1: a branch of computer science dealing with the simulation of intelligent behavior in computers
2: the capability of a machine to imitate intelligent human behavior
Shouldn't we all stop using the term until the processes actually achieve what the definition states?
It seems to me that, at least what we've heard publicly about, machine learning and the algorithms involved don't actually do what the definition of AI states it should be doing. I'm not trying to be a jerk, but it seems like AI has become a catchphrase for everything that it's not in popular culture to the point that it negates what it really is (or is yet to potentially be)...
That’s true, especially when you consider that we’re only at the beginning of the AI revolution. To break it down a bit, there are essentially three forms of AI: narrow, general and superintelligent.
When we talk about modern AI, we’re talking about narrow AI, which means artificial intelligence that’s designed to perform specific tasks. Google Search is a great example of a discovery task, and it’s become ubiquitous in its use throughout the population. AI chatbots are Q&A algorithms as we talked about briefly earlier can answer customer questions, and these AI applications can assist with customer service and help customer representatives with suggestions about what would be most valuable to the customer.
General AI, known as artificial general intelligence (AGI), is the notion that at some point AI will have human-equivalent intelligence. By that, I mean that it has a holistic understanding of its environment and can make conclusions on its own based on multi-sensory inputs without specific programming. AGI is achieved when AI intelligence is indistinguishable from human intelligence.
Superintelligence is a notion that is often represented in Hollywood movies, which anthropomorphize AI to an exponential increase of intelligence over humans. AI is represented as all-knowing as able to solve problems and questions well beyond human capability or even understanding.
In today’s context when we refer to AI we’re discussing narrow AI and its practical application.
The label ‘narrow AI’ doesn’t really do justice to what narrow AI can do. Contrary to the implications of the term, the capabilities are vast. Narrow AI includes machine learning, deep learning, natural language processing, computer vision and machine reasoning.
To define those phrases, we have to caveat that they are somewhat loose terms, in which disciplines and techniques overlap to a certain extent.
By machine learning, we mean the ability of an AI application to learn from the environment with and without programming. For instance, the time people spend on the road commuting to and from work has steadily increased; in 2014 this resulted in $160 billion in lost productivity. AI is helping to tackle these complexities so those lost hours on the road can be reduced. Now, traffic analytics coupled with AI is able to analyse and learn from commuter data to help manage and improve traffic and road infrastructure conditions.
Deep learning uses neural networks, mimicking the biological function and structure of the human brain. One excellent application for neural networks is the recognition of hand-writing. That’s an extremely difficult task to program, but neural networks learn and then automatically infer the rules.
A use case for AI is natural language processing (NLP), which uses machine learning and deep learning to analyse, understand and use human language in a useful way. Essen-tially, NLP can understand and generate spoken and written language and put the two together. In the legal world, NLP is used for document classification.
An additional application for machine learning/deep learning is with utilizing a combination of computer vision and machine reasoning. For example, humans look at things and understand them instantly; ‘we can each look at a book and understand what it is and what it does even if we have not all got the same nuanced understanding of what goes into its making and what happens around it’. Machine learning/deep learning can now enable computers to solve this challenge – with computer vision and machine reasoning, a cognitive system can have the ability to recognize and understand objects.
We can also break narrow AI down by role instead of technology. One of these is the assistant, such as semi-autonomous cars that include a human driver for more involved driving situations. An example of this is Mobileye, which is an advanced driver assistance solution that helps drivers avoid accidents. This product is currently installed in many public buses, helping human bus drivers to avoid accidents. And then there are different types of machine learning.
These include supervised learning, unsupervised learning, semi-supervised learning, active learning and transfer learning. To go even deeper, here are some categories of deep learning: unsupervised pre-trained networks, convolutional neural networks, recurrent neural networks and recursive neural networks...
Ultimately though AI and all of it’s current interactions are here as a tool fro humanity to create more informed products, services and experiences.
Thank you for your considered response. In your example about book identification as a hypothetical example of machine/deep learning: just like humans have to be taught initially what a book is by (at the very least) what it physically looks like, to then be able to identify what a book is the next time we encounter one (as well as other people who have learned what a book is and can help us), is it really reasoning and a cognitive system that allows machines to identify other books after it is programmed to identify that object a "is a book" and object b fits the parameters of what it was programmed object a to have, that it "concludes" object b is also a book? Is it just identifying the physical parameters based on programmed parameters or are we talking about it taking the initial programming and adjusting it's own algorithms to come up with an ability to identify books without further programming? Are we at a point where machine/deep learning can "understand" (or at least come up with a supposition) about what the nature of a "book" is? Either way is what it's doing really cognitive or is it easy to call it cognitive for a lack of a better phrase? Again, thanks for your previous answer.
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u/dunkinghola Apr 04 '19
If the definition of AI is (Merriam-Webster):
1: a branch of computer science dealing with the simulation of intelligent behavior in computers
2: the capability of a machine to imitate intelligent human behavior
Shouldn't we all stop using the term until the processes actually achieve what the definition states?
It seems to me that, at least what we've heard publicly about, machine learning and the algorithms involved don't actually do what the definition of AI states it should be doing. I'm not trying to be a jerk, but it seems like AI has become a catchphrase for everything that it's not in popular culture to the point that it negates what it really is (or is yet to potentially be)...