Artificial Intelligence (AI) and Machine Learning (ML) technologies are rapidly making their way into every industry, geography, system and process. So in B2B sales and marketers need a primer for artificial intelligence and machine learning to quickly catch up so that it could benefit them in their growth.
Artificial intelligence is the simulation of human intelligence processes by machines. These processes include learning basically the acquisition of information and rules for using the information, reasoning means sing the rules to reach approximate or definite conclusions, and self-correction means continuous and tireless learning.
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.
Algorithmic advances, data proliferation, huge increases in computing power and storage and the ability to work 24x7x365 without tiring and enabling continuous learning.
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.
Types of Machine Learning
Machine Learning a 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 algorithm learning from the training data is similar to a teacher supervising the learning process.
Machine Learning as 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 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 as 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 goal of maximizing the long term reward.
Machine Learning as Deep Learning
Deep learning is a type of machine learning as
process a wider range of data resources
requires less data preprocessing by humans, and
often produce more accurate results than traditional machine learning approaches.
In deep learning, interconnected layers of software based calculators 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. There is a direct correlation between time and intelligence.
Lastly 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.