Aug
01
2018
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Altru raises $1.3M to improve recruiting with employee videos

Marketers are increasingly looking for social media celebrities and influencers who can promote their products with more authenticity (or at least, the appearance of authenticity) than a traditional ad.

So Altru CEO Alykhan Rehmatullah wondered: Why can’t businesses do something similar with recruiting?

And that’s what Altru is trying to accomplish, powering a page on a company’s website that highlights videos from real employees answering questions that potential hires might be asking. The videos are searchable (thanks to Altru’s transcriptions), and they also can be shared on social media.

The startup was part of the recent winter batch at Techstars NYC, and it’s already working with companies like L’Oréal, Dell and Unilever. Today, Altru is announcing that it’s raised $1.3 million in new funding led by Birchmere Ventures.

Rehmatullah contrasted Altru’s approach with Glassdoor, which he said features “more polarized” content (since it’s usually employees with really good or really bad experiences who want to write reviews) and where companies are often forced to “play defense.”

On Altru, on the other hand, employers can take the informal conversations that often take place when someone’s deciding whether to accept a job and turn them into an online recruiting tool. Over time, Rehmatullah said the platform could expand beyond recruiting to areas like on-boarding new employees.

Since these videos are posted to the company website, with the employees’ name and face attached, they may not always feel comfortable being completely honest, particularly about a company’s flaws. But at least it’s a message coming from a regular person, not the corporate-speak of a recruiter or manager.

Rehmatullah acknowledged that there’s usually “an educational process” involved in making employers more comfortable with this kind of content.

“These conversations are already happening outside your organization,” he said. “In the long-term, candidates expect more authenticity, more transparency, more true experiences.”

Jul
30
2018
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A pickaxe for the AI gold rush, Labelbox sells training data software

Every artificial intelligence startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it’s doctors assessing the size of cancer from a scan or drivers circling street signs in self-driving car footage, all this labeling has to happen somewhere. Often that means wasting six months and as much as a million dollars just developing a training data system. With nearly every type of business racing to adopt AI, that spend in cash and time adds up.

Labelbox builds artificial intelligence training data labeling software so nobody else has to. What Salesforce is to a sales team, Labelbox is to an AI engineering team. The software-as-a-service acts as the interface for human experts or crowdsourced labor to instruct computers how to spot relevant signals in data by themselves and continuously improve their algorithms’ accuracy.

Today, Labelbox is emerging from six months in stealth with a $3.9 million seed round led by Kleiner Perkins and joined by First Round and Google’s Gradient Ventures.

“There haven’t been seamless tools to allow AI teams to transfer institutional knowledge from their brains to software,” says co-founder Manu Sharma. “Now we have over 5,000 customers, and many big companies have replaced their own internal tools with Labelbox.”

Kleiner’s Ilya Fushman explains that “If you have these tools, you can ramp up to the AI curve much faster, allowing companies to realize the dream of AI.”

Inventing the best wheel

Sharma knew how annoying it was to try to forge training data systems from scratch because he’d seen it done before at Planet Labs, a satellite imaging startup. “One of the things that I observed was that Planet Labs has a superb AI team, but that team had been for over six months building labeling and training tools. Is this really how teams around the world are approaching building AI?,” he wondered.

Before that, he’d worked at DroneDeploy alongside Labelbox co-founder and CTO Daniel Rasmuson, who was leading the aerial data startup’s developer platform. “Many drone analytics companies that were also building AI were going through the same pain point,” Sharma tells me. In September, the two began to explore the idea and found that 20 other companies big and small were also burning talent and capital on the problem. “We thought we could make that much smarter so AI teams can focus on algorithms,” Sharma decided.

Labelbox’s team, with co-founders Ysiad Ferreiras (third from left), Manu Sharma (fourth from left), Brian Rieger (sixth from left) Daniel Rasmuson (seventh from left)

Labelbox launched its early alpha in January and saw swift pickup from the AI community that immediately asked for additional features. With time, the tool expanded with more and more ways to manually annotate data, from gradation levels like how sick a cow is for judging its milk production to matching systems like whether a dress fits a fashion brand’s aesthetic. Rigorous data science is applied to weed out discrepancies between reviewers’ decisions and identify edge cases that don’t fit the models.

“There are all these research studies about how to make training data” that Labelbox analyzes and applies, says co-founder and COO Ysiad Ferreiras, who’d led all of sales and revenue at fast-rising grassroots campaign texting startup Hustle. “We can let people tweak different settings so they can run their own machine learning program the way they want to, instead of being limited by what they can build really quickly.” When Norway mandated all citizens get colon cancer screenings, it had to build AI for recognizing polyps. Instead of spending half a year creating the training tool, they just signed up all the doctors on Labelbox.

Any organization can try Labelbox for free, and Ferreiras claims hundreds have. Once they hit a usage threshold, the startup works with them on appropriate SaaS pricing related to the revenue the client’s AI will generate. One called Lytx makes DriveCam, a system installed on half a million trucks with cameras that use AI to detect unsafe driver behavior so they can be coached to improve. Conde Nast is using Labelbox to match runway fashion to related items in their archive of content.

Eliminating redundancy, and jobs?

The big challenge is convincing companies that they’re better off leaving the training software to the experts instead of building it in-house where they’re intimately, though perhaps inefficiently, involved in every step of development. Some turn to crowdsourcing agencies like CrowdFlower, which has their own training data interface, but they only work with generalist labor, not the experts required for many fields. Labelbox wants to cooperate rather than compete here, serving as the management software that treats outsourcers as just another data input.

Long-term, the risk for Labelbox is that it’s arrived too early for the AI revolution. Most potential corporate customers are still in the R&D phase around AI, not at scaled deployment into real-world products. The big business isn’t selling the labeling software. That’s just the start. Labelbox wants to continuously manage the fine-tuning data to help optimize an algorithm through its entire life cycle. That requires AI being part of the actual engineering process. Right now it’s often stuck as an experiment in the lab. “We’re not concerned about our ability to build the tool to do that. Our concern is ‘will the industry get there fast enough?’” Ferreiras declares.

Their investor agrees. Last year’s big joke in venture capital was that suddenly you couldn’t hear a startup pitch without “AI” being referenced. “There was a big wave where everything was AI. I think at this point it’s almost a bit implied,” says Fushman. But it’s corporations that already have plenty of data, and plenty of human jobs to obfuscate, that are Labelbox’s opportunity. “The bigger question is ‘when does that [AI] reality reach consumers, not just from the Googles and Amazons of the world, but the mainstream corporations?’”

Labelbox is willing to wait it out, or better yet, accelerate that arrival — even if it means eliminating jobs. That’s because the team believes the benefits to humanity will outweigh the transition troubles.

“For a colonoscopy or mammogram, you only have a certain number of people in the world who can do that. That limits how many of those can be performed. In the future, that could only be limited by the computational power provided so it could be exponentially cheaper” says co-founder Brian Rieger. With Labelbox, tens of thousands of radiology exams can be quickly ingested to produce cancer-spotting algorithms that he says studies show can become more accurate than humans. Employment might get tougher to find, but hopefully life will get easier and cheaper too. Meanwhile, improving underwater pipeline inspections could protect the environment from its biggest threat: us.

“AI can solve such important problems in our society,” Sharma concludes. “We want to accelerate that by helping companies tell AI what to learn.”

Jul
26
2018
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Facebook acquires Redkix to enhance communications on Workplace by Facebook

Facebook had a rough day yesterday when its stock plunged after a poor earnings report. What better way to pick yourself up and dust yourself off than to buy a little something for yourself. Today the company announced it has acquired Redkix, a startup that provides tools to communicate more effectively by combining email with a more formal collaboration tool. The companies did not reveal the acquisition price.

Redkix burst out of the gate two years ago with a $17 million seed round, a hefty seed amount by any measure. What prompted this kind of investment was a tool that combined a collaboration tool like Slack or Workplace by Facebook with email. People could collaborate in Redkix itself, or if you weren’t a registered user, you could still participate by email, providing a more seamless way to work together.

Alan Lepofsky, who covers enterprise collaboration at Constellation Research, sees this tool as providing a key missing link. “Redkix is a great solution for bridging the worlds between traditional email messaging and more modern conversational messaging. Not all enterprises are ready to simply switch from one to the other, and Redkix allows for users to work in whichever method they want, seamlessly communicating with the other,” Lepofsky told TechCrunch.

As is often the case with these kinds of acquisitions, the company bought the technology  itself along with the team that created it. This means that the Redkix team including the CEO and CTO will join Facebook and they will very likely be shutting down the application after the acquisition is finalized.

Lepofsky thinks that enterprises that are adopting Facebook’s enterprise tool will be able to more seamlessly transition between the two modes of communication, the Workplace by Facebook tool and email, as they prefer.

Although a deal like this has probably been in the works for some time, after yesterday’s earning’s debacle, Facebook could be looking for ways to enhance its revenue in areas beyond the core Facebook platform. The enterprise collaboration tool does offer a possible way to do that in the future, and if they can find a way to incorporate email into it, it could make it a more attractive and broader offering.

Facebook is competing with Slack, the darling of this space and others like Microsoft, Cisco and Google around communications and collaboration. When it launched in 2015, it was trying to take that core Facebook product and put it in a business context, something Slack had been doing since the beginning.

To succeed in business, Facebook had to think differently than as a consumer tool, driven by advertising revenue and had to convince large organizations that they understood their requirements. Today, Facebook claims 30,000 organizations are using the tool and over time they have built in integrations to other key enterprise products, and keep enhancing it.

Perhaps with today’s acquisition, they can offer a more flexible way to interact with the platform and could increase those numbers over time.

Jul
25
2018
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Snark AI looks to help companies get on-demand access to idle GPUs

Riding on a wave of an explosion in the use of machine learning to power, well, just about everything is the emergence of GPUs as one of the go-to methods to handle all the processing for those operations.

But getting access to those GPUs — whether using the cards themselves or possibly through something like AWS — might still be too difficult or too expensive for some companies or research teams. So Davit Buniatyan and his co-founders decided to start Snark AI, which helps companies rent GPUs that aren’t in use across a distributed network of companies that just have them sitting there, rather than through a service like Amazon. While the larger cloud providers offer similar access to GPUs, Buniatyan’s hope is that it’ll be attractive enough to companies and developers to tap a different network if they can lower that barrier to entry. The company is launching out of Y Combinator’s Summer 2018 class.

“We bet on that there will always be a gap between mining and AWS or Google Cloud prices,” Buniatyan said. “If the mining will be [more profitable than the cost of running a GPU], anyone can get into AWS and do mining and be profitable. We’re building a distributed cloud computing platform for clients that can easily access the resources there but are not used.”

The startup works with companies with a lot of spare GPUs that aren’t in use, such as gaming cloud companies or crypto mining companies. Teams that need GPUs for training their machine learning models get access to the raw hardware, while teams that just need those GPUs to handle inference get access to them through a set of APIs. There’s a distinction between the two because they are two sides to machine learning — the former building the model that the latter uses to execute some task, like image or speech recognition. When the GPUs are idle, they run mining to pay the hardware providers, and Snark AI also offers the capability to both mine and run deep learning inference on a piece of hardware simultaneously, Buniatyan said.

Snark AI matches the proper amount of GPU power to whatever a team needs, and then deploys it across a network of distributed idle cards that companies have in various data centers. It’s one way to potentially reduce the cost of that GPU over time, which may be a substantial investment initially but get a return over time while it isn’t in use. If that’s the case, it may also encourage more companies to sign up with a network like this — Snark AI or otherwise — and deploy similar cards.

There’s also an emerging trend of specialized chips that focus on machine learning or inference, which look to reduce the cost, power consumption or space requirements of machine learning tasks. That ecosystem of startups, like Cerebras Systems, Mythic, Graphcore or any of the other well-funded startups, all potentially have a shot at unseating GPUs for machine learning tasks. There’s also the emergence of ASICs, customized chips that are better suited to tasks like crypto mining, which could fracture an ecosystem like this — especially if the larger cloud providers decide to build or deploy something similar (such as Google’s TPU). But this also means that there’s room to potentially create some new interface layer that can snap up all the leftovers for tasks that companies might need, but don’t necessarily need bleeding-edge technology like that from those startups.

There’s always going to be the same argument that was made for Dropbox prior to its significant focus on enterprises and collaboration: the price falls dramatically as it becomes more commoditized. That might be especially true for companies like Amazon and Google, which have already run that playbook, and could leverage their dominance in cloud computing to put a significant amount of pressure on a third-party network like Snark AI. Google also has the ability to build proprietary hardware like the TPU for specialized operations. But Buniatyan said the company’s focus on being able to juggle inference and mining, in addition to keeping that cost low for idle GPUs of companies that are just looking to deploy, should keep it viable, even amid a changing ecosystem that’s focusing on machine learning.

Jul
24
2018
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Outlier raises $6.2 M Series A to change how companies use data

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of insights right to your inbox that matter most for your job, company and industry. Today the company announced a $6.2 million Series A to further develop that vision.

The round was led by Ridge Ventures with assistance from 11.2 Capital, First Round Capital, Homebrew, Susa Ventures and SV Angel. The company has raised over $8 million.

The startup is trying to solve a difficult problem around delivering meaningful insight without requiring the customer to ask the right questions. With traditional BI tools, you get your data and you start asking questions and seeing if the data can give you some answers. Outlier wants to bring a level of intelligence and automation by pointing out insight without having to explicitly ask the right question.

Company founder and CEO Sean Byrnes says his previous company, Flurry, helped deliver mobile analytics to customers, but in his travels meeting customers in that previous iteration, he always came up against the same question: “This is great, but what should I look for in all that data?”

It was such a compelling question that after he sold Flurry in 2014 to Yahoo for more than $200 million, that question stuck in the back of his mind and he decided to start a business to solve it. He contends that the first 15 years of BI was about getting answers to basic questions about company performance, but the next 15 will be about finding a way to get the software to ask good questions based on the huge amounts of data.

Byrnes admits that when he launched, he didn’t have much sense of how to put this notion into action, and most people he approached didn’t think it was a great idea. He says he heard “No” from a fair number of investors early on because the artificial intelligence required to fuel a solution like this really wasn’t ready in 2015 when he started the company.

He says that it took four or five iterations to get to today’s product, which lets you connect to various data sources, and using artificial intelligence and machine learning delivers a list of four or five relevant questions to the user’s email inbox that points out data you might not have noticed, what he calls “shifts below the surface.” If you’re a retailer that could be changing market conditions that signal you might want to change your production goals.

Outlier email example. Photo: Outlier

The company launched in 2015. It took some time to polish the product, but today they have 14 employees and 14 customers including Jack Rogers, Celebrity Cruises and Swarovski.

This round should allow them to continuing working to grow the company. “We feel like we hit the right product-market fit because we have customers [generating] reproducible results and really changing the way people use the data,” he said.

Jul
24
2018
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InVision CEO Clark Valberg to talk design at Disrupt SF

To Clark Valberg, the screen is the most important place in the world. And he’s not the only one who thinks so. It isn’t just tech companies spending their money on design. The biggest brands in the world are pouring money into their digital presence, for many, the first step is InVision.

InVision launched back in 2011 with a simple premise: What if, instead of the back-and-forth between designers and engineers and executives, there was a program that let these interested parties collaborate on a prototype?

The first iteration simply let designers build out prototypes, complete with animations and transitions, so that engineers didn’t spend time building things that would only change later.

As that tool grew, InVision realized that it was in conversation with designers across the industry, and that it hadn’t yet fixed one of their biggest pain points. That’s why, in 2017, InVision launched Studio, a design platform that was built specifically for designers building products.

Alongside Studio, InVision also launched its own app store for design programs to loop into the larger InVision platform. And the company also launched a fund to invest in early-stage design companies.

The idea here is to become the Salesforce of the design world, with the entire industry centering around this company and its various offerings.

InVision has raised more than $200 million, and serves 4 million users, including 80 percent of the Fortune 500. We’re absolutely thrilled to have Clark Valberg, InVision cofounder and CEO, join us at Disrupt SF in September.

The full agenda is here. Passes for the show are available at the Early-Bird rate until July 25 here.

Jul
24
2018
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YC-backed Send Reality makes 3D virtual walkthroughs for residential listings

The fields of computer vision and VR are difficult. But a new company, Send Reality, is entering the race. The Y Combinator-backed company is looking to offer full 3D-modeling for virtual walkthroughs of real estate listings.

Founder and CEO Andrew Chen said he was the kid back in middle school and high school that spent hours walking around the streets of Paris, NYC and SF on Google Streetview.

“The thing I always wanted was to walk through the inside of all the interesting places of the world,” said Chen. “90 percent of the world’s most interesting physical content is inside, but I couldn’t do that.”

Chen explained that the field of computer vision has been able to make substantial technical breakthroughs, now allowing companies like Send Reality to create a videogame-style replica of the world.

For now, however, Send Reality is focused on luxury residential real estate.

Here’s how it works:

Send Reality sends photographers out to the listing with an iPad, a $250 commodity depth sensor, and a specialized Send Reality app. These photographers take hundreds of thousands of photos, and the Send Reality technology stitches those photos together to create a complete 3D model, as shown in the above .gif.

Chen says that what makes Send Reality tech special is how efficiently it’s able to stitch together these photos, explaining that the company can put together over 100K photos in the same time it takes for top academic labs in the world to put together 5,000.

“What this means is that the 3D models we create are so much more realistic than anything else anyone else has made,” said Chen.

For the luxury residential market that Send Reality is currently targeting, most listings are put up on their own website. Given this is still in beta, the numbers on Send Reality demoes are still rough. But Chen says that listing websites that include the Send Reality product see a 5x to 10x increase in the amount of time people spend on the website, with 75 percent to 80 percent of that extra time spent directly in the Send Reality viewer.

Send Reality sells directly to realtors, offering the product for $500 to $800 depending on the size and complexity of the home. In the future, the company can bring down that price point by allowing realtors to scan the home themselves from their own smartphone.

Send Reality has received funding from Y Combinator .

Jul
24
2018
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Rescale reels in $32 million Series B to bring high performance computing to cloud

Rescale, the startup that wants to bring high performance computing to the cloud, announced a $32 million Series B investment today led by Initialized Capital, Keen Venture Partners and SineWave Ventures.

They join a list of well-known early investors that included Sam Altman, Jeff Bezos, Richard Branson, Paul Graham, Ron Conway, Chris Dixon, Peter Thiel and others. Today’s investment brings the total amount raised to $52 million, according to the company.

Rescale works with engineering, aerospace, scientific and other verticals and helps them move their legacy high performance computing applications to the cloud. The idea is to provide a set of high performance computing resources, whether that’s on prem or in the cloud, and help customers tune their applications to get the maximum performance.

Traditionally HPC has taken place on prem in a company’s data center. These companies often have key legacy applications they want to move to the cloud and Rescale can help them do that in the most efficient manner, whether that involves bare metal a virtual machine or a container.

“We help take a portfolio of [legacy] applications running on prem and help enable them in the cloud or in a hybrid environment. We tune and optimize the applications on our platform and take advantage of capital assets on prem, then we help extend that environment to different cloud vendors or tune to best practices for the specific application,” company CEO and co-founder Joris Poort explained.

Photo: Rescale

Ben Verwaayen, who is a partner at one of the lead investors, Keen Venture Partners, sees a company going after a large legacy market with a new approach. “The market is currently 95% on-premise, and Rescale supports customers as they move to hybrid and eventually to a fully cloud native solution. Rescale helps CIOs enable the digital transformation journey within their enterprise, to optimize IT resources and enable meaningful productivity and cost improvements,” Verwaayen said in a statement.

The new influx of cash should help Rescale, well, scale, and that will involve hiring more developers, solutions architects and the like. The company wants to also use the money to expand its presence in Asia and Europe and establish relationships with systems integrators, who would be a good fit for a product like this and help expand their market beyond what they can do as a young startup.

The company, which is based in San Francisco, was founded in 2011 and has 80 employees. They currently have 150 customers including Sikorsky Innovation, Boom Aerospace and Trek Bikes.

Jul
23
2018
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Xage secures $12 million Series A for IoT security solution on blockchain

Xage (pronounced Zage), a blockchain security startup based in Silicon Valley, announced a $12 million Series A investment today led by March Capital Partners. GE Ventures, City Light Capital and NexStar Partners also participated.

The company emerged from stealth in December with a novel idea to secure the myriad of devices in the industrial internet of things on the blockchain. Here’s how I described it in a December 2017 story:

Xage is building a security fabric for IoT, which takes blockchain and synthesizes it with other capabilities to create a secure environment for devices to operate. If the blockchain is at its core a trust mechanism, then it can give companies confidence that their IoT devices can’t be compromised. Xage thinks that the blockchain is the perfect solution to this problem.

It’s an interesting approach, one that attracted Duncan Greatwood to the company. As he told me in December his previous successful exits — Topsy to Apple in 2013 and PostPath to Cisco in 2008 — gave him the freedom to choose a company that really excited him for his next challenge.

When he saw what Xage was doing, he wanted to be a part of it, and given the unorthodox security approach the company has taken, and Greatwood’s pedigree, it couldn’t have been hard to secure today’s funding.

The Industrial Internet of Things is not like its consumer cousin in that it involves getting data from big industrial devices like manufacturing machinery, oil and gas turbines and jet engines. While the entire Internet of Things could surely benefit from a company that concentrates specifically on keeping these devices secure, it’s a particularly acute requirement in industry where these devices are often helping track data from key infrastructure.

GE Ventures is the investment arm of GE, but their involvement is particularly interesting because GE has made a big bet on the Industrial Internet of Things. Abhishek Shukla of GE Ventures certainly saw the connection. “For industries to benefit from the IoT revolution, organizations need to fully connect and protect their operation. Xage is enabling the adoption of these cutting edge technologies across energy, transportation, telecom, and other global industries,” Shukla said in a statement.

The company was founded just last year and is based in Palo Alto, California.

Jul
23
2018
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SessionM customer loyalty data aggregator snags $23.8 M investment

SessionM announced a $23.8 million Series E investment led by Salesforce Ventures. A bushel of existing investors including Causeway Media Partners, CRV, General Atlantic, Highland Capital and Kleiner Perkins Caufield & Byers also contributed to the round. The company has now raised over $97 million.

At its core, SessionM aggregates loyalty data for brands to help them understand their customer better, says company co-founder and CEO Lars Albright. “We are a customer data and engagement platform that helps companies build more loyal and profitable relationships with their consumers,” he explained.

Essentially that means, they are pulling data from a variety of sources and helping brands offer customers more targeted incentives, offers and product recommendations “We give [our users] a holistic view of that customer and what motivates them,” he said.

Screenshot: SessionM (cropped)

To achieve this, SessionM takes advantage of machine learning to analyze the data stream and integrates with partner platforms like Salesforce, Adobe and others. This certainly fits in with Adobe’s goal to build a customer service experience system of record and Salesforce’s acquisition of Mulesoft in March to integrate data from across an organization, all in the interest of better understanding the customer.

When it comes to using data like this, especially with the advent of GDPR in the EU in May, Albright recognizes that companies need to be more careful with data, and that it has really enhanced the sensitivity around stewardship for all data-driven businesses like his.

“We’ve been at the forefront of adopting the right product requirements and features that allow our clients and businesses to give their consumers the necessary control to be sure we’re complying with all the GDPR regulations,” he explained.

The company was not discussing valuation or revenue. Their most recent round prior to today’s announcement, was a Series D in 2016 for $35 million also led by Salesforce Ventures.

SessionM, which was founded in 2011, has around 200 employees with headquarters in downtown Boston. Customers include Coca-Cola, L’Oreal and Barney’s.

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