Unless you were living under the rocks, the chances of you not hearing about Artificial Intelligence and Machine Learning are slim.
These two disruptive technologies are found everywhere today, right from your banking website all the way to your smartphone.
Often, these two buzzwords are used interchangeably; Although both seem similar, they’re both vastly different technologies.
They both have their own utilities and uses when it comes to designing solutions for modern-day consumers.
In this post, let us understand the key differences between them.
Machine Learning
Machine learning is the practice of using algorithms that allow computers to identify patterns and find solutions through the analysis of large data sets.
Machine Learning enables computers to find these patterns through the accumulation of experience – very similar to how humans find patterns in objects by repeatedly using them in real life. For example, humans, with practice, can quickly identify the difference between different varieties of apple – Red Delicious, Granny Smith, Honey Crisp, etc.
Machine learning aims to educate and enable computer systems to be able to find similar patterns across large data sets, much larger than the different types of apples that humans can analyze on their own.
Machine learning is touted as a subset of Artificial Intelligence. Remember – by itself, machine learning is not designed to mimic human intelligence. It’s designed to learn, identify patterns, and perform analyses on large data sets – often using statistical methods.
Types of Machine Learning
Depending on the kind of learning that’s permitted for ML systems, there are different types –
Supervised Learning
This type of ML is the use of well-defined datasets created by humans to learn a pattern and perform analyses. Once the system understands what the pattern is, the algorithm can find new patterns in those new datasets.
Unsupervised Learning
These are the machine learning algorithms that analyze and group unstructured and unlabeled datasets. These algorithms are used to discover hidden patterns and information without the need for human intervention.
Here, the goal is to find hidden patterns that are not noticeable to human perception. The reason could be the inability of humans to easily discern, or simply the datasets are too large for them to make any accurate predictions. Unsupervised learning often doesn’t have a set idea of what the end goal will be when the algorithm completes analyzing the dataset.
Reinforcement Learning
Reinforcement learning is a type of machine learning where the algorithm is rewarded whenever it makes a certain type of a decision, such as winning a chess game or capturing a target in a virtual environment.
Reinforcement learning helps algorithms find pathways designed to maximize the idea of a reward. Machine learning typically uses the sample datasets to discover the pattern and uses new data to rediscover the pattern in the new dataset. At the same time, the new data can also be a source for self-correction and self-learning.
Applications Of Machine Learning
You don’t have to go very far to understand the application of machine learning. An application that most of us could easily relate to is search recommendations on Google. After understanding the pattern of your searches, Google will adapt and alter its search recommendations based on your interests.
Another common application of machine learning is the auto-tagging feature on photos that you can find on social media platforms like Facebook. Through the use of existing data sets, the machine learning algorithm can identify the difference between your face and your friend’s face, making suggestions on the fly as you’re tagging the photo.
Artificial Intelligence
In simple terms, Artificial Intelligence uses computers and machines to mimic the decision-making and problem-solving capabilities of human beings.
You can find great examples of AI in most of the software around us such as Google’s AI Voice Assistant, Amazon’s Alexa, Microsoft’s Cortana, and so on.
AI is the superset of Machine Learning as it can work with structured, semi-structured, and completely unstructured data, while ML usually deals with only structured and semi-structured data.
Based on the type of method used by Artificial Intelligence to learn, they can be categorized into the following.
Weak AI
Weak AI is also called Artificial Narrow Intelligence (AN)I, or simply Narrow AI. Currently, this is the only type of AI that we’ve been able to successfully realize. Weak AI is designed to be goal-oriented toward a singular task such as speech recognition, facial recognition, automated driving, etc. Such a type of AI is very good at completing the task it’s programmed for.
While an application employing weak AI may seem intelligent, they’re only able to operate intelligently within its particular constraints. They cannot replicate human intelligence because once you take them out of their predefined parameters, they’ll be unable to produce any meaningful output.
Examples of weak AI include IBM’s Watson, Google’s RankBrain page ranking algorithm, Microsoft’s Cortana, Amazon’s Alexa, and modern email spam filters.
Strong AI
Strong AI is more commonly referred to as Artificial General Intelligence (AGI). This is the concept of a machine with a more generalized intelligence and behavior. Strong AI will mimic human intelligence and behaviors, with the ability to learn, adapt, and apply this intelligence to solve problems that are presented before it. Strong AI is meant to act exactly like humans in any given situation.
Currently, researchers haven’t achieved strong AI yet. That’s because of the complexities involved in modeling an AI after the human brain, which is the goal behind strong AI. Strong AI uses the mind AI framework, which is the ability to discern the nuances of other intelligent entities.
Artificial Superintelligence
Artificial Superintelligence is a hypothetical concept that goes beyond mimicking human intelligence. ASI is also where machines and computers become self-aware and surpass the capabilities of human intelligence and ability. As of now, ASIs remain strictly in the realm of science fiction. But the ideation of ASIs brings interesting questions, such as self-preservation, co-existence with other intelligent beings, and more.
How do AI and ML differ from each other?
To understand the difference between ML and AI, we need to dive a little deeper into their work. In this present era, Deep Learning using Neural Networks has come to dominate the way the computer learns from data. A Neural Network is simply a computer system that is designed to work by classifying information in the same way a human brain does.
Thanks to this, AI is more capable of understanding nuanced patterns, complex classification, and advanced decision-making. This is often demonstrated in advanced applications such as voice transcription, advanced language understanding with question answering, text summarization, medical diagnostics, playing chess, or learning any new game with superhuman abilities.
On the other hand, ML uses statistical methods and software for broader pattern recognition that mirrors the data but often lacks in performing very nuanced classification or understanding that humans possess.
Machine learning in the past has been used for simpler pattern recognition, such as spam detection, clustering, sentiment or anomaly detection, image classification, etc.
How does Rafiki use AI and ML to empower sales teams?
Rafiki is a self-coaching sales platform that uses AI and ML to bolster your sales process. Thanks to its powerful AI and ML engine, Rafiki is able to transcribe with the highest accuracy (85+ accuracy for any accent), diarize i.e. detect speakers and the parts they speak, and apply voice biometrics to know who is speaking. Transcription, Diarization, and bio-metric (using voice patterns) all use state-of-the-art advanced Deep learning AI algorithms that are continuously upgraded as it learns more from data.
At the same time, it also tracks sales topic signals automatically and groups transcribed content into meaningful sales topics using natural language processing and AI-based topic extraction and detection techniques. It also allows one to extend/add topics of interest such as product names, industry lingo, and competitor mentions and personalize the transcription and conversational search to suit their business needs.
Rafiki AI team works with the best in the AI and cloud computing industry to bring the latest technology to the sales and customer-facing teams at an affordable cost using run-time optimization to run on less expensive machines. Rafiki uses these advanced algorithms and techniques to drive down costs that result in its ability to offer one of the most affordable conversations and revenue intelligence platforms to all fast-growing businesses.
If you’re interested in knowing more about artificial intelligence and machine learning, check out Rafiki’s blogs on these topics. Or, if you are ready to infuse the power of AI and ML into your sales process, contact us now.
