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Why Machine Learning is not Artificial Intelligence?

Last updated: 04-17-2021

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Why Machine Learning is not Artificial Intelligence?

Why Machine Learning is not Artificial Intelligence?
Dec 18, 2020·10 min read
It is simple: because ML is only a contributing branch of AI. If we follow Norvig and Russell book -and other authors-, saying that machine learning is equivalent to artificial intelligence is grossly misleading. ML is a contributing discipline of AI, just like search, reasoning, planning, decision making, natural language processing, vision, and robotics.
For instance, ML by itself cannot be intelligent because lacks reasoning, planning, logic, and doesn’t interact with the environment. ML detects patterns and performs predictions based on statistical analysis of data using math based algorithms. These algorithms are not intelligent per se.
Intelligence is much more than that.
Stating that ML is part of AI dramatically lowers the bar of what John McCarthy meant by AI. Chapter two of the AIAMA ( Artificial Intelligence: A Modern Approach ) book -which counts five editions- contains examples and schematics of what an AI agent is. In essence, an AI agent perceives its environment by means of sensors, and acts upon the environment through actuators; in between these two, there is logic, reasoning, decision making that makes the agent act autonomously, correcting itself with no human intervention.
The figure is my crude attempt of explaining what the AI contributors are (brown).
The view of AI by Peter Norvig and Stuart Russell (2010)
Background
Our first instinct would bring us to say Machine Learning is a subset of Artificial intelligence. Then, starts getting fuzzy and nebulous when we find papers, articles, posts claiming to be on Artificial intelligence. You start reading them and turns out they are about Machine Learning or Data Science. At this point we just don’t know, if the author(s) made the mistake by ignorance (didn’t research or read enough literature), or purposely tried to deceive readers. Commercialism also plays a huge part in all this. Although, Machine Learning has had -still has- tremendous achievements to show, marketing and PR departments prefer to call it Artificial intelligence.
Books on artificial intelligence DO NOT do that. They are written by scientists -generally- with strong ethical codes.
So, today’s Artificial intelligence, Machine Learning, and Data Science atmosphere is charged with false stories, inflated achievements. That’s bad for all of us. Because in the end what we receive is pseudo-science.
Science is hard — it’s supposed to be. Artificial intelligence, at the top of the food chain, was one of the ultimate frontiers. I’m sure John McCarthy would not agree to qualify a linear regression, a neural network, or a bare robot, as being Artificial intelligence. Of course, they could be AI with the right combination of AI components. Machine Learning is only one of them.
“You are spreading misinformation …”
If I may invite you to read “Artificial Intelligence — The Revolution Hasn’t Happened Yet” by Prof. Michael I. Jordan . With that clarity that comes from a mixed background of engineering and science, Prof. Jordan diagnose the current state of AI. These are my main takeaways:
1- Clearly, there is a misunderstanding of the word Artificial Intelligence, not only by the general public but also scientists.
2- It is difficult to grasp the concept of AI because we are living it. But it’s time that universities come up with a career on AI Engineering.
3- Most of the successes we attribute to AI are actually Machine Learning. All technological leading companies use ML throughout all their business; read Google, Netflix, Facebook, Amazon, Twitter, Microsoft, IBM, etc.
4- AI originally was meant to focus on the cognitive capability of humans. After almost 70 years still remains elusive.
5- What we call AI today arose from the fields of pattern recognition, movement control and statistics.
6- Although document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing are not AI but Machine Learning, they are very successful. And, still we insist in calling them AI.
There is also eye an opening interview by Lex Friedman to Prof. Jordan in YouTube. Here is the link , in which Lex and Yann LeCun refer to Prof. Jordan as “the Miles Davis of Machine Learning”.
The view of Artificial Intelligence by Prof. John McCarthy (2007)
It is interesting to note how the personal computer and computerized electronics completely warped the AI landscape. If we compare Prof. McCarthy to the one by Norvig and Russell, the latter gives way to Robotics, Object Recognition (or Vision), Machine Learning, and Natural Language Processing. Several disciplines that Prof. McCarthy considered as branches of AI have merged by 2010.
It is mind boggling to see Machine Learning, having brought most of the successes that are attributed to AI, public and general, and even scientists insist in calling it AI. It is not because of the word “artificial”; we are irremediably fascinated and in continuous awe by anything labeled with “intelligence”.
And sadly to say, good part of it is commercialism and competition.
The view of AI by Poole, Mackworth and Goebel (1998)
The view of AI by Nils Nilsson (1998)
The view of AI by Edward A. Bender (1986)
Although Professor Bender classifies Artificial Intelligence, from the viewpoint of Goals, in six groups, he start with two main groups: Goals as Results (Reasoning, Planning, and Learning), and Specific Goals (Robotics, Vision, and Language). He dedicates most of the book to reasoning, search, and learning.
Prof. Bender covers robotics, vision and language succinctly in the final chapter.
What Artificial Intelligence is not
Machine Learning gluttony
Definitely the following diagrams do NOT represent what Artificial Intelligence is.
The main flaw is that each of the figures makes it look like Machine Learning is the biggest contributor of AI and makes it a subset of AI, when ML is only a contributing branch of AI.
The interaction with the environment, called here, “technologies physical enablement” does not belong to Machine Learning, it is part of an AI agent. Sensors and actuators (or effectors) are used by an Artificial Intelligence agent to interact with the environment; they are not contributors. Interaction with the environment is another dimension of an AI agent.
Robotics, Vision and NLP are contributing branches of AI
In the next figure, I would move the Machine Learning circle outside of AI. The other smaller circles (vision, robotics, NLP), should be surrounding AI, just as Machine Learning. Autonomous Vehicles is an application of AI, not a contributing branch.
The most successful techniques come from Machine Learning not AI
These all are Machine Learning techniques; they are not AI.
Data Science is not Artificial Intelligence
Here we see the two disciplines, Machine Learning and Data Science. They both complement each other but not necessarily intersect with Artificial Intelligence. Data Science is a reciprocal main contributor of Machine Learning but also minor contributor of Natural Language Processing and Object Recognition. ML and DS are fundamental to get a basic understanding of AI.
Deep Learning and Neural Networks are part of Machine Learning not AI
The following diagram is wrong twice. Deep Learning is a subset of Neural Networks. Machine Learning is not a subset of Artificial Intelligence; it is a contributing branch. ML by itself cannot be intelligent.
On the figure below. Some AI authors think Cognitive Computing should split from Artificial Intelligence as the former uses psychology, neurobiology to search for human intelligence.
Neural Networks and Deep Learning are not branches of Artificial Intelligence but rather of Machine Learning.
Artificial Intelligence representations
Disciplines such as operations research, problem solving, machine learning, reasoning, knowledge representation, are all enabling branches of an artificial intelligence agent.
Fuzzy Logic and Neural Networks could have been included in Machine Learning. I am not so sure about Fuzzy Logic. I think it should be in Process Control or Optimization, though. There is some debate there. Same with Genetic Algorithms; not AI but a method also used in ML and Optimization.
Originally, Prof. McCarthy had a branch for Genetic Algorithms under an independent branch he called Genetic Programming. Today, some AI authors consider it part of Search.
Separate is better. Deep Learning should be included in Machine Learning. The two disciplines Machine Learning and Data Science complement each other. ML, DS, computer science, mathematics are necessary to get a fundamental understanding of AI.
The arrows should point the other way around. The disciplines around enable AI. I don’t know what the author meant by “big data”. Big data is a misnomer. Again, neural networks and big data (which is a wrong term), should be in Machine Learning. Speech recognition should be under Natural Language Processing.
Neural Networks and “big data” -whatever the heck means -, should be part of Machine Learning. Arrows should point in direction of AI and no the other way around.
This diagram slightly follows the book “Artificial Intelligence: A Modern Approach” by Peter Norvig and Russell. Neural Networks, though, has to be moved to Machine Learning.
Computational Intelligence is another preferred term for Artificial Intelligence. At some point, Professor McCarthy wished he had called the discipline “Computational Intelligence” according to Prof. Noel Sharkey.
This diagram is focused to algorithms that do not necessarily belong to Machine Learning but to optimization and process control: Fuzzy Logic, Evolutionary Computing, and Swarm Intelligence. They could be grouped in a common branch but not ML if they don’t learn from data.
We see something new here: “physical algorithms” and “immune algorithms”. They would not have a branch in my diagram; they are not ML if they don’t learn from data. I will do some research and come up with a common branch.
The following figure needs some grouping but gives the idea of what tools you could use to make an AI agent. It has good intentions but mixes applications of AI with disciplines contributing to AI.
A different take on AI, from the point of view of the cognitive mode. I could not find here, though, anything related to decision making, logic, reasoning, or problem solving. Cognitive Computing seems to be a discipline branching out of AI, according to some authors.
In the next figure, I would just add reasoning, problem solving, and decision making. Deep Learning would go under supervised learning. Although not for long. There have been successes in some types of Generative Adversarial Networks or GANs -some of which use Deep Learning through Convolutional Neural Networks- where the GAN can discriminate and classify unlabeled data.
This is another perspective. What are the disciplines that have made possible Artificial Intelligence. In this diagram we see few. I would add also: Economics, Neuroscience, Computer Engineering, Control Theory, Cybernetics, Linguistics, Statistics.
A different view on classification of contributing branches of AI.
References


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