Curious about what it's like to be an intern at Salesforce? Welcome to the Salesforce Spotlight: A podcast series that highlights the extraordinary stories of the Salesforce employees and Futureforce interns. I'm Barbara Alberts, and on this episode of the Salesforce Spotlight, APM Intern Hanslei Cuoscia and PhD Intern Marchian Josenia talk all things Einstein and what it's like to work with artificial intelligence. Tune in for more, and happy listening.
So on this week's podcast, we have APM Intern Hanslei Cuoscia and Software Engineering Intern Marjan Josenia, and they're here to talk about their projects this summer and what they've been working on in their internships. Thank you guys for joining.
One main reason for why I picked Salesforce over other APM programs, and just other internships in general, was the things I heard about the APM program from the Inaugural Batch which was last year's APM program. What they told me was there's a ton of executive exposure and interaction with executive leaders, both in product and engineering, just all around. There are regular APM Thursdays, in which you and all the other 11 interns in your batch meet up on a regular basis, have workshops, meetings, and just question and answer sessions with a lot of these general managers, executive vice presidents and all these people that are literally setting and creating the vision for the next few quarters, or even years of Salesforce. I heard about that, I thought it was pretty crazy to be in a room with them, not only once or twice in an internship, but once a week. That was definitely a huge factor.
In addition to that, I also think that the network for our program is pretty awesome. By network, I mean that it's a 12 person program. Other programs are either one or two people for APM's, or maybe even a 50-person program for a couple other companies. I thought 12 was a perfect size to genuinely and personally know everyone else in the program, and just have a good time and be able build a quality, lasting friendship that would go years onwards, after this.
This summer, I'm working on Einstein's first Business Card Scanner, meaning that you upload a picture of a business card currently as for web, and later on we're going to expand to mobile. After uploading that picture, then essentially all that information's parsed and pulled out of it, and you automatically have a new lead, record or a contact created in your Salesforce dashboard. Couple of the technologies use our Optical Character Recognition to pull the text from the image. Then, after the text is pulled, then we use Name Entity Recognition to pull the names, and locations, and organizations from that text. Finally, the end product is a new lead that's automatically added to your Salesforce records. So, that's what I've been working on this summer.
In addition that, one other thing I did was a Sentiment Analysis Lightning Web Component, meaning that it's just one of those components that you can easily drag around in your Salesforce set-up and embed it onto the screen, and you can literally have that tell you the sentiment, whether it be positive, neutral, or negative sentiment, of any customer's survey response, or any field that you want to get the general vibe of.
True. Einstein, in my opinion, is just the brain behind all of Salesforce's products. It's how the machine learning, embedded in every single cloud, or in every single functionality of the product that can potentially have predictions made, or can be a little smarter than just a simple row-based kind of system. That's what Einstein is, from what I've seen, but my team particularly creates 8-pix AI services, meaning that we create simple API's from which, with one line of code, you can easily have a machine learning API call, and on the Salesforce platform. Literally, in any field or any standard custom object, you can instantly get a response for translation, you can use that API. You can also use Sentiment Analysis. You can also use name Entity Recognition. You can also use Optical Character Recognition. So, these are all things that, with one line, you can easily utilize that power of machine learning. It can essentially be democratized for any Salesforce admin, any Salesforce user, or even an internal Salesforce user.
So, that's what I'm working on with Einstein, but as an overview, Einstein is essentially the brain behind all of Salesforce's products, and is embedding machine learning in a seamless manner throughout the entire platform.
So, when modeling this business card scanner out, the name entity recognition that I mentioned earlier was definitely difficult to, not only get the names, not only get the organizations, but it was difficult to get the location and address from a business card. There's no way to easily identify the address, unless you have some very, very large database of addresses and some maps API that it's linked to, which we didn't want to go into because that would be outside of the scale of my internship. We only had around eight weeks to develop this product. So, keeping that in mind, we decided not to go with address right now, but we instead decided to optimize for the other fields that we could, in time, have a very high accuracy for. So that includes something that regular expressions can do, which is email addresses and phone numbers. In addition to that, we could focus more on fine tuning the name that pulled with the NER, or Name Entity Recognition API, and also the organization name, which was also successfully pulled at a high accuracy, as well.
So we optimize for those, and we had five to six fields that were with very high quality populated every time, any business card was pretty much scanned. We focused on getting a really good NVP out, instead of focusing on a very difficult and longer term problem, which was address, and I think that was definitely the right decision to do.
I think some potential avenues for my business card scanner product would be: there's a few. The first one could be that we could put it on the app exchange, which is Salesforce's apps. A second thing could be, we could create an unmanaged package for it, meaning that it's an unmanaged snippet of code that people can instantly run on their Salesforce platform without needing it to be actively maintained, or bugs-patched, or any of that. It's kind of like the [inaudible 00:11:45] where they can make modifications to it themselves, or if they feel like it's kind of lacking any features that they really need for their specific customer use case. And the third thing would be, we could create a managed package, which, based off of customer requests, we could add more features to it.
So, unmanaged package, managed package, or just put it on the app exchange as a whole. Those are three possible avenues for my project, and what that means is we, of course, want customers to use this scanner as much as possible. Zooming out even more, I think a next step, on the larger scale of things, would be to extend this to mobile, because business cards are generally scanned on phones. Because a lot of the technology, in terms of Optical Character Recognition and Name Entity Recognition, has already been developed over the course's internship, it's simply a matter of extending this use case to mobile and focusing more on the UI. Where as the back end has pretty much stayed the same. Because we've also done a lot of quality and metric testing and it seems like the quality's doing pretty well, so far. So it can easily be exposed to the public.
International relations is definitely a very, very important and high priority task for every single product on Einstein and throughout the company, as a whole. We really value our customers that speak different languages and that are in different countries. So, for my team particularly, this business card scanner will most definitely, with full intent, be extended to non-English speakers and people that have business cards in other languages. One quick way on how to do that is, remember how I mentioned that we have easy, simple, one-line machine learning services, right? What we're going to do is, first of all, we want to build the English product first, right? Simply in one language, to have an NVP, and approval concept that this business card scanner can in fact be made on the Salesforce platform, for [inaudible 00:14:33] And then after that, then we'll extend it to the next most popular language, which right now it seems like it will be Chinese, and what we'll do is, we'll still use Optical Character Recognition. We'll use that API to pull the text from an image, and after that text is pulled, then we already have the Translation API that has been built a few months back. We'll then translate that text, so then it's in the given language of interest. And once we have that translated, then we can pull all of the entities using Name Entity Recognition, and then service and populate all of the fields in the Salesforce object.
So that's exactly how we would go about doing it. This is something that we actually have thought about from the beginning, because a lot of people on my team, and on other sister teams of mine, have told us that internationalization is a huge priority, is very important for Einstein and Salesforce as a whole, and that's something that we should definitely keep in mind, moving forward. We're glad to say that we're definitely working on that right now.
I'd say that the engine's pretty simple for the business card scanner because that just adds another tool to the tool kit of Einstein, and, especially in the form of a lightning web component, which is a component that can easily be dragged and dropped into a Salesforce display, or a Salesforce grid set-up, where you have your file upload area, where you have your main panel, where you can have your dashboard leads, or any cases, or anything that you're viewing as a Salesforce user, which would be our customers. You could instantly just drag and drop that business card scanning lightning component. That could just be something that they instantly upload files into and get a response from. That's one thing that, I think, it's useful for Einstein for, just expands its tool kit from the existing number of lightning components that they already have.
In addition to that, I think the other project I worked on, which is the Sentiment Analysis Lightning Component, that also adds another really cutting edge and avant garde kind of technology to the Einstein tool belt, which is getting the sentiment for any case or any snippet of text, for our customers. One really useful case that this could be seen in, in the industry, is when a customer wants to look at all of the surveys that have been filled out by their customers, they don't care about the servers that are really good in sentiment or really happy, or really neutral. They want to see the ones that are bad, that are voicing some complaint, or voicing some action for change, because they want to see what needs to be changed, and they want to really act upon that and fix their product, and add a new feature, or remove a feature, to make their product better, and more geared to what their customers preferences are.
I think the Sentiment Analysis Lightning Component is something that you could easily just drag and drop and select the field that you want to get the sentiment of, will very, very easily allow you to see which case are relevant, and which you should look at, and will save the companies, I guess, thousands of hours. Because they'll only be looking at the complaints, instead of everything, and just filtering manually through what's a complaint, and what's not a complaint.
This internship has helped me in countless ways. The first of which is it's the first time I've experienced product management as an internship. Parts of this I was doing program management, parts of that I was purely software stuff. So, this is definitely a new experience and it's been nothing but great. First of all, the mentorship has been great, right? So that's what everyone at Salesforce says. But for me in particular, I really resonate with this point because everyone on my team is very credible, very experienced in this domain. They've learned product for several years, but not only several years, but several years on a machine learning product. So it's definitely very relevant to what I'm working on right now.
In addition to that, they always go out of their way to help me out, even when they have a very, very, very busy schedule. One day, my manager came back from a three-day conference and I was like okay he has no time to help me out. He said I'm very busy, but that same day he ended up staying up with me 'till 7:30pm in the office, and just helping me out on a product bug that was stopping me from making progress into the rest of the week. So I think that was definitely an indication, one of the first few indications that my team is here for me and has my back, unconditionally, and they just want me to succeed. So that feeling of your team just genuinely wanting your success, instead of wanting their product built alone, is very huge right there. I think this has grown my career significantly, and there is no better internship that I've seen.