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Google Cloud launches Vertex AI, a new managed machine learning platform
At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. It’s a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesn’t traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.
The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. “Machine learning in the enterprise is in crisis, in my view,” Craig Wiley, the director of product management for Google Cloud’s AI Platform, told me. “As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”
Image Credits: Google
Wiley, who was also the general manager of AWS’s SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, “many of which were dead ends,” according to him (including some of Google’s own). “Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models they’re building.”
Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.
Image Credits: Google
The service is also integrated with Vizier, Google’s AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.
Vertex also offers a “Feature Store” that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.
Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Cloud’s AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.
To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and — and this may be a bit of a surprise — BigQuery ML, Google’s tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.
“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production,” said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. “We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”
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Father and son duo take on global logistics with Optimal Dynamics’ sequential decision AI platform
Like “innovation,” machine learning and artificial intelligence are commonplace terms that provide very little context for what they actually signify. AI/ML spans dozens of different fields of research, covering all kinds of different problems and alternative and often incompatible ways to solve them.
One robust area of research here that has antecedents going back to the mid-20th century is what is known as stochastic optimization — decision-making under uncertainty where an entity wants to optimize for a particular objective. A classic problem is how to optimize an airline’s schedule to maximize profit. Airlines need to commit to schedules months in advance without knowing what the weather will be like or what the specific demand for a route will be (or, whether a pandemic will wipe out travel demand entirely). It’s a vibrant field, and these days, basically runs most of modern life.
Warren B. Powell has been exploring this problem for decades as a researcher at Princeton, where he has operated the Castle Lab. He has researched how to bring disparate areas of stochastic optimization together under one framework that he has dubbed “sequential decision analytics” to optimize problems where each decision in a series places constraints on future decisions. Such problems are common in areas like logistics, scheduling and other key areas of business.
The Castle Lab has long had industry partners, and it has raised tens of millions of dollars in grants from industry over its history. But after decades of research, Powell teamed up with his son, Daniel Powell, to spin out his collective body of research and productize it into a startup called Optimal Dynamics. Father Powell has now retired full-time from Princeton to become chief analytics officer, while son Powell became CEO.
The company raised $18.4 million in new funding last week from Bessemer led by Mike Droesch, who recently was promoted to partner earlier this year with the firm’s newest $3.3 billion fundraise. The company now has 25 employees and is centered in New York City.
So what does Optimal Dynamics actually do? CEO Powell said that it’s been a long road since the company’s founding in mid-2017 when it first raised a $450,000 pre-seed round. We were “drunkenly walking in finding product-market fit,” Powell said. This is “not an easy technology to get right.”
What the company ultimately zoomed in on was the trucking industry, which has precisely the kind of sequential decision-making that father Powell had been working on his entire career. “Within truckload, you have a whole series of uncertain variables,” CEO Powell described. “We are the first company that can learn and plan for an uncertain future.”
There’s been a lot of investment in logistics and trucking from VCs in recent years as more and more investors see the potential to completely disrupt the massive and fragmented market. Yet, rather than building a whole new trucking marketplace or approaching it as a vertically integrated solution, Optimal Dynamics decided to go with the much simpler enterprise SaaS route to offer better optimization to existing companies.
One early customer, which owned 120 power units, saved $4 million using the company’s software, according to Powell. That was a result of better utilization of equipment and more efficient operations. They “sold off about 20 vehicles that they didn’t need anymore due to the underlying efficiency,” he said. In addition, the company was able to reduce a team of 10 who used to manage trucking logistics down to one, and “they are just managing exceptions” to the normal course of business. As an example of an exception, Powell said that “a guy drove half way and then decided he wanted to quit,” leaving a load stranded. “Trying to train a computer on weird edge events [like that] is hard,” he said.
Better efficiency for equipment usage and then saving money on employee costs by automating their work are the two main ways Optimal Dynamics saves money for customers. Powell says most of the savings come in the former rather than the latter, since utilization is often where the most impact can be felt.
On the technical front, the key improvement the company has devised is how to rapidly solve the ultra-complex optimization problems that logistics companies face. The company does that through value function approximation, which is a field of study where instead of actually computing the full range of stochastic optimization solutions, the program approximates the outcomes of decisions to reduce compute time. We “take in this extraordinary amount of detail while handling it in a computationally efficient way,” Powell said. That’s where we have really “wedged ourselves as a company.”
Early signs of success with customers led to a $4 million seed round led by Homan Yuen of Fusion Fund, which invests in technically sophisticated startups (i.e. the kind of startups that take decades of optimization research at Princeton to get going). Powell said that raising the round was tough, transpiring during the first weeks of the pandemic last year. One corporate fund pulled out at the last minute, and it was “chaos ensuing with everyone,” he said. This Series A process meanwhile was the opposite. “This round was totally different — closed it in 17 days from round kickoff to closure,” he said.
With new capital in the bank, the company is looking to expand from 25 employees to 75 this year, who will be trickling back to the company’s office in the Flatiron neighborhood of Manhattan in the coming months. Optimal Dynamics targets customers with 75 trucks or more, either fleets for rent or private fleets owned by companies like Walmart who handle their own logistics.
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Explorium scores $75M Series C just 10 months after B round
Without good data, it’s impossible to build an accurate predictive machine learning model. Explorium, a company that has been building a solution over the last several years to help data pros find the best data for a given model, announced a $75 million Series C today — just 10 months after announcing a $31 million Series B.
Insight Partners led today’s investment with participation from existing investors Zeev Ventures, Emerge, F2 Venture Capital, 01 Advisors and Dynamic Loop Capital. The company reports it has now raised a total of $127 million. George Mathew, managing partner at Insight, and former president and COO at Alteryx, will be joining the board, giving the company someone with solid operator experience to help guide them into the next phase.
Company co-founder and CEO Maor Shlomo, says that in spite of how horrible COVID has been from a human perspective, it has been a business accelerator for his company and he saw revenue quadruple last year (although he didn’t share specific numbers beyond that). “It’s related to the nature of our business. We’re helping enterprises and data practitioners find new data sources that can help them solve business challenges,” Sholmo explained.
He says that during the pandemic, a lot of companies had to find new data sources because the old data wasn’t especially helpful for predictive models. That meant that customers required new sources to give them visibility into the shifts and movements in the market to help them adjust and make decisions during pandemic. “And given that’s basically what our platform does in its essence, we’ve seen a lot of growth [over the past year],” he says.
With the revenue growth the company has been experiencing, it has been adding employees at rapid clip. When we spoke to Explorium last July, the company had 87 people. Today that number has grown to 130 with plans to get to 200 perhaps by the end of 2021 or early 2022, depending on how the business continues to grow.
The company has offices in Tel Aviv and San Mateo, California with plans to open a new office in New York City whenever it’s possible to do so. While Shlomo wants a flexible workplace, he’s not going fully remote with plans to allow people to work two days from home and three in the office as local rules allow.
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Artificial raises $21M led by Microsoft’s M12 for a lab automation platform aimed at life sciences R&D
Automation is extending into every aspect of how organizations get work done, and today comes news of a startup that is building tools for one industry in particular: life sciences. Artificial, which has built a software platform for laboratories to assist with, or in some cases fully automate, research and development work, has raised $21.5 million.
It plans to use the funding to continue building out its software and its capabilities, to hire more people, and for business development, according to Artificial’s CEO and co-founder David Fuller. The company already has a number of customers including Thermo Fisher and Beam Therapeutics using its software directly and in partnership for their own customers. Sold as aLab Suite, Artificial’s technology can both orchestrate and manage robotic machines that labs might be using to handle some work; and help assist scientists when they are carrying out the work themselves.
“The basic premise of what we’re trying to do is accelerate the rate of discovery in labs,” Fuller said in an interview. He believes the process of bringing in more AI into labs to improve how they work is long overdue. “We need to have a digital revolution to change the way that labs have been operating for the last 20 years.”
The Series A is being led by Microsoft’s venture fund M12 — a financial and strategic investor — with Playground Global and AME Cloud Ventures also participating. Playground Global, the VC firm co-founded by ex-Google exec and Android co-creator Andy Rubin (who is no longer with the firm), has been focusing on robotics and life sciences and it led Artificial’s first and only other round. Artificial is not disclosing its valuation with this round.
Fuller hails from a background in robotics, specifically industrial robots and automation. Before founding Artificial in 2019, he was at Kuka, the German robotics maker, for a number of years, culminating in the role of CTO; prior to that, Fuller spent 20 years at National Instruments, the instrumentation, test equipment and industrial software giant. Meanwhile, Artificial’s co-founder, Nikhita Singh, has insight into how to bring the advances of robotics into environments that are quite analogue in culture. She previously worked on human-robot interaction research at the MIT Media Lab, and before that spent years at Palantir and working on robotics at Berkeley.
As Fuller describes it, he saw an interesting gap (and opportunity) in the market to apply automation, which he had seen help advance work in industrial settings, to the world of life sciences, both to help scientists track what they are doing better, and help them carry out some of the more repetitive work that they have to do day in, day out.
This gap is perhaps more in the spotlight today than ever before, given the fact that we are in the middle of a global health pandemic. This has hindered a lot of labs from being able to operate full in-person teams, and increased the reliance on systems that can crunch numbers and carry out work without as many people present. And, of course, the need for that work (whether it’s related directly to Covid-19 or not) has perhaps never appeared as urgent as it does right now.
There have been a lot of advances in robotics — specifically around hardware like robotic arms — to manage some of the precision needed to carry out some work, but up to now no real efforts made at building platforms to bring all of the work done by that hardware together (or in the words of automation specialists, “orchestrate” that work and data); nor link up the data from those robot-led efforts, with the work that human scientists still carry out. Artificial estimates that some $10 billion is spent annually on lab informatics and automation software, yet data models to unify that work, and platforms to reach across it all, remain absent. That has, in effect, served as a barrier to labs modernising as much as they could.
A lab, as he describes it, is essentially composed of high-end instrumentation for analytics, alongside then robotic systems for liquid handling. “You can really think of a lab, frankly, as a kitchen,” he said, “and the primary operation in that lab is mixing liquids.”
But it is also not unlike a factory, too. As those liquids are mixed, a robotic system typically moves around pipettes, liquids, in and out of plates and mixes. “There’s a key aspect of material flow through the lab, and the material flow part of it is much more like classic robotics,” he said. In other words, there is, as he says, “a combination of bespoke scientific equipment that includes automation, and then classic material flow, which is much more standard robotics,” and is what makes the lab ripe as an applied environment for automation software.
To note: the idea is not to remove humans altogether, but to provide assistance so that they can do their jobs better. He points out that even the automotive industry, which has been automated for 50 years, still has about 6% of all work done by humans. If that is a watermark, it sounds like there is a lot of movement left in labs: Fuller estimates that some 60% of all work in the lab is done by humans. And part of the reason for that is simply because it’s just too complex to replace scientists — who he described as “artists” — altogether (for now at least).
“Our solution augments the human activity and automates the standard activity,” he said. “We view that as a central thesis that differentiates us from classic automation.”
There have been a number of other startups emerging that are applying some of the learnings of artificial intelligence and big data analytics for enterprises to the world of science. They include the likes of Turing, which is applying this to helping automate lab work for CPG companies; and Paige, which is focusing on AI to help better understand cancer and other pathology.
The Microsoft connection is one that could well play out in how Artificial’s platform develops going forward, not just in how data is perhaps handled in the cloud, but also on the ground, specifically with augmented reality.
“We see massive technical synergy,” Fuller said. “When you are in a lab you already have to wear glasses… and we think this has the earmarks of a long-term use case.”
Fuller mentioned that one area it’s looking at would involve equipping scientists and other technicians with Microsoft’s HoloLens to help direct them around the labs, and to make sure people are carrying out work consistently by comparing what is happening in the physical world to a “digital twin” of a lab containing data about supplies, where they are located, and what needs to happen next.
It’s this and all of the other areas that have yet to be brought into our very AI-led enterprise future that interested Microsoft.
“Biology labs today are light- to semi-automated—the same state they were in when I started my academic research and biopharmaceutical career over 20 years ago. Most labs operate more like test kitchens rather than factories,” said Dr. Kouki Harasaki, an investor at M12, in a statement. “Artificial’s aLab Suite is especially exciting to us because it is uniquely positioned to automate the masses: it’s accessible, low code, easy to use, highly configurable, and interoperable with common lab hardware and software. Most importantly, it enables Biopharma and SynBio labs to achieve the crowning glory of workflow automation: flexibility at scale.”
Harasaki is joining Peter Barratt, a founder and general partner at Playground Global, on Artificial’s board with this round.
“It’s become even more clear as we continue to battle the pandemic that we need to take a scalable, reproducible approach to running our labs, rather than the artisanal, error-prone methods we employ today,” Barrett said in a statement. “The aLab Suite that Artificial has pioneered will allow us to accelerate the breakthrough treatments of tomorrow and ensure our best and brightest scientists are working on challenging problems, not manual labor.”
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Industrial automation startup Bright Machines hauls in $435M by going public via SPAC
Bright Machines is going public via a SPAC-led combination, it announced this morning. The transaction will see the 3-year-old company merge with SCVX, raising gross cash proceeds of $435 million in the process.
After the transaction is consummated, the startup will sport an anticipated equity valuation of $1.6 billion.
The Bright Machines news indicates that the great SPAC chill was not a deep freeze. And the transaction itself, in conjunction with the previously announced Desktop Metal blank-check deal, implies that there is space in the market for hardware startup liquidity via SPACs. Perhaps that will unlock more late-stage capital for hardware-focused upstarts.
Today we’re first looking at what Bright Machines does, and then the financial details that it shared as part of its news.
What’s Bright Machines?
Bright Machines is trying to solve a hard problem related to industrial automation by creating microfactories. This involves a complex mix of hardware, software and artificial intelligence. While robotics has been around in one form or another since the 1970s, for the most part, it has lacked real intelligence. Bright Machines wants to change that.
The company emerged in 2018 with a $179 million Series A, a hefty amount of cash for a young startup, but the company has a bold vision and such a vision takes extensive funding. What it’s trying to do is completely transform manufacturing using machine learning.
At the time of that funding, the company brought in former Autodesk co-CEO Amar Hanspal as CEO and former Autodesk founder and CEO Carl Bass to sit on the company board of directors. AutoDesk itself has been trying to transform design and manufacturing in recent years, so it was logical to bring these two experienced leaders into the fold.
The startup’s thesis is that instead of having what are essentially “unintelligent” robots, it wants to add computer vision and a heavy dose of sensors to bring a data-driven automation approach to the factory floor.
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Opportunity knocks: Exhibit at TC Sessions: Mobility 2021
No matter what slice of the mobility market you’ve claimed as your own — AVs, EVs, data mining, AI, dockless scooters, robotics or the batteries that will charge and change the world — you won’t find a better place to showcase your extraordinary tech and talent than TC Sessions: Mobility 2021.
Buy a Startup Exhibitor Package and virtually plant your early-stage mobility startup in front of a global audience that’s focused exclusively on one of the most complex, rapidly evolving industries. TC Sessions: Mobility, which takes place on June 9, features the top minds and makers, draws thousands of attendees, fosters collaborative community and creates a networking environment ripe with opportunities.
Pro tip: This package is for pre-Series A, early-stage startups only.
The Startup Exhibitor Package costs $380, and it comes with four all-access passes to the event. But wait (insert infomercial voice here), there’s more!
Your virtual expo booth features lead-generation capabilities. You can highlight your pitch deck, run a video loop and/or host live demos. Network with CrunchMatch, our AI-powered platform, to find and connect with the people who can help move your business forward. CrunchMatch lets you host private video meetings — pitch investors, recruit new talent or grow your customer base.
You’ll have access to all the presentations, panel discussions and breakout sessions, too. And video-on-demand means you won’t miss out.
Here’s a peek at just some of the agenda’s great programming you and, thanks to those extra passes, your team can attend — or catch later with VOD:
- EV Founders in Focus: We sit down with the founders poised to take advantage of the rise in electric vehicle sales. This time, we will chat with Kameale Terry, co-founder and CEO of ChargerHelp! a startup that enables on-demand repair of electric vehicle charging stations.
- Will Venture Capital Drive the Future of Mobility? Clara Brenner, Quin Garcia and Rachel Holt will discuss how the pandemic changed their investment strategies, the hottest sectors within the mobility industry, the rise of SPACs as a financial instrument and where they plan to put their capital in 2021 and beyond.
- Driving Innovation at General Motors: GM is in the midst of sweeping changes that will eventually turn it into an EV-only producer of cars, trucks and SUVs. But the auto giant’s push to electrify passenger vehicles is just one of many efforts to be a leader in innovation and the future of transportation. We’ll talk with Pam Fletcher, vice president of innovation at GM, one of the key people behind the 113-year-old automaker’s push to become a nimble, tech-centric company.
TC Sessions: Mobility 2021 takes place June 9. Buy a Startup Exhibitor Package and set yourself up for global exposure and networking success. Show us your extraordinary tech and talent!
Is your company interested in sponsoring or exhibiting at TC Sessions: Mobility 2021? Contact our sponsorship sales team by filling out this form.
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Worksome pulls $13M into its high skill freelancer talent platform
More money for the now very buzzy business of reshaping how people work: Worksome is announcing it recently closed a $13 million Series A funding round for its “freelance talent platform” — after racking up 10x growth in revenue since January 2020, just before the COVID-19 pandemic sparked a remote working boom.
The 2017 founded startup, which has a couple of ex-Googlers in its leadership team, has built a platform to connect freelancers looking for professional roles with employers needing tools to find and manage freelancer talent.
It says it’s seeing traction with large enterprise customers that have traditionally used Managed Service Providers (MSPs) to manage and pay external workforces — and views employment agency giants like Randstad, Adecco and Manpower as ripe targets for disruption.
“Most multinational enterprises manage flexible workers using legacy MSPs,” says CEO and co-founder Morten Petersen (one of the Xooglers). “These largely analogue businesses manage complex compliance and processes around hiring and managing freelance workforces with handheld processes and outdated technology that is not built for managing fluid workforces. Worksome tackles this industry head on with a better, faster and simpler solution to manage large freelancer and contractor workforces.”
Worksome focuses on helping medium/large companies — who are working with at least 20+ freelancers at a time — fill vacancies within teams rather than helping companies outsource projects, per Petersen, who suggests the latter is the focus for the majority of freelancer platforms.
“Worksome helps [companies] onboard people who will provide necessary skills and will be integral to longer-term business operations. It makes matches between companies and skilled freelancers, which the businesses go on to trust, form relationships with and come back to time and time again,” he goes on.
“When companies hire dozens or hundreds of freelancers at one time, processes can get very complicated,” he adds, arguing that on compliance and payments Worksome “takes on a much greater responsibility than other freelancing platforms to make big hires easier”.
The startup also says it’s concerned with looking out for (and looking after) its freelancer talent pool — saying it wants to create “a world of meaningful work” on its platform, and ensure freelancers are paid fairly and competitively. (And also that they are paid faster than they otherwise might be, given it takes care of their payroll so they don’t have to chase payments from employers.)
The business started life in Copenhagen — and its Series A has a distinctly Nordic flavor, with investment coming from the Danish business angel and investor on the local version of the Dragons’ Den TV program Løvens Hule; the former Minister for Higher Education and Science, Tommy Ahlers; and family home manufacturer Lind & Risør.
It had raised just under $6M prior to thus round, per Crunchbase, and also counts some (unnamed) Google executives among its earlier investors.
Freelancer platforms (and marketplaces) aren’t new, of course. There are also an increasing number of players in this space — buoyed by a new flush of VC dollars chasing the ‘future of work’, whatever hybrid home-office flexible shape that might take. So Worksome is by no means alone in offering tech tools to streamline the interface between freelancers and businesses.
A few others that spring to mind include Lystable (now Kalo), Malt, Fiverr — or, for techie job matching specifically, the likes of HackerRank — plus, on the blue collar work side, Jobandtalent. There’s also a growing number of startups focusing on helping freelancer teams specifically (e.g. Collective), so there’s a trend towards increasing specialism.
Worksome says it differentiates vs other players (legacy and startups) by combining services like tax compliance, background and ID checks and handling payroll and other admin with an AI powered platform that matches talent to projects.
Although it’s not the only startup offering to do the back-office admin/payroll piece, either, nor the only one using AI to match skilled professionals to projects. But it claims it’s going further than rival ‘freelancer-as-a-service’ platforms — saying it wants to “address the entire value chain” (aka: “everything from the hiring of freelance talent to onboarding and payment”).
Worksome has 550 active clients (i.e. employers in the market for freelancer talent) at this stage; and has accepted 30,000 freelancers into its marketplace so far.
Its current talent pool can take on work across 12 categories, and collectively offers more than 39,000 unique skills, per Petersen.
The biggest categories of freelancer talent on the platform are in Software and IT; Design and Creative Work; Finance and Management Consulting; plus “a long tail of niche skills” within engineering and pharmaceuticals.
While its largest customers are found in the creative industries, tech and IT, pharma and consumer goods. And its biggest markets are the U.K. and U.S.
“We are currently trailing at +20,000 yearly placements,” says Petersen, adding: “The average yearly spend per client is $300,000.”
Worksome says the Series A funding will go on stoking growth by investing in marketing. It also plans to spend on product dev and on building out its team globally (it also has offices in London and New York).
Over the past 12 months the startup doubled the size of its team to 50 — and wants to do so again within 12 months so it can ramp up its enterprise client base in the U.S., U.K. and euro-zone.
“Yes, there are a lot of freelancer platforms out there but a lot of these don’t appreciate that hiring is only the tip of the iceberg when it comes to reducing the friction in working with freelancers,” argues Petersen. “Of the time that goes into hiring, managing and paying freelancers, 75% is currently spent on admin such as timesheet approvals, invoicing and compliance checks, leaving only a tiny fraction of time to actually finding talent.”
Worksome woos employers with a “one-click-hire” offer — touting its ability to find and hire freelancers “within seconds”.
If hiring a stranger in seconds sounds ill-advised, Worksome greases this external employment transaction by taking care of vetting the freelancers itself (including carrying out background checks; and using proprietary technology to asses freelancers’ skills and suitability for its marketplace).
“We have a two-step vetting process to ensure that we only allow the best freelance talent onto the Worksome platform,” Petersen tells TechCrunch. “For step one, an inhouse-built robot assesses our freelancer applicants. It analyses their skillset, social media profiles, profile completeness and hourly or daily rate, as well as their CV and work history, to decide whether each person is a good fit for Worksome.
“For step two, our team of talent specialists manually review and decline or approve the freelancers that pass through step one with a score of 85% or more. We have just approved our 30,000th freelancer and will be able to both scale and improve our vetting procedure as we grow.”
A majority of freelancer applicants fail Worksome’s proprietary vetting processes. This is clear because it says it has received 80,000 applicants so far — but only approved 30,000.
That raises interesting questions about how it’s making decisions on who is (and isn’t) an ‘appropriate fit’ for its talent marketplace.
It says its candidate assessing “robot” looks at “whether freelancers can demonstrate the skillset, matching work history, industry experience and profile depth” deemed necessary to meet its quality criteria — giving the example that it would not accept a freelancer who says they can lead complex IT infrastructure projects if they do not have evidence of relevant work, education and skills.
On the AI freelancer-to-project matching side, Worksome says its technology aims to match freelancers “who have the highest likelihood of completing a job with high satisfaction, based on their work-history, and performance and skills used on previous jobs”.
“This creates a feedback loop that… ensure that both clients and freelancers are matched with great people and great work,” is its circular suggestion when we ask about this.
But it also emphasizes that its AI is not making hiring decisions on its own — and is only ever supporting humans in making a choice. (An interesting caveat since existing EU data protection rules, under Article 22 of the GDPR, provide for a right for individuals to object to automated decision making if significant decisions are being taken without meaningful human interaction.)
Using automation technologies (like AI) to make assessments that determine whether a person gains access to employment opportunities or doesn’t can certainly risk scaled discrimination. So the devil really is in the detail of how these algorithmic assessments are done.
That’s why such uses of technology are set to face close regulatory scrutiny in the European Union — under incoming rules on ‘high risk’ users of artificial intelligence — including the use of AI to match candidates to jobs.
The EU’s current legislative proposals in this area specifically categorize “employment, workers management and access to self-employment” as a high risk use of AI, meaning applications like Worksome are likely to face some of the highest levels of regulatory supervision in the future.
Nonetheless, Worksome is bullish when we ask about the risks associated with using AI as an intermediary for employment opportunities.
“We utilise fairly advanced matching algorithms to very effectively shortlist candidates for a role based solely on objective criteria, rinsed from human bias,” claims Petersen. “Our algorithms don’t take into account gender, ethnicity, name of educational institutions or other aspects that are usually connected to human bias.”
“AI has immense potential in solving major industry challenges such as recruitment bias, low worker mobility and low access to digital skills among small to medium sized businesses. We are firm believers that technology should be utilized to remove human bias’ from any hiring process,” he goes on, adding: “Our tech was built to this very purpose from the beginning, and the new proposed legislation has the potential to serve as a validator for the hard work we’ve put into this.
“The obvious potential downside would be if new legislation would limit innovation by making it harder for startups to experiment with new technologies. As always, legislation like this will impact the Davids more than the Goliaths, even though the intentions may have been the opposite.”
Zooming back out to consider the pandemic-fuelled remote working boom, Worksome confirms that most of the projects for which it supplied freelancers last year were conducted remotely.
“We are currently seeing a slow shift back towards a combination of remote and onsite work and expect this combination to stick amongst most of our clients,” Petersen goes on. “Whenever we are in uncertain economic times, we see a rise in the number of freelancers that companies are using. However, this trend is dwarfed by a much larger overall trend towards flexible work, which drives the real shift in the market. This shift has been accelerated by COVID-19 but has been underway for many years.
“While remote work has unlocked an enormous potential for accessing talent everywhere, 70% of the executives expect to use more temporary workers and contractors onsite than they did before COVID-19, according to a recent McKinsey study. This shows that businesses really value the flexibility in using an on-demand workforce of highly skilled specialists that can interact directly with their own teams.”
Asked whether it’s expecting growth in freelancing to sustain even after we (hopefully) move beyond the pandemic — including if there’s a return to physical offices — Petersen suggests the underlying trend is for businesses to need increased flexibility, regardless of the exact blend of full-time and freelancer staff. So platforms like Worksome are confidently poised to keep growing.
“When you ask business leaders, 90% believe that shifting their talent model to a blend of full-time and freelancers can give a future competitive advantage (Source: BCG),” he says. “We see two major trends driving this sentiment; access to talent, and building an agile and flexible organization. This has become all the more true during the pandemic — a high degree of flexibility is allowing organisations to better navigate both the initial phase of the pandemic as well the current pick up of business activity.
“With the amount of change that we’re currently seeing in the world, and with businesses are constantly re-inventing themselves, the access to highly skilled and flexible talent is absolutely essential — now, in the next 5 years, and beyond.”
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The Last Gameboard raises $4M to ship its digital tabletop gaming platform
The tabletop gaming industry has exploded over the last few years as millions discovered or rediscovered its joys, but it too is evolving — and The Last Gameboard hopes to be the venue for that evolution. The digital tabletop platform has progressed from crowdfunding to $4 million seed round, and having partnered with some of the biggest names in the industry, plans to ship by the end of the year.
As the company’s CEO and co-founder Shail Mehta explained in a TC Early Stage pitch-off earlier this year, The Last Gameboard is a 16-inch square touchscreen device with a custom OS and a sophisticated method of tracking game pieces and hand movements. The idea is to provide a digital alternative to physical games where that’s practical, and do so with the maximum benefit and minimum compromise.
If the pitch sounds familiar… it’s been attempted once or twice before. I distinctly remember being impressed by the possibilities of D&D on an original Microsoft Surface… back in 2009. And I played with another at PAX many years ago. Mehta said that until very recently there simply wasn’t the technology and the market wasn’t ready.
“People tried this before, but it was either way too expensive or they didn’t have the audience. And the tech just wasn’t there; they were missing that interaction piece,” she explained, and certainly any player will recognize that the, say, iPad version of a game definitely lacks physicality. The advance her company has achieved is in making the touchscreen able to detect not just taps and drags, but game pieces, gestures and movements above the screen, and more.
“What Gameboard does, no other existing touchscreen or tablet on the market can do — it’s not even close,” Mehta said. “We have unlimited touch, game pieces, passive and active… you can use your chess set at home, lift up and put down the pieces, we track it the whole time. We can do unique identifiers with tags and custom shapes. It’s the next step in how interactive surfaces can be.”
It’s accomplished via a not particularly exotic method, which saves the Gameboard from the fate of the Surface and its successors, which cost several thousand dollars due to their unique and expensive makeups. Mehta explained that they work strictly with ordinary capacitive touch data, albeit at a higher framerate than is commonly used, and then use machine learning to characterize and track object outlines. “We haven’t created a completely new mechanism, we’re just optimizing what’s available today,” she said.
Image Credits: The Last Gameboard
At $699 for the Gameboard it’s not exactly an impulse buy, either, but the fact of the matter is people spend a lot of money on gaming, with some titles running into multiple hundreds of dollars for all the expansions and pieces. Tabletop is now a more than $20 billion industry. If the experience is as good as they hope to make it, this is an investment many a player will not hesitate (much, anyway) to make.
Of course, the most robust set of gestures and features won’t matter if all they had on the platform were bargain-bin titles and grandpa’s-parlor favorites like “Parcheesi.” Fortunately, The Last Gameboard has managed to stack up some of the most popular tabletop companies out there, and aims to have the definitive digital edition for their games.
Asmodee Digital is probably the biggest catch, having adapted many of today’s biggest hits, from modern classics “Catan” and “Carcassonne” to crowdfunded breakout hit “Scythe” and immense dungeon-crawler “Gloomhaven.” The full list of partners right now includes Dire Wolf Digital, Nomad Games, Auroch Digital, Restoration Games, Steve Jackson Games, Knights of Unity, Skyship Studios, EncounterPlus, PlannarAlly and Sugar Gamers, as well as individual creators and developers.
Image Credits: The Last Gameboard
These games may be best played in person, but have successfully transitioned to digital versions, and one imagines that a larger screen and inclusion of real pieces could make for an improved hybrid experience. There will be options both to purchase games individually, like you might on mobile or Steam, or to subscribe to an unlimited access model (pricing to be determined on both).
It would also be something that the many gaming shops and playing venues might want to have a couple of on hand. Testing out a game in-store and then buying a few to stock, or convincing consumers to do the same, could be a great sales tactic for all involved.
In addition to providing a unique and superior digital version of a game, the device can connect with others to trade moves, send game invites and all that sort of thing. The whole OS, Mehta said, “is alive and real. If we didn’t own it and create it, this wouldn’t work.” This is more than a skin on top of Android with a built-in store, but there’s enough shared that Android-based ports will be able to be brought over with little fuss.
Head of content Lee Allentuck suggested that the last couple years (including the pandemic) have started to change game developers’ and publishers’ minds about the readiness of the industry for what’s next. “They see the digital crossover is going to happen — people are playing online board games now. If you can be part of that new trend at the very beginning, it gives you a big opportunity,” he said.
CEO Shail Mehta (center) plays Stop Thief on the Gameboard with others on the team. Image Credits: The Last Gameboard
Allentuck, who previously worked at Hasbro, said there’s widespread interest in the toy and tabletop industry to be more tech-forward, but there’s been a “chicken and egg scenario,” where there’s no market because no one innovates, and no one innovates because there’s no market. Fortunately things have progressed to the point where a company like The Last Gameboard can raise $4 million to help cover the cost of creating that market.
The round was led by TheVentureCity, with participation from SOSV, Riot Games, Conscience VC, Corner3 VC and others. While the company didn’t go to HAX’s Shenzhen program as planned, they are still HAX-affiliated. SOSV partner Garrett Winther gave a glowing recommendation of its approach: “They are the first to effectively tie collaborative physical and digital gameplay together while not losing the community, storytelling or competitive foundations that we all look for in gaming.”
Mehta noted that the pandemic nearly cooked the company by derailing their funding, which was originally supposed to come through around this time last year when everything went pear-shaped. “We had our functioning prototype, we had filed for a patent, we got the traction, we were gonna raise, everything was great… and then COVID hit,” she recalled. “But we got a lot of time to do R&D, which was actually kind of a blessing. Our team was super small so we didn’t have to lay anyone off — we just went into survival mode for like six months and optimized, developed the platform. 2020 was rough for everyone, but we were able to focus on the core product.”
Now the company is poised to start its beta program over the summer and (following feedback from that) ship its first production units before the holiday season when purchases like this one seem to make a lot of sense.
(This article originally referred to this raise as The Last Gameboard’s round A — it’s actually the seed. This has been updated.)
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SightCall raises $42M for its AR-based visual assistance platform
Long before COVID-19 precipitated “digital transformation” across the world of work, customer services and support was built to run online and virtually. Yet it too is undergoing an evolution supercharged by technology.
Today, a startup called SightCall, which has built an augmented reality platform to help field service teams, the companies they work for, and their customers carry out technical and mechanical maintenance or repairs more effectively, is announcing $42 million in funding, money that it plans to use to invest in its tech stack with more artificial intelligence tools and expanding its client base.
The core of its service, explained CEO and co-founder Thomas Cottereau, is AR technology (which comes embedded in their apps or the service apps its customers use, with integrations into other standard software used in customer service environments including Microsoft, SAP, Salesforce and ServiceNow). The augmented reality experience overlays additional information, pointers and other tools over the video stream.
This is used by, say, field service engineers coordinating with central offices when servicing equipment; or by manufacturers to provide better assistance to customers in emergencies or situations where something is not working but might be repaired quicker by the customers themselves rather than engineers that have to be called out; or indeed by call centers, aided by AI, to diagnose whatever the problem might be. It’s a big leap ahead for scenarios that previously relied on work orders, hastily drawn diagrams, instruction manuals and voice-based descriptions to progress the work in question.
“We like to say that we break the barriers that exist between a field service organization and its customer,” Cottereau said.
The tech, meanwhile, is unique to SightCall, built over years and designed to be used by way of a basic smartphone, and over even a basic mobile network — essential in cases where reception is bad or the locations are remote. (More on how it works below.)
Originally founded in Paris, France before relocating to San Francisco, SightCall has already built up a sizable business across a pretty wide range of verticals, including insurance, telecoms, transportation, telehealth, manufacturing, utilities and life sciences/medical devices.
SightCall has some 200 big-name enterprise customers on its books, including the likes of Kraft-Heinz, Allianz, GE Healthcare and Lincoln Motor Company, providing services on a B2B basis as well as for teams that are out in the field working for consumer customers, too. After seeing 100% year-over-year growth in annual recurring revenue in 2019 and 2020, SightCall’s CEO says it’s looking like it will hit that rate this year as well, with a goal of $100 million in annual recurring revenue.
The funding is being led by InfraVia, a European private equity firm, with Bpifrance also participating. The valuation of this round is not being disclosed, but I should point out that an investor told me that PitchBook’s estimate of $122 million post-money is not accurate (we’re still digging on this and will update as and when we learn more).
For some further context on this investment, InfraVia invests in a number of industrial businesses, alongside investments in tech companies building services related to them such as recent investments in Jobandtalent, so this is in part a strategic investment. SightCall has raised $67 million to date.
There has been an interesting wave of startups emerging in recent years building out the tech stack used by people working in the front lines and in the field, a shift after years of knowledge workers getting most of the attention from startups building a new generation of apps.
Workiz and Jobber are building platforms for small business tradespeople to book jobs and manage them once they’re on the books; BigChange helps manage bigger fleets; and Hover has built a platform for builders to be able to assess and estimate costs for work by using AI to analyze images captured by their or their would-be customers’ smartphone cameras.
And there is Streem, which I discovered is a close enough competitor to SightCall that they’ve acquired AdWords ads based on SightCall searches in Google. Just ahead of the COVID-19 pandemic breaking wide open, General Catalyst-backed Streem was acquired by Frontdoor to help with the latter’s efforts to build out its home services business, another sign of how all of this is leaping ahead.
What’s interesting in part about SightCall and sets it apart is its technology. Co-founded in 2007 by Cottereau and Antoine Vervoort (currently SVP of product and engineering), the two are long-time telecoms industry vets who had both worked on the technical side of building next-generation networks.
SightCall started life as a company called Weemo that built video chat services that could run on WebRTC-based frameworks, which emerged at a time when we were seeing a wider effort to bring more rich media services into mobile web and SMS apps. For consumers and to a large extent businesses, mobile phone apps that work “over the top” (distributed not by your mobile network carrier but the companies that run your phone’s operating system, and thus partly controlled by them) really took the lead and continue to dominate the market for messaging and innovations in messaging.
After a time, Weemo pivoted and renamed itself as SightCall, focusing on packaging the tech that it built into whichever app (native or mobile web) where one of its enterprise customers wanted the tech to live.
The key to how it works comes by way of how SightCall was built, Cottereau explained. The company has spent 10 years building and optimizing a network across data centers close to where its customers are, which interconnects with Tier 1 telecoms carriers and has a lot of latency in the system to ensure uptime. “We work with companies where this connectivity is mission critical,” he said. “The video solution has to work.”
As he describes it, the hybrid system SightCall has built incorporates its own IP that works both with telecoms hardware and software, resulting in a video service that provides 10 different ways for streaming video and a system that automatically chooses the best in a particular environment, based on where you are, so that even if mobile data or broadband reception don’t work, video streaming will. “Telecoms and software are still very separate worlds,” Cottereau said. “They still don’t speak the same language, and so that is part of our secret sauce, a global roaming mechanism.”
The tech that the startup has built to date not only has given it a firm grounding against others who might be looking to build in this space, but has led to strong traction with customers. The next steps will be to continue building out that technology to tap deeper into the automation that is being adopted across the industries that already use SightCall’s technology.
“SightCall pioneered the market for AR-powered visual assistance, and they’re in the best position to drive the digital transformation of remote service,” said Alban Wyniecki, partner at InfraVia Capital Partners, in a statement. “As a global leader, they can now expand their capabilities, making their interactions more intelligent and also bringing more automation to help humans work at their best.”
“SightCall’s $42M Series B marks the largest funding round yet in this sector, and SightCall emerges as the undisputed leader in capital, R&D resources and partnerships with leading technology companies enabling its solutions to be embedded into complex enterprise IT,” added Antoine Izsak of Bpifrance. “Businesses are looking for solutions like SightCall to enable customer-centricity at a greater scale while augmenting technicians with knowledge and expertise that unlocks efficiencies and drives continuous performance and profit.”
Cottereau said that the company has had a number of acquisition offers over the years — not a surprise when you consider the foundational technology it has built for how to architect video networks across different carriers and data centers that work even in the most unreliable of network environments.
“We want to stay independent, though,” he said. “I see a huge market here, and I want us to continue the story and lead it. Plus, I can see a way where we can stay independent and continue to work with everyone.”
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DataRobot expands platform and announces Zepl acquisition
DataRobot, the Boston-based automated machine learning startup, had a bushel of announcements this morning as it expanded its platform to give technical and nontechnical users alike something new. It also announced it has acquired Zepl, giving it an advanced development environment where data scientists can bring their own code to DataRobot. The two companies did not share the acquisition price.
Nenshad Bardoliwalla, SVP of Product at DataRobot says that his company aspires to be the leader in this market and it believes the path to doing that is appealing to a broad spectrum of user requirements, from those who have little data science understanding to those who can do their own machine learning coding in Python and R.
“While people love automation, they also want it to be [flexible]. They don’t want just automation, but then you can’t do anything with it. They also want the ability to turn the knobs and pull the levers,” Bardoliwalla explained.
To resolve that problem, rather than building a coding environment from scratch, it chose to buy Zepl and incorporate its coding notebook into the platform in a new tool called Composable ML. “With Composable ML and with the Zepl acquisition, we are now providing a really first-class environment for people who want to code,” he said.
Zepl was founded in 2016 and raised $13 million along the way, according to Crunchbase data. The company didn’t want to reveal the number of employees or the purchase price, but the acquisition gives it advanced capabilities, especially a notebook environment to call its own to attract those more advanced users to the platform. The company plans to incorporate the Zepl functionality into the platform, while also leaving the standalone product in place.
Bardoliwalla said that they see the Zepl acquisition as an extension of the automated side of the house, where these tools can work in conjunction with one another with machines and humans working together to generate the best models. “This [generates an] organic mixture of the best of what a system can generate using DataRobot AutoML and the best of what human beings can do and kind of trying to compose those together into something really interesting […],” Bardoliwalla said.
The company is also introducing a no-code AI app builder that enables nontechnical users to create apps from the data set with drag and drop components. In addition, it’s adding a tool to monitor the accuracy of the model over time. Sometimes, after a model is in production for a time, the accuracy can begin to break down as the data on which the model is based is no longer valid. This tool monitors the model data for accuracy and warns the team when it’s starting to fall out of compliance.
Finally, the company is announcing a model bias monitoring tool to help root out model bias that could introduce racist, sexist or other assumptions into the model. To avoid this, the company has built a tool to identify when it sees this happening both in the model-building phase and in production. It warns the team of potential bias, while providing them with suggestions to tweak the model to remove it.
DataRobot is based in Boston and was founded in 2012. It has raised more than $750 million and has a valuation of over $2.8 billion, according to PitchBook.
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