Machine learning differ in AI
The terms "artificial intelligence" and "machine
learning" are often used interchangeably, but one is more specific than
the other.
Artificial intelligence (AI) is
the broader of the two terms. It originated in the 1950s and can be used to
describe any application or machine that mimics human intelligence. This
includes both simple programs, such as a virtual checkers player, and
sophisticated machines, such as self-driving cars. Some in the field
distinguish between AI tools that exist today and general artificial
intelligence—thinking, autonomous agents—that do not yet exist.
Machine learning describes a
subset of artificial intelligence. This term arose in the 1970s. Machine
learning is distinguished by a machine or program that is fed and trained on
existing data and then is able to find patterns, make predictions, or perform
tasks when it encounters data it has never seen before.
Machine learning can be thought of as the process of converting data and
experience into new knowledge, usually in the form of a mathematical model.
Once it is created, this model can then be used to perform other tasks. This
allows for the design of applications that would be too complex or time
consuming to develop without computer assistance. For example, a machine
learning system may be trained on millions of examples of labeled tumors in MRI
images. On the basis of these examples, the system recognizes patterns of
characteristics that constitute a tumor. This serves as a model that can then
determine if tumors are present in new MRI images. These systems are often able
to outperform experts.
Machine learning is a powerful tool that increasingly is incorporated
into more computer applications. Its ubiquity makes it harder to spot AI
applications that are not trained on data but that rely on
human-written and readable rules and facts. Applications that use artificial
intelligence but do not learn from or produce new results based on exposure to
data are sometimes referred to as "good old-fashioned AI" or
"GOFAI." And some are still in operation. For example, a
simple chatbot may address questions solely by supplying pre-written answers
that contain relevant keywords.
Finally, deep learning is a subset of machine learning.
Deep learning uses machine learning algorithms but structures the algorithms in
layers to create "artificial neural networks." These networks are
modeled after the human brain and have been effective in many situations. Deep
learning applications are most likely to provide an experience that feels like
interacting with a real human.
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
Visit Our Website : computer.scifat.com
Nominate now : https://computer-vision-conferences.scifat.com/award-nomination/?ecategory=Awards&rcategory=Awardee
Contact us : computersupport@scifat.com
#researchawards #shorts #technology #researchers #conference #awards #professors #teachers #lecturers #biologybiologiest #OpenCV #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #DataScience #physicist #coordinator #business #genetics #medicirne #bestreseracher #bestpape
Get Connected Here:
==================
Twitter : x.com/sarkar23498
Youtube : youtube.com/channel/UCUytaCzHX00QdGbrFvHv8zA
Pinterest : pinterest.com/computervision69/
Instagram : instagram.com/saisha.leo/?next=%2F
Tumblr : tumblr.com/blog/computer-vision-research
No comments:
Post a Comment