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Helping machines perceive some laws of physics

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Humans have an early understanding of the laws of physical reality. Infants, for instance, hold expectations for how objects should move and interact with each other, and will show surprise when they do something unexpected, such as disappearing in a sleight-of-hand magic trick.

Now MIT researchers have designed a model that demonstrates an understanding of some basic “intuitive physics” about how objects should behave. The model could be used to help build smarter artificial intelligence and, in turn, provide information to help scientists understand infant cognition.

The model, called ADEPT, observes objects moving around a scene and makes predictions about how the objects should behave, based on their underlying physics. While tracking the objects, the model outputs a signal at each video frame that correlates to a level of “surprise” — the bigger the signal, the greater the surprise. If an object ever dramatically mismatches the model’s predictions — by, say, vanishing or teleporting across a scene — its surprise levels will spike.

In response to videos showing objects moving in physically plausible and implausible ways, the model registered levels of surprise that matched levels reported by humans who had watched the same videos.  

“By the time infants are 3 months old, they have some notion that objects don’t wink in and out of existence, and can’t move through each other or teleport,” says first author Kevin A. Smith, a research scientist in the Department of Brain and Cognitive Sciences (BCS) and a member of the Center for Brains, Minds, and Machines (CBMM). “We wanted to capture and formalize that knowledge to build infant cognition into artificial-intelligence agents. We’re now getting near human-like in the way models can pick apart basic implausible or plausible scenes.”

Joining Smith on the paper are co-first authors Lingjie Mei, an undergraduate in the Department of Electrical Engineering and Computer Science, and BCS research scientist Shunyu Yao; Jiajun Wu PhD ’19; CBMM investigator Elizabeth Spelke; Joshua B. Tenenbaum, a professor of computational cognitive science, and researcher in CBMM, BCS, and the Computer Science and Artificial Intelligence Laboratory (CSAIL); and CBMM investigator Tomer D. Ullman PhD ’15.

Mismatched realities

ADEPT relies on two modules: an “inverse graphics” module that captures object representations from raw images, and a “physics engine” that predicts the objects’ future representations from a distribution of possibilities.

Inverse graphics basically extracts information of objects — such as shape, pose, and velocity — from pixel inputs. This module captures frames of video as images and uses inverse graphics to extract this information from objects in the scene. But it doesn’t get bogged down in the details. ADEPT requires only some approximate geometry of each shape to function. In part, this helps the model generalize predictions to new objects, not just those it’s trained on.

“It doesn’t matter if an object is rectangle or circle, or if it’s a truck or a duck. ADEPT just sees there’s an object with some position, moving in a certain way, to make predictions,” Smith says. “Similarly, young infants also don’t seem to care much about some properties like shape when making physical predictions.”

These coarse object descriptions are fed into a physics engine — software that simulates behavior of physical systems, such as rigid or fluidic bodies, and is commonly used for films, video games, and computer graphics. The researchers’ physics engine “pushes the objects forward in time,” Ullman says. This creates a range of predictions, or a “belief distribution,” for what will happen to those objects in the next frame.

Next, the model observes the actual next frame. Once again, it captures the object representations, which it then aligns to one of the predicted object representations from its belief distribution. If the object obeyed the laws of physics, there won’t be much mismatch between the two representations. On the other hand, if the object did something implausible — say, it vanished from behind a wall — there will be a major mismatch.

ADEPT then resamples from its belief distribution and notes a very low probability that the object had simply vanished. If there’s a low enough probability, the model registers great “surprise” as a signal spike. Basically, surprise is inversely proportional to the probability of an event occurring. If the probability is very low, the signal spike is very high.  

“If an object goes behind a wall, your physics engine maintains a belief that the object is still behind the wall. If the wall goes down, and nothing is there, there’s a mismatch,” Ullman says. “Then, the model says, ‘There’s an object in my prediction, but I see nothing. The only explanation is that it disappeared, so that’s surprising.’”

Violation of expectations

In development psychology, researchers run “violation of expectations” tests in which infants are shown pairs of videos. One video shows a plausible event, with objects adhering to their expected notions of how the world works. The other video is the same in every way, except objects behave in a way that violates expectations in some way. Researchers will often use these tests to measure how long the infant looks at a scene after an implausible action has occurred. The longer they stare, researchers hypothesize, the more they may be surprised or interested in what just happened.

For their experiments, the researchers created several scenarios based on classical developmental research to examine the model’s core object knowledge. They employed 60 adults to watch 64 videos of known physically plausible and physically implausible scenarios. Objects, for instance, will move behind a wall and, when the wall drops, they’ll still be there or they’ll be gone. The participants rated their surprise at various moments on an increasing scale of 0 to 100. Then, the researchers showed the same videos to the model. Specifically, the scenarios examined the model’s ability to capture notions of permanence (objects do not appear or disappear for no reason), continuity (objects move along connected trajectories), and solidity (objects cannot move through one another).

ADEPT matched humans particularly well on videos where objects moved behind walls and disappeared when the wall was removed. Interestingly, the model also matched surprise levels on videos that humans weren’t surprised by but maybe should have been. For example, in a video where an object moving at a certain speed disappears behind a wall and immediately comes out the other side, the object might have sped up dramatically when it went behind the wall or it might have teleported to the other side. In general, humans and ADEPT were both less certain about whether that event was or wasn’t surprising. The researchers also found traditional neural networks that learn physics from observations — but don’t explicitly represent objects — are far less accurate at differentiating surprising from unsurprising scenes, and their picks for surprising scenes don’t often align with humans.

Next, the researchers plan to delve further into how infants observe and learn about the world, with aims of incorporating any new findings into their model. Studies, for example, show that infants up until a certain age actually aren’t very surprised when objects completely change in some ways — such as if a truck disappears behind a wall, but reemerges as a duck.

“We want to see what else needs to be built in to understand the world more like infants, and formalize what we know about psychology to build better AI agents,” Smith says.


Topics: Research, Computer science and technology, Algorithms, Artificial intelligence, Machine learning, Computer vision, Computer Science and Artificial Intelligence Laboratory (CSAIL), Brain and cognitive sciences, Electrical Engineering & Computer Science (eecs), School of Engineering, Center for Brains Minds and Machines

Source: http://news.mit.edu/2019/adept-ai-machines-laws-physics-1202

AR/VR

Emerging Technologies Achievable Through The Cloud: 4 Practical Examples

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Steve Sangapu

Cloud computing is the foundation beneath some of the fastest growing industries in the world, So it’s not difficult to get lost in all the buzzwords that are thrown around cloud computing and digress from actual technological advances and benefits that are achievable with smart and efficient use of the cloud.

So what’s behind the hype? Some extremely powerful technologies and workflows. And that’s exactly what we’re going to take a look at in this article — the top 4 practical examples of technologies achievable through the cloud in 2020.

Contrary to popular belief, information alone won’t give companies a competitive advantage — executives also need to be able to base their decisions on data before the opportunities pass. However, most companies generate terabytes of data every week but are unable to capitalize on any of the data. Big data analytics is a solution to this problem.

Thanks to the advanced evolution of the cloud, companies are able to gather and analyze data at a nearly instantaneous rate. Leveraging big data analytics empowers organizations to run more efficiently in terms of cost and decision making. Companies can make data-driven decisions brought to them by data analysis tools that are provided through the cloud.

BigQuery from Google Cloud has many powerful features that allow users to view their data in real-time, providing continual up-to-date information to help guide business decisions. Big Query is a serverless NoOps (no operations) platform that separates compute and storage, meaning that better autoscaling is offered as they can be independently scaled as required. BigQuery’s Machine Learning and Business Intelligence Engine analysis of various data models are quite powerful. It integrates seamlessly with the Google Cloud AI platform and other tools like Data Studio.

Cloud service providers like Google Cloud Platform (GCP) use shared computing to process large datasets extremely quickly. Also known as cluster computing, Google uses hundreds of computers interconnected together for quick data analysis and completing complex computing tasks. Businesses like yours can also make use of similar services like cloud service providers to improve insights and decision-making.

(Sources: SAS, Google Cloud, Hostingtribunal)

1. How VR could bring transhumanism to the masses

2. How Augmented Reality (AR) is Reshaping the Food Service Industry

3. ExpiCulture — Developing an Original World-Traveling VR Experience

4. Enterprise AR: 7 real-world use cases for 2021

Automating mundane and repetitive tasks is and should be the top priority for businesses in this age. Even automating the simplest tasks, most business environments can free up to 30% time for employees — allowing them to focus on more important matters.

Cloud service providers have made it extremely easy for businesses of all sizes to dabble with business process automation. For instance, at the most fundamental level, businesses can automate how they receive and sort documents through document management, to automating entire workflows including delivery pipelines and testing updates in a controlled cloud environment. Tools such as Google’s Document Understanding AI can actually help you ensure your data is accurate and compliant. This is especially helpful in highly regulated industries where accuracy and precision are crucial to operations. It is also quick and easy to request more compute if needed for deep learning and complex ML training by requesting GPUs or using a managed service like Kubeflow.

Another emerging technology that is now accessible to small to medium enterprises is machine learning. Put simply, machine learning refers to training computer algorithms to interpret and interact with data without human interference. With increasing accuracy, MI (a subset of AI) is becoming incredibly valuable to businesses as it has virtually unlimited use cases.

You can read more about how cloud solutions using AI and ML can help save time, cut costs, and improve rates of human error.

(Sources: Google Cloud, Interactions)

Although lesser-known among legacy businesses, the Internet of Things is one of the fastest-growing industries in the world and was valued at $190 billion in 2018. Alexa and Google Home are two of the most popular examples of IoT devices of which you’re most likely very familiar. Apart from that, smart TVs, smart refrigerators, smart LEDs, security systems, thermostats, and even cars (think Tesla) that operate over WiFi are all a part of the internet of things.

Think of IoT devices as part of a much larger network all of which have a backbone in the cloud. Aside from pure convenience, IoT can be seen making significant breakthroughs in other spaces such as health tech. Fitbit, for example, has partnered with Google to transform how their product integrates between fitness and the cloud. The device uses Google’s Cloud Healthcare API. The API is a service that “helps facilitate the exchange of data among healthcare applications and services that run on Google’s Cloud.” Even more interesting is that the API also integrates analytics tools like BigQuery, AI tools like AI Platform, and data processing tools like Dataflow.

Similar tools and APIs are available for businesses in different industries so they too can help connect their device to an online network and introduce security patches, fix bugs, add features, and more.

(Sources: internetofbusiness, Google Healthcare API)

Though it has become significantly more popular in the last few years, augmented and virtual reality are not new technologies. Leftronic reports that the number of augmented reality users will reach 3.5 billion by 2023. Furthermore, they estimate that the AR and VR device market will hit $198 billion by 2025. In fact, large institutions like Boeing and NASA have been developing their own AR and VR technologies for training purposes for quite some time now. However, thanks to cloud proliferation, technologies like virtual reality are finally becoming accessible and more importantly, affordable for the average business to experiment with.

So how does it work?

When applications superimpose a CG image into the real world, they create an augmented reality experienced. Augmented reality places computer-generated objects in the human world, whereas virtual reality places you into a computer-generated world. Businesses can use this technology in a number of ways including giving consumers a virtual reality tour of their product or use it for training in a safe environment.

It’s also quite easy to get started with. Google’s Cloud Anchor allows developers to create experiences within their app for users to add virtual objects into an augmented reality environment. Thanks to Google’s ARCore Cloud Anchor service, experiences are allowed to be hosted and shared between users. Virtual Reality allows you to be transported to distant places and immerse yourself in foreign environments. Devices such as the Oculus Rift or Quest and the HTC Vive provide outstanding experiences that can run independently of a computer. When used at its capacity, Virtual Reality can be transformative for gaming, education, and immersive experiences.

These emerging technologies unlock a completely new frontier that businesses can compete in without exorbitant investments or technical knowledge. With all the right tools already available at their disposal, most businesses only need a helping hand to get started. If your organization is considering using the cloud to leverage an emerging technology but are unsure about the intricacies, reach out to D3V and set up a free strategic consultation with our certified cloud experts. Our team can help determine the best set of options for your company based on your business needs and aspirations.

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Source: https://arvrjourney.com/emerging-technologies-achievable-through-the-cloud-4-practical-examples-3e2b72d5e349?source=rss—-d01820283d6d—4

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Optimal Dynamics nabs $22M for AI-powered freight logistics

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Join Transform 2021 this July 12-16. Register for the AI event of the year.


Optimal Dynamics, a New York-based startup applying AI to shipping logistics, today announced that it closed a $18.4 million round led by Bessemer Venture Partners. Optimal Dynamics says that the funds will be used to more than triple its 25-person team and support engineering efforts, as well as bolster sales and marketing departments.

Last-mile delivery logistics tends to be the most expensive and time-consuming part of the shipping process. According to one estimate, last-mile accounts for 53% of total shipping costs and 41% of total supply chain costs. With the rise of ecommerce in the U.S., retail providers are increasingly focusing on fulfilment and distribution at the lowest cost. Particularly in the construction industry, the pandemic continues to disrupt wholesalers — a 2020 Statista survey found that 73% of buyers and users of freight transportation and logistics services experienced an impact on their operations.

Founded in 2016, Optimal Dynamics offers a platform that taps AI to generate shipment plans likely to be profitable — and on time. The fruit of nearly 40 years of R&D at Princeton, the company’s product generates simulations for freight transportation, enabling logistics companies to answer questions about what equipment they should buy, how many drivers they need, daily dispatching, load acceptance, and more.

Simulating logistics

Roughly 80% of all cargo in the U.S. is transported by the 7.1 million people who drive flatbed trailers, dry vans, and other heavy lifters for the country’s 1.3 million trucking companies. The trucking industry generates $726 billion in revenue annually and is forecast to grow 75% by 2026. Even before the pandemic, last-mile delivery was fast becoming the most profitable part of the supply chain, with research firm Capgemini pegging its share of the pie at 41%.

Optimal Dynamics’ platform can perform strategic, tactical, and real-time freight planning, forecasting shipment events as far as two weeks in advance. CEO Daniel Powell — who cofounded the company with his father, Warren Princeton, a professor of operations research and financial engineering — says that the underlying technology was deployed, tested, and iterated with trucking companies, railroads, and energy companies, along with projects in health, ecommerce, finance, and materials science.

“Use of something called ‘high-dimensional AI’ allows us to take in exponentially greater detail while planning under uncertainty. We also leverage clever methods that allow us to deploy robust AI systems even when we have very little training data, a common issue in the logistics industry,” Powell told VentureBeat via email. “The results are … a dramatic increase in companies’ abilities to plan into the future.”

The global logistics market was worth $10.32 billion in 2017 and is estimated to grow to $12.68 billion USD by 2023, according to Research and Markets. Optimal Dynamics competes with Uber, which offers a logistics service called Uber Freight. San Francisco-based startup KeepTruckin recently secured $149 million to further develop its shipment marketplace. Next Trucking closed a $97 million investment. And Convoy raised $400 million at a $2.75 billion valuation to make freight trucking more efficient.

But 25-employee Optimal Dynamics investor Mike Droesch, a partner at BVP, says that demand remains strong for the company’s products. “Logistics operators need to consider a staggering number of variables, making this an ideal application for a software-as-a-service product that can help operators make more informed decisions by leveraging Optimal Dynamics industry leading technology. We were really impressed with the combination of their deep technology and the commercial impact that Optimal Dynamics is already delivering to their customers,” he said in a statement.

With the latest funding round, a series A, Optimal Dynamics has raised over $22 million to date. Beyond Bessemer, Fusion Fund, The Westly Group, TenOneTen Ventures, Embark Ventures, FitzGate Ventures, and John Larkin and John Hess also contributed .

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Source: https://venturebeat.com/2021/05/13/optimal-dynamics-nabs-22m-for-ai-powered-freight-logistics/

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Code-scanning platform BluBracket nabs $12M for enterprise security

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Join Transform 2021 this July 12-16. Register for the AI event of the year.


Code security startup BluBracket today announced it has raised $12 million in a series A round led by Evolution Equity Partners. The capital will be used to further develop BluBracket’s products and grow its sales team.

Detecting exploits in source code can be a pain point for enterprises, especially with the onset of containerization, infrastructure as code, and microservices. According to a recent Flexera report, the number of vulnerabilities remotely exploitable in apps reached more than 13,300 from 249 vendors in 2020. In 2019, Barracuda Networks found that 13% of security pros hadn’t patched their web apps over the past 12 months. And in a 2020 survey from Edgescan, organizations said it took them an average of just over 50 days to address critical vulnerabilities in internet-facing apps.

BluBracket, which was founded in 2019 and is headquartered in Palo Alto, California, scans codebases for secrets and blocks future commits from introducing new risks. The platform can monitor real-time risk scores across codebases, git configurations, infrastructure as code, code copies, and code access and resolve issues, detecting passwords and over 50 different types of tokens, keys, and IDs.

Code-scanning automation

Coralogix estimates that developers create 70 bugs per 1,000 lines of code and that fixing a bug takes 30 times longer than writing a line of code. In the U.S., companies spend $113 billion annually on identifying and fixing product defects.

BluBracket attempts to prevent this by proactively monitoring public repositories with the highest risk factors, generating reports for dev teams. It prioritizes commits based on their risk scores, minimizing duplicates using a tracking hash for every secret. A rules engine reduces false positives and scans for regular expressions, as well as sensitive words. And BluBracket sanitizes commit history both locally and remotely, supporting the exporting of reports via download or email.

BluBracket offers a free product in its Community Edition. Both it and the company’s paid products, Teams and Enterprise, work with GitHub, BitBucket, and Gitlab and offer CI/CD integration with Jenkins, GitHub Actions, and Azure Pipelines.

BluBracket

Above: The Community Edition of BluBracket’s software.

Image Credit: BluBracket

“Since our introduction early last year, the industry has seen through Solar Winds how big of an attack surface code is. Hackers are exploiting credentials and secrets in code, and valuable code is available in the public domain for virtually every company we engage with,” CEO Prakash Linga, who cofounded BluBracket with Ajay Arora, told VentureBeat via email.

BluBracket competes on some fronts with Sourcegraph, a “universal code search” platform that enables developer teams to manage and glean insights from their codebase. It has another rival in Amazon’s CodeGuru, an AI-powered developer tool that provides recommendations for improving code quality. There’s also cloud monitoring platform Datadog, codebase coverage tester Codecov, and feature-piloting solution LaunchDarkly, to name a few.

But BluBracket, which has about 30 employees, says demand for its code security solutions has increased “dramatically” since 2020. Its security products are being used in “dozens” of companies with “thousands” of users, according to Linga.

“DevSecOps and AppSec teams are scrambling, as we all know, to address this growing threat. By enabling their developers to keep these secrets out of code in the first place, our solutions make everyone’s life easier,” Linga continued. “We are excited to work with Evolution on this next stage of our company’s growth.”

Unusual Ventures, Point72 Ventures, SignalFire, and Firebolt Ventures also participated in BluBracket’s latest funding round. The startup had previously raised $6.5 million in a seed round led by Unusual Ventures.

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Source: https://venturebeat.com/2021/05/13/code-scanning-platform-blubracket-nabs-12m-for-enterprise-security/

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Data governance and security startup Cyral raises $26M

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Data security and governance startup Cyral today announced it has raised $26 million, bringing its total to date to $41.1 million. The company plans to put the funds toward expanding its platform and global workforce.

Managing and securing data remains a challenge for enterprises. Just 29% of IT executives give their employees an “A” grade for following procedures to keep files and documents secure, according to Egnyte’s most recent survey. A separate report from KPMG found only 35% of C-suite leaders highly trust their organization’s use of data and analytics, with 92% saying they were concerned about the reputational risk of machine-assisted decisions.

Redwood City, California-based Cyral, which was founded in 2018 by Manav Mital and Srini Vadlamani, uses stateless interception technology to deliver enterprise data governance across platforms, including Amazon S3, Snowflake, Kafka, MongoDB, and Oracle. Cyral monitors activity across popular databases, pipelines, and data warehouses — whether on-premises, hosted, or software-as-service-based. And it traces data flows and requests, sending output logs, traces, and metrics to third-party infrastructure and management dashboards.

Cyral can prevent unauthorized access from users, apps, and tools and provide dynamic attribute-based access control, as well as ephemeral access with “just-enough” privileges. The platform supports both alerting and blocking of disallowed accesses and continuously monitors privileges across clouds, tracking and enforcing just-in-time and just-enough privileges for all users and apps.

Identifying roles and anomalies

Beyond this, Cyral can identify users behind shared roles and service accounts to tag all activity with the actual user identity, enabling policies to be specified against them. And it can perform baselining and anomaly detection, analyzing aggregated activity across data endpoints and generating policies for normal activity, which can be set to alert or block anomalous access.

“Cyral is built on a high-performance stateless interception technology that monitors all data endpoint activity in real time and enables unified visibility, identity federation, and granular access controls. [The platform] automates workflows and enables collaboration between DevOps and Security teams to automate assurance and prevent data leakage,” the spokesperson said.

Cyral

Existing investors, including Redpoint, Costanoa Ventures, A.Capital, and strategic investor Silicon Valley CISO Investments, participated in Cyral’s latest funding round. Since launching in Q2 2020, Cyral — which has 40 employees and occupies a market estimated to be worth $5.7 billion by 2025, according to Markets and Markets — says it has nearly doubled the size of its team and close to quadrupled its valuation.

“This is an emerging market with no entrenched solutions … We’re now working with customers across a variety of industries — finance, health care, insurance, supply chain, technology, and more. They include some of the world’s largest organizations with complex environments and some of the fastest-growing tech companies,” the spokesperson said. “With Cyral, our company was built during the pandemic. We have grown the majority of our company during this time, and it has allowed us to start our company with a remote-first business model.”

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VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

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Source: https://venturebeat.com/2021/05/13/data-governance-and-security-startup-cyral-raises-26m/

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