Explore the benefits which MNCs aregetting from AI/ML and emphasizing the enhancement of AI provided to their products and make them the top notch companies of this generation.

What is Artificial Intelligence(AI)?

Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task.
That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not.
AI systems will typically demonstrate at least some of the following behaviours associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity.
WHAT ARE THE USES FOR AI?

• Smart assistants (like Siri and Alexa)
• Disease mapping and prediction tools
• Manufacturing and drone robots
• Optimized, personalized healthcare treatment recommendations
• Conversational bots for marketing and customer service
• Robo-advisors for stock trading
• Spam filters on email
• Social media monitoring tools for dangerous content or false news
• Song or TV show recommendations from Spotify and Netflix

Now from above hope you understand what is AI and where it is use…..

Now lets study about Machine Learning , basically your machine learning is comes under the AI or you can think it is a subcategory of AI.

machine learning is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training — typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Disney uses Machine Learning Algorithm and IoT to power CX
After years of testing Disney launched a MagicBand, a wristband that is integrated with RFID technology and a long-range radio. These MagicBand communicate with thousands of sensors present in the Disney premise, for a smart Customer Experience.
MagicBand act as tickets, FastPasses, credit cards, hotel keys, and a lot more. With the swipe of the band, the giant computer system across the park knows, what you are doing, where you are, and what do you need. Additionally, your favorite Disney cartoon characters can find and greet you and your children wherever you are. Candid photos of your family can be clicked the entire day. And then sent to you at night in your hotel rooms.
Machine Learning based Magicband were implemented to weed out the friction within the Disney World Experience. And one of the biggest challenges of Disney was the waiting line. They know when a visitor is at the waiting line, they are not spending money on food or rides. As a result they developed this band to nullify this problem and enhance their customers’ experience.
Disney’s Next-gen tech — Recognizing customers through Shoe Soles
Disney last year had applied for a patent for a system that will recognize customers by their shoe sole. Their aim is to provide a Next-gen experience that aims to offer a more seamless, immersive, and personal experience to every visitor.

Examples of Multi-million Dollar Companies leveraging AI & ML to improve their Customer Experiences
Disney

Disney uses Machine Learning Algorithm and IoT to power CX
After years of testing Disney launched a MagicBand, a wristband that is integrated with RFID technology and a long-range radio. These MagicBand communicate with thousands of sensors present in the Disney premise, for a smart Customer Experience.
MagicBand act as tickets, FastPasses, credit cards, hotel keys, and a lot more. With the swipe of the band, the giant computer system across the park knows, what you are doing, where you are, and what do you need. Additionally, your favorite Disney cartoon characters can find and greet you and your children wherever you are. Candid photos of your family can be clicked the entire day. And then sent to you at night in your hotel rooms.
Machine Learning based Magicband were implemented to weed out the friction within the Disney World Experience. And one of the biggest challenges of Disney was the waiting line. They know when a visitor is at the waiting line, they are not spending money on food or rides. As a result they developed this band to nullify this problem and enhance their customers’ experience.
Disney’s Next-gen tech — Recognizing customers through Shoe Soles
Disney last year had applied for a patent for a system that will recognize customers by their shoe sole. Their aim is to provide a Next-gen experience that aims to offer a more seamless, immersive, and personal experience to every visitor.

Thank you for reading!!!!