HDS, from the Borders and Webvan founder, raises $3M as it gears up to launch its robot-run grocery and general merchandise play

The online grocery market is poised to get a little more crowded in the next several months, with the launch of a startup led by a veteran founder who has taken big hits from Amazon in the past, but now hopes to come back swinging with the help of an army of robots.

Home Delivery Services, a delivery startup founded by Louis Borders that plans to sell groceries and general merchandise online using a massive, automated system to power the fulfillment and logistics, is today announcing funding of $3 million to finalize the finishing touches on an AI-based robotic demonstration center outside of Indianapolis.

The plan is for the center to showcase the technology that HDS Global has been building over the last several years (plans first emerged as long ago as 2014), robots and other automation under the name RoboFS, that will power a wide fulfillment system extending from stocking, sorting and picking items that will then be delivered, mostly by humans, to consumers, to take on what Borders describes as a $1 trillion grocery market in the US.

“The $1 trillion grocery in the US is not well penetrated,” he said, comparing the opportunity to the one that Walmart seized 20 years ago in physical stores. “We want to offer a complete selection of groceries and general merchandise in one order.” The idea is to build warehouses that cover some 150,000 square feet to do $200 million in revenue over millions of SKUs for one-hour deliveries.

A funding round of $3 million — which is coming from Bob DiRumauldo, the chairman of Ulta and CEO of Naples Ventures — might sound a little modest, especially considering the hundreds of millions of dollars that have been collectively raised by online grocery players in the last several months — all of them racing to scale up their businesses in the wake of huge consumer demand for online shopping alternatives to visiting stores in person in the wake of the Covid-19 pandemic.

Borders said in an interview that this small round is primarily to kick off the demo center to show off RoboFS to help bring on new investors and new partners with the proof of concept. It already has a few investor partners, Ingram Micro and Toyota, and the idea will be to add more.

And he confirmed that HDS — which will unveil a different name when it launches commercially, he added — is also working on a much bigger round of funding, likely to close in the next 15 months, to fuel that wider commercial launch. It has raised $38 million to date, he said.

Borders’ name will ring a bell to many in the worlds of retail and technology: he was the founder and head of the Borders book superstores and later started Webvan, a very early mover in the world of online grocery ordering and delivery. Both companies crashed hard in their times and became case studies, and more specifically cautionary tales, around how to build businesses in the digital era: beware the specter of Amazon, of innovating too early or too late, of being less agile, too inefficient, and of not correctly identifying where the puck was going and skating to it.

This time around, the idea is that he’s focusing first and foremost on technology to try to head those problems off in ways that his previous ventures did not. This is one reason why HDS has spent so many years on building the technology: automation, specifically in areas like picking groceries, is one area that has foxed a lot of companies to date — Amazon continues to work on this, and Ocado, a leader in the space, has yet to launch robotic picking although it says this is coming soon. Borders estimates that bringing in automation can bring down the cost of labor by two-thirds, with people instead focused on delivering and selling at people’s doors.

“When we went out to buy the tech we didn’t see what we wanted,” Borders said. “We’re tryin to be smart about technology but the tech was just not there when we decided to build this 5 years ago. So we started with building that system. This became our opportunity.”

The interesting opportunity is not just to build services that don’t quite exist yet, but to provide a set of infrastructure that can be a viable alternative and supply chain to Amazon — a common goal that brings together players from a lot of disparate yet interconnected areas in the grocery value chain. This is one reason why companies like Toyota and Ingram have come on board to work with the startup.

Given that it’s been so many years in the making and has yet to see the proof of concept, there will continue to be a lot of factors that could not come together, but it’s a play that HDS, Borders and their partners are willing to make.

“Ecommerce has become an essential component in people’s daily lives but what many don’t realize is that it can be exponentially better than what is offered today,” said DiRumauldo in a statement. “I was attracted to working with Louis again and to the company’s big idea approach – an all-new robotic fulfillment system purpose-built for ecommerce – which can deliver a vastly improved experience at lower cost. I am excited to be a part of bringing this vision to life.”

#automation, #ecommerce, #food, #funding, #online-grocery, #robotics, #tc

0

Volvo to supply Chinese ride hailing giant Didi with autonomous driving cars

As the autonomous driving race in China heats up, Didi is rushing to expand its car fleets by picking Swedish automaker Volvo, an old partner of Uber, as its ally.

Didi said on Monday it will be using the XC90 SUVs of Volvo, which has been owned by Chinese auto company Geely since 2010, for its network of robotaxis in the long term. Didi created a subsidiary dedicated to autonomous driving last year and the unit has since raised about $800 million from investors including SoftBank Vision Fund and IDG Capital. The subsidiary now has over 500 employees.

Didi started out as a ride-share app in 2012 and gobbled up Uber China in 2016. It now offers a range of mobility services including taxi hailing, ride-hailing, carpooling, shared bikes and scooters, as well as financial services for drivers. The company is seeking a valution north of $100 billion in an initial public offering, Reuters reported last month.

Didi’s autonomous driving arm has been testing robotaxis for the past two years in China and the United States, but Volvo’s XC90 model will be the first to adopt Didi’s freshly minted self-driving hardware system called Gemini, which contains sensors like short, mid and long-range lidars, radars, cameras, a thermal imager; a fallback system; and remote assistance through 5G networks.

Didi said that its Gemini platform, coupled with Volvo’s backup functions including steering, braking and electric power, will eventually allow its robotaxis to remove safety drivers. If any of the primary systems fails during a ride, Volvo’s backup systems can act to bring the vehicle to a safe stop.

Didi is competing against a clutch of well-funded robotaxi startups in China, such as Pony.ai and WeRide, which are busy tesing in major Chinese cities and California while splurging on R&D expenses to reach Level 4 driving. AutoX, another Chinese robotaxi company, announced last week that it will be using Honda’s Accord and Inspire sedans for its test drives in China. The edge of Didi, some suggest, is the mountains of driving data accumulated from its ride-hailing business spanning Asia, Latin America, Africa and Russia.

Rising electric automakers like Nio and Xpeng have also joined in the race to automate vehicles, making bold claims that they, too, will be able to remove safety drivers soon. Meanwhile, traditional car manufacturers don’t want to fall behind. BAIC, a state-owned enterprise, for instance, is adding Huawei’s advanced automation system and smart cockpit to its new electric passenger cars.

#accord, #artificial-intelligence, #asia, #automation, #automotive, #autox, #china, #didi, #idg-capital, #robotaxi, #robotics, #self-driving-cars, #softbank, #softbank-vision-fund, #transportation, #uber, #volvo, #volvo-cars, #xpeng

0

Andrew Yang Hasn’t Done the Math

Was his economic story too good to check?

#automation, #elections-mayors, #income-inequality, #layoffs-and-job-reductions, #new-york-city, #politics-and-government, #productivity, #unemployment, #yang-andrew-1975

0

IBM acquires Italy’s MyInvenio to integrate process mining directly into its suite of automation tools

Automation has become a big theme in enterprise IT, with organizations using RPA, no-code and low-code tools, and other  technology to speed up work and bring more insights and analytics into how they do things every day, and today IBM is announcing an acquisition as it hopes to take on a bigger role in providing those automation services. The IT giant has acquired MyInvenio, an Italian startup that builds and operates process mining software.

Process mining is the part of the automation stack that tracks data produced by a company’s software, as well as how the software works, in order to provide guidance on what a company could and should do to improve it. In the case of myInvenio, the company’s approach involves making a “digital twin” of an organization to help track and optimize processes. IBM is interested in how myInvenio’s tools are able to monitor data in areas like sales, procurement, production and accounting to help organizations identify what might be better served with more automation, which it can in turn run using RPA or other tools as needed.

Terms of the deal are not being disclosed. It is not clear if myInvenio had any outside investors (we’ve asked and are awaiting a response). This is the second acquisition IBM has made out of Italy. (The first was in 2014, a company called CrossIdeas that now forms part of the company’s security business.)

IBM and myInvenio are not exactly strangers: the two inked a deal as recently as November 2020 to integrate the Italian startup’s technology into IBM’s bigger automation services business globally.

Dinesh Nirmal, GM of IBM Automation, said in an interview that the reason IBM acquired the company was two-fold. First, it lets IBM integrate the technology more closely into the company’s Cloud Pak for Business Automation, which sits on and is powered by Red Hat OpenShift and has other automation capabilities already embedded within it, specifically robotic process automation (RPA), document processing, workflows and decisions.

Second and perhaps more importantly, it will mean that IBM will not have to tussle for priority for its customers in competition with other solution partners that myInvenio already had. IBM will be the sole provider.

“Partnerships are great but in a partnership you also have the option to partner with others, and when it comes to priority who decides?” he said. “From the customer perspective, will they will work just on our deal, or others first? Now, our customers will get the end result of this… We can bring a single solution to an end user or an enterprise, saying, ‘look you have document processing, RPA, workflow, mining. That is the beauty of this and what customers will see.”

He said that IBM currently serves customers across a range of verticals including financial, insurance, healthcare and manufacturing with its automation products.

Notably, this is not the first acquisition that IBM has made to build out this stack. Last year, it acquired WDG to expand into robotic process automation.

And interestingly, it’s not even the only partnership that IBM has had in process mining. Just earlier this month, it announced a deal with one of the bigger names in the field, Celonis, a German startup valued at $2.5 billion in 2019.

Ironically, at the time, my colleague Ron wondered aloud why IBM wasn’t just buying Celonis outright in that deal. It’s hard to speculate if price was one reason. Remember: we don’t know the terms of this acquisition, but given myInvenio was off the fundraising radar, chances are it’s possibly a little less than Celonis’s pricetag.

We’ve asked and IBM has confirmed that it will continue to work with Celonis alongside now offering its own native process mining tools.

“In keeping with IBM’s open approach and $1 billion investment in ecosystem, [Global Business Services, IBM’s enterprise services division] works with a broad range of technologies based on client and market demand, including IBM AI and Automation software,” a spokesperson said in a statement. “Celonis focuses on execution management which supports GBS’ transformation of clients’ business processes through intelligent workflows across industries and domains. Specifically, Celonis has deep connectivity into enterprise systems such as Salesforce, SAP, Workday or ServiceNow, so the Celonis EMS platform helps GBS accelerate clients’ transformations and BPO engagements with these ERP platforms.”

Indeed, at the end of the day, companies that offer services, especially suites of services, are working in environments where they have to be open to customers using their own technology, or bringing in something else.

There may have been another force pushing IBM to bring more of this technology in-house, and that’s wider competitive climate. Earlier this year, SAP acquired another European startup in the process mining space, Signavio, in a deal reportedly worth about $1.2 billion. As more of these companies get snapped up by would-be IBM rivals, and those left standing are working with a plethora of other parties, maybe it was high time for IBM to make sure it had its own horse in the race.

“Through IBM’s planned acquisition of myInvenio, we are revolutionizing the way companies manage their process operations,” said Massimiliano Delsante, CEO, myInvenio, who will be staying on with the deal. “myInvenio’s unique capability to automatically analyze processes and create simulations — what we call a ‘Digital Twin of an Organization’ —  is joining with IBM’s AI-powered automation capabilities to better manage process execution. Together we will offer a comprehensive solution for digital process transformation and automation to help enterprises continuously transform insights into action.”

#automation, #enterprise, #europe, #fundings-exits, #ibm, #italy, #ma, #myinvenio, #process-mining, #rpa

0

China’s Xpeng in the race to automate EVs with lidar

Elon Musk famously said any company relying on lidar is “doomed.” Tesla instead believes automated driving functions are built on visual recognition and is even working to remove the radar. China’s Xpeng begs to differ.

Founded in 2014, Xpeng is one of China’s most celebrated electric vehicle startups and went public when it was just six years old. Like Tesla, Xpeng sees automation as an integral part of its strategy; unlike the American giant, Xpeng uses a combination of radar, cameras, high-precision maps powered by Alibaba, localization systems developed in-house, and most recently, lidar to detect and predict road conditions.

“Lidar will provide the 3D drivable space and precise depth estimation to small moving obstacles even like kids and pets, and obviously, other pedestrians and the motorbikes which are a nightmare for anybody who’s working on driving,” Xinzhou Wu, who oversees Xpeng’s autonomous driving R&D center, said in an interview with TechCrunch.

“On top of that, we have the usual radar which gives you location and speed. Then you have the camera which has very rich, basic semantic information.”

Xpeng is adding lidar to its mass-produced EV model P5, which will begin delivering in the second half of this year. The car, a family sedan, will later be able to drive from point A to B based on a navigation route set by the driver on highways and certain urban roads in China that are covered by Alibaba’s maps. An older model without lidar already enables assisted driving on highways.

The system, called Navigation Guided Pilot, is benchmarked against Tesla’s Navigate On Autopilot, said Wu. It can, for example, automatically change lanes, enter or exit ramps, overtake other vehicles, and maneuver another car’s sudden cut-in, a common sight in China’s complex road conditions.

“The city is super hard compared to the highway but with lidar and precise perception capability, we will have essentially three layers of redundancy for sensing,” said Wu.

By definition, NGP is an advanced driver-assistance system (ADAS) as drivers still need to keep their hands on the wheel and take control at any time (Chinese laws don’t allow drivers to be hands-off on the road). The carmaker’s ambition is to remove the driver, that is, reach Level 4 autonomy two to four years from now, but real-life implementation will hinge on regulations, said Wu.

“But I’m not worried about that too much. I understand the Chinese government is actually the most flexible in terms of technology regulation.”

The lidar camp

Musk’s disdain for lidar stems from the high costs of the remote sensing method that uses lasers. In the early days, a lidar unit spinning on top of a robotaxi could cost as much as $100,000, said Wu.

“Right now, [the cost] is at least two orders low,” said Wu. After 13 years with Qualcomm in the U.S., Wu joined Xpeng in late 2018 to work on automating the company’s electric cars. He currently leads a core autonomous driving R&D team of 500 staff and said the force will double in headcount by the end of this year.

“Our next vehicle is targeting the economy class. I would say it’s mid-range in terms of price,” he said, referring to the firm’s new lidar-powered sedan.

The lidar sensors powering Xpeng come from Livox, a firm touting more affordable lidar and an affiliate of DJI, the Shenzhen-based drone giant. Xpeng’s headquarters is in the adjacent city of Guangzhou about 1.5 hours’ drive away.

Xpeng isn’t the only one embracing lidar. Nio, a Chinese rival to Xpeng targeting a more premium market, unveiled a lidar-powered car in January but the model won’t start production until 2022. Arcfox, a new EV brand of Chinese state-owned carmaker BAIC, recently said it would be launching an electric car equipped with Huawei’s lidar.

Musk recently hinted that Tesla may remove radar from production outright as it inches closer to pure vision based on camera and machine learning. The billionaire founder isn’t particularly a fan of Xpeng, which he alleged owned a copy of Tesla’s old source code.

In 2019, Tesla filed a lawsuit against Cao Guangzhi alleging that the former Tesla engineer stole trade secrets and brought them to Xpeng. XPeng has repeatedly denied any wrongdoing. Cao no longer works at Xpeng.

Supply challenges

While Livox claims to be an independent entity “incubated” by DJI, a source told TechCrunch previously that it is just a “team within DJI” positioned as a separate company. The intention to distance from DJI comes as no one’s surprise as the drone maker is on the U.S. government’s Entity List, which has cut key suppliers off from a multitude of Chinese tech firms including Huawei.

Other critical parts that Xpeng uses include NVIDIA’s Xavier system-on-the-chip computing platform and Bosch’s iBooster brake system. Globally, the ongoing semiconductor shortage is pushing auto executives to ponder over future scenarios where self-driving cars become even more dependent on chips.

Xpeng is well aware of supply chain risks. “Basically, safety is very important,” said Wu. “It’s more than the tension between countries around the world right now. Covid-19 is also creating a lot of issues for some of the suppliers, so having redundancy in the suppliers is some strategy we are looking very closely at.”

Taking on robotaxis

Xpeng could have easily tapped the flurry of autonomous driving solution providers in China, including Pony.ai and WeRide in its backyard Guangzhou. Instead, Xpeng becomes their competitor, working on automation in-house and pledges to outrival the artificial intelligence startups.

“The availability of massive computing for cars at affordable costs and the fast dropping price of lidar is making the two camps really the same,” Wu said of the dynamics between EV makers and robotaxi startups.

“[The robotaxi companies] have to work very hard to find a path to a mass-production vehicle. If they don’t do that, two years from now, they will find the technology is already available in mass production and their value become will become much less than today’s,” he added.

“We know how to mass-produce a technology up to the safety requirement and the quarantine required of the auto industry. This is a super high bar for anybody wanting to survive.”

Xpeng has no plans of going visual-only. Options of automotive technologies like lidar are becoming cheaper and more abundant, so “why do we have to bind our hands right now and say camera only?” Wu asked.

“We have a lot of respect for Elon and his company. We wish them all the best. But we will, as Xiaopeng [founder of Xpeng] said in one of his famous speeches, compete in China and hopefully in the rest of the world as well with different technologies.”

5G, coupled with cloud computing and cabin intelligence, will accelerate Xpeng’s path to achieve full automation, though Wu couldn’t share much detail on how 5G is used. When unmanned driving is viable, Xpeng will explore “a lot of exciting features” that go into a car when the driver’s hands are freed. Xpeng’s electric SUV is already available in Norway, and the company is looking to further expand globally.

#alibaba, #artificial-intelligence, #asia, #automation, #automotive, #baic, #bosch, #cars, #china, #cloud-computing, #driver, #electric-car, #elon-musk, #emerging-technologies, #engineer, #founder, #huawei, #lasers, #li-auto, #lidar, #livox, #machine-learning, #nio, #norway, #nvidia, #qualcomm, #robotaxi, #robotics, #self-driving-cars, #semiconductor, #shenzhen, #tc, #tesla, #transport, #transportation, #u-s-government, #united-states, #wu, #xavier, #xiaopeng, #xpeng

0

Docugami’s new model for understanding documents cuts its teeth on NASA archives

You hear so much about data these days that you might forget that a huge amount of the world runs on documents: a veritable menagerie of heterogeneous files and formats holding enormous value yet incompatible with the new era of clean, structured databases. Docugami plans to change that with a system that intuitively understands any set of documents and intelligently indexes their contents — and NASA is already on board.

If Docugami’s product works as planned, anyone will be able to take piles of documents accumulated over the years and near-instantly convert them to the kind of data that’s actually useful to people.

Because it turns out that running just about any business ends up producing a ton of documents. Contracts and briefs in legal work, leases and agreements in real estate, proposals and releases in marketing, medical charts, etc, etc. Not to mention the various formats: Word docs, PDFs, scans of paper printouts of PDFs exported from Word docs, and so on.

Over the last decade there’s been an effort to corral this problem, but movement has largely been on the organizational side: put all your documents in one place, share and edit them collaboratively. Understanding the document itself has pretty much been left to the people who handle them, and for good reason — understanding documents is hard!

Think of a rental contract. We humans understand when the renter is named as Jill Jackson, that later on, “the renter” also refers to that person. Furthermore, in any of a hundred other contracts, we understand that the renters in those documents are the same type of person or concept in the context of the document, but not the same actual person. These are surprisingly difficult concepts for machine learning and natural language understanding systems to grasp and apply. Yet if they could be mastered, an enormous amount of useful information could be extracted from the millions of documents squirreled away around the world.

What’s up, .docx?

Docugami founder Jean Paoli says they’ve cracked the problem wide open, and while it’s a major claim, he’s one of few people who could credibly make it. Paoli was a major figure at Microsoft for decades, and among other things helped create the XML format — you know all those files that end in x, like .docx and .xlsx? Paoli is at least partly to thank for them.

“Data and documents aren’t the same thing,” he told me. “There’s a thing you understand, called documents, and there’s something that computers understand, called data. Why are they not the same thing? So my first job [at Microsoft] was to create a format that can represent documents as data. I created XML with friends in the industry, and Bill accepted it.” (Yes, that Bill.)

The formats became ubiquitous, yet 20 years later the same problem persists, having grown in scale with the digitization of industry after industry. But for Paoli the solution is the same. At the core of XML was the idea that a document should be structured almost like a webpage: boxes within boxes, each clearly defined by metadata — a hierarchical model more easily understood by computers.

Illustration showing a document corresponding to pieces of another document.

Image Credits: Docugami

“A few years ago I drank the AI kool-aid, got the idea to transform documents into data. I needed an algorithm that navigates the hierarchical model, and they told me that the algorithm you want does not exist,” he explained. “The XML model, where every piece is inside another, and each has a different name to represent the data it contains — that has not been married to the AI model we have today. That’s just a fact. I hoped the AI people would go and jump on it, but it didn’t happen.” (“I was busy doing something else,” he added, to excuse himself.)

The lack of compatibility with this new model of computing shouldn’t come as a surprise — every emerging technology carries with it certain assumptions and limitations, and AI has focused on a few other, equally crucial areas like speech understanding and computer vision. The approach taken there doesn’t match the needs of systematically understanding a document.

“Many people think that documents are like cats. You train the AI to look for their eyes, for their tails… documents are not like cats,” he said.

It sounds obvious, but it’s a real limitation: advanced AI methods like segmentation, scene understanding, multimodal context, and such are all a sort of hyper-advanced cat detection that has moved beyond cats to detect dogs, car types, facial expressions, locations, etc. Documents are too different from one another, or in other ways too similar, for these approaches to do much more than roughly categorize them.

And as for language understanding, it’s good in some ways but not in the ways Paoli needed. “They’re working sort of at the English language level,” he said. “They look at the text but they disconnect it from the document where they found it. I love NLP people, half my team is NLP people — but NLP people don’t think about business processes. You need to mix them with XML people, people who understand computer vision, then you start looking at the document at a different level.”

Docugami in action

Illustration showing a person interacting with a digital document.

Image Credits: Docugami

Paoli’s goal couldn’t be reached by adapting existing tools (beyond mature primitives like optical character recognition), so he assembled his own private AI lab, where a multi-disciplinary team has been tinkering away for about two years.

“We did core science, self-funded, in stealth mode, and we sent a bunch of patents to the patent office,” he said. “Then we went to see the VCs, and Signalfire basically volunteered to lead the seed round at $10 million.”

Coverage of the round didn’t really get into the actual experience of using Docugami, but Paoli walked me through the platform with some live documents. I wasn’t given access myself and the company wouldn’t provide screenshots or video, saying it is still working on the integrations and UI, so you’ll have to use your imagination… but if you picture pretty much any enterprise SaaS service, you’re 90 percent of the way there.

As the user, you upload any number of documents to Docugami, from a couple dozen to hundreds or thousands. These enter a machine understanding workflow that parses the documents, whether they’re scanned PDFs, Word files, or something else, into an XML-esque hierarchical organization unique to the contents.

“Say you’ve got 500 documents, we try to categorize it in document sets, these 30 look the same, those 20 look the same, those 5 together. We group them with a mix of hints coming from how the document looked, what it’s talking about, what we think people are using it for, etc,” said Paoli. Other services might be able to tell the difference between a lease and an NDA, but documents are too diverse to slot into pre-trained ideas of categories and expect it to work out. Every set of documents is potentially unique, and so Docugami trains itself anew every time, even for a set of one. “Once we group them, we understand the overall structure and hierarchy of that particular set of documents, because that’s how documents become useful: together.”

Illustration showing a document being turned into a report and a spreadsheet.

Image Credits: Docugami

That doesn’t just mean it picks up on header text and creates an index, or lets you search for words. The data that is in the document, for example who is paying whom, how much and when, and under what conditions, all that becomes structured and editable within the context of similar documents. (It asks for a little input to double check what it has deduced.)

It can be a little hard to picture, but now just imagine that you want to put together a report on your company’s active loans. All you need to do is highlight the information that’s important to you in an example document — literally, you just click “Jane Roe” and “$20,000” and “5 years” anywhere they occur — and then select the other documents you want to pull corresponding information from. A few seconds later you have an ordered spreadsheet with names, amounts, dates, anything you wanted out of that set of documents.

All this data is meant to be portable too, of course — there are integrations planned with various other common pipes and services in business, allowing for automatic reports, alerts if certain conditions are reached, automated creation of templates and standard documents (no more keeping an old one around with underscores where the principals go).

Remember, this is all half an hour after you uploaded them in the first place, no labeling or pre-processing or cleaning required. And the AI isn’t working from some preconceived notion or format of what a lease document looks like. It’s learned all it needs to know from the actual docs you uploaded — how they’re structured, where things like names and dates figure relative to one another, and so on. And it works across verticals and uses an interface anyone can figure out a few minutes. Whether you’re in healthcare data entry or construction contract management, the tool should make sense.

The web interface where you ingest and create new documents is one of the main tools, while the other lives inside Word. There Docugami acts as a sort of assistant that’s fully aware of every other document of whatever type you’re in, so you can create new ones, fill in standard information, comply with regulations, and so on.

Okay, so processing legal documents isn’t exactly the most exciting application of machine learning in the world. But I wouldn’t be writing this (at all, let alone at this length) if I didn’t think this was a big deal. This sort of deep understanding of document types can be found here and there among established industries with standard document types (such as police or medical reports), but have fun waiting until someone trains a bespoke model for your kayak rental service. But small businesses have just as much value locked up in documents as large enterprises — and they can’t afford to hire a team of data scientists. And even the big organizations can’t do it all manually.

NASA’s treasure trove

Image Credits: NASA

The problem is extremely difficult, yet to humans seems almost trivial. You or I could glance through 20 similar documents and a list of names and amounts easily, perhaps even in less time than it takes for Docugami to crawl them and train itself.

But AI, after all, is meant to imitate and excel human capacity, and it’s one thing for an account manager to do monthly reports on 20 contracts — quite another to do a daily report on a thousand. Yet Docugami accomplishes the latter and former equally easily — which is where it fits into both the enterprise system, where scaling this kind of operation is crucial, and to NASA, which is buried under a backlog of documentation from which it hopes to glean clean data and insights.

If there’s one thing NASA’s got a lot of, it’s documents. Its reasonably well maintained archives go back to its founding, and many important ones are available by various means — I’ve spent many a pleasant hour perusing its cache of historical documents.

But NASA isn’t looking for new insights into Apollo 11. Through its many past and present programs, solicitations, grant programs, budgets, and of course engineering projects, it generates a huge amount of documents — being, after all, very much a part of the federal bureaucracy. And as with any large organization with its paperwork spread over decades, NASA’s document stash represents untapped potential.

Expert opinions, research precursors, engineering solutions, and a dozen more categories of important information are sitting in files searchable perhaps by basic word matching but otherwise unstructured. Wouldn’t it be nice for someone at JPL to get it in their head to look at the evolution of nozzle design, and within a few minutes have a complete and current list of documents on that topic, organized by type, date, author, and status? What about the patent advisor who needs to provide a NIAC grant recipient information on prior art — shouldn’t they be able to pull those old patents and applications up with more specificity than any with a given keyword?

The NASA SBIR grant, awarded last summer, isn’t for any specific work, like collecting all the documents of such and such a type from Johnson Space Center or something. It’s an exploratory or investigative agreement, as many of these grants are, and Docugami is working with NASA scientists on the best ways to apply the technology to their archives. (One of the best applications may be to the SBIR and other small business funding programs themselves.)

Another SBIR grant with the NSF differs in that, while at NASA the team is looking into better organizing tons of disparate types of documents with some overlapping information, at NSF they’re aiming to better identify “small data.” “We are looking at the tiny things, the tiny details,” said Paoli. “For instance, if you have a name, is it the lender or the borrower? The doctor or the patient name? When you read a patient record, penicillin is mentioned, is it prescribed or prohibited? If there’s a section called allergies and another called prescriptions, we can make that connection.”

“Maybe it’s because I’m French”

When I pointed out the rather small budgets involved with SBIR grants and how his company couldn’t possibly survive on these, he laughed.

“Oh, we’re not running on grants! This isn’t our business. For me, this is a way to work with scientists, with the best labs in the world,” he said, while noting many more grant projects were in the offing. “Science for me is a fuel. The business model is very simple – a service that you subscribe to, like Docusign or Dropbox.”

The company is only just now beginning its real business operations, having made a few connections with integration partners and testers. But over the next year it will expand its private beta and eventually open it up — though there’s no timeline on that just yet.

“We’re very young. A year ago we were like five, six people, now we went and got this $10M seed round and boom,” said Paoli. But he’s certain that this is a business that will be not just lucrative but will represent an important change in how companies work.

“People love documents. Maybe it’s because I’m French,” he said, “but I think text and books and writing are critical — that’s just how humans work. We really think people can help machines think better, and machines can help people think better.”

#artificial-intelligence, #automation, #documents, #enterprise, #language, #natural-language-understanding, #saas, #startups, #tc

0

Tines raises $26M Series B for its no-code security automation platform

Tines, a no-code automation platform co-founded by two senior cybersecurity operators, today announced that it has raised a $26 million Series B funding round led by Addition. Existing investors Accel and Blossom Capital participated in this round, which also includes strategic investments from CrowdStrike and Silicon Valley CISO Investments. After this round, which brings the total funding in the company to $41.1 million, Tines is now valued at $300 million.

Given that Tines co-founders Eoin Hinchy and Thomas Kinsella were both in senior security roles at DocuSign before they left to start their own company in 2018, it’s maybe no surprise that the company’s platform launched with a strong focus on security operations. As such, it combines security orchestration and robotic process automation with a low-code/no-code user interface.

“Tines is on a mission to allow frontline employees to focus on more business-critical tasks and improve their wellbeing by reducing the burden of ‘busy work’ by helping automate any manual workflow and making existing teams more efficient, effective, and engaged,” the company notes in today’s announcement.

The idea here is to free analysts from spending time on routine repetitive tasks and allow them to focus on those areas where they can have the most impact. The tools features pre-configured integrations with a variety of business and security tools, but for more sophisticated users, it also features the ability to hook into virtually any API.

Image Credits: Tines

The company argues that even non-technical employees should be able to learn the ins and outs of its platform within about three hours (sidenote: it’s nice to see a no-code platform acknowledge that users will actually need to spend some time with it before they can become productive).

“If software is eating the world, automation is eating the enterprise,” Hinchy said. “Yet, the majority of progress in this space still requires non-technical teams to depend on software engineers to implement their automation. Other platforms are generally either too hard to use, not flexible enough or not sufficiently robust for mission-critical workflows like cybersecurity. Tines empowers enterprise teams to automate any of their own manual workloads independently, making their jobs more rewarding while simultaneously delivering enormous value for their organizations.”

Current Tines customers include the likes of Box, Canva, OpenTable and Sophos.

The company, which was founded in Dublin, Ireland and recently opened an office in Boston, plans to use the new funding to double its 18-person team in order to support its product growth.

“Tines has quickly established itself as a market leader in enterprise automation,” said Lee Fixel, founder of Addition. “We look forward to supporting Eoin and the Tines team as they continue to scale the business and enhance their product — which is beloved by their unmatched customer base.”

Image Credits: Tines

#addition, #api, #automation, #boston, #box, #business, #business-process-automation, #canva, #crowdstrike, #docusign, #dublin, #ireland, #lee-fixel, #low-code, #market-leader, #no-code, #opentable, #recent-funding, #security, #security-tools, #silicon-valley-ciso-investments, #sophos, #startups, #tc, #tines, #tools

0

Hyundai IONIQ 5 will be Motional and Lyft’s first robotaxi

Motional will integrate its driverless technology into Hyundai’s new all-electric SUV to create the company’s first robotaxi. At the start of 2023, customers in certain markets will be able to book the fully electric, fully autonomous taxi through the Lyft app.

The Hyundai IONIQ 5, which was revealed in February with a consumer release date expected later this year, will be fully integrated with Motional’s driverless system. The vehicles will be equipped with the hardware and software needed for Level 4 autonomous driving capabilities, including LiDAR, radar and cameras to provide the vehicle’s sensing system with 360 degrees of vision, and the ability to see up to 300 meters away. This level of driverless technology means a human will not be required to take over driving.

The interior living space will be similar to the consumer model, but additionally equipped with features needed for robotaxi operation, according to a Motional spokesperson. Motional did not reveal whether or not the vehicle would still have a steering wheel, and images of the robotaxi aren’t yet available.

Motional’s IONIQ 5 robotaxis have already begun testing on public roads and closed courses, and they’ll be put through more months of testing and real-world experience before being deployed on Lyft’s platform. The company says it’ll complete testing only once it’s confident that the taxis are safer than a human driver.

Motional, the Aptiv-Hyundai $4 billion joint venture aimed at commercializing driverless cars, announced its partnership with Lyft in December, signaling the ride-hailing company’s primary involvement in Motional’s plans. The company recently announced that it began testing its driverless tech on public roads in Las Vegas. Hyundai’s IONIQ 5 is Motional’s second platform to go driverless on public roads.

#aptiv, #automation, #driver, #emerging-technologies, #hyundai, #hyundai-motor-company, #las-vegas, #lyft, #mobility, #motional, #robotaxi, #robotics, #self-driving-cars, #tc, #technology, #transport

0

No code, workflow, and RPA line up for their automation moment

We’ve seen a lot of trend lines moving throughout 2020 and into 2021 around automation, workflow, robotic process automation (RPA) and the movement to low-code and no-code application building. While all of these technologies can work on their own, they are deeply connected and we are starting to see some movement towards bringing them together.

While the definition of process automation is open to interpretation, and could include things like industrial automation, Statista estimates that the process automation market could be worth $74 billion in 2021. Those are numbers that are going to get the attention of both investors and enterprise software executives.

Just this week, Berlin-based Camunda announced a $98 million Series B to help act as a layer to orchestrate the flow of data between RPA bots, microservices and human employees. Meanwhile UIPath, the pure-play RPA startup that’s going to IPO any minute now, acquired Cloud Elements, giving it a way to move beyond RPA into API automation.

Not enough proof for you? How about ServiceNow announcing this week that it is buying Indian startup Intellibot to give it — you guessed it — RPA capabilities. That acquisition is part of a broader strategy by the company to move into full-scale workflow and automation, which it discussed just a couple of weeks ago.

Meanwhile at the end of last year, SAP bought a different Berlin process automation startup, Signavio, for $1.2 billion after announcing new automated workflow tools and an RPA tool at the beginning of December. Microsoft is in on it too, having acquired process automation startup Softmotive last May, which it then combined with its own automation tool PowerAutomate.

What we have here is a frothy mix of startups and large companies racing to provide a comprehensive spectrum of workflow automation tools to empower companies to spin up workflows quickly and move work involving both human and machine labor through an organization.

The result is hot startups getting prodigious funding, while other startups are exiting via acquisition to these larger companies looking to buy instead of build to gain a quick foothold in this market.

Cathy Tornbohm, Distinguished Research Vice President at Gartner, says part of the reason for the rapidly growing interest is that these companies have stayed on the sidelines up until now, but they see an opportunity and are using their checkbooks to play catch up.

“IBM, SAP, Pega, Appian, Microsoft, ServiceNow all bought into the RPA market because for years they didn’t focus on how data got into their systems when operating between organizations or without a human. [Instead] they focused more on what happens inside the client’s organization. The drive to be digitally more efficient necessitates optimizing data ingestion and data flows,” Tornbohm told me.

For all the bluster from the big vendors, they do not control the pure-play RPA market. In fact, Gartner found that the top three players in this space are UIPath, Automation Anywhere and Blue Prism.

But Tornbohm says that, even as the traditional enterprise vendors try to push their way into the space, these pure-play companies are not sitting still. They are expanding beyond their RPA roots into the broader automation space, which could explain why UIPath came up from its pre-IPO quiet period to make the Cloud Elements announcement this week.

Dharmesh Thakker, managing partner at Battery Ventures, agrees with Tornbohm, saying that the shift to the cloud, accelerated by COVID-19, has led to an expansion of what RPA vendors are doing.

“RPA has traditionally focused on automation-UI flow and user steps, but we believe a full automation suite requires that ability to automate processes across the stack. For larger companies, we see their interest in the category as a way to take action on data within their systems. And for standalone RPA vendors, we see this as validation of the category and an invitation to expand their offerings to other pillars of automation,” Thakker said.

The activity we have seen across the automation and workflow space over the last year could be just the beginning of what Thakker and Tornbohm are describing, as companies of all sizes fight to become the automation stack of choice in the coming years.


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#automation, #cloud, #dharmesh-thakker, #enterprise, #no-code, #rpa, #sap, #tc, #uipath, #workflow

0

ServiceNow takes RPA plunge by acquiring India-based startup Intellibot

ServiceNow became the latest company to take the robotic process automation (RPA) plunge when it announced it was acquiring Intellibot, an RPA startup based in Hyderabad, India. The companies did not reveal the purchase price.

The purchase comes at a time where companies are looking to automate workflows across the organization. RPA provides a way to automate a set of legacy processes, which often involve humans dealing with mundane repetitive work.

The announcement comes on the heels of the company’s no-code workflow announcements earlier this month and is part of the company’s broader workflow strategy, according to Josh Kahn, SVP of Creator Workflow Products at ServiceNow.

“RPA enhances ServiceNow’s current automation capabilities including low code tools, workflow, playbooks, integrations with over 150 out of the box connectors, machine learning, process mining and predictive analytics,” Khan explained. He says that the company can now bring RPA natively to the platform with this acquisition, yet still use RPA bots from other vendors if that’s what the customer requires.

“ServiceNow customers can build workflows that incorporate bots from the pure play RPA vendors such as Automation Anywhere, UiPath and Blue Prism, and we will continue to partner with those companies. There will be many instances where customers want to use our native RPA capabilities alongside those from our partners as they build intelligent, end-to-end automation workflows on the Now Platform,” Khan explained.

The company is making this purchase as other enterprise vendors enter the RPA market. SAP announced a new RPA tool at the end of December and acquired process automation startup Signavio in January. Meanwhile Microsoft announced a free RPA tool earlier this month, as the space is clearly getting the attention of these larger vendors.

ServiceNow has been on a buying spree over the last year or so buying five companies including Element AI, Loom Systems, Passage AI and Sweagle. Khan says the acquisitions are all in the service of helping companies create automation across the organization.

“As we bring all of these technologies into the Now Platform, we will accelerate our ability to automate more and more sophisticated use cases. Things like better handling of unstructured data from documents such as written forms, emails and PDFs, and more resilient automations such as larger data sets and non-routine tasks,” Khan said.

Intellibot was founded in 2015 and will provide the added bonus of giving ServiceNow a stronger foothold in India. The companies expect to close the deal no later than June.

 

#automation, #cloud, #enterprise, #exit, #fundings-exits, #ma, #mergers-and-acquisitions, #rpa, #servicenow, #startups, #tc

0

DeepSee.ai raises $22.6M Series A for its AI-centric process automation platform

DeepSee.ai, a startup that helps enterprises use AI to automate line-of-business problems, today announced that it has raised a $22.6 million Series A funding round led by led by ForgePoint Capital. Previous investors AllegisCyber Capital and Signal Peak Ventures also participated in this round, which brings the Salt Lake City-based company’s total funding to date to $30.7 million.

The company argues that it offers enterprises a different take on process automation. The industry buzzword these days is ‘robotic process automation,’ but DeepSee.ai argues that what it does is different. I describe its system as ‘knowledge process automation’ (KPA). The company itself defines this as a system that “mines unstructured data, operationalizes AI-powered insights, and automates results into real-time action for the enterprise.” But the company also argues that today’s bots focus on basic task automation that doesn’t offer the kind of deeper insights that sophisticated machine learning models can bring to the table. The company also stresses that it doesn’t aim to replace knowledge workers but help them leverage AI to turn the plethora of data that businesses now collect into actionable insights.

Image Credits: DeepSee.ai

“Executives are telling me they need business outcomes and not science projects,” writes DeepSee.ai CEO Steve Shillingford. “And today, the burgeoning frustration with most AI-centric deployments in large-scale enterprises is they look great in theory but largely fail in production. We think that’s because right now the current ‘AI approach’ lacks a holistic business context relevance. It’s unthinking, rigid, and without the contextual input of subject-matter experts on the ground. We founded DeepSee to bridge the gap between powerful technology and line-of-business, with adaptable solutions that empower our customers to operationalize AI-powered automation – delivering faster, better, and cheaper results for our users.”

To help businesses get started with the platform, DeepSee.ai offers three core tools. There’s DeepSee Assembler, which ingests unstructured data and gets it ready for labeling, model review and analysis. Then, DeepSee Atlas can use this data to train AI models that can understand a company’s business processes and help subject-matter experts define templates, rules and logic for automating a company’s internal processes. The third tool, DeepSee Advisor, meanwhile focuses on using text analysis to help companies better understand and evaluate their business processes.

Currently, the company’s focus is on providing these tools for insurance companies, the public sector and capital markets. In the insurance space, use cases include fraud detection, claims prediction and processing, and using large amounts of unstructured data to identify patterns in agent audits, for example.

That’s a relatively limited number of industries for a startup to operate in, but the company says it will use its new funding to accelerate product development and expand to new verticals.

“Using KPA, line-of-business executives can bridge data science and enterprise outcomes, operationalize AI/ML-powered automation at scale, and use predictive insights in real time to grow revenue, reduce cost, and mitigate risk,” said Sean Cunningham, Managing Director of ForgePoint Capital. “As a leading cybersecurity investor, ForgePoint sees the daily security challenges around insider threat, data visibility, and compliance. This investment in DeepSee accelerates the ability to reduce risk with business automation and delivers much-needed AI transparency required by customers for implementation.”

#allegiscyber-capital, #articles, #artificial-intelligence, #automation, #automation-anywhere, #business-process-automation, #business-process-management, #business-software, #cloud, #emerging-technologies, #enterprise, #forgepoint-capital, #machine-learning, #recent-funding, #robotic-process-automation, #salt-lake-city, #signal-peak-ventures, #startups

0

SkyMul’s drones secure rebar on the fly to speed up construction

There are many jobs in the construction industry that fall under the “dull, dirty, and dangerous” category said to be ripe for automation — but only a few can actually be taken on with today’s technology. One such job is the crucial but repetitive task of rebar tying, which a startup called SkyMul is aiming to completely automate using fleets of drones.

Unless you’ve put together reinforced concrete at some point in your life, you may not know what rebar tying is. The steel rebar that provides strength to concrete floors, walls, and other structures is held in place during the pouring process by tying it to the other rebar where the rods cross. For a good-size building or bridge this can easily be thousands of ties — and the process is generally done manually.

Rodbusters (as rebar tying specialists are called, or so I’m told) are masters of the art of looping a short length of plastic or wire around an intersection between two pieces of rebar, then twisting and tying it tightly so that the rods are secured in multiple directions. It must be done precisely and efficiently, and so it is — but it’s backbreaking, repetitive work. Though any professional must feel pride in what they do, I doubt anyone cherishes the chronic pain they get from doing that task thousands of times in an hour. As you might expect, rodbusters have high injury rates and develop chronic issues.

Automation of rebar tying is tricky because it happens in so many different circumstances. A prominent semi-robotic solution is the TyBot, which is a sort of rail-mounted gantry that suspends itself over the surface — but while this makes sense for a bridge, it makes far less for the 20th floor of an office building.

Animated image of a drone floating over rebar and tying it together at intersections.

Image Credits: SkyMul

Enter SkyMul, a startup still in the very early stages but with a compelling pitch: rebar tying done by a fleet of drones. When you consider that the tying process doesn’t involve too much force, and that computer vision has gotten more than good enough to locate the spots that need work… it starts sounding kind of obvious.

CEO and co-founder Eohan George said that they evaluated a number of different robotic solutions but that drones are the only ones that make sense. The only legged robots with the dexterity to pick their way through the rebar are too expensive, and treads and wheels are too likely to move the unsecured rebar.

Diagram showing how SkyMul's drones map an area of rebar then divide it up for tying.

Image Credits: SkyMul

Here’s how the company’s SkyTy system works. First, a mapper drone flies over the site to mark the boundaries and then, in an automated closer flyover, to build a map of the rebar itself and where the ties will need to go. This map is then double-checked by the rodbuster technician running the show, which George said only takes about a minute per thousand square feet of rebar (though that adds up quickly).

Then the tying drones are released, as many as needed or wanted. Each one moves from spot to spot, hovering and descending until its tying tool (much like those used by human rodbusters) spans the rebar intersection; the tie is wrapped, twisted, and the drone is off to the next spot. They need their batteries swapped every 25 minutes, which means they generally have time to put down 70-80 ties; right now each drone does one tie every 20 seconds, which is in line with humans, who can do it faster but generally go at about that speed or slower, according to numbers George cited.

It’s difficult to estimate the cost savings and value of the work SkyTy does, because the value of the labor varies widely. In some places rodbusters are earning north of $80/hour, meaning the draw of automation is in cost savings. But in other markets the pay is less than a third of that, which compounded with the injury risk makes rodbusters a scarce quantity — so the value is in availability and reliability. Drone-based tying seems to offer value one way or the other, but that means the business model is somewhat in flux as SkyMul figures out what makes the most sense. Generally contractors at one level or another would lease and eventually own their own drones, though other methods are being looked into.

Animated image of a computer-generated grid overlaid on images of rebar.

Image Credits: SkyMul

The system offers value-add services as well, for instance the precise map of the rebar generated at the beginning, which can be archived and used later for maintenance, quality assurance, comparison with plans, and other purposes. Once a contractor is convinced it’s as good or better than the manually-produced ones currently used, this could save hours, turning a 3-day job into a 2-day job or otherwise simplifying logistics.

The plan at the company is to first offer SkyTy as an option for bridge construction, which is a simpler environment than a multi-story building for the drones. The market there is on the order of $30-40 million per year for rebar tying services, providing an easier path to the more complex deployments.

SkyMul is looking for funding, having spun out of Georgia Tech and going through Comcast-NBC accelerator The Farm and then being granted a National Science Foundation SBIR Phase I award (with hopes for a Phase II). They have demonstrated the system but have yet to enter into any pilot programs — there are partnerships in the works but the construction business isn’t a nimble one and a drone-based solution isn’t trivial to swap in for human rodbusters on short notice. But once a few projects are under its belt the company seems likely to find serious traction among forward-thinking contractors.

#artificial-intelligence, #automation, #construction, #drones, #gadgets, #hardware, #startups, #tc, #uavs

0

Microsoft’s Power Automate Desktop is now free for all Windows 10 users

Microsoft today announced that it is making Power Automate Desktop, its enterprise-level tool for creating automated desktop-centric workflows, available to all Windows 10 users for free. Power Automate Desktop is what Microsoft calls its “attended Robotic Process Automation” solution, but you can think of it as a macro recorder on steroids. It comes with 370 prebuilt actions that help you build flows across different applications, but its real power is in letting you build your own scripts to automate repetitive and time-consuming tasks.

Power Automate Desktop originally launched last September. It’s based on Microsoft’s acquisition of Softomotive in early 2020, but Microsoft has since extended Softomotive’s technology and integrated it deeper into its own stack.

Users who want to give Power Automate Desktop a try can now download it from Microsoft, but in the coming weeks, it’ll become part of Microsoft’s Insider Builds for Windows 10 and then eventually become a built-in part of Windows 10, all the way down to the standard Windows Home version. Until now, a per-user license for Power Automate Desktop would set you back at least $15 per month.

“We’ve had this mission of wanting to go democratize development for everybody with the Power Platform,” Charles Lamanna, the CVP of Power Platform engineering at Microsoft, told me. “And that means, of course, making products which are accessible to anybody — and that’s what no-code/low-code is all about, whether it’s building applications with Power Apps or automating with Power Automate. But another big part of that is just, how do you also expand the imagination of a typical PC user to make them believe they can be a developer?”

This move, Lamanna believes, reduces the licensing friction and sends a message to Windows users that they can build bots and automate tasks, too. “The way we’ve designed it — and the experience we have, particularly around the recording abilities like a macro recorder — makes it so you don’t have to think about for loops or what is this app I’m clicking on or this text box — you can just record it and run it,” he said.

#automation, #computing, #developer, #macro, #microsoft, #microsoft-ignite-2021, #microsoft-windows, #operating-systems, #softomotive, #tc, #windows-10

0

Pipe17 closes $8M to connect a range of e-commerce tools without any code required

This morning Pipe17, a software startup focused on the e-commerce market, announced that it has closed $8 million in funding.

Pipe17’s service helps smaller e-commerce merchants connect their digital tools, without the need to code. With the startup’s service, e-commerce operations that may lack an in-house IT function can quickly connect their selling platform to shipping, or point-of-sale data to their ERP.

The venture arm of a large logistics investor GLP, GLP Capital Partners led the round.

Pipe17 co-founders Mo Afshar and Dave Shaffer told TechCrunch in an interview that the idea for their startup came from examining the e-commerce market, noting the energy to be found concerning selling platforms, and the comparative dearth of software to help get e-commerce tools to work together; Shopify and BigCommerce and Shippo are just fine, but if you can’t code you might wind up schlepping data from one platform to the next to keep your e-commerce operation humming.

So they built Pipe17 to fill in the gap.

According to Afshar, Pipe17 wants to simplify operations for e-commerce merchants through the lens of connection; the pair of co-founders believe that easy cross-compatibility is the key missing ingredient in the modern-day e-commerce software stack, likening the current e-commerce maket to the IT and datacenter worlds before the advent of Splunk and Datadog.

The prevailing view in the e-commerce industry, the co-founders explained, is that to fix a problem e-commerce players should purchase another application. Pipe17 thinks that most ecommerce companies probably have enough tooling, and that they instead need to get their existing tooling to communicate.

What’s neat about the startup is that it’s building something that we might call no-code-no-code, or no-code to a higher degree. Instead of offering a interface for non-developers to visually map out connections between different software services, it has pre-built what might need to be mapped. Just pick the two e-commerce services you want to link, and Pipe17 will connect them for you in an intelligent manner. For folks who find any sort of coding hard (which probably describes a lot of indie online store operators), the method could be an attractive pitch.

The startup’s customer target are sellers doing single-digit millions to nine-figures in year sales.

Why did Pipe17 raise capital now? The co-founders said that there are only so many chances to simplify a large market, akin to what Plaid and Twilio did for their own niches, so taking on funds now made sense. In Afshar’s view, e-commerce operations is going to be simply massive. Given the growth in digital selling that we saw last year, it’s a perspective that is hard to dispute.

The niche that Pipe17 wants to fill has more than one player. While the startups themselves might quibble about just how much competitive space they share, Y Combinator-backed Alloy recently raised $4 million to build a no-code e-commerce automation service. Which is related to what Pipe17 does. It will be interesting to see if they wind up in competition, and, if so, who comes out on top.

#alloy, #automation, #bigcommerce, #e-commerce, #ecommerce, #fundings-exits, #shopify, #startups, #tc

0

Notable Health seeks to improve COVID-19 vaccine administration through intelligent automation

Efficient and cost-effective vaccine distribution remains one of the biggest challenges of 2021, so it’s no surprise that startup Notable Health wants to use their automation platform to help. Initially started to help address the nearly $250 billion annual administrative costs in healthcare, Notable Health launched in 2017 to use automation to replace time-consuming and repetitive simple tasks in health industry admin. In early January of this year, they announced plans to use that technology as a way to help manage vaccine distribution.

“As a physician, I saw firsthand that with any patient encounter, there are 90 steps or touchpoints that need to occur,” said Notable Health medical director Muthu Alagappan in an interview. “It’s our hypothesis that the vast majority of those points can be automated.”

Notable Health’s core technology is a platform that uses robotic process automation (RPA), natural language processing (NLP), and machine learning to find eligible patients for the COVID-19 vaccine. Combined with data provided by hospital systems’ electronic health records, the platform helps those qualified to receive the vaccine set up appointments and guides them to other relevant educational resources.

“By leveraging intelligent automation to identify, outreach, educate and triage patients, health systems can develop efficient and equitable vaccine distribution workflows,” said Notable Health strategic advisor and Biden Transition COVID-19 Advisory Board Member Dr. Ezekiel Emanuel, in a press release.

Making vaccine appointments has been especially difficult for older Americans, many of whom have reportedly struggled with navigating scheduling websites. Alagappan sees that as a design problem. “Technology often gets a bad reputation, because it’s hampered by the many bad technology experiences that are out there,” he said.

Instead, he thinks Notable Health has kept the user in mind through a more simplified approach, asking users only for basic and easy-to-remember information through a text message link. “It’s that emphasis on user-centric design that I think has allowed us to still have really good engagement rates even with older populations,” he said.

While the startup’s platform will likely help hospitals and health systems develop a more efficient approach to vaccinations, its use of RPA and NLP holds promise for future optimization in healthcare. Leaders of similar technology in other industries have already gone on to have multi-billion dollar valuations, and continue to attract investors’ interest.

Artificial intelligence is expected to grow in healthcare over the next several years, but Alagappan argues that combining that with other, more readily available intelligent technologies is also an important step towards improved care. “When we say intelligent automation, we’re really referring to the marriage of two concepts: artificial intelligence—which is knowing what to do—and robotic process automation—which is knowing how to do it,” he said. That dual approach is what he says allows Notable Health to bypass administrative bottlenecks in healthcare, instructing bots to carry out those tasks in an efficient and adaptable way.

So far, Notable Health has worked with several hospital systems across multiple states in using their platform for vaccine distribution and scheduling, and are now using the platform to reach out to tens of thousands of patients per day.

#artificial-intelligence, #automation, #biden, #covid-19, #covid-19-vaccine, #health, #healthcare, #machine-learning, #natural-language-processing, #robotic-process-automation, #science, #startups, #vaccine

0

Levity is a ‘no-code’ AI tool to let anyone create workflow automations

Levity, which has been operating in stealth (until now), is the latest no-code company to throw its wares into the ring, having picked up $1.7M in pre-seed funding led by Gil Dibner’s Angular Ventures. The Berlin-based startup wants to bring AI-powered workflow automation to anyone, letting knowldge workers automate tedious, repetitive and manual parts of their job without the need to learn how to code.

Suitable for customer service, marketing, operations, HR, and more, Levity has elected to be a horizontal offering from the get-go. Typical repetitive tasks that can be automated includes reviewing and categorizing documents, images, or text. The premise is that conventional, rule-based automation software isn’t able to automate tasks like these as it requires cognitive abilities, meaning that they usually done manually. This, of course, is where machine learning come into play.

“We want to solve the problem that people spend so much time at their jobs doing boring, repetitive stuff that can be automated to free up space and time for fun and interesting work,” says Gero Keil, co-founder and CEO. “Even though this is what AI has been promising us for decades, there are very few solutions out there, and even less for non-technical people who can’t code”.

To that end, Keil says Levity’s entire mission is to help non-technical knowledge workers automate what they couldn’t automate before. Specifically, the startup targets work processes that involve making decisions on unstructured data, such as images, text, PDFs and other documents.

“For example, if a company receives hundreds or thousands of emails from partners and customers with attachments every day, someone typically has to download the attachment, look at it and then decide what to do with it,” explains Keil. “With Levity, they can train their own custom AI on all of the historic data that they have accumulated, and once it has learned from that it seamlessly integrates with their existing tools and workflows e.g. Dropbox, Gmail, Slack etc.”

More broadly, he says there are many companies struggling to “productionize AI” that would really benefit from having an end-to-end platform “that enables them to build their own AI solutions and make them part of their processes”.

Keil argues that Levity’s main competitor is people doing work manually, but concedes that there is crossover with automation machine learning tools, workflow automation offerings, and labeling tools,

“Instead of going deep into every domain of the ML value chain and making the lives of developers and data scientists at large corporations easier, we focus only the most essential bits and pieces, wrap them in simple and enjoyable UX and abstract the rest away,” he says. “That makes us the best for non-developers in small and medium-sized businesses that want to automate previously non automatable processes in the most straightforward way. The people that have the automation problem become the same people that solve the automation problem; it’s a paradigm shift just like what Wix and Squarespace did to websites”.

Adds Gil Dibner, general partner and founder at Angular Ventures, in a statement: “Levity is driving a massive shift that will affect all knowledge workers. By allowing knowledge workers to easily train AI engines, build AI-powered automations, and integrate them into their everyday workflows, Levity is radically democratizing the benefits of AI.”

Alongside Angular, Levity’s other backers include: System.One, Discovery Ventures (founders of SumUp), Martin Henk (founder of Pipedrive) and various additional unnamed angel investors.

#angular-ventures, #artificial-intelligence, #automation, #berlin, #business-process-management, #europe, #fundings-exits, #levity, #startups, #tc

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Tesla is willing to license Autopilot and has already had “preliminary discussions” about it with other automakers

Tesla is open to licensing its software, including its Autopilot highly-automated driving technology, and the neural network training it has built to improve its autonomous driving technology. Tesla CEO Elon Musk revealed those considerations on the company’s Q4 earnings call on Wednesday, adding that the company has in fact already “had some preliminary discussions about licensing Autopilot to other OEMs.”

The company began rolling out its beta version of the so-called ‘full self-driving’ or FSD version of Autopilot late last year. The standard Autopilot features available in general release provide advanced driver assistance (ADAS) which provide essentially advanced cruise control capabilities designed primarily for use in highway commutes. Musk said on the call that he expects the company will seek to prove out its FSD capabilities before entering into any licensing agreements, if it does end up pursuing that path.

Musk noted that Tesla’s “philosophy is definitely not to create walled gardens” overall, and pointed out that the company is planning to allow other automakers to use its Supercharger networks, as well as its autonomy software. He characterized Tesla as “more than happy to license” those autonomous technologies to “other car companies,” in fact.

One key technical hurdle required to get to a point where Tesla’s technology is able to demonstrate true reliability far surpassing that of a standard human driver is transition the neural networks operating in the cars and providing them with the analysis that powers their perception engines is to transition those to video. That’s a full-stack transition across the system away from basing it around neural nets trained on single cameras and single frames.

To this end, the company has developed video labelling software that has had “a huge effect on the efficiency of labeling,” with the ultimate aim being enabling automatic labeling. Musk (who isn’t known for modesty around his company’s achievements, it should be said) noted that Tesla believes “it may be the best neural net training computer in the world by possibly an order of magnitude,” adding that it’s also “something we can offer potentially as a service.”

Training huge quantities of video data will help Tesla push the reliability of its software from 100% that of a human driver, to 200% and eventually to “2,000% better than the average human,” Musk said, while again suggesting that it won’t be a technological achievement the company is interested into keeping to themselves.

#automation, #automotive, #car, #ceo, #driver, #elon-musk, #neural-network, #neural-networks, #self-driving-car, #tc, #tesla, #tesla-autopilot, #transport, #transportation

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Span, the smart fusebox replacement founded by an ex-Tesla engineer, gets an Alexa upgrade

Automating and controlling devices and energy usage in homes has potentially become a bit easier thanks to an integration between Span, the startup making a digital fusebox replacement, and  Amazon’s voice recognition interface, Alexa.

The integration also comes with a $20 million new cash infusion from Amazon’s Alexa Fund and the massive insurance company Munich Re Ventures’ HSB Fund.

Through the Alexa integration, homeowners using Span’s electrical panels can turn on or off any circuit or appliance in their home, monitor which appliances are using power, and determine which electrical source is generating the most power for a home.

Questions like “Alexa, ask Span what is consuming the most power right now?” will get a response. The Alexa integration opens up new opportunities for home owners to integrate their devices and appliances, because of the connection to the home’s wiring, according to Span chief executive, Arch Rao.

Rao sees the Alexa integration as a way for Span to become the home automation hub that tech companies have been promising for a long time. “There are far too many devices in the hoe today… with too many apps,” Rao said. “The advantage we have is, once installed, we’re persistent in the home and connected to everything electric in the home for the next 30 to 40 years.”

In addition to monitoring energy usage and output, Alexa commands could turn off the power for any device or switch that a homeowner has programmed into the system.

“The most material way to state it is, our panel is providing a virtual interface to the home in the build environment,” said Rao. “We’re building a very capable edge device… it becomes sort of a true aggregation point and nerve center to give you real-time visibility and control.”

Going forward, Rao envisions Span integrating with other devices like water sensors, fire alarm sensors, and other equipment to provide other types of controls that could be useful for insurers like Munich Re.

With the $20 million that the company raised, Rao intends to significantly increase sales and marketing efforts working through partners like Munich Re and Amazon to get Span’s devices into as many homes as possible.

The company has significant tailwinds thanks to home automation and energy efficiency upgrade efforts that are now wending their way through Washington, but could mean subsidies for the deployment of technology’s like Span’s electric panels.

 

Rao also intends to boost headcount at Span. The company currently has 35 employees and Rao would like to see that number double to roughly 70 by the end of the year.

Span’s growth is part of a broad movement in home technologies toward increasingly sustainable options. In many cases that’s the penetration of electrical appliances in things like water heaters and stove tops, but also the integration of electric vehicle charging stations, home energy storage units, and other devices that push energy generation and management to the edge of electricity grids.

“It’s cutting that pipe that’s bringing natural gas to the home and bringing all electric everything… as consumers are continuing to cut the cord on fossils, your existing home system is not efficient. That’s one ecosystem of products where we are starting to see partnership opportunities,” Rao said. “When it comes to applications like monitoring the health of your appliances… and services to the home. Having the data that we provide will be unprecedented.”

#alexa, #amazon, #arch-rao, #automation, #charging-station, #emerging-technologies, #home-automation, #natural-gas, #tc, #voice-recognition, #washington

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How artificial intelligence will be used in 2021

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.

#artificial-intelligence, #automation, #covid-19, #emerging-technologies, #enterprise, #machine-learning, #natural-language-processing, #scale-ai, #virtual-reality

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National Grid sees machine learning as the brains behind the utility business of the future

If the portfolio of a corporate venture capital firm can be taken as a signal for the strategic priorities of their parent companies, then National Grid has high hopes for automation as the future of the utility industry.

The heavy emphasis on automation and machine learning from one of the nation’s largest privately held utilities with a customer base numbering around 20 million people is significant. And a sign of where the industry could be going.

Since its launch, National Grid’s venture firm, National Grid Partners, has invested in 16 startups that featured machine learning at the core of their pitch. Most recently, the company backed AI Dash, which uses machine learning algorithm to analyze satellite images and infer the encroachment of vegetation on National Grid power lines to avoid outages.

Another recent investment, Aperio uses data from sensors monitoring critical infrastructure to predice loss of data quality from degradation or cyberattacks.

Indeed, of the $175 million in investments the firm has made roughly $135 million has been committed to companies leveraging machine learning for their services.

“AI will be critical for the energy industry to achieve aggressive decarbonization and decentralization goals,” Lisa Lambert, the chief technology and innovation officer at National Grid and the founder and president of National Grid Partners.

National Grid started the year off slowly because of the COVID-19 epidemic, but the pace of its investments picked up and the company is on track to hit its investment targets for the year, Lambert said.

Modernization is critical for an industry that still mostly runs on spreadsheets and collective knowledge that’s locked in an aging employee base, with no contingency plans in the event of retirement, Lambert said. It’s that situation that’s compelling National Grid and other utilities to automate more of their business.

“Most companies in the utility sector are trying to automate now for efficiency reasons and cost reasons. Today, most companies have everything written down in manuals; as an industry, we basically still run our networks off spreadsheets, and the skills and experience of the people who run the networks. So we’ve got serious issues if those people retire. Automating [and] digitizing is top of mind for all the utilities we’ve talked to in the Next Grid Alliance.

To date, a lot of the automation work that’s been done has been around basic automation of business processes. But there are new capabilities on the horizon that will push the automation of different activities up the value chain, Lambert said.

“ ML is the next level — predictive maintenance of your assets, delivering for the customer. Uniphore, for example: you’re learning from every interaction you have with your customer, incorporating that into the algorithm, and the next time you meet a customer, you’re going to do better. So that’s the next generation,” Lambert said. “Once everything is digital, you’re learning from those engagements – whether engaging an asset or a human being.”

Lambert sees another source of demand for new machine learning tech in the need for utilities to rapidly decarbonize. The move away from fossil fuels will necessitate entirely new ways of operating and managing a power grid. One where humans are less likely to be in the loop.

“In the next five years, utilities have to get automation and analytics right if they’re going to have any chance at a net-zero world – you’re going to need to run those assets differently,” said Lambert. “Windmills and solar panels are not [part of] traditional distribution networks. A lot of traditional engineers probably don’t think about the need to innovate, because they’re building out the engineering technology that was relevant when assets were built decades ago – whereas all these renewable assets have been built in the era of OT/IT.”

 

#artificial-intelligence, #automation, #corporate-venture-capital, #electrical-grid, #energy, #energy-industry, #machine-learning, #national-grid-partners, #president, #smart-grid, #tc

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Increasing diversity in tech hiring requires a common-ground approach

The pandemic is surging in America once more. If this past year is any indication, it will hurt all of us — but communities of color will continue to suffer disproportionately.

Black and brown folks will make up more of the sick and the dying, and Black and brown businesses and employees will make up more of the people struggling financially.

Here is the good news: Interest in finding common ground and concrete solutions is also surging. That means there are some paths out of the mess we are in.

America’s biggest, best-funded, most-profitable companies are struggling to hire and retain diverse talent.

Let’s take stock: The longer the pandemic lasts, the more it could accelerate ongoing trends. Automation and advanced computing was changing how we work and undermining livelihoods before COVID-19, but by 2030, technology and automation will negatively affect hundreds of thousands of jobs that exist today.

The situation is worse for communities of color. Because people of color are overrepresented in fields that are likely to be automated, a McKinsey report estimated that 23.1% of African Americans and 25.1% of Hispanic Americans will see their jobs disappear or transform in the next decade. Even before COVID-19, the situation was bleak.

Perhaps this shift will create new, high-tech jobs, or those same people can retrain, retool and find employment in the economy of the future?

In practice, it is not nearly so easy. In 2019, the average cost for online coding bootcamps was $14,623 per person. Even with loans, installment plans or income-sharing agreements, that is far beyond the reach of many of the people whose current jobs are going away.

The pandemic is making this worse. Nearly 80% of low-income households do not have enough savings to last three months, and a third of Americans will have trouble paying their bills this month.

Waiting for the good news? America’s biggest, best-funded, most-profitable companies are struggling to hire and retain diverse talent. The good news is that they know it. They know they cannot compete without the genius in underrepresented communities, and they know they are not doing well enough right now.

Many companies will spend an average of $20,000 just on recruiting fees for a single IT hire, but hiring an IT candidate from a diverse community can cost three times as much, and once hired, there is a massive retention problem. Since 2016, the retention rate of Black and Latinx employees in Big Tech has fallen from 7% to 5%. There is a revolving door of diverse talent entering and leaving organizations.

In other words, you have a whole bunch of talented, creative people crying out for high-tech jobs — and a whole bunch of powerhouse, innovative companies desperate to hire and hold onto talent and creativity.

These overlapping needs mean we can find common ground. One model for this was the Dream Corps TECH Town Hall this month, where activists and educators from underrepresented communities shared panels with industry leaders. Instead of lobbing bombs at each other, both groups came to talk about the problems they face and how they can work together.

For instance, industry and educational leaders can devote resources to scholarships and training programs that come with job guarantees. Activists and CEOs can both push for universal broadband access, especially in the midst of a pandemic that is damaging learning opportunities for children, so that the next generation of coders has a shot at success.

Untapped talent in underrepresented communities can help companies avoid algorithmic bias and compete in a diverse, global world, and companies can help people thrive as the economy changes.

This common-ground approach is built on the recognition that both sides need each other in order to succeed. It can be a model for other thorny problems and produce necessary solutions. The pandemic is surging once more — but so is the demand for common ground. We can choose how we respond.

#automation, #column, #diversity, #diversity-in-technology, #labor, #opinion, #startups

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Inside Zoox’s six-year ride from prototype to product

Zoox, the autonomous vehicle company that was acquired this year by Amazon, revealed this week the product of six years of work: A purpose-built self-driving vehicle designed to carry people — and someday maybe even packages — in dense urban environments.

The company’s story has captured the attention of skeptics and supporters alike, perhaps because of its secretive nature and outsized mission. Unlike its rivals, Zoox is developing the self-driving software stack, the on-demand ride-sharing app and the vehicle itself. Zoox also plans to own, manage and operate its robotaxi fleet.

Unlike its rivals, Zoox is developing the self-driving software stack, the on-demand ride-sharing app and the vehicle itself.

It’s been an expensive pursuit that almost led to its demise before Amazon snapped it up — and the mission is still far from over. But today, as an independent Amazon subsidiary, it has the financial support of one of the world’s most valuable public companies.

TechCrunch interviewed Zoox co-founder and CTO Jesse Levinson about the company’s milestone, the vehicle design, its exit to Amazon and what lies ahead.

This interview has been edited for brevity and clarity.

TechCrunch: What was your trick or how did you remain focused for six years on something that is futuristic, expensive and possibly could fail? What did you personally do to keep that focus?

Jesse Levinson: Well, doing something like this is definitely challenging and it requires patience. I think the advice I would give is first to convince yourself that what you’re doing makes sense and is important and worth doing. If you’re starting a company because your goal is to make as much money as possible, if it turns out to be hard it’s going to be really difficult to convince yourself and your team and investors to stick with the idea.

One of the great things about Zoox is that the idea itself just makes a lot of sense. From first principles, there’s really a compelling reason to solve the problem the way we’ve been solving it and the market opportunity is unquestionably enormous. So armed with those facts and a team of wonderful employees and investors who strongly believed in that, we were able to weather some of the ups and downs of the industry, even though it’s not always been an easy ride.

Let’s go back in time to the very first concept when you started to think of what a purpose-built vehicle would look like. Those early drawings showed a very, very different looking type of vehicle.

Are you referring to maybe like the sports-car-looking vehicle? We were actually never planning on launching a sports car as our first product; that was more of like a vision statement. Honestly, if you’re trying to move people around cities, it makes much more sense to have the kind of compact carriage like we showed this morning. We were never actually building a sports car. 

What is it like to create a long-term, cutting-edge product that exists at the edge of regulation? It seems like a very unique problem. 

I would say that if you have a big idea and you’re confident that it makes sense, you should at least explore the idea, rather than giving up because the current regulations aren’t designed for it.

At the same time, it’s very important to be respectful of the regulatory process, and you can’t assume that you can ignore it. I think companies that have tried that approach have usually found that doesn’t work very well either. We’ve taken a very proactive approach to working with regulatory agencies at the local, state and federal level, and we’ve been very forthcoming with “this is how we look at the problem” and “this is what we want to do.”

We’ve also been fortunate because over time the regulations on the local, state and federal level have really evolved to accommodate what we’ve been working on since 2014, even though when we started the company in 2014, those regulations did not exist. 

The vehicle today, was that what you had in your mind, or what the team had in mind, from the very beginning? Or was it a bit different?

Yeah, honestly, there have been very few substantive changes to the vehicle’s design since we started working on it in 2014 and 2015. Obviously, we refined it and actually had to make it work from an engineering and crash perspective. But if you look at some of the drawings that we were exploring in 2014 and 2015, it’s extremely similar.

#automation, #automotive, #jesse-levinson, #robotaxi, #robotics, #self-driving-cars, #tc, #transportation, #zoox

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Microsoft brings new process mining features to Power Automate

Power Automate is Microsoft’s platform for streamlining repetitive workflows — you may remember it under its original name: Microsoft Flow. The market for these robotic process automation (RPA) tools is hot right now, so it’s no surprise that Microsoft, too, is doubling down on its platform. Only a few months ago, the team launched Power Automate Desktop, based on its acquisition of Softomotive, which helps users automate workflows in legacy desktop-based applications, for example. After a short time in preview Power Automate Desktop is now generally available.

The real news today, though, is that the team is also launching a new tool, the Process Advisor, which is now in preview as part of the Power Automate platform. This new process mining tool provides users with a new collaborative environment where developers and business users can work together to create new automations.

The idea here is that business users are the ones who know exactly how a certain process works. With Process Advisor, they can now submit recordings of how they process a refund, for example, and then submit that to the developers, who are typically not experts in how these processes usually work.

What’s maybe just as important is that a system like this can identify bottlenecks in existing processes where automation can help speed up existing workflows.

Image Credits: Microsoft

“This goes back to one of the things that we always talk about for Power Platform, which, it’s a corny thing, but it’s that development is a team sport,” Charles Lamanna, Microsoft’s corporate VP for its Low Code Application Platform, told me. “That’s one of our big focuses: how do bring people to collaborate and work together who normally don’t. This is great because it actually brings together the business users who live the process each and every day with a specialist who can build the robot and do the automation.”

The way this works in the backend is that Power Automate’s tools capture exactly what the users do and click on. All this information is then uploaded to the cloud and — with just five or six recordings — Power Automate’s systems can map how the process works. For more complex workflows, or those that have a lot of branches for different edge cases, you likely want more recordings to build out these processes, though.

Image Credits: Microsoft

As Lamanna noted, building out these workflows and process maps can also help businesses better understand the ROI of these automations. “This kind of map is great to go build an automation on top of it, but it’s also great because it helps you capture the ROI of each automation you do because you’ll know for each step, how long it took you,” Lamanna said. “We think that this concept of Process Advisor is probably going to be one of the most important engines of adoption for all these low-code/no-code technologies that are coming out. Basically, it can help guide you to where it’s worth spending the energy, where it’s worth training people, where it’s worth building an app, or using AI, or building a robot with our RPA like Power Automate.”

Lamanna likened this to the advent of digital advertising, which for the first time helped marketers quantify the ROI of advertising.

The new process mining capabilities in Power Automate are now available in preview.

#artificial-intelligence, #automation, #business, #business-process-automation, #cloud, #developer, #developers, #enterprise, #microsoft, #process-mining, #robotic-process-automation, #rpa

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AutoX becomes China’s first to remove safety drivers from robotaxis

Residents of Shenzhen will see truly driverless cars on the road starting Thursday. AutoX, a four-year-old startup backed by Alibaba, MediaTek and Shanghai Motors, is deploying a fleet of 25 unmanned vehicles in downtown Shenzhen, marking the first time any autonomous driving car in China tests without safety drivers or remote operators on public roads.

The cars, meant as robotaxis, are not yet open to the public, an AutoX spokesperson told TechCrunch.

The milestone came just five months after AutoX landed a permit from California to start driverless tests, following in the footsteps of Waymo and Nuro.

It also indicates that China wants to bring its smart driving industry on par with the U.S. Cities from Shenzhen to Shanghai are competing to attract autonomous driving upstarts by clearing regulatory hurdles, touting subsidies and putting up 5G infrastructure.

As a result, each city ends up with its own poster child in the space: AutoX and Deeproute.ai in Shenzhen, Pony.ai and WeRide in Guangzhou, Momenta in Suzhou, Baidu’s Apollo fleet in Beijing, to name a few. The autonomous driving companies, in turn, work closely with traditional carmakers to make their vehicles smarter and more suitable for future transportation.

“We have obtained support from the local government. Shenzhen is making a lot of rapid progress on legislation for self-driving cars,” said the AutoX representative.

The decision to remove drivers from the front and operators from a remote center appears a bold move in one of China’s most populated cities. AutoX equips its vehicles with its proprietary vehicle control unit called XCU, which it claims has faster processing speed and more computational capability to handle the complex road scenarios in China’s cities.

“[The XCU] provides multiple layers of redundancy to handle this kind of situation,” said AutoX when asked how its vehicles will respond should the machines ever go rogue.

The company also stressed the experience it learned from “millions of miles” driven in China’s densest city centers through its 100 robotaxis in the past few years. Its rivals are also aggressively accumulating mileage to train their self-driving algorithms while banking sizable investments to fund R&D and pilot tests. AutoX itself, for instance, has raised more than $160 million to date.

#alibaba, #artificial-intelligence, #asia, #automation, #autox, #beijing, #china, #mediatek, #momenta, #robotaxi, #robotics, #self-driving-cars, #shanghai, #shenzhen, #transportation, #waymo

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Salesforce applies AI to workflow with Einstein Automate

While Salesforce made a big splash yesterday with the announcement that it’s buying Slack for $27.7 billion, it’s not the only thing going on for the CRM giant this week. In fact Dreamforce, the company’s customer extravaganza is also on the docket. While it is virtual this year, there are still product announcements aplenty and today the company announced Einstein Automate, a new AI-fueled set of workflow solutions.

Sarah Franklin, EVP & GM of Platform, Trailhead and AppExchange at Salesforce says that she is seeing companies facing a digital imperative to automate processes as things move ever more quickly online, being driven there even faster by the pandemic. “With Einstein Automate, everyone can change the speed of work and be more productive through intelligent workflow automation,” she said in a statement.

Brent Leary, principal analyst at CRM Essentials says that combined these tools are designed to help customers get to work more quickly. “It’s not only about identifying the insight, it’s about making it easier to leverage it at the the right time. And this should make it easier for users to do it without spending more time and effort,” Leary told TechCrunch.

Einstein is the commercial name given to Salesforce’s artificial intelligence platform that touches every aspect of the company’s product line, bringing automation to many tasks and making it easier to find the most valuable information on customers, which is often buried in an avalanche of data.

Einstein Automate encompasses several products designed to improve workflows inside organizations. For starters, the company has created Flow Orchestrator, a tool that uses a low-code, drag and drop approach for building workflows, but it doesn’t stop there. It also relies on AI to provide help suggest logical next steps to speed up workflow creation.

Salesforce is also bringing Mulesoft, the integration company it bought for $6.5 billion in 2018 into the mix. Instead of processes like a mortgage approval workflow, the Mulesoft piece lets IT build complex integrations between applications across the enterprise, and the Salesforce family of products more easily.

To make it easier to build these workflows, Salesforce is announcing the Einstein Automate collection page available in AppExchange, the company’s application marketplace. The collection includes over 700 pre-built connectors so customers can grab and go as they build these workflows, and finally it’s updating the OmniStudio, their platform for generating customer experiences. As Salesforce describes it, “Included in OmniStudio is a suite of resources and no-code tools, including pre-built guided experiences, templates and more, allowing users to deploy digital-first experiences like licensing and permit applications quickly and with ease. ”

Per usual with Salesforce Dreamforce announcements, the Flow Orchestrator being announced today won’t be available in beta until next summer. The Mulesoft component will be available in early 2021, but the OmniStudio updates and the Einstein connections collection are available today.

#artificial-intelligence, #automation, #cloud, #customer-experience, #einstein, #enterprise, #saas, #salesforce, #tc, #workflows

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Honda to mass-produce Level 3 autonomous cars by March

Honda claims it will be the first automaker to mass-produce vehicles with autonomous capabilities that meet SAE Level 3 standards, with plans to begin producing and selling a version of its Honda Legend luxury sedan with fully approved automated driving equipment in Japan from next March. Honda announced the news via press release (via Reuters) and this follows the approval by the Japanese government of the company’s ‘Traffic Jam Pilot’ autonomous tech, which for the first time will allow drivers to actually take their eyes off the road while it’s engaged.

Honda’s Pro Pilot Assist is the feature that predates this forthcoming one, but it’s a Level 2 feature per the SAE scale, which means that while it can automatically control both speed and steering, drivers behind the wheel have to be constantly ready to take over manual control should the system require it. SAE Level 3 is the first that falls under a categorization that most experts feels qualifies as actually autonomous – wherein a driver can fully allow their vehicle to take over control. Level 3 still requires that a driver be able to take over driving when the system requests, while Levels 4 and 5 have no such requirement.

Tesla has also launched its own ‘full self-driving’ feature in its vehicles in a beta program that it’s expanding to more drivers gradually, but critics suggest that despite it’s name, it’s not actually a fully autonomous system, and it isn’t yet classified as such according to regulations. Honda’s launch of its Level 3 Legend in March 2021 will be one watched by regulators and ordinary drivers alike around the world as one of the first true tests of a mass-produced and regulator-approved autonomous vehicle system.

#artificial-intelligence, #automation, #automotive, #cars, #driver, #driving, #emerging-technologies, #honda, #japan, #japanese-government, #legend, #pilot, #robotics, #self-driving-car, #tc, #tesla, #transport, #transportation

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