Apr
17
2018
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Enterprise AI will make the leap — who will reap the benefits?

This year, artificial intelligence will further elevate the enterprise by transforming the way we work, securing digital assets, increasing collaboration and ushering in a new era of AI-powered innovation. Enterprise AI is rapidly moving beyond hype and into reality, and is primed to become one of the most consequential technological segments. Although startups have already realized AI’s power in redefining industries, enterprise executives are still in the process of understanding how it will transform their business and reshape their teams across all departments.

Throughout the past year, early adopting businesses of all sizes and industries began to reap benefits. AI applications with AI-powered capabilities introduced opportunities to change the way the enterprise engaged customers, segmented markets, assessed sales leads and engaged influencers. Enterprises are on the edge of taking this a step further because of the amount of knowledge and tools leveraging the potential of AI within their entire organization.

“New breakthroughs in AI, enabled by new hardware architectures, will create new intelligent business models for enterprises,” says Nigel Toon, co-founder and CEO at U.K.-based Graphcore. “Companies that can build an initial knowledge model and launch an initial intelligent service or product, then use this first product to capture new data and improve the knowledge model on a continuing basis, will quickly create clear class-leading products and services that competitors will struggle to keep up with.”

The category is evolving, and large companies are finding distinct ways to innovate. They can uniquely tap into decades of industry experience to develop horizontal AI, built for specific industries like healthcare, financial services, automotive, retail and more. These implementations, though, require deep industry expertise and industry-specific design, training, monitoring, security and implementation to meet the high-stakes IT requirements of global organizations.

“In 2018, AI is entering the enterprise. I believe we will see many enterprises adopt AI technology, but the (few) leaders will be those that can align AI with their strategic business goals,” says Ronny Fehling, associate director of Gamma Artificial Intelligence at BCG.

2018: AI will start separating the winners from the losers

Early industry successes (and failures) proved AI’s inevitability, but also the reality that wide-scale adoption would come through incremental progress only. This year, we’ll see AI move from influencing product or business functions to an organization-wide AI strategy. Expect the winners to move fast and remain nimble to keep implementing off-the-shelf and proprietary AI.

The companies that win the AI talent war will gain exponential advantages, given the category’s rapid growth.

Hans-Christian Boos, CEO and founder of Germany-based Arago, adds: “2018 will be a make or break year for enterprise and the established economy in general. I believe AI is the only viable path for innovation, new business models and digital disruption in companies from the industrial era. General AI can enable these enterprises to finally make use of the only advantage they have in the battle against new business models and giants from the Silicon Valley, or rather giants from the new age of knowledge based business models.”

The AI talent challenge

A boon in enterprise AI will also mean a further shortage of talent. Industries like telecommunications, financial services and manufacturing will feel the talent squeeze the most. The companies that win the AI talent war will gain exponential advantages, given the category’s rapid growth.

Hence, enterprises will try to attract talent by offering a powerful vision, a track record of product success, a bench of early client implementations and the potential to impact the masses. It’s about developing high-functioning and reliable solutions that become a new foundation for clients.

Developers and data scientists, however, are only the beginning. Winning enterprises must adopt their organizational structures that attract a new generation of product managers, sales, marketing, communications and other delivery teams that understand AI. This requires an informed, passionate and forward-thinking group of professionals that will help customers understand the future of work and customer engagement powered by AI.

AI adoption and employee training

Digital transformation, powered in large part by new AI capabilities, requires enterprises to understand how to extract data and utilize data-driven intelligence. Data is one of the greatest assets and essentials in maximizing the value in an AI application, yet data is often underutilized and misunderstood. Executives must establish teams and hold individuals across departments accountable for the successful and ongoing implementation of digital tools that extract full value from available internal and external data.

This transformation into an AI-native organization requires it to hire, train and re-skill all levels of employees, and provide the resources for individuals to adopt AI-powered disciplines that enhance their performance. Most workforce, from top to bottom, should be encouraged to rethink and evolve their role by incorporating new digital tools, often enabled by AI itself.

Expect AI and other digital technologies to become more prevalent in all business disciplines, not only at the application layer, as Vishal Chatrath, co-founder and CEO of U.K.-based Prowler.io emphasises. “Decision-making in enterprise is dominated by expert-systems that are born obsolete. The AI tools available till now that rely on deep-neural nets which are great for classification problems (identifying cats, dogs, words etc.) are not really fit for purpose for decision-making in large, complex and dynamic environments, because they are very data inefficient (needs millions of data points) and effectively act like black-boxes. 2018 will see Enterprise AI move beyond classification to decision-making.”

What’s next

However, the spotlight will shine on data governance as businesses adjust entire departments and workflows around data. In turn, data management and integrity will be an essential component of success as consumers and enterprises gain greater awareness about how companies use customers’ data. This opens a large field of opportunities, but also will require transparency in how companies are using, sharing and building applications on top of customer data to ensure trust.

“Every single industry will be enhanced with AI in the coming years. In the last years there was a lot of foundation work on gathering standardized data and now we can start to use some of the advanced AI techniques to bring huge efficiency and quality gains to enterprise companies,” says Rasmus Rothe, co-founder and CTO of Germany-based research lab and venture builder Merantix. “Enterprises should therefore thoroughly analyze their business units to understand how AI can help them to improve. Partnering with external AI experts instead of trying to build everything yourself is often more capital efficient and also leads to better results.”

The shift toward AI-native enterprises is in a defining phase. The pie of the AI-enabled market will continue to grow and everyone has an opportunity to take a slice. Enterprises need to quickly leverage their assets and extract the value of their data as AI algorithms themselves will become the most valuable part when data has become a commodity. The question is, who will move first, and who will have the biggest appetite.

Apr
17
2018
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Google Cloud releases Dialogflow Enterprise Edition for building chat apps

Building conversational interfaces is a hot new area for developers. Chatbots can be a way to reduce friction in websites and apps and to give customers quick answers to commonly asked questions in a conversational framework. Today, Google announced it was making Dialogflow Enterprise Edition generally available. It had previously been in beta.

This technology came to them via the API.AI acquisition in 2016. Google wisely decided to change the name of the tool along the way, giving it a moniker that more closely matched what it actually does. The company reports that hundreds of thousands of developers are using the tool already to build conversational interfaces.

This isn’t just an all-Google tool, though. It works across voice interface platforms, including Google Assistant, Amazon Alexa and Facebook Messenger, giving developers a tool to develop their chat apps once and use them across several devices without having to change the underlying code in a significant way.

What’s more, with today’s release the company is providing increased functionality and making it easier to transition to the enterprise edition at the same time.

“Starting today, you can combine batch operations that would have required multiple API calls into a single API call, reducing lines of code and shortening development time. Dialogflow API V2 is also now the default for all new agents, integrating with Google Cloud Speech-to-Text, enabling agent management via API, supporting gRPC, and providing an easy transition to Enterprise Edition with no code migration,” Dan Aharon, Google’s product manager for Cloud AI, wrote in a company blog post announcing the tool.

The company showed off a few new customers using Dialogflow to build chat interfaces for their customers, including KLM Royal Dutch Airlines, Domino’s and Ticketmaster.

The new tool, which is available today, supports more than 30 languages and as a generally available enterprise product comes with a support package and service level agreement (SLA).

Apr
17
2018
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Resy rolls out a new suite of tools for restaurants

Resy launched in the summer of 2014 with a simple premise: If you want a premium reservation at a restaurant on short notice, you should be able to pay for it. Four years and 160 markets later, Resy has changed a lot since then.

But today, the company is about to change things up even more.

This morning, Resy has announced a brand new suite of tools for restaurants, including a new inventory management system called ResyFly.

As it stands now, restaurants have two options when it comes to inventory management for their reservations. They can choose a slot system, where diners are seated at 6pm, 8pm and 10pm, or they can opt for a flex system, where they take reservations as they’re called in and build the night’s reservations based off what comes in first.

Unfortunately, most restaurants have to choose between these two systems, as there are no inventory management systems that offer the ability to do both, according to Resy.

ResyFly uses Resy’s troves of data to determine the best way for restaurants to eliminate gaps in their inventory throughout a given night, taking into account things like date, time, weather and even the average time spent eating at a given restaurant. The tool gives restaurants the ability to schedule different floor plans, reservation grids and hours of operation for special days like Valentine’s Day.

Alongside ResyFly, the company is also introducing Business Intelligence, a window into important information like KPIs, revenue and ratings with third-party information from platforms like Foursquare layered in and integrated with POS software providers to offer real-time revenue reporting.

But sometimes you want direct feedback from the customer. To that end, Resy is launching Resy Surveys, which gives a restaurant the opportunity to send a custom survey to customers about their experience. Resy is also integrating with Upserve, giving Resy’s restaurant partners insights into their guests’ preferences and favorite dishes, as well as info on dining companions, frequency of bookings and historical spend.

And while Resy is focused on refining the product, the company is also focused on growth. That’s why Resy has announced the launch of Resy Global Service, which lets Resy distribute inventory to partners like Airbnb. (It’s worth noting that Airbnb led Resy’s $13 million funding round in 2017.)

Finally, Resy is working on a new membership loyalty program called Resy Select, which will launch at the end of the month. Resy Select is an invite-only program that gives restaurants insights into Resy’s hungriest users, and gives those users benefits such as exclusive booking windows, priority waitlist, early access tickets to events and other exclusive experiences like meeting the chef or touring the kitchen.

Resy books more than 1 million reservations on the platform each week. The company no longer charges users for reservations, but rather charges restaurants by feature, instead of cover, with three tiers ranging from $189/month to $899/month. That said, the company is not yet self-serve on the restaurant side, but founder and CEO Ben Leventhal said the team is thinking about introducing it in the future.

“The key challenge and key opportunity is to do everything we can to make the right choices about what we build and the order we build it in,” said Leventhal. “Our goal is to stay focused on restaurants, as a significant amount of the tech we build is built in conjunction with our restaurant partners.”

Apr
09
2018
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Juro grabs $2M to take the hassle out of contracts

UK startup Juro, which is applying a “design centric approach” and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital.

Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise’s co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com).

Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round — with Point Nine Capital only joining “late” in the day.

“We actually could have strung it out to Series A,” he says of the funding that’s being announced now. “But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that it made sense to do a round now rather than wait for the A. The whole legal industry is undergoing radical change and we want to be leading it.”

Juro’s SaaS product is an integrated contracts workflow that combines contract creation, e-signing and commenting capabilities with AI-powered contract analytics.

Its general focus is on customers that have to manage a high volume of contacts — such as marketplaces.

The 2016-founded startup is not breaking out any customer numbers yet but says its client list includes the likes of Estee Lauder, Deliveroo and Nested. And Mabey adds that “most” of its demand is coming from enterprise at this point, noting it has “several tech unicorns and Fortune 500 companies in trial”.

While design is clearly a major focus — with the startup deploying clean-looking templates and visual cues to offer a user-friendly ‘upgrade’ on traditional legal processes — the machine learning component is its scalable, value-added differentiator to serve the target b2b users by helping them identify recurring sticking points in contract negotiations and keep on top of contract renewals.

Mabey tells TechCrunch the new funding will be used to double down on development of the machine learning component of the product.

“We’re not the first to market in contract management by about 25 years,” he says with a smilie. “So we have always needed to prove out our vision of why the incumbents are failing. One part of this is clunky UX and we’ve succeeded so far in replacing legacy providers through better design (e.g. we replace DocuSign at 80% of our customers).

“But the thing we and our investors are really excited about is not just helping businesses with contract workflow but helping them understand their contract data, auto-tag contracts, see pattens in negotiations and red flag unusual contract terms.”

While this machine learning element is where he sees Juro cutting out a competitive edge in an existing and established market, Mabey concedes it takes “quite a lot of capital to do well”. Hence taking more funding now.

“We need a level of predictive accuracy in our models that risk averse lawyers can get comfortable with and that’s a big ask!” he says.

Specifically, Juro will be using the funding to hire data scientists and machine learning engineers — building out the team at both its London and Riga offices. “We’re doing it like crazy,” adds Mabey. “For example, we just hired from the UK government Digital Service the data scientist who delivered the first ML model used by the UK government (on the gov.uk website).

“There is a huge opportunity here but great execution is key and we’re building a world class team to do it. It’s a big bet to grow revenue as quickly as we are and do this kind of R&D but that’s just what the market is demanding.”

Juro’s HQ remains in London for now, though Mabey notes its entire engineering team is based in the EU — between Riga, Amsterdam and Barcelona — “in part to avoid ‘Brexit risk’”.

“Only 27% of the team is British and we have customers operating in 12 countries — something I’m quite proud of — but it does leave us rather exposed. We’re very open minded about where we will be based in the future and are waiting to hear from the government on the final terms of Brexit,” he says when asked whether the startup has any plans to Brexit to Berlin.

“We always look beyond the UK for talent: if the government cannot provide certainty to our Romanian product designer (ex Kalo, Entrepreneur First) that she can stay in the UK post Brexit without risking a visa application, tbh it makes me less bullish on London!”

Apr
05
2018
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Suplari raises $10.3M Series A round to bring AI to procurement

Procurement isn’t the most exciting topic in the world, but for large businesses, it’s an area where inefficiencies can quickly affect the bottom line. Simply getting a complete view of all of the products and services that a company buys is a challenge in itself, though, which in turn makes it hard to find savings, ensure compliance with company policy or government regulations or detect potential fraud. Suplari wants to change this by bringing its AI systems to bear on this problem.

The company today announced that it has raised a $10.3 million Series A round led by Shasta Ventures. Existing investors Madrona Ventures and Amplify Partners also joined this round, as well as new investors Two Sigma Ventures and Workday Ventures.

Suplari uses advanced artificial intelligence on top of existing enterprise systems to proactively uncover the highest-value opportunities to pursue and empower the CFO or Chief Procurement Officer to unlock savings and profit that can be invested in growth, innovation, and their people,” said Suplari CEO and co-founder Nikesh Parekh in today’s announcement.

The company’s cloud-based service allows businesses to analyze all of their procurement data across platforms and formats. This data can include contracts, purchasing data, product usage information and data from corporate credit card accounts.

A number of Fortune 1000 customers have already signed up for the service and Supplari argues that it has helped its customers save software licensing fees by 33 percent and consolidate $200 million in professional service and temporary labor suppliers.

Apr
03
2018
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Zendesk hits $500M run rate, launches enterprise content management platform

Over the last several years, Zendesk has been making the transition from a company that caters mostly to small businesses to one with larger enterprise customers — and their revenue reflects that. The company announced it has crossed the $.5 billion annual run rate since its last earning report in February. It also announced a new enterprise content management product specifically geared for large customer service organizations.

The company was just shy of the goal after its most recent earnings report (pdf) with $123.4 million for the quarter. They say they have since passed that goal, but have not announced it until now, based on revenue that closed March 31, 2018. The company is projecting between $555 and $565 million in revenue for fiscal 2018, according to its last earnings report. When you consider that when the company went public in 2014, it was at $100 million in annual revenue, reaching a half billion dollars in 4 years is significant.

Zendesk reports that 40 percent of its revenue now comes from larger enterprise customers, which they define as 100 seats or more. The company is predicting it will cross the $1 billion run rate by some time in 2020.

“When we IPOed, our run rate was $100 million. We had great momentum, but we were seen as SMB scaling to mid market. To reach a half a billion dollars shows momentum for building up enterprise market and enterprise products,” Adrian McDermott, Zendesk’s president of products told TechCrunch.

As for the new product, it’s called Guide Enterprise and it’s designed to provide those larger customer service organizations with a knowledge base and a content management platform for editorial planning and review. The idea is to empower customer service reps to write up solutions to problems they encounter and build up that knowledge base as part of the natural act of doing their jobs.

Zendesk Guide Enterprise. Photo: Zendesk

That gives organizations a couple of advantages. First of all, the reps can find their fellow employees’ notes and not have to reinvent the wheel every time, and the notes and articles they write can pass through editorial review and become part of the permanent knowledge base.

When customers hit the site or app, they can access solutions to common problems before having to talk to a human. The platform also includes reminders to check the content regularly so the knowledge base stays fresh and stale content is removed.

Finally, the company is applying AI to the problem. The artificial intelligence component can review the corpus of information currently available in the entire knowledge base and identify gaps in content that the company might want to add, allowing for proactive content creation.

The content management idea isn’t new to Zendesk. McDermott says they shipped the first content management product years ago, but what’s different is that this is geared to larger organizations and that the AI piece allows for some automation of this process. “The new workflow brings rich AI concepts like content analytics into the publishing flow,” he said.

Mar
30
2018
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Asana introduces Timeline, lays groundwork for AI-based monitoring as the “team brain” for productivity

When workflow management platform Asana announced a $75 million round of funding in January led by former Vice President Al Gore’s Generation Investment Management, the startup didn’t give much of an indication of what it planned to do with the money, or what it was that won over investors to a new $900 million valuation (a figure we’ve now confirmed with the company).

Now, Asana is taking off the wraps on the next phase of its strategy. This week, the company announced a new feature it’s calling Timeline — composite, visual, and interactive maps of the various projects assigned to different people within a team, giving the group a wider view of all the work that needs to be completed, and how the projects fit together, mapped out in a timeline format.

Timeline is a new premium product: Asana’s 35,000 paying users will be able to access it for no extra charge. Those who are among Asana’s millions of free users will have to upgrade to the premium tier to access it.

The Timeline that Asana is making is intended to be used in scenarios like product launches, marketing campaigns and event planning, and it’s not a matter of a new piece of software where you have to duplicate work, but each project automatically becomes a new segment on a team’s Timeline. Viewing projects through the Timeline allows users to identify if different segments are overlapping and adjust them accordingly.

Perhaps one of the most interesting aspects of the Timeline, however, is that it’s the first instalment of a bigger strategy that Asana plans to tackle over the next year to supercharge and evolve its service, making it the go-to platform for helping keep you focused on work, when you’re at work.

While Asana started out as a place where people go to manage the progress of projects, its ambition going forward is to become a platform that, with a machine-learning engine at the back end, will aim to manage a team’s and a company’s wider productivity and workload, regardless of whether they are actively in the Asana app or not.

“The long term vision is to marry computer intelligence with human intelligence to run entire companies,” Asana co-founder Justin Rosenstein said in an interview. “This is the vision that got investors excited.”

The bigger product — the name has not been revealed — will include a number of different features. Some that Rosenstein has let me see in preview include the ability for people to have conversations about specific projects — think messaging channels but less dynamic and more contained. And it seems that Asana also has designs to move into the area of employee monitoring: it has also been working on a widget of sorts that installs on your computer and watches you work, with the aim of making you more efficient.

“Asana becomes a team brain to keep everyone focused,” said Rosenstein.

Given that Asana’s two co-founders, Dustin Moskovitz and Rosenstein, previously had close ties to Facebook — Moskovitz as a co-founder and Rosenstein as its early engineering lead — you might wonder if Timeline and the rest of its new company productivity engine might be bringing more social elements to the table (or desk, as the case may be).

In fact, it’s quite the opposite.

Rosenstein may have to his credit the creation of the “like” button and other iconic parts of the world’s biggest social network, but he has in more recent times become a very outspoken critic of the distracting effects of services like Facebook’s. It’s part of a bigger trend hitting Silicon Valley, where a number of leading players have, in a wave of mea culpa, turned against some of the bigger innovations particularly in social media.

Some have even clubbed together to form a new organization called the Center for Humane Technology, whose motto is “Reversing the digital attention crisis and realigning technology with humanity’s best interests.” Rosenstein is an advisor, although when I tried to raise the issue of the backlash that has hit Facebook on multiple fronts, he responded pretty flatly, “It’s not something I want to talk about right now.” (That’s what keeping focussed is all about, I guess.)

Asana, essentially, is taking the belief that social can become counterproductive when you have to get something done, and applying it to the enterprise environment.

This is an interesting twist, given that one of the bigger themes in enterprise IT over the last several years has been how to turn business apps and software more “social” — tapping into some of the mechanics and popularity of social networking to encourage employees to collaborate and communicate more with each other even when (as is often the case) they are not in the same physical space.

But social working might not be for everyone, all the time. Slack, the wildly popular workplace chat platform that interconnects users with each other and just about every enterprise and business app, is notable for producing “a gazillion notifications”, in Rosenstein’s words, leading to distraction from actually getting things done. “I’m not saying services like Slack can’t be useful,” he explained. (Slack is also an integration partner of Asana’s.) “But companies are realising that, to collaborate effectively, they need more than communication. They need content and work management. I think that Slack has a lot of useful purposes but I don’t know if all of it is good all the time.”

The “team brain” role that Asana envisions may be all about boosting productivity by learning about you and reducing distraction — you will get alerts, but you (and presumably the brain) prioritise which ones you get, if any at all — but interestingly it has kept another feature characteristic of a lot of social networking services: amassing data about your activities and using that to optimise engagement. As Rosenstein described it, Asana will soon be able to track what you are working on, and how you work on it, to figure out your working patterns.

The idea is that, by using machine learning algorithms, you can learn what a person does quickly, and what might take longer, to help plan that person’s tasks better, and ultimately make that person more productive. Eventually, the system will be able to suggest to you what you should be working on and when.

All of that might sound like music to managers’ ears, but for some, employee monitoring programs sound a little alarming for how closely they monitor your every move. Given the recent wave of attention that social media services have had for all the data they collect, it will be interesting to see how enterprise services like this get adopted and viewed. It’s also not at all clear how these sorts of programs will sit in respect of new directives like GDPR in Europe, which put into place a new set of rules for how any provider of an internet service needs to inform users of how their data is used, and any data collecting needs to have a clear business purpose.

Still, with clearly a different aim in mind — helping you work better — the end could justify the means for some, not just for bosses, but for people who might feel overwhelmed with what is on their work plate every day. “When you come in in the morning, you might have a list [many things] to do today,” Rosenstein said. “We take over your desktop to show the one thing you need to do.”

Mar
30
2018
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IoT devices could be next customer data frontier

At the Adobe Summit this week in Las Vegas, the company introduced what could be the ultimate customer experience construct, a customer experience system of record that pulls in information, not just from Adobe tools, but wherever it lives. In many ways it marked a new period in the notion of customer experience management, putting it front and center of the marketing strategy.

Adobe was not alone, of course. Salesforce, with its three-headed monster, the sales, marketing and service clouds, was also thinking of a similar idea. In fact, they spent $6.5 billion dollars last week to buy MuleSoft to act as a data integration layer to access  customer information from across the enterprise software stack, whether on prem, in the cloud, or inside or outside of Salesforce. And they announced the Salesforce Integration Cloud this week to make use of their newest company.

As data collection takes center stage, we actually could be on the edge of yet another data revolution, one that could be more profound than even the web and mobile were before it. That is…the Internet of Things.

Here comes IoT

There are three main pieces to that IoT revolution at the moment from a consumer perspective. First of all, there is the smart speaker like the Amazon Echo or Google Home. These provide a way for humans to interact verbally with machines, a notion that is only now possible through the marriage of all this data, sheer (and cheap) compute power and the AI algorithms that fuel all of it.

Next, we have the idea of a connected car, one separate from the self-driving car. Much like the smart speaker, humans can interact with the car, to find directions and recommendations and that leaves a data trail in its wake. Finally we, have sensors like iBeacons sitting in stores, providing retailers with a world of information about a customer’s journey through the store — what they like or don’t like, what they pick up, what they try on and so forth.

There are very likely a host of other categories too, and all of this information is data that needs to be processed and understood just like any other signals coming from customers, but it also has unique characteristics around the volume and velocity of this data — it is truly big data with all of the issues inherent in processing that amount of data.

The means it needs to be ingested, digested and incorporated into that central customer record-keeping system to drive the content and experiences you need to create to keep your customers happy — or so the marketing software companies tell us, at least. (We also need to consider the privacy implications of such a record, but that is the subject for another article.)

Building a better relationship

Regardless of the vendor, all of this is about understanding the customer better to provide a central data gathering system with the hope of giving people exactly what they want. We are no longer a generic mass of consumers. We are instead individuals with different needs, desires and requirements, and the best way to please us they say, is to understand us so well, that the brand can deliver the perfect experience at exactly the right moment.

Photo: Ron Miller

That involves listening to the digital signals we give off without even thinking about it. We carry mobile, connected computers in our pockets and they send out a variety of information about our whereabouts and what we are doing. Social media acts as a broadcast system that brands can tap into to better understand us (or so the story goes).

Part of what Adobe, Salesforce and others can deliver is a way to gather that information, pull it together into his uber record keeping system and apply a level of machine and learning and intelligence to help further the brand’s ultimate goals of serving a customer of one and delivering an efficient (and perhaps even pleasurable) experience.

Getting on board

At an Adobe Summit session this week on IoT (which I moderated), the audience was polled a couple of times. In one show of hands, they were asked how many owned a smart speaker and about three quarters indicated they owned at least one, but when asked how many were developing applications for these same devices only a handful of hands went up. This was in a room full of marketers, mind you.

Photo: Ron Miller

That suggests that there is a disconnect between usage and tools to take advantage of them. The same could be said for the other IoT data sources, the car and sensor tech, or any other connected consumer device. Just as we created a set of tools to capture and understand the data coming from mobile apps and the web, we need to create the same thing for all of these IoT sources.

That means coming up with creative ways to take advantage of another interaction (and data collection) point. This is an entirely new frontier with all of the opportunity involved in that, and that suggests startups and established companies alike need to be thinking about solutions to help companies do just that.

Mar
27
2018
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Pure Storage teams with Nvidia on GPU-fueled Flash storage solution for AI

As companies gather increasing amounts of data, they face a choice over bottlenecks. They can have it in the storage component or the backend compute system. Some companies have attacked the problem by using GPUs to streamline the back end problem or Flash storage to speed up the storage problem. Pure Storage wants to give customers the best of both worlds.

Today it announced, Airi, a complete data storage solution for AI workloads in a box.

Under the hood Airi starts with a Pure Storage FlashBlade, a storage solution that Pure created specifically with AI and machine learning kind of processing in mind. NVidia contributes the pure power with four NVIDIA DGX-1 supercomputers, delivering four petaFLOPS of performance with NVIDIA ® Tesla ® V100 GPUs. Arista provides the networking hardware to make it all work together with Arista 100GbE switches. The software glue layer comes from the NVIDIA GPU Cloud deep learning stack and Pure Storage AIRI Scaling Toolkit.

Photo: Pure Storage

One interesting aspect of this deal is that the FlashBlade product operates as a separate product inside of the Pure Storage organization. They have put together a team of engineers with AI and data pipeline understanding with the focus inside the company on finding ways to move beyond the traditional storage market and find out where the market is going.

This approach certainly does that, but the question is do companies want to chase the on-prem hardware approach or take this kind of data to the cloud. Pure would argue that the data gravity of AI workloads would make this difficult to achieve with a cloud solution, but we are seeing increasingly large amounts of data moving to the cloud with the cloud vendors providing tools for data scientists to process that data.

If companies choose to go the hardware route over the cloud, each vendor in this equation — whether Nvidia, Pure Storage or Arista — should benefit from a multi-vendor sale. The idea ultimately is to provide customers with a one-stop solution they can install quickly inside a data center if that’s the approach they want to take.

Mar
26
2018
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The Linux Foundation launches a deep learning foundation

Despite its name, the Linux Foundation has long been about more than just Linux. These days, it’s a foundation that provides support to other open source foundations and projects like Cloud Foundry, the Automotive Grade Linux initiative and the Cloud Native Computing Foundation. Today, the Linux Foundation is adding yet another foundation to its stable: the LF Deep Learning Foundation.

The idea behind the LF Deep Learning Foundation is to “support and sustain open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.”

The founding members of the new foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa and ZTE. Others will likely join in the future.

“We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems,” said Jim Zemlin, executive director of The Linux Foundation.

The foundation’s first official project is the Acumos AI Project, a collaboration between AT&T and Tech Mahindra that was already hosted by the Linux Foundation. Acumos AI is a platform for developing, discovering and sharing AI models and workflows.

Like similar Linux Foundation-based organizations, the LF Deep Learning Foundation will offer different membership levels for companies that want to support the project, as well as a membership level for non-profits. All LF Deep Learning members have to be Linux Foundation members, too.

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