Artificial Intelligence and Salesforce Einstein

Jayant Joshi,
Technology Architecture Delivery Manager,
Accenture Gmbh.

Why are we starting with an Article on Artificial Intelligence first?

Well, SFDC Einstein is an Artificial Intelligence tool for CRM and to understand Einstein, it is better to start with the basics first. Hold your horses for some time until we reach Salesforce Einstein. Note: If you are an AI champ or already know the basics of AI jump to the SFDC Einstein section.

What is AI?

So what exactly is AI?

Actually, in the last ten years a lot has been written about AI. There are many ways to describe Artificial intelligence but let us start with Wiki definition (Yeah...You know that i Love Wiki). As per Wiki, Artificial intelligence (AI), sometimes called machine intelligence, "is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals". In computer science, AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem-solving".

Okay, too technical definition? I will return to the definition in a minute. Firstly let us look at a brief history of Artificial Intelligence.

Brief History of AI:

It is important to first always have some background information about the topic we want to learn so let us look back in time and understand how modern AI was gradually shaped.

Turing Test: The Turing test, developed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behavior (we will come to this point shortly) equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses.

The Term 'Artificial Intelligence': The term “artificial intelligence” is given in a proposal for a “2 month, 10 man study of artificial intelligence” submitted by John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

Unimate was the first industrial robot which worked on a General Motors assembly line at the Inland Fisher Guide Plant in Ewing Township, New Jersey, in 1961.

ELIZA is an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.

Shakey the Robot was the first general-purpose mobile robot to be able to reason about its own actions (how cool...). While other robots would have to be instructed on each individual step of completing a larger task, Shakey could analyze commands and break them down into basic chunks by itself Unfortunately there were no major breakthrough in the Area of AI between 1967 to early 1990.

Apple Siri: The voice assistant was released as an app for iOS in February 2010, and it was acquired by Apple two months later. Siri was then integrated into iPhone 4S at its release in October 2011.

Google Now was a feature of Google Search that offered predictive cards with information and daily updates in the Google app for Android and iOS. In 2016, Google launched a new intelligent personal assistant Google Assistant, in some ways an evolution of Google Now.

Amazon Alexa: Amazon Alexa is a virtual assistant developed by Amazon, first used in the Amazon Echo and the Amazon Echo Dot smart speakers developed by Amazon.

Tay was an artificial intelligence chatter bot that was originally released by Microsoft Corporation via Twitter on March 23, 2016.

Cortana is a virtual assistant created by Microsoft an set reminders, recognize natural voice without the requirement for keyboard input, and answer questions using information from the Bing search engine.

Source: Wiki

Understood...so What is the typical usage of AI?

Let us look at some examples of AI already used currently (as of 2018):p>

• Google Assistant for engaging in a two-way conversations.

• A powerful and a very successful recommendation engine is used by Amazon for Products Search and Recommendations. They use machine learning behind the scenes.

• Image recognition by companies like Google (Google Photos), Facebook (Facebook App) etc. They use the Image recognition algorithms for this purpose.

• Navigation Apps like Apple Maps, Google Maps, Waze etc.

• Cab sharing Apps like Uber, Ola, Lyft are using advanced Machine learning techniques to provide best user experience to their customers.

• Harvard scientists used Deep Learning to teach a computer to perform viscoelastic computations, these are the computations used in predictions of earthquakes.

As promised, let us again jump to the definition of Artificial Intelligence: So AI is essentially 'Intelligent Behavior'. So how do we describe 'Intelligent Behavior'?

Well, Intelligent behavior have some of the below characteristics:

• Learn from experience

• Determine what is important (Prioritize)

• Solve Problems (as human do)

• Handle complex situations (As human do)

• Apply knowledge acquired from experience (Very Important trait be being human)

Great!!!, let us again try to understand AI formally and which brings us to 'What are the disciplines of AI'?

There are many disciplines of Artificial Intelligence. Below is the list (Partial):

The disciplines on the left side are quite self-explanatory and make perfect sense but you must be wondering why human behavior, psychology, philosophy are considered as disciplines of AI? Well, The field of AI was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This brings us to 'philosophy' and opens up debate on nature of mind. There has been a lot of debates on human mind Vs. AI in last fifty years and as depicted in many movies 'Artificial Intelligence' in the future can be a challenge for the human race if not built in a controlled way. In January 2015, Stephen Hawking, Elon Musk, and dozens of artificial intelligence experts signed an open letter on artificial intelligence calling for research on the societal impacts of AI. The letter affirmed that society can reap great potential benefits from artificial intelligence, but called for concrete research on how to prevent certain potential "pitfalls".

What are the Applications of Artificial Intelligence?

We have already seen the current applications of AI in one of the above sections but let us see the broader definitions in this section:

AI can be (and is being used) in a lot of areas. Let us take these one by one:

Natural Language Processing

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence that is focused on enabling computers to understand and process human languages. NPL makes it possible for the computers to hear speech, read text, interpret it, measure the sentiments and determine which parts of the speech/text are important.

Voice Recognition:

Voice recognition is a computer software program or a hardware device with the ability to decode the human voice. Voice recognition is commonly used to operate a device, perform commands, or write without having to use a keyboard, mouse, or press any buttons. Many of us are already using voice recognition in our daily life. This started with the introduction to Siri in 2011 and continues with the widespread usage of Amazon Alexa in 2018. There are many vendors like Apple, Amazon, Microsoft, Google etc. which provides Voice Recognition as one of the features in their Mobile devices/Operation Systems.

Neural Networks

Neural networks is programming paradigm which enables a computer to learn from observational data. It is actually inspired from Biology (biological neural networks). The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules./p>

Computer Vision

Computer vision is gradually becoming a base technology for traffic sensing systems. Formally, Computer vision is an interdisciplinary field that 'deals with how computers can be made to gain high-level understanding from digital images or videos'. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. It is used extensively in the Self-Driving Cars. How? read this article.

What is the differences between AI, Machine Learning and Deep Learning?

It is quite interesting to know the difference between Artificial Intelligence, Machine Learning and Deep Learning.

• Machine Learning: It is a subset of AI technique which uses statistical methods to enable machines to improve the experiences. Essentially, it is the application of AI with the idea that machine be fed with data and they will learn by themselves. Essentially, instead of coding software programs with a specific set of instructions to accomplish a particular task, the machine is “trained” using large chunks of data (and algorithms) that give it the ability to learn how to perform the task.

• Deep Learning: Deep learning is a subset of Machine Learning which make the computation of multi-layer neural networks feasible. Essentially, Deep learning is 'a Technique for Implementing Machine Learning'. It allows machines to solve complex problems with unstructured and diverse data sets. Good examples of deep learning are Speech recognition, image classification, recommendation systems.

Okay, that was a good summary of AI. How about our cute loving Salesforce Einstein?

Actually, behind the scenes Salesforce Einstein is not a Single products but a set of products. Salesforce bought quite a lot (9+) of companies few years back to create presence in the 'Artificial Intelligence space related to CRM'. The features are designed to discover insights, predict outcomes, recommend actions and automate tasks. Essentially, Einstein is Your Smart CRM Assistant.

Einstein offers a variety of Deep Learning APIs that allow developers of all skill levels to leverage the power of image recognition and natural language processing to build AI-enabled apps fast. Essentially Salesforce Einstein use Machine Learning, Deep Learning and other AI related stuff to provide recommendations based on the data available on your salesforce ecosystem (sales cloud, service cloud etc.). It also helps you in taking better decisions and identifying the next best steps.

See the below table with the SFDC Einstein Products and features:

Note: You should have some basic understanding of Salesforce Product family to comprehend what we are talking about here

Salesforce Einstein is also available on other Salesforce Products:

Note:

a. Salesforce also provide Einstein Voice to selected customers through a pilot program that requires agreement to specific terms and conditions.

b. Salesforce Einstein Bots can handle routine requests and free customer agents to handle more complex issues. Bots can also gather pre-chat information to save your agents time.