Tag Archives: Assistant

Mist automates WLAN monitoring with new AI features

Mist Systems announced this week that its Marvis virtual network assistant now understands how to respond to hundreds of inquiries related to wireless LAN performance. And, in some cases, it can detect anomalies in those networks before they cause problems for end users.

IT administrators can ask Marvis questions about the performance of wireless networks — and the devices connected to it — using natural language commands, such as, “What’s wrong with John’s laptop?” The vendor said the technology helps customers identify client-level problems, rather than just network-wide trends.

Marvis could only handle roughly a dozen basic questions at launch in February. But Mist’s machine learning platform has used data from customers that have started using the product to improve Marvis’ natural language processing (NLP) skills for WLAN monitoring. Marvis can now field hundreds of queries, with less specificity required in asking each question.

Mist also announced an anomaly detection feature for Marvis that uses deep learning to determine when a wireless network is starting to behave abnormally, potentially flagging issues before they happen. Using the product’s APIs, IT departments can integrate Marvis with their help desk software to set up automatic alerts.

Mist has a robust platform for network management, and the advancements announced this week represent “solid steps forward for the company and the industry,” said Brandon Butler, analyst at IDC.

Cisco and Aruba Networks, a subsidiary of Hewlett Packard Enterprise, have also been investing in new technologies for automated WLAN monitoring and management, Butler said.

“Mist has taken a unique approach in the market with its focusing on NLP capabilities to provide users an intuitive way of interfacing with the management platform,” Butler said. “It is one of many companies … that are building up their anomaly detection and auto-remediation capabilities using machine learning capabilities.”

Applying AI to radio resource management

The original promise of radio resource management (RRM), which has been around for 15 years, was the service would detect noise and interference in wireless networks and adjust access points and channels accordingly, said Jeff Aaron, vice president of marketing at Mist, based in Cupertino, Calif.

“The problem is it’s never really worked that way,” Aaron said. “RRM has never been real-time; it’s usually done at night, because it doesn’t really have the level of data you need to make the decision.”

Now, Mist has revamped its RRM service using AI, so it can monitor the coverage, capacity, throughput and performance of Wi-Fi networks on a per-user basis. The service makes automatic changes and quantifies what impact — positive or negative — those changes have on end users.

Mist has RRM in its flagship product for WLAN monitoring and management, Wi-Fi Assurance.

Service-level expectations for WAN performance

Mist will now let customers establish and enforce service-level expectations (SLEs) for WAN performance. The agreements will help Mist customers track the impact of latency, jitter and packet loss on end users.

The release of SLEs for the WAN comes as Mist pursues partnerships with Juniper and VMware to reduce friction between the performance and user experience of the WLAN and the WAN.

Mist also lets customers set service levels for Wi-Fi performance based on metrics that include capacity, coverage, throughput, latency, access point uptime and roaming.

Data Center Scale Computing and Artificial Intelligence with Matei Zaharia, Inventor of Apache Spark

Matei Zaharia, Chief Technologist at Databricks & Assistant Professor of Computer Science at Stanford University, in conversation with Joseph Sirosh, Chief Technology Officer of Artificial Intelligence in Microsoft’s Worldwide Commercial Business


At Microsoft, we are privileged to work with individuals whose ideas are blazing a trail, transforming entire businesses through the power of the cloud, big data and artificial intelligence. Our new “Pioneers in AI” series features insights from such pathbreakers. Join us as we dive into these innovators’ ideas and the solutions they are bringing to market. See how your own organization and customers can benefit from their solutions and insights.

Our first guest in the series, Matei Zaharia, started the Apache Spark project during his PhD at the University of California, Berkeley, in 2009. His research was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in Computer Science. He is a co-founder of Databricks, which offers a Unified Analytics Platform powered by Apache Spark. Databricks’ mission is to accelerate innovation by unifying data science, engineering and business. Microsoft has partnered with Databricks to bring you Azure Databricks, a fast, easy, and collaborative Apache Spark based analytics platform optimized for Azure. Azure Databricks offers one-click set up, streamlined workflows and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts to generate great value from data faster.

So, let’s jump right in and see what Matei has to say about Spark, machine learning, and interesting AI applications that he’s encountered lately.

Video and podcast versions of this session are available at the links below. The podcast is also available from your Spotify app and via Stitcher. Alternatively, just continue reading the text version of their conversation below, via this blog post.

Joseph Sirosh: Matei, could you tell us a little bit about how you got started with Spark and this new revolution in analytics you are driving?

Matei Zaharia: Back in 2007, I started doing my PhD at UC Berkeley and I was very interested in data center scale computing, and we just saw at the time that there was an open source MapReduce implementation in Apache Hadoop, so I started early on by looking at that. Actually, the first project was profiling Hadoop workloads to identify some bottlenecks and, as part of that, we made some improvements to the Hadoop job scheduler and that actually went into Hadoop and I started working with some of the early users of that, especially Facebook and Yahoo. And what we saw across all of these is that this type of large data center scale computing was very powerful, there were a lot of interesting applications they could do with them, but just the map-reduce programming model alone wasn’t really sufficient, especially for machine learning – that’s something everyone wanted to do where it wasn’t a good fit but also for interactive queries and streaming and other workloads.

So, after seeing this for a while, the first project we built was the Apache Mesos cluster manager, to let you run other types of computations next to Hadoop. And then we said, you know, we should try to build our own computation engine which ended up becoming Apache Spark.

JS: What was unique about Spark?

MZ: I think there were a few interesting things about it. One of them was that it tried to be a general or unified programming model that can support many types of computations. So, before the Spark project, people wanted to do these different computations on large clusters and they were designing specialized engines to do particular things, like graph processing, SQL custom code, ETL which would be map-reduce, they were all separate projects and engines. So in Spark we kind of stepped back at them and looked at these and said is there any way we can come up with a common abstraction that can handle these workloads and we ended up with something that was a pretty small change to MapReduce – MapReduce plus fast data sharing, which is the in-memory RDDs in Spark, and just hooking these up into a graph of computations turned out to be enough to get really good performance for all the workloads and matched the specialized engines, and also much better performance if your workload combines a bunch of steps. So that is one of the things.

I think the other thing which was important is, having a unified engine, we could also have a very composable API where a lot of the things you want to use would become libraries, so now there are hundreds maybe thousands of third party packages that you can use with Apache Spark which just plug into it that you can combine into a workflow. Again, none of the earlier engines had focused on establishing a platform and an ecosystem but that’s why it’s really valuable to users and developers, is just being able to pick and choose libraries and arm them.

JS: Machine Learning is not just one single thing, it involves so many steps. Now Spark provides a simple way to compose all of these through libraries in a Spark pipeline and build an entire machine learning workflow and application. Is that why Spark is uniquely good at machine learning?

MZ: I think it’s a couple of reasons. One reason is much of machine learning is preparing and understanding the data, both the input data and also actually the predictions and the behavior of the model, and Spark really excels at that ad hoc data processing using code – you can use SQL, you can use Python, you can use DataFrames, and it just makes those operations easy, and, of course, all the operations you do also scale to large datasets, which is, of course, important because you want to train machine learning on lots of data.

Beyond that, it does support iterative in-memory computation, so many algorithms run pretty well inside it, and because of this support for composition and this API where you can plug in libraries, there are also quite a few libraries you can plug in that call external compute engines that are optimized to do different types of numerical computation.

JS: So why didn’t some of these newer deep learning toolsets get built on top of Spark? Why were they all separate?

MZ: That’s a good question. I think a lot of the reason is probably just because people, you know, just started with a different programming language. A lot of these were started with C++, for example, and of course, they need to run on the GPU using CUDA which is much easier to do from C++ than from Java. But one thing we’re seeing is really good connectors between Spark and these tools. So, for example, TensorFlow has a built-in Spark connector that can be used to get data from Spark and convert it to TFRecords. It also actually connects to HDFS and different sorts of big data file systems. At the same time, in the Spark community, there are packages like deep learning pipelines from Databricks and quite a few other packages as well that let you setup a workflow of steps that include these deep learning engines and Spark processing steps.

“None of the earlier engines [prior to Apache Spark] had focused on establishing a platform and an ecosystem.”

JS: If you were rebuilding these deep learning tools and frameworks, would you recommend that people build it on top of Spark? (i.e. instead of the current approach, of having a tool, but they have an approach of doing distributed computing across GPUs on their own.)

MZ: It’s a good question. I think initially it was easier to write GPU code directly, to use CUDA and C++ and so on. And over time, actually, the community has been adding features to Spark that will make it easier to do that in there. So, there’s definitely been a lot of proposals and design to make GPU a first-class resource. There’s also this effort called Project Hydrogen which is to change the scheduler to support these MPI-like batch jobs. So hopefully it will become a good platform to do that, internally. I think one of the main benefits of that is again for users that they can either program in one programming language, they can learn just one way to deploy and manage clusters and it can do deep learning and the data preprocessing and analytics after that.

JS: That’s great. So, Spark – and Databricks as commercialized Spark – seems to be capable of doing many things in one place. But what is not good at? Can you share some areas where people should not be stretching Spark?

MZ: Definitely. One of the things it doesn’t do, by design, is it doesn’t do transactional workloads where you have fine grained updates. So, even though it might seem like you can store a lot of data in memory and then update it and serve it, it is not really designed for that. It is designed for computations that have a large amount of data in each step. So, it could be streaming large continuous streams, or it could be batch but is it not these point queries.

And I would say the other thing it does not do it is doesn’t have a built-in persistent storage system. It is designed so it’s just a compute engine and you can connect it to different types of storage and that actually makes a lot of sense, especially in the cloud, with separating compute and storage and scaling them independently. But it is different from, you know, something like a database where the storage and compute are co-designed to live together.

JS: That makes sense. What do you think of frameworks like Ray for machine learning?

MZ: There are lot of new frameworks coming out for machine learning and it’s exciting to see the innovation there, both in the programming models, the interface, and how to work with it. So I think Ray has been focused on reinforcement learning which is where one of the main things you have to do is spawn a lot of little independent tasks, so it’s a bit different from a big data framework like Spark where you’re doing one computation on lots of data – these are separate computations that will take different amounts of time, and, as far as I know, users are starting to use that and getting good traction with it. So, it will be interesting to see how these things come about.

I think the thing I’m most interested in, both for Databricks products and for Apache Spark, is just enabling it to be a platform where you can combine the best algorithms, libraries and frameworks and so on, because that’s what seems to be very valuable to end users, is they can orchestrate a workflow and just program it as easily as writing a single machine application where you just import a bunch of libraries.

JS: Now, stepping back, what do you see as the most exciting applications that are happening in AI today?

MZ: Yeah, it depends on how recent. I mean, in the past five years, deep learning is definitely the thing that has changed a lot of what we can do, and, in particular, it has made it much easier to work with unstructured data – so images, text, and so on. So that is pretty exciting.

I think, honestly, for like wide consumption of AI, the cloud computing AI services make it significantly easier. So, I mean, when you’re doing machine learning AI projects, it’s really important to be able to iterate quickly because it’s all about, you know, about experimenting, about finding whether something will work, failing fast if a particular idea doesn’t work. And I think the cloud makes it much easier.

JS: Cloud AI is super exciting, I completely agree. Now, at Stanford, being a professor, you must see a lot of really exciting pieces of work that are going on, both at Stanford and at startups nearby. What are some examples?

MZ: Yeah, there are a lot of different things. One of the things that is really useful for end users is all the work on transfer learning, and in general all the work that lets you get good results with AI using smaller training datasets. There are other approaches as well like weak supervision that do that as well. And the reason that’s important is that for web-scale problems you have lot of labeled data, so for something like web search you can solve it, but for many scientific or business problems you don’t have that, and so, how can you learn from a large dataset that’s not quite in your domain like the web and then apply to something like, say, medical images, where only a few hundred patients have a certain condition so you can’t get a zillion images. So that’s where I’ve seen a lot of exciting stuff.

But yeah, there’s everything from new hardware for machine learning where you throw away the constraints that the computation has to be precise and deterministic, to new applications, to things like, for example security of AI, adversarial examples, verifiability, I think they are all pretty interesting things you can do.

JS: What are some of the most interesting applications you have seen of AI?

MZ: So many different applications to start with. First of all, we’ve seen consumer devices that bring AI into every home, or every phone, or every PC – these have taken off very quickly and it’s something that a large fraction of customers use, so that’s pretty cool to see.

In the business space, probably some of the more exciting things are actually dealing with image data, where, using deep learning and transfer learning, you can actually start to reliably build classifiers for different types of domain data. So, whether it’s maps, understanding satellite images, or even something as simple as people uploading images of a car to a website and you try to give feedback on that so it’s easier to describe it, a lot of these are starting to happen. So, it’s kind of a new class of data, visual data – we couldn’t do that much with it automatically before, and now you can get both like little features and big products that use it.

JS: So what do you see as the future of Databricks itself? What are some of the innovations you are driving?

MZ: Databricks, for people not familiar, we offer basically, a Unified Analytics Platform, where you can work with big data mostly through Apache Spark and collaborate with it in an organization, so you can have different people, developing say notebooks to perform computations, you can have people developing production jobs, you can connect these together into workflows, and so on.

So, we’re doing a lot of things to further expand on that vision. One of the things that we announced recently is what we call machine learning runtime where we have preinstalled versions of popular machine learning libraries like XGBoost or TensorFlow or Horovod on your Databricks cluster, so you can set those up as easily as you can set up as easily as you can setup an Apache Spark cluster in the past. And then another product that we featured a lot at our Spark Summit conference this year is Databricks Delta which is basically a transactional data management layer on top of cloud objects stores that lets us do things like indexing, reliable exactly once stream processing, and so on at very massive scale, and that’s a problem that all our users have, because all our users have to setup a reliable data ingest pipeline.

JS: Who are some of the most exciting customers of Databricks and what are they doing?

MZ: There are a lot of really interesting customers doing pretty cool things. So, at our conference this year, for example, one of the really cool presentations we saw was from Apple. So, Apple’s internal information security group – this is the group that does network monitoring, basically gets hundreds of terabytes of network events per day to process, to detect intrusions and information security problems. They spoke about using Databricks Delta and streaming with Apache Spark to handle all of that – so it’s one of the largest applications people have talked about publicly, and it’s very cool because the whole goal there – it’s kind of an arms race between the security team and attackers – so you really want to be able to design new rules, new measurements and add new data sources quickly. And so, the ease of programming and the ease of collaborating with this team of dozens of people was super important.

We also have some really exciting health and life sciences applications, so some of these are actually starting to discover new drugs that companies can actually productionize to tackle new diseases, and this is all based on large scale genomics and statistical studies.

And there are a lot of more fun applications as well. Like actually the largest video game in the world, League of Legends, they use Databricks and Apache Spark to detect players that are misbehaving or to recommend items to people or things like that. These are all things that were featured at the conference.

JS: If you had one advice to developers and customers using Spark or Databricks, or guidance on what they should learn, what would that be?

MZ: It’s a good question. There are a lot of high-quality training materials online, so I would say definitely look at some of those for your use case and see what other people are doing in that area. The Spark Summit conference is also a good way to see videos and talks and we make all of those available for free, the goal of that is to help and grow the developer community. So, look for someone who is doing similar things and be inspired by that and kinda see what the best practices are around that, because you might see a lot of different options for how to get started and it can be hard to see what the right path is.

JS: One last question – in recent years there’s been a lot of fear, uncertainty and doubt about AI, and a lot of popular press. Now – how real are they, and what do you think people should be thinking?

MZ: That’s a good question. My personal view is – this sort of evil artificial general intelligence stuff – we are very far away from it. And basically, if you don’t believe that, I would say just try doing machine learning tutorials and see how these models break down – you get a sense for how difficult that is.

But there are some real challenges that will come from AI, so I think one of them is the same challenge as with all technology which is, automation – how quickly does it happen. Ultimately, after automation, people usually end up being better off, but it can definitely affect some industries in a pretty bad way and if there is no time for people to transition out, that can be a problem.

I think the other interesting problem there is always a discussion about is basically access to data, privacy, managing the data, algorithmic discrimination – so I think we are still figuring out how to handle that. Companies are doing their best, but there are also many unknowns as to how these techniques will do that. That’s why we’ll see better best practices or regulations and things like that.

JS: Well, thank you Matei, it’s simply amazing to see the innovations you have driven, and looking forward to more to come.

MZ: Thanks for having me.

“When you’re doing machine learning AI projects, it’s really important to be able to iterate quickly because it’s all about experimenting, about finding whether something will work, failing fast if a particular idea doesn’t work.


And I think the cloud makes it much easier.”

We hope you enjoyed this blog post. This being our first episode in the series, we are eager to hear your feedback, so please share your thoughts and ideas below.

The AI / ML Blog Team

Resources

Alexa for Business sounds promising, but security a concern

Virtual assistant technology, popular in the consumer world, is migrating toward businesses with the hopes of enhancing employee productivity and collaboration. Organizations could capitalize on the familiarity of home-based virtual assistants, such as Siri and Alexa, to boost productivity in the office and launch meetings quicker.

Last week, Amazon announced Alexa for Business, a virtual assistant that connects Amazon Echo devices to the enterprise. Alexa for Business allows organizations to equip conference rooms with Echo devices that can turn on video conferencing equipment and dial into a conference via voice commands.

“Virtual assistants, such as Alexa, greatly enhance the user experience and reduce the complexity in joining meetings,” Frost & Sullivan analyst Vaishno Srinivasan said.

Personal Echo devices connected to the Alexa for Business platform can also be used for hands-free calling and messaging, scheduling meetings, managing to-do lists and finding information on business apps, such as Salesforce and Concur.

Overcoming privacy and security hurdles

Before enterprise virtual assistants like Alexa for Business can see widespread adoption, they must overcome security concerns.

“Amazon and other providers will have to do some evangelizing to demonstrate to CIOs and IT leaders that what they’re doing is not going to compromise any security,” Gartner analyst Werner Goertz said.

Amazon is well-positioned to grab this opportunity much ahead of Microsoft Cortana, Google Assistant and Apple’s Siri.
Vaishno Srinivasananalyst, Frost & Sullivan

Srinivasan said organizations may have concerns about Alexa for Business collecting data and sharing it in a cloud environment. Amazon has started to address these concerns, particularly when connecting personal Alexa accounts and home Echo devices to a business account.

Goertz said accounts are sandboxed, so users’ personal information will not be visible to the organization. The connected accounts must also comply with enterprise authentication standards. The platform also includes administrative controls that offer shared device provisioning and management capabilities, as well as user and skills management.

Another key challenge is ensuring a virtual assistant device, like the Amazon Echo, responds to a user with information that is highly relevant and contextual, Srinivasan said.

“These devices have to be trained to enhance its intelligence to deliver context-sensitive and customized user experience,” she said.

Integrating with enterprise IT systems

End-user spending on virtual assistant devices is expected to reach $3.5 billion by 2021, up from $720 million in 2016, according to Gartner. Enterprise adoption is expected to ramp up by 2019.

Goertz said Amazon had to do a lot of work “under the hood” to enable the integrations with business apps and vendors such as Microsoft, Cisco, Polycom and BlueJeans. The deep integrations with enterprise IT systems is required to enable future capabilities, such as dictating and sending emails from an Echo device, he said.

Srinivasan said Alexa for Business can extend beyond conference rooms through APIs provided by Amazon’s Alexa Skills Kit for developers.

“Thousands of developers utilize these APIs and have created ‘skills’ that enable automation and increase efficiency within enterprises,” she said.

Taking use cases beyond productivity tools

While enterprise virtual assistants could be deployed in any type of company looking to boost productivity, Alexa for Business has already seen deployments in industries such as hospitality.

Wynn Las Vegas is equipping its rooms with Amazon Echo devices, which are managed with Alexa for Business, Goertz said. Guests of the hotel chain can use voice commands, called skills, to turn on the lights, close the blinds or order room service.

Another industry that could see adoption of virtual assistants is healthcare. Currently, Alexa for Business supports audio-only devices. But the platform could potentially support devices with a camera and display that could add video conferencing and telemedicine capabilities, Goertz said.

Alexa for Business also has the potential to disrupt the huddle room market by turning Echo devices into stand-alone conference phones, Srinivasan said.

Amazon Echo prices range from $50 to $200, and the most recent generation of devices offers improved audio quality. The built-in virtual assistant with Alexa for Business and developer ecosystem fills a gap that exists in the conference phone market, she wrote in a blog post.

“Amazon is well-positioned to grab this opportunity much ahead of Microsoft Cortana, Google Assistant and Apple’s Siri,” she said.

How State Bank of India, a 215-year-old bank, hit refresh to become a modern workplace – Microsoft News Center India

It is a big day for Rachna Gupta, an Assistant Manager at State Bank of India (SBI). After dropping her 11-year-old daughter at school, she hurries to the Mayur Vihar Metro station for her daily hour-long commute to Chandni Chowk. Her thoughts are preoccupied with the upcoming presentation. “Will there be any last-minute hiccups?” she nervously wonders.

Her smartphone pings as she exits from the Chandni Chowk Metro station and hails a cycle rickshaw. It is an email from her team asking for changes in her presentation. Unlike earlier, when she’d have to wait to reach her desk to get any work done, Gupta opens the PowerPoint file from OneDrive on her phone. As the rickshaw snakes through the narrow lanes of the original walled city of old Delhi, she makes the changes and shares the new file with her team.

“For customers, banking has transformed completely. But technology has also made the life of employees’ smoother, and tension-free,” she says as she gets off at the 200-year-old heritage building that was once a palace belonging to Begum Samru. The building is a fitting location for SBI, which also traces its roots to pre-Independence India, with the formation of the Bank of Calcutta in 1802.

Gupta is one of the 263,000 employees at SBI, who are reaping the benefits of a modern workplace. This is the story of how one of the oldest banks in the world embarked upon a digital transformation journey for more than quarter-of-a-million employees, who serve over half-a-billion customer accounts.

The challenge

As with any organization with the scale and size of SBI, different technology solutions implemented at different times meant that most solutions were not talking to each other while some were archaic.

Emails were taking hours to get delivered and employees had to clear their inboxes frequently to ensure they had enough space for new emails to come. Documents were being shared as attachments with multiple versions getting created. There was no Global Address List and most employees could not access the official intranet network on their phones – they had to be on their desk, in front of their PC to do anything.

Things were even more difficult for senior employees, who had to travel to various branches for meetings as they could not remotely check-in. Teams in different branches, even in big cities, had no seamless way to connect with each other.

It was becoming clear to the senior leadership that for a behemoth like SBI, workplace transformation was essential to fulfill the service expected by its 500 million customer accounts, and to retain its leadership position in the super-competitive banking sector in India.

“It was vital to take digital transformation to our workforce – empowering them to become digitally enabled. We had to ‘Hit Refresh’,” says Arundhati Bhattacharya, who recently retired as SBI’s Chairperson and who had initiated the digital transformation at the bank.

The need of the hour

SBI required a solution that would address three key challenges. An integrated platform approach for all productivity requirements; simple to apply use-cases allowing for employees even in remote areas to be included; and an agile platform leveraging cloud that would give the bank the scale of operations required. Additionally, it was important to give employees a seamless experience across various devices like mobiles, tablets and PCs.

Microsoft’s modern cloud technology fitted perfectly with SBI’s vision of a contemporary workplace. Microsoft assessed the work environment and created role-based access profiles, including all employees from Chief Managers to Officers, clerical staff and other categories of employees, even those who have retired. Today, 263,000 SBI employees are on Office 365, using services like Exchange Online, OneDrive, Skype for Business, SharePoint Online, among others as a part of their daily work tools.

“The mobility solution from Microsoft helps us exercise continuous control over all the enabled devices. Our employees will now experience a modern digital workplace platform that will empower them to collaborate effectively from any device anywhere (Android, iOS, Mac and Windows), provide an integrated experience and reduce complexity,” says Mrityunjay Mahapatra, Deputy Managing Director & Chief Information Officer, SBI.

“We are excited about our partnership with Microsoft. As India’s economy continues to grow, the BFSI sector needs to be well-equipped to address the dynamic market pressures and evolving industry needs. It has become imperative to transform technologically to sustain a competitive edge. A digital culture shift, designing a modern workplace that harnesses digital intelligence and enabling mobility are key aspects. Microsoft’s cutting-edge technology is helping us lead this digital transformation by making it part of our DNA,” says Rajnish Kumar, Chairman, SBI.

On ground zero

At SBI’s Chandni Chowk branch, in the meanwhile, Gupta’s presentation went off better than expected. “The work in banking is the same, but the way we have do it is different. Everyone’s productivity has increased. We are doing the same work with more enthusiasm,” says a beaming Gupta.

As she prepares to leave for home to spend time with her daughter and family, we ask her what her ideal workplace of the future would be. She closes her eyes, “There may be a point in future, where I may close my eyes and imagine myself in office and I will be in office.”

Well, who knows what the future has in store?