Qlik’s Top Researcher Describes New Ways for Human Cognition and Augmented Intelligence to Join Forces

Transcript of a discussion on how the latest research and products bring the power of people and machine intelligence closer together to make analytics consumable across more business processes.
 

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Qlik. 

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you’re listening to BriefingsDirect. Our next business intelligence (BI) trends discussion explores the latest research and products that bring the power of people and machine intelligence closer together.

Gardner

As more data becomes available to support augmented intelligence -- and the power of analytics platforms increasingly goes to where the data is -- the next stage of value is in how people can interact with the results.

Stay with us now as we examine the latest strategies for not only visualizing data-driven insights but making them conversational and even presented through a form of storytelling.

To learn more about making the consumption and refinement of analytics delivery an interactive exploit open to more types of users, we are now joined by Elif Tutuk, Head of Research at Qlik. Welcome to BriefingsDirect.

Elif Tutuk: Thank you. It’s a great pleasure to be here.

Gardner: Strides have been made in recent years for better accessing data and making it available to analytics platforms, but the democratization of the results and making insights consumable by more people is just beginning. What are the top technical and human interaction developments that will broaden the way that people interact differently with analytics?

Trusted data for all

Tutuk: That’s a great question. We are doing a lot of research in this area in terms of creating new user experiences where we can bring about more data literacy and help improve people’s understanding of reading, analyzing, and arguing with the data.

Tutuk

In terms of the user experience, a conversational aspect has a big impact. But we also believe that it’s not only through the conversation, especially when you want to understand data. The visual exploration part should also be there. We are creating experiences that combine the unique nature, language, and visual exploration capabilities of a human. We think that it is the key to building a good collaboration between the human and the machine.

Gardner: As a result, are we able to increase the number and types of people impacted by data by going directly to them -- rather than through a data scientist or an IT department? How are the interaction elements broadening this to a wider clientele?

Tutuk: The idea is to make analysis available from C-level users to the business end users.

If you want to broaden the use of analytics and lower the barrier, you also need to make sure that the data machines and the system are governed and trusted.

Our enterprise data management strategy therefore becomes important for our Cognitive Engine technology. We are combining those two so that the machines use a governed data source to provide trusted information.

Gardner: What strikes me as quite new now is more interaction between human cognition and augmented intelligence. It’s almost a dance. It creates new types of insights, and new and interesting things can happen.

How do you attain the right balance in the interactions between human cognition and AI?

Tutuk: It is about creating experiences between what the human is good at -- perception, awareness, and ultimately decision-making -- and what the machine technology is good at, such as running algorithms on large amounts of data.

As the machine serves insights to the user, it needs to first create trust about what data is used and the context around it. Without the context you cannot really take that insight and make an action on it. And this is where the human part comes in, because as humans you have the intuition and the business knowledge to understand the context of the insight. Then you can explore it further by being augmented. Our vision is for making decisions by leveraging that [machine-generated] insight.

Gardner: In addition to the interactions, we are hearing about the notion of storytelling. How does that play a role in ways that people get better analytics outcomes?

Storytelling insights support

Tutuk: We have been doing a lot of research and thinking in this area because today, in the analytics market, AI is becoming robust. These technologies are developing very well. But the challenge is that most of the technologies provide results like a black box. As a user, you don’t know why the machine is making a suggestion and insight. And that creates a big trust issue.

To have greater adoption of the AI results, you need to create an experience that builds trust, and that is why we are looking at one of the most effective and timeless forms of communication that humans use, which is storytelling.

To have greater adoption of the AI results, you need to create an experience that builds trust, and that is why we are looking at one of the most effective and timeless forms of communication that humans use, which is storytelling.

So we are creating unique experiences where the machine generates an insight. And then, on the fly, we create data stories generated by the machine, thereby providing more context. As a user, you can have a great narrative, but then that narrative is expanded with insightful visualizations. From there, based on what you gain from the story, we are also looking at capabilities where you can explore further.

And in that third step you are still being augmented, but able to explore. It is user-driven. That is where you start introducing human intuition as well.

And when you think about the machine first surfacing insights, then getting more context with the data story, and lastly going to exploration -- all three phases can be tied together in a seamless flow. You don’t lose the trust of the human. The context becomes really important. And you should be able to carry the context between all of the stages so that the user knows what the context is. Adding the human intuition expands that context.

Gardner: I really find this fascinating because we are talking not just about problem-solution, we are talking about problem-solution-resolution, then readjusting and examining the problem for even more solution and resolution. We are also now, of course, in the era of augmented reality, where we can bring these types of data analysis outputs to people on a factory floor, wearing different types of visual and audio cue devices.

So the combination of augmented reality, augmented intelligence, storytelling, and bringing it out to the field strikes me as something really unprecedented. Is that the case? Are we charting an entirely new course here?

Tutuk: Yes, I think so. It’s an exciting time for us. I am glad that you pointed out the augmented reality because it’s another research area that we are looking at. One of the research projects we have done augments people on retail store floors, the employees.

The idea is, if you are trying to do shelf arrangement, for example, we can provide them information -- right when they look at the product – about that product and what other products are being sold together. Then, right away at that moment, they are being augmented and they will make a decision. It’s an extremely exciting time for us, yes.

Gardner: It throws the idea of batch-processing out the window. You used to have to run the data, come up with report, and then adjust your inventory. This gets directly to the interaction with the end-consumer in mind and allows for entirely new types of insights and value.

Tutuk: As part of that project, we also allow for being able to pin things on the space. So imagine that you are in a warehouse, looking at a product, and you develop an interesting insight. Now you can just pin it on the space on that product. And as you do that on different products, you can take a step back, take a look, and discover different insights on the product.

The idea is having a tray that you carry with you, like your own analytics coming with you, and when you find something interesting that matches with the tray – with, for example, the product that you are looking at -- you can pin it. It’s like having a virtual board with products and with the analytics being augmented reality.

Gardner: We shouldn’t lose track that we are often talking about billions of rows of data supporting this type of activity, and that new data sets can be brought to bear on a problem very rapidly.

Putting data in context with AI²

Tutuk: Exactly, and this is where our Associative Big Data Index technology comes into play. We are bringing the power of our unique associative engine to massive datasets. And, of course, with the latest acquisition that we have done with Attunity, we gain data streaming and real-time analytics.

Gardner: Digging down to the architecture to better understand how it works, the Qlik cognitive engine increasingly works with context awareness. I have heard this referred to as AI². What do you all mean by AI²?

Tutuk: AI² is augmented intelligence powered by an associative index. So augmented intelligence is our vision for the use of artificial intelligence, where the goal is to augment the human, not to replace them. And now we are making sure that we have the unique component in terms of our associative index as well.

Allow me to explain the advantage of the associative index. One of the challenges for using AI and machine learning is bias. The system has bias because it doesn’t have access to all of the data.

With the associative index, our technology provides a system with visibility to all of the data at any point, including the data that is associated with your context, and also what's not associated. That part provides a good learning source for the algorithms that we are using.

For example, you maybe are trying to make a prediction for churn analysis in the western sales region. Normally if you select the west region the system -- if the AI is running with a SQL or relational database -- it will only have access to that slice of data. It will never have the chance to learn what is not associated, such as the customers from the other regions, to look at their behavior.

With the associative index, our technology provides a system with visibility to all of the data at any point, including the data that is associated with your context, and also what’s not associated. And that part that is not associated provides a good learning source for the algorithms that we are using. This is where we are differentiating ourselves and providing unique insights to our users that will be very hard to get with an AI tool that works only with SQL and relational data structures.

Gardner: Not only is Qlik is working on such next-generation architectures, you are also undertaking a larger learning process with the Data Literacy Program to, in a sense, make the audience more receptive to the technology and its power.

Please explain, as we move through this process of making intelligence accessible and actionable, how we can also make democratization of analytics possible through education and culturally rethinking the process.

Data literacy drives cognitive engine

Tutuk: Data literacy is important to help make people able to read, analyze, and argue with the data. We have an open program -- so you don’t have to be a Qlik customer. It’s now available. Our goal is to make everyone data literate. And through that program you can firstly understand the data literacy level of your organization. We have some free tests you can take, and then based on that need we have materials to help people to become data literate.

As we build the technology, our vision with AI is to make the analytics platform much easier to use in a trusted way. So that’s why our vision is not only focused on prescriptive probabilities, it’s focused on the whole analytics workflow -- from data acquisition, to visualization, exploration, and sharing. You should always be augmented by the system.

We are at just the beginning of our cognitive framework journey. We introduced Qlik Cognitive Engine last year, and since then we have exposed more features from the framework in different parts of the product, such as on the data preparation. Our users, for example, get suggestions on the best way of associating data coming from different data sources.

And, of course, on the visualization part and dashboarding, we have visual insights, where the Cognitive Engine right away suggests insights. And now we are adding natural language capabilities on top of that, so you can literally conversationally interact with the data. More things will be coming on that.

Gardner: As an interviewer, as you can imagine, I am very fond of the Socratic process of questioning and then reexamining. It strikes me that what you are doing with storytelling is similar to a Socratic learning process. You had an acquisition recently that led to the Qlik Insight Bot, which to me is like interviewing your data analysis universe, and then being able to continue to query, and generate newer types of responses.

Tell us about how the Qlik Insight Bot works and why that back-and-forth interaction process is so powerful.

Tutuk: We believe any experiences you have with the system should be in the form of a conversation, it should have a conversational nature. There’s a unique thing about human-to-human conversation – just as we are having this conversation. I know that we are talking about AI and analytics. You don’t have to tell me that as we are talking. We know we are having a conversation about that.

That is exactly what we have achieved with the Qlik Insight Bot technology. As you ask questions to the Qlik Insight Bot, it is keeping track of the context. You don’t have to reiterate the context and ask the question with the context. And that is also a unique differentiator when you compare that experience to just having a search box, because when you use Google, it doesn’t, for example, keep the context. So that’s one of the important things for us to be able to keep -- to have a conversation that allows the system to keep the context.

Gardner: Moving to the practical world of businesses today, we see a lot of use of Slack and Microsoft Teams. As people are using these to collaborate and organize work, it seems to me that presents an opportunity to bring in some of this human-level cognitive interaction and conversational storytelling.

Do you have any examples of organizations implementing this with things like Slack and Teams?

Collaborate to improve processes

Tutuk: You are on the right track. The goal is to provide insights wherever and however you work. And, as you know, there is a big trend in terms of collaboration. People are using Slack instead of just emailing, right?

So, the Qlik Insight Bot is available with an integration to Microsoft Teams, Slack, and Skype. We know this is where the conversations are happening. If you are having a conversation with a colleague on Slack and neither of the parties know the answer, then right away they can just continue their conversation by including Qlik Insight Bot and be powered with the Cognitive Engine insights that they can make decisions with right away.

Gardner: Before we close out, let’s look to the future. Where do you take this next, particularly in regard to process? We also hear a lot these days about robotic process automation (RPA). There is a lot of AI being applied to how processes can be improved and allowing people to do what they do best.

The Qlik insight Bot is available with an integration to Microsoft Teams, Slack, and Skype. We know this is where the conversations are happening. They can just continue their conversation by including the Qlik Insight Bot and be powered with the Cognitive Engine insights that they can make decisions with.

Do you see an opportunity for the RPA side of AI and what you are all doing with augmented intelligence and the human cognitive interactions somehow reinforcing one another?

Tutuk: We realized with RPA processes that there are challenges with the data there as well. It’s not only about the human and the interaction of the human with the automation. Every process automation generates data. And one of the things that I believe is missing right now is to have a full view on the full automation process. You may have 65 different robots automating different parts of a process, but how do you provide the human a 360-degree view of how the process is performing overall?

A platform can gather associated data from different robots and then provide the human a 360-degree view of what’s going on in the processes. Then that human can make decisions, again, because as humans we are very good at making decisions by seeing nonlinear connections. Feeding the right data to us to be able to use that capability is very important, and our platform provides that.

Gardner: Elif, for organizations looking to take advantage of all of this, what should they be doing now to get ready? To set the foundation, build the right environment, what should enterprises be doing to be in the best position to leverage and exploit these capabilities in the coming years?

Replace repetitive processes

Tutuk: Look for the processes that are repetitive. Those aren’t the right places to use unique human capabilities. Determine those repetitive processes and start to replace them with machines and automation.

Then make sure that whatever data that they are feeding into this is trustable and comes from a governed environment. The data generated by those processes should be governed as well. So have a governance mechanism around those processes.

I also believe there will be new opportunities for new jobs and new ideas that the humans will be able to start doing. We are at an exciting new era. It’s a good time to find the right places to use human intelligence and creativity just as more automation will happen for repetitive tasks. It’s an incredible and exciting time. It will be great.

Gardner: These strike me as some of the most powerful tools ever created in human history, up there with first wheel and other things that transformed our existence and our quality of life. It is very exciting.

I’m afraid we will have to leave it there. You have been listening to a sponsored BriefingsDirect discussion on the latest research and products that bring the power of people and augmented intelligence closer than ever.

And we have learned about strategies for not only visualizing data-driven insights but making them conversational -- and even presented through storytelling. So a big thank you to our guest, Elif Tutuk, Head of Research at Qlik. Thank you very much.

Tutuk: Thank you very much.

Gardner: And a big thank you to our audience as well for joining this BriefingsDirect business intelligence trends discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host throughout this series of Qlik-sponsored BriefingsDirect interviews.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Qlik.

 

Transcript of a discussion on how the latest research and products bring the power of people and machine intelligence closer together to make analytics consumable across more business processes. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.

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Using AI to Solve Data and IT Complexity -- And Better Enable AI

A discussion on how the rising tidal wave of data must be better managed, and how new tools are emerging to bring artificial intelligence to the rescue.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of the Innovator podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on the latest in IT innovation.

Gardner

Our next discussion focuses on why the rising tidal wave of data must be better managed, and how new tools are emerging to bring artificial intelligence (AI) to the rescue. Stay with us now as we learn how the latest AI innovations improve both data and services management across a cloud deployment continuum -- and in doing so set up an even more powerful way for businesses to exploit AI.

To learn how AI will help conquer complexity to allow for higher abstractions of benefits from across all sorts of data for better analysis, please join me in welcoming Rebecca Lewington, Senior Manager of Innovation Marketing at Hewlett Packard Enterprise (HPE). Welcome to BriefingsDirect, Rebecca.

Rebecca Lewington: Hi, Dana. It’s very nice to talk to you.

Gardner: We have been talking about massive amounts of data for quite some time. What’s new about data buildup that requires us to look to AI for help?

Lewington: Partly it is the sheer amount of data. IDC’s Data Age Study predicts the global data sphere will be 175 zettabytes by 2025, which is a rather large number. That’s what, 1 and 21 zeros? But we have always been in an era of exploding data.

Lewington

Yet, things are different. One, it’s not just the amount of data; it’s the number of sources the data comes from. We are adding in things like mobile devices, and we are connecting factories’ operational technologies to information technology (IT). There are more and more sources.

Also, the time we have to do something with that data is shrinking to the point where we expect everything to be real-time or you are going to make a bad decision. An autonomous car, for example, might do something bad. Or we are going to miss a market or competitive intelligence opportunity.

So it’s not just the amount of data -- but what you need to do with it that is challenging.

Gardner: We are also at a time when Al and machine learning (ML) technologies have matured. We can begin to turn them toward the data issue to better exploit the data. What is new and interesting about AI and ML that make them more applicable for this data complexity issue?

Data gets smarter with AI

Lewington: A lot of the key algorithms for AI were actually invented long ago in the 1950s, but at that time, the computers were hopeless relative to what we have today; so it wasn’t possible to harness them.

For example, you can train a deep-learning neural net to recognize pictures of kittens. To do that, you need to run millions of images to train a working model you can deploy. That’s a huge, computationally intensive task that only became practical a few years ago. But now that we have hit that inflection point, things are just taking off.

Gardner: We can begin to use machines to better manage data that we can then apply to machines. Does that change the definition of AI?

Lewington: The definition of AI is tricky. It’s malleable, depending on who you talk to. For some people, it’s anything that a human can do. To others, it means sophisticated techniques, like reinforcement learning and deep learning.

How to Remove Complexity
From Multicloud and Hybrid IT

One useful definition is that AI is what you use when you know what the answer looks like, but not how to get there.

Traditional analytics effectively does at scale what you could do with pencil and paper. You could write the equations to decide where your data should live, depending on how quickly you need to access it.

But with AI, it’s like the kittens example. You know what the answer looks like, it’s trivial for you to look at the photograph and say, “That is a cat in the picture.” But it’s really, really difficult to write the equations to do it. But now, it’s become relatively easy to train a black box model to do that job for you.

Gardner: Now that we are able to train the black box, how can we apply that in a practical way to the business problem that we discussed at the outset? What is it about AI now that helps better manage data? What's changed that gives us better data because we are using AI?

The heart of what makes AI work is good data; the right data, in the right place, with the right properties you can use to train a model, which you can then feed new data into to get results that you couldn't get otherwise.

Lewington: It’s a circular thing. The heart of what makes AI work is good data; the right data, in the right place, with the right properties you can use to train a model, which you can then feed new data into to get results that you couldn’t get otherwise.

Now, there are many ways you can apply that. You can apply it to the trivial case of the cat we just talked about. You can apply it to helping a surgeon review many more MRIs, for example, by allowing him to focus on the few that are borderline, and to do the mundane stuff for him.

But, one of the other things you can do with it is use it to manipulate the data itself. So we are using AI to make the data better -- to make AI better.

Gardner: Not only is it circular, and potentially highly reinforcing, but when we apply this to operations in IT -- particularly complexity in hybrid cloud, multicloud, and hybrid IT -- we get an additional benefit. You can make the IT systems more powerful when it comes to the application of that circular capability -- of making better AI and better data management.

AI scales data upward and outward

Lewington: Oh, absolutely. I think the key word here is scale. When you think about data -- and all of the places it can be, all the formats it can be in -- you could do it yourself. If you want to do a particular task, you could do what has traditionally been done. You can say, “Well, I need to import the data from here to here and to spin up these clusters and install these applications.” Those are all things you could do manually, and you can do them for one-off things.

But once you get to a certain scale, you need to do them hundreds of times, thousands of times, even millions of times. And you don’t have the humans to do it. It’s ridiculous. So AI gives you a way to augment the humans you do have, to take the mundane stuff away, so they can get straight to what they want to do, which is coming up with an answer instead of spending weeks and months preparing to start to work out the answer.

Gardner: So AI directed at IT, what some people call AIOps could be an accelerant to this circular advantageous relationship between AI and data? And is that part of what you are doing within the innovation and research work at HPE?

Lewington: That’s true, absolutely. The mission of Hewlett Packard Labs in this space is to assist the rest of the company to create more powerful, more flexible, more secure, and more efficient computing and data architectures. And for us in Labs, this tends to be a fairly specific series of research projects that feed into the bigger picture.

For example, we are now doing the Deep Learning Cookbook, which allows customers to find out ahead of time exactly what kind of hardware and software they are going to need to get to a desired outcome. We are automating the experimenting process, if you will.

And, as we talked about earlier, there is the shift to the edge. As we make more and more decisions -- and gain more insights there, to where the data is created -- there is a growing need to deploy AI at the edge. That means you need a data strategy to get the data in the right place together with the AI algorithm, at the edge. That’s because there often isn’t time to move that data into the cloud before making a decision and waiting for the required action to return.

Once you begin doing that, once you start moving from a few clouds to thousands and millions of endpoints, how do you handle multiple deployments? How do you maintain security and data integrity across all of those devices? As researchers, we aim to answer exactly those questions.

And, further out, we are looking to move the natural learning phase itself to the edge, to do the things we call swarm learning, where devices learn from their environment and each other, using a distributed model that doesn’t use a central cloud at all.

Gardner: Rebecca, given your title is Innovation Marketing Lead, is there something about the very nature of innovation that you have come to learn personally that’s different than what you expected? How has innovation itself changed in the past several years?

Innovation takes time and space 

Lewington: I began my career as a mechanical engineer. For many years, I was offended by the term innovation process, because that’s not how innovation works. You give people the space and you give them the time and ideas appear organically. You can’t have a process to have ideas. You can have a process to put those ideas into reality, to wean out the ones that aren’t going to succeed, and to promote the ones that work.

How to Better Understand
What AI Can do For Your Business

But the term innovation process to me is an oxymoron. And that’s the beautiful thing about Hewlett Packard Labs. It was set up to give people the space where they can work on things that just seem like a good idea when they pop up in their heads. They can work on these and figure out which ones will be of use to the broader organization -- and then it’s full steam ahead.

Gardner: It seems to me that the relationship between infrastructure and AI has changed. It wasn’t that long ago when we thought of business intelligence (BI) as an application -- above the infrastructure. But the way you are describing the requirements of management in an edge environment -- of being able to harness complexity across multiple clouds and the edge -- this is much more of a function of the capability of the infrastructure, too. Is that how you are seeing it, that only a supplier that’s deep in its infrastructure roots can solve these problems? This is not a bolt-on benefit.

Lewington: I wouldn’t say it’s impossible as a bolt-on; it’s impossible to do efficiently and securely as a bolt-on. One of the problems with AI is we are going to use a black box; you don’t know how it works. There were a number of news stories recently about AIs becoming corrupted, biased, and even racist, for example. Those kinds of problems are going to become more common.

And so you need to know that your systems maintain their integrity and are not able to be breached by bad actors. If you are just working on the very top layers of the software, it’s going to be very difficult to attest that what’s underneath has its integrity unviolated.

If you are someone like HPE, which has its fingers in lots of pies, either directly or through our partners, it’s easier to make a more efficient solution.

You need to know that your systems maintain their integrity and are not able to be breached by bad actors. If you are just working on the very top layers of the software, it's going to be very difficult to attest that what's underneath has its integrity unviolated.

Gardner: Is it fair to say that AI should be a new core competency, for not only data scientists and IT operators, but pretty much anybody in business? It seems to me this is an essential core competency across the board.

Lewington: I think that's true. Think of AI as another layer of tools that, as we go forward, becomes increasingly sophisticated. We will add more and more tools to our AI toolbox. And this is one set of tools that you just cannot afford not to have.

Gardner: Rebecca, it seems to me that there is virtually nothing within an enterprise that won't be impacted in one way or another by AI.

Lewington: I think that’s true. Anywhere in our lives where there is an equation, there could be AI. There is so much data coming from so many sources. Many things are now overwhelmed by the amount of data, even if it’s just as mundane as deciding what to read in the morning or what route to take to work, let alone how to manage my enterprise IT infrastructure. All things that are rule-based can be made more powerful, more flexible, and more responsive using AI.

Gardner: Returning to the circular nature of using AI to make more data available for AI -- and recognizing that the IT infrastructure is a big part of that -- what are doing in your research and development to make data services available and secure? Is there a relationship between things like HPE OneView and HPE OneSphere and AI when it comes to efficiency and security at scale?

Let the system deal with IT 

Lewington: Those tools historically have been rules-based. We know that if a storage disk gets to a certain percentage full, we need to spin up another disk -- those kinds of things. But to scale flexibly, at some point that rules-based approach becomes unworkable. You want to have the system look after itself, to identify its own problems and deal with them.

Including AI techniques in things like HPE InfoSight, HPE Clearpath, and network user identity behavior software on the HPE Aruba side allows the AI algorithms to make those tools more powerful and more efficient.

You can think of AI here as another class of analytics tools. It’s not magic, it’s just a different and better way of doing IT analytics. The AI lets you harness more difficult datasets, more complicated datasets, and more distributed datasets.

Gardner: If I’m an IT operator in a global 2000 enterprise, and I’m using analytics to help run my IT systems, what should I be thinking about differently to begin using AI -- rather than just analytics alone -- to do my job better?

Lewington: If you are that person, you don’t really want to think about the AI. You don’t want the AI to intrude upon your consciousness. You just want the tools to do your job.

For example, I may have 1,000 people starting a factory in Azerbaijan, or somewhere, and I need to provision for all of that. I want to be able to put on my headset and say, “Hey, computer, set up all the stuff I need in Azerbaijan.” You don’t want to think about what’s under the hood. Our job is to make those tools invisible and powerful.

Composable, invisible, and insightful 

Gardner: That sounds a lot like composability. Is that another tangent that HPE is working on that aligns well with AI?

Lewington: It would be difficult to have AI be part of the fabric of an enterprise without composability, and without extending composability into more dimensions. It’s not just about being able to define the amount of storage and computer networking with a line of code, it’s about being able to define the amount of memory, where the data is, where the data should be, and what format the data should be in. All of those things – from the edge to cloud – need to be dimensions in composability.

How to Achieve Composability
Across Your Datacenter

You want everything to work behind the scenes for you in the best way with the quickest results, with the least energy, and in the most cost-effective way possible. That’s what we want to achieve -- invisible infrastructure.

Gardner: We have been speaking at a fairly abstract level, but let’s look to some examples to illustrate what we’re getting at when we think about such composability sophistication.

Do you have any concrete examples or use cases within HPE that illustrate the business practicality of what we’ve been talking about?

Lewington: Yes, we have helped a tremendous number of customers either get started with AI in their operations or move from pilot to volume use. A couple of them stand out. One particular manufacturing company makes electronic components. They needed to improve the yields in their production lines, and they didn’t know how to attack the problem. We were able to partner with them to use such things as vision systems and photographs from their production tools to identify defects that only could be picked up by a human if they had a whole lot of humans watching everything all of the time.

This gets back to the notion of augmenting human capabilities. Their machines produce terabytes of data every day, and it just gets turned away. They don’t know what to do with it.

We began running some research projects with them to use some very sophisticated techniques, visual autoencoders, that allow you, without having a training set, to characterize a production line that is performing well versus one that is on the verge of moving away from the sweet spot. Those techniques can fingerprint a good line and also identify when the lines go just slightly bad. In that case, a human looking at line would think it was working just perfectly.

This takes the idea of predictive maintenance further into what we call prescriptive maintenance, where we have a much more sophisticated view into what represents a good line and what represents a bad line. Those are couple of examples for manufacturing that I think are relevant.

Gardner: If I am an IT strategist, a Chief Information Officer (CIO) or a Chief Technology Officer (CTO), for example, and I’m looking at what HPE is doing -- perhaps at the HPE Discover conference -- where should I focus my attention if I want to become better at using AI, even if it’s invisible? How can I become more capable as an organization to enable AI to become a bigger part of what we do as a company?

The new company man is AI

Lewington: For CIOs, their most important customers these days may be developers and increasingly data scientists, who are basically developers working with training models as opposed to programs and code. They don’t want to have to think about where that data is coming from and what it’s running on. They just want to be able to experiment, to put together frameworks that turn data into insights.

It’s very much like the programming world, where we’ve gradually abstracted things from bare-metal, to virtual machines, to containers, and now to the emerging paradigm of serverless in some of the walled-garden public clouds. Now, you want to do the same thing for that data scientist, in an analogous way.

Today, it’s a lot of heavy lifting, getting these things ready. It’s very difficult for a data scientist to experiment. They know what they want. They ask for it, but it takes weeks and months to set up a system so they can do that one experiment. Then they find it doesn’t work and move on to do something different. And that requires a complete re-spin of what’s under the hood.

Now, using things like software from the recent HPE BlueData acquisition, we can make all of that go away. And so the CIO’s job becomes much simpler because they can provide their customers the tools they need to get their work done without them calling up every 10 seconds and saying, “I need a cluster, I need a cluster, I need a cluster.”

That’s what a CIO should be looking for, a partner that can help them abstract complexity away, get it done at scale, and in a way that they can both afford and that takes the risk out. This is complicated, it’s daunting, and the field is changing so fast.

Gardner: So, in a nutshell, they need to look to the innovation that organizations like HPE are doing in order to then promulgate more innovation themselves within their own organization. It’s an interesting time.

Containers contend for the future 

Lewington: Yes, that’s very well put. Because it’s changing so fast they don’t just want a partner who has the stuff they need today, even if they don’t necessarily know what they need today. They want to know that the partner they are working with is working on what they are going to need five to 10 years down the line -- and thinking even further out. So I think that’s one of the things that we bring to the table that others can’t.

Gardner: Can give us a hint as to what some of those innovations four or five years out might be? How should we not limit ourselves in our thinking when it comes to that relationship, that circular relationship between AI, data, and innovation?

Lewington: It was worth coming to HPE Discover in June, because we talked about some exciting new things around many different options. The discussion about increasing automation abstractions is just going to accelerate.

We are going to get to the point where using containers seems as complicated as bare-metal today and that's really going to help simplify the whole data pipelines thing.

For example, the use of containers, which have a fairly small penetration rate across enterprises, is at about 10 percent adoption today because they are not the simplest thing in the world. But we are going to get to the point where using containers seems as complicated as bare-metal today and that’s really going to help simplify the whole data pipelines thing.

Beyond that, the elephant in the room for AI is that model complexity is growing incredibly fast. The compute requirements are going up, something like 10 times faster than Moore’s Law, even as Moore’s Law is slowing down.

We are already seeing an AI compute gap between what we can achieve and what we need to achieve -- and it’s not just compute, it’s also energy. The world’s energy supply is going up, can only go up slowly, but if we have exponentially more data, exponentially more compute, exponentially more energy, and that’s just not going to be sustainable.

So we are also working on something called Emergent Computing, a super-energy-efficient architecture that moves data around wherever it needs to be -- or not move data around but instead bring the compute to the data. That will help us close that gap.

How to Transform
The Traditional Datacenter

And that includes some very exciting new accelerator technologies: special-purpose compute engines designed specifically for certain AI algorithms. Not only are we using regular transistor-logic, we are using analog computing, and even optical computing to do some of these tasks, yet hundreds of times more efficiently and using hundreds of times less energy. This is all very exciting stuff, for a little further out in the future.

Gardner: I’m afraid we’ll have to leave it there. We have been exploring how the rising tidal wave of data must be better managed and how new tools are emerging to bring AI to the rescue. And we’ve heard how new AI approaches and tools create a virtuous adoption pattern between better data and better analytics, and therefore better business outcomes.

So please join me in thanking our guest, Rebecca Lewington, Senior Manager for Innovation Marketing at HPE. Thank you so much, Rebecca.

Lewington: Thanks Dana, this was fun.

Gardner: And thank you as well to our audience for joining this BriefingsDirect Voice of the Innovator interview. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-sponsored discussions. Thanks again for listening, please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

A discussion on how the rising tidal wave of data must be better managed, and how new tools are emerging to bring artificial intelligence to the rescue. Copyright Interarbor Solutions, LLC, 2005-2019. All rights reserved.

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