Jul
12
2018
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Microsoft wants to make you a better team player by nudging you into submission

Microsoft announced a number of new tools for its MyAnalytics tool for Office 365 users today that are geared toward giving employees more data about how they work, as well as ways to improve how teams work together. In today’s businesses, everybody has to be a team player, after all, and if you want to bring technology to bear on this, you first need data — and once you have data, you can go into full-on analytics mode and maybe even throw in a smidge of machine learning, too.

So today, Microsoft is launching two new products: Workplace Analytics and MyAnalytics nudges. Yes, Office 365 will now nudge you to be a better team player. “Building better teams starts with transparent, data-driven dialog—but no one is perfect and sticking to good collaboration habits can be challenging in a fast-paced job,” Microsoft’s Natalie McCullough and Noelle Beaujon, using language only an MBA could love, write in today’s announcement.

I’m not sure what exactly that means or whether I have good collaboration habits or not, but in practice, Office 365 can now nudge you when you need more focus time as your calendar fills up, for example. You can block off those times without leaving your Inbox (or, I guess, you could always ignore this and just set up a standing block of time every day where you don’t accept meetings and just do your job…). MyAnalytics can also now nudge you to delegate meetings to a co-worker when your schedule is busy (because your co-workers aren’t busy and will love you for putting more meetings on your calendar) and tell you to avoid after-hours emails as you draft them to co-workers so they don’t have to work after hours, too (that’s actually smart, but may not work well in every company).

With this new feature, Microsoft is also using some machine learning smarts, of course. MyAnalytics was already able to remind you of tasks you promised to co-workers over email, and now it’ll nudge you when you read new emails from those co-workers, too. Because the more you get nudged, the more likely you are to finish that annoying task you never intended to do but promised your co-worker you would do so he’d go away.

If you’re whole team needs some nudging, Microsoft will also allow the group to enroll in a change program and provide you with lots of data about how you are changing. And if that doesn’t work, you can always set up a few meetings to discuss what’s going wrong.

These new features will roll out this summer. Get ready to be nudged.

Jul
11
2018
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Facial recognition startup Kairos acquires Emotion Reader

Kairos, the face recognition technology used for brand marketing, has announced the acquisition of EmotionReader.

EmotionReader is a Limerick, Ireland-based startup that uses algorithms to analyze facial expressions around video content. The startup allows brands and marketers to measure viewers emotional response to video, analyze viewer response via an analytics dashboard, and make different decisions around media spend based on viewer response.

The acquisition makes sense considering that Kairos core business is focused on facial identification for enterprise clients. Knowing who someone is, paired with how they feel about your content, is a powerful tool for brands and marketers.

The idea for Kairos started when founder Brian Brackeen was making HR time-clocking systems for Apple. People were cheating the system, so he decided to implement facial recognition to ensure that employees were actually clocking in and out when they said they were.

That premise spun out into Kairos, and Brackeen soon realized that facial identification as a service was much more powerful than any niche time clocking service.

But Brackeen is very cautious with the technology Kairos has built.

While Kairos aims to make facial recognition technology (and all the powerful insights that come with it) accessible and available to all businesses, Brackeen has been very clear about the fact that Kairos isn’t interested in selling this technology to government agencies.

Brackeen recently contributed a post right here on TechCrunch outlining the various reasons why governments aren’t ready for this type of technology. Alongside the outstanding invasion of personal privacy, there are also serious issues around bias against people of color.

From the post:

There is no place in America for facial recognition that supports false arrests and murder. In a social climate wracked with protests and angst around disproportionate prison populations and police misconduct, engaging software that is clearly not ready for civil use in law enforcement activities does not serve citizens, and will only lead to further unrest.

As part of the deal, EmotionReader CTO Dr. Stephen Moore will run Kairos’ new Singapore-based R&D center, allowing for upcoming APAC expansion.

Kairos has raised approximately $8 million from investors New World Angels, Kapor Capital, 500 Startups, Backstage Capital, Morgan Stanley, Caerus Ventures, and Florida Institute, and is now closing on its $30 million crowd sale.

Jul
10
2018
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Box acquires Butter.ai to make search smarter

Box announced today that it has acquired Butter.ai, a startup that helps customers search for content intelligently in the cloud. The terms of the deal were not disclosed, but the Butter.AI team will be joining Box.

Butter.AI was started by two ex-Evernote employees, Jack Hirsch and Adam Walz. The company was partly funded by Evernote founder and former CEO Phil Libin’s Turtle Studios. The latter is a firm established with a mission to use machine learning to solve real business problems like finding the right document wherever it is.

Box has been adding intelligence to its platform for some time, and this acquisition brings the Butter.AI team on board and gives them more machine learning and artificial intelligence known-how while helping to enhance search inside of the Box product.

“The team from Butter.ai will help Box to bring more intelligence to our Search capabilities, enabling Box’s 85,000 customers to more easily navigate through their unstructured information — making searching for files in Box more contextualized, predictive and personalized,” Box’s Jeetu Patel wrote in a blog post announcing the acquisition.

That means taking into account the context of the search and delivering documents that make sense given your role and how you work. For instance, if you are a salesperson and you search for a contract, you probably want a sales contract and not one for a freelancer or business partnership.

For Butter, the chance to have access to all those customers was too good to pass up. “We started Butter.ai to build the best way to find documents at work. As it turns out, Box has 85,000 customers who all need instant access to their content. Joining Box means we get to build on our original mission faster and at a massive scale,” company CEO and co-founder Jack Hirsch said.

The company launched in September 2017, and up until now it has acted as a search assistant inside Slack you can call upon to search for documents and find them wherever they live in the cloud. The company will be winding down that product as it becomes part of the Box team.

As is often the case in these deals, the two companies have been working closely together and it made sense for Box to bring the Butter.AI team into the fold where it can put its technology to bear on the Box platform.

“After launching in September 2017 our customers were loud and clear about wanting us to integrate with Box and we quickly delivered. Since then, our relationship with Box has deepened and now we get to build on our vision for a MUCH larger audience as part of the Box team,” the founders wrote in a Medium post announcing the deal.

The company raised $3.3 million over two seed rounds. Investors included Slack and General Catalyst.

Jun
29
2018
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Leena AI builds HR chatbots to answer policy questions automatically

Say you have a job with a large company and you want to know how much vacation time you have left, or how to add your new baby to your healthcare. This usually involves emailing or calling HR and waiting for an answer, or it could even involve crossing multiple systems to get what you need.

Leena AI, a member of the Y Combinator Summer 2018 class, wants to change that by building HR bots to answer questions for employees instantly.

The bots can be integrated into Slack or Workplace by Facebook and they are built and trained using information in policy documents and by pulling data from various back-end systems like Oracle and SAP.

Adit Jain, co-founder at Leena AI, says the company has its roots in another startup called Chatteron, which the founders started after they got out of college in India in 2015. That product helped people build their own chatbots. Jain says along the way, they discovered while doing their market research a particularly strong need in HR. They started Leena AI last year to address that specific requirement.

Jain says when building bots, the team learned through its experience with Chatteron that it’s better to concentrate on a single subject because the underlying machine learning model gets better the more it’s used. “Once you create a bot, for it to really add value and be [extremely] accurate, and for it to really go deep, it takes a lot of time and effort and that can only happen through verticalization,” Jain explained.

Photo: Leena AI

What’s more, as the founders have become more knowledgeable about the needs of HR, they have learned that 80 percent of the questions cover similar topics, like vacation, sick time and expense reporting. They have also seen companies using similar back-end systems, so they can now build standard integrators for common applications like SAP, Oracle and NetSuite.

Of course, even though people may ask similar questions, the company may have unique terminology or people may ask the question in an unusual way. Jain says that’s where the natural language processing (NLP) comes in. The system can learn these variations over time as they build a larger database of possible queries.

The company just launched in 2017 and already has a dozen paying customers. They hope to double that number in just 60 days. Jain believes being part of Y Combinator should help in that regard. The partners are helping the team refine its pitch and making introductions to companies that could make use of this tool.

Their ultimate goal is nothing less than to be ubiquitous, to help bridge multiple legacy systems to provide answers seamlessly for employees to all their questions. If they can achieve that, they should be a successful company.

Jun
28
2018
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Facebook is using machine learning to self-tune its myriad services

Regardless of what you may think of Facebook as a platform, they run a massive operation, and when you reach their level of scale you have to get more creative in how you handle every aspect of your computing environment.

Engineers quickly reach the limits of human ability to track information, to the point that checking logs and analytics becomes impractical and unwieldy on a system running thousands of services. This is a perfect scenario to implement machine learning, and that is precisely what Facebook has done.

The company published a blog post today about a self-tuning system they have dubbed Spiral. This is pretty nifty, and what it does is essentially flip the idea of system tuning on its head. Instead of looking at some data and coding what you want the system to do, you teach the system the right way to do it and it does it for you, using the massive stream of data to continually teach the machine learning models how to push the systems to be ever better.

In the blog post, the Spiral team described it this way: “Instead of looking at charts and logs produced by the system to verify correct and efficient operation, engineers now express what it means for a system to operate correctly and efficiently in code. Today, rather than specify how to compute correct responses to requests, our engineers encode the means of providing feedback to a self-tuning system.”

They say that coding in this way is akin to declarative code, like using SQL statements to tell the database what you want it to do with the data, but the act of applying that concept to systems is not a simple matter.

“Spiral uses machine learning to create data-driven and reactive heuristics for resource-constrained real-time services. The system allows for much faster development and hands-free maintenance of those services, compared with the hand-coded alternative,” the Spiral team wrote in the blog post.

If you consider the sheer number of services running on Facebook, and the number of users trying to interact with those services at any given time, it required sophisticated automation, and that is what Spiral is providing.

The system takes the log data and processes it through Spiral, which is connected with just a few lines of code. It then sends commands back to the server based on the declarative coding statements written by the team. To ensure those commands are always being fine-tuned, at the same time, the data gets sent from the server to a model for further adjustment in a lovely virtuous cycle. This process can be applied locally or globally.

The tool was developed by the team operating in Boston, and is only available internally inside Facebook. It took lots of engineering to make it happen, the kind of scope that only Facebook could apply to a problem like this (mostly because Facebook is one of the few companies that would actually have a problem like this).

Jun
27
2018
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IQ Capital is raising £125M to invest in deep tech startups in the UK

The rapid pace of technology innovation and applications in recent decades — you could argue that just about every kind of business is a “tech” business these days — has spawned a sea of tech startups and larger businesses that are focused on serving that market, and equally demanding consumers, on a daily basis. Today, a venture capital firm in the UK is announcing a fund aimed at helping to grow the technologies that will underpin a lot of those daily applications.

Cambridge-based IQ Capital is raising £125 million ($165 million) that it will use specifically to back UK startups that are building “deep tech” — the layer of research and development, and potentially commercialised technology, that is considered foundational to how a lot of technology will work in the years and decades to come. So far, some £92 million has been secured, and partner Kerry Baldwin said that the rest is coming “without question” — pointing to strong demand.

There was a time when it was more challenging to raise money for very early stage companies working at the cusp of new technologies, even more so in smaller tech ecosystems like the UK’s. As Ed Stacey, another partner in the firm acknowledges, there is often a very high risk of failure at even more stages of the process, with the tech in some cases not even fully developed, let alone rolled out to see what kind of commercial interest there might be in the product.

However, there has been a clear shift in the last several years.

There a lot more money floating around in tech these days — so much so that it’s created a stronger demand for projects to invest in. (Another consequence of that is that when you do get a promising startup, funds are potentially giving them hundreds of millions and causing other disruptions in how they grow and exit, which is another story…)

And while there are definitely a lot of startups out there in the world today, a lot of them are what you might describe as “me too”, or at least making something that is easily replicated by another startup, making the returns and the wins harder to find among them.

A new focus that we are seeing on “deep tech” is a consequence of both of those trends.

“The low-hanging fruit has been discovered… Shallow tech is a solved problem,” Stacey said, in reference to areas like the basics of e-commerce services and mobile apps. “These are easy to build with open source components, for example. It’s shallow when it can be copied very quickly.”

In contrast, deep tech is “by definition is something that can’t easily be copied,” he continued. “The underlying algorithm is deep, with computational complexity.”

But the challenges run deep in deep tech: not only might a product or technology never come together, or find a customer, but it might face problems scaling if it does take off. IQ Capital’s focus on deep tech is coupled with the company trying to  determine which ideas will scale, not just work or find a customer. As we see more deep tech companies emerging and growing, I’m guessing scalability will become an ever more prominent factor in deciding whether a startup gets backing.

IQ Capital’s investments to date span areas like security (Privitar), marketing tech (Grapeshot, which was acquired by Oracle earlier this year), AI (such as speech recognition API developer Speechmatics) and biotechnology (Fluidic Analytics, which measures protein concentrations), all areas that will be the focus of this fund, along with IoT and other emerging technologies and gaps in the current market.

IQ Capital is not the only fund starting to focus on deep tech, nor is its portfolio the only range of startups focusing on this (Allegro.AI and deep-learning chipmaker Hailo are others, to name just two).

LPs in this latest fund include family offices, wealth managers, tech entrepreneurs and CEOs from IQ’s previous investments, as well as British Business Investments, the commercial arm of the British Business Bank, the firm said.

Jun
26
2018
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Ping Identity acquires stealthy API security startup Elastic Beam

At the Identiverse conference in Boston today, Ping Identity announced that it has acquired Elastic Beam, a pre-Series A startup that uses artificial intelligence to monitor APIs and help understand when they have been compromised.

Ping also announced a new product, PingIntelligence for APIs, based on the Elastic Beam technology. They did not disclose the sale price.

The product itself is a pretty nifty piece of technology. It automatically detects all the API IP addresses and URLs running inside a customer. It then uses artificial intelligence to search for anomalous behavior and report back when it finds it (or it can automatically shut down access depending on how it’s configured).

“APIs are defined either in the API gateway because that facilitates creation or implemented on an application server like node.js. We created a platform that could bring a level of protection to both,” company founder Bernard Harguindeguy told TechCrunch.

It may seem like an odd match for Ping, which after all, is an enterprise identity company, but there are reasonable connections here. Perhaps the biggest is that CEO Andre Durand wants to see his company making increasing use of AI and machine learning for identity security in general. It’s also worth noting that his company has had an API security product in its portfolio for over five years, so it’s not a huge stretch to buy Elastic Beam.

With this purchase, Ping has not only acquired some advanced technology, it has also acqui-hired a team of AI and machine learning experts that could help inject the entire Ping product line with AI and machine learning smarts. “Nobody should be surprised who has been watching that Ping will drive machine learning AI and general intelligence into our identity platform,” Durand said.

Harguindeguy certainly sees the potential here. “I think we can over time bring a high level of monitoring and intelligence to Ping to understand whether an identity may have been used by someone else or being misused somehow,” he said.

Elastic Beam interface. Photo: Elastic Beam website

Harguindeguy will join Ping Identity as Senior Vice President of Intelligence along with his entire team. Neither company would divulge the exact number of employees, but Durand did acknowledge it fell somewhere between the 11 and 50 mentioned in the company Crunchbase profile. The original team consisted of around 10 according to  Harguindeguy and they have been hiring for some time, so fair to say more than 11, but less than 50.

Harguindeguy says they were pursued by more than one company (although he wouldn’t say who those other companies were), but he felt that Ping provided a good cultural match for his company and could take them where they wanted to go faster than they could on their own, even with Series A money.

“We realized this is going to be really big. How do we go after the market really strongly really fast? We saw that we could fuse this really fast with Ping and have strong go to market with them,” he said.

Durand acknowledged that Ping, which was itself acquired by Vista Equity Partners for $600 million two years ago, couldn’t have made such an acquisition without the backing of a larger firm like this. “There was there was no chance we could have done either UnboundID (which the company acquired in August 2016) or Elastic Beam on our own. This was purely an artifact of being part of the Vista family portfolio,” he said.

PingIntelligence for APIs, the product based on Elastic Beam’s technology, is currently in private preview. It should be generally available some time later this year.

Jun
20
2018
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Beamery closes $28M Series B to stoke support for its ‘talent CRM’

Beamery, a London-based startup that offers self-styled “talent CRM”– aka ‘candidate relationship management’ — and recruitment marketing software targeted at fast-growing companies, has closed a $28M Series B funding round, led by EQT Ventures.

Also participating in the round are M12, Microsoft’s venture fund, and existing investors Index Ventures, Edenred Capital Partners and Angelpad Fund. Beamery last raised a $5M Series A, in April 2017, led by Index.

Its pitch centers on the notion of helping businesses win a ‘talent war’ by taking a more strategic and pro-active approach to future hires vs just maintaining a spreadsheet of potential candidates.

Its platform aims to help the target enterprises build and manage a talent pool of people they might want to hire in future to get out ahead of the competition in HR terms, including providing tools for customized marketing aimed at nurture relations with possible future hires.

Customer numbers for Beamery’s software have stepped up from around 50 in April 2017 to 100 using it now — including the likes of Facebook (which is using it globally), Continental, VMware, Zalando, Grab and Balfour Beatty.

It says the new funding will be going towards supporting customer growth, including by ramping up hiring in its offices in London (HQ), Austin and San Francisco.

It also wants to expand into more markets. “We’re focusing on some of the world’s biggest global businesses that need support in multiple timezones and geographies so really it’s a global approach,” said a spokesman on that.

“Companies adopting the system are large enterprises doing talent at scale, that are innovative in terms of being proactive about recruiting, candidate experience and employer brand,” he added.

A “significant” portion of the Series B funds will also go towards R&D and produce development focused on its HR tech niche.

“Across all sectors, there’s a shift towards proactive recruitment through technology, and Beamery is emerging as the category leader,” added Tom Mendoza, venture lead and investment advisor at EQT, in a supporting statement.

“Beamery has a fantastic product, world-class high-ambition founders, and an outstanding analytics-driven team. They’ve been relentless about building the best talent CRM and marketing platform and gaining a deep understanding of the industry-wide problems.”

Jun
19
2018
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Google injects Hire with AI to speed up common tasks

Since Google Hire launched last year it has been trying to make it easier for hiring managers to manage the data and tasks associated with the hiring process, while maybe tweaking LinkedIn while they’re at it. Today the company announced some AI-infused enhancements that they say will help save time and energy spent on manual processes.

“By incorporating Google AI, Hire now reduces repetitive, time-consuming tasks, like scheduling interviews into one-click interactions. This means hiring teams can spend less time with logistics and more time connecting with people,” Google’s Berit Hoffmann, Hire product manager wrote in a blog post announcing the new features.

The first piece involves making it easier and faster to schedule interviews with candidates. This is a multi-step activity that involves scheduling appropriate interviewers, choosing a time and date that works for all parties involved in the interview and scheduling a room in which to conduct the interview. Organizing these kind of logistics tend to eat up a lot of time.

“To streamline this process, Hire now uses AI to automatically suggest interviewers and ideal time slots, reducing interview scheduling to a few clicks,” Hoffmann wrote.

Photo: Google

Another common hiring chore is finding keywords in a resume. Hire’s AI now finds these words for a recruiter automatically by analysing terms in a job description or search query and highlighting relevant words including synonyms and acronyms in a resume to save time spent manually searching for them.

Photo: Google

Finally, another standard part of the hiring process is making phone calls, lots of phone calls. To make this easier, the latest version of Google Hire has a new click-to-call function. Simply click the phone number and it dials automatically and registers the call in call a log for easy recall or auditing.

While Microsoft has LinkedIn and Office 365, Google has G Suite and Google Hire. The strategy behind Hire is to allow hiring personnel to work in the G Suite tools they are immersed in every day and incorporate Hire functionality within those tools.

It’s not unlike CRM tools that integrate with Outlook or GMail because that’s where sales people spend a good deal of their time anyway. The idea is to reduce the time spent switching between tools and make the process a more integrated experience.

While none of these features individually will necessarily wow you, they are making use of Google AI to simplify common tasks to reduce some of the tedium associated with every-day hiring tasks.

Jun
13
2018
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Tableau gets AI shot in the arm with Empirical Systems acquisition

When Tableau was founded back in 2003, not many people were thinking about artificial intelligence to drive analytics and visualization, but over the years the world has changed and the company recognized that it needed talent to keep up with new trends. Today, it announced it was acquiring Empirical Systems, an early stage startup with AI roots.

Tableau did not share the terms of the deal.

The startup was born just two years ago from research on automated statistics at the MIT Probabilistic Computing Project. According to the company website, “Empirical is an analytics engine that automatically models structured, tabular data (such as spreadsheets, tables, or csv files) and allows those models to be queried to uncover statistical insights in data.”

The product was still in private Beta when Tableau bought the company. It is delivered currently as an engine embedded inside other applications. That sounds like something that could slip in nicely into the Tableau analytics platform. What’s more, it will be bringing the engineering team on board for some AI knowledge, while taking advantage of this underlying advanced technology.

Francois Ajenstat, Tableau’s chief product officer says this ability to automate findings could put analytics and trend analysis into the hands of more people inside a business. “Automatic insight generation will enable people without specialized data science skills to easily spot trends in their data, identify areas for further exploration, test different assumptions, and simulate hypothetical situations,” he said in a statement.

Richard Tibbetts, Empirical Systems CEO, says the two companies share this vision of democratizing data analysis. “We developed Empirical to make complex data modeling and sophisticated statistical analysis more accessible, so anyone trying to understand their data can make thoughtful, data-driven decisions based on sound analysis, regardless of their technical expertise,” Tibbets said in a statement.

Instead of moving the team to Seattle where Tableau has its headquarters, it intends to leave the Empirical Systems team in place and establish an office in Cambridge, Massachusetts.

Empirical was founded in 2016 and has raised $2.5 million.

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