ByteDance’s Pico reveals its latest VR headset as it aims to compete with Meta Quest 2

subsidiary Pico has . The Pico 4 will initially be available in Japan, South Korea, the UK, France, Germany, Spain, Italy and eight other European countries. Pico hasn’t revealed US release plans as yet, but it aims to bring the device to Singapore and Malaysia by the end of the year, and China at a later date.

The headset — which has a Qualcomm XR2 processor, an Adreno 650 GPU and 8GB of RAM — can be used as a standalone device. Pico claims the battery, which is in the rear strap to help keep things balanced, offers around three hours of use on a single charge, as notes. The device weighs 295 grams without the strap and 586 grams when it’s attached.

You can also connect Pico 4 to a gaming PC for higher-end VR experiences. That might be necessary to make full use of the dual displays, which offer higher than 4K resolution at 4,320 x 2,160 resolution for each eye. The displays have a 90Hz refresh rate and 105-degree field of view, according to   

The Pico 4 uses inside-out tracking with no need for external beacons. It comes with Pico 4 motion controllers (which have vibration features) and there are four external cameras, as points out. According to the Pico website, the device will offer full-color passthrough — something Meta is working on for its Project Cambria headset. 

A person wearing the Pico 4 virtual reality headset.


Given that ByteDance also owns , it shouldn’t be surprising that there’s a way to view videos from that app. You’ll be able to share VR experiences to TikTok as well. There will be hundreds more things to watch in VR and 360 formats. Pico is working to bring live sports and “avatar-based concerts” to the platform as well. 

As for games, there are 165 of them in the Pico store and more being added each week. The headset will support the likes of Peaky Blinders: The King’s Ransom, Demeo, , All-in-One Summer Sports VR and Just Dance VR (which will arrive in 2023 as a Pico exclusive). There will also be SteamVR support.

Meanwhile, there are plans to launch a metaverse-style experience called Pico Worlds next year. Unlike in Meta’s , Pico’s avatars appear to have legs.

Considering the price and specs, it’s Pico is trying to compete with Meta Quest 2 (Meta recently of that product). Whether the brand can hang with Meta on the content front remains to be seen. Users are unlikely to be able to play Beat Saber, for instance. Pico also revealed its latest device just a few weeks before Meta will show off at least one VR headset, likely to be the higher-end Project Cambria model, .

A Pico 4 with 128GB of storage costs €429 (around $422). A model with double the storage capacity will run you €499 (approximately $491). Preorders open next month and the headset will ship on October 18th. Pico also to release some accessories next year. A more accurate fitness tracker, a wireless dongle for PC connectivity and a carrying case will each cost €50 (or around $49).

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ServiceNow evolves from ITSM, aims to simplify business processes

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ServiceNow cut its teeth in IT service management (ITSM) and IT operations management (ITOM). The platform helps streamline the process of reporting and resolving IT problems. A significant update to the core platform, called the Now Platform Tokyo release, takes a major step toward the broader realm of enterprise service management (ESM) to respond to issues at a business level rather than just an IT level. 

Monish Mishra, VP for service line markets and strategic engagements at Mindtree, told VentureBeat, “By adopting ESM, enterprises can leverage service management capabilities and framework throughout the organization.”

For example, ServiceNow is adding new solutions for enterprise asset management (EAM), supplier lifecycle management (SLM), and environment, social, and governance (ESG) management. It also includes new tools for improving experience and engagement for customers and employees. A new ServiceNow Vault also promises to centralize data security and privacy management across the Now Platform. 

It is all about helping businesses to become digital first. At a practical level, this means simplifying the underlying platform and the business processes built on top. 


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ServiceNow chief innovation officer, Dave Wright, told VentureBeat, “When implemented well, a single platform, like ServiceNow, should touch far-reaching corners of the company, seamlessly connecting disparate systems, breaking down data silos and making things easier, everywhere, for both employees and the business itself.”

Now Platform Tokyo was designed to help businesses focus on improving experiences rather than just service levels. For example, the new Manager Hub provides a single destination for leaders to create learning and development plans for their teams and get personalized training. 

The new release also improves connectivity between disparate systems to simplify complex processes. For example, this can help companies move from an SLM process based on emails and spreadsheets to an automated process spanning employees and suppliers.

Start at the process level

When executives sit around the conference table, they may start with vague goals like improving the use of assets like buildings, factories and expensive equipment, enabling supply chain resilience, or becoming net zero by 2030. Turning each of these goals into measurable outcomes requires the coordination of people, processes and equipment. 

New purpose-built features in the Tokyo release take a first stab at aligning high-level goals for EAM, SLM and ESG with business processes running across multiple apps. ServiceNow started with these solutions to help enterprises address some of the most pressing challenges facing customers. 

“We are simplifying complex supply chains, automating asset management and delivering investor-grade sustainability data so our customers can more effectively safeguard their businesses and manage risk and compliance,” Wright said.

Wright said they also fill an important gap with their expanded ESG management capabilities. Most solutions focus on individual areas of ESG or even singular goals like reducing carbon emissions. But the United Nations has identified 17 broad sustainable development goals (SDGs) and 169 measurable targets. 

The danger in pursuing individual targets lies in compromising others in the process or adding additional work. A broader approach like ServiceNow’s new ESG Command Center for managing multiple simultaneous targets and the processes for achieving them will be required to increase all of them in tandem. It combines ESG management and reporting with enterprise risk management and strategic project management. 

ServiceNow steps up collaboration 

ServiceNow is collaborating with leading systems integrators like Mindtree, NTT DATA Corporation and RSM US LLP to customize these new capabilities for each enterprise. This will help enterprises implement and fine-tune the latest release for their specific goals. Systems integrators believe the new solutions will be essential in meeting broader enterprise goals. 

NTT DATA head of ServiceNow business, Tomoyuki Azuma, told VentureBeat, “ServiceNow is a complete breakthrough in terms of the way software development is made and in terms of the conventional wisdom of efficiency.”

Azuma says it will play a significant role in creating the employee experience required to collectively drive ESG goals. Most businesses he works with struggle with a sustainability dilemma in which the extra work necessary to manage new KPIs drags down financial sustainability. A better ESG management experience will help employees identify ways to assess minor changes to achieve the optimal state of business processes. 

“The ESG Management solution empowers our clients to shape the future of our society with sustainability in a way they can measure the ROI, manage the risk and demonstrate the impact to their local and global footprint. Awareness of the benefits of ESG will spread overall participation and innovation in ESG,” NTT DATA’s VP ServiceNow practice, Marci Parker, said. 

Boosting engagement

The update also includes new tools for improving employee experiences for common workflows. All these build on ServiceNow’s recently launched Next Experience UX.

Manager Hub provides a single place to review employee journeys and respond to requests. The tool lets managers create personalized experiences for each employee. They can edit tasks, add mentors, include AI-based learning recommendations from learning posts and integrate satisfaction surveys to understand how employees feel about their experience and journey at the company. 

Admin Center allows system administrators to discover, install and configure ServiceNow solutions. Previously, ServiceNow administrators relied on their account managers when administering new applications or manually sorted through apps or ServiceNow Knowledge Management resources. With Admin Center, system administrators can now discover, install and configure ServiceNow solutions in one place.

Issue Auto Resolution for Human Resources applies natural language understanding to analyze requirements and deliver self-service content. Issue Auto Resolution was previously available for ITSM to help IT agents resolve routine incidents much more quickly by proactively deflecting them to an AI-powered virtual agent. The new capabilities for HR teams automate common HR inquiries like PTO requests, HR policy or benefits enrollment questions, and payroll issues. 

Privacy and security controls

Enterprises often spread data across dozens of separate applications, databases and workflows. A new ServiceNow Vault promises to centralize privacy and security control. It includes a tool for simplifying the management and protection of machine credentials and validating the authenticity and integrity of code being deployed to ensure no malicious insertion. 

Wright said the Vault applies to all apps and data running on the Now Platform. However, it does not manage data from other apps. 

Cautious optimism for EAM, SLM and ESG

Yugal Joshi, partner at Everest Group, an advisory firm, told VentureBeat that the addition of new solutions for EAM, SLM and ESG indicates ServiceNow’s persistence in moving out of its ITSM and ITOM heritage to become an enterprise platform for clients for solving complex business problems. These new solutions have the potential to help IT leaders enhance their positioning and working relationships with business teams.

However, Joshi cautions new customers to do a thorough analysis before committing. This should include a cost analysis of subscription, integration, maintenance and upgrade factors. “Leaders need to understand the functionalities of these newer offerings and their relevance to their environment,” Joshi said.

It’s also essential to evaluate the maturity of these solutions. Everest research suggests that enterprises aren’t fully satisfied with the maturity of newer ServiceNow launches and the service partnerships to implement and scale them. 

“This will be important for the CIO organization engaging with ServiceNow as a strategic platform vendor,” he said. 

In addition, enterprises will need to understand the licensing policy. Everest research suggests enterprises struggle with ServiceNow licensing.

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Nvidia-Deloitte partnership aims to accelerate AI adoption

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Despite the much-touted benefits of AI, 74% of businesses are still in the AI experimentation stage. 

This means that just 26% of enterprises are focused on deploying high-impact AI use cases at scale. 

But industry leaders and experts alike call AI adoption and deployment an imperative — and to help fuel this, Nvidia and Deloitte today announced an expanded alliance at Nvidia’s GTC event. This new partnership will help Deloitte customers innovate and expand AI and metaverse services. 

“AI and metaverse technologies are reshaping the foundations of our economy,” Jensen Huang, founder and CEO of Nvidia said in a statement. “Together, Nvidia and Deloitte can help enterprises apply AI to create new products and services that reinvent their industries.”


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AI transformation: Never complete

The “privileged partnership” announced today builds on a two-year Deloitte-Nvidia relationship, said Nitin Mittal, Deloitte’s U.S. AI strategic growth offering consulting leader. 

Deloitte’s customers will now have access to the Nvidia AI and Nvidia Omniverse enterprise platforms, thus enabling the development, implementation and deployment of cutting-edge tools such as edge AI, speech AI, recommender systems, cybersecurity, chatbots and digital twins.

Nvidia DGX A100 systems already power the Deloitte Center for AI Computing, which launched in March 2021. Deloitte’s Unlimited Reality services also leverage the Nvidia Omniverse Enterprise platform for 3D design collaboration and virtual world simulation, and Deloitte and Nvidia are together creating hybrid replicas of real-world environments and processes, said Mittal. 

Further expanding its commitment to AI, Deloitte has trademarked the phrase Age of With, which the company describes as “a world where humans work with machines to enable far greater outcomes.” The Deloitte AI Institute teams with tech leaders to help realize this future.  

“Becoming an AI-fueled organization is to understand that the transformation process is never complete, but rather a journey of continuous learning and improvement,” said Mittal. 

Automating and enhancing processes, detecting fraud

For instance, by leveraging Deloitte-Nvidia capabilities, enterprises in the financial services industry are automating debt collection and serving clients through chatbots and natural language processing (NLP), Mittal and Irfan Saif write in an AI dossier.  

ML models are estimating customer lifetime value and predicting customer churn and propensity to accept additional offers by analyzing their profiles and historical and real-time data. Similarly, text mining and NLP can automate the underwriting process; facial recognition and other AI-based biometric technologies can process payments; and AI can adjust insurance coverage and rates based on a customer’s past behavior, according to Mittal and Saif. 

When it comes to the widespread issue of financial industry fraud, meanwhile, AI algorithms can identify and analyze risk factors by continuously scanning for clues across numerous data sources such as social media and deep web forums. This can help transactional and account takeover fraud in real-time and spot suspicious activity that could be missed by humans, write Mittal and Saif. 

“With AI, financial services firms finally have a chance to get in front of criminal behavior, instead of being a step behind,” contend Mittal and Saif. 

Broad-reaching use cases

The Deloitte-Nvidia partnership has also enabled the U.S. Postal Service to leverage vision AI to improve delivery efficiency, said Mittal. Other use cases include customer service processes and interactions, where AI can be used to evolve experiences from “human-human, to human-machine and ultimately machine-machine,” thus increasing efficiency and convenience.

Meanwhile, in retail, AI can instantly determine the best fitting clothing items by leveraging ML, computer vision and 3D scanning. 

In healthcare, the combination of AI and wearable and nonwearable devices can monitor health and provide real-time feedback and coaching. Self-learning systems apply data from millions of users to enable personalized coaching that “drives behavior change” and help manage and prevent chronic disease, said Mittal. 

“That’s the future of health and wellness, and with the latest advances in AI (and the proliferation of devices such as smartwatches) it’s already starting,” write Mittal and Saif. 

Deloitte and Nvidia have also supported innovations in industries including energy, resources and industrials; government and public services; life sciences and healthcare; and technology, media and telecommunications. 

The partnership has enabled autonomous driving technology, which combines onboard sensors and localization technologies with AI-based decision models. This can reduce human error and lead to smarter, more informed decisions about steering, braking, and navigation, said Mittal. 

New capabilities

Deloitte’s expanded portfolio of services will run in the cloud and will be powered by several Nvidia products and technologies, including the following: 

Nvidia Omniverse Enterprise, a platform that helps build custom 3D pipelines and simulate virtual worlds. 

Nvidia Omniverse Avatar Cloud Engine: AI microservices combined with Nvidia Project Tokkio, which helps build, customize and deploy interactive service avatars at scale. 

Nvidia AI Enterprise, a cloud-native suite of AI and data analytics software.

Nvidia Riva, a GPU-accelerated SDK for building speech AI applications. 

NVIDIA Merlin, an open-source framework for building recommender systems at scale. 

Nvidia Metropolis, a set of developer tools and partner ecosystem that combine visual data and AI. 

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How Tymely aims to improve chatbot conversations

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As digitization continues to shape consumer behavior toward ecommerce businesses, consumers are increasingly demanding fast and convenient online shopping experiences. Fueled by the COVID-19 pandemic, that demand also increased the online presence of ecommerce businesses. With more enterprises riding the digital transformation wave, positive customer experience (CX) is crucial to customer acquisition and improving sales.

In 2021, Vonage listed chatbots (40%) as the second most preferred communication channel for consumers. Shopify’s Future of Commerce Trend 2022 Report revealed 58% of consumers purchased from brands where they’ve experienced excellent CX. The report further showed more brands (44%) plan to invest in asynchronous chat experiences to manage customer responses. Undoubtedly, many ecommerce brands are becoming more aware of the impacts of CX, and are turning to artificial intelligence (AI) tools like chatbots to improve customer service.

However, while chatbots have become a critical part of the customer journey today, issues around personalization persist. In 2019, Forrester reported that 54% of online consumers in the U.S. believed interacting with a chatbot “has a negative impact on the quality of their life.”

This implies that even though chatbots are great tools, they aren’t perfect yet. Though, Ohad Rozen, cofounder and CEO of chatbot provider, Tymely, believes that human supervision in its processes provides a solution that enables human-level personalizations.


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The company, which today announced it raised $7 million to “make AI converse better,” claims it uses AI-human hybrid tech to enable brands to provide email and chat support services in a more human, empathetic and precise way. 

Rozen told VentureBeat in an interview that in addition to Tymely’s cutting-edge natural language processing (NLP) models, the company takes the human-in-the-loop approach to close the gap between today’s current technology stack and optimal customer service.

The rise and fall of chatbots

Chatbots are AI-powered programs that provide on-demand customer services — and unlike human customer services, chatbots are always available. In 2018, Drift reported that 64% of consumers listed 24-hour service as chatbots’ most helpful feature, while 55% were impressed with its swift response. 

Although chatbots are fast and readily available, creating personalized messages is still a blocker. This is because of their inability to comprehend the nuanced industry-specific languages customers use. WATConsult’s 2021 research adds more weight to this stance, revealing the main blockers to using chatbots are lack of understanding (50%), inability to solve complex issues (47%), and lack of personal service experience (45%). 

According to a report by Gartner, Chatbots’ self-service report is also statistically underwhelming. The report showed chatbots’ self-service solves only 9% of queries without a human touch. Besides, chatbots have limited use for customer engagements, and chatbots with poor customer service output are bad news for sales. For example, chatbots caused sales to decline by 80% in 2019

Because of its limited customer service functionality, many companies are slow to adopt the technology. For instance, fashion retailer Everlane ditched the Facebook Messenger chatbot after it recorded high failure rates in 2017. Along those same lines, in 2018, Accenture reported that 53% of organizations “have no plans” to invest in chatbots. 

Tymely intervention

Tymely claims its AI technology can create personalized messages. Launched in 2022, the company says that it is building an AI that understands complex human language to improve CX. Unlike most chatbots and other fully automated solutions, Tymely claims it has a human-level understanding of the customers’ language, with its technology being a mix of people and AI. 

Rozen also believes the human touch is the answer to creating empathetic messages that regular chatbots lack.

“Tymely employs experts that review each AI input and, if needed, correct it in real-time. This results in human-level accuracy that enables us to understand tiny and implicit nuances in a customer’s text; a high-resolution understanding that also allows us to generate hyper-personalized and empathetic responses to customers, according to the brand’s voice and policy,” he said.

Rozen also noted that Tymely can improve the efficiency of contact centers because it’s fully digital, helping businesses save labor-head costs. He further noted that Tymely AI costs 50% to 80% less than outsourced contact centers. “And unlike contact centers, Tymley commits to SLA in minutes, not hours,” he added.

This new funding boost was led by venture capital firm Hetz Ventures and DESCOvery, the D. E. Shaw group’s venture studio. In a statement announcing the funding, Rozen revealed that Tymely plans to use the funding to “improve its natural language understanding (NLU) technology” for better service offerings.

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Google’s open-source bug bounty aims to clamp down on supply chain attacks

Google has introduced a new vulnerability rewards program to pay researchers who find security flaws in its open-source software or in the building blocks that its software is built on. It’ll pay anywhere from $101 to $31,337 for information about bugs in projects like Angular, GoLang, and Fuchsia or for vulnerabilities in the third-party dependencies that are included in those projects’ codebases.

While it’s important for Google to fix bugs in its own projects (and in the software that it uses to keep track of changes to its code, which the program also covers), perhaps the most interesting part is the bit about third-party dependencies. Programmers often use code from open-source projects so they don’t continuously have to reinvent the same wheel. But since developers often directly import that code, as well as any updates to it, that introduces the possibility of supply chain attacks. That’s when hackers don’t target the code directly controlled by Google itself but go after these third-party dependencies instead.

As SolarWinds showed, this type of attack isn’t limited to open-source projects. But in the past few years, we’ve seen several stories where big companies have had their security put at risk thanks to dependencies. There are ways to mitigate this sort of attack vector — Google itself has begun vetting and distributing a subset of popular open-source programs, but it’s almost impossible to check over all the code a project uses. Incentivizing the community to check through dependencies and first-party code helps Google cast a wider net.

According to Google’s rules, payouts from the Open Source Software Vulnerability Rewards Program will depend on the severity of the bug, as well as the importance of the project it was found in (Fuchsia and the like are considered “flagship” projects and thus have the biggest payouts). There are also some additional rules around bounties for supply chain vulnerabilities — researchers will have to inform whoever’s actually in charge of the third-party project first before telling Google. They also have to prove that the issue affects Google’s project; if there’s a bug in a part of the library the company’s not using, it won’t be eligible for the program.

Google also says that it doesn’t want people poking around at third-party services or platforms it uses for its open-source projects. If you find an issue with how its GitHub repository is configured, that’s fine; if you find an issue with GitHub’s login system, that’s not covered. (Google says it can’t authorize people to “conduct security research of assets that belong to other users and companies on their behalf.”)

For researchers who aren’t motivated by money, Google offers to donate their rewards to a charity picked by the researcher — the company even says it’ll double those donations.

Obviously, this isn’t Google’s first crack at a bug bounty — it had some form of vulnerability reward program for over a decade. But it’s good to see that the company’s taking action on a problem that it’s been raising the alarm about. Earlier this year, in the wake of the Log4Shell exploit found in the popular open-source Log4j library, Google said the US government needs to be more involved in finding and dealing with security issues in critical open-source projects. Since then, as BleepingComputer notes, the company has temporarily bumped up payouts for people who find bugs in certain open-source projects like Kubernetes and the Linux kernel.

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‘Mario + Rabbids: Sparks of Hope’ aims to be a more modern tactical adventure

Mario + Rabbids: Kingdom Battle was a pleasant surprise. A charming game that married Nintendo’s Mushroom Kingdom with the chaos of Ubisoft’s Rabbid mascots and crammed it into a game that was, well, pretty much a cartoon interpretation of the tactical strategy series XCOM.

It was an unlikely early hit on the Switch. Ubisoft was able to offer a different kind of game than Nintendo was offering in its first-party titles. Apparently, that was the seed that led Ubisoft Milan Creative Director Davide Soliani to Mario + Rabbids. Talking to Engadget, he said, “[We] should create something that makes sense from Ubisoft’s point of view, something not happening in Nintendo’s catalog.”

Ubisoft fulfilled that brief with Kingdom Battle. Soliani added: “We can match the aesthetics [of Super Mario], using and misusing the elements…. The contrast is the drive.” That’s the context for this sequel, too. 

In Mario + Rabbids: Sparks of Hope, Mario et al. (and their Rabbid equivalents) face a shared threat, called Cursa. The blended worlds of Mario and Rabbids are being contorted by darkness, but this time it’s a little more galactic. Expect to see varied worlds, à la Mario Galaxy, with the Lumas of that game being transformed into Sparks, elemental sprites that work like summonable magic attacks in the many, many battles.

That may sound new to anyone that played Kingdom Battle, but there are far bigger changes afoot. We’re yet to play the game, but judging from the new teaser and Davide Soliani’s explanation, it’s going to feel different – less of an XCOM tribute and something between tactical strategy conventions and the manic dashing and leaping of typical Mario games.

Mario + Rabbids: Sparks of Hope


Your party of three heroes can now move around in real-time, no more grids. You’ll be able to see how far a character can move within their environment thanks to a white outline, but you’ll be able to figure out cover and optimal attacks on the fly. Each hero will get their turn before the baddies get to, well, return the favor. Soliani says this should help the game to feel more “natural”.

Crucial elements will include where you move your hero (as long as you don’t shoot), utilizing items to extend movement and even using some enemies against other enemies – like hurling a Bob-omb towards some unsuspecting enemies on the other side of an area. Like Kingdom Battle, the synergy with other heroes will be crucial in tackling the biggest enemies.

Alongside companion elemental Sparks, which will grow in abilities as your characters do, each hero will have their own unique weapon this time, running the gamut from melee weapons like swords through to dual pistols and even bows. (You can’t have a game in the 2020s without including a bow.)

You’ll be joined by some new characters, including a Rabbid with a sword called Edge. (Dumb, I love it.) and age-old rival Bowser, who’s apparently a heavy-hitter equipped with what appears to be a bazooka.

More freedom in battles is mirrored in the game too. The worlds you’ll explore should feel more open-ended than the areas of its predecessor. Explore planets, take on fetch quests (this is a Ubisoft game after all), solve the major darkness problems of this specific planet – or just do the bare minimum and move on to the next part of the game.

Mario + Rabbids: Sparks of Hope


This should all help Sparks of Hope feel a little more contemporary – aided by a pretty incredible array of musical talent. Kingdom Battle composer Grant Kirkhope, who also contributed to Rare’s epic run of Nintendo 64 games, returns, joined by Gareth Croker (Ori and the Will of the Wisps, Halo Infinite) and Yoko Shimomura (Kingdom Hearts, Final Fantasy XV). Those are some gaming music heavyweights which should help ensure all these different worlds sound as different as they’ll look.

Judging from the teaser and Soliani’s comments, Ubisoft is evolving Mario + Rabbids at a swift clip, modernizing the battle system and adding further strategic wrinkles and customization to fights. Sparks of Hope could feel like a different sort of tactical battle game, and if they nail the synergy like the first game, it could be just as entertaining.

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Mabl aims to automate software testing, nabs $40M

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In the enterprise, the pandemic brought about sharp increases in software release cadences and automation. That’s thanks in part to the adoption of low-code development platforms, which abstract away the programming traditionally required to build apps and software. Respondents to a 2021 Statista survey report that low-code tools have enabled them to release apps 40% to 60% faster, while a full 60% of developers tell GitLab that they’re committing code two times faster and bringing higher-impact technologies into their processes.

However, the accelerated pace of development has introduced new blockers, like a backlog of testing and code reviews. GitLab reports that only 45% of developers review code weekly, with 22% opting to do it every other week instead. It’s not that the pace of innovation in software testing is slowing — it isn’t — but that aspects like installation, maintenance, security, and incompatible problems remain hurdles.

The urgency of the challenge — insufficient testing can lead to security vulnerabilities — has put a spotlight on startups like Mabl, which today announced that it raised $40 million in a series C investment led by Vista Equity Partners. Founded in 2017 by Dan Belcher and Izzy Azeri, Mabl is among a crop of newer companies developing platforms that enable software teams to create, run, manage, and automate software and API tests.

“Despite the fact that the testing category is massive, no software-as-a-service leader had emerged in the space [prior to Mabl] … This void left enterprises to choose between legacy testing solutions that were incredibly complex and expensive and self-managed open source frameworks coupled with bespoke infrastructure that were inaccessible to quality assurance professionals,” Belcher told VentureBeat via email. “Mabl envisioned and delivered an end-to-end testing solution for the enterprise that would combine a low-code framework, a software-as-a-service delivery model, deep integration with enterprise environments and workflows, data analytics, and machine intelligence to disrupt the testing industry.”

Automating testing at scale

Belcher and Azeri are second-time founders, having previously launched Stackdriver, a service that provides performance and diagnostics data to public cloud users. Stackdriver was acquired by Google in 2014 for an undisclosed sum, and recently became a part of Google Cloud’s operations suite.

With Mabl, Belcher and Azeri set out to build a product that lets developers orchestrate browser, API, and mobile and web app tests from a single location. Mabl’s low-code dashboard is designed to automate end-to-end tests locally or in the cloud, running tests with different permutations of data to strengthen individual test cases.

“Lack of effective test automation is a major problem in the software industry, because ineffective testing leads to dramatically lower team productivity due to rework, overhead, and a general bottleneck in throughput, as quality engineering cannot execute with the same velocity as development and operations. Likewise, ineffective testing leads to poor product quality — from not only a functional perspective but also in terms of user experience, performance, accessibility, and more,” Belcher added. “Low-code is critical because it democratizes testing — making it accessible to quality engineers, manual testers, developers, product owners, and others — rather than limiting efforts to a quality engineer or dev.”


Above: Mabl’s automated testing orchestration dashboard.

Image Credit: Mabl

Mabl can generate tests from the contents of emails and PDFs, adapting as the tested app’s UI evolves with development. An AI-driven screenshot comparison feature attempts to mimic real-life visual UI testing to help spot unwanted changes to the UI, while a link-crawling capability autonomously generates tests that cover reachable paths within the app — giving insight into broken links.

“There are a number of areas where we use machine intelligence to benefit customers. In particular … Mabl automatically trains and updates structural and performance models of page elements, and incorporates these models into the timing and logic of test execution,” Belcher explained. “Mabl [also leverages] visual anomaly detection that uses visual models to differentiate between expected changes resulting from dynamic data and dynamic elements from unexpected changes and defects.”

Mabl allows customers to update and debug tests without affecting master versions. API endpoints can be used to trigger Mabl tests, as well as plugins for CI/CD platforms including GitHub, Bitbucket Pipelines, and Azure Pipelines. On the analytics side, Mabl shows metrics quantifying how well tests cover an app, identifying gaps based on statistics and interactive elements on a page.

Growing trend

Mabl’s users include dev teams at Charles Schwab, ADP, Stack Overflow, and JetBlue, and the Boston, Massachusetts-based company expects to more than double its recurring revenue this year. But the company faces competition from Virtuoso, ProdPerfect, Testim, Functionize, Mesmer, and Sauce Labs, among others. Markets and Markets predicts that the global automation testing market size will grow in size from $12.6 billion in 2019 to $28.8 billion by 2024.

Automated tests aren’t a silver bullet. In a blog post, Amir Ghahrai, lead test consultant at Amido, lays out a few of the common issues that can come up, like wrong expectations of automated tests and automating tests at the wrong layer of the app stack.


Still, a growing number of companies are adopting automated testing tools — especially in light of high-profile ransomware and software supply chain attacks. According to a LogiGear survey, at least 38% of developers have at least tried test automation — even if it failed in its first implementation. Another source estimates that 44% of IT companies automated 50% or more of all testing in 2020.

“Mabl has over 200 customers globally, which includes 10 of the Fortune 500 and 32 of the Fortune Global 2000 companies. We have active, paid users in over 60 countries,” Belcher said. “The Mabl community in Japan has more than doubled in the past six months — reaching nearly 1,500 members. The Japanese customer base has increased by 322% and revenue has increased by over 300% in the past year, now representing 10% of the company’s total global revenue.”

Existing investors Amplify Partners, Google Ventures, Presidio Capital, and CRV also participated in 55-employee Mabl’s round. It brings the startup’s total capital raised to over $77 million.


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Darktrace aims to expand into ‘proactive’ security AI by end of year

Darktrace plans to expand its AI-powered security offerings to include attack prevention by the end of 2021, the company told VentureBeat.

On Tuesday, executives from the company described plans for upcoming product updates that will expand the Darktrace portfolio to include proactive security AI capabilities, joining the company’s detection and response technologies.

The upcoming launch of “prevent” capabilities will extend Darktrace “into the offensive area for the first time ever,” said Nicole Eagan, chief strategy officer and AI officer at Darktrace, while speaking at the virtual Gartner Security & Risk Management Summit – Americas conference on Tuesday.

In a statement provided to VentureBeat, Eagan said that “development of this breakthrough innovation known as our ‘prevent’ capability is on track, and we expect this to be released to early adopters by the end of this calendar year.”

Founded in 2013, the Cambridge, U.K.-based firm went public in April and now has a market capitalization of $4.25 billion.

Security AI growth

While Darktrace is a pioneer in the realm of security AI with its self-learning technology for detecting and responding to cyber threats, the company is now part of a fast-growing field of companies that are turning to AI and machine learning to counter increasingly sophisticated cyber threats.

Startups getting major traction in the space include Securiti, Vectra AI and Salt Security, while cybersecurity giants such as Fortinet, Palo Alto Networks and Microsoft have invested heavily into AI-based security. Today, for instance, Palo Alto Networks unveiled a cloud security platform that taps ML and AI to enable many of its new capabilities, such as improved data loss prevention.

Alongside its growth, Darktrace has also demonstrated the potential for AI-powered security with responses to high-profile cyber incidents, such as an incident this summer at the Olympic Games in Tokyo.

There, Darktrace identified a malicious Raspberry Pi IoT device that an intruder had planted into the office of a national sporting body directly involved in the Olympics. The company’s technology detected the device port scanning nearby devices, blocked the connections, and supplied human analysts with insights into the scanning activity so they could investigate further.

But even with outcomes like that, there is much more that Darktrace’s security AI technology can do, company executives said during the conference Tuesday.

During “all the time that you aren’t actually under attack,” a customer could be using the Darktrace technology in order to prevent future attacks, Eagan said.

The company’s self-learning AI has “an immense amount of insights” from a customer’s data, she said. “We could use this data to help you move from a reactive state to a proactive, and even an adaptive, state.”

Attack path modeling

Specifically, Darktrace is looking at capabilities that include attack path modeling, which in the past has typically been a “human-centric” capability, said Max Heinemeyer, director of threat hunting at Darktrace, during the conference session.

With the self-learning AI technology, Darktrace knows a customer’s digital estate inside and out, he said. The technology knows what type of data is being accessed, how it’s being accessed, what types of emails are being sent, what variety of internet-facing systems a customer has, and whether there is shadow IT in the environment, Heinemeyer said.
This could provide customers with potential attack paths that they otherwise would never have been able to figure out, he said.

The Darktrace system could proactively tell a customer, “this is your core crown jewel, based on what we see—and it’s actually just two hops from this new [employee] to one your IT administrators to compromise that,” Heinemeyer said. “And that could be one of thousands of possible attack pathways. So we can really have an impact in telling you where your risks lie, and where your most vulnerable paths are, without having to predefine everything and try to tell the system what your environment looks like. That situational awareness, that context, comes with the self-learning AI.”

In this scenario, Darktrace would be able to then feed that knowledge back into the detection and response side of the product, “wrapping a safety blanket around these critical assets,” he said.

Other “prevent” capabilities in development at Darktrace include AI-powered red teaming to automatically test security controls, company executives said.

“Continuous AI loop”

Eventually, the goal is for Darktrace’s expanded security AI offerings to “form a continuous AI loop that’s always improving your overall cyber posture,” Eagan said.

The plan even further down the road is to bring AI-driven recovery capabilities after an attack, she said.

“We feel that we’re very well positioned to be able to actually help in that recovery,” Eagan said. “Our vision is really to be able to help you do the cleanup very quickly—bring the organization back to its normal state of business operations.”

Ultimately, she said, Darktrace sees each of its AI systems “reinforcing the other, minimizing any impacts of any breach or attack in real-time, and allowing the AI to preemptively lower your risk.”


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Sama aims to bring greater equality to crowd-labeling of datasets with new $70M

Sama, a company providing data to train machine learning systems, has raised $70 million in a series B found led by CDPQ with participation from First Ascent Ventures, Salesforce Ventures, Vistara Capital Partners, and existing investors. CEO Wendy Gonzalez says that the company will use the funding to grow its platform with new products that “enable teams to manage the complete AI lifecycle.”

Data scientists spend about 45% of their time on data preparation tasks including loading and cleaning data, according to Anaconda. A separate report from Alation found that 97% of data leaders have suffered the consequences of ignoring data, either missing out on new revenue opportunities, poorly forecasting performance, or making bad investments. Yet another study — this by MIT Technology Review Insights and commissioned by Databricks — reveals that machine learning’s business impact is limited largely by challenges in managing its end-to-end lifecycle.

Founded by Leila Janah, San Francisco, California-based Sama — formerly Samasource — developed its first relationships with partner delivery centers in 2018, focusing on data entry, sentiment analysis, and data transcription. In 2009, the company launched the initial version of its technology platform, SamaHub, and embarked on a slew of commercial projects — including providing images and annotations used by Microsoft to build out the company’s Xbox Kinect.

“Janah believed that giving meaningful, living-wage work was the best way to permanently lift people out of poverty,” Gonzalez told VentureBeat via email. “To date, we’re the only AI training data provider with a responsible training and employment program that provides actionable career skills for underserved communities to bring us closer to a more equitable future of AI.”

Data platform

Today, Sama hosts a crowd-powered platform through which companies can obtain data labeled to train AI models, like videos, images, computer-generated shapes, radar, and natural language. Customers in industries such as transportation and navigation, retail and ecommerce, and robotics and manufacturing pay for datasets while “crowdworkers” supply annotations in exchange for payment from Sama.

Sama competes with a host of data labeling and annotation platforms in the market, including DefinedCrowd, CrowdFlower, Labelbox, Superb AI, and as well as incumbents like Amazon Mechanical Turk. But the company asserts that it delivers a superior product by tracking 160 million events per month to improve its platform and processes, like machine learning-assisted annotation tools for crowdworkers.


Above: Objects labeled with Sama’s backend tools.

Image Credit: Sama

“Our labelers have three-year average tenure and are subject-matter experts who work with our customers to identify edge cases and recommend annotation best practices,” Sama explains on its website. “Sampling provides feedback to quality managers to ensure teams are working efficiently and effectively, while ‘hold’ tasks and advanced scripting detect errors early in the pipeline.”

When a company contracts with Sama, Sama’s platform creates “micromodels” that are used to generate prelabeled data to assist labelers with annotation. Annotators validate the machine learning-generated labels while Sama works with the company to identify edge cases and recommend annotation best practices.

Post-annotation and deployment, Sama can provide ongoing feedback and monitor models in production. Beyond this, the platform can generate data on “frame-level” annotation and edge cases, producing reports designed to help get models to market faster.


Supervised learning — one of the types of models that requires labels to train — is the most common form of machine learning used in the enterprise. In a recent O’Reilly report, 82% of respondents said that their organization opted to adopt supervised learning versus unsupervised (which doesn’t require labels) or semi-supervised learning (which only requires a small amount of labels). And according to Gartner, supervised learning will remain the type of machine learning that organizations leverage most through 2022.

Labels can bear the hallmarks of inequality, however. For example, an estimated less than 2% of Mechanical Turk workers come from Global South countries, with the vast majority originating from the U.S. and India. ImageNet — a dataset that’s been essential to recent progress in computer vision — wouldn’t have been possible without the work of data labelers. But the ImageNet workers themselves made a median wage of $2 per hour, with only 4% making more than the U.S. federal minimum wage of $7.25 per hour — itself a far cry from a living wage.

Sama claims that it pays a higher annotator rate than its competitors — about $8 a day — with the mission of providing opportunities to communities in underserved regions. In a three-year randomized trial conducted by MIT and Innovations for Poverty Action, crowdworkers in Nairobi, Kenya who received both training and inclusion in Sama’s hiring pool had lower unemployment rates and higher average monthly earnings in comparison to crowdworkers who only received training.


The study didn’t compare the outcomes of Sama’s crowdworkers with those employed with other data labeling startups. But Gonzalez says that the results “point to the indisputable facts” and “demonstrate the value of [Sama’s] impact-model on communities globally.”

Sama — which employs 120 full-time workers and 3,500 annotators — has customers in Google, Nvidia, GM, Walmart, Getty, and over 25% of the Fortune 50. Its crowdworkers annotated 1.5 billion data points in 2020 alone, and with the latest funding round, Sama’s total capital raised stands at nearly $85 million.

“Our customers include Fortune 2000 companies,” Gonzalez said. “Notably, Sama’s … training data was recently tapped by Google to power its AI algorithm for Project Guideline, which helps those with visual impairments run independently. With our high-quality, accurate training data, the application is able to accurately approximate the runner’s position and provide audio feedback so the runner can self-correct. Now, we’re working to scale Project Guideline with a goal of making the solution an accessible option for the blind [and] visually impaired community.”


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Biden admin’s bug fix mandate aims to prevent the next major cybersecurity attack

The Biden administration is requiring civilian federal agencies to fix hundreds of cybersecurity flaws, as reported earlier by The Wall Street Journal. As the WSJ states, the BOD 22-01 directive from the Cybersecurity and Infrastructure Security Agency (CISA) covers around 200 known threats that cybersecurity experts discovered between 2017 and 2020, as well as 90 more flaws that were found in 2021. Federal agencies have six months to patch older threats and just two weeks to fix the ones that were discovered within the past year.

The WSJ report points out that federal agencies are usually left to their own devices when it comes to security, sometimes resulting in poor security management. The goal is to force federal agencies to fix all potential threats, whether they’re major or not, and establish a basic list for other private and public organizations to follow. While zero-day vulnerabilities that exploit previously unknown openings get major headlines, addressing “the subset of vulnerabilities that are causing harm now” can get ahead of many incidents.

Previously, a 2015 order gave federal agencies one month to fix threats deemed “critical risk.” This was changed in 2019 to include threats categorized as “high risk,” as pointed out by the WSJ. The new mandate distances itself from prioritizing specific threat levels and instead acknowledges that small holes can quickly cause larger problems if hackers can find a way to take advantage of them.

“The Directive lays out clear requirements for federal civilian agencies to take immediate action to improve their vulnerability management practices and dramatically reduce their exposure to cyber attacks,” says CISA director Jen Easterly. “While this Directive applies to federal civilian agencies, we know that organizations across the country, including critical infrastructure entities, are targeted using these same vulnerabilities. It is therefore critical that every organization adopt this Directive and prioritize mitigation of vulnerabilities listed in CISA’s public catalog.”

CISA’s newly released list of known vulnerabilities notably includes the Microsoft Exchange Server flaw. In March, emails from over 30,000 US governmental and commercial organizations were hacked by a Chinese group, thanks to four known security holes that, had they been patched, would’ve prevented the attacks. CISA’s list requires patching the “Microsoft Exchange Remote Code Execution Vulnerability” and is calling on federal agencies to install available SolarWinds patches by May 2022.

The Solarwinds Orion Platform is also on the list, which was the victim of a major hack in late 2020 that compromised US government agencies. The CISA notes that the “SolarWinds Orion API is vulnerable to an authentication bypass that could allow a remote attacker to execute API commands.”

Cybersecurity has been a priority for President Biden since he entered office. In May, he signed an executive order to help prevent future cybersecurity disasters. The order mandates two-factor authentication across the federal government, establishes a protocol for responding to cyberattacks, and forms a Cybersecurity Safety Review Board, among other safety measures.

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