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Main article: Artificial Intelligence

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Facebook brings its 3D photos feature to users with single-camera phones

22:04 | 28 February

Facebook first showed off its 3D photos back in 2018, and shared the technical details behind it a month later. But unless you had one of a handful of phones with dual cameras back then (when they weren’t so common), you couldn’t make your own. Today an update brings 3D photos to those of us still rocking a single camera.

In case you don’t remember or haven’t seen one lately, the 3D photos work by analyzing a 2D picture and slicing it into a ton of layers that move separately when you tilt the phone or scroll. I’m not a big fan of 3D anything, and I don’t even use Facebook, but the simple fact is this feature is pretty cool.

The problem is it used the dual camera feature to help the system determine distance, which informed how the picture should be sliced. That meant I, with my beautiful iPhone SE, was out of the running — along with about a billion other people who hadn’t bought into the dual-camera thing yet.

But over the last few years the computer vision team over at Facebook has been working on making it possible to do this without dual-camera input. At last they succeeded, and this blog post explains, in terms technical enough that I’m not even going to attempt to summarize them here, just how they did it.

The advances mean that many — though not all — relatively modern single-camera phones should be able to use the feature. Google’s Pixel series is now supported, and single-camera iPhones from the 7 forward. The huge diversity of Android devices makes it hard to say which will and won’t be supported — it depends on a few things not usually listed on the spec sheet — but you’ll be able to tell once your Facebook app updates and you take a picture.

 


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Microsoft’s Cortana drops consumer skills as it refocuses on business users

20:44 | 28 February

With the next version of Windows 10, coming this spring, Microsoft’s Cortana digital assistant will lose a number of consumer skills around music and connected homes, as well as some third-party skills. That’s very much in line with Microsoft’s new focus for Cortana, but it may still come as a surprise to the dozens of loyal Cortana fans.

Microsoft is also turning off Cortana support in its Microsoft Launcher on Android by the end of April and on older versions of Windows that have reached their end-of-service date, which usually comes about 36 months after the original release.

cortana

As the company explained last year, it now mostly thinks of Cortana as a service for business users. The new Cortana is all about productivity, with deep integrations into Microsoft’s suite of Office tools, for example. In this context, consumer services are only a distraction, and Microsoft is leaving that market to the likes of Amazon and Google .

Because the new Cortana experience is all about Microsoft 365, the subscription service that includes access to the Office tools, email, online storage and more, it doesn’t come as a surprise that the assistant’s new feature will give you access to data from these tools, including your calendar, Microsoft To Do notes and more.

And while some consumer features are going away, Microsoft stresses that Cortana will still be able to tell you a joke, set alarms and timers, and give you answers from Bing.

For now, all of this only applies to English-speaking users in the U.S. Outside of the U.S., most of the productivity features will launch in the future.

 


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Indian research firm Convergence Catalyst is ready for its second act

10:07 | 28 February

A 9-year-old is smashing the shuttle far and wide, and frantically pacing back and forth on the court in Bangalore, India, as her competition refuses to back down. Her rival is not a human. She is playing against a machine that is mimicking the game of badminton legend P.V. Sindhu, toned down a few notches to adjust for the age difference.

By the court, her father, Jayanth Kolla, is watching the game and taking notes. Kolla is a familiar name in the tech startup and business ecosystem in India. For the last eight years, he has been helming the research firm Convergence Catalyst, which covers mobility, telecom, AI and IoT.

When his daughter showed interest in badminton, Kolla rushed to explore options, only to realize that the centuries old sports could use some deep tech.

He reached out to a few friends to explore if they could build a device. “I have always wondered how a younger version of players who have made it to the professional arena must have played like,” he said in an interview.

Months later, they had something better.

Sensate Technologies

Kolla founded Sensate Technologies last year and has hired many industry experts and data scientists from Stanford, MIT, and India’s IIT. Sensate is building solutions on deep technologies such as AI, ML, advanced analytics, IoT, robotics and blockchain.

In the last year, the bootstrapped startup has developed seven prototypes, five of which are for sports. It holds eight patents. Which brings us back to the court.

One of the prototypes that Sensate has built is the machine that Kolla’s daughter is playing against. In a recent interview, he demonstrated how Sensate was able to accurately map how a player moves on the court and goes about smashing the shuttle by just looking at two-dimensional videos on YouTube and mobile camera feed. This has been built using Computer Vision AI.

It then fine tunes the gameplay in accordance with the age difference, which is input into a machine that can now mimic that player to a great level, said Kolla.

A handful of startups and established players have sought to address the sports tech market in recent years. SeeHow, another India-based startup, builds and embeds sensors in bats and balls to track specific types of data that batsmen and bowlers generate.

Kolla’s aim is to turn Sensate Technologies into a global deep tech venture foundry and build 20 odd products that would then branch into multiple companies operating in 11 different industries.

Microsoft last year partnered with Indian cricket legend Anil Kumble’s company Spektacom to work on a number of solutions including a smart sticker for bats that contains sensor tech designed to track the performance.

But Kolla’s ambitions go way beyond sports tech.

“The best part about deep technology solutions and platforms is that you build solutions on these technologies to solve a problem in a particular sector and with very little incremental effort, they can solve problems in a completely different sector,” he said.

Kolla, a former product manager at Motorola and Nokia, among other companies, said the startup is also in discussion with one of the world’s biggest companies that is looking to license its tech for their healthcare stack. “This validates our approach.” He declined to name any potential clients as the talks have not materialized yet.

 


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DocuSign acquires Seal Software for $188M to enhance its AI chops

02:03 | 28 February

Contract management service DocuSign today announced that it is acquiring Seal Software for $188 million in cash. The acquisition is expected to close later this year. DocuSign, it’s worth noting, previously invested $15 million in Seal Software in 2019.

Seal Software was founded in 2010 and while it may not be a mainstream brand, its customers include the likes of PayPal, Dell, Nokia and DocuSign itself. These companies use Seal for its contract management tools, but also for its analytics, discovery and data extraction services. And it’s these AI smarts the company developed over time to help businesses analyze their contracts that made DocuSign acquire the company. This can help them significantly reduce their time for legal reviews, for example.

“Seal was built to make finding, analyzing, and extracting data from contracts simpler and faster,” DocuSign CEO John O’Melia said in today’s announcement. “We have a natural synergy with DocuSign, and our team is excited to leverage our AI expertise to help make the Agreement Cloud even smarter. Also, given the company’s scale and expansive vision, becoming part of DocuSign will provide great opportunities for our customers and partners.”

DocuSign says it will continue to sell Seal’s analytics tools. What’s surely more important to DocuSign, though, is that it will also leverage the company’s AI tools to bolster its DocuSign CLM offering. CLM is DocuSign’s service for automating the full contract lifecycle, with a graphical interface for creating workflows and collaboration tools for reviewing and tracking changes, among other things. And integration with Seal’s tools, DocuSign argues, will allow it to provide its customers with a “faster, more efficient agreement process,” while Seal’s customers will benefit from deeper integrations with the DocuSign Agreement Cloud.

 


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Fear and liability in algorithmic hiring 

01:37 | 28 February

It would be a foolish U.S. business that tried to sell chlorine-washed chicken in Europe — a region where very different food standards apply. But in the high-tech world of algorithmically assisted hiring, it’s a different story.

A number of startups are selling data-driven tech tools designed to comply with U.S. equality laws into the European Union, where their specific flavor of anti-discrimination compliance may be as legally meaningless as the marketing glitter they’re sprinkling — with eye-catching (but unquantifiable) claims of “fairness metrics” and “bias beating” AIs.

First up, if your business is trying to crystal-ball-gaze something as difficult to quantify (let alone predict) as “job fit” and workplace performance, where each individual hire will almost certainly be folded into (and have their performance shaped by) a dynamic mix of other individuals commonly referred to as “a team” — and you’re going about this job matchmaking “astrology” by working off of data sets that are absolutely not representative of our colorful, complex, messy human reality — then the most pressing question is probably, “what are you actually selling?”

Snake oil in software form? Automation of something math won’t ever be able to “fix?” An impossibly reductionist dream of friction-free recruitment?

Deep down in the small print, does your USP sum to claiming to do the least possible damage? And doesn’t that sound, well, kind of awkward?

 


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London-based Gyana raises $3.9M for a no-code approach to data science

19:31 | 27 February

Coding and other computer science expertise remain some of the more important skills that a person can have in the working world today, but in the last few years, we have also seen a big rise in a new generation of tools providing an alternative way of reaping the fruits of technology: “no-code” software, which lets anyone — technical or non-technical — build apps, games, AI-based chatbots, and other products that used to be the exclusive terrain of engineers and computer scientists.

Today, one of the newer startups in the category — London-based Gyana, which lets non-technical people run data science analytics on any structured dataset — is announcing a round of £3 million to fuel its next stage of growth.

Led by UK firm Fuel Ventures, other investors in this round include Biz Stone of Twitter, Green Shores Capital and U+I , and it brings the total raised by the startup to $6.8 million since being founded in 2015.

Gyana (Sanskrit for “knowledge”) was co-founded by Joyeeta Das and David Kell, who were both pursuing post-graduate degrees at Oxford: Das, a former engineer, was getting an MBA, and Kell was doing a PhD in physics.

Das said that the idea of building this tool came out of the fact that the pair could see a big disconnect emerging not just in their studies, but also in the world at large — not so much a digital divide, as a digital light year in terms of the distance between the groups of who and who doesn’t know how to work in the realm of data science.

“Everyone talks about using data to inform decision making, and the world becoming data-driven, but actually that proposition is available to less than one percent of the world,” she said.

Out of that, the pair decided to work on building a platform that Das describes as a way to empower “citizen data scientists”, by letting users upload any structured data set (for example, a .CSV file) and running a series of queries on it to be able to visualise trends and other insights more easily.

While the longer term goal may be for any person to be able to produce an analytical insight out of a long list of numbers, the more practical and immediate application has been in enterprise services and building tools for non-technical knowledge workers to make better, data-driven decisions.

To prove out its software, the startup first built an app based on the platform that it calls Neera (Sanskrit for “water”), which specifically parses footfall and other “human movement” metrics, useful for applications in retail, real estate and civic planning — for example to determine well certain retail locations are performing, footfall in popular locations, decisions on where to place or remove stores, or how to price a piece of property.

Starting out with the aim of mid-market and smaller companies — those most likely not to have in-house data scientists to meet their business needs — startup has already picked up a series of customers that are actually quite a lot bigger than that. They include Vodafone, Barclays, EY, Pret a Manger, Knight Frank and the UK Ministry of Defense. It says it has some £1 million in contracts with these firms currently.

That, in turn, has served as the trigger to raise this latest round of funding and to launch Vayu (Sanskrit for “air”) — a more general purpose app that covers a wider set of parameters that can be applied to a dataset. So far, it has been adopted by academic researchers, financial services employees, and others that use analysis in their work, Das said.

With both Vayu and Neera, the aim — refreshingly — is to make the whole experience as privacy-friendly as possible, Das noted. Currently, you download an app if you want to use Gyana, and you keep your data local as you work on it. Gyana has no “anonymization” and no retention of data in its processes, except things like analytics around where your cursor hovers, so that Gyana knows how it can improve its product.

“There are always ways to reverse engineer these things,” Das said of anonymization. “We just wanted to make sure that we are not accidentally creating a situation where, despite learning from anaonyised materials, you can’t reverse engineer what people are analysing. We are just not convinced.”

While there is something commendable about building and shipping a tool with a lot of potential to it, Gyana runs the risk of facing what I think of as the “water, water everywhere” problem. Sometimes if a person really has no experience or specific aim, it can be hard to think of how to get started when you can do anything. Das said they have also identified this, and so while currently Gyana already offers some tutorials and helper tools within the app to nudge the user along, the plan is to eventually bring in a large variety of datasets for people to get started with, and also to develop a more intuitive way to “read” the basics of the files in order to figure out what kinds of data inquiries a person is most likely to want to make.

The rise of “no-code” software has been a swift one in the world of tech spanning the proliferation of startups, big acquisitions, and large funding rounds. Companies like Airtable and DashDash are aimed at building analytics leaning on interfaces that follow the basic design of a spreadsheet; AppSheet, which is a no-code mobile app building platform, was recently acquired by Google; and Roblox (for building games without needing to code) and Uncorq (for app development) have both raised significant funding just this week.

Gartner predicts that by 2024, some 65% of all app development will be made on low- or no-code platforms, and Forrester estimates that the no- and low-code market will be worth some $10 billion this year, rising to $21.2 billion by 2024.

That represents a big business opportunity for the likes of Gyana, which has been unique in using the no-code approach specifically to tackle the area of data science.

However, in the spirit of citizen data scientists, the intention is to keep a consumer version of the apps free to use as it works on signing up enterprise users with more enhanced paid products, which will be priced on an annual license basis (currently clients are paying between $6,000 and $12,000 depending on usage, she said).

“We want to do free for as long as we can,” Das said, both in relation to the data tools and the datasets that it will offer to users. “The biggest value add is not about accessing premium data that is hard to get. We are not a data marketplace but we want to provide data that makes sense to access,” adding that even with business users, “we’d like you to do 90% of what you want to do without paying for anything.”

 


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Tractable claims $25M to sell damage-assessing AIs to more insurance giants

14:39 | 27 February

London-based insurtech AI startup Tractable, which is applying artificial intelligence to speed up accident and disaster recovery by using computer vision to perform visual damage appraisal instead of getting humans to do the job, has closed a $25 million Series C, led by Canadian investment fund Georgian Partners.

Existing investors also participated, including Insight Partners and Ignition Partners. The round nearly doubles the 2014-founded startup’s total funding, taking it to $55M raised to date.

When TechCrunch spoke to Tractable’s co-founder and CEO Alexandre Dalyac, back in 2018, he said the company’s aim is to speed up insurance-related response times around events like car accidents and natural disasters by as much as 10x.

Two years on the startup isn’t breaking out any hard metrics — but says its product is used by a number of multinational insurance firms, including Ageas in the UK, France’s Covéa, Japan’s Tokio Marine and Polish insurer Talanx-Warta — to analyse vehicle damage “effectively and efficiently”.

It also says the technology has been involved in accelerating insurance-related assessments for “hundreds of thousands of people worldwide”.

Tractable’s pitch is that AI appraisals of damage to vehicles/property can take place via its platform “in minutes”, thereby allowing for repairs to begin sooner and people’s livelihoods to be restored more quickly.

Though of course if the AI algorithm denies a person’s claim the opposite would happen.

The startup said its new funding will go on expanding its market footprint. It has customers across nine markets, globally, at this point. And in addition to its first offices in the UK and US recently opened a permanent office in Japan — with the stated aim of serving new clients in the Asia region.

It also said the Series C will be used for continued product development by further enhancing its AI.

Its current product line up includes AI for assessing damage to vehicles and another focused on the appraisal of damage caused by natural disasters, such as to buildings by hurricanes.

“Our AI solutions capture and process photos and damage and predict repair costs — at scale,” Tractable claims on its website, noting its proprietary algorithms can be fed by “satellite, drone or smartphone imagery”.

Commenting on the funding in a statement Lonne Jaffe, MD at Insight Partners and also Tractable board director, said: “Tractable has achieved tremendous scale in the past year with a customer base across nine countries, a differentiated data asset, and the expansion of their team to over 100 employees across London, New York, and now Tokyo. We are excited to continue to invest in Tractable as the team brings its powerful AI technology to many more countries.”

Emily Walsh, principal at Georgian Partners, added that the startup’s “sophisticated approach to computer vision applied to accident recovery is resonating with the largest players globally, who are using the platform to make real-time, data-driven decisions while dramatically improving the customer experience”.

“We’re incredibly excited to partner with the Tractable team to help them move even faster on bringing the next wave of technological innovation to accident and disaster recovery across the world,” she added.

It’s worth noting that in the EU citizens have a right, under data protection law, to (human) review of algorithmic decisions if they a legal or similarly significant impact — and insurance would likely fall into that category.

EU policymakers also recently laid out a proposal to regulate certain “high risk” AI systems and said they intend to expand the bloc’s consumer protection rules by bringing in a testing and certification program for the data-sets that feed algorithms powering AI-driven services to support product safety.

 


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Grab your ticket: Only one week to TC Sessions: Robotics + AI 2020

00:00 | 27 February

It’s T-minus one week to the big day, March 3, when more than 1,000 startuppers will convene in San Jose, Calif. for TC Sessions: Robotics + AI 2020. We’re talking a hefty cross-section representing big companies and exciting new startups. We’re talking some of the most innovative thinkers, makers, researchers, investors and influencers — all focused on creating the future of these two world-changing technologies.

Don’t miss out on this one-day conference of interviews, panel discussions, Q&As, workshops and demos dedicated to every aspect of robotics and A.I. General admission tickets cost $345. Snag your ticket now and save, because prices go up at the door. Want to save even more? Save 15 percent when you buy four or more tickets. Are you a student? Grab a ticket for just $50.

What do we have planned for this TC Session? Here’s a small sample of the fab programming that awaits you, and be sure to check out the full TC Session agenda here.

  • Q&A with Founders: This is your chance to ask questions of Sébastien Boyer, co-founder and CEO of FarmWise and Noah Ready-Campbell, founder and CEO of Built Robotics — some of the most successful robotics founders on our stage.
  • Disney Robotics: Imagineers from Disney will present state-of-the-art robotics built to populate its theme parks.
  • Investing in Robotics and AI: Lessons from the Industry’s VCs: Dror Berman, founding partner at Innovation Endeavors, Jocelyn Goldfein, managing director at Zetta Venture Partners and Eric Migicovsky, general partner at Y Combinator will discuss the rising tide of venture capital funding in robotics and AI. The investors bring a combination of early stage investing and corporate venture capital expertise, sharing a fondness for the wild world of robotics and AI investing.

And — new this year — don’t miss watching the finalists from our Pitch Night competition. Founders of these early-stage companies, hand-picked by TechCrunch editors, will take the stage and have just five minutes to present their wares.

With just one more week until TC Sessions: Robotics + AI 2020 kicks off, you don’t have much time left to save on tickets. Why pay more at the door? Buy your ticket now and join the best and brightest for a full day dedicated to all things robotics.

 


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Cartesiam helps developers bring AI to microcontrollers

22:02 | 26 February

Cartesiam, a startup that aims to bring machine learning to edge devices powered by microcontrollers, has launched a new tool for developers who want an easier way to build services for these devices. The new NanoEdge AI Studio is the first IDE specifically designed for enabling machine learning and inferencing on Arm Cortex-M microcontrollers, which power billions of devices already.

As Cartesiam GM Marc Dupaquier, who co-founded the company in 2016, told me, the company works very closely with Arm, given that both have a vested interest in having developers create new features for these devices. He noted that while the first wave of IoT was all about sending data to the cloud, that has now shifted and most companies now want to limit the amount of data they send out and do a lot more on the device itself. And that’s pretty much one of the founding theses of Cartesiam. “It’s just absurd to send all this data — which, by the way, also exposes the device from a security standpoint,” he said. “What if we could do it much closer to the device itself?”

The company first bet on Intel’s short-lived Curie SoC platform. That obviously didn’t work out all that well, given that Intel axed support for Curie in 2017. Since then, Cartesiam has focused on the Cortex-M platform, which worked out for the better, given how ubiquitous it has become. Since we’re talking about low-powered microcontrollers, though, it’s worth noting that we’re not talking about face recognition or natural language understanding here. Instead, using machine learning on these devices is more about making objects a little bit smarter and, especially in an industrial use case, detecting abnormalities or figuring out when it’s time to do preventive maintenance.

Today, Cartesiam already works with many large corporations that build Cortex-M-based devices. The NanoEdge Studio makes this development work far easier, though. “Developing a smart object must be simple, rapid and affordable — and today, it is not, so we are trying to change it,” said Dupaquier. But the company isn’t trying to pitch its product to data scientists, he stressed. “Our target is not the data scientists. We are actually not smart enough for that. But we are unbelievably smart for the embedded designer. We will resolve 99% of their problems.” He argues that Cartesiam reduced time to market by a factor of 20 to 50, “because you can get your solution running in days, not in multiple years.”

One nifty feature of the NanoEdge Studio is that it automatically tries to find the best algorithm for a given combination of sensors and use cases and the libraries it generates are extremely small and use somewhere between 4K to 16K of RAM.

NanoEdge Studio for both Windows and Linux is now generally available. Pricing starts at €690/month for a single user or €2,490/month for teams.

 


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Freshworks acquires AnsweriQ

19:08 | 26 February

Customer engagement platform Freshworks today announced that it has acquired AnsweriQ, a startup that provides AI tools for self-service solutions and agent-assisted use cases where the ultimate goal is to quickly provide customers with answers and make agents more efficient.

The companies did not disclose the acquisition price. AnsweriQ last raised a funding round in 2017, when it received $5 million in a Series A round from Madrona Venture Group.

Freshworks founder and CEO Girish Mathrubootham tells me that he was introduced to the company through a friend, but that he had also previously come across AnsweriQ as a player in the customer service automation space for large clients in high-volume call centers.

“We really liked the team and the product and their ability to go up-market and win larger deals,” Mathrubootham said. “In terms of using the AI/ML customer service, the technology that they’ve built was perfectly complementary to everything else that we were building.”

He also noted the client base, which doesn’t overlap with Freshworks’, and the talent at AnsweriQ, including the leadership team, made this a no-brainer.

AnsweriQ, which has customers that use Freshworks and competing products, will continue to operate its existing products for the time being. Over time, Freshworks, of course, hopes to convert many of these users into Freshworks users as well. The company also plans to integrate AnsweriQ’s technology into its Freddy AI engine. The exact branding for these new capabilities remains unclear, but Mathrubootham suggested FreshiQ as an option.

As for the AnsweriQ leadership team, CEO Pradeep Rathinam will be joining Freshworks as chief customer officer.

Rathinam told me that the company was at the point where he was looking to raise the next round of funding. “As we were going to raise the next round of funding, our choices were to go out and raise the next round and go down this path, or look for a complementary platform on which we can vet our products and then get faster customer acquisition and really scale this to hundreds or thousands of customers,” he said.

He also noted that as a pure AI player, AnsweriQ had to deal with lots of complex data privacy and residency issues, so a more comprehensive platform like Freshworks made a lot of sense.

Freshworks has always been relatively acquisitive. Last year, the company acquired the customer success service Natero, for example. With the $150 million Series H round it announced last November, the company now also has the cash on hand to acquire even more customers. Freshworks is currently valued at about $3.5 billion and has 2,7000 employees in 13 offices. With the acquisition of AnsweriQ, it now also has a foothold in Seattle, which it plans to use to attract local talent to the company.

 


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