Artificial Intelligence and Machine Learning are the buzzwords of this century. Their wide range of applications has changed the facets of technology in every field, ranging from Healthcare, Manufacturing, Business, Education, Banking, Information Technology, and what not!
Artificial intelligence and machine learning are the part of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems.
You can think of deep learning, machine learning, and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth
In this blog, we will explore these buzzwords and learn the difference between them.
Artificial intelligence is a field of computer science that makes a computer system that can mimic human intelligence. It is comprised of two words "Artificial" and "intelligence", which means "a human-made thinking power." Hence, we can define it as,
There are a lot of ways to simulate human intelligence, and some methods are more intelligent than others.
AI can be a pile of if-then statements, or a complex statistical model mapping raw sensory data to symbolic categories. The if-then statements are simply rules explicitly programmed by a human hand. Taken together, these if-then statements are sometimes called rules engines, expert systems, knowledge graphs, or symbolic AI. Collectively, these are known as Good, Old-Fashioned AI (GOFAI).
Usually, when a computer program designed by AI researchers succeeds at something – like winning at chess – many people say it’s “not really intelligent” because the algorithm’s internals is well understood. The critics think intelligence must be something intangible, and exclusively human. A wag would say that true AI is whatever computers can’t do yet.
Artificial intelligence (AI), machine learning, and deep learning are three terms often used interchangeably to describe software that behaves intelligently. However, it is useful to understand the key distinctions among them.
With AI, you can ask a machine questions – out loud – and get answers about sales, inventory, customer retention, fraud detection, and much more. The computer can also discover information that you never thought to ask. It will offer a narrative summary of your data and suggest other ways to analyze it. It will also share information related to previous questions from you or anyone else who asked similar questions. You’ll get the answers on a screen or just conversationally.
How will this play out in the real world? In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented instead of just detected. And so much more.
In each of these examples, the machine understands what information is needed, looks at relationships between all the variables, formulates an answer – and automatically communicates it to you with options for follow-up queries.
We have decades of artificial intelligence research to thank for where we are today. And we have decades of intelligent human-to-machine interactions to come.
These are services focus on a single job, whether that’s scheduling meetings, automating repetitive work, etc. Vertical AI Bots perform just one job for you and do it so well, that we might mistake them for a human.
These services are such that they can handle multiple tasks. There is no single job to be done. Cortana, Siri, and Alexa are some of the examples of Horizontal AI. These services work more massively as the question and answer settings, such as “What is the temperature in New York?” or “Call Alex”. They work for multiple tasks and not just for a particular task entirely.
AI is achieved by analyzing how the human brain works while solving an issue and then using that analytical problem-solving techniques to build complex algorithms to perform similar tasks. AI is an automated decision-making system, which continuously learns, adapts, suggests, and takes actions automatically. At the core, they require algorithms that can learn from their experience. This is where Machine Learning comes into the picture.
Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that can learn things from past cases. If some behavior exists in past, then you may predict if or it can happen again. This means if there are no past cases then there is no prediction.
ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars, and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond to different situations for which they have not been explicitly programmed. The machines learn from history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.
Artificial Intelligence and Machine Learning always interest and surprises us with their innovations. AI and Ml have reached industries like Customer Service, E-commerce, Finance and where not. By 2020, 85% of customer interactions will be managed without a human. There are certain implications of AI and ML to incorporate data analysis like Descriptive analytics, Prescriptive Analytics, and Predictive analytics.
Some of the top applications of AI & MI include the following:
Just like anything else, Machine Learning has its shortcomings and that is where Deep Learning comes into the picture!
Machine Learning models don’t perform very well when the volume and complexity of data increase multifold. They need some sort of human intervention and guidance whereas Deep Learning models learn from data and previous experience and correct themselves progressively.
|Artificial Intelligence||Machine learning|
|Artificial intelligence is a technology that enables a machine to simulate human behavior.||Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.|
|The goal of AI is to make a smart computer system like humans to solve complex problems.||The goal of ML is to allow machines to learn from data so that they can give accurate output.|
|In AI, we make intelligent systems to perform any task like a human.||In ML, we teach machines with data to perform a particular task and give an accurate result.|
|AI has a very wide range of scope.||Machine learning has a limited scope.|
|AI is working to create an intelligent system that can perform various complex tasks.||Machine learning is working to create machines that can perform only those specific tasks for which they are trained.|
|The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, an intelligent humanoid robot, etc.||The main applications of ML are the Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.|
|AI completely deals with Structured, semi-structured, and unstructured data.||ML deals with Structured and semi-structured data.|
The advances made by researchers at DeepMind, Google Brain, OpenAI, and various universities are accelerating. AI can solve harder and harder problems better than humans can.
This means that AI is changing faster than its history can be written, so predictions about its future quickly become obsolete as well. Are we chasing a breakthrough like nuclear fission (possible), or attempt to wring intelligence from silicon more like trying to turn lead into gold
Given that the power of AI progresses hand in hand with the power of computational hardware, advances in computational capacity, such as better chips or quantum computing, will set the stage for advances in AI. On a purely algorithmic level, most of the astonishing results produced by labs such as DeepMind come from combining different approaches to AI, much as AlphaGo combines deep learning and reinforcement learning. Combining deep learning with symbolic reasoning, analogical reasoning, Bayesian and evolutionary methods all show promise.
Finally, there are the pragmatists, plugging along at the math, struggling with messy data, scarce AI talent, and user acceptance. They are the least religious of the groups making prophecies about AI – they just know that it’s hard.