Aug
21
2018
--

Talla builds a smarter customer knowledge base

Talla is taking aim at the customer service industry with its latest release, an AI-infused knowledge base. Today, the company released version 2.0 of the Talla Intelligent Knowledge Base.

The company also announced that Paula Long, most recently CEO at Data Gravity, has joined the company as SVP of engineering.

This tool combines customer content with automation, chatbots and machine learning. It’s designed to help teams who work directly with customers get at the information they need faster and the machine learning element should allow it to improve over time.

You can deploy the product as a widget on your website to give customers direct access to the information, but Rob May, company founder and CEO says the most common use case involves helping sales, customer service and customer success teams get access to the most relevant and current information, whether that’s maintenance or pricing.

The information can get into the knowledge base in several ways. First of all you can enter elements like product pages and FAQs directly in the Talla product as with any knowledge base. Secondly if an employee asks a question and there isn’t an adequate answer, it exposes the gaps in information.

Talla Knowledge Base gap list. Screenshot: Talla

“It really shows you the unknown unknowns in your business. What are the questions people are asking that you didn’t realize you don’t have content for or you don’t have answers for. And so that allows you to write new content and better content,” May explained.

Finally, the company can import information into the knowledge base from Salesforce, ServiceNow, Jira or wherever it happens to live, and that can be added to a new page or incorporated into existing page as appropriate.

Employees interact with the system by asking a bot questions and it supplies the answers if one exists. It works with Slack, Microsoft Teams or Talla Chat.

Talla bot in action in Talla Chat. Screenshot: Talla

Customer service remains a major pain point for many companies. It is the direct link to customers when they are having issues. A single bad experience can taint a person’s view of a brand, and chances are when a customer is unhappy they let their friends know on social media, making an isolated incident much bigger. Having quicker access to more accurate information could help limit negative experiences.

Today’s announcement builds on an earlier version of the product that took aim at IT help desks. Talla found customers kept asking for a solution that provided similar functionality with customer-facing information and they have tuned it for that.

May launched Talla in 2015 after selling his former startup Backupify to Datto in 2014. The company, which is based near Boston, has raised $12.3 million.

Aug
17
2018
--

Incentivai launches to simulate how hackers break blockchains

Cryptocurrency projects can crash and burn if developers don’t predict how humans will abuse their blockchains. Once a decentralized digital economy is released into the wild and the coins start to fly, it’s tough to implement fixes to the smart contracts that govern them. That’s why Incentivai is coming out of stealth today with its artificial intelligence simulations that test not just for security holes, but for how greedy or illogical humans can crater a blockchain community. Crypto developers can use Incentivai’s service to fix their systems before they go live.

“There are many ways to check the code of a smart contract, but there’s no way to make sure the economy you’ve created works as expected,” says Incentivai’s solo founder Piotr Grudzie?. “I came up with the idea to build a simulation with machine learning agents that behave like humans so you can look into the future and see what your system is likely to behave like.”

Incentivai will graduate from Y Combinator next week and already has a few customers. They can either pay Incentivai to audit their project and produce a report, or they can host the AI simulation tool like a software-as-a-service. The first deployments of blockchains it’s checked will go out in a few months, and the startup has released some case studies to prove its worth.

“People do theoretical work or logic to prove that under certain conditions, this is the optimal strategy for the user. But users are not rational. There’s lots of unpredictable behavior that’s difficult to model,” Grudzie? explains. Incentivai explores those illogical trading strategies so developers don’t have to tear out their hair trying to imagine them.

Protecting crypto from the human x-factor

There’s no rewind button in the blockchain world. The immutable and irreversible qualities of this decentralized technology prevent inventors from meddling with it once in use, for better or worse. If developers don’t foresee how users could make false claims and bribe others to approve them, or take other actions to screw over the system, they might not be able to thwart the attack. But given the right open-ended incentives (hence the startup’s name), AI agents will try everything they can to earn the most money, exposing the conceptual flaws in the project’s architecture.

“The strategy is the same as what DeepMind does with AlphaGo, testing different strategies,” Grudzie? explains. He developed his AI chops earning a masters at Cambridge before working on natural language processing research for Microsoft.

Here’s how Incentivai works. First a developer writes the smart contracts they want to test for a product like selling insurance on the blockchain. Incentivai tells its AI agents what to optimize for and lays out all the possible actions they could take. The agents can have different identities, like a hacker trying to grab as much money as they can, a faker filing false claims or a speculator that cares about maximizing coin price while ignoring its functionality.

Incentivai then tweaks these agents to make them more or less risk averse, or care more or less about whether they disrupt the blockchain system in its totality. The startup monitors the agents and pulls out insights about how to change the system.

For example, Incentivai might learn that uneven token distribution leads to pump and dump schemes, so the developer should more evenly divide tokens and give fewer to early users. Or it might find that an insurance product where users vote on what claims should be approved needs to increase its bond price that voters pay for verifying a false claim so that it’s not profitable for voters to take bribes from fraudsters.

Grudzie? has done some predictions about his own startup too. He thinks that if the use of decentralized apps rises, there will be a lot of startups trying to copy his approach to security services. He says there are already some doing token engineering audits, incentive design and consultancy, but he hasn’t seen anyone else with a functional simulation product that’s produced case studies. “As the industry matures, I think we’ll see more and more complex economic systems that need this.”

Aug
17
2018
--

Klarity uses AI to strip drudgery from contract review

Klarity, a member of the Y Combinator 2018 Summer class, wants to automate much of the contract review process by applying artificial intelligence, specifically natural language processing.

Company co-founder and CEO Andrew Antos has experienced the pain of contract reviews first hand. After graduating from Harvard Law, he landed a job spending 16 hours a day reviewing contract language, a process he called mind-numbing. He figured there had to be a way to put technology to bear on the problem and Klarity was born.

“A lot of companies are employing internal or external lawyers because their customers, vendors or suppliers are sending them a contract to sign,” Antos explained They have to get somebody to read it, understand it and figure out whether it’s something that they can sign or if it requires specific changes.

You may think that this kind of work would be difficult to automate, but Antos said that  contracts have fairly standard language and most companies use ‘playbooks.’ “Think of the playbook as a checklist for NDAs, sales agreements and vendor agreements — what they are looking for and specific preferences on what they agree to or what needs to be changed,” Antos explained.

Klarity is a subscription cloud service that checks contracts in Microsoft Word documents using NLP. It makes suggestions when it sees something that doesn’t match up with the playbook checklist. The product then generates a document, and a human lawyer reviews and signs off on the suggested changes, reducing the review time from an hour or more to 10 or 15 minutes.

Screenshot: Klarity

They launched the first iteration of the product last year and have 14 companies using it with 4 paying customers so far including one of the world’s largest private equity funds. These companies signed on because they have to process huge numbers of contracts. Klarity is helping them save time and money, while applying their preferences in a consistent fashion, something that a human reviewer can have trouble doing.

He acknowledges the solution could be taking away work from human lawyers, something they think about quite a bit. Ultimately though, they believe that contract reviewing is so tedious, it is freeing up lawyers for work that requires a greater level of intellectual rigor and creativity.

Antos met his co-founder and CTO, Nischal Nadhamuni, at an MIT entrepreneurship class in 2016 and the two became fast friends. In fact, he says that they pretty much decided to start a company the first day. “We spent 3 hours walking around Cambridge and decided to work together to solve this real problem people are having.”

They applied to Y Combinator two other times before being accepted in this summer’s cohort. The third time was the charm. He says the primary value of being in YC is the community and friendships they have formed and the help they have had in refining their approach.

“It’s like having a constant mirror that helps you realize any mistakes or any suboptimal things in your business on a high speed basis,” he said.

Aug
13
2018
--

New Uber feature uses machine learning to sort business and personal rides

Uber announced a new program today called Profile Recommendations that takes advantage of machine intelligence to reduce user error when switching between personal and business accounts.

It’s not unusual for a person to have both types of accounts. When you’re out and about, it’s easy to forget to switch between them when appropriate. Uber wants to help by recommending the correct one.

“Using machine learning, Uber can predict which profile and corresponding payment method an employee should be using, and make the appropriate recommendation,” Ronnie Gurion, GM and Global Head of Uber for Business wrote in a blog post announcing the new feature.

Uber has been analyzing a dizzying amount of trip data for so long, it can now (mostly) understand the purpose of a given trip based on the details of your request. While it’s certainly not perfect because it’s not always obvious what the purpose is, Uber believes it can determine the correct intention 80 percent of the time. For that remaining 20 percent, when it doesn’t get it right, Uber is hoping to simplify corrections too.

Photo: Uber

Business users can now also assign trip reviewers — managers or other employees who understand the employee’s usage patterns, and can flag questionable rides. Instead of starting an email thread or complicated bureaucratic process to resolve an issue, the employee can now see these flagged rides and resolve them right in the app. “This new feature not only saves the employee’s and administrator’s time, but it also cuts down on delays associated with closing out reports,” Gurion wrote in the blog post announcement.

Uber also announced that it’s supporting a slew of new expense reporting software to simplify integration with these systems. They currently have integrations with Certify, Chrome River, Concur and Expensify. They will be adding support for Expensya, Happay, Rydoo, Zeno by Serko and Zoho Expense starting in September.

All of this should help business account holders deal with Uber expenses more efficiently, while integrating with many of the leading expense programs to move data smoothly from Uber to a company’s regular record-keeping systems.

Jul
30
2018
--

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
25
2018
--

Google is baking machine learning into its BigQuery data warehouse

There are still a lot of obstacles to building machine learning models and one of those is that in order to build those models, developers often have to move a lot of data back and forth between their data warehouses and wherever they are building their models. Google is now making this part of the process a bit easier for the developers and data scientists in its ecosystem with BigQuery ML, a new feature of its BigQuery data warehouse, by building some machine learning functionality right into BigQuery.

Using BigQuery ML, developers can build models using linear and logistical regression right inside their data warehouse without having to transfer data back and forth as they build and fine-tune their models. And all they have to do to build these models and get predictions is to write a bit of SQL.

Moving data doesn’t sound like it should be a big issue, but developers often spend a lot of their time on this kind of grunt work — time that would be better spent on actually working on their models.

BigQuery ML also promises to make it easier to build these models, even for developers who don’t have a lot of experience with machine learning. To get started, developers can use what’s basically a variant of standard SQL to say what kind of model they are trying to build and what the input data is supposed to be. From there, BigQuery ML then builds the model and allows developers to almost immediately generate predictions based on it. And they won’t even have to write any code in R or Python.

These new features are now available in beta.

Jul
24
2018
--

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
23
2018
--

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.

Jul
23
2018
--

Cogito scores $37M as AI-driven sentiment analysis biz grows

Cogito announced a $37 million Series C investment today led by Goldman Sachs Growth Equity. Previous investors Salesforce Ventures and OpenView also chipped in. Mark Midle of Goldman Sachs’ Merchant Banking Division, has joined Cogito’s Board of Directors

The company has raised over $64 million since it emerged from the MIT Human Dynamics Lab back in 2007 trying to use the artificial intelligence technology available at the time to understand sentiment and apply it in a business context.

While it took some time for the technology to catch up with the vision, and find the right use case, company CEO and founder Joshua Feast says today they are helping customer service representatives understand the sentiment and emotional context of the person on the line and give them behavioral cues on how to proceed.

“We sell software to very large software, premium brands with many thousands of people in contact centers. The purpose of our solution is to help provide a really wonderful service experience in moments of truth,” he explained. Anyone who deals with a large company’s customer service has likely felt there is sometimes a disconnect between the person on the phone and their ability to understand your predicament and solve your problem.

Cogito in action giving customer service reps real-time feedback.

He says using his company’s solution, which analyzes the contents of the call in real time, and provides relevant feedback, the goal is to not just complete the service call, but to leave the customer feeling good about the brand and the experience. Certainly a bad experience can have the opposite effect.

He wants to use technology to make the experience a more human interaction and he recognizes that as an organization grows, layers of business process make it harder for the customer service representative to convey that humanity. Feast believes that technology has helped create this problem and it can help solve it too.

While the company is not talking about valuation or specific revenue at this point, Feast reports that revenue has grown 3X over the last year. Among their customers are Humana and Metlife, two large insurance companies, each with thousands of customer service agents.

Cogito is based in downtown Boston with 117 employees at last count, and of course they hope to use the money to add on to that number and help scale this vision further.

“This is about scaling our organization to meet client’s needs. It’s also about deepening what we do. In a lot of ways, we are only scratching the surface [of the underlying technology] in terms of how we can use AI to support emotional connections and help organizations be more human,” Feast said.

Jul
18
2018
--

Swim.ai raises $10M to bring real-time analytics to the edge

Once upon a time, it looked like cloud-based serviced would become the central hub for analyzing all IoT data. But it didn’t quite turn out that way because most IoT solutions simply generate too much data to do this effectively and the round-trip to the data center doesn’t work for applications that have to react in real time. Hence the advent of edge computing, which is spawning its own ecosystem of startups.

Among those is Swim.ai, which today announced that it has raised a $10 million Series B funding round led by Cambridge Innovation Capital, with participation from Silver Creek Ventures and Harris Barton Asset Management. The round also included a strategic investment from Arm, the chip design firm you may still remember as ARM (but don’t write it like that or their PR department will promptly email you). This brings the company’s total funding to about $18 million.

Swim.ai has an interesting take on edge computing. The company’s SWIM EDX product combines both local data processing and analytics with local machine learning. In a traditional approach, the edge devices collect the data, maybe perform some basic operations against the data to bring down the bandwidth cost and then ship it to the cloud where the hard work is done and where, if you are doing machine learning, the models are trained. Swim.ai argues that this doesn’t work for applications that need to respond in real time. Swim.ai, however, performs the model training on the edge device itself by pulling in data from all connected devices. It then builds a digital twin for each one of these devices and uses that to self-train its models based on this data.

“Demand for the EDX software is rapidly increasing, driven by our software’s unique ability to analyze and reduce data, share new insights instantly peer-to-peer – locally at the ‘edge’ on existing equipment. Efficiently processing edge data and enabling insights to be easily created and delivered with the lowest latency are critical needs for any organization,” said Rusty Cumpston, co-founder and CEO of Swim.ai. “We are thrilled to partner with our new and existing investors who share our vision and look forward to shaping the future of real-time analytics at the edge.”

The company doesn’t disclose any current customers, but it is focusing its efforts on manufacturers, service providers and smart city solutions. Update: Swim.ai did tell us about two customers after we published this story: The City of Palo Alto and Itron.

Swim.ai plans to use its new funding to launch a new R&D center in Cambridge, UK, expand its product development team and tackle new verticals and geographies with an expanded sales and marketing team.

Powered by WordPress | Theme: Aeros 2.0 by TheBuckmaker.com