State-of-the-art text-to-speech models can produce snippets that sound nearly human-like on first listen. In point of fact, they underpin the neural voices available through Google Assistant, as well as the newscaster voice that recently came to Alexa and Amazon’s Polly service. But because most of the models share the same synthesis approaches in common — this is, they generate a mel-spectrogram (a representation of a sound’s power) from text and then synthesize speech using a vocoder (a codec that analyzes and synthesizes voice signals) — they suffer the same shortcomings, namely slow inference for mel-spectrogram generation and skipped and repeated words in synthesized speech.In an attempt to solve these and other text-to-speech-related challenges, researchers from Microsoft and Zhejiang University developed FastSpeech, a novel machine learning model that they detail in a paper (“FastSpeech: Fast, Robust and Controllable Text to Speech“) accepted to the NeurIPS 2019 conference in Vancouver. It features a unique architecture that not only improves performance in a number of areas compared with other text-to-speech models (its mel-spectrogram generation is 270 times faster than the baseline and its voice generation is 38 times faster), but that eliminates errors like word skipping and affords fine-grained adjustment of speed and word break.

Google says it’s implementing AI and machine learning techniques to improve story suggestions in Google Search. In a blog post this morning, the tech giant announced that users in the U.S. in English (with more languages and locations to come in the next few months) who search for a news topic will now see an article carousel at the top of the results page. When there are multiple stories related to a search, each will be organized by relevance and quality in a way that accounts for a diversity of perspectives.“People come to Search for all types of information to help them form a better understanding of the world and the topics they care about most,” wrote Google Search product manager Duncan Osborn. “We’ve continued to bring new improvements to Search to help people better orient themselves around a topic and easily explore related ideas, so they can more quickly go from having a question in mind to developing deeper understanding … Our research has shown that clustering results into clearly-defined stories is critical in helping people easily navigate the results and identify the best content for their needs.”

While AIs are increasingly beating us mere mortals at many things, racing drones is something we still have the upper hand at. The Drone Racing League (DRL) orchestrated its first AI racing competitions this year, with the final of a four-part series held in Texas earlier this month. The races aim to advance the development […] The post Humans are still beating AIs at drone racing, for now appeared first on AI News.

Factories regularly face inventory shortages and missed deliveries, but they’re wounds of a self-inflicted nature. Just ask LeanDNA cofounder and CEO Richard Lebovitz, who previously founded Factory Logic, which was acquired by SAP for an undisclosed amount. LeanDNA is an Austin, Texas-based startup building a cloud-based actionable intelligence platform for factory enterprise resource planning systems. In the four years since its founding, LeanDNA has nabbed the business of customers including Ibis Tek, JR Automation, Knoll, Milacron, Thales, Garrett Metal Detectors, and Swenson. Now, roughly two years after it raised $4.5 million in series A funding, it’s closing a venture capital round ahead of a planned expansion.

Microsoft today launched Microsoft Teams for Linux in public preview. No, that’s not a typo — Microsoft Teams is indeed the first Office app that the company has ported to Linux. You can download Microsoft Teams as a native Linux package in .deb and .rpm formats from here.Teams is the company’s Office 365 chat-based collaboration tool that competes with Google’s Hangouts Chat, Facebook’s Workplace, and Slack. It’s also Microsoft’s fastest-growing business app ever. In March, Microsoft shared that Teams is used by 500,000 organizations, just two years after launch. In November, Microsoft said Teams had passed 20 million daily active users.There is a lot of nuance in that figure. Slack, Microsoft Teams’ main rival, is particularly critical of how Microsoft got to 20 million daily active users (Slack has over 12 million daily active users as of October). Nevertheless, Slack is already available for Linux (although still in beta), so Microsoft is playing catch up here. If you were thinking about adopting Microsoft Teams or Slack at your company, and Linux was the deal-breaker, the decision just got harder.

Lidar sensors, which measure the distance to a target by illuminating the target with laser light and measuring the reflected light, hasn’t been Intel’s domain historically. But that’s changing today with the launch of the RealSense Lidar Depth Camera, a product family that incorporates solid-state lidar technology — i.e., lidar without moving parts — into cameras designed to measure depth.The first RealSense Lidar Depth Camera — the L515, which starts at $350 and is available for preorder starting today, with shipments expected the week of April 27 — uses a proprietary micro-electro-mechanical mirror-scanning technique that enables better efficiency compared with time-of-flight alternatives. Intel claims its power consumption for depth streaming — less than 3.5 watts — makes the L515 the most energy-efficient high-resolution lidar camera on the market.

Nuro, a self-driving car company founded by two former Google employees, today announced a partnership with Walmart to deliver groceries to customers in Houston, Texas. This follows pilots in Scottsdale, Arizona that kicked off last August, first with a fleet of modified Toyota Priuses and then with Nuro’s custom R1 vehicle, and an existing delivery program with grocery giant Kroger in Houston.Nuro says self-driving deliveries will initially be available to a select group of participants who have opted in to try the service. It will launch in the coming weeks, ahead of general availability sometime next year.“Walmart’s dedication to its customers aligns with our desire to help people save time and money while making shopping easier. We are excited to join forces with Walmart to help provide the best possible delivery experience to customers,” said Nuro head of partner relations Cosimo Leipold. “Working alongside Walmart gives us an incredible opportunity to improve our door-to-door operations, serve Walmart’s loyal customers, and continue to integrate and engage with the Houston community.”

Outsight announced it has raised $20 million in venture capital to develop the first commercial version of its 3D camera that promises more rapid and accurate processing of images to power smart cities and autonomous vehicles.The Paris-based company says its 3D Semantic Camera can detect objects and at the same time recognize them within the context of their broader surroundings. Outsight claims these cameras, which combine the company’s hardware and software, provide data that accelerates the work of AI algorithms.Outsight intends for the cameras to initially power monitoring services in places like airports and malls and eventually target vehicles and remote robots.“We see the first applications in smart cities and smart monitoring infrastructure,” said CEO and cofounder Cedric Hutchings. “The environment is fixed, and it has fewer constraints than putting [cameras] on vehicles. We’ll have a version for autonomous vehicles, but that’s a longer journey.”

Deepfakes, or media that takes a person in an existing image, audio recording, or video and replaces them with someone else’s likeness using AI algorithms, are multiplying quickly. Amsterdam-based cybersecurity startup Deeptrace found 14,698 deepfake videos on the internet during its most recent tally in June and July, up from 7,964 last December — an 84% increase within only seven months. That’s troublesome not only because deepfakes might be used to sway public opinion during, say, an election, or to implicate someone in a crime they didn’t commit, but because the technology has already generated pornographic material and swindled firms out of hundreds of millions of dollars.In an effort to fight deepfakes’ spread, Facebook — along with Amazon Web Services (AWS), Microsoft, the Partnership on AI, Microsoft, and academics from Cornell Tech, MIT, University of Oxford, UC Berkeley, University of Maryland, College Park, and University at Albany-SUNY — are spearheading the Deepfake Detection Challenge, which was announced in September. It’s launching globally at the NeurIPS 2019 conference in Vancouver this week, with the goal of catalyzing research to ensure the development of open source detection tools.

The European Laboratory for Learning and Intelligent Systems (ELLIS) today announced the selection of 17 cities in 10 European countries and Israel where it’s establishing project that it hope grows into AI research institutes keen on societal impact.Each selected site — in places like Amsterdam, Copenhagen, Helsinki, Tel Aviv, and Zurich — will begin with about half a dozen AI researchers and sustained funding of at least €1.5 million a year for the next 5 years. The funding is meant to provide researchers with salaries that can compete with tech giants in an industry known for aggressively hiring top talent. Units begin operations in spring 2020.“A step toward realizing this vision of having these institutes is creating these units which are a little bit like the seed. So that if the units are successful, the plan is that it will grow and it will eventually become an institute,” AI researcher and ELLIS board member Nuria Oliver told VentureBeat in an interview today at the Neural Information Processing Systems (NeurIPS) conference in Vancouver, Canada.

Facebook’s Portal TV now lets you sing on an augmented reality stage while wearing AR to look like an ’80s rock star, hippie, or punk rocker in one of several feature upgrades announced today.Facebook Messenger video calls with the new Mic Drop feature let you sing up to 90-second clips with songs like “Roar” from Katy Perry and “Viva La Vida” from Coldplay, as well as music from Guns N’ Roses, Bon Jovi, and Backstreet Boys. Part of your karaoke performance can then be shared in a GIF.Facebook’s Portal TV for video calls, second-generation Portal, and Portal Mini with Amazon’s Alexa hit the market for the first time this fall.Also new: Watch Together is now available for all Portal devices. The feature allows you to watch videos while on video calls and was initially only available for Portal TV. Don’t get too excited though: Watch Together still only works for Facebook Watch videos.

Selecting a machine learning algorithm for a predictive modeling problem involves evaluating many different models and model configurations using k-fold cross-validation. The super learner is an ensemble machine learning algorithm that combines all of the models and model configurations that you might investigate for a predictive modeling problem and uses them to make a prediction …

Tines, a cybersecurity startup that helps enterprise security teams automate repetitive workflows, has added a further $11 million to its series A funding round. The company had initially announced a $4.1 million series A investment six weeks ago, meaning this fresh cash injection takes its total raised in this round to $15.1 million.The funding extension was spearheaded by Accel, with participation from Index Ventures and Blossom Capital.Founded out of Dublin in 2018, Tines enables companies to prebuild what it calls “automation stories” to perform an unlimited number of steps that would normally have to be carried out manually. This could include log and threat intelligence searches to help establish whether a security alert requires further action.

At NeurIPS this week in Vancouver, Canada more than 1,400 pieces of AI research are being examined for their novel approaches or breakthroughs — but one of these papers is unlike all the rest.Microsoft Research’s Lin Xiao was named winner of the Test of Time award this week, a title granted to AI research that’s made important and lasting contributions to the AI field over the last 10 years.A specially made committee is convened to look back at papers published at NeurIPS 10 years ago and narrows the list down to 18 papers that have had a lasting influence on machine learning, measured in part by which papers garnered the most citations in the past decade. To date, Xiao’s paper has been cited more than 600 times by other researchers.

Like Google, Uber, and countless other startups and tech giants, Lyft is developing cars it hopes will someday ferry passengers sans driver — a task that’s easier said than done. In a Medium post published this morning, Lyft’s Level 5 team — the division devoted to autonomous vehicle (AV) research and development — laid out a few of the challenges its engineers have encountered to date, while pulling back the curtains on solutions and general progress.Picture this: A car driving down a freeway encounters another car cutting across multiple lanes to make an exit. Preventing a collision would require the first car to slow down, but just how dramatic should that slowdown be? Lyft’s self-driving prototypes tap what the company calls a “human-inspired” planning approach to determine this. Originally, they used a baseline AI model that didn’t account for the velocity of obstacles, but the newest model naturally learned from human driving to marginally slow down for cars performing high-velocity cut-ins.

This week at NeurIPS, the biggest AI research conference in the world, attendees of the Queer in AI workshop could choose multiple stickers for their badges. There were stickers for preferred gender pronouns, and next to the nonprofit’s rainbow brain logo stickers was a stack of red and blue stickers that indicate if a member is comfortable being photographed. Blue = photo, red = no photo. For some it may be about privacy, and for others it’s about feeling safe.Most Queer in AI speakers’ names were publicly available in the NeurIPS diversity workshop brochure, a livestream, and a poster session together with groups like Black in AI, Latinx in AI, and Women in Machine Learning, but unlike virtually every other workshop collocated at NeurIPS, poster presenters and attendees who didn’t want to be photographed were also given the choice to keep their identity private.

The European Laboratory for Learning and Intelligent Systems (ELLIS) has announced a new initiative designed to help keep AI talent in the continent. Retaining AI talent is difficult. AI News has already reported on academia’s struggle to keep researchers helping to equip the next generation with AI skills, but it’s not just the education industry […] The post ELLIS commits €220m in a bid to keep AI talent in Europe appeared first on AI News.

Woe to the enterprise that pinches pennies and opts for a lax cybersecurity policy. Kaspersky Lab reported a threefold year-over-year increase in smart gadget hacks in the first half of 2018, with one malware variant managing to infect 57,000 wireless security cameras in a week. And with 30 billion devices expected to be connected by 2020, including deadbolts and office printers, the problem only stands to worsen.Santa Clara, California-based Ordr asserts that the client-side solutions installed on workstations, laptops, and handhelds are a poor fit for today’s hyper-connected work environments. By contrast, its software-as-a-service (SaaS) platform — Systems Control Engine, or SCE — autonomously identifies, regulates, and protects local networks by classifying attached systems and applying traffic flow and access policies accordingly.

There’s a reason firms in agriculture, energy, and industry are increasingly opting to adopt drone-based monitoring and analytics products: They promise to cut down on operational expenses while removing human inspectors from harm’s way. According to a recent report, flying drones cost between $200 and $300 per mile compared to conventional helicopter flights that cost upwards of $1,200 to $1,600 per mile. And the Centers for Disease Control reports that from 2003 to 2013, the number of work-related fatalities in the oil and gas extraction industry increased 27.6%, with a total of 1,189 deaths.A cottage industry has emerged to meet the demand, and one of the startups at the forefront is Raleigh, North Carolina-based PrecisionHawk. Founded as WineHawk Labs in 2010, an unmanned aerial systems and remote sensing company for wine producers, it pivoted in 2013 to general drone solutions and consultancy. This was prescient — the Federal Aviation Administration (FAA) forecasts that the drone industry could triple between now and 2023, with an estimated 823,000 drones flying by that time. And perhaps unsurprisingly, PrecisionHawk hasn’t had much trouble convincing investors to fund its ventures.

Life insurance isn’t as popular as it once was, despite the fact that a majority of people consider it to be important. Over 80% of consumers agreed in a recent survey that people need a life insurance policy, yet just 62% say they have one themselves. In fact, only 44% of U.S. households held individual life insurance as of 2010 — a 50-year low — compared with the 72% of Americans who owned life insurance in 1960.A New York- and Hartford, Connecticut-based startup hopes to reverse the trend with a novel service that rewards policyholders for making smart lifestyle choices. Dubbed Sproutt Insurance, it’s the brainchild of insurance tech company Akitbo CEO Yoav Shaham, who nearly two years ago set out to blend analytics and health insights with AI to match people with life insurance providers.

Remember the Lenovo Smart Clock? It’s a miniature speaker with a 5-inch display tailor-made for bedside nightstands. It’s powered by Google Assistant, meaning a voice command is all it takes to kick off timers, news briefings, recipes, and more. And it’s becoming even more capable thanks to an over-the-air update.The latest Smart Clock firmware, which began rolling out globally today, introduces a new alarm option called Impromptu. According to Google, it plays a ringtone that’s contextually sensitive to factors like the time of day and weather. For instance, if an alarm goes off early in the morning and the outside temperature is less than 50 degrees, Impromptu might play this ringtone:

Leaders in the AI community came together to release the 2019 AI Index report today, an annual attempt to examine the biggest trends shaping the AI industry, breakthrough research, and AI’s impact to society.It also examine trends like AI hiring practices, private investment, AI research contributions by nation, researchers leaving academia for industry, and how much AI plays a role in specific industries. The report also notes strides in the reduction of the amount of time it takes to train AI systems and computing costs, two of the biggest hindrances to AI adoption rates.“In a year and a half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July, 2019,” the report reads.

Object recognition models have improved by leaps and bounds over the past decade, but they’ve got a long way to go where accuracy is concerned. That’s the conclusion of a joint team from the Massachusetts Institute of Technology and IBM, which recently released a data set — ObjectNet — designed to illustrate the performance gap between machine learning algorithms and humans.Unlike many existing data sets, which feature photos taken from Flickr and other social media sites, ObjectNet’s data samples were captured by paid freelancers. Depicted objects like oranges, banana, and clothing are tipped on their side, shot at odd angles, and displayed in clutter-strewn rooms — scenarios with which even state-of-the-art algorithms have trouble contending. In point of fact, when “leading” object-detection models were tested on ObjectNet, their accuracy rates fell from a high of 97% on the publicly available ImageNet corpus to just 50% to 55%

Executing cross-channel marketing campaigns isn’t easy no matter what resources a brand has at its disposal. According to a recent survey, only 30% of marketers are highly confident in their ability to deliver a mulichannel strategy, followed by 67% who are only somewhat confident. Be that as it may, 95% of salespeople say they consider multichannel marketing important for customer targeting, which is likely why an estimated 51% of companies use at least eight channels to interact with customers.That’s why Andrew Boni (who worked at Google on AdSense) and Justin Zhu (who built user growth systems at Twitter) cofounded Iterable, a startup developing a platform that enables brands to create, execute, and optimize cross-channel campaigns with flexibility. Roughly six years after the launch of its first product, the San Francisco-based company today announced that it has raised $60 million in series D funding led by Viking Global Investors, with participation from Index Ventures, CRV, Blue Cloud Ventures, Harmony Partners, and Stereo Capital.

Some AI systems achieve goals in challenging environments by drawing on representations of the world informed by past experiences. They generalize these to novel situations, enabling them to complete tasks even in settings they haven’t encountered before. As it turns out, reinforcement learning — a training technique that employs rewards to drive software policies toward goals — is particularly well-suited to learning world models that summarize an agent’s experience, and by extension to facilitating the learning of novel behaviors.Researchers hailing from Google, Alphabet subsidiary DeepMind, and the University of Toronto sought to exploit this with an agent — Dreamer — designed to internalize a world model and plan ahead to select actions by “imagining” their long-term outcomes. They say that it not only works for any learning objective, but that Dreamer exceeds existing approaches in data efficiency and computation time as well as final performance.

Countless tech platforms are setting out to help call centers automate conversations with their customers. But U.S-Indian startup and Y Combinator alum Observe.ai is bucking that trend by using AI to help coach human call center workers, rather than replace them. Today the company announced it has raised $26 million in a series A round of funding to help with this mission.Founded in 2017, Observe.ai uses natural language processing (NLP) to analyze conversations between human agents and customers. It then automatically transcribes each call and carries out sentiment analysis to determine customer satisfaction, while drawing correlations between the words and actions of the support agent and the happiness of the customer.

From my perspective, attending this year’s Snapdragon Tech Summit was all but mandatory for a journalist covering 5G: It offered the very first opportunity to go hands-on with low-band (600MHz) 5G, and the first looks at the chips that will power many of 2020’s most powerful 5G devices. Qualcomm revealed the Snapdragon XR2 5G, a chipset that will power standalone 5G VR and AR devices, as well as new Snapdragon chips for ultra-premium and midrange smartphones, plus entry-level and midrange Windows PCs.I was genuinely impressed by all of the new offerings, as well as Qualcomm’s competitive undertone during the keynotes. By the end of the third day’s keynote, it was targeting laptop OEMs with more power-efficient, AI-capable chips than Intel, and willing to call its rival out on its still stunning inabilities to make 7-nanometer CPUs or consistently supply older-process parts to customers.

AI self-driving cars need to be prepared with a set of strategies and action plans in case they get enmeshed into a car chase.

The Eclipse Foundation today announced the formation of the Edge Native Working Group, which will drive the adoption of open source software for edge computing. Founding members include Adlink, Bosch, Edgeworx, Eurotech, Kynetics, Huawei, Intel, and Siemens. The working group plans to focus on creating an end-to-end software stack that will support deployments of today’s most transformative technologies, including the internet of things (IoT), artificial intelligence, and autonomous vehicles.Edge computing is a distributed computing paradigm that brings compute power and storage physically closer to applications. The goal of doing so is to improve performance, increase efficiency, and save bandwidth. Over the years, the Eclipse Foundation has built up a sizable IoT community — 41 open source projects from over 40 members, with over 4 million lines of code produced. Now the foundation wants to do the same thing for edge computing.

Keeping abreast of shopping trends online is straightforward enough — whole categories of startups achieve this with predictive modeling. But what about when that shopping takes place in-store? Tracking the behaviors of mall, outlet, and department store shoppers is of critical importance to physical store brands, particularly considering that the percentage of brick-and-mortar sales increased by 2% from $2.99 trillion in 2016 to $3.04 trillion in 2017.To meet this need, Miron Mironiuk founded Cosmose AI, a Shanghai-based analytics software provider that anticipates how people shop offline. Brands like Subway, Samsung, Walmart, Airbnb, Tencent, Burberry, Omnicom, Mercedes-Benz, Anheuser-Busch InBev, LVMH, Kering, L’Oréal, Gucci, Cartier, P&G, Nestle, and Coca-Cola use its tool suite to granularly track offline visitors’ purchasing habits and target them with online ads via WeChat, Weibo, Facebook, Google, and over 100 other internet platforms.

Medical imaging is among the most popular application of AI and machine learning, and with good reason. Computer vision algorithms are naturally adept at spotting anomalies experts sometimes miss, in the process reducing wait times and lightening clinical workloads. Perhaps that’s why although the percentage of health care organizations that have adopted AI remains relatively low (22%) globally, the majority of practitioners (77%) believe the technology is important to the medical imaging field as a whole.Unsurprisingly, data scientists have devoted outsize time and attention to developing AI imaging models for use in health care systems, a few of which Google scientists detail in a paper accepted to this week’s NeurIPS conference in Vancouver.  In “Transfusion: Understanding Transfer Learning for Medical Imaging,” coauthors hailing from Google Research (the R&D-focused arm of Google’s business) investigate the role transfer learning plays in developing image classification algorithms.

Facebook AI Research (FAIR) says they’ve created the latest AI to achieve state-of-the-art performance when playing the card game Hanabi. The AI system achieves a score of 24.61 out of 25 while the previous best got 23.92 out of 25. In February, researchers from Google, DeepMind, Carnegie Mellon University, and Oxford University proposed a Hanabi benchmark and for the creation of more AI that can play the game in order to achieve “a new frontier for AI research.”Unlike other game challenges that pit AI versus humans like chess or Go, Hanabi is a cooperative card game where participants play together to work towards a common goal.“One of the really exciting things about this is that the improvement we’re observing is really orthogonal to the improvements that are being observed with deep reinforcement learning: You can add this on top of any strategy, and it will make it much stronger,” Facebook AI researcher Noam Brown told VentureBeat in a phone interview. “We’re seeing that the results are far beyond what we or other researchers expected. In fact, the benefits that we get from search are stronger than the benefits that have been gained through all of the deep reinforcement learning algorithms that have been used in the past.”

Four key events set the pace for each year’s smartphone camera innovations: Samsung’s February Galaxy Unpacked, Apple’s September iPhone event, Google’s October Made by Google event, and Qualcomm’s December Snapdragon Tech Summit. While the first three events focus entirely on products for the current year, Qualcomm’s is uniquely about what’s coming up next, though other announcements sometimes obscure the camera news.That didn’t happen at this year’s Tech Summit, as camera innovations were some of the biggest tentpole features of the new flagship Snapdragon 865 processor. On day one, the company promised the chip will deliver “gigapixel image processing” and 8K video capture capabilities, and on day two, it promised real Snapdragon 865 devices in early 2020, including a 200-megapixel camera in actual smartphones at some point next year.

Presented by Samsung NEXTThe reality of human-level artificial intelligence is still a dream. Even with all of the recent advancements in state-of-the-art AI, its ability to understand the world around us is only at the level of a one-year-old child. We don’t yet know how to build a robot to match a two-year-old’s ability to empathize or her ability to define new goals to help others.Still, it’s mind-blowingly impressive how well our current AI technology scales. The software industry has entered a race to apply AI in every vertical possible. While AI technology adoption is accelerating, our ability to understand its potential impact isn’t keeping up.To share some of the crazy cool advancements happening in the AI world, we recently launched a 12-part video series where some of the world’s leading AI researchers from the likes of Google, Uber, and Giphy shared some of the technology they’re working on. We called this effort “How AI is Changing the World”.

I’ve been thinking a lot about the Alphabet-Google (my colleague Kyle Wiggers prefers “Googlebet”) news this week. Why has it been so difficult to come up with a whole lot to say? It finally dawned on me last night: There isn’t much to say. That’s the point. This wasn’t a CEO shakeup. This has all been a long time coming.In August 2015, Google cofounders Larry Page and Sergey Brin formed Alphabet, a new umbrella company that includes Google. Page became CEO of Alphabet, Brin took on the role of president, and they named Sundar Pichai as CEO of Google. This week, Page and Brin stepped down, leaving Sundar Pichai as CEO of Google and CEO of Alphabet. (The cofounders still hold board seats and a majority of voting power.)

WalkMe, a so-called “digital adoption platform” (DAP) that helps companies integrate on-screen product walkthroughs into their software, has raised $90 million in a round of funding led by Vitruvian Partners, with participation from Insight Partners.This takes WalkMe’s total funding to more than $300 million, including its $50 million series F round last November at which point it was valued at more than $1 billion — the company now says that it has nearly doubled its valuation over the past year, meaning that it should be valued at around the $2 billion mark.Founded in 2011, San Francisco-based WalkMe creates tools that enable software makers to build user-focused help functionality into their products. This may include tip-balloon overlays to highlight new or notable features, for example, which can be leveraged to train employees or onboard new customers. The technology can also be used to push users toward redeeming a free trial, or converting from a free to a paid user. The WalkMe platform also leans heavily on automation to make real-time contextual suggestions based on how a user is interacting with an interface.

If Google’s Pixel 4 is your daily driver, good news: It’s now able to screen robocalls — and more. Google has announced an update to the Pixel 4’s Call Screen feature in the U.S. that automatically declines calls from unknown parties and filters out suspected robocallers, alongside an improved video calling experience on Duo, the rollout of the new Google Assistant to more users, and a zippier software experience made possible by memory usage optimizations.It’s part of what Google is calling feature drops, which will deliver “bigger updates” to Pixel devices with “more helpful and fun features” going forward. The first batch arrives today, with others to follow on a monthly cadence. “Pixel phones have always received monthly updates to improve performance and make your device safe,” wrote Google group product manager Shenaz Zack Mistry in a blog post. “Now, [Pixel devices] will also get [more substantial] updates in new … feature drops.”

Seeing around corners and through walls is old hat for AI and machine learning algorithms, which are at the heart of systems (some of which use lasers) that produce images outside a sight line. But what about the much more challenging task of reconstructing hidden objects without special equipment?Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory say they’ve developed exactly that. Their system, which they lay out in a preprint paper published this week, can reconstruct hidden videos from the shadows and reflections on an observed pile of clutter. With nothing more than a video camera switched on in a room, it’s capable of “seeing” around corners even when those corners (and live-action performances) fall outside the camera’s field of view.

A novel experimental facility integrates automation and active learning, illuminating a path to accelerated scientific discovery.

Bustling San Jose — the third-largest metropolis in California, according to the 2010 census — is about to become a testbed for autonomous cars developed by Bosch and Mercedes-Benz parent Daimler. With a memorandum of understanding last November, the two jointly reaffirmed that they’ll pilot driverless vehicles in Silicon Valley as part of an on-demand offering revealed in July 2018. This morning, the companies announced the launch of a service in San Jose with a fleet of around 30 autonomous Mercedes-Benz S-Class vehicles, which only a select group of users will be able to hail initially.Mercedes-Benz sedans capable of level 4/level 5 driving, or automated navigation requiring little-to-no human input or oversight (as outlined by the Society of Automotive Engineers), service communities along the San Carlos Street and Stevens Creek Boulevard thoroughfares between downtown and west San Jose. Intrepid riders tap Daimler’s Car2Go car-sharing service — plus MyTaxi (which the automaker acquired in 2014) and Moovel, an app that compares the price and duration of ride- and bike-sharing services, public transit, and other means of transportation — to summon rides from designated pickup locations.

Nvidia Research created an AI system that can predict 3D properties of 2D images without any 3D training data. The work will be presented at the annual conference on Neural Information Processing Systems, where researchers in academia and industry share the latest in cutting-edge machine learning. Now in its 33rd year, the conference formerly known as NIPS will take place this week in Vancouver, Canada. With more than 18,000 participants, NeurIPS is the largest AI research conference of the year.The work, which was conducted by researchers from Vector Institute, University of Toronto, Nvidia Research, and Aalto University, is detailed in the paper “Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer.”

Today concludes Amazon’s re:Invent 2019 conference in Las Vegas, where the Seattle company’s Amazon Web Services (AWS) division unveiled enhancements heading down its public cloud pipeline. Just Tuesday, Amazon announced the general availability of AWS Outposts, a fully managed service that extends AWS’ infrastructure and services to customer datacenters, co-location spaces, and on-premises facilities. And it debuted in preview Amazon Detective, which helps to analyze, investigate, and identify the root cause of potential security issues and suspicious activities. That’s not to mention AI-powered fraud detection and code review products and an expanded machine learning experimentation and development studio, as well as a dedicated instance for AI inferencing workloads.

This week marks the kickoff of Neural Information Processing Systems (NeurIPS), one of the largest AI and machine learning conferences globally. NeurIPS 2017 and NeuIPS 2018 received 3,240 and 4,854 research paper submissions, respectively, and this year’s event — which takes place from December 8 to December 14 in Vancouver — is on track to handily break those records. (Submissions this year overwhelmed NeurIPS’ website, which crashed minutes before the official deadline.)Researchers from Intel will be in attendance, as will those from tech giants like Google, Facebook, Apple, Uber, Alibaba, Baidu, and countless others. For its part, the Santa Clara, California-based chipmaker said it intends to host three dozen conference, workshop, and spotlight sessions covering topics like deep equilibrium models, imitation learning, machine programming, and more.

Google Assistant on smartphones, smart displays, and smart speakers is getting a series of upgrades today, like the ability to create notes and lists with third-party apps and search podcasts by topic.Google Assistant on smartphones is also gaining the ability to share photos with voice so you can say “Hey Google, show me photos from last weekend.” Once Google Photos results appear on your screen, you can choose the photos you want to share and tell Google Assistant to send them to one of your contacts. Last fall, Google Nest smart displays got the ability to share photos that appear on the device’s digital photo frame or in voice search results after users say things like “Hey Google, show me my photos from San Diego.”

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, providing a method that is easy to use for scenarios where our intuition often fails. The best way to develop an intuition for Bayes Theorem is to think about the meaning of the terms in the equation and …

DeepMind co-founder Mustafa Suleyman has announced he’s making the full jump to Google to work on AI policy at the company. Google acquired DeepMind for $400 million in 2014 and the firm became a subsidiary of Google’s parent company Alphabet in 2015. Suleyman co-founded DeepMind and originally became its chief product officer. After Google’s acquisition […] The post DeepMind co-founder moves to Google to work on AI policy appeared first on AI News.

AWS SageMaker, the machine learning brand of AWS, announced the release of SageMaker Studio, branded an “IDE for ML,” on Tuesday. Machine-learning has been gaining traction and, with its compute-heavy training workloads, could prove a decisive factor in the growing battle over public cloud. So what does this new IDE mean for AWS and the public cloud market?First, the big picture (skip below for the feature by feature analysis of Studio): It’s no secret that SageMaker’s market share is minuscule (the Information put it around $11 million in July of 2019). SageMaker Studio attempts to solve important pain points for data scientists and machine-learning (ML) developers by streamlining model training and maintenance workloads. However, its implementation falls short due to common, long-standing, complaints about AWS in general — its steep learning curve and sheer complexity.

One of machine learning’s promises is to help humans do things faster and more efficiently. Ironically, one of the roadblocks that keeps businesses and independent developers from capitalizing on ML’s capabilities is that it can be time-consuming and difficult to build, train, and deploy models. PerceptiLabs, a two-person Swedish startup, developed a visual drag-and-drop interface to streamline and simplify the entire process.It’s designed specifically to offload some of the labor a data scientist or developer would usually have to perform, thereby accelerating the process of development. But it also has pragmatic implications for any business or organization struggling with developing ML tools, because in addition to giving a dev team a speed boost, it allows non-technical people to better understand the process and collaborate.

Systems that monitor vital signs are critically important in any health system with seriously ill patients; it’s estimated that about 53% of U.S. hospitals have computerized remote patient monitoring systems. Perhaps unsurprisingly, the remote patient monitoring market is anticipated to be worth more than $31.3 billion by the end of 2023, up from $15.8 billion in 2017 (an increase of more than 97%).One startup chasing after it is Current Health (formerly snap40), which Christopher McCann and Microsoft veteran Stewart Whiting cofounded in 2015 with the goal of preventing illness for one million patients around the world by 2021. The Edinburgh-based firm provides a patient management product that predicts diseases and recommends specific interventions to physicians and nurses, using a combination of machine learning, clinical indicators, symptoms, and vital signs monitoring.

A team of researchers from Macquarie University are developing an AI-powered scribe as concerns grow about tech giants having too much power in healthcare. Many of us know how voice services often struggle with even “normal” conversations, despite vast training datasets available. National and regional accents are difficult enough without adding in industry-specific jargon. However, […] The post Macquarie Uni researchers aim to beat tech giants at AI-powered medical scribing appeared first on AI News.

What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They’re entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging.Of course, there’s always room for improvement. In pursuit of assistants capable of tailoring jokes to individual users’ tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches “positively” impacted user satisfaction and potentially improved joke-telling.

As in just about every area of the health and fitness market, technology is increasingly infiltrating yoga, with startups and investors pushing to capitalize on the $80 billion market. Last year, Germany-based Asana Rebel raised more than $17 million from notable backers that include Greycroft to grow its virtual yoga platform, while New York’s Mirror has raised sizable funding rounds for a connected mirror that delivers virtual fitness classes, such as yoga and Pilates.Zenia recently entered the fray with a mobile app that leverages machine learning, computer vision, and motion tracking with the promise of helping improve your yoga poses. The company calls it “the world’s first AI-powered yoga assistant,” and plans to expand its technology to cover all areas of health and fitness.

The innovation is a critical step in pushing the limits for researchers who want to explore large libraries of polymers, including plastics and fibers, for chemical and biological applications such as drugs and regenerative medicine through tissue engineering.While a human researcher may be able to make a few polymers a day, the new automated system -- featuring custom software and a liquid-handling robot -- can create up to 384 different polymers at once, a huge increase over current methods.Synthetic polymers are widely used in advanced materials with special properties, and their continued development is crucial to new technologies, according to a study in the journal Advanced Intelligent Systems. Such technologies include diagnostics, medical devices, electronics, sensors, robots and lighting.

Sophisticated machine learning applications require not only enormous amounts of training data, but powerful computer hardware on which to train. An analysis conducted by San Francisco research firm OpenAI found that since 2012, the amount of compute used in the largest training runs has been increasing exponentially with a 3.4-month doubling time, and that it’s grown by more than 300,000 times over that same time period.The trend spurred the development of supercomputers like the U.S. Department of Energy’s Sierra and Summit, which leverage dedicated accelerator chips to speed up AI computation. Now, IBM’s Hardware Center, in collaboration with New York State, SUNY Polytechnic Institute, and other members of IBM’s AI Hardware Center, has delivered a new machine for the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) that’s optimized for state-of-the-art machine learning workloads.

These days, Segment CEO Peter Reinhardt can boast that his customer management company has raised almost $300 million in venture capital for a $1.5 billion valuation. But he hasn’t forgotten the moment when the company came within inches of total failure.It took pivoting from its initial plan and moving in an entirely different direction with a homegrown data management tool to get on track to success.Founded in 2011, Segment (formerly known as Segment.io) seemed at first to be on the glide path to victory. Reinhardt was studying aerospace engineering at MIT, and his roommates were studying computer science. They had an idea to create a classroom lecture tool. Basically, the tool would allow students to signal to a professor that they were lost or confused during a presentation. The professor could see a graph that would let them know the overall status of the class’ comprehension and thus if the lecture was missing the mark.

Generative AI language models like OpenAI’s GPT-2 produce impressively coherent and grammatical text, but controlling the attributes of this text — such as the topic or sentiment — requires architecture modification or tailoring to specific data. That’s why a team of scientists at Uber, Caltech, and the Hong Kong University of Science and Technology devised what they call the Plug and Play Language Model (PPLM), which combines a pretrained language model with one or more attribute classifiers that guide novel text generation. Preliminary results in a preprint paper show that PPLM is able to control a “range” of topics and sentiment styles, importantly without sacrificing fluency and while retaining flexibility that in any combination of differentiable models steers text generation.

Augtera today announced that it raised $4 million for its operation that takes data from datacenter networks to create AI systems for management and optimization. The money will be used to grow Augtera’s engineering and research and development teams and accelerate product adoption.Augtera uses network domain knowledge and semantics, together with data from a variety of sources, to train its AI, from hardware data to interface errors and virtualized system activity.“We have taken our network domain expertise, and really used that to model the network in a very deep way,” Augtera CEO and founder Rahul Aggarwal told VentureBeat in a phone interview. “You can drop us in any network, and depending on the value proposition, we can learn in a few hours or a few weeks the behavior of the network, and then start finding what’s anomalous, or what the relationships are and what’s related and so on and so forth.”

Following the rollout of its cloudless, edge device-focused voice assistant stack, which comprises wake word, speech-to-text translation, and speech-to-intent capabilities, Picovoice announced a web console that lets you easily create and train your own voice models. Alongside the web console release, the company joined the Arm AI Ecosystem Partner Program, which gives Picovoice deeper access to ARM IP and to chip manufacturers like NXP. Specifically, Picovoice is focused on ARM Cortex-M chip designs, which are extremely low power and can integrate into all manner of IoT devices — but are powerful enough to support its voice assistant without the need for a cloud connection.The big idea is that OEMs can use the Picovoice web console to whip up voice controls for their devices large and small, for minimal cost. Products with voice assistants on board are hot, and although the likes of smart speakers and smart displays get the bulk of the attention, some level of voice control is possible on all manner of lower-power edge devices, from coffee makers to lights. Amazon in particular has aggressively pushed into this internet of things (IoT) space with household devices like its microwave and lamp, but those are all part of Amazon’s Alexa ecosystem.

AI for Good is a United Nations platform that fosters a dialogue on the beneficial use of AI by developing concrete projects.

A series of new features appear to be on the way for Microsoft’s AirSim, a robotics and AI simulation platform. The Unreal Engine-based simulator will be adapted to better suit Game of Drones, which pits quadcopter drone racing AI systems against each other in an AirSim simulation. Game of Drones is in its first year and today Microsoft said it plans to keep the competition open after a winner is named next week.Game of Drones requires AI that can carry out trajectory planning, computer vision, and opponent drone avoidance. Microsoft brought AirSim to the Unity game engine last year.“While the first iteration brought an array of new features to AirSim, there are still many essential ingredients for trustable autonomy in real-world scenarios and effective simulation-to-reality transfer of learned policies,” the company said in a blog post today. “These include reliable state estimation; camera sensor models and motion blur; robustness to environmental conditions like weather, brightness, and diversity in texture and shape of the drone racing gates; and robustness against dynamics of the quadcopter. Over the next iterations, we aim to extend the competition to focus on these components of autonomy as well.”

As machine learning algorithms have an increasing impact on society, safeguards are needed to prevent "undesirable outcomes."

Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions. Out-of-fold predictions play an important role in machine learning …

Adyen has turned its payment processing business into one of Europe’s biggest entrepreneurial success stories. Now it’s quietly deploying artificial intelligence to keep its remarkable growth streak going.The Amsterdam-based company’s philosophy of building tools from within rather than through acquisitions has been crucial to its rise, from its founding in 2006 to its2018 IPO. Its approach to AI reflects that same philosophy. The company hasn’t been talking much publicly about its work in this area, but has instead been cautiously deploying its algorithms internally.Adyen CEO Pieter van der Does told VentureBeat that the company has studiously avoided hype-driven trends and so initially took a conservative approach to AI. But after developing in-house fraud-detection algorithms, van der Does was quickly convinced of AI’s potential.

These plastics, made from thermo-responsive liquid crystal polymer networks and a coat of dye, are soft actuators that can convert energy into mechanical motion. Initially, the actuator is only respondent to heat, but by associating light with heat, it learns to respond to light. In response, the actuator similarly bends itself as a human curls its index finger. By irradiating the actuator periodically, it "walks" like an inchworm with a speed of 1 mm/s, about the same pace of a snail."Our research is essentially asking the question if an inanimate material can somehow learn in a very simplistic sense," says senior author Arri Priimägi (@APriimagi), of Tampere University. "My colleague, Professor Olli Ikkala from Aalto University, posed the question: Can materials learn, and what does it mean if materials would learn? We then joined forces in this research to make robots that would somehow learn new tricks." The research team also includes postdoctoral researchers Hao Zeng, Tampere University, and Hang Zhang, Aalto University.

For obvious reasons, trade secrets are highly valuable — they’re the innovations that confer competitive advantage. The damages sought and obtained in trade secret cases are predictably high, in some instances exceeding hundreds of millions of dollars. And plaintiffs not uncommonly estimate that theft costs hundreds of jobs and billions in combined shareholder equity.Five security researchers hailing from Stanford, MIT, the Swiss Federal Institute of Technology, and the University of California, Berkeley — Cristian Zamfir, George Candea, Radu Banabic, Vitaly Chipounov, and Volodymyr Kuznetsov — made protecting trade secrets their mission when they founded Cyberhaven four years ago. The Palo Alto-based firm, which has offices in Boston and Lausanne, Switzerland, provides an analytics solution capable of observing data’s behavior while delivering context — like which users accessed what information and when.

Exactly one year ago, Waymo, the self-driving vehicle venture of Google parent company Alphabet, launched Waymo One, which it characterized at the time as the world’s first commercial autonomous taxi service. Waymo One enabled select customers — initially only those who were involved in the early stages of Waymo’s trial program — to hail a robo-taxi, beginning in Phoenix, Arizona and surrounding metro areas.Waymo One hasn’t grown far beyond Phoenix geographically, but it’s moving incrementally forward toward a broader launch in the continental U.S. To this end, a Waymo One app for iOS launched this morning in the App Store, marking the first time it’s been made available on a platform other than Android.

When Qualcomm announced a dedicated processor solution for laptops in 2018, the company said the Snapdragon 8cx would offer OEMs not only the world’s first 7-nanometer chip for Windows PCs, but also continued advancements on a “smartphone pace.” One year later, Qualcomm is introducing two new 8cx siblings — the Snapdragon 8c and Snapdragon 7c — though as their names suggest, they’re here to make Qualcomm PC chips more diverse with “various price points,” not move the platform forward.It’s worth noting up front that Qualcomm previously offered high-end smartphone processors to Windows laptop makers, later positioning the Snapdragon 8cx as an “extreme” computer-focused alternative to the Snapdragon 850 with Intel Core-caliber performance. Today’s announcements keep the 8cx at the top of the line, while the 8c is a step down — but still better than the 850 — and the 7c is closer to the company’s 7 series mobile processors.

DeepSpeech, a suite of speech-to-text and text-to-speech engines maintained by Mozilla’s Machine Learning Group, this morning received an update (to version 0.6) that incorporates one of the fastest open source speech recognition models to date. In a blog post, senior research engineer Reuben Morais lays out what’s new and enhanced, as well as other spotlight features coming down the pipeline.The latest version of DeepSpeech adds support for TensorFlow Lite, a version of Google’s TensorFlow machine learning framework that’s optimized for compute-constrained mobile and embedded devices. It’s reduced DeepSpeech’s package size from 98MB to 3.7MB and its built-in English model size — which has a 7.5% word error rate on a popular benchmark and which was trained on 5,516 hours of transcribed audio from WAMU (NPR), LibriSpeech, Fisher, Switchboard, and Mozilla’s Common Voice English data sets — from 188MB to 47MB. Plus, it’s cut down DeepSpeech’s memory consumption by 22 times and boosted its startup speed by over 500 times.

The Institute's largest academic department reorganizes with new leadership as part of the formation of the MIT Schwarzman College of Computing.

After yesterday’s announcement of names and bare concept outlines for its upcoming Snapdragon 865 and 765 mobile platforms, Qualcomm today detailed how the upcoming chipsets will perform, as well as how they’ll differ in 5G features. It also offered an expected on-sale date for the first consumer devices using both platforms: the first quarter of 2020.Designed for flagship-class devices, the Snapdragon 865 includes all of Qualcomm’s latest wireless and processor components, including a new 2.84GHz Kryo 585 CPU, Adreno 650 GPU, fifth-generation AI engine, and Spectra 480 image signal processor (ISP). Collectively, the improvements pave the way for next-generation Android phones with major leaps forward in camera, graphics, and networking performance.

AI and machine learning algorithms capable of reading lips from videos aren’t anything out of the ordinary, in truth. Back in 2016, researchers from Google and the University of Oxford detailed a system that could annotate video footage with 46.8% accuracy, outperforming a professional human lip-reader’s 12.4% accuracy. But even state-of-the-art systems struggle to overcome ambiguities in lip movements, preventing their performance from surpassing that of audio-based speech recognition.In pursuit of a more performant system, researchers at Alibaba, Zhejiang University, and the Stevens Institute of Technology devised a method dubbed Lip by Speech (LIBS), which uses features extracted from speech recognizers to serve as complementary clues. They say it manages industry-leading accuracy on two benchmarks, besting the baseline by a margin of 7.66% and 2.75% in character error rate.

We reached out to a range of AI practitioners for their predictions on AI trends in 2020. Here is a selection of responses.

Several experts have given their thoughts on what threats AI poses, and unsurprisingly fake content is the current biggest danger. The experts, who were speaking on Tuesday at the WSJ Pro Cybersecurity Executive Forum in New York, believe that AI-generated content is of pressing concern to our societies. Camille François, chief innovation officer at social […] The post Experts discuss the current biggest threats posed by AI appeared first on AI News.

Companies are responding to expanding efforts to guard data privacy by offering consumers more options for granting or withholding permission.

The mixture of AI self-driving cars and human-driven cars on the road for the foreseeable future will require a mix of driving strategies.

Gosu.ai, the AI-driven video game coach, unveiled its plans for a desktop voice assistant that proactively offers strategy, tactics, and feedback to gamers as they play League of Legends.Previously, Gosu.ai’s virtual assistant used machine learning to offer detailed post-match analysis with recommendations regarding training matches, weapons and strategies. The desktop assistant, now with emotive voice and chat features, uses the same sophisticated machine learning to offer such advice in real-time.It is currently in open beta and will be developed further with the help of community feedback. The Vilnius, Lithuania-based startup was founded by Alisa Chumachenko, the founder of Game Insight.

AI models invariably encounter ambiguous situations to which they struggle to respond with instructions alone. That’s problematic for autonomous agents tasked with, say, navigating an apartment, because they run the risk of becoming stuck when presented with several paths.To solve this, researchers at Amazon’s Alexa AI division developed a framework that endows agents with the ability to ask for help in certain situations. Using what’s called a model-confusion-based method, the agents ask questions based on their level of confusion as determined by a predefined confidence threshold, which the researchers claim boosts the agents’ success by at least 15%.“Consider the situation in which you want a robot assistant to get your wallet on the bed … with two doors in the scene and an instruction that only tells it to walk through the doorway,” wrote the team in a preprint paper describing their work. “In this situation, it is clearly difficult for the robot to know exactly through which door to enter. If, however, the robot is able to discuss the situation with the user, the situational ambiguity can be resolved.”

For most brands, guiding and tracking customers through every step of their journeys is of critical operational importance. According to a recent PricewaterhouseCoopers report, the number of companies investing in omnichannel experiences has jumped from 20% to 80%, and an Adobe study found that those with the strongest omnichannel customer engagement strategies enjoy 10% year-over-year growth on average and a 25% increase in close rates.In an effort to further accelerate the adoption of omnichannel strategies, AOL and Salesforce veterans Brad Birnbaum and Jeremy Suriel founded New York-based Kustomer in 2015, a software-as-a-service (SaaS) provider that automates repetitive customer service processes by applying analytics atop data from multiple sources. Business is booming, according to the two cofounders — among Kustomer’s marquee customers are Sweetgreen, ThirdLove, Ring, Glossier, Rent the Runway, Away, Glovo, and UNTUCKit. And that’s perhaps why they had no trouble winning over venture backers.

GitGuardian, a cybersecurity platform that helps companies detect sensitive data hidden in public and private code repositories, has raised $12 million in a series A round of funding led by London-based Balderton Capital, with participation from GitHub cofounder Scott Chacon and Docker co-creator Solomon Hykes.Founded out of Paris in 2017, GitGuardian scans all GitHub public activity in real time to identify private data such as database login credentials, API keys, cryptographic keys, and more. The company works with over 200 API providers, spanning payment systems, cloud services, messaging apps, crypto wallets, and more to ensure any private information that does leak into the public domain is swiftly identified and the company is notified. The French startup said it has sent out more than 400,000 alerts since its inception.

Lifecycle management might not be the most exciting software segment on the block, but it’s poised to grow from $50.7 billion in 2019 to $73.7 billion by 2024. That’s likely because it’s very useful to companies juggling solutions from multiple vendors. Case in point: Poorly managed projects are estimated to go over budget by 27%, on average, and project management initiatives to save companies 28 times more capital, owing to “more reliable” output from those projects.Three years ago, entrepreneurs Matan Or-El and Meir Antar began developing an automated lifecycle management product to address challenges in cybersecurity. Their startup — Panorays — grew into a multimillion-dollar operation in a matter of months, with monitoring and alerting systems designed to map a company’s cyber posture without requiring installation or integration with third-party systems.