Tag Archives: understand

New Everbridge CEO talks education, NC4 acquisition

The new Everbridge CEO said he wants people to understand the importance of a critical event management platform.

“It just needs to be something everyone has, because it does save lives,” said David Meredith, previously the COO of Rackspace. He took over on July 15 for Jaime Ellertson, the Everbridge CEO since 2011 who is transitioning to the role of executive chairman of the board.

“We need to get out there as the leader and we need to be more aggressive in having conversations like we’re having today, and educating people about what are the best practices, and how they can best prepare,” Meredith said.

Two weeks into his time as CEO, Everbridge acquired NC4 Inc., a risk intelligence provider that Meredith said will improve his company’s Critical Event Management (CEM) suite. The two companies had previously been partners.

“A lot of acquisitions, companies may be competing with each other or they’re maybe in an adjacent space, but they haven’t worked together very much,” said Karl Kotalik, who will be general manager of NC4 after serving as its president and CEO. “We’ve been exchanging information for years, not just together, but in combination with customers.”

The NC4 acquisition gives Everbridge 10 products it sells as a SaaS company, Meredith said. Everbridge, which is based in Burlington, Mass., claims about 4,700 enterprise customers. The company now has about 950 employees, including the entire team of more than 70 workers from NC4, which is based in El Segundo, Calif. The acquisition payment was $83 million in cash and Everbridge stock, and it’s expected to fully close at the end of the third quarter.

We need to really be more proactive in terms of educating the marketplace on what can be done to keep people safe and keep businesses running.
David MeredithCEO, Everbridge

NC4 claimed more than 300 customers. One hundred of those customers are in the Fortune 500. About 50% of NC4 customers were also Everbridge users as well. Kotalik said the acquisition will help NC4 “scale down” into Everbridge’s base for smaller companies that still need risk intelligence.

Meredith said he wants Everbridge to be for CEM what Salesforce is for customer relationship management, in a “platform that really makes the ecosystem” around CRM.

“You can have one place to get all the data if you are an enterprise, or a state, local or federal government,” Meredith said. “Then if something is happening, we can move very quickly to manage that with the rest of the tools in the suite.”

We recently spoke with Meredith and Kotalik to discuss their plans for the Critical Event Management suite and NC4.

What led you to take the Everbridge CEO job?

David MeredithDavid Meredith

David Meredith: I’ve known of Everbridge as a customer for years and was a very happy and satisfied customer. What pulled me into this role, first and foremost, is the mission of Everbridge — the mission of keeping people safe and businesses running faster. It’s a very powerful draw. We are a mission-driven company.

The technology is the leader in the space. They used to say you’d never get fired for hiring IBM in technology. And in the critical event management space, Everbridge is the leader and I think it’s safe to say you would never get fired for picking Everbridge. If you look at the ability to scale, the global reach, the resiliency, the fact that we’re a public company, our size, the breadth of our offerings, we’re the clear leader in the space, and that’s very exciting.

But I still think there’s a lot of room to grow from there.

What is it about the technology that makes Everbridge a leader?

Meredith: Everbridge has been investing heavily on building out our technology platform and doing acquisitions as well. If you look at the Critical Event Management suite, critical event management, or CEM, is an area that we’re sort of a pioneer in. It starts with a single pane of glass, and this is our Visual Command Center, and that’s where we can aggregate thousands and thousands of pieces of data. The ability to curate all that data, using technology, machine learning, artificial intelligence, as well as expert human analysts, [create] that added level of validation.

Our systems are extremely scalable. We’ve moved everything to the cloud now and we’re very resilient. … We have the ability to deliver the messages when you need them in a timely manner, and we’ve got backups in place at every level of the supply chain. We’re the leader in that.

Everbridge and NC4 were partners previously — how did you work together in the past?

Karl KotalikKarl Kotalik

Karl Kotalik: I started NC4 18 years ago, right after 9/11. … A natural partnership developed about 10 years ago because Everbridge was already emerging as the leader in mass notification and communication — at the time they called it unified communications. And NC4, our specialty, we were very focused on risk intelligence. We were emerging as the leader in real-time event incident monitoring, all hazards — everything from water main breaks and one-alarm fires and shootings up to terrorist attacks, hurricanes, tornadoes, major floods.

We were getting the information, but to deliver it at scale, to the large customers we were serving, we needed that assist from Everbridge. So, we partnered, where we did something really well, on the front end of the process, and Everbridge handled the downstream messaging, unified communications to people who needed to know. And they started getting into more response and coordination, and they’ve grown the CEM platform today.

When you’re partnered, you’re not coordinating on strategy. It’s a nice relationship, but we realized we could do so much more coming together. … Putting the two together is a killer combination. And a lot of the work had already been done because of the partnerships with these large enterprises.

How has the integration been going?

Kotalik: Everbridge has had access to every iteration and evolution of our APIs going back 10 years, and they’ve seen our data streams and fed it to their platforms over all these years. So, their development team, their product management team, their operations team, understand the [kind and volume of data NC4 deals in]. We’re doing 700 incidents a day on critical events.

You might think of a critical event as something big, but a critical event for an individual customer could be as minor as a water main break. But [it’s a major event] if it’s across the street and you’re a data center and you depend on that water pressure for the cooling of the equipment in the data center. With the CEM platform, to very quickly orchestrate all the mitigation steps you want to take, shutting down the servers, turning on alternate cooling systems, whatever those steps are, not being able to do that, could turn that into a disaster. It could take down your customers and you don’t want to do that.

Where do you see the CEM suite progressing? Is there anything you want to see added?

Meredith: We have a whole roadmap that we’re going to continue to be building out. There are some big market drivers that we’re tapping into. One is internet of things. There’s going to be 75 billion connected devices in the next six years. One of the things Everbridge does, in addition to keeping your employees or citizens or customers safe, we also help to keep your assets and things safe as well. And that’s going to get much more complex with the advent of more IoT.

Another big trend we see is around mobility. If you look at what’s happening with the workforce today, in the next few years, over 70% of U.S. workers are going to be mobile. If you’re trying to keep your employees safe, it’s not as simple as when everyone is just in one building, from 9 to 5. Now they’re spread everywhere, working from home and other places.

Big data is another one. I think NC4 is a great example where aggregating all that data, being able to curate it, sort through it, and get to actionable intelligence for our customers as quickly as possible, even to the point of being predictive, is going to be strategically important for us. We’re going to continue to invest and drive more analytics-type solutions out of all the data that we have and all the data that we see.

Photo of Everbridge's Global Operations Center
Everbridge’s Global Operations Center at its headquarters in Burlington, Mass., tracks critical events worldwide, 24/7.

What are you seeing as trends in customers?

Meredith: One big trend, and another reason I was drawn to the company, is Everbridge is really creating a network effects business.

We recently announced that the state of Florida did a five-year renewal with us. So, what happens when you win a state like Florida? Over the years, we’ve added 64 of 67 counties as customers. We’ve added 26 cities, including the 10 largest in Florida, almost 50 corporations, 15 state agencies, almost 20 higher education universities [and] 29 healthcare organizations.

When you start to add all that on, it creates this network effect, where when something happens, it’s all interrelated — you’ve got emergency responders, you’ve got the state, the county, the city, transportation. If there’s a hurricane in Florida, all of these groups are impacted. Our ability to have all of them on our platform is really powerful. It’s beneficial to them, it’s beneficial to us. That’s really that ecosystem effect, that network effect we create.

We just announced that we won the country of Australia as a customer. If you think about what I just talked about with Florida, now we’re doing it for the country of Australia — the states, the cities, the healthcare, the higher education, the corporations and tying all that together.

What’s really interesting, looking forward, the European Union has come out and said all of the EU countries need to have population alerting systems in place in the next few years, so that’s an opportunity for us to take what we’ve done in Australia and other countries and now move faster in terms of spreading that in Europe.

We’re getting all this data coming in from all these sources. The data is the lifeblood of the system. As you’re looking at that Visual Command Center — and we’re getting data from our analysts, we’re getting data from the web, from our customers — it allows us to be much more accurate in terms of false positives and false negatives. There have been some highly publicized examples recently about false alarms and how disruptive that can be. With NC4, you’ve got 24/7 analysts looking at all the feeds, highly trained, highly skilled, and can say, ‘I’m looking at all my data, I’m curating all the data and this is not a critical event. This is a false alarm.’

Or, alternatively, potentially minutes can save lives. And being able to shrink that time and know something is really happening, know we’re getting into a critical event, and be able to get people to safety, be able to protect your assets, that is very important and has a huge impact in terms of the overall return on investment the customer makes in a platform like this.

What else have you learned as Everbridge CEO in a month and what are your short- and long-term plans?

Meredith: Having been in technology for many years now, I will say, you need great people, you need great technology; you also need timing to line up. Unfortunately, we’re at a period now, we have the data — unfortunately, it’s up in terms of weather events, in terms of cyber, malware attacks, terrorist attacks. The rate’s increasing.

We’re creating a whole new category. We need to really be more proactive in terms of educating the marketplace on what can be done to keep people safe and keep businesses running. … We’ve got to be out there and educating and talking about the story. I really believe if you’re a Global 2000 or Fortune 1000 company, really every one of those companies should have technology and plans in place for what to do in the event of a critical event, whether they use Everbridge or not.

Do you think that not enough people and organizations know about what you do?

Meredith: I think that’s correct. When we go talk to a company, a lot of times, it’s not that they already have a solution, but they have maybe a couple point solutions and they’ve sort of jury-rigged some standard operating procedures. We don’t see the level of preparation that you would like to see. It’s something that you don’t want to ever have to use, but you want to have it in place.

Kotalik: We will go in to customers and they won’t even realize they can get real-time information that’s impacting their travelers, their assets, their locations, in enough time to really mitigate. When they hear the stories about how it saved lives or it reduced downtime, it stopped an event from turning into a disaster for the company because they were able to mitigate it, that helps drive our business for these less sophisticated organizations that haven’t really thought about this. They don’t think they have a big enough budget or enough people.

Go to Original Article

Machine reading comprehension with Dr. T.J. Hazen

Dr. TJ Hazen

Episode 86, August 21, 2019

The ability to read and understand unstructured text, and then answer questions about it, is a common skill among literate humans. But for machines? Not so much. At least not yet! And not if Dr. T.J. Hazen, Senior Principal Research Manager in the Engineering and Applied Research group at MSR Montreal, has a say. He’s spent much of his career working on machine speech and language understanding, and particularly, of late, machine reading comprehension, or MRC.

On today’s podcast, Dr. Hazen talks about why reading comprehension is so hard for machines, gives us an inside look at the technical approaches applied researchers and their engineering colleagues are using to tackle the problem, and shares the story of how an a-ha moment with a Rubik’s Cube inspired a career in computer science and a quest to teach computers to answer complex, text-based questions in the real world.



T.J. Hazen: Most of the questions are fact-based questions like, who did something, or when did something happen? And most of the answers are fairly easy to find. So, you know, doing as well as a human on a task is fantastic, but it only gets you part of the way there. What happened is, after this was announced that Microsoft had this great achievement in machine reading comprehension, lots of customers started coming to Microsoft saying, how can we have that for our company? And this is where we’re focused right now. How can we make this technology work for real problems that our enterprise customers are bringing in?

Host: You’re listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. I’m your host, Gretchen Huizinga.

Host: The ability to read and understand unstructured text, and then answer questions about it, is a common skill among literate humans. But for machines? Not so much. At least not yet! And not if Dr. T.J. Hazen, Senior Principal Research Manager in the Engineering and Applied Research group at MSR Montreal, has a say. He’s spent much of his career working on machine speech and language understanding, and particularly, of late, machine reading comprehension, or MRC.

On today’s podcast, Dr. Hazen talks about why reading comprehension is so hard for machines, gives us an inside look at the technical approaches applied researchers and their engineering colleagues are using to tackle the problem, and shares the story of how an a-ha moment with a Rubik’s Cube inspired a career in computer science and a quest to teach computers to answer complex, text-based questions in the real world. That and much more on this episode of the Microsoft Research Podcast.

(music plays)

Host: T.J. Hazen, welcome to the podcast!

T.J. Hazen: Thanks for having me.

Host: Researchers like to situate their research, and I like to situate my researchers so let’s get you situated. You are a Senior Principal Research Manager in the Engineering and Applied Research group at Microsoft Research in Montreal. Tell us what you do there. What are the big questions you’re asking, what are the big problems you’re trying to solve, what gets you up in the morning?

T.J. Hazen: Well, I’ve spent my whole career working in speech and language understanding, and I think the primary goal of everything I do is to try to be able to answer questions. So, people have questions and we’d like the computer to be able to provide answers. So that’s sort of the high-level goal, how do we go about answering questions? Now, answers can come from many places.

Host: Right.

T.J. Hazen: A lot of the systems that you’re probably aware of like Siri for example, or Cortana or Bing or Google, any of them…

Host: Right.

T.J. Hazen: …the answers typically come from structured places, databases that contain information, and for years these models have been built in a very domain-specific way. If you want to know the weather, somebody built a system to tell you about the weather.

Host: Right.

T.J. Hazen: And somebody else might build a system to tell you about the age of your favorite celebrity and somebody else might have written a system to tell you about the sports scores, and each of them can be built to handle that very specific case. But that limits the range of questions you can ask because you have to curate all this data, you have to put it into structured form. And right now, what we’re worried about is, how can you answer questions more generally, about anything? And the internet is a wealth of information. The internet has got tons and tons of documents on every topic, you know, in addition to the obvious ones like Wikipedia. If you go into any enterprise domain, you’ve got manuals about how their operation works. You’ve got policy documents. You’ve got financial reports. And it’s not typical that all this information is going to be curated by somebody. It’s just sitting there in text. So how can we answer any question about anything that’s sitting in text? We don’t have a million or five million or ten million librarians doing this for us…

Host: Right.

T.J. Hazen: …uhm, but the information is there, and we need a way to get at it.

Host: Is that what you are working on?

T.J. Hazen: Yes, that’s exactly what we’re working on. I think one of the difficulties with today’s systems is, they seem really smart…

Host: Right?

T.J. Hazen: Sometimes. Sometimes they give you fantastically accurate answers. But then you can just ask a slightly different question and it can fall on its face.

Host: Right.

T.J. Hazen: That’s the real gap between what the models currently do, which is, you know, really good pattern matching some of the time, versus something that can actually understand what your question is and know when the answer that it’s giving you is correct.

Host: Let’s talk a bit about your group, which, out of Montreal, is Engineering and Applied Research. And that’s an interesting umbrella at Microsoft Research. You’re technically doing fundamental research, but your focus is a little different from some of your pure research peers. How would you differentiate what you do from others in your field?

T.J. Hazen: Well, I think there’s two aspects to this. The first is that the lab up in Montreal was created as an offshoot of an acquisition. Microsoft bought Maluuba, which was a startup that was doing really incredible deep learning research, but at the same time they were a startup and they needed to make money. So, they also had this very talented engineering team in place to be able to take the research that they were doing in deep learning and apply it to problems where it could go into products for customers.

Host: Right.

T.J. Hazen: When you think about that need that they had to actually build something, you could see why they had a strong engineering team.

Host: Yeah.

T.J. Hazen: Now, when I joined, I wasn’t with them when they were a startup, I actually joined them from Azure where I was working with outside customers in the Azure Data Science Solution team, and I observed lots of problems that our customers have. And when I saw this new team that we had acquired and we had turned into a research lab in Montreal, I said I really want to be involved because they have exactly the type of technology that can solve customer problems and they have this engineering team in place that can actually deliver on turning from a concept into something real.

Host: Right.

T.J. Hazen: So, I joined, and I had this agreement with my manager that we would focus on real problems. They were now part of the research environment at Microsoft, but I said that doesn’t restrict us on thinking about blue sky, far-afield research. We can go and talk to product teams and say what are the real problems that are hindering your products, you know, what are the difficulties you have in actually making something real? And we could focus our research to try to solve those difficult problems. And if we’re successful, then we have an immediate product that could be beneficial.

Host: Well in any case, you’re swimming someplace in a “we could do this immediately” but you have permission to take longer, or is there a mandate, as you live in this engineering and applied research group?

T.J. Hazen: I think there’s a mandate to solve hard problems. I think that’s the mandate of research. If it wasn’t a hard problem, then somebody…

Host: …would already have a product.

T.J. Hazen: …in the product team would already have a solution, right? So, we do want to tackle hard problems. But we also want to tackle real problems. That’s, at least, our focus of our team. And there’s plenty of people doing blue sky research and that’s an absolute need as well. You know, we can’t just be thinking one or two years ahead. Research should be also be thinking five, ten, fifteen years ahead.

Host: So, there’s a whole spectrum there.

T.J. Hazen: So, there’s a spectrum. But there is a real need, I think, to fill that gap between taking an idea that works well in a lab and turning it into something that works well in practice for a real problem. And that’s the key. And many of the problems that have been solved by Microsoft have not just been blue sky ideas, but they’ve come from this problem space where a real product says, ahh, we’re struggling with this. So, it could be anything. It can be, like, how does Bing efficiently rank documents over billions of documents? You don’t just solve that problem by thinking about it, you have to get dirty with the data, you have to understand what the real issues are. So, many of these research problems that we’re focusing on, and we’re focusing on, how do you answer questions out of documents when the questions could be arbitrary, and on any topic? And you’ve probably experienced this, if you are going into a search site for your company, that company typically doesn’t have the advantage of having a big Bing infrastructure behind it that’s collecting all this data and doing sophisticated machine learning. Sometimes it’s really hard to find an answer to your question. And, you know, the tricks that people use can be creative and inventive but oftentimes, trying to figure out what the right keywords are to get you to an answer is not the right thing.

Host: You work closely with engineers on the path from research to product. So how does your daily proximity to the people that reify your ideas as a researcher impact the way you view, and do, your work as a researcher?

T.J. Hazen: Well, I think when you’re working in this applied research and engineering space, as opposed to a pure research space, it really forces you to think about the practical implications of what you’re building. How easy is it going to be for somebody else to use this? Is it efficient? Is it going to run at scale? All of these problems are problems that engineers care a lot about. And sometimes researchers just say, let me solve the problem first and everything else is just engineering. If you say that to an engineer, they’ll be very frustrated because you don’t want to bring something to an engineer that works ten times slower than needs to be, uses ten times more memory. So, when you’re in close proximity to engineers, you’re thinking about these problems as you are developing your methods.

Host: Interesting, because those two things, I mean, you could come up with a great idea that would do it and you pay a performance penalty in spades, right?

T.J. Hazen: Yeah, yeah. So, sometimes it’s necessary. Sometimes you don’t know how to do it and you just say let me find a solution that works and then you spend ten years actually trying to figure out how to make it work in a real product.

Host: Right.

T.J. Hazen: And I’d rather not spend that time. I’d rather think about, you know, how can I solve something and have it be effective as soon as possible?

(music plays)

Host: Let’s talk about human language technologies. They’ve been referred to by some of your colleagues as “the crown jewel of AI.” Speech and language comprehension is still a really hard problem. Give us a lay of the land, both in the field in general and at Microsoft Research specifically. What’s hope and what’s hype, and what are the common misconceptions that run alongside the remarkable strides you actually are making?

T.J. Hazen: I think that word we mentioned already: understand. That’s really the key of it. Or comprehend is another way to say it. What we’ve developed doesn’t really understand, at least when we’re talking about general purpose AI. So, the deep learning mechanisms that people are working on right now that can learn really sophisticated things from examples. They do an incredible job of learning specific tasks, but they really don’t understand what they’re learning.

Host: Right.

T.J. Hazen: So, they can discover complex patterns that can associate things. So in the vision domain, you know, if you’re trying to identify objects, and then you go in and see what the deep learning algorithm has learned, it might have learned features that are like, uh, you know, if you’re trying to identify a dog, it learns features that would say, oh, this is part of a leg, or this is part of an ear, or this is part of the nose, or this is the tail. It doesn’t know what these things are, but it knows they all go together. And the combination of them will make a dog. And it doesn’t know what a dog is either. But the idea that you could just feed data in and you give it some labels, and it figures everything else out about how to associate that label with that, that’s really impressive learning, okay? But it’s not understanding. It’s just really sophisticated pattern-matching. And the same is true in language. We’ve gotten to the point where we can answer general-purpose questions and it can go and find the answer out of a piece of text, and it can do it really well in some cases, and like, some of the examples we’ll give it, we’ll give it “who” questions and it learns that “who” questions should contain proper names or names of organizations. And “when” questions should express concepts of time. It doesn’t know anything about what time is, but it’s figured out the patterns about, how can I relate a question like “when” to an answer that contains time expression? And that’s all done automatically. There’s no features that somebody sits down and says, oh, this is a month and a month means this, and this is a year, and a year means this. And a month is a part of a year. Expert AI systems of the past would do this. They would create ontologies and they would describe things about how things are related to each other and they would write rules. And within limited domains, they would work really, really well if you stayed within a nice, tightly constrained part of that domain. But as soon as you went out and asked something else, it would fall on its face. And so, we can’t really generalize that way efficiently. If we want computers to be able to learn arbitrarily, we can’t have a human behind the scene creating an ontology for everything. That’s the difference between understanding and crafting relationships and hierarchies versus learning from scratch. We’ve gotten to the point now where the algorithms can learn all these sophisticated things, but they really don’t understand the relationships the way that humans understand it.

Host: Go back to the, sort of, the lay of the land, and how I sharpened that by saying, what’s hope and what’s hype? Could you give us a “TBH” answer?

T.J. Hazen: Well, what’s hope is that we can actually find reasonable answers to an extremely wide range of questions. What’s hype is that the computer will actually understand, at some deep and meaningful level, what this answer actually means. I do think that we’re going to grow our understanding of algorithms and we’re going to figure out ways that we can build algorithms that could learn more about relationships and learn more about reasoning, learn more about common sense, but right now, they’re just not at that level of sophistication yet.

Host: All right. Well let’s do the podcast version of your NERD Lunch and Learn. Tell us what you are working on in machine reading comprehension, or MRC, and what contributions you are making to the field right now.

T.J. Hazen: You know, NERD is short for New England Research and Development Center

Host: I did not!

T.J. Hazen: …which is where I physically work.

Host: Okay…

T.J. Hazen: Even though I work closely and am affiliated with the Montreal lab, I work out of the lab in Cambridge, Massachusetts, and NERD has a weekly Lunch and Learn where people present the work they’re doing, or the research that they’re working on, and at one of these Lunch and Learns, I gave this talk on machine reading comprehension. Machine reading comprehension, in its simplest version, is being able to take a question and then being able to find the answer anywhere in some collection of text. As we’ve already mentioned, it’s not really “comprehending” at this point, it’s more just very sophisticated pattern-matching. But it works really well in many circumstances. And even on tasks like the Stanford Question Answering Dataset, it’s a common competition that people have competed in, question answering, by computer, has achieved a human level of parity on that task.

Host: Mm-hmm.

T.J. Hazen: Okay. But that task itself is somewhat simple because most of the questions are fact-based questions like, who did something or when did something happen? And most of the answers are fairly easy to find. So, you know, doing as well as a human on a task is fantastic, but it only gets you part of the way there. What happened is, after this was announced that Microsoft had this great achievement in machine reading comprehension, lots of customers started coming to Microsoft saying, how can we have that for our company? And this is where we’re focused right now. Like, how can we make this technology work for real problems that our enterprise customers are bringing in? So, we have customers coming in saying, I want to be able to answer any question in our financial policies, or our auditing guidelines, or our operations manual. And people don’t ask “who” or “when” questions of their operations manual. They ask questions like, how do I do something? Or explain some process to me. And those answers are completely different. They tend to be longer and more complex and you don’t always, necessarily, find a short, simple answer that’s well situated in some context.

Host: Right.

T.J. Hazen: So, our focus at MSR Montreal is to take this machine reading comprehension technology and apply it into these new areas where our customers are really expressing that there’s a need.

Host: Well, let’s go a little deeper, technically, on what it takes to enable or teach machines to answer questions, and this is key, with limited data. That’s part of your equation, right?

T.J. Hazen: Right, right. So, when we go to a new task, uh, so if a company comes to us and says, oh, here’s our operations manual, they often have this expectation, because we’ve achieved human parity on some dataset, that we can answer any question out of that manual. But when we test the general-purpose models that have been trained on these other tasks on these manuals, they don’t generally work well. And these models have been trained on hundreds of thousands, if not millions, of examples, depending on what datasets you’ve been using. And it’s not reasonable to ask a company to collect that level of data in order to be able to answer questions about their operations manual. But we need something. We need some examples of what are the types of questions, because we have to understand what types of questions they ask, we need to understand the vocabulary. We’ll try to learn what we can from the manual itself. But without some examples, we don’t really understand how to answer questions in these new domains. But what we discovered through some of the techniques that are available, transfer learning is what we refer to as sort of our model adaptation, how do you learn from data in some new domain and take an existing model and make it adapt to that domain? We call that transfer learning. We can actually use transfer learning to do really well in a new domain without requiring a ton of data. So, our goal is to have it be examples like hundreds of examples, not tens of thousands of examples.

Host: How’s that working now?

T.J. Hazen: It works surprisingly well. I’m always amazed at how well these machine learning algorithms work with all the techniques that are available now. These models are very complex. When we’re talking about our question answering model, it has hundreds of millions of parameters and what you’re talking about is trying to adjust a model that is hundreds of millions of parameters with only hundreds of examples and, through a variety of different techniques where we can avoid what we call overfitting, we can allow the generalizations that are learned from all this other data to stay in place while still adapting it so it does well in this specific domain. So, yeah, I think we’re doing quite well. We’re still exploring, you know, what are the limits?

Host: Right.

T.J. Hazen: And we’re still trying to figure out how to make it work so that an outside company can easily create the dataset, put the dataset into a system, push a button. The engineering for that and the research for that is still ongoing, but I think we’re pretty close to being able to, you know, provide a solution for this type of problem.

Host: All right. Well I’m going to push in technically because to me, it seems like that would be super hard for a machine. We keep referring to these techniques… Do we have to sign an NDA, as listeners?

T.J. Hazen: No, no. I can explain stuff that’s out…

Host: Yeah, do!

T.J. Hazen: … in the public domain. So, there are two common underlying technical components that make this work. One is called word embeddings and the other is called attention. Word embeddings are a mechanism where it learns how to take words or phrases and express them in what we call vector space.

Host: Okay.

T.J. Hazen: So, it turns them into a collection of numbers. And it does this by figuring out what types of words are similar to each other based on the context that they appear in, and then placing them together in this vector space, so they’re nearby each other. So, we would learn, that let’s say, city names are all similar because they appear in similar contexts. And so, therefore, Boston and New York and Montreal, they should all be close together in this vector space.

Host: Right.

T.J. Hazen: And blue and red and yellow should be close together. And then advances were made to figure this out in context. So that was the next step, because some words have multiple meanings.

Host: Right.

T.J. Hazen: So, you know, if you have a word like apple, sometimes it refers to a fruit and it should be near orange and banana, but sometimes it refers to the company and it should be near Microsoft and Google. So, we’ve developed context dependent ones, so that says, based on the context, I’ll place this word into this vector space so it’s close to the types of things that it really represents in that context.

Host: Right.

T.J. Hazen: That’s the first part. And you can learn these word embeddings from massive amounts of data. So, we start off with a model that’s learned on far more data than we actually have question and answer data for. The second part is called attention and that’s how you associate things together. And it’s the attention mechanisms that learn things like a word like “who” has to attend to words like person names or company names. And a word like “when” has to attend to…

Host: Time.

T.J. Hazen: …time. And those associations are learned through this attention mechanism. And again, we can actually learn on a lot of associations between things just from looking at raw text without actually having it annotated.

Host: Mm-hmm.

T.J. Hazen: Once we’ve learned all that, we have a base, and that base tells us a lot about how language works. And then we just have to have it focus on the task, okay? So, depending on the task, we might have a small amount of data and we feed in examples in that small amount, but it takes advantage of all the stuff that it’s learned about language from all these, you know, rich data that’s out there on the web. And so that’s how it can learn these associations even if you don’t give it examples in your domain, but it’s learned a lot of these associations from all the raw data.

Host: Right.

T.J. Hazen: And so, that’s the base, right? You’ve got this base of all this raw data and then you train a task-specific thing, like a question answering system, but even then, what we find is that, if we train a question answering system on basic facts, it doesn’t always work well when you go to operation manuals or other things. So, then we have to have it adapt.

Host: Sure.

T.J. Hazen: But, like I said, that base is very helpful because it’s already learned a lot of characteristics of language just by observing massive amounts of text.

(music plays)

Host: I’d like you to predict the future. No pressure. What’s on the horizon for machine reading comprehension research? What are the big challenges that lie ahead? I mean, we’ve sort of laid the land out on what we’re doing now. What next?

T.J. Hazen: Yeah. Well certainly, more complex questions. What we’ve been talking about so far is still fairly simple in the sense that you have a question, and we try to find passages of text that answer that question. But sometimes a question actually requires that you get multiple pieces of evidence from multiple places and you somehow synthesize them together. So, a simple example we call the multi-hop example. If I ask a question like, you know, where was Barack Obama’s wife born? I have to figure out first, who is Barack Obama’s wife? And then I have to figure out where she was born. And those pieces of information might be in two different places.

Host: Right.

T.J. Hazen: So that’s what we call a multi-hop question. And then, sometimes, we have to do some operation on the data. So, you could say, you know like, what players, you know, from one Super Bowl team also played on another Super Bowl team? Well there, what you have to do is, you have to get the list of all the players from both teams and then you have to do an intersection between them to figure out which ones are the same on both. So that’s an operation on the data…

Host: Right.

T.J. Hazen: …and you can imagine that there’s lots of questions like that where the information is there, but it’s not enough to just show the person where the information is. You also would like to go a step further and actually do the computation for that. That’s a step that we haven’t done, like, how do you actually go from mapping text to text, and saying these two things are associated, to mapping text to some sequence of operations that will actually give you an exact answer. And, you know, it can be quite difficult. I can give you a very simple example. Like, just answering a question, yes or no, out of text, is not a solved problem. Let’s say I have a question where someone says, I’m going to fly to London next week. Am I allowed to fly business class according to my policies from my company, right? We can have a system that would be really good at finding the section of the policy that says, you know, if you are a VP-level or higher and you are flying overseas, you can fly business class, otherwise, no. Okay? But, you know, if we actually want the system to answer yes or no, we have to actually figure out all the details, like okay, who’s asking the question? Are they a VP? Where are they located? Oh, they’re in New York. What does flying overseas mean??

Host: Right. They’re are layers.

T.J. Hazen: Right. So that type of comprehension, you know, we’re not quite there yet for all types of questions. Usually these things have to be crafted by hand for specific domains. So, all of these things about how can you answer complex questions, and even simple things like common sense, like, things that we all know… Um. And so, my manager, Andrew McNamara, he was supposed to be here with us, one of his favorite examples is this concept of coffee being black. But if you spill coffee on your shirt, do you have a black stain on your shirt? No, you’ve got a brown stain on your shirt. And that’s just common knowledge. That is, you know, a common-sense thing that computers may not understand.

Host: You’re working on research, and ultimately products or product features, that make people think they can talk to their machines and that their machines can understand and talk back to them. So, is there anything you find disturbing about this? Anything that keeps you up at night? And if so, how are you dealing with it?

T.J. Hazen: Well, I’m certainly not worried about the fact that people can ask questions of the computer and the computer can give them answers. What I’m trying to get at is something that’s helpful and can help you solve tasks. In terms of the work that we do, yeah, there are actually issues that concern me. So, one of the big ones is, even if a computer can say, oh, I found a good answer for you, here’s the answer, it doesn’t know anything about whether that answer is true. If you go and ask your computer, was the Holocaust real? and it finds an article on the web that says no, the Holocaust was a hoax, do I want my computer to show that answer? No, I don’t. But…

Host: Or the moon landing…!

T.J. Hazen: …if all you are doing is teaching the computer about word associations, it might think that’s a perfectly reasonable answer without actually knowing that this is a horrible answer to be showing. So yeah, the moon landing, vaccinations… The easy way that people can defame people on the internet, you know, even if you ask a question that might seem like a fact-based question, you can get vast differences of opinion on this and you can get extremely biased and untrue answers. And how does a computer actually understand that some of these things are not things that we should represent as truth, right? Especially if your goal is to find a truthful answer to a question.

Host: All right. So, then what do we do about that? And by we, I mean you!

T.J. Hazen: Well, I have been working on this problem a little bit with the Bing team. And one of the things that we discovered is that if you can determine that a question is phrased in a derogatory way, that usually means the search results that you’re going to get back are probably going to be phrased in a derogatory way. So, even if we don’t understand the answer, we can just be very careful about what types of questions we actually want to answer.

Host: Well, what does the world look like if you are wildly successful?

T.J. Hazen: I want the systems that we build to just make life easier for people. If you have an information task, the world is successful if you get that piece of information and you don’t have to work too hard to get it. We call it task completion. If you have to struggle to find an answer, then we’re not successful. But if you can ask a question, and we can get you the answer, and you go, yeah, that’s the answer, that’s success to me. And we’ll be wildly successful if the types of things where that happens become more and more complex. You know, where if someone can start asking questions where you are synthesizing data and computing answers from multiple pieces of information, for me, that’s the wildly successful part. And we’re not there yet with what we’re going to deliver into product, but it’s on the research horizon. It will be incremental. It’s not going to happen all at once. But I can see it coming, and hopefully by the time I retire, I can see significant progress in that direction.

Host: Off script a little… will I be talking to my computer, my phone, a HoloLens? Who am I asking? Where am I asking? What device? Is that so “out there” as well?

T.J. Hazen: Uh, yeah, I don’t know how to think about where devices are going. You know, when I was a kid, I watched the original Star Trek, you know, and everything on there, it seemed like a wildly futuristic thing, you know? And then fifteen, twenty years later, everybody’s got their own little “communicator.”

Host: Oh my gosh.

T.J. Hazen: And so, uh, you know, the fact that we’re now beyond where Star Trek predicted we would be, you know, that itself, is impressive to me. So, I don’t want to speculate where the devices are going. But I do think that this ability to answer questions, it’s going to get better and better. We’re going to be more interconnected. We’re going to have more access to data. The range of things that computers will be able to answer is going to continue to expand. And I’m not quite sure exactly what it looks like in the future, to be honest, but, you know, I know it’s going to get better and easier to get information. I’m a little less worried about, you know, what the form factor is going to be. I’m more worried about how I’m going to actually answer questions reliably.

Host: Well it’s story time. Tell us a little bit about yourself, your life, your path to MSR. How did you get interested in computer science research and how did you land where you are now working from Microsoft Research in New England for Montreal?

T.J. Hazen: Right. Well, I’ve never been one to long-term plan for things. I’ve always gone from what I find interesting to the next thing I find interesting. I never had a really serious, long-term goal. I didn’t wake up some morning when I was seven and say, oh, I want to be a Principal Research Manager at Microsoft in my future! I didn’t even know what Microsoft was when I was seven. I went to college and I just knew I wanted to study computers. I didn’t know really what that meant at the time, it just seemed really cool.

Host: Yeah.

T.J. Hazen: I had an Apple II when I was a kid and I learned how to do some basic programming. And then I, you know, was going through my course work. I was, in my junior year, I was taking a course in audio signal processing and in the course of that class, we got into a discussion about speech recognition, which to me was, again, it was Star Trek. It was something I saw on TV. Of course, now it was Next Generation….!

Host: Right!

T.J. Hazen: But you know, you watch the next generation of Star Trek and they’re talking to the computer and the computer is giving them answers and here somebody is telling me you know there’s this guy over in the lab for computer science, Victor Zue, and he’s building systems that recognize speech and give answers to questions! And to me, that was science-fiction. So, I went over and asked the guy, you know, I heard you’re building a system, and can I do my bachelor’s thesis on this? And he gave me a demo of the system – it was called Voyager – and he asked a question, I don’t remember the exact question, but it was probably something like, show me a map of Harvard Square. And the system starts chugging along and it’s showing results on the screen as it’s going. And it literally took about two minutes for it to process the whole thing. It was long enough that he actually explained to me how the entire system worked while it was processing. But then it came back, and it popped up a map of Harvard Square on the screen. And I was like, ohhh my gosh, this is so cool, I have to do this! So, I did my bachelor’s thesis with him and then I stayed on for graduate school. And by seven years later, we had a system that was running in real time. We had a publicly available system in 1997 that you could call up on a toll-free number and you could ask for weather reports and weather information for anywhere in the United States. And so, the idea that it went from something that was “Star Trek” to something that I could pick up my phone, call a number and, you know, show my parents, this is what I’m working on, it was astonishing how fast that developed! I stayed on in that field with that research group. I was at MIT for another fifteen years after I graduated. At some point, a lot of the things that we were doing, they moved from the research lab to actually being real.

Host: Right.

T.J. Hazen: So, like twenty years after I went and asked to do my bachelor’s thesis, Siri comes out, okay? And so that was our goal. They were like, twenty years ago, we should be able to have a device where you can talk to it and it gives you answers and twenty years later there it was. So, that, for me, that was a queue that maybe it’s time to go where the action is, which was in companies that were building these things. Once you have a large company like Microsoft or Google throwing their resources behind these hard problems, then you can’t compete when you’re in academia for that space. You know, you have to move on to something harder and more far out. But I still really enjoyed it. So, I joined Microsoft to work on Cortana…

Host: Okay…

T.J. Hazen: …when we were building the first version of Cortana. And I spent a few years working on that. I’ve worked on some Bing products. I then spent some time in Azure trying to transfer these things so that companies that had the similar types of problems could solve their problems on Azure with our technology.

Host: And then we come full circle to…

T.J. Hazen: Then full circle, yeah. You know, once I realized that some of the stuff that customers were asking for wasn’t quite ready yet, I said, let me go back to research and see if I can improve that. It’s fantastic to see something through all the way to product, but once you’re successful and you have something in a product, it’s nice to then say, okay, what’s the next hard problem? And then start over and work on the next hard problem.

Host: Before we wrap up, tell us one interesting thing about yourself, maybe it’s a trait, a characteristic, a life event, a side quest, whatever… that people might not know, or be able to find on a basic web search, that’s influenced your career as a researcher?

T.J. Hazen: Okay. You know, when I was a kid, maybe about eleven years old, the Rubik’s Cube came out. And I got fascinated with it. And I wanted to learn how to solve it. And a kid down the street from my cousin had taught himself from a book how to solve it. And he taught me. His name was Jonathan Cheyer. And he was actually in the first national speed Rubik’s Cube solving competition. It was on this TV show, That’s Incredible. I don’t know if you remember that TV show.

Host: I do.

T.J. Hazen: It turned out what he did was, he had learned what is now known as the simple solution. And I learned it from him. And I didn’t realize it until many years later, but what I learned was an algorithm. I learned, you know, a sequence of steps to solve a problem. And once I got into computer science, I discovered all that problem-solving I was doing with the Rubik’s Cube and figuring out what are the steps to solve a problem, that’s essentially what things like machine learning are doing. What are the steps to figure out, what are the features of something, what are the steps I have to do to solve the problem? I didn’t realize that at the time, but the idea of being able to break down a hard problem like solving a Rubik’s Cube, and figuring out what are the stages to get you there, is interesting. Now, here’s the interesting fact. So, Jonathan Cheyer, his older brother is Adam Cheyer. Adam Cheyer is one of the co-founders of Siri.

Host: Oh my gosh. Are you kidding me?

T.J. Hazen: So, I met the kid when I was young, and we didn’t really stay in touch. I discovered, you know, many years later that Adam Cheyer was actually the older brother of this kid who taught me the Rubik’s Cube years and years earlier, and Jonathan ended up at Siri also. So, it’s an interesting coincidence that we ended up working in the same field after all those years from this Rubik’s Cube connection!

Host: You see, this is my favorite question now because I’m getting the broadest spectrum of little things that influenced and triggered something…!

Host: At the end of every podcast, I give my guests a chance for the proverbial last word. Here’s your chance to say anything you want to would-be researchers, both applied and other otherwise, who might be interested in working on machine reading comprehension for real-world applications.

T.J. Hazen: Well, I could say all the things that you would expect me to say, like you should learn about deep learning algorithms and you should possibly learn Python because that’s what everybody is using these days, but I think the single most important thing that I could tell anybody who wants to get into a field like this is that you need to explore it and you need to figure out how it works and do something in depth. Don’t just get some instruction set or some high-level overview on the internet, run it on your computer and then say, oh, I think I understand this. Like get into the nitty-gritty of it. Become an expert. And the other thing I could say is, of all the people I’ve met who are extremely successful, the thing that sets them apart isn’t so much, you know, what they learned, it’s the initiative that they took. So, if you see a problem, try to fix it. If you see a problem, try to find a solution for it. And I say this to people who work for me. If you really want to have an impact, don’t just do what I tell you to do, but explore, think outside the box. Try different things. OK? I’m not going to have the answer to everything, so therefore, if I don’t have the answer to everything, then if you’re only doing what I’m telling you to do, then we both, together, aren’t going to have the answer. But if you explore things on your own and take the initiative and try to figure out something, that’s the best way to really be successful.

Host: T.J. Hazen, thanks for coming in today, all the way from the east coast to talk to us. It’s been delightful.

T.J. Hazen: Thank you. It’s been a pleasure.

(music plays)

To learn more about Dr. T.J. Hazen and how researchers and engineers are teaching machines to answer complicated questions, visit Microsoft.com/research

Go to Original Article
Author: Microsoft News Center

Learn the tricks for using Microsoft Teams with Exchange

Using Microsoft Teams means Exchange administrators need to understand how this emerging collaboration service connects to the Exchange Online and Exchange on-premises systems.

At its 2017 Ignite conference, Microsoft unveiled its intelligent communications plan, which mapped out the movement of features from Skype for Business to Microsoft Teams, the Office 365 team collaboration service launched in March 2017. Since that September 2017 conference, Microsoft has added meetings and calling features to Teams, while also enhancing the product’s overall functionality.

Organizations that run Exchange need to understand how Microsoft Teams relies on Office 365 Groups, as well as the setup considerations Exchange administrators need to know.

How Microsoft Teams depends on Office 365 Groups

Each team in Microsoft Teams depends on the functionality provided by Office 365 Groups, such as shared mailboxes or SharePoint Online team sites. An organization can permit all users to create a team and Office 365 Group, or it can limit this ability by group membership. 

When creating a new team, it can be linked to an existing Office 365 Group; otherwise, a new group will be created.

Microsoft Teams layout
Microsoft Teams is Microsoft’s foray into the team collaboration space. Using Microsoft Teams with Exchange will require administrators to stay abreast of roadmap plans for proper configuration and utilization of the collaboration offering.

Microsoft adjusted settings recently so new Office 365 Groups created by Microsoft Teams do not appear in Outlook by default. If administrators want new groups to show in Outlook, they can use the Set-UnifiedGroup PowerShell command.

Microsoft Teams’ reliance on Office 365 Groups affects organizations that run an Exchange hybrid configuration. In this scenario, the Azure AD Connect group writeback feature can be enabled to synchronize Office 365 Groups to Exchange on premises as distribution groups. But this setting could lead to the creation of many Office 365 Groups created via Microsoft Teams that will appear in Exchange on premises. Administrators will need to watch this to see if the configuration will need to be adjusted.

Using Microsoft Teams with Exchange Online vs. Exchange on premises

As an Exchange Online customer, subscribers also get access to all the Microsoft Teams features. However, if the organization uses Exchange on premises, then certain functionality, such as the ability to modify user profile pictures and add connectors, is not available.

Microsoft Teams’ reliance on Office 365 Groups affects organizations that run an Exchange hybrid configuration.

Without connectors, users cannot plug third-party systems into Microsoft Teams; certain add-ins, like the Twitter connector that delivers tweets into a Microsoft Teams channel, cannot be used. Additionally, organizations that use Microsoft Teams with Exchange on-premises mailboxes must run on Exchange 2016 cumulative update 3 or higher to create and view meetings in Microsoft Teams.

Message hygiene services and Microsoft Teams

Antispam technology might need to be adjusted due to some Microsoft Teams and Exchange integration issues.

When a new member joins a team, the email.teams.microsoft.com domain sends an email to the new member. Microsoft owns this domain name, which the tenant administrator cannot adjust.

Because the domain is considered an external email domain to the organization’s Exchange Online deployment, the organization’s antispam configuration in Exchange Online Protection may mark the notification email as spam. Consequently, the new member might not receive the email or may not see it if it goes into the junk email folder.

To prevent this situation, Microsoft recommends adding email.teams.microsoft.com to the allowed domains list in Exchange Online Protection.

Complications with security and compliance tools

Administrators need to understand the security and compliance functionality when using Microsoft Teams with Exchange Online or Exchange on premises. Office 365 copies team channel conversations in the Office 365 Groups shared mailbox in Exchange Online so its security and compliance tools, such as eDiscovery, can examine the content. However, Office 365 stores copies of chat conversations in the users’ Exchange Online mailboxes, not the shared mailbox in Office 365 Groups.

Historically, Office 365 security and compliance tools could not access conversation content in an Exchange on-premises mailbox in a hybrid environment. Microsoft made changes to support this scenario, but customers must request this feature via Microsoft support.

Configure Exchange to send email to Microsoft Teams

An organization might want its users to have the ability to send email messages from Exchange Online or Exchange on premises to channels in Microsoft Teams. To send an email message to a channel, users need the channel’s email address and permission from the administrator. A right-click on a channel reveals the Get email address option. All the channels have a unique email address.

Administrators can restrict the domains permitted to send email to a channel in the Teams administrator settings in the new Microsoft Teams and Skype for Business admin center.

Democratizing technology, according to Nate – Microsoft Life

To understand Nate Yohannes’s journey, you must start in Eritrea.

In the Horn of Africa, the country’s evolving political climate fueled a struggle for democracy in the 30-year Eritrean War for Independence. Taking up arms to break down social injustice, a young peasant herder named Tes Yohannes, Yohannes’s father, became a freedom fighter in 1978. As he walked alongside compatriots with similar fates, Tes Yohannes stepped on one of the region’s innumerable landmines and was blinded in one eye as a result. Despite this life-changing injury, Tes Yohannes held strong to his belief in democracy, equality, and self-autonomy. He passed these values to his son, who would grow up to put them to work lessening disparities between the haves and the have-nots.

Born and raised in the United States during the 1980s, Yohannes wanted to be Ronald Reagan or a lawyer when he grew up. In that order. “I would hold a broom and give these speeches as if I was Reagan, and my parents knew that their son was (a) a dork and (b) a little too ambitious for his age,” he said.

Yohannes’s lawyerly ambitions were more grounded in reality, as his family’s entry to the United States was sponsored by a lawyer named Peter Oddleifson. Upon arriving, the Yohanneses lived with Oddleifson for weeks and remained close for years to come.

“He became a second dad to me. He’s our family superhero. He’s the one that led the legal agreements and helped us establish life here in the United States,” Yohannes said.

Early in youth, the guidance of mentors began to shape Yohannes’s conscience. What he didn’t know is where that guidance would one day lead him.

Yohannes’s childhood held the imprints of a fellow human’s generosity, a gift that followed him to the University at Buffalo School of Law in New York, where he studied human rights and immigration. During his studies, he received the Barbara and Thomas Wolfe Human Rights Fellowship to clerk at the Monroe County, New York, Public Defender’s office. Later, he went on to clerk for the chief justice of the New York Supreme Court, Eighth District. His public service was underway.

Near the end of his post-graduate work, he received some interesting advice from a Buffalo law alumnus—a former advisor to former US President Jimmy Carter. The advisor told Yohannes that although his aspirations to support the disenfranchised were well founded, Yohannes might make more of an impact by taking a different route to advocacy. The advisor saw in Yohannes the potential for big success equaled by a propensity for deep compassion—a combination that could position him well for a career in the private sector. Through that career, he could lift up individuals into opportunity.

It’s a rare person who can champion others with the same fervor as they do themselves, but Yohannes knows no other way.

“Help others—professionally and personally. The ability to learn and perform will eventually cap you, [so] you have to be able to work with other people. That will enable you to rise professionally,” he said. “We are in a people business. Life is personal.”

After he graduated law school, Yohannes set off for Washington, DC, where he became the assistant general counsel of the Money Management Institute, a trade group that represents the financial industry. There, Yohannes reestablished a nonprofit called Gateway to Leadership, designed to recruit the best and brightest undergraduate women and minorities to take internships at big investment banks.

“Although we were working in the securities space representing the Goldman Sachs of the world, that compassion of continuing to help was through a different route, by economic empowerment—by bringing those who are not at the table to lucrative industries and uplift folks,” he said.

In the coming years, Yohannes took opportunities that led him to some of the bedrock names in finance, industry, and entrepreneurial ventures. Always searching for ways to outsmart systematic barriers to social equality, in 2016 Yohannes found himself in a chance Uber ride that proved providential.

At the time, Yohannes was working for former US President Barack Obama’s administration as senior advisor to the head of the Office of Investments and Innovation. A work trip took him to San Francisco, where he was tasked with promoting women venture capital opportunities at the NASDAQ Entrepreneurial Center. He used a rideshare app to carpool to an engagement and met Ana White, general manager of Human Resources at Microsoft.

What followed was an unexpected conversation between the Uber driver, White, and Yohannes—an organic connection that turned out to be Yohannes’s gateway to a job at Microsoft. White wasn’t the first leader to recognize Yohannes’s singular depth of character and his ability to adapt and grow. Yohannes, too, was intrigued by conversations with Sarah Richmond, senior director of Business Development, and Priya Priyadarshini, director of Human Resources, and was drawn to the cultural shift that was occurring at Microsoft.

Employee Nate Yohannes

Even when he didn’t come to work at Microsoft right away, Nate Yohannes said people he met at the company stayed interested in what he was doing and continued show him what Microsoft’s culture was like.

After those interactions, Yohannes went through a series of Microsoft interviews where he met like-minded people who, Yohannes says, “advocate for economic equality through the use of technology.”

It wasn’t an immediate life change, though. Even as Yohannes decided to pursue other work, he kept thinking about the energizing meetings he had and couldn’t get Microsoft out of his mind. Meanwhile, the people he had met at Microsoft stayed connected with him and were interested in what he was up to. It was clear to him that Microsoft believed in Yohannes and his value, shaped by all the experiences he had and the drive to effect change that kept him moving forward.

Finally, inspired by the thought leadership and impressed by how Microsoft had engaged and followed up with him, Yohannes joined the company nine months later as director of business development on the Office and Artificial Intelligence team. In part because of Microsoft’s unique culture and the ways that he says employees evangelized for the company and the opportunities he could pursue there, Yohannes found his calling in the world’s next platform for freedom: technology.

Democratizing technology, according to Yohannes, originated in Microsoft when it brought computing to the individual level, not just the enterprise level. Today, it has evolved into a pivotal tool for the marginalized. In this new era of digital redlining, there are blockers to connectivity around the globe. In some parts of the United States, students who rely on technology to complete homework assignments sit outside of fast food restaurants that offer internet connection. In response, Microsoft just launched its Rural Broadband Initiative, offering rural connectivity at affordable prices. Freedom comes in the form of access to knowledge and access to technology, something Yohannes never loses sight of.

Yohannes champions transparency for every citizen—through shared media, language translation, medical technology, educational resources, and communication. Democratizing technology, says Yohannes, means “empowering human beings from the human rights level to the e-commerce level. It’s allowing tech to hit every corner of Earth to uplift society.”

He knows personally the roadblocks that language barriers can bring; he says his parents’ potential wasn’t unleashed until they could speak English in their new country. Currently, he’s delivering services to an AI product called MS Language Translator, which allows people to speak in their native language and have it immediately translated in real time. This is the type of work in tech that amplifies human ingenuity and improves livelihoods.

Connectivity, education, and diverse representation in the digital world are now the focus of the industry’s humanitarian goals. However, in a relatively short time, there will be a different kind of disparity in society—a gap between those who are trained in tech and the demand for those workers. Yohannes sees this deficit as another opportunity to level the playing field. Through a connection from his White House days, Yohannes’s office just hosted a nonprofit called Code2040, which empowers women and minorities to code with the goal of narrowing the gap by 2040.

What started as a concrete battle in Eritrea has paved the way for an abstract, yet equally relevant, defense of the have-nots. In Yohannes’s vocational coming-of-age, he discovered his responsibility in the new world order. With a profession built upon the foundation of his parents’ ethos, he says “I am most proud of having them as my parents.” For a family that radiates this ethos of equity for all, it’s hard to believe that they are banned from returning to their homeland because of his father’s vocal stance against the dictator in power—a former comrade. Yohannes hadn’t met his extended family, trapped in Eritrea, until adulthood, when technology reunited them through Facebook and Skype. Ultimately, there is justice in knowing that individuals will connect and opportunity will increasingly arise from the cloud.

Yohannes nurtures this hope, his family’s hope, for “moral integrity, humility, that passion to make everything human.”

On tap for Microsoft Ignite conference: Adobe, Dynamics 365, LinkedIn

To understand the direction Microsoft is moving with its Dynamics 365 platform, you have to go to 2014, when CEO Satya Nadella was put in charge of the company. One of his primary goals was to take a larger share of the CRM market, where Microsoft currently sits fourth behind leader Salesforce, Oracle and SAP.

This process — and progress, according to analysts — was multipronged, starting with better alignment with Microsoft’s existing business applications, namely Office 365 and Outlook; adding cloud hosting with Azure and business intelligence with Power BI; continuing the Microsoft-Adobe partnership; and acquiring LinkedIn, both of which added significant value to the Dynamics 365 platform.

The Adobe partnership was announced at the annual Microsoft Ignite conference last year. For Microsoft Ignite 2017, analysts expect the Adobe partnership to deepen and possibly expand, and for the LinkedIn purchase — which was announced but not yet finalized during last year’s conference — to play a more significant role. The conference, located in Orlando this year, takes place from Sept. 25 to 29.

“If Microsoft was just going to try and replace existing technology in the ERP and CRM market, it would be a pretty difficult battle,” said Kevin Armstrong, vice president of Microsoft Sales at Tribridge, a technology services firm in Chelmsford, Mass. “For Dynamics 365 to take hold, Satya had to change the vision of Dynamics, and the direction has been fast and impressive.”

Building on the Microsoft-Adobe partnership

Microsoft has been tight-lipped about any announcements ahead of Microsoft Ignite 2017. Unlike Salesforce, which had its CEO Marc Benioff take the lid off the Einstein announcement prior to Dreamforce last year, Microsoft keeps its news closer to the chest, only teasing out the news of a partnership last year, before announcing the relationship with Adobe at the conference.

Session descriptions for the upcoming conference do mention the possibility of further Microsoft-Adobe integrations or products.

There is some joint go-to-market efforts and selling across [Microsoft and Adobe].
Michael Fauscettechief research officer, G2 Crowd

“We share current and upcoming capabilities, including demos of integrations between Adobe Campaign, Adobe Experience Manager, Dynamics 365 and Power BI,” one description read.

Adobe recently announced new features for email marketing in its Campaign Manager, which is part of Adobe Marketing Cloud, one of the main products Microsoft is partnering with to better provide marketing capabilities for its enterprise-sized customers.

“The thing about Adobe in the digital marketing side of the house is they’ve had this reputation — this ‘cool’ factor — for Adobe’s marketing suite,” said Michael Fauscette, chief research officer at Chicago-based G2 Group. “There’s a cache about that integration and capabilities that Microsoft doesn’t have.”

Fauscette said he also believes that Microsoft and Adobe could be gearing up to go to market together on joint product offerings — an attractive option for Microsoft-heavy shops looking for better customer and marketing management.

“There is some joint go-to-market efforts and selling across companies,” Fauscette said. “I think there is some economic advantage selling directly, versus a referral partnership.”

Further integration with existing products

Microsoft wasted little time integrating Dynamics 365 with other in-house business applications and putting them on the same common data model — a big computing advantage, according to Armstrong. But further integration with Microsoft’s host of products could still be beneficial, and some of that could be unveiled at the upcoming Microsoft Ignite conference.

“I’m a big believer in Microsoft’s move toward machine learning and AI with Cortana,” Armstrong said. “The biggest advantage of having all this data in one solution is leveraging Cortana to look at the data and tell us things about our customers or prospects. The reason that’s important is you’re not talking about integrating with [AI], you’re talking about interacting with it.”

Microsoft hinted at new tools for innovation around Dynamics 365 at Microsoft Ignite 2017. Building off its integrations with its existing products and expanding to others — like Cortana and Power BI — could provide some of those new innovations.

The LinkedIn whale

Microsoft’s acquisition of LinkedIn in 2016 was arguably the biggest tech splash of the year, with the Seattle-based company spending $26.2 billion on the professional social network. Yet at last year’s conference, there was little official discussion about the capabilities and integrations on the Microsoft side, as the sale was still under the approving eyes of the Securities and Exchange Commission (SEC). After the sale was finalized a couple of months later, Microsoft continued to say that the LinkedIn and Sales Navigator tool APIs would remain open for all to use.

“Microsoft is trying to do two things: one is leave LinkedIn and their tools in their existing position — keep them Switzerland,” Fauscette said. “At the same time, Microsoft wants to integrate them more deeply in the background so that if you’re a Dynamics customer you can get an advantage of using it.”

Still, analysts see an advantage for Microsoft having LinkedIn in its stable of products with the capability of baking its features directly into Dynamics 365, rather than a third-party integration that other CRM companies have to do.

“Anytime you have separate systems combine, you have potential for connection points to break,” Armstrong said.

It’s unlikely that Microsoft spent $26.2 billion for an existing revenue stream, but it also can’t use LinkedIn as a proprietary product without upsetting the SEC. However, Microsoft users should expect enhanced features moving forward with LinkedIn, and the first tidbit of those features could be heard at the Microsoft Ignite conference.

For Sale – Lian li PC-C60 case

I found the pictures in the previous post. I like them.
I understand also the missing silver cover blanking pieces on the back for PCI cards. Is fine for me – no problem with missing them.

However it seems that there is missing one sigle hdd racking (the 6th one) and the longitudinal support for the second cluster of hdd. I will use a mini ITX MB with all on board and I really need all hdd and odd. And Lian Li does not offer the possibility to buy these type of hdd cages. I attached a generic picture to see the second cluster of hdd.
Do you still have these parts: the 6th sigle hdd racking and the the longitudinal support for the second cluster of hdd?

For Sale – Lian li PC-C60 case

I found the pictures in the previous post. I like them.
I understand also the missing silver cover blanking pieces on the back for PCI cards. Is fine for me – no problem with missing them.

However it seems that there is missing one sigle hdd racking (the 6th one) and the longitudinal support for the second cluster of hdd. I will use a mini ITX MB with all on board and I really need all hdd and odd. And Lian Li does not offer the possibility to buy these type of hdd cages. I attached a generic picture to see the second cluster of hdd.
Do you still have these parts: the 6th sigle hdd racking and the the longitudinal support for the second cluster of hdd?

For Sale – Lian li PC-C60 case

I found the pictures in the previous post. I like them.
I understand also the missing silver cover blanking pieces on the back for PCI cards. Is fine for me – no problem with missing them.

However it seems that there is missing one sigle hdd racking (the 6th one) and the longitudinal support for the second cluster of hdd. I will use a mini ITX MB with all on board and I really need all hdd and odd. And Lian Li does not offer the possibility to buy these type of hdd cages. I attached a generic picture to see the second cluster of hdd.
Do you still have these parts: the 6th sigle hdd racking and the the longitudinal support for the second cluster of hdd?

For Sale – Lian li PC-C60 case

I found the pictures in the previous post. I like them.
I understand also the missing silver cover blanking pieces on the back for PCI cards. Is fine for me – no problem with missing them.

However it seems that there is missing one sigle hdd racking (the 6th one) and the longitudinal support for the second cluster of hdd. I will use a mini ITX MB with all on board and I really need all hdd and odd. And Lian Li does not offer the possibility to buy these type of hdd cages. I attached a generic picture to see the second cluster of hdd.
Do you still have these parts: the 6th sigle hdd racking and the the longitudinal support for the second cluster of hdd?