Tag: machine learning

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Researchers developed a sensing system to constantly track the performance of workers

Researchers have come up with a mobile-sensing system that can track and rate the performance of workers by combining a smartphone, fitness bracelets and a custom app.

The mobile-sensing system, as the researchers call it, is able to classify high and low performers. The team used the system to track 750 U.S. workers for one year. The system was able to tell the difference between high performers and low performers with 80% accuracy.

The aim, the researchers say, is to give employees insight into physical, emotional and behavioral well-being. But that constant flow of data also has a downside, and if abused, can put employees under constant surveillance by the companies they work for.

The researchers, including Dartmouth University computer science professor Andrew Campbell, whose earlier work on a student monitoring app provided the underlying technology for this system, see this as a positive gateway to improving worker productivity.

“This is a radically new approach to evaluating workplace performance using passive sensing data from phones and wearables,” said Campbell. “Mobile sensing and machine learning might be the key to unlocking the best from every employee.”

The researchers argue that the technology can provide a more objective measure of performance than self-evaluations and interviews, which they say can be unreliable.

The mobile-sensing system developed by the researchers has three distinct pieces. A smartphone tracks physical activity, location, phone use and ambient light. The fitness tracker monitors heart functions, sleep, stress and body measurements like weight and calorie consumption. Meanwhile, location beacons placed in the home and office provide information on time at work and breaks from the desk.

From here, cloud-based machine learning algorithms are used to classify workers by performance level.

The study found that higher performers typically had lower rates of phone usage, had longer periods of deep sleep and were more physically active.

Privacy experts and labor advocates have long raised concerns about the practice of tracking employees. That hasn’t stopped companies from incentivizing employees to wear fitness tracks in exchange for savings on insurance or other benefits. Startups have popped up to offer even more ways to track employees.

For instance, WeWork acquired in February Euclid, a data platform that tracks the identity and behavior of people in the physical world. Shiva Rajaraman, WeWork’s chief product officer, told TechCrunch at the time that the Euclid platform and its team will become integrated into a software analytics package that WeWork plans to sell to companies that aren’t renting WeWork space but want to WeWork-ify their own offices.

Meanwhile, the team of researchers suggests that while its system of continuous monitoring via wearables and other devices is not yet available, it could be coming in the next few years. It’s unclear if the team is making a calculated guess or if there are designs to try and launch this system as a product.

The team, led by Dartmouth University, included researchers from University of Notre Dame, Georgia Institute of Technology, University of Washington, University of Colorado Boulder, University of California, Irvine, Ohio State University, University of Texas at Austin and Carnegie Mellon University .

A paper describing the study will be published in the Proceedings of the ACM on Interactive, Mobile Wearable and Ubiquitous Technology.

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MIT’s new interactive machine learning prediction tool could give everyone AI superpowers

Soon, you might not need anything more specialized than a readily accessible touchscreen device and any existing data sets you have access to in order to build powerful prediction tools. A new experiment from MIT and Brown University researchers have added a capability to their ‘Northstar’ interactive data system that can “instantly generate machine-learning models” to use with their exiting data sets in order to generate useful predictions.

One example the researchers provide is that doctors could make use of the system to make predictions about the likelihood their patients have of contracting specific diseases based on their medial history. Or, they suggest, a business owner could use their historical sales data to develop more accurate forecasts, quickly and without a ton of manual analytics work.

Researchers are calling this feature the Northstar system’s “virtual data scientist,” (or VDS) and it sounds like it could actually replace the human equivalent, especially in settings where one would never actually be readily available or resourced anyway. Your average doctor’s office doesn’t have a dedicated data scientist headcount, for instance, and nor do most small- to medium-sized businesses for that matter. Independently owned and operated coffee shops and retailers definitely wouldn’t otherwise have access to this kind of insight.

touchscreen analytics 1

This new tool is built on automated machine-learning techniques that are becoming much more ‘au courant,’ since it helps expand the number of people for whom AI technology is accessible.

Northstar itself is the product of more than four years of work, and presents a blank canvas that’s compatible across multiple platforms, and then users can upload their own data sets, which show up as boxes on the interface. They can then drag and drop those into the centre area of the canvas and then draw connecting lines to indicate to that they should be processed with an algorithm of their choosing in combination with one another.

So basically, they could theoretically grab a dataset detailing metabolic rates of patients, and another one detailing their age, and then derive from that how often a specific disease occurs across those two factors. Now, with the new virtual data scientist feature, they’ll be able to combine inputs to generate predictive, AI-based analysis across these combined factors as well.

touchscreen analytics 3

Researchers have also designed this VDS system so that it’s actually the fastest application of automated machine learning to date. That’s another key piece for making it usable by everyone, since it’s not really feasible to imagine people working with this digital whitetable and then waiting ours for results to come out. Next up, it’s going to improve error reporting to help ensure that non-specialist users not only find it easy to use, but also get clear indicators when they do something wrong so they can fix it next time.

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Network with CrunchMatch at TC Sessions: Enterprise 2019

Ready to tackle the colossus that is enterprise software? Join us and more than 1,000 attendees for TC Sessions Enterprise 2019 on September 5 at the Yerba Buena Center for the Arts in San Francisco. We’re talking founders, technologists and investors digging deep into the challenges facing established and emerging enterprise companies today. Get your early-bird tickets now and save.

TechCrunch’s first ever event focused on Enterprise is a prime networking opportunity that will feature a crowd drawn to a day of intensive, on-stage interviews (led by TechCrunch editors) with the king pins of enterprise as well as breakout sessions, exhibiting startups, receptions and much more.  Naturally, we have a fantastic networking app to help attendees wring the most opportunity out of the show.

CrunchMatch (powered by Brella), is TechCrunch’s free business match-making service. Effective networking is more than just meeting people. CrunchMatch helps you search for the right people based on specific mutual criteria, goals and interests. The platform’s combination of curation and automation lets you easily find, vet, schedule and connect with the people you want to meet — founders, investors, technologists, researchers or MBA students. You decide, and CrunchMatch delivers.

CrunchMatch is available to all attendees. When the platform launches, keep an eye out for an email with a sign-up link. Fill out your profile with the pertinent details — your role (technologist, founder, investor, etc.) and who you want to connect with at the event. CrunchMatch will make meet-up suggestions, which you can approve or decline.

Now that you’re up to speed on the networking situation, all you need to do is buy a ticket to TC Sessions: Enterprise.  Early-bird passes cost $395, and you can save an extra 15 percent when you buy group tickets (four or more) for $335 each. Student passes sell for $245. Bonus: for every TC Sessions: Enterprise ticket you buy, we’ll register you for one free Expo Only pass to Disrupt San Francisco 2019. Holla!

There are a limited number of Startup Demo Packages available for $2,000, which includes four tickets to attend the event.

TC Sessions: Enterprise takes place on September 5 in San Francisco. Join your community of enterprise-minded founders, investors, CTOs, CIOs and engineers to talk machine learning, AI, intelligent marketing automation, the cloud, quantum computing, blockchain and so much more. Buy your early-bird tickets now.

Interested in sponsoring TC Sessions: Enterprise? Fill out this form and a member of our sales team will contact you.

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Europe should ban AI for mass surveillance and social credit scoring, says advisory group

An independent expert group tasked with advising the European Commission to inform its regulatory response to artificial intelligence — to underpin EU lawmakers’ stated aim of ensuring AI developments are “human centric” — has published its policy and investment recommendations.

This follows earlier ethics guidelines for “trustworthy AI”, put out by the High Level Expert Group (HLEG) for AI back in April, when the Commission also called for participants to test the draft rules.

The AI HLEG’s full policy recommendations comprise a highly detailed 50-page document — which can be downloaded from this web page. The group, which was set up in June 2018, is made up of a mix of industry AI experts, civic society representatives, political advisers and policy wonks, academics and legal experts.

The document includes warnings on the use of AI for mass surveillance and scoring of EU citizens, such as China’s social credit system, with the group calling for an outright ban on “AI-enabled mass scale scoring of individuals”. It also urges governments to commit to not engage in blanket surveillance of populations for national security purposes. (So perhaps it’s just as well the UK has voted to leave the EU, given the swingeing state surveillance powers it passed into law at the end of 2016.) 

“While there may be a strong temptation for governments to ‘secure society’ by building a pervasive surveillance system based on AI systems, this would be extremely dangerous if pushed to extreme levels,” the HLEG writes. “Governments should commit not to engage in mass surveillance of individuals and to deploy and procure only Trustworthy AI systems, designed to be respectful of the law and fundamental rights, aligned with ethical principles and socio-technically robust.”

The group also calls for commercial surveillance of individuals and societies to be “countered” — suggesting the EU’s response to the potency and potential for misuse of AI technologies should include ensuring that online people-tracking is “strictly in line with fundamental rights such as privacy”, including (the group specifies) when it concerns ‘free’ services (albeit with a slight caveat on the need to consider how business models are impacted).

Last week the UK’s data protection watchdog fired an even more specific shot across the bows of the online behavioral ad industry — warning that adtech’s mass-scale processing of web users’ personal data for targeting ads does not comply with EU privacy standards. The industry was told its rights-infringing practices must change, even if the Information Commissioner’s Office isn’t about to bring down the hammer just yet. But the reform warning was clear.

As EU policymakers work on fashioning a rights-respecting regulatory framework for AI, seeking to steer  the next ten years+ of cutting-edge tech developments in the region, the wider attention and scrutiny that will draw to digital practices and business models looks set to drive a clean up of problematic digital practices that have been able to proliferate under no or very light touch regulation, prior to now.

The HLEG also calls for support for developing mechanisms for the protection of personal data, and for individuals to “control and be empowered by their data” — which they argue would address “some aspects of the requirements of trustworthy AI”.

“Tools should be developed to provide a technological implementation of the GDPR and develop privacy preserving/privacy by design technical methods to explain criteria, causality in personal data processing of AI systems (such as federated machine learning),” they write.

“Support technological development of anonymisation and encryption techniques and develop standards for secure data exchange based on personal data control. Promote the education of the general public in personal data management, including individuals’ awareness of and empowerment in AI personal data-based decision-making processes. Create technology solutions to provide individuals with information and control over how their data is being used, for example for research, on consent management and transparency across European borders, as well as any improvements and outcomes that have come from this, and develop standards for secure data exchange based on personal data control.”

Other policy suggestions among the many included in the HLEG’s report are that AI systems which interact with humans should include a mandatory self-identification. Which would mean no sneaky Google Duplex human-speech mimicking bots. In such a case the bot would have to introduce itself up front — thereby giving the human caller a chance to disengage.

The HLEG also recommends establishing a “European Strategy for Better and Safer AI for Children”. Concern and queasiness about rampant datafication of children, including via commercial tracking of their use of online services, has been raised  in multiple EU member states.

“The integrity and agency of future generations should be ensured by providing Europe’s children with a childhood where they can grow and learn untouched by unsolicited monitoring, profiling and interest invested habitualisation and manipulation,” the group writes. “Children should be ensured a free and unmonitored space of development and upon moving into adulthood should be provided with a “clean slate” of any public or private storage of data related to them. Equally, children’s formal education should be free from commercial and other interests.”

Member states and the Commission should also devise ways to continuously “analyse, measure and score the societal impact of AI”, suggests the HLEG — to keep tabs on positive and negative impacts so that policies can be adapted to take account of shifting effects.

“A variety of indices can be considered to measure and score AI’s societal impact such as the UN Sustainable Development Goals and the Social Scoreboard Indicators of the European Social Pillar. The EU statistical programme of Eurostat, as well as other relevant EU Agencies, should be included in this mechanism to ensure that the information generated is trusted, of high and verifiable quality, sustainable and continuously available,” it suggests. “AI-based solutions can help the monitoring and measuring its societal impact.”

The report is also heavy on pushing for the Commission to bolster investment in AI — calling particularly for more help for startups and SMEs to access funding and advice, including via the InvestEU program.

Another suggestion is the creation of an EU-wide network of AI business incubators to connect academia and industry. “This could be coupled with the creation of EU-wide Open Innovation Labs, which could be built further on the structure of the Digital Innovation Hub network,” it continues. 

There are also calls to encourage public sector uptake of AI, such as by fostering digitalisation by transforming public data into a digital format; providing data literacy education to government agencies; creating European large annotated public non-personal databases for “high quality AI”; and funding and facilitating the development of AI tools that can assist in detecting biases and undue prejudice in governmental decision-making.

Another chunk of the report covers recommendations to try to bolster AI research in Europe — such as strengthening and creating additional Centres of Excellence which address strategic research topics and become “a European level multiplier for a specific AI topic”.

Investment in AI infrastructures, such as distributed clusters and edge computing, large RAM and fast networks, and a network of testing facilities and sandboxes is also urged; along with support for an EU-wide data repository “through common annotation and standardisation” — to work against data siloing, as well as trusted data spaces for specific sectors such as healthcare, automative and agri-food.

The push by the HLEG to accelerate uptake of AI has drawn some criticism, with digital rights group Access Now’s European policy manager, Fanny Hidvegi, writing that: “What we need now is not more AI uptake across all sectors in Europe, but rather clarity on safeguards, red lines, and enforcement mechanisms to ensure that the automated decision making systems — and AI more broadly — developed and deployed in Europe respect human rights.”

Other ideas in the HLEG’s report include developing and implementing a European curriculum for AI; and monitoring and restricting the development of automated lethal weapons — including technologies such as cyber attack tools which are not “actual weapons” but which the group points out “can have lethal consequences if deployed. 

The HLEG further suggests EU policymakers refrain from giving AI systems or robots legal personhood, writing: “We believe this to be fundamentally inconsistent with the principle of human agency, accountability and responsibility, and to pose a significant moral hazard.”

The report can downloaded in full here.

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Snowflake co-founder and president of product Benoit Dageville is coming to TC Sessions: Enterprise

When it comes to a cloud success story, Snowflake checks all the boxes. It’s a SaaS product going after industry giants. It has raised bushels of cash and grown extremely rapidly — and the story is continuing to develop for the cloud data lake company.

In September, Snowflake’s co-founder and president of product Benoit Dageville will join us at our inaugural TechCrunch Sessions: Enterprise event on September 5 in San Francisco.

Dageville founded the company in 2012 with Marcin Zukowski and Thierry Cruanes with a mission to bring the database, a market that had been dominated for decades by Oracle, to the cloud. Later, the company began focusing on data lakes or data warehouses, massive collections of data, which had been previously stored on premises. The idea of moving these elements to the cloud was a pretty radical notion in 2012.

It began by supporting its products on AWS, and more recently expanded to include support for Microsoft Azure and Google Cloud.

The company started raising money shortly after its founding, modestly at first, then much, much faster in huge chunks. Investors included a Silicon Valley who’s who such as Sutter Hill, Redpoint, Altimeter, Iconiq Capital and Sequoia Capital .

Snowflake fund raising by round. Chart: Crunchbase

Snowflake fund raising by round. Chart: Crunchbase

The most recent rounds came last year, starting with a massive $263 million investment in January. The company went back for more in October with an even larger $450 million round.

It brought on industry veteran Bob Muglia in 2014 to lead it through its initial growth spurt. Muglia left the company earlier this year and was replaced by former ServiceNow chairman and CEO Frank Slootman.

TC Sessions: Enterprise (September 5 at San Francisco’s Yerba Buena Center) will take on the big challenges and promise facing enterprise companies today. TechCrunch’s editors will bring to the stage founders and leaders from established and emerging companies to address rising questions, like the promised revolution from machine learning and AI, intelligent marketing automation and the inevitability of the cloud, as well as the outer reaches of technology, like quantum computing and blockchain.

Tickets are now available for purchase on our website at the early-bird rate of $395.

Student tickets are just $245 – grab them here.

We have a limited number of Startup Demo Packages available for $2,000, which includes four tickets to attend the event.

For each ticket purchased for TC Sessions: Enterprise, you will also be registered for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.

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At last, a camera app that automatically removes all people from your photos

As a misanthrope living in a vibrant city, I’m never short of things to complain about. And in particular the problem of people crowding into my photos, whatever I happen to shoot, is a persistent one. That won’t be an issue any more with Bye Bye Camera, an app that simply removes any humans from photos you take. Finally!

It’s an art project, though a practical one (art can be practical!), by Do Something Good. The collective, in particular the artist damjanski, has worked on a variety of playful takes on the digital era, such as a CAPTCHA that excludes humans, and setting up a dialogue between two Google conversational agents.

The new app, damjanski told Artnome, is “an app for the post-human era… The app takes out the vanity of any selfie and also the person.” Fortunately, it leaves dogs intact.

Of course it’s all done in a self-conscious, arty way — are humans necessary? What defines one? What will the world be like without us? You can ponder those questions or not; fortunately, the app doesn’t require it of you.

Bye Bye Camera works using some of the AI tools that are already out there for the taking in the world of research. It uses YOLO (You Only Look Once), a very efficient object classifier that can quickly denote the outline of a person, and then a separate tool that performs what Adobe has called “context-aware fill.” Between the two of them a person is reliably — if a bit crudely — deleted from any picture you take and credibly filled in by background.

It’s a fun project (though the results are a mixed bag) and it speaks not only to the issues it supposedly raises about the nature of humanity, but also the accessibility of tools under the broad category of “AI” and what they can and should be used for.

You can download Bye Bye Camera for $3 on the iOS App Store.

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Google brings together BigQuery and Kaggle in new integration

Google bought Kaggle in 2017 to provide a data science community for its big data processing tools on Google Cloud. Today, the company announced a new direct integration between Kaggle and BigQuery, Google’s cloud data warehouse.

More specifically, data scientists can build a model in a Kaggle Jupyter Notebook, known as Kaggle Kernels in the community. You can then link directly to BigQuery through the tool’s API, making it much simpler to query against the data in the data warehouse using SQL, a language data scientists tend to be very familiar with.

The benefit of this approach, according to Google, is that you don’t have to actually move or download the data to query it or perform machine learning on it. “Once your Google Cloud account is linked to a Kernels notebook or script, you can compose queries directly in the notebook using the BigQuery API Client library, run it against BigQuery, and do almost any kind of analysis from there with the data,” Google wrote in a blog post introducing the integration.

Data scientists, who have a particular way of working, get to work in a familiar fashion and it reduces the friction involved in building a model and conducting machine learning against it. Instead of moving back and forth between tools, you can do all your work in a smoother, more integrated way and it should save time and effort in the long run.

What’s more, because Kaggle is a public community of data scientists, you can share Kernels should you choose to do so. Conversely, you can search the public repository and use existing Kernels as a starting point or as a reference to experiment with different types of data sets.

The Kaggle community also provides a means to discuss issues with other data scientists in an open way. The community has 3 million users and there are currently 200,000 Kernels available to explore in the public repository.

Google confirms its acquisition of data science community Kaggle

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Get your early-bird tickets to TC Sessions: Enterprise 2019

In a world where the enterprise market hovers around $500 billion in annual sales, is it any wonder that hundreds of enterprise startups launch into that fiercely competitive arena every year? It’s a thrilling, roller-coaster ride that’s seen it all: serious success, wild wealth and rapid failure.

That’s why we’re excited to host our inaugural TC Sessions Enterprise 2019 event on September 5 at the Yerba Buena Center for the Arts in San Francisco. Like TechCrunch’s other TC Sessions, this day-long intensive goes deep on one specific topic. Early-bird tickets are on sale now for $395 — and we have special pricing for MBA students and groups, too. Buy your tickets now and save.

Bonus ROI: For every ticket you buy to TC Sessions: Enterprise, we’ll register you for a free Expo Only pass to TechCrunch Disrupt SF on October 2-4. Sweet!

Expect a full day of programming featuring the people making it happen in enterprise today. We’re talking founders and leaders from established and emerging companies, plus proven enterprise-focused VCs. Discussions led by TechCrunch’s editors, including Connie Loizos, Frederic Lardinois and Ron Miller, will explore machine learning and AI, intelligent marketing automation and the inevitability of the cloud. We’ll even touch on topics like quantum computing and blockchain.

Tired of the hype and curious about what it really takes to build a successful enterprise company? We’ve got you. You’ll hear from proven serial entrepreneurs who’ve been there, done that and what they might like to build next.

We’re building the agenda of speakers, panelists and demos, and we have a limited number of speaking opportunities available. If you have someone in mind, submit your recommendation here.

This event is perfect for enterprise-minded founders, investors, MBA students, engineers, CTOs and CIOs. If you need four or more tickets, take advantage of our group rate and save 15% over the early-bird price when you buy in bulk. Are you an MBA student? Save your dough — buy a student ticket for $245.

TC Sessions: Enterprise 2019 takes place September 5 in San Francisco. Join us for actionable insights and world-class networking. Buy your early-bird tickets today.

Is your company interested in sponsoring or exhibiting at TC Sessions: Enterprise 2019? Contact our sponsorship sales team by filling out this form.

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Tally’s Jason Brown on fintech’s first debt roboadvisor and an automated financial future

Yesterday, Tally, the startup looking to automate consumers financial lives, announced it had raised a $50 million Series C round led by Andreessen Horowitz and with participation from Valley heavy hitters Kleiner Perkins, Shasta Ventures, Cowboy Ventures and Sway Ventures.

On the back of the announcement, TechCrunch’s fintech contributor Gregg Schoenberg sat down with Tally’s founder and CEO Jason Brown to discuss the round, Tally’s growth strategy and the company’s vision for an automated financial future.

Gregg Schoenberg: I never like to congratulate people when they raise a big load of capital, because if anything, the pressure is on even more. But just to level set real quickly, are there any numbers you can share that Andreessen Horowitz and the other investors saw that underscored your traction?

Jason Brown: So I agree with you. Internally, the metaphor I use is that it’s kind of like going on a long road trip where you’re stopping in the gas station to get more fuel so you can make it to your destination. You should really celebrate when you’re delivering value to customers.

Schoenberg: In terms of total credit card debt you’re managing, you were at $250mm towards the end of last year.

Brown: Yes. Now, we’re getting close to $400mm.

Schoenberg: And the savings vehicle – it’s new and totally free?

Brown: Yes, it’s completely free and just to recap, it takes 35-45 seconds to set-up, it automates the process of setting money aside every week and it gives you points. It’s still in beta, but we’re getting close to the end of beta, and have over 30,000 people on the waitlist.

AI is a non-technical term, right? I like to use the word automation because it means things are being done for you.

Schoenberg: With respect to the fundraise you just announced, the big takeaway I got was your aspiration to automate people’s entire financial lives. That’s big talk.

Brown: That is big talk.

Schoenberg: You obviously knew what you were doing when you decided to frame it that way. Where do you go from here? Obviously, credit card payments and the savings vehicle are good, but there are many other financial services out there that you’ll need to tackle.

Brown: Well one of the key portions of the investment thesis for Andreessen Horowitz is actually what’s under the hood. So we actually took three years to build the underlying infrastructure to automate the pay off my cards job. And there are two fundamental layers to the tech.

There’s the “decide what’s best for me,” which addresses the complexity of ingesting data across your entire financial life, and being able to validate that it’s accurate and consistent, and then having algorithms that can make sense of it and figure out what’s best for you. The next layer is actually doing what’s best for you, which involves being able to move money around and lend money.

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Cruise is sharing its data visualization tool with robotics geeks everywhere

Cruise is sharing a software platform with roboticists that was initially created to give its own engineers a better understanding of the petabytes of data generated every month from its fleet of autonomous vehicles.

The platform is a data visualization tool called Webviz, a web-based application aimed at anyone working in robotics, a field that includes autonomous vehicles. Researchers, students and engineers can now access the tool and get a visual insight into their data by dragging their robotics data into a ROS bag file.

Robots and, specifically autonomous vehicles, capture loads of data from various sensors like lidar, radar and cameras. The tool is supposed to make it easier to take that data and turn it from binary code into something visual. The tool lets users configure different layouts of panels, each one displaying information like text logs, 2D charts and 3D depictions of the AV’s environment.

The tool is a product of a Cruise hackathon that was held a couple of years ago. It was apparently such a hit that engineers at the self-driving car company now use it daily to calibrate lidar sensors, verify machine learning models and debug test rides. Webviz now has 1,000 monthly active users within the company, according to Cruise.

As engineers developed Webviz they found it could have applications outside of Cruise. The company decided to open source it as general robotics data inspection tool. For this initial release, Cruise settled on a suite of general panels that any robotics developer can leverage to explore their own data, with minimal setup, the company said in a Medium post Tuesday.

A demo video provided by Cruise is posted below.

Prior to Webviz, Cruise engineers who wanted to turn binary AV data into something more visual would have to access a suite of tools within the ROS open source community. While the system worked well, setting up the platform and then replicating it on a co-worker’s machine was time consuming effort. It also required manually positioning windows running separate tools such as logging message or viewing camera images.

The tool created out of the hackathon essentially helped lower the barrier to entry for engineers to explore and understand its autonomous vehicle data.

Cruise shared a piece, or an application, of Webviz earlier this year called Worldview — a library that can turn data into 3D scenes. Cruise has also developed and open sourced rosbag.js, a JavaScript library for reading ROS bag files. Both of these projects were developed as engineers created and built out Webviz, according to Cruise.

Cruise isn’t the only robotics-focused company (or autonomous vehicle company for that matter) to open source datasets or other tools. For instance, Aptiv released last year nuScenes, a large-scale dataset from an autonomous vehicle sensor suite.

And it likely won’t be the last. Not only are moves like this part of the engineering culture, there are other benefits as well, including recruitment. Plus, by releasing it into the world, it’s likely that other outsiders will build upon the tool and improve it, or use it to make engineering breakthroughs in robotics.