Through the power of AI and machine learning, the Merchandising and Marketing feature enables retailers to:

Introduction

On the off chance that you’ve shopped online for a dress, gear, makeup, shoes, home stylistic theme—or anything—you’ve most likely shopped on one of the 3,000 or more sites controlled by B2C Commerce. B2C Commerce is the thing that made your shopping encounters at those sites conceivable. Also, it offers some strong abilities.

B2C Commerce allows retailers to make and arrange customers’ online encounters and exchanges across advanced channels and gadgets. These encounters, by and large, occur on the web or versatile.

However, customers don’t restrict themselves to web and portable locales. They likewise cooperate with brands through email and social media. On the off chance that retailers incorporate their web-based business destinations with their showcasing and administration arrangements, they can make email and social media connections more customized and applicable to customers.

A) B2C Commerce:

B) Through the power of AI and machine learning, the Merchandising and Marketing feature enables retailers to:

Show Answers

What is AI in machine learning?

Ai (Artificial Intelligence) is a broad term that describes any software program that exhibits intelligent behavior. In general, AI applications fall into three categories:

• Narrow AI – systems that perform tasks using human knowledge, such as playing chess or recognizing speech patterns
• General AI – systems capable of carrying out any task given enough computing power
• Superintelligence – artificial intelligence that could surpass human intellectual capacity

In the realm of cannabis, narrow AI encompasses software that performs specific tasks, such as automated lab testing or extraction. General AI would consist of an algorithm that learns how to accomplish a wide variety of tasks. Finally, superintelligence is defined as an intelligence that exceeds human capabilities.

Narrow AI is already being applied to the cannabis industry, and we will look at some examples of these programs below.

Are AI and machine learning the same?

1. Artificial Intelligence (AI)

Artificial intelligence is a technology that simulates intelligent behavior and performance in machines. These systems use algorithms to learn information and make decisions based on those lessons. AI is a broad term that includes deep learning, cognitive computing, natural language processing, robotic automation, and machine vision. Machine learning uses data-driven methods to teach computers how to perform tasks without being explicitly programmed.

2. Machine Learning (ML)

Machine learning involves using software programs to analyze information and make predictions about future outcomes. ML techniques have been applied to many different situations, including computer vision, speech recognition, self-driving cars, and medical diagnosis. There are two subtypes of machine learning: supervised and unsupervised. Supervised ML requires training examples where each example contains known input and output pairs. Unsupervised ML does not require labeled examples; instead, it relies on mathematical models to find patterns in unlabeled data.

3. Deep Learning

Deep learning is a subset of machine learning that employs neural networks, a type of artificial intelligence algorithm inspired by neurons. Neural networks are composed of layers of interconnected nodes, called units. Units at the bottom of the network learn simple concepts while higher level units interpret their inputs and pass them down to lower levels. Deep learning combines several types of neural networks together. One major benefit of deep learning is its ability to identify complex nonlinear relationships between variables.

4. Reinforcement Learning (RL)

Reinforcement learning is an approach to artificial intelligence that gives agents goals, rewards, and punishments. RL is often used in the context of video games to predict player actions and adjust game play accordingly. In business applications, RL can be used to train autonomous vehicles or robots.

5. Automated Reasoning (AR)

Automated reasoning refers to the capability of a computer system to solve logical problems. An automated reasoning system takes information provided by an expert and applies logic rules to determine facts, draw conclusions, and decide what action should be taken. AR is commonly used to help programmers design databases.

6. Cognitive Computing

Cognitive computing is an emerging field of research that focuses on the interaction of humans and machines. Cognitive computing seeks to create technologies that mimic human cognition to improve decision making and problem solving.

7. Biologically Inspired Computation (BIC)

Biologically inspired computation attempts to simulate the brain’s way of thinking using electrical signals. BIC aims to develop computational devices that are capable of performing mental processes similar to those performed by the human brain. The goal is to enable machines to think and act intelligently.

What’s the difference between ML and AI?

I would say ML is the newest technology in terms of what we call AI.

ML is Machine Learning, while AI is Artificial Intelligence.

AI tries to mimic human intelligence, where ML doesn’t really try to mimic anything.

AI just uses specific algorithms to accomplish tasks, while ML uses machine learning techniques to teach the computer how to learn for itself.

ML is much less computationally intensive than AI and can be used to train any algorithm.

It’s been around since about 2004-2005, but it wasn’t until recently, 2017, that they started using it in production.

The problem with AI is that it takes a lot of computational power and memory to run it properly.

If you don’t have enough memory or CPU cycles, then the whole thing shuts down.

So they had to use ML over AI instead.

We’re still working on getting our models to work at scale, but I think it’ll be a great tool for growers.

You can start small and build your way up, and learn something along the way.

There are a few different types of AI out there today, and each type can be broken down further.

There are three broad categories of AI today, Reinforcement Learning, Deep Learning, and General AI.

Reinforcement Learning (RL) is based on trial and error.

Where is AI and machine learning used?

1. Image recognition

AI and machine learning is being used in image recognition systems. These help machines recognize images and classify them based on different factors, including objects, animals, people, etc.

2. Voice control

Voice control technology uses artificial intelligence and machine learning algorithms to identify what someone says and then perform some action. An example of voice control would be using these to operate smart home products, like lights, thermostats, appliances, and door locks, among others.

3. Speech synthesis

Speech synthesis is the use of AI techniques to convert human speech into computer-readable text. So, for example, if we wanted to type out a sentence instead of speaking it out loud, we could simply speak the words instead of saying them. This saves time and money.

4. Chatbots

Chatbots use AI in order to converse with humans via natural language processing (NLP). NLP is a field of computer science that focuses on understanding and generating natural languages through computers.

5. Language translation

Language translation involves translating text written in one language to another. Since many languages are not mutually intelligible, this is a complex task. However, with the advent of AI, this is becoming easier. Machine learning software learns over time and becomes more accurate at its job.

6. Customer service

Customer service agents provide assistance and support to customers via phone calls, chat rooms, email, and social media platforms. Customers may have questions about billing information, product usage, specific products, and/or general inquiries. By leveraging AI, customer service agents can handle customer requests faster than ever before.

7. Recommendation engines

Recommendation engines recommend items to users based on their previous purchases, browsing habits, or interests. Amazon’s recommendation engine is a great example of how recommendations work. If you buy a book, the system will suggest similar books that you might enjoy reading. Other examples include Facebook’s newsfeed, Netflix’s suggestions, and YouTube’s watch later feature.