Apr
17
2019
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The Exit: an AI startup’s McPivot

Five years ago, Dynamic Yield was courting an investment from The New York Times as it looked to shift how publishers paywalled their content. Last month, Chicago-based fast food king McDonald’s bought the Israeli company for $300 million, a source told TechCrunch, with the purpose of rethinking how people order drive-thru chicken nuggets.

The pivot from courting the grey lady to the golden arches isn’t as drastic as it sounds. In a lot of ways, it’s the result of the company learning to say “no” to certain customers. At least, that’s what Bessemer’s Adam Fisher tells us.

The Exit is a new series at TechCrunch. It’s an exit interview of sorts with a VC who was in the right place at the right time but made the right call on an investment that paid off. 

Fisher

Fisher was Dynamic Yield founder Liad Agmon’s first call when he started looking for funds from institutional investors. Bessemer bankrolled the bulk of a $1.7 million funding round which valued the startup at $5 million pre-money back in 2013. The firm ended up putting about $15 million into Dynamic Yield, which raised ~$85 million in total from backers including Marker Capital, Union Tech Ventures, Baidu and The New York Times.

Fisher and I chatted at length about the company’s challenging rise and how Israel’s tech scene is still being underestimated. Fisher has 11 years at Bessemer under his belt and 14 exits including Wix, Intucell, Ravello and Leaba.

The interview has been edited for length and clarity. 


Saying “No”

Lucas Matney: So, right off the bat, how exactly did this tool initially built for publishers end up becoming something that McDonalds wanted?

Adam Fisher: I mean, the story of Dynamic Yield is unique. Liad, the founder and CEO, he was an entrepreneur in residence in our Herzliya office back in 2011. I’d identified him earlier from his previous company, and I just said, ‘Well, that’s the kind of guy I’d love to work with.’ I didn’t like his previous company, but there was something about his charisma, his technology background, his youth, which I just felt like “Wow, he’s going to do something interesting.” And so when he sold his previous company, coincidentally to another Chicago based company called Sears, I invited him and I think he found it very flattering, so he joined us as an EIR.

And really only at the very end of his residence did he come up with this idea that would become Dynamic Yield. He came about it very much focused on the problem he saw with publishers being outwitted by ad buyers. He felt like all the big publishers really didn’t understand their digital businesses, didn’t understand their users, didn’t understand how performance ad buying was working, and he began to build a product that could dynamically optimize a publisher’s website to maximize revenue, hence the yield … the dynamic yield.

But very quickly, we told him, ‘That’s interesting, but we’re not sure how big that market is. And, you know it’s not always great to sell to those kind of weak customers. Sometimes they’re weak for a reason.’

Apr
14
2019
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Diving into Google Cloud Next and the future of the cloud ecosystem

Extra Crunch offers members the opportunity to tune into conference calls led and moderated by the TechCrunch writers you read every day. This week, TechCrunch’s Frederic Lardinois and Ron Miller offered up their analysis on the major announcements that came out of Google’s Cloud Next conference this past week, as well as their opinions on the outlook for the company going forward.

Google Cloud announced a series of products, packages and services that it believes will improve the company’s competitive position and differentiate itself from AWS and other peers. Frederic and Ron discuss all of Google’s most promising announcements, including its product for managing hybrid clouds, its new end-to-end AI platform, as well as the company’s heightened effort to improve customer service, communication, and ease-of-use.

“They have all of these AI and machine learning technologies, they have serverless technologies, they have containerization technologies — they have this whole range of technologies.

But it’s very difficult for the average company to take these technologies and know what to do with them, or to have the staff and the expertise to be able to make good use of them. So, the more they do things like this where they package them into products and make them much more accessible to the enterprise at large, the more successful that’s likely going to be because people can see how they can use these.

…Google does have thousands of engineers, and they have very smart people, but not every company does, and that’s the whole idea of the cloud. The cloud is supposed to take this stuff, put it together in such a way that you don’t have to be Google, or you don’t have to be Facebook, you don’t have to be Amazon, and you can take the same technology and put it to use in your company”

Image via Bryce Durbin / TechCrunch

Frederic and Ron dive deeper into how the new offerings may impact Google’s market share in the cloud ecosystem and which verticals represent the best opportunity for Google to win. The two also dig into the future of open source in cloud and how they see customer use cases for cloud infrastructure evolving.

For access to the full transcription and the call audio, and for the opportunity to participate in future conference calls, become a member of Extra Crunch. Learn more and try it for free. 

Apr
11
2019
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Rasa raises $13M led by Accel for its developer-friendly open-source approach to chatbots

Conversational AI and the use of chatbots have been through multiple cycles of hype and disillusionment in the tech world. You know the story: first you get a launch from the likes of Apple, Facebook, Microsoft, Amazon, Google or any number of other companies, and then you get the many examples of how their services don’t work as intended at the slightest challenge. But time brings improvements and more focused expectations, and today a startup that has been harnessing all those learnings is announcing funding to take to the next level its own approach to conversational AI.

Rasa, which has built an open-source platform for third parties to design and manage their own conversational (text or voice) AI chatbots, is today announcing that it has raised $13 million in a Series A round of funding led by Accel, with participation from Basis Set Ventures, Greg Brockman (co-founder & CTO OpenAI), Daniel Dines (founder & CEO UiPath) and Mitchell Hashimoto (co-founder & CTO Hashicorp).

Rasa was founded in Berlin, but with this round, it will be moving its headquarters to San Francisco, with a plan to hire more people there in sales, marketing and business development; and to continue its tech development with its roadmap including plans to expand the platform to cover images, too.

The company was founded 2.5 years ago, by co-founder/CEO Alex Weidauer’s own admission “when chatbot hype was at its peak.”

Rasa itself was not immune to it, too: “Everyone wanted to automate conversations, and so we set out to build something, too,” he said. “But we quickly realised it was extremely hard to do and that the developer tools were just not there yet.”

Rather than posing an insurmountable roadblock, the shortcomings of chatbots became the problem that Rasa set out to fix.

Alan Nichols, the co-founder who is now the CTO, is an AI PhD, not in natural language as you might expect, but in machine learning.

“What we do is more is address this as a mathematical, machine learning problem rather than one of language,” Weidauer said. Specifically, that means building a model that can be used by any company to tap its own resources to train their bots, in particular with unstructured information, which has been one of the trickier problems to solve in conversational AI.

At a time when many have raised concerns about who might “own” the progress of artificial intelligence, and specifically the data that goes into building these systems, Rasa’s approach is a refreshing one.

Typically, when an organization wants to build an AI chatbot either to interact with customers or to run something in the back end of their business, their developers most commonly opt for third-party cloud APIs that have restrictions on how they can be customized, or they build their own from scratch — but if the organization is not already a large tech company, it will be challenged to have the human or other resources to execute this.

Rasa underscores an emerging trend for a strong third contender. The company has built a stack of tools that it has open-sourced, meaning that anyone can (and thousands of developers do) use it for free, with a paid enterprise version that includes extra tools, including customer support, testing and training tools, and production container deployment. (It’s priced depending on size of organization and usage.)

Importantly, whichever package is used, the tools run on a company’s own training data; and the company can ultimately host their bots wherever they choose, which have been some of the unique selling points for those using Rasa’s platform, when they are less interested in working with organizations that might also be competitors.

Adobe’s new AI assistant for searching on Adobe Stock, which has some 100 million images, was built on Rasa.

“We wanted to give our users an AI assistant that lets them search with natural language commands,” said Brett Butterfield, director of software development at Adobe, in a statement. “We looked at several online services, and, in the end, Rasa was the clear choice because we were able to host our own servers and protect our user’s data privacy. Being able to automate full conversations and the fact it is open source were key elements for us.”

Other customers include Parallon and TalkSpace, Zurich and Allianz, Telekom and UBS.

Open source has become big business in the last several years, and so a startup that’s built an AI platform that has a very direct application in the enterprise built on it presents an obvious attraction for VCs.

“Automation is the next battleground for the enterprise, and while this is a very difficult space to win, especially for unstructured information like text and voice, we are confident Rasa has what it takes given their impressive adoption by developers,” said Andrei Brasoveanu, partner at Accel, in a statement.

“Existing solutions don’t let in-house developer teams control their own automation destiny. Rasa is applying commercial open source software solutions for AI environments similarly to what open source leaders such as Cloudera, Mulesoft, and Hashicorp have done for others.”

Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

Apr
10
2019
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Google Cloud takes aim at verticals starting with new set of tools for retailers

Google might not be Adobe or Salesforce, but it has a particular set of skills, which fit nicely with retailer requirements and can over time help improve the customer experience, even if that means just simply making sure the website or app is running, even on peak demand. Today, at Google Cloud Next, the company showed off a package of solutions as an example its vertical strategy.

Just this morning, the company announced a new phase of its partnership with Salesforce, where it’s using its contact center AI tools and chatbot technology in combination with Salesforce data to produce a product that plays to each company’s strengths and helps improve the customer service experience.

But Google didn’t stop with a high profile partnership. It has a few tricks of its own for retailers, starting with the classic retailer Black Friday kind of scenario. The easiest way to explain the value of cloud scaling is to look at a retail event like Black Friday when you know servers are going to be bombarded with traffic.

The cloud has always been good at scaling up for those kind of events, but it’s not perfect, as Amazon learned last year when it slowed down on Prime Day. Google wants to help companies avoid those kinds of disasters because a slow or down website translates into lots of lost revenue.

The company offers eCommerce Hosting, designed specifically for online retailers, and it is offering a special premium program, so retailers get “white glove treatment with technical architecture reviews and peak season operations support…” according to the company. In other words, it wants to help these companies avoid disastrous, money-losing results when a site goes down due to demand.

In addition, Google is offering real-time inventory tools, so customers and clerks can know exactly what stock is on hand, and it’s applying its AI expertise to this, as well with tools like Google Contact Center AI solution to help deliver better customer service experiences or Cloud Vision technology to help customers point their cameras at a product and see similar or related products. They also offer Recommendations AI, a tool, that says, if you bought these things, you might like this too, among other tools.

The company counts retail customers like Shopify and Ikea. In addition, the company is working with SI partners like Accenture, CapGemini and Deloitte and software partners like Salesforce, SAP and Tableau.

All of this is about creating a set of services created specifically for a given vertical to help that industry take advantage of the cloud. It’s one more way for Google Cloud to bring solutions to market and help increase its marketshare.

Apr
10
2019
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Salesforce and Google want to build a smarter customer service experience

Anyone who has dealt with bad customer service has felt frustration with the lack of basic understanding of who you are as a customer and what you need. Google and Salesforce feel your pain, and today the two companies expanded their partnership to try and create a smarter customer service experience.

The goal is to combine Salesforce’s customer knowledge with Google’s customer service-related AI products and build on the strengths of the combined solution to produce a better customer service experience, whether that’s with an agent or a chatbot..

Bill Patterson, executive vice president for Salesforce Service Cloud, gets that bad customer service is a source of vexation for many consumers, but his goal is to change that. Patterson points out that Google and Salesforce have been working together since 2017, but mostly on sales- and marketing-related projects. Today’s announcement marks the first time they are working on a customer service solution together.

For starters, the partnership is looking at the human customer service agent experience.”The combination of Google Contact Center AI, which highlights the language and the stream of intelligence that comes through that interaction, combined with the customer data and the business process information that that Salesforce has, really makes that an incredibly enriching experience for agents,” Patterson explained.

The Google software will understand voice and intent, and have access to a set of external information like weather or news events that might be having an impact on the customers, while Salesforce looks at the hard data it stores about the customer such as who they are, their buying history and previous interactions.

The companies believe that by bringing these two types of data together, they can surface relevant information in real time to help the agent give the best answer. It may be the best article or it could be just suggesting that a shipment might be late because of bad weather in the area.

Customer service agent screen showing information surfaced by intelligent layers in Google and Salesforce

The second part of the announcement involves improving the chatbot experience. We’ve all dealt with rigid chatbots, who can’t understand your request. Sure, it can sometimes channel your call to the right person, but if you have any question outside the most basic ones, it tends to get stuck, while you scream “Operator! I said OPERATOR!” (Or at least I do.)

Google and Salesforce are hoping to change that by bringing together Einstein, Salesforce’s artificial intelligence layer and Google Natural Language Understanding (NLU) in its Google Dialogflow product to better understand the request, monitor the sentiment and direct you to a human operator before you get frustrated.

Patterson’s department, which is on a $3.8 billion run rate, is poised to become the largest revenue producer in the Salesforce family by the end of the year. The company itself is on a run rate over $14 billion.

“So many organizations just struggle with primitives of great customer service and experience. We have a lot of passion for making everyday interaction better with agents,” he said. Maybe this partnership will bring some much needed improvement.

Apr
09
2019
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Accenture announces intent to buy French cloud consulting firm

As Google Cloud Next opened today in San Francisco, Accenture announced its intent to acquire Cirruseo, a French cloud consulting firm that specializes in Google Cloud intelligence services. The companies did not share the terms of the deal.

Accenture says that Cirruseo’s strength and deep experience in Google’s cloud-based artificial intelligence solutions should help as Accenture expands its own AI practice. Google TensorFlow and other intelligence solutions are a popular approach to AI and machine learning, and the purchase should help give Accenture a leg up in this area, especially in the French market.

“The addition of Cirruseo would be a significant step forward in our growth strategy in France, bringing a strong team of Google Cloud specialists to Accenture,” Olivier Girard, Accenture’s geographic unit managing director for France and Benelux said in a statement.

With the acquisition, should it pass French regulatory muster, the company would add a team of 100 specialists trained in Google Cloud and G Suite to the an existing team of 2,600 Google specialists worldwide.

The company sees this as a way to enhance its artificial intelligence and machine learning expertise in general, while giving it a much stronger market placement in France in particular and the EU in general.

As the company stated, there are some hurdles before the deal becomes official. “The acquisition requires prior consultation with the relevant works councils and would be subject to customary closing conditions,” Accenture indicated in a statement. Should all that come to pass, then Cirruseo will become part of Accenture.

Apr
09
2019
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Talk key takeaways from Google Cloud Next with TechCrunch writers

Google’s Cloud Next conference is taking over the Moscone Center in San Francisco this week and TechCrunch is on the scene covering all the latest announcements.

Google Cloud already powers some of the world’s premier companies and startups, and now it’s poised to put even more pressure on cloud competitors like AWS with its newly-released products and services. TechCrunch’s Frederic Lardinois will be on the ground at the event, and Ron Miller will be covering from afar. Thursday at 10:00 am PT, Frederic and Ron will be sharing what they saw and what it all means with Extra Crunch members on a conference call.

Tune in to dig into what happened onstage and off and ask Frederic and Ron any and all things cloud or enterprise.

To listen to this and all future conference calls, become a member of Extra Crunch. Learn more and try it for free.

Mar
29
2019
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ServiceNow teams with Workplace by Facebook on service chatbot

One of the great things about enterprise chat applications, beyond giving employees a common channel to communicate, is the ability to integrate with other enterprise applications. Today, Workplace, Facebook’s enterprise collaboration and communication application, and ServiceNow announced a new chatbot to make it easier for employees to navigate a company’s help desks inside Workplace Chat.

The beauty of the chatbot is that employees can get answers to common questions whenever they want, wherever they happen to be. The Workplace-ServiceNow integration happens in Workplace Chat and can can involve IT or HR help desk scenarios. A chatbot can help companies save time and money, and employees can get answers to common problems much faster.

Previously, getting these kind of answers would have required navigating multiple systems, making a phone call or submitting a ticket to the appropriate help desk. This approach provides a level of convenience and immediacy.

Companies can brainstorm common questions and answers and build them in the ServiceNow Virtual Agent Designer. It comes with some standard templates, and doesn’t require any kind of advanced scripting or programming skills. Instead, non-technical end users can adapt pre-populated templates to meet the needs, language and workflows of an individual organization.

Screenshot: ServiceNow

This is all part of a strategy by Facebook to integrate more enterprise applications into the tool. In May at the F8 conference, Facebook announced 52 such integrations from companies like Atlassian, SurveyMonkey, HubSpot and Marketo (the company Adobe bought in September for $4.75 billion).

This is part of a broader enterprise chat application trend around making these applications the center of every employee’s work life, while reducing task switching, the act of moving from application to application. This kind of integration is something that Slack has done very well and has up until now provided it with a differentiator, but the other enterprise players are catching on and today’s announcement with ServiceNow is part of that.

Mar
28
2019
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Vizion.ai launches its managed Elasticsearch service

Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.

Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.

Vizion.ai GM and VP Geoff Tudor

“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.

What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.

There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.

He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”

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