Salesforce Einstein Vision is a part of Einstein Platform services technology, a set of powerful new APIs that allow force.com and Heroku developers of all skill levels to bring image recognition to CRM and build AI-powered apps fast.
Einstein AI is probably the most recent technology on the planet – an innovation that learns by itself. Artificial intelligence is growing through all parts of our lives, whether that be artificial hearing or the voice-controlled Siri and Alexa. Self-driving vehicles are an example as well– with the abilities to prescient.
Einstein is AI for Salesforce and the Salesforce engineers are very eager to gain the expertise on the most proficient method to use technology like Einstein AI. It resembles black box – too hard to even consider opening. So first we will focus on “What is Einstein AI?” and “What isn’t a part of Einstein AI?
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What is a Neuron?
In Biological term, Neurons is the unit of nervous system which is responsible for flow of message in the form of electrical impulse in Human brain. So, Neuron is responsible for Human intelligence. But, in Today’s scenario it is used in Artificial Intelligence as well. Recurrent Neural Network (RNN) is a class of Artificial neural network in which connections between the neurons form a directed graph, or in simpler words, having a self-loop in the hidden layers. This helps RNNs to utilize the previous state of the hidden neurons to learn current state. Along with the current input, RNNs utilize the information they have learnt previously. Among all the neural networks, they are the only one with an internal memory. A usual RNN has a short-term memory. Because of their internal memory, RNN are able to remember things.
The term ‘Forecasting’ by far is coined by the words ‘fore’ & ‘casting’, meaning predicting in advance… First Thoughts… are we trying to play GOD… Predicting in advance??
Oh no… That’s not it…
Artificial intelligence (AI) has been changing our lives for decades, but today its presence is bigger than ever before. AI has powered HR automation. Human resource processes plays vital role in every company. Whether its recruitment, onboarding process, training, learning & development. Artificial Intelligence is entering the HR space, and very soon everything that can be automated, will be automated.
Times are changing; this has been a term in use since the rapid progress of humanity since the 18th century. Today, it can be used even on a daily basis. Technology remains the cornerstone through which humanity evolves, it is now embedded in every single part of our lives today and imagining a future without it would be quite challenging. This is why going forward, there is always going to be a discussion about its impact and how it will change as time comes.
Machine Learning and Artificial Intelligence are considered as an integral part of future technologies.
Artificial Intelligence is an area focused on developing intelligent machines that work and react like humans. To achieve this Artificial Intelligence considers all the traits that can help achieve the feat, these traits include perception, learning, and planning. Salesforce Machine learning on the other hand focuses on development of programs in such a way that systems can access data and use it to learn for themselves Artificial Intelligence focuses on making machines smart i.e. react as the situation demands whereas machine learning is based on providing machines access to data, making them learn themselves which makes their decisions learned rather than smart. Continue reading
Since you are reading this, I assume you are aware of, or at least have heard about Machine Learning and Artificial Intelligence. Being two of the hottest buzzwords in the industry right now, these are often used interchangeably leading to some confusion. However, these two have different meanings and applications. The two terms are very strongly related though, as they share a containership relationship between them where the former is a subset of the later. Lets dive deep into these topics and try to find the reason for this confusion and related solutions.
Why this confusion?
The main culprit behind this confusion is the interchangeable use of these two terms and the limited knowledge of the subject among the developer as well as the user community. Artificial intelligence is heavily dependent on machine learning,
Machine Learning and AI confusion
which has led to the perception that both terms refer to the same thing. This confusion has spread like wildfire in the industry and only people who are experts in this field, know the clear distinction among these terms.
Artificial Intelligence-The Big Brother
Artificial Intelligence is the intelligence demonstrated by machines which emulates a human like thinking and behavior, allowing them to make their own decisions in real life situations. Going by the computer science definition, AI is referred to as the study of intelligent agents, which are devices that perceive their environment and take actions accordingly in order to maximum fulfillment of their goals. These agents mimic certain cognitive functions, which humans relate with the human mind, like problem solving and learning. AI, traditionally, attempts to solve problems such as Reasoning, Knowledge Representation, Learning, Planning, Natural Language Processing etc. Generating an intelligent agent which can think like humans is the long-term goal since it makes use of all the former techniques mentioned.
Now, there are two ways in which the intelligent agents can achieve this, the first one being by using a set of if/else
Artificial Intelligence and Machine Learning
statements which provide the answer for each problem statement. This is a very orthodox, ineffective and tedious way of doing this. Another way is to use Machine Learning which is more flexible & dynamic, being able to learn from the data it processes and improve upon the results incrementally in real time.
Due to disagreements on any established paradigm to be used in machine learning, there is still no fixed approach which works effectively. Some of the popular approaches are as follows:
Machine Learning-An Overview
Machine Learning is a field of artificial intelligence which makes use of statistical techniques and functions to give a computer the ability to learn itself from the provided data, without the need of any explicit programming. This act of the machine system learning by itself progressively improve its performance for a specific task. The heart of machine learning models lies in the dataset which is being used to train the model as well as the algorithms which operate on the data. These algorithms learn from the provide data and make predictions, overcoming static and fixed programming instructions by employing data-driven decisions through a model which it builds from the training data given.
There are two learning types in machine learning- supervised learning and unsupervised learning. The former takes a well labeled data set to operate upon and makes deductions based on it. The later one takes in raw, unlabeled data and finds patterns in the data based on which new data predictions are made.
Closely related to the field of Computational Statistics, machine learning also focuses on making predictions though the use of computers. Mathematical optimization is also tightly coupled with ML since it provides the theory, methods and application domains to the field. Machine Learning is also used in the field of Data Analytics to generate complex models and algorithms which lend themselves to prediction; commercially this is referred to as Predictive Analytics. These models help data scientists, researchers, engineers and experts, to produce reliable results, repeatedly and uncover hidden insights within the data
There are N number of algorithms present which can be utilized to solve your machine learning problem. Each has its own strengths and weaknesses and its fit depends completely on the dataset and the use case. Some of the popular algorithms/methods being used are Decision tree learning, Artificial Neural networks, Inductive Logic Programming, Deep Learning, Bayesian Networks and many more.
Conclusion
Well, this confusion, however small it is, must be cleared since this might lead to problems down the line as the scale of artificial intelligence and machine learning increases. In large and critical implementations, using them interchangeable should be unacceptable and the community should be made more aware about these concepts and their differences. You can reach us for all your AI needs since we, being the AI & ML experts, can help you design a custom solution for your machine learning problem.
References
Salesforce released TransmogrifAI, a machine learning library written in Scala that runs on top of Spark. This can be potentially deployed on any cloud such as Heroku/PostgreSQL platform. What all is involved in TransmogrifAI?
Let us explore a bit more about these new players in the scene and whether they will align with our need to build robust machine learning models. The entry barrier to using the TransmogrifAI library is likely to be the new tech stack that a typical Salesforce developer needs to scale up to. Continue reading
