AI News, Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone

Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone

Starting two years ago, a band of artificial-intelligence acolytes within Salesforce escaped the towering headquarters with the goal of crazily multiplying the impact of the machine learning models that increasingly shape our digital world—by automating the creation of those models.

They named it after the Transformers leader because, as one participant recalls, “machine learning is all about transforming data.” Whether the marketing department thought better of it, or the rights weren’t available, the Transformers tie-in didn't make it far out of that basement.

The company’s original mantra was “no software.” Its customers wouldn’t have to purchase and install complex programs and then pay to maintain and upgrade them—Salesforce would take care of all that at its data centers in the cloud.

So the Einstein Intelligence module—a little add-on column at the far right of the basic Salesforce screen—will do it for you, ranking them based on, say, “most likely to close.” For marketers, who also make up a big chunk of Salesforce customers, it can take a big mailing list and sort individual recipients by the likelihood that they’ll open an email.

The machine learning difference is simple but profound: The program studies the history of the data and figures out for itself which factors best predict the future—and then it keeps adjusting its model based on new information over time.

Salesforce director of product marketing Ally Witherspoon uses the example of a solar-panel sales outfit using the machine learning tool to discover that a key factor in predicting a customer’s chances of saying “yes” is whether the house’s roof is pitched in a solar-friendly way.

This roof info might start out as a major ingredient in how the machine learning program sorts its list—and, in one of Einstein’s nifty design flourishes, users can click to reveal which factors shaped each priority scoring.

If you’re the likes of Facebook, Google, or Amazon, you can hire the field’s leading lights and put them to work optimizing algorithms and inventing new ways of serving billions of customers with more artificial intelligence.

(Inside Salesforce, developers still use that name.) It’s the system that automates the creation of machine learning models for each Salesforce customer so that data scientists don’t have to spend weeks babysitting each new model as it is born and trained to deliver good answers.

Benioff said then, “AI is the next platform—all future applications, all future capabilities for all companies will be built on AI.” Benioff even told analysts on a quarterly earnings call that he uses Einstein at weekly executive meetings to forecast results and settle arguments: “I will literally turn to Einstein in the meeting and say, ‘OK, Einstein, you’ve heard all of this, now what do you think?’ And Einstein will give me the over and under on the quarter and show me where we’re strong and where we’re weak, and sometimes it will point out a specific executive, which it has done in the last three quarters, and say that this executive is somebody who needs specific attention.” That may sound a little Big Brother-ish, but everyone I spoke with at Salesforce is careful to keep the AI talk friendly.

He teaches a wildly popular AI course at Stanford, and co-publishes papers with titles like “Pointer Sentinel Mixture Models” and “Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization.” With his unruly straw mop of hair, Socher still looks like the grad student he was not that long ago—and he has a youthful enthusiasm for testing the limits of what we think AI can handle.

But he thinks the company is on the right track: At this stage in AI’s evolution, there’s more to be gained from putting basic tools in more people’s hands than from squeezing an extra few percentages of efficiency from an algorithm.

Just because you’re a late starter doesn’t mean that in a few years you can’t become a leader.” Salesforce faces a crowded field in the fight to put AI tools to work on behalf of the warm-handshake crowd.

If Salesforce does succeed in moving to the front rank of today’s crazy corporate AI race, company insiders point to one advantage as its not-so-secret weapon: its well-tended warehouses of consistently labeled and organized customer data.

They spend enormous amounts of time today “preparing data,” which means figuring out how to prep piles of information so that it can be digested by machine learning programs and produce good results.

“What they do at the end of the day is, they actually have human labor do this stuff,” he says, “That makes money but is not scalable.” But Salesforce customers have all already entered their data into a single software platform, even if many of them have added their own custom flourishes.

For all the utopian dreams and Skynet nightmares that today’s advances in artificial intelligence provoke, the winners and losers in this transition will probably be determined by what computer scientists call “data hygiene.” In other words: No matter how smart our programs get in the AI future, tidiness still counts.

Get Started with Einstein

After completing this unit, you’ll be able to: There’s been a ton of buzz about Salesforce Einstein, but what is it exactly?

today, or Amazon’s Alexa to order you some new kitchen supplies, or Google’s Assistant to play you the newest Drake album, you’ve interacted with a smart assistant.  What makes this assistant so smart?

all of the Salesforce apps (Sales Cloud, Service Cloud, and so on)—a built-in smart assistant—so that every business user in every role, function, and industry can be assisted right inside of the Salesforce product that they use every day.  Einstein Platform Each business operates differently, and therefore, uses Salesforce differently.

voice input, natural language understanding, voice output, intelligent interpretation, and agency components to better help your business interact with and understand its customers.

In order to predict anything, your team needs a list of customers who bought that item, customers who didn’t, and all of the attributes surrounding those customers -

“length of time being a customer,” “customer address,” or “last purchased item.” But we know you have a massive amount of data, and we don’t want you to sift through it all.

As you feed the system more cleaned data, your data gets trained, and the more accurate the identification of the features will be.  OK, so your data is trained and the features of the dataset have been engineered to know which ones might influence a purchase.

The higher the weight—relative to the other weights—the more significant the feature is for predicting propensity to buy.  Even better, Einstein tells you the most significant features and determines the percentages of the impact they have on the purchase.

So now you have enough information to decide how best to engage your customer to influence a purchase.  With AutoML, the data cleansing, feature engineering, and automated model selection are automated, so no need to hire a data scientist to get those same business predictions.

It Lives on the Salesforce Platform And finally, because Einstein is part of Salesforce’s trusted platform, all Einstein insights, predictions, recommendations, and actions are served up inside Salesforce—meaning you can take advantage of the same model management and monitoring tools you’ve

Using the Einstein Platform, you can build AI-enabled smart assistants and applications for your business and your customers using easy, declarative, point-and-click tools.  And with Einstein out-of-the-box applications, there are built-in intelligent features for each cloud that cover the most widespread use cases for sales, service, marketing, and commerce.

Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone

Starting two years ago, a band of artificial-intelligence acolytes within Salesforce escaped the towering headquarters with the goal of crazily multiplying the impact of the machine learning models that increasingly shape our digital world—by automating the creation of those models.

They named it after the Transformers leader because, as one participant recalls, “machine learning is all about transforming data.” Whether the marketing department thought better of it, or the rights weren’t available, the Transformers tie-in didn't make it far out of that basement.

The company’s original mantra was “no software.” Its customers wouldn’t have to purchase and install complex programs and then pay to maintain and upgrade them—Salesforce would take care of all that at its data centers in the cloud.

So the Einstein Intelligence module—a little add-on column at the far right of the basic Salesforce screen—will do it for you, ranking them based on, say, “most likely to close.” For marketers, who also make up a big chunk of Salesforce customers, it can take a big mailing list and sort individual recipients by the likelihood that they’ll open an email.

The machine learning difference is simple but profound: The program studies the history of the data and figures out for itself which factors best predict the future—and then it keeps adjusting its model based on new information over time.

Salesforce director of product marketing Ally Witherspoon uses the example of a solar-panel sales outfit using the machine learning tool to discover that a key factor in predicting a customer’s chances of saying “yes” is whether the house’s roof is pitched in a solar-friendly way.

This roof info might start out as a major ingredient in how the machine learning program sorts its list—and, in one of Einstein’s nifty design flourishes, users can click to reveal which factors shaped each priority scoring.

If you’re the likes of Facebook, Google, or Amazon, you can hire the field’s leading lights and put them to work optimizing algorithms and inventing new ways of serving billions of customers with more artificial intelligence.

(Inside Salesforce, developers still use that name.) It’s the system that automates the creation of machine learning models for each Salesforce customer so that data scientists don’t have to spend weeks babysitting each new model as it is born and trained to deliver good answers.

Benioff said then, “AI is the next platform—all future applications, all future capabilities for all companies will be built on AI.” Benioff even told analysts on a quarterly earnings call that he uses Einstein at weekly executive meetings to forecast results and settle arguments: “I will literally turn to Einstein in the meeting and say, ‘OK, Einstein, you’ve heard all of this, now what do you think?’ And Einstein will give me the over and under on the quarter and show me where we’re strong and where we’re weak, and sometimes it will point out a specific executive, which it has done in the last three quarters, and say that this executive is somebody who needs specific attention.” That may sound a little Big Brother-ish, but everyone I spoke with at Salesforce is careful to keep the AI talk friendly.

He teaches a wildly popular AI course at Stanford, and co-publishes papers with titles like “Pointer Sentinel Mixture Models” and “Your TL;DR by an AI: A Deep Reinforced Model for Abstractive Summarization.” With his unruly straw mop of hair, Socher still looks like the grad student he was not that long ago—and he has a youthful enthusiasm for testing the limits of what we think AI can handle.

But he thinks the company is on the right track: At this stage in AI’s evolution, there’s more to be gained from putting basic tools in more people’s hands than from squeezing an extra few percentages of efficiency from an algorithm.

Just because you’re a late starter doesn’t mean that in a few years you can’t become a leader.” Salesforce faces a crowded field in the fight to put AI tools to work on behalf of the warm-handshake crowd.

If Salesforce does succeed in moving to the front rank of today’s crazy corporate AI race, company insiders point to one advantage as its not-so-secret weapon: its well-tended warehouses of consistently labeled and organized customer data.

They spend enormous amounts of time today “preparing data,” which means figuring out how to prep piles of information so that it can be digested by machine learning programs and produce good results.

“What they do at the end of the day is, they actually have human labor do this stuff,” he says, “That makes money but is not scalable.” But Salesforce customers have all already entered their data into a single software platform, even if many of them have added their own custom flourishes.

For all the utopian dreams and Skynet nightmares that today’s advances in artificial intelligence provoke, the winners and losers in this transition will probably be determined by what computer scientists call “data hygiene.” In other words: No matter how smart our programs get in the AI future, tidiness still counts.

Learning Salesforce Einstein: Add artificial intelligence capabilities to your business solutions with Heroku, PredictiveIO, and Force Paperback – June 28, 2017

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