical data. The techniques needed to build decision making criteria based on historical data are collectively treated as their own discipline, called "Data Science".
One of these techniques, is to build "models" that allow us to estimate unknown values of some entity, using its known values. This process uses historical data to build a model that gives us the best possible estimate (maximum likelihood estimate) given the available data. Building models in this fashion is called "Machine Learning".
So, in summary, Machine Learning is a subset of Data Science. Data Science is a subset of AI.
Are those videos all one continuous take or the editing is so good I can't notice the cut? Great content, really well explained.
Thanks for the kind complements! That video was recorded all in one continuous shot. Nearly all of the ones I’ve done to date have been done that way but we are starting to experiment with segments and transitions, but those will be obvious. Not trying to trick anyone
but, NLP, computer vision, and robotics it could be a part of ML and/or DL? i mean, it involves using neural networks like CNN, DNN, and RNN in most cases, so i always struggle when try to explain the application of AI except ML and DL?
Yes, NLP *uses* ML, which is why I put it where I did in outer layer of the Venn diagram, which was not meant to be a precise definition.Just to get the general notion across about the relationship of ML to AI
Aren't NLP, Vision, TxtSP all subsets of ML at least in how they are executed?
ML can help achieve them as an underlying technology
I remembered to like this video and subscribe to this channel so that you could continue to bring content that matters to me.
You are the best!!! 😊
I'll reference your video in my upcoming vid about bionic arm development@@jeffcrume
Awesome, the Venn diagram wins in explanation power. I'll never forget this.
So glad you liked it!
Super knowledgeable! Thank you very much. 😊
I love AI topic, thanks for sharing such great content 👏
Amazing sir, love you 1000 ❤
Good stuff! More journalists need to watch this type of PSA to stop inadvertently spread misinformation!
Thanks. So clear.