The Power and Limitations of Neural Networks

Neural networks are one of the key ways of doing deep learning today. The idea behind them is modeled on the way our brain is thought to function, with neurons combining to get together and propagate signals based on certain thresholds being met. As inputs are presented, the network can learn and adapt using feedback systems. This allows it to recognize patterns in general and exhibit a form of pattern recognition that's akin to "I see a dog" or "I see a car." However, neural networks were not the only method of machine learning, and there are many different algorithms used for machine learning. Neural networks, however, have proven to be particularly effective and are often considered the most powerful tool in this field.

Artificial Intelligence: A Broader Concept

Artificial intelligence, on the other hand, refers to a much broader concept that encompasses not just pattern recognition, but also abstract thinking, creativity, strategy, and context. This is often referred to as "general AI," and it's an area of research that's still in its infancy. Artificial intelligence is different from machine learning in that it involves creating systems that can think for themselves, rather than simply recognizing patterns. While machine learning algorithms like neural networks are great at recognizing patterns, they're not necessarily capable of true creativity or self-awareness.

The Distinction Between Machine Learning and AI

It's worth noting that the terms "machine learning" and "AI" are often used interchangeably, but they're actually quite different. Machine learning is a specific technique for recognizing patterns and making predictions based on those patterns. It's a tool within the broader field of artificial intelligence. AI, on the other hand, refers to the entire field of research and development aimed at creating systems that can think like humans.

The Limits of Neural Networks

Neural networks are incredibly powerful tools, but they're not without their limitations. One of the biggest challenges is getting them to work in situations where there's no clear pattern or structure. In other words, if you give a neural network something outside of what it's used to, it can often fail completely. This is because neural networks rely on patterns and structures that are already present in the data they're trained on.

The Example of Machine Learning

To illustrate this point, consider the example of a machine learning system trying to recognize a new pattern or image. If you show it an infinite number of dictionaries for Chinese Mandarin and ask it to translate words, it may be able to recognize certain patterns and respond accordingly. However, if you give it a completely new word that's not in any of its training data, it will likely fail to recognize it. This is because machine learning systems are only as good as the data they're trained on.

The Limits of Artificial Intelligence

Artificial intelligence, on the other hand, refers to the idea of creating systems that can think for themselves and exhibit true creativity or self-awareness. While some researchers believe that it may be possible to create a computer that's truly conscious, this is still a topic of debate among experts. The problem is that consciousness is notoriously difficult to define, and it's not clear what exactly constitutes "intelligence" in a human being.

The Idea of General AI

General AI refers to the idea of creating systems that can think for themselves and exhibit true creativity or self-awareness. This is often referred to as "weak AI," meaning that the system is capable of thinking like humans but does not possess consciousness or sentience. Strong AI, on the other hand, refers to the idea of creating a system that is truly conscious and self-aware.

The Importance of Abstract Thinking

Abstract thinking is an important aspect of artificial intelligence, as it allows systems to reason about complex concepts and make decisions based on incomplete information. This is in contrast to machine learning, which relies on pattern recognition and data-driven decision-making. While machine learning algorithms like neural networks are great at recognizing patterns, they're not necessarily capable of true abstract thinking.

The Future of Artificial Intelligence

The future of artificial intelligence is a topic of much debate among experts. Some believe that it may be possible to create a computer that's truly conscious, while others argue that this is impossible. Regardless of the outcome, one thing is clear: artificial intelligence has the potential to revolutionize many areas of our lives, from healthcare and finance to education and entertainment.

Conclusion

In conclusion, neural networks are powerful tools for machine learning, but they're not without their limitations. Artificial intelligence, on the other hand, refers to a much broader concept that encompasses abstract thinking, creativity, strategy, and context. While machine learning algorithms like neural networks are great at recognizing patterns, they're not necessarily capable of true creativity or self-awareness. As we move forward in the development of artificial intelligence, it's essential to consider both the limitations and possibilities of these technologies.

"WEBVTTKind: captionsLanguage: enhello there i'm gary sims from andrew authority now some of the catchphrases that you hear a lot of today are artificial intelligence machine learning and neural networks and they are often used interchangeably but actually they're very separate different things so if you want to know what the differences are between artificial intelligence and machine learning then please let me explain basically it works like this when a company's trying to sell a product they call everything artificial intelligence so my smartphone has artificial intelligence your your house has artificial intelligence when they're talking to maybe to the engineers or talking about job descriptions then they may put in the word machine learning and then when it comes to actually writing some actual specifications actually developing the technology then they're probably going to be using neural networks so let's have a look at what these three things are and see how really they're quite different but of course very interconnected we'll start with a simpler one which is machine learning simpler in that it's simpler to define now machine learning is the process by which a machine can learn from its experience now I've got a whole video on this about what is machine learning and I'll link it up here so you can go and watch that now basically machine learning in our context like learning to recognize voice learning to recognize images learning to recognize text is basically you take a data set you feed it into a program that is capable of learning from that data set and then of the output it knows how to use and recognize things that fit inside that data set even if they've never actually seen that specific example before so you can teach the computer the word you know hello and you can say in lots of different accents in British accents like mine and maybe in an American accent and a sort of South deep in the south part of America maybe from Scotland you know maybe from Australia and you can say hello in all these different ways and because it's actually learnt these different formats of understanding the word hello then you can understand hello when anybody says it all all around the world that's basically the idea now the thing about machine learning is very very specific so if you teach a computer to recognize the word hello from America and with the British accent from Scotland from Australia that's great but if you then say goodbye it doesn't understand doesn't recognize what you're trying to say so let me give an example we've got this alphago system that can play go the chinese ancient game of go much better than any human player now the point is that go is played on a 19 by 19 board now they trained up this program they just taught it how to play go it played millions of games and then it worked out how to play go but if they actually present it now this with a twenty by twenty board the machine learning program would just fail you would not know how to play go there's no creativity there's no abstract thinking there's no strategy there's no like oh well I know how to play this I've seen it similar before Ingo bon a 1919 board but a human player particularly one who's an expert at go would have a very good attempt at playing that game at a fairly high level so machine learning is impressive when it's in a very particular thing and it says well this computer knows how to play goal this could be to knows how to recognize voices but if you give it something outside of what it's used to that it just completely fails because a very specific application now when it comes to neural network neural networks are one of the key ways of doing deep learning today so the idea is and it's modeled on the way our brain is thought to function with the idea of neurons and when the neurons are combining to get together you can have a set of inputs and then the signals when those inputs are propagated further on down the network if a certain threshold is met and then that threshold can be tweaked and trained using a feedback system so as you present it it's kind of distal zeros at the beginning as empty and then as you present it data and as you tell it what the desired result is it can kind of have this feedback and it creates this ability to recognize things in a general pattern and it's pattern recognition you know that's a dog that's the word hello and so on that gives it its power but neural net was not the only method of machine know and there are lots of different algorithms that can be used for machine learning however neural networks are the ones that are being you maybe for the greatest benefit at the moment now artificial intelligence is different intimate intelligence actually talks about the idea of being able to be creative to have abstract thinking to be able to analyze things inside a context now I've got a whole video on art of emergence which I will link up here but basically the thing we have to remember is that machine learning is very about pattern recognition about seeing a particular situation whereas artificial intelligence is being able to be intelligent about that and of course we could have a whole discussion about what intelligence is but basically you have to remember that a computer can exhibit the ideas of intelligence without being intelligent itself the example that is often used is if you have a man inside of a room that has an infinite number of dictionaries about Chinese Mandarin and it has an infinite number of responses that it can give to any number of questions that if you stick some Chinese Mandarin under the door of his room he can pick it up he can look it up in the book and he can give you the reply and you would be convinced that the man inside the room actually is fluent in speaking Mandarin in fact he's not he's just looking up an input and giving you the appropriate output so it exhibits the idea of intelligence without actually being intelligent and then further on we've got the idea that of course an AI is generalistic so machine learning is very specific but an AI would be generalistic so if it was presented with a 20 by 20 go board it would have a good crack at saying well I can play this game I've never seen it before but I can play this game because I'm gonna use my sort of abstract thinking my strategy my context here to work out what's going on and then AI you've also got the idea of a weak AI and strong AI weak AI basically being a system that exhibits the ideas of intelligence the characteristics of intelligence without actually being self-aware without actually being sentient whereas strong AI is the idea that people some people believe that you can create a computer that actually forms its consciousness it becomes self-aware and that cause brings in the whole issues of morality and ethics which actually we're going to look at in another video soon so to recap machine learning is a algorithm a technique for recognizing patterns and then doing something according to those patterns whether it be words images you know strategy games it's just basically pattern recognition if I was in this position before what did I do that would lead me to a win that's the strategy one or if I've seen this picture before what does it look like when it renders mbers another picture I've seen of a cat or a dog or a car or whatever and neural networks are a part of machine learning in how you actually build the software to do that process of learning artificial tones on the other hand is the idea of exhibiting intelligence which includes abstract thinking creativity strategy context which is much broader and much more generalistic so some people even talk about it as general AI general artificial intelligence so although the marketing people will kind of talk about AI and machine learning and neural networks all in the same phrase and they will just mix them up they are actually very specifically different things of course neural networks are part of machine learning machine learning has to be part of AI but they're not all exactly the same thing so if you have a smartphone that claims it has AI because actually it's got machine learning well now you know different I'm going to since render Authority and really hope you enjoyed this video if you did please do give it a thumbs up don't forget to subscribe to hand Rathore T's YouTube channel hit that notification symbol so you always get told when we release a new video and last but not least do go over to andrew authority comm because we are your source for all things Androidhello there i'm gary sims from andrew authority now some of the catchphrases that you hear a lot of today are artificial intelligence machine learning and neural networks and they are often used interchangeably but actually they're very separate different things so if you want to know what the differences are between artificial intelligence and machine learning then please let me explain basically it works like this when a company's trying to sell a product they call everything artificial intelligence so my smartphone has artificial intelligence your your house has artificial intelligence when they're talking to maybe to the engineers or talking about job descriptions then they may put in the word machine learning and then when it comes to actually writing some actual specifications actually developing the technology then they're probably going to be using neural networks so let's have a look at what these three things are and see how really they're quite different but of course very interconnected we'll start with a simpler one which is machine learning simpler in that it's simpler to define now machine learning is the process by which a machine can learn from its experience now I've got a whole video on this about what is machine learning and I'll link it up here so you can go and watch that now basically machine learning in our context like learning to recognize voice learning to recognize images learning to recognize text is basically you take a data set you feed it into a program that is capable of learning from that data set and then of the output it knows how to use and recognize things that fit inside that data set even if they've never actually seen that specific example before so you can teach the computer the word you know hello and you can say in lots of different accents in British accents like mine and maybe in an American accent and a sort of South deep in the south part of America maybe from Scotland you know maybe from Australia and you can say hello in all these different ways and because it's actually learnt these different formats of understanding the word hello then you can understand hello when anybody says it all all around the world that's basically the idea now the thing about machine learning is very very specific so if you teach a computer to recognize the word hello from America and with the British accent from Scotland from Australia that's great but if you then say goodbye it doesn't understand doesn't recognize what you're trying to say so let me give an example we've got this alphago system that can play go the chinese ancient game of go much better than any human player now the point is that go is played on a 19 by 19 board now they trained up this program they just taught it how to play go it played millions of games and then it worked out how to play go but if they actually present it now this with a twenty by twenty board the machine learning program would just fail you would not know how to play go there's no creativity there's no abstract thinking there's no strategy there's no like oh well I know how to play this I've seen it similar before Ingo bon a 1919 board but a human player particularly one who's an expert at go would have a very good attempt at playing that game at a fairly high level so machine learning is impressive when it's in a very particular thing and it says well this computer knows how to play goal this could be to knows how to recognize voices but if you give it something outside of what it's used to that it just completely fails because a very specific application now when it comes to neural network neural networks are one of the key ways of doing deep learning today so the idea is and it's modeled on the way our brain is thought to function with the idea of neurons and when the neurons are combining to get together you can have a set of inputs and then the signals when those inputs are propagated further on down the network if a certain threshold is met and then that threshold can be tweaked and trained using a feedback system so as you present it it's kind of distal zeros at the beginning as empty and then as you present it data and as you tell it what the desired result is it can kind of have this feedback and it creates this ability to recognize things in a general pattern and it's pattern recognition you know that's a dog that's the word hello and so on that gives it its power but neural net was not the only method of machine know and there are lots of different algorithms that can be used for machine learning however neural networks are the ones that are being you maybe for the greatest benefit at the moment now artificial intelligence is different intimate intelligence actually talks about the idea of being able to be creative to have abstract thinking to be able to analyze things inside a context now I've got a whole video on art of emergence which I will link up here but basically the thing we have to remember is that machine learning is very about pattern recognition about seeing a particular situation whereas artificial intelligence is being able to be intelligent about that and of course we could have a whole discussion about what intelligence is but basically you have to remember that a computer can exhibit the ideas of intelligence without being intelligent itself the example that is often used is if you have a man inside of a room that has an infinite number of dictionaries about Chinese Mandarin and it has an infinite number of responses that it can give to any number of questions that if you stick some Chinese Mandarin under the door of his room he can pick it up he can look it up in the book and he can give you the reply and you would be convinced that the man inside the room actually is fluent in speaking Mandarin in fact he's not he's just looking up an input and giving you the appropriate output so it exhibits the idea of intelligence without actually being intelligent and then further on we've got the idea that of course an AI is generalistic so machine learning is very specific but an AI would be generalistic so if it was presented with a 20 by 20 go board it would have a good crack at saying well I can play this game I've never seen it before but I can play this game because I'm gonna use my sort of abstract thinking my strategy my context here to work out what's going on and then AI you've also got the idea of a weak AI and strong AI weak AI basically being a system that exhibits the ideas of intelligence the characteristics of intelligence without actually being self-aware without actually being sentient whereas strong AI is the idea that people some people believe that you can create a computer that actually forms its consciousness it becomes self-aware and that cause brings in the whole issues of morality and ethics which actually we're going to look at in another video soon so to recap machine learning is a algorithm a technique for recognizing patterns and then doing something according to those patterns whether it be words images you know strategy games it's just basically pattern recognition if I was in this position before what did I do that would lead me to a win that's the strategy one or if I've seen this picture before what does it look like when it renders mbers another picture I've seen of a cat or a dog or a car or whatever and neural networks are a part of machine learning in how you actually build the software to do that process of learning artificial tones on the other hand is the idea of exhibiting intelligence which includes abstract thinking creativity strategy context which is much broader and much more generalistic so some people even talk about it as general AI general artificial intelligence so although the marketing people will kind of talk about AI and machine learning and neural networks all in the same phrase and they will just mix them up they are actually very specifically different things of course neural networks are part of machine learning machine learning has to be part of AI but they're not all exactly the same thing so if you have a smartphone that claims it has AI because actually it's got machine learning well now you know different I'm going to since render Authority and really hope you enjoyed this video if you did please do give it a thumbs up don't forget to subscribe to hand Rathore T's YouTube channel hit that notification symbol so you always get told when we release a new video and last but not least do go over to andrew authority comm because we are your source for all things Android\n"