Jan
17
2019
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Former Facebook engineer picks up $15M for AI platform Spell

In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former director of Engineering for Facebook AI Research had every intention to keep working on AI, but quickly realized a huge issue.

Unless you’re under the umbrella of one of these big tech companies like Facebook, it can be very difficult and incredibly expensive to get your hands on the hardware necessary to run machine learning experiments.

So he built Spell, which today received $15 million in Series A funding led by Eclipse Ventures and Two Sigma Ventures.

Spell is a collaborative platform that lets anyone run machine learning experiments. The company connects clients with the best, newest hardware hosted by Google, AWS and Microsoft Azure and gives them the software interface they need to run, collaborate and build with AI.

“We spent decades getting to a laptop powerful enough to develop a mobile app or a website, but we’re struggling with things we develop in AI that we haven’t struggled with since the 70s,” said Piantino. “Before PCs existed, the computers filled the whole room at a university or NASA and people used terminals to log into a single main frame. It’s why Unix was invented, and that’s kind of what AI needs right now.”

In a meeting with Piantino this week, TechCrunch got a peek at the product. First, Piantino pulled out his MacBook and opened up Terminal. He began to run his own code against MNIST, which is a database of handwritten digits commonly used to train image detection algorithms.

He started the program and then moved over to the Spell platform. While the original program was just getting started, Spell’s cloud computing platform had completed the test in less than a minute.

The advantage here is obvious. Engineers who want to work on AI, either on their own or for a company, have a huge task in front of them. They essentially have to build their own computer, complete with the high-powered GPUs necessary to run their tests.

With Spell, the newest GPUs from Nvidia and Google are virtually available for anyone to run their tests.

Individual users can get on for free, specify the type of GPU they need to compute their experiment and simply let it run. Corporate users, on the other hand, are able to view the runs taking place on Spell and compare experiments, allowing users to collaborate on their projects from within the platform.

Enterprise clients can set up their own cluster, and keep all of their programs private on the Spell platform, rather than running tests on the public cluster.

Spell also offers enterprise customers a “spell hyper” command that offers built-in support for hyperparameter optimization. Folks can track their models and results and deploy them to Kubernetes/Kubeflow in a single click.

But perhaps most importantly, Spell allows an organization to instantly transform their model into an API that can be used more broadly throughout the organization, or used directly within an app or website.

The implications here are huge. Small companies and startups looking to get into AI now have a much lower barrier to entry, whereas large traditional companies can build out their own proprietary machine learning algorithms for use within the organization without an outrageous upfront investment.

Individual users can get on the platform for free, whereas enterprise clients can get started for $99/month per host you use over the course of a month. Piantino explains that Spell charges based on concurrent usage, so if the customer has 10 concurrent things running, the company considers that the “size” of the Spell cluster and charges based on that.

Piantino sees Spell’s model as the key to defensibility. Whereas many cloud platforms try to lock customers in to their entire suite of products, Spell works with any language framework and lets users plug and play on the platforms of their choice by simply commodifying the hardware. In fact, Spell doesn’t even share with clients which cloud cluster (Microsoft Azure, Google or AWS) they’re on.

So, on the one hand the speed of the tests themselves goes up based on access to new hardware, but, because Spell is an agnostic platform, there is also a huge advantage in how quickly one can get set up and start working.

The company plans to use the funding to further grow the team and the product, and Piantino says he has his eye out for top-tier engineering talent, as well as a designer.

Jan
16
2019
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HyperScience, the machine learning startup tackling data entry, raises $30 million Series B

HyperScience, the machine learning company that turns human readable data into machine readable data, has today announced the close of a $30 million Series B funding round led by Stripes Group, with participation from existing investors FirstMark Capital and Felicis Ventures, as well as new investors Battery Ventures, Global Founders Capital, TD Ameritrade and QBE.

HyperScience launched out of stealth in 2016 with a suite of enterprise products focused on the healthcare, insurance, finance and government industries. The original products were HSForms (which handled data-entry by converting hand-written forms to digital), HSFreeForm (which did a similar function for hand-written emails or other non-form content) and HSEvaluate (which could parse through complex data on a form to help insurance companies approve or deny claims by pulling out all the relevant info).

Now, the company has combined all three of those products into a single product called HyperScience. The product is meant to help companies and organizations reduce their data-entry backlog and better serve their customers, saving money and resources.

The idea is that many of the forms we use in life or in the workplace are in an arbitrary format. My bank statements don’t look the same as your bank statements, and invoices from your company might look different than invoices from my company.

HyperScience is able to take those forms and pipe them into the system quickly and easily, without help from humans.

Instead of charging by seat, HyperScience charges by documents, as the mere use of HyperScience should mean that fewer humans are actually “using” the product.

The latest round brings HyperScience’s total funding to $50 million, and the company plans to use a good deal of that funding to grow the team.

“We have a product that works and a phenomenally good product market fit,” said CEO Peter Brodsky. “What will determine our success is our ability to build and scale the team.”

Jan
14
2019
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Salesforce Commerce Cloud updates keep us shopping with AI-fueled APIs

As people increasingly use their mobile phones and other devices to shop, it has become imperative for vendors to improve the shopping experience, making it as simple as possible, given the small footprint. One way to do that is using artificial intelligence. Today, Salesforce announced some AI-enhanced APIs designed to keep us engaged as shoppers.

For starters, the company wants to keep you shopping. That means providing an intelligent recommendation engine. If you searched for a particular jacket, you might like these similar styles, or this scarf and gloves. That’s fairly basic as shopping experiences go, but Salesforce didn’t stop there. It’s letting developers embed this ability to recommend products in any app, whether that’s maps, social or mobile.

That means shopping recommendations could pop up anywhere developers think it makes sense, like on your maps app. Whether consumers see this as a positive thing, Salesforce says when you add intelligence to the shopping experience, it increases sales anywhere from 7-16 percent, so however you feel about it, it seems to be working.

The company also wants to make it simple to shop. Instead of entering a multi-faceted search, as has been the traditional way of shopping in the past — footwear, men’s, sneakers, red — you can take a picture of a sneaker (or anything you like) and the visual search algorithm should recognize it and make recommendations based on that picture. It reduces data entry for users, which is typically a pain on the mobile device, even if it has been simplified by checkboxes.

Salesforce has also made inventory availability as a service, allowing shoppers to know exactly where the item they want is available in the world. If they want to pick up in-store that day, it shows where the store is on a map and could even embed that into your ridesharing app to indicate exactly where you want to go. The idea is to create this seamless experience between consumer desire and purchase.

Finally, Salesforce has added some goodies to make developers happy, too, including the ability to browse the Salesforce API library and find the ones that make the most sense for what they are creating. This includes code snippets to get started. It may not seem like a big deal, but as companies the size of Salesforce increase their API capabilities (especially with the MuleSoft acquisition), it’s harder to know what’s available. The company has also created a sandboxing capability to let developers experiment and build capabilities with these APIs in a safe way.

The basis of Commerce Cloud is Demandware, the company Salesforce acquired two years ago for $2.8 billion. Salesforce’s intelligence platform is called Einstein. In spite of its attempt to personify the technology, it’s really about bringing artificial intelligence across the Salesforce platform of products, as it has with today’s API announcements.

Jan
08
2019
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Amid a legal fight in LA, IBM’s Weather Company launches hyperlocal weather forecasts globally

While IBM is getting sued by the city of Los Angeles, accusing it of covertly mining user data in the Weather Channel app in the US, it’s testing the waters for another hyperlocal weather feature that — coincidentally — relies on data that it picks up from sensors on app users’ smartphones, among other devices, combined with AI at IBM’s end to help model the information.

Today at CES, the company announced new service called the Global High-Resolution Atmospheric Forecasting System — GRAF for short — a new weather forecasting system that says it will provide the most accurate weather for anywhere in the world, running every hour, and in increments of every three kilometers everywhere by way of crunching around 10 terabytes of data every day.

The new hyperlocal weather data will start to become available in 2019.

This is a key piece of news particularly for the developing world. There has been some effort already to create and use hyperlocal weather information in the US market using things like in-built sensors that can pick up information on, for example, barometric pressure — the very feature that is now the subject of a lawsuit — but there have been fewer efforts to bring that kind of service to a wider, global audience.

“If you’re a farmer in Kenya or Kansas, you will get a way better weather prediction,” said Ginny Rometty, the CEO of IBM, announcing the service today at CES.

She added that other potential end users of the data could include airlines to better predict when a plane might encounter turbulence or other patterns that could affect a flight; insurance companies managing recovery operations and claims around natural disasters; and utility companies monitoring for faults or preparing for severe weather strains on their systems.

Rometty said that the Weather Channel app’s 100 million users — and, in an estimation from Mary Glackin, the Weather Channel’s VP of business solutions, 300 million monthly active users when considering the wider network of places where the data gets used including Weather.com and Weather Underground — will be providing the data “with consent”. Data sourced from businesses will be coming from customers that are partners and are also likely to become users of the data.

That data in turn will be run through IBM’s Power9 supercomputers, the same ones used in the US Department of Energy’s Summit and Sierra  supercomputers, and modelled using suplementary data from the National Center for Atmospheric Research (NCAR).

The news represents a big step change for the Weather Company and for meteorology research, Glackin said in an interview.

“This is going to be the first significant implementation of GPUs at the Weather Company,” she told me. “The weather community has been slow to adopt to technology, but this is providing much improved performance for us, with higher resolutions and a much finer scale and focus of short-term forecasts.”

The new service of providing hyperlocal data also underscores an interesting turn for IBM as it turns its efforts to building the Weather Channel business into a more global operation, and one that helps deliver more business returns for IBM itself.

Glackin said the Weather Channel app was the most-downloaded weather app in India last year, underscoring how it, like other consumer apps, is seeing more growth outside of the US at the moment after already reaching market saturation in its home market.

Saturation, and some controversy. It’s not clear how the lawsuit in LA will play out, but the fact that it’s been filed definitely points to changing opinions and sensibilities when it comes to the use of personal data, and more generally how consumers and authorities are starting to think about how all that data that we are generating every day on our connected devices is getting used.

IBM is by far not the only company, nor the most vilified, when it comes to this issue, but at a time when the company is still trying to capitalise on the potential of how to commercialise the trove of information and customer connections in its wider business network, this will be something that will impact it as well.

Notably, Rometty closed off her keynote today at CES with a few parting words that reference that.

“As we work on these technologies, all that data that we talked about, that ownership, they belong to the user, and with their permission, we use that,” she said, adding, “These technologies also need to be open and explainable.”

Jan
08
2019
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Daily Crunch: The age of quantum computing is here

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 9am Pacific, you can subscribe here:

1. IBM unveils its first commercial quantum computer

The 20-qubit system combines the quantum and classical computing parts it takes to use a machine like this for research and business applications into a single package. While it’s worth stressing that the 20-qubit machine is nowhere near powerful enough for most commercial applications, IBM sees this as the first step towards tackling problems that are too complex for classical systems.

2. Apple’s trillion-dollar market cap was always a false idol

Nothing grows forever, not even Apple. Back in August we splashed headlines across the globe glorifying Apple’s brief stint as the world’s first $1 trillion company, but in the end it didn’t matter. Fast-forward four months and Apple has lost more than a third of its stock value, and last week the company lost $75 billion in market cap in a single day.

3. GitHub Free users now get unlimited private repositories

Starting today, free GitHub users will now get unlimited private projects with up to three collaborators. Previously, GitHub had a caveat for its free users that code had to be public if they didn’t pay for the service.

Photo credit: Chesnot/Getty Images

4. Uber’s IPO may not be as eye-popping as we expected

Uber’s public debut later this year is undoubtedly the most anticipated IPO of 2019, but the company’s lofty valuation (valued by some as high as $120 billion) has some investors feeling uneasy.

5. Amazon is getting more serious about Alexa in the car with Telenav deal

Amazon has announced a new partnership with Telenav, a Santa Clara-based provider of connected car services. The collaboration will play a huge role in expanding Amazon’s ability to give drivers relevant information and furthers the company’s mission to bake Alexa into every aspect of your life.

6. I used VR in a car going 90 mph and didn’t get sick

The future of in-vehicle entertainment could be VR. Audi announced at CES that it’s rolling out a new company called Holoride to bring adaptive VR entertainment to cars. The secret sauce here is matching VR content to the slight movements of the vehicle to help those who often get motion sickness.

7. Verizon and T-Mobile call out AT&T over fake 5G labels

Nothing like some CES drama to start your day. AT&T recently shared a shady marketing campaign that labeled its 4G networks as 5G and rivals Verizon and T-Mobile are having none of it.

Dec
18
2018
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Ex-Googlers meld humans & machines at new cobotics startup Formant

Our distinct skill sets and shortcomings mean people and robots will join forces for the next few decades. Robots are tireless, efficient and reliable, but in a millisecond through intuition and situational awareness, humans can make decisions machine can’t. Until workplace robots are truly autonomous and don’t require any human thinking, we’ll need software to supervise them at scale. Formant comes out of stealth today to “help people speak robot,” says co-founder and CEO Jeff Linnell. “What’s really going to move the needle in the innovation economy is using humans as an empowering element in automation.”

Linnell learned the grace of uniting flesh and steel while working on the movie Gravity. “We put cameras and Sandra Bullock on dollies,” he bluntly recalls. Artistic vision and robotic precision combined to create gorgeous zero-gravity scenes that made audiences feel weightless. Google bought his startup Bot & Dolly, and Linnell spent four years there as a director of robotics while forming his thesis.

Now with Formant, he wants to make hybrid workforce cooperation feel frictionless.

The company has raised a $6 million seed round from SignalFire, a data-driven VC fund with software for recruiting engineers. Formant is launching its closed beta that equips businesses with cloud infrastructure for collecting, making sense of and acting on data from fleets of robots. It allows a single human to oversee 10, 20 or 100 machines, stepping in to clear confusion when they aren’t sure what to do.

“The tooling is 10 years behind the web,” Linnell explains. “If you build a data company today, you’ll use AWS or Google Cloud, but that simply doesn’t exist for robotics. We’re building that layer.”

A beautiful marriage

“This is going to sound completely bizarre,” Formant CTO Anthony Jules warns me. “I had a recurring dream [as a child] in which I was a ship captain and I had a little mechanical parrot on my should that would look at situations and help me decide what to do as we’d sail the seas trying to avoid this octopus. Since then I knew that building intelligent machines is what I would do in this world.”

So he went to MIT, left a robotics PhD program to build a startup called Sapient Corporation that he built into a 4,000-employee public company, and worked on the Tony Hawk video games. He too joined Google through an acquisition, meeting Linnell after Redwood Robotics, where he was COO, got acquired. “We came up with some similar beliefs. There are a few places where full autonomy will actually work, but it’s really about creating a beautiful marriage of what machines are good at and what humans are good at,” Jules tells me.

Formant now has SaaS pilots running with businesses in several verticals to make their “robot-shaped data” usable. They range from food manufacturing to heavy infrastructure inspection to construction, and even training animals. Linnell also foresees retail increasingly employing fleets of robots not just in the warehouse but on the showroom floor, and they’ll require precise coordination.

What’s different about Formant is it doesn’t build the bots. Instead, it builds the reins for people to deftly control them.

First, Formant connects to sensors to fill up a cloud with LiDAR, depth imagery, video, photos, log files, metrics, motor torques and scalar values. The software parses that data and when something goes wrong or the system isn’t sure how to move forward, Formant alerts the human “foreman” that they need to intervene. It can monitor the fleet, sniff out the source of errors, and suggest options for what to do next.

For example, “when an autonomous digger encounters an obstacle in the foundation of a construction site, an operator is necessary to evaluate whether it is safe for the robot to proceed or stop,” Linnell writes. “This decision is made in tandem: the rich data gathered by the robot is easily interpreted by a human but difficult or legally questionable for a machine. This choice still depends on the value judgment of the human, and will change depending on if the obstacle is a gas main, a boulder, or an electrical wire.”

Any single data stream alone can’t reveal the mysteries that arise, and people would struggle to juggle the different feeds in their minds. But not only can Formant align the data for humans to act on, it also can turn their choices into valuable training data for artificial intelligence. Formant learns, so next time the machine won’t need assistance.

The industrial revolution, continued

With rock-star talent poached from Google and tides lifting all automated boats, Formant’s biggest threat is competition from tech giants. Old engineering companies like SAP could try to adapt to the new real-time data type, yet Formant hopes to out-code them. Google itself has built reliable cloud scaffolding and has robotics experience from Boston Dynamics, plus buying Linnell’s and Jules’ companies. But the enterprise customization necessary to connect with different clients isn’t typical for the search juggernaut.

Linnell fears that companies that try to build their own robot management software could get hacked. “I worry about people who do homegrown solutions or don’t have the experience we have from being at a place like Google. Putting robots online in an insecure way is a pretty bad problem.” Formant is looking to squash any bugs before it opens its platform to customers in 2019.

With time, humans will become less and less necessary, and that will surface enormous societal challenges for employment and welfare. “It’s in some ways a continuation of the industrial revolution,” Jules opines. “We take some of this for granted but it’s been happening for 100 years. Photographer — that’s a profession that doesn’t exist without the machine that they use. We think that transformation will continue to happen across the workforce.”

Dec
18
2018
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Box releases Skills, which lets developers apply AI and machine learning to Box content

When you have as much data under management as Box does, you have the key ingredient for artificial intelligence and machine learning, which feeds on copious amounts of data. Box is giving developers access to this data, while letting them choose the AI and machine learning algorithms they want to use. Today, the company announced the general availability of the Box Skills SDK, originally announced at BoxWorks a year ago.

Jeetu Patel, Box’s chief product officer and chief strategy officer, says beta customers have been focusing on use cases specific to each company. They have been pulling information from different classes of content that matter most to them to bring an element of automation to their content management. “If there’s a way to bring a level of automation with machine learning, rather than doing it manually, that would meaningfully change the way that business processes can function,” Patel told TechCrunch.

Among the use cases Box has been seeing with the 300 beta testers is using artificial intelligence to recognize the contents of a photo for the purpose of auto tagging, thereby eliminating the need for humans to do that tagging. Another example is in contract management, where the terms are pulled automatically from the contract, saving the legal team from having to do this.

Where this can get really powerful though is that the Skills SDK can drive a more complex automated workflow inside of Box. If, for example, Skills is driving the creation of automated metadata, that can in turn drive a workflow, Patel said.

Box is providing the means to ingest Box data into a given AI or machine learning algorithm, but instead of trying to create those on its own, it’s been relying on partners that have more specific expertise, such as IBM Watson, Microsoft Azure, Google Cloud Platform and Amazon Web Services. In fact, Box says it is working with dozens of AI and machine learning partners.

For customers that aren’t comfortable doing any of this on their own, Box is also providing a consulting service, where it can come into a customer and help work through a set of requirements and choose the best algorithm for the job.

Dec
13
2018
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New tool uses AI to roll back problematic continuous delivery builds automatically

As companies shift to CI/CD (continuous integration/continuous delivery), they face a problem around monitoring and fixing problems in builds that have been deployed. How do you deal with an issue after moving onto the next delivery milestone? Harness, the startup launched last year by AppDynamics founder Jyoti Bansal, wants to fix that with a new tool called 24×7 Service Guard.

The new tool is designed to help companies working with a continuous delivery process by monitoring all of the builds, regardless of when they were launched. What’s more, the company claims that using AI and machine learning, it can dial back a problematic build to one that worked in an automated fashion, freeing developers and operations to keep working without worry.

The company launched last year with a tool called Continuous Verification to verify that a continuous delivery build got deployed. With today’s announcement, Bansal says the company is taking this to another level to help understand what happens after you deploy.

The tool watches every build, even days after deployment, taking advantage of data from tools like AppDynamics, New Relic, Elastic and Splunk, then using AI and machine learning to identify problems and bring them back to a working state without human intervention. What’s more, your team can get a unified view of performance and the quality of every build across all of your monitoring and logging tools.

“People are doing Continuous Delivery and struggling with it. They are also using these AI Ops kinds of products, which are watching things in production, and trying to figure out what’s wrong. What we are doing is we’re bringing the two together and ensuring nothing goes wrong,” Bansal explained.

24×7 Service Guard Console. Screenshot: Harness

He says that he brought this product to market because he saw enterprise companies struggling with CI/CD. He said the early messaging that you should move fast and break things really doesn’t work in enterprise settings. They need tooling that ensures that critical applications will keep running even with continuous builds (however you define that). “How do you enable developers so that they can move fast and make sure the business doesn’t get impacted. I feel that industry was underserved by this [earlier] message,” he said.

While it’s hard for any product to absolutely guarantee up-time, this one is providing tooling for companies who see the value of CI/CD, but are looking for a way to keep their applications up and running, so they aren’t constantly on this deploy/repair treadmill. If it works as described, it could help advance CI/CD, especially for large companies that need to learn to move faster and want assurances that when things break, they can be fixed in an automated fashion.

Dec
13
2018
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Chorus.ai rings up $33M for its platform that analyses sales calls to close more deals

Chorus.ai, a service that listens to sales calls in real time, and then transcribes and analyses them to give helpful tips to the salesperson, has raised $33 million to double down on the current demand for more AI-based tools in the enterprise.

The Series B is being led by Georgian Partners, with participation also from Redpoint Ventures and Emergence Capital, previous investors that backed Israeli-founded, SF-based Chorus.ai in its $16 million Series A two years ago.

In the gap between then and now, the startup has seen strong growth, listening in to some 5 million calls, and performing hundreds of thousands of hours of transcriptions for around 200 customers, including Adobe, Zoom, and Outreach (among others that it will not name).

Micha Breakstone, the co-founder (who has a pretty long history in conversational AI, heading up R&D at Ginger Software and then Intel after it acquired the startup; and before that building the tech that eventually became Summly and got acquired by Yahoo, among other roles), says that while the platform gives information and updates to salespeople in real time, much of the focus today is on providing information to users post-conversation, based on both audio and video calls.

One of its big areas is “smart themes” — patterns and rules Chorus has learned through all those calls. For example, it has identified what kind of language the most successful sales people are using and in turn prompts those who are less successful to use it more. Two general tips Breakstone told me about: using more collaborative terms like we and us; and giving more backstory to clients, although there will be more specific themes and approaches based on Chorus’s specific customers and products.

“I’d say we are super attuned to our customers and what they need and want,” Breakstone said. Which makes sense given the whole premise of Chorus.

It also creates smart “playlists” for managers who will almost certainly never have the time to review hundreds of hours of calls but might want to hear instructive highlights or ‘red alert’ moments where a more senior person might need to step in to save or close a deal.

There are currently what seems like dozens of startups and larger businesses that are currently tackling the opportunity to provide “conversational intelligence” to sales teams, using advances in natural language processing, voice recognition, machine learning and big data to help turn every sales person into a Jerry Maguire (yes, I know he’s an agent, but still, he needs to close deals, and he’s a salesman). They include TalkIQ (which has now been acquired by Dialpad), People.AI, Gong, Voicera, VoiceOps, and I’m pulling from a long list.

“We were among the very first to start this, no one knew what conversational intelligence was before us,” Breakstone says. He describes most of what was out in the market at the time as “Nineties technology” and adds that “our tech is superior because we built it in the correct way from the ground up, with nothing sent to a third party.”

He says that this is one reason why the company has negative churn — it essentially wins customers and hasn’t lost any. And having the tech all in-house not only means the platform is smarter and more accurate, but that helps with compliance around regulations like GDPR, which also has been a boost to its business. It’s also scored well on metrics around reps hitting targets better with its tools (the company claims its products lead to 50 percent greater quota attainment and ‘ramp time’ up by 30 percent for new sales people who use it).

Chorus.ai has helped us become a smarter sales organization as we’ve scaled. We have visibility into our sales conversations and what is working across all of our offices”, said Greg Holmes, Head of Sales for Zoom Video Communications, in a statement. “We’ve seen a drastic reduction in new hire ramp times and higher sales productivity with even more reps hitting quota. Chorus.ai is a game changer.”

Chorus has raised $55 million to date and Breakstone said he would not disclose its valuation — despite my best attempts to use some of those sales tips to winkle the information out of him. But I understand it to be “significantly higher” than in its last round, and definitely in the hundreds of millions.

As a point of reference, after its Series A two years ago, it was only valued at around $33 million post-money according to PitchBook.

“Maintaining high-quality sales conversations as you scale a sales organization is hard for many companies, but key to delivering predictable revenue growth. Chorus.ai’s Conversation Intelligence platform solves that challenge with a market-leading solution that is easy-to-use and delivers best-in-class results.” said Simon Chong, Managing Partner at Georgian Partners, in a statement. (Chong is joining the board with this round.) “Chorus.ai works with some of the best sales teams in the world and they love the product. We are very excited to partner with Chorus.ai on their next phase of growth as they help world class sales teams reach higher quota attainment and efficiency.”

Dec
12
2018
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Wandelbots raises $6.8M to make programming a robot as easy as putting on a jacket

Industrial robotics is on track to be worth around $20 billion by 2020, but while it may have something in common with other categories of cutting-edge tech — innovative use of artificial intelligence, pushing the boundaries of autonomous machines that are disrupting pre-existing technology — there is one key area where it differs: each robotics firm uses its own proprietary software and operating systems to run its machines, making programming the robots complicated, time-consuming and expensive.

A startup out of Germany called Wandelbots (a portmanteau of “change” and “robots” in German) has come up with an innovative way to skirt around that challenge: using software built by the company, a person wearing a jacket fitted with dozens of sensors can now program the actions of robots from the 12 most popular industrial robotics makers.

“We are providing a universal language to teach those robots in the same way, independent of the technology stack,” said CEO Christian Piechnick said in an interview. Essentially reverse engineering the process of how a lot of software is built, Wandelbots has created a Linux-like underpinning to all of it.

With some very big deals under its belt with the likes of Volkwagen, Infineon and Midea, the startup out of Dresden has now raised €6 million ($6.8 million), a Series A to take it to its next level of growth and specifically to double down on its operations in China. The funding comes from Paua VenturesEQT Ventures and other unnamed previous investors. (It had previously raised a seed round around the time it was a finalist in our Disrupt Battlefield last year, pre-launch.)

Paua has a bit of a history backing transformational software companies (it also invests in Stripe), and EQT, being connected to a private equity firm, is treating this as a strategic investment that might be deployed across its own assets.

Piechnick — who co-founded Wandelbots with Georg Püschel, Maria Piechnick, Sebastian Werner, Jan Falkenberg and Giang Nguyen on the back of research they did at university — said that typical programming of industrial robots to perform a task could have in the past taken three months, the employment of specialist systems integrators, and of course an extra cost on top of the machines themselves.

Someone with no technical knowledge, wearing one of Wandelbots’ jackets, can bring that process down to 10 minutes, with costs reduced by a factor of ten.

“In order to offer competitive products in the face of the rapid changes within the automotive industry, we need more cost savings and greater speed in the areas of production and automation of manufacturing processes,” said Marco Weiß, Head of New Mobility & Innovations at Volkswagen Sachsen GmbH, in a statement. “Wandelbots’ technology opens up significant opportunities for automation. Using Wandelbots offering, the installation and setup of robotic solutions can be implemented incredibly quickly by teams with limited programming skills.”

Wandelbots’ focus at the moment is on programming robotic arms rather than the mobile machines that you may have seen Amazon and others using to move goods around warehouses. For now, this means that there is not a strong crossover in terms of competition between these two branches of enterprise robotics.

However, Amazon has been expanding and working on new areas beyond warehouse movements: it has, for example, been working ways of using computer vision and robotic arms to identify and pick out the most optimal fruits and vegetables out of boxes to put into grocery orders.

Innovations like that from Amazon and others could see more pressure for innovation among robotics makers, although Piechnick notes that up to now we’ve seen very little in the way of movement, and there may never be (creating more opportunity for companies like his that build more usability).

“Attempts to build robotics operating systems have been tried over and over again, and each time it’s failed,” he said. “But robotics has completely different requirements, such as real time computing, safety issues and many other different factors. A robot in operation is much more complicated than a phone.” He also added that Wandelbots itself has a number of innovations of its own currently going through the patent process, which will widen its own functionality too in terms of what and how its software can train a robot to do. (This may see more than jackets enter the mix.)

As with companies in the area of robotic process automation — which uses AI to take over more mundane back-office features — Piechnick maintains that what he has built, and the rise of robotics overall, is not going to replace workers, but put them on to other roles, while allowing businesses to expand the scope of what they can do that a human might never have been able to execute.

“No company we work with has ever replaced a human worker with a robot,” he said, explaining that generally the upgrade is from machine to better machine. “It makes you more efficient and cost reductive, and it allows you to put your good people on more complicated tasks.”

Currently, Wandelbots is working with large-scale enterprises, although ultimately, it’s smaller businesses that are its target customer, he said.

“Previously the ROI on robots was too difficult for SMEs,” he said. “With our tech this changes.”

“Wandelbots will be one of the key companies enabling the mass-adoption of industrial robotics by revolutionizing how robots are trained and used,” said Georg Stockinger, Partner at Paua Ventures, in a statement. “Over the last few years, we’ve seen a steep decline in robotic hardware costs. Now, Wandelbots’ resolves the remaining hurdle to disruptive growth in industrial automation – the ease and speed of implementation and teaching. Both factors together will create a perfect storm, driving the next wave of industrial revolution.”

 

 

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