According to many thought leaders, analysts and early adopters, Artificial Intelligence (AI) and Machine Learning (ML) technologies are rapidly making their way into every industry, geography, system and process. This means that B2B sales and marketers need a primer for artificial intelligence and machine learning to quickly catch up on how they can benefit. This primer on AI and ML will explain how.

In general, artificial intelligence is the simulation of human intelligence processes by machines. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction (continuous and tireless learning).

Artificial Intelligence (AI)

AI is typically defined as the ability of a machine to perform cognitive functions usually performed by people. These functions include perceiving, reasoning, learning, interacting with the environment, problem solving, and possibly some level of creativity. Examples of technologies that enable AI to solve business problems are robotics and autonomous vehicles, computer vision, language, virtual agents and machine learning.

Why Now is the Right Time for AI

Algorithmic advances, data proliferation, huge increases in computing power and storage (especially from the cloud) and the ability to “work” 24 * 7 * 365 without tiring and enabling continuous learning, has propelled AI from a concept to many real use cases.

Machine Learning (ML)

The most recent advances in AI have been achieved by applying machine learning to very large, diverse data sets or data lakes. ML algorithms detect patterns and learn how to make models, predictions and recommendations by continuously processing vast amounts of data and experiences, as opposed to using a rigid set of commands programmed by a person or team of people based on the information available at a specific point in time. A huge differentiator is that the ML algorithms constantly learn and adapt as new data and experiences are processes and that means continuous learning over time – plus they never tire and make far fewer mistakes than humans.

The Four Primary Types of Machine Learning

Machine Learning – Supervised Learning

In supervised learning, algorithms use training data and feedback from humans to learn the relationship of given inputs to a given output. The goal of supervised learning is to approximate the mapping function so well that it generates a new input data that can be used to predict the output variables.

It is called supervised learning because the process of an algorithm learning from the training data is similar to a teacher supervising the learning process. The teacher knows the answer while the algorithm iteratively makes predictions on the training data and is “corrected” by the teacher.

Machine Learning – Unsupervised Learning

Unsupervised learning is where the only input is data and there are no corresponding output variables. In other words, an algorithm explores input data without being given an explicit output variable (the answer) – an example is sales and marketers exploring customer demographic data to identify patterns of existing and potential customers.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. It’s referred to as unsupervised learning because the answer is not known – algorithms discover and present patterns and insights in the data.

Machine Learning – Reinforcement Learning

In reinforcement learning the answer is not known, so the reinforcement learning agent still has to decide how to act to successfully perform its task. Because there is no training data available, the agent learns from experiences. It collects the training examples and through a trial-and-error process and it relentlessly pursues the the goal of maximizing the long-term reward.

Machine Learning – Deep Learning

Deep learning is a type of machine learning that can:

  • process a wider range of data resources
  • requires less data preprocessing by humans, and
  • can often produce more accurate results than traditional machine-learning approaches (although it requires a larger amount of data to do so)

In deep learning, interconnected layers of software-based calculators (referred to as neurons from a neural network) can ingest vast amounts of input data. In the next step, the data is processed through multiple layers that learn increasingly complex features of the data at each layer. The network then makes a determination about the data, the accuracy of the determination and then incorporates what it has learned to make smarter determinations the next time. In short, there is a direct correlation between time and intelligence.

Types of Neural Networks

Convolutional Neural Network (CNN)

In machine learning, a convolutional neural network is a deep, feed-forward artificial neural network that has successfully been applied to analyzing visual imagery. In this scenario, the CNN learns the filters that in traditional algorithms were hand-engineered by an individual.

Recurrent Neural Network (RNN)

Recurrent neural networks are a type of artificial neural network designed to recognize patterns in sequences of data such as text, genomes, handwriting, the spoken word, or numerical time series data emanating from sensors, stock markets or government agencies.

RNNs are considered by some to be the most powerful and useful type of neural network which can be decomposed into a series of patches and treated as a sequence. Since recurrent neural networks possess a certain type of memory, and memory is also part of the human condition, RNNs have many similarities to the human brain.

Summing it Up

All of the necessary associated infrastructure and services are available for organizations to incorporate AI and ML into their business models. Everything is available for B2B sales and marketers to step up their game, including:

  • cloud-based data stores capable of holding the vast amount of data needed to train machine-learning model
  • services to transform data to prepare it for analysis
  • visualization tools to display the results clearly
  • software that simplifies the building of models

Many cloud platforms even simplify the creation of custom machine-learning models for B2B sales and marketers.

By automating the creation of AI models for B2B sales and marketers through declarative or drag and drop capabilities, users can build custom image recognition models without the need for machine learning expertise or programming knowledge.

The time is now for B2B sales and marketers to embrace AI and ML to find, attract, engage, acquire, onboard, retain and expand existing and new markets.


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