Daily Archives: January 9, 2022

Totaling 4,233 universe simulations, millions of galaxies and 350 terabytes of data, a new release from the CAMELS project is a treasure trove for cosmologists. CAMELS which stands for Cosmology and Astrophysics with MachinE Learning Simulations aims to use those simulations to train artificial intelligence models to decipher the universes properties.

Scientists are already using the data, which is free to download, to power new research, says project co-leader Francisco Villaescusa-Navarro, a research scientist with the Simons Foundations CMB (Cosmic Microwave Background) Analysis and Simulation group.

Villaescusa-Navarro leads the project with associate research scientists at the Flatiron Institutes Center for Computational Astrophysics (CCA) Shy Genel and Daniel Angls-Alczar, who is also a UConn Associate Professor of Physics.

Machine learning is revolutionizing many areas of science, but it requires a huge amount of data to exploit, says Angls-Alczar. The CAMELS public data release, with thousands of simulated universes covering a broad range of plausible physics, will provide the galaxy formation and cosmology communities with a unique opportunity to explore the potential of new machine-learning algorithms to solve a variety of problems.

The CAMELS team generated the simulations using code taken from the IllustrisTNG and Simba projects. The CAMELS team includes members of both projects, with Genel a part of the core team of IllustrisTNG and Angls-Alczar on the team that developed Simba.

About half of the simulations combine the physics of the cosmos with the smaller-scale physics essential for galaxy formation. Each simulation is run with slightly different assumptions about the universe for instance, regarding how much of the universe is invisible dark matter versus the dark energy pulling the cosmos apart, or how much energy supermassive black holes inject into the space between galaxies.

The researchers designed the simulations to feed machine-learning models, which will then be able to extract information from observations of the real, observable universe. With 4,233 universe simulations, CAMELS is the largest ever suite of detailed cosmological simulations designed to train machine-learning algorithms.

The data will enable new discoveries and connect cosmology with astrophysics through machine learning, says Villaescusa-Navarro. There has never been anything similar to this, with this many universe simulations.

The CAMELS dataset is already powering research projects, with a wide range of papers utilizing the data in the works.

Pablo Villanueva-Domingo of the University of Valencia in Spain led one such paper. He and his colleagues leveraged the CAMELS simulations to train an artificial intelligence model to measure the mass of our Milky Way galaxy plus its surrounding dark matter halo, and the nearby Andromeda galaxy and its halo. The measurements the first ever done using AI put our galaxys heft at 1 trillion to 2.6 trillion times the suns mass. Those estimates are roughly in line with those made by other methods, demonstrating the AI approachs accuracy.

Meanwhile, Villaescusa-Navarro headed an effort to use the CAMELS data to estimate the value of two parameters that govern the fundamental properties of the universe: what fraction of the universe is matter, and how evenly mass is distributed throughout the cosmos. First, he and his colleagues used CAMELS to generate maps such as the distribution of dark matter, gas and different properties of stars. Then, using the maps, they trained a machine-learning tool called a neural network to predict the values of the two parameters.

This is the same kind of algorithm used to tell the difference between a cat and a dog from the pixels of an image, says Genel, who co-authored the paper. The human eye cant determine how much dark matter there is in a simulation, but a neural network can do that.

The results showed the promise of leveraging CAMELS to precisely estimate such parameters in the future based on new observations of the universe, says Villaescusa-Navarro.

Its exciting to see what other new discoveries this will enable, he says.

Read more here:

The Largest Suite of Cosmic Simulations for AI Training Is Now Free to Download; Already Spurring Discoveries - UConn Today - UConn Today

In this special guest feature, Carlos Melendez, COO, Wovenware, discusses best practices for The Third Mile in AI Development the huge market subsector in data labeling companies, as they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. The article addresses this trend and outlines how it is not really a commodity market, but can comprise different strategies for successful outcomes. Wovenware is a Puerto Rico-based design-driven company that delivers customized AI and other digital transformation solutions that create measurable value for government and private business customers across the U.S.

The growth of AI has spawned a huge market subsector and increasing interest among investors in data labeling. In the past year, companies specializing in data labeling have secured millions of dollars in funding and they continue to come up with new ways to monetize this often-considered tedious aspect of AI development. Yet, what can be viewed as the third mile in AI development, data labeling, is also perhaps the most crucial one to effective AI solutions.

In very general terms, AI development can be broken down into four key phases:

Data Labeling is Not Created Equal

The third mile in AI development is where the action begins. Massive amounts of data is needed to train and refine the AI model our experience has showed us that a minimum of 10,000 labeled data points are needed and it must be in a structured format to test and validate it, and train the model to identify and understand recurring patterns. The labels can be in the form of boxes around objects, tagging items visually or with text labels in images or in a text-based database that accompanies the original data.

Once trained with annotated data, the algorithm can begin to recognize the same patterns in new unstructured data. To get the raw data into the shape it needs to be in, it is cleaned (errors fixed and duplicate information deleted); and labeled with its proper identification.

Much of data labeling is a manual and laborious process. It involves groups of people who must label images as cars, or more specifically, white cars, or whatever the specifics might be, so that the algorithm can go out and find them. As with many things that can take time, data labeling firms are looking for a quick fix to this process. Theyre turning to automated systems to tag and identify data-sets. While automation can expedite part of the process, it needs to be kept in check to ensure that AI solutions making critical decisions are not faulty. Consider the ramifications of an algorithm trained to identify children at the cross-walk of a busy intersection not recognizing those of a certain height because the data set used to train the algorithm didnt have data about these children.

Since data is the lifeblood to effective AI, its no wonder that investors are seeing huge growth opportunities for the market. Effective data labeling firms are in hot demand as companies look to find a faster path to AI transformation. To aggregate and label data not only takes months of time, but effective algorithms get better over time, so its a constant process. But when selecting a data labeling firm that automates the process, buyers must beware. Data labeling is not yet a commodity market, and there are many ways to approach it. Consider the following when determining how to accomplish your critical data labeling process:

As data continues to become the oil that fuels effective AI, its critical that getting it into shape for algorithm training is not treated as a commodity, but given the attention it deserves. Data labeling can never be a one-size-fits all task, but requires the expertise, customization, collaboration and strategic approach that results in smarter solutions.

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The Secret Weapon Behind Quality AI: Effective Data Labeling - insideBIGDATA

New York City will be the latest jurisdiction to regulate the use of artificial intelligence in the workplace. The City has just passed alawrequiring employers to perform bias audits not more than one year before using automated employment decision tools in connection with hiring and promotion. The law goes into effect on January 1, 2023.

The law defines an automated employment decision tool as any computational process, derived from machine learning, statistical modeling, data analytics, or artificial intelligence that is used to assist an employer in making a decision on an individual based on the score or recommendation calculated by the AI. The law also defines an acceptable audit as an impartial evaluation by an independent auditor that includes the testing of the tool to assess its disparate impact on persons of any federal EEO-1 component category.

The law applies only to decisions related to a prospective candidates hire or promotion. It is unclear whether passive recruitment tools, such as ZipRecruiters or LinkedIns suggested jobs, are covered under the law.

The law prohibits an employer from using an automated decision tool to screen for hiring or promotion unless (1) the tool was subject to an independent bias audit no more than one year before its use, and (2) a summary of the audit results, as well as the distribution date of the tool to which the audit applied, has been made publicly available on the employers website.

An employer who uses an automated employment decision tool for hiring or promotions must notify each candidate who resides in New York City of the following at least 10 business days before the decision tool is used:

An employer may be subject to a $500 fine for a first violation, and up to $1,500 per offense for repeat violations.

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New York City To Regulate Use Of AI In Hiring And Promotion - JD Supra

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Despite all the stories about big companies bailing out of CES 2022 amidst the latest surge in COVID-19 cases, the consumer electronics show in Las Vegas is still the place to be for robots, autonomous vehicles, smart gadgets, and their inventors an opportunity to take stock of whats required to build practical machine intelligence into a consumer product.

A sampling of the innovations VentureBeat heard about in advance briefings:

OrCam and Sonatus are among the companies no longer planning to travel to Las Vegas or announce products at CES, and its possible some of the other vendors VentureBeat interviewed in advance of the event will also be no-shows. Big names like Microsoft, Google, Intel, Amazon, and T-Mobile backed out in recent weeks. Augmented reality, virtual reality, and the metaverse will be topics of discussion that will have to proceed without Meta (the company formerly known as Facebook). Automotive tech will be a big theme of the event, but General Motors, BMW, and Mercedes-Benz decided not to make the drive (GMs all-digital presence is still supposed to include a video keynote from CEO Mary Barra on Wednesday). On the other hand, some like Perrone Robotics had already shipped vehicles and a test track set up, indicating their commitment

Still, the Consumer Technology Association, which sponsors the event, determined that the show must go on. Despite the big company drama, exhibiting and networking at CES remains an opportunity for thousands of smaller companies, entrepreneurs, and innovators who have made investments in building their exhibits and are counting on CES for their business, inspiration, and future, CTA CEO Gary Shapiro wrote in an op-ed for the Las Vegas Review-Journal.

Although CES exhibitors pitched VentureBeat on everything, including sexual wellness products, VB sought out briefings related to the uses of data and AI that readers can learn from. In particular, consumer device makers tend to want to take advantage of the cloud for software and data updates without being dependent on the cloud with the smarts of the smart device resident on the device itself. So theyre worth studying as pioneers in edge computing.

Much as enterprise tech may have to learn from the consumer technology world, the opposite is also true. For example, Jeffrey Chou, CEO, and founder at Sonatus, said that one way for computerized systems in automobiles to improve is by learning from the model of the enterprise datacenter. In other words, siloed software running on lots of little computers (electronics control units or ECUs in automotive jargon) needs to be simplified and tied together with middleware, which Sonatus provides. That unification has to happen while preserving real-time performance for vehicle safety systems and addressing new concerns like vehicle cybersecurity.

Theres no short-term fix. The long-term fix is to do software right, Chou said.

Apex.ai has a somewhat similar story about improving the software foundations for autonomous driving and other smart vehicle tech, which, in its case, involved a series of enhancements and optimizations for ROS, the open source Robotics Operating System (considered as more of a programming framework). There isnt a company in automotive that doesnt use ROS for prototyping, CEO Jan Becker said, adding that his companys products help with turning successful prototypes into production products.

Auto processor consolidation is paving the way for more sophisticated software, according to Becker. The trend that we see now that Tesla introduced that a couple of years ago and everybody else is introducing that in the next three years is having those more powerful central more central computers, for infotainment for driver systems, potentially for a gateway potentially then also for vehicle safety functions like ESP and ABS anti-lock braking, he said.

At the same time, Becker noted its been years since self-driving car enthusiasts began predicting that robot taxis would be roaming the streets any day now. The truth is, the problem is really, really hard. What our industry has begun to understand better in the last couple of years is which applications are commercially reasonable, he said. For example, long before fully autonomous driving becomes available and affordable in passenger cars that need to be able to go anywhere, it can be practical for commercial vehicles navigating well-known and profitable routes.

Perrone Robotics is applying that approach to autonomous commercial vehicles that can navigate freight yards or circulate through urban or campus bus routes. Although it has partnerships with electric vehicle manufacturers like GreenPower Motor Company, Perrone also sells a retrofit kit that works with the pedals, transmission, and steering wheel of conventional vehicles to render them autonomous for low-speed operation over a known route. Theres going to be a very long path to autonomy, CEO Paul Perrone said. My focus is on, heres what you can do now.

In fact, hes a bit of a contrarian: rather than chasing bleeding edge AI applications, he leans toward deterministic software, the logic of which is easier to certify as safe for the operation of a vehicle. You cant just train it with some probabilistic learning system that will probably get it to its destination, he said.

Meanwhile, Ottobot is capitalizing on automotive innovations, such as lidar rangefinders, for use in its delivery robots, which began navigating the concourses at Cleveland International Airport in December 2020. Ottobot also recently announced a partnership with restaurant tech company Presto for curbside and parking lot delivery of food orders with less labor required.

While taking advantage of autonomous vehicle tech, Ottobot has innovated in other directions to allow its bots to go where many other delivery bots cant because, like cars, they rely on GPS navigation. To work within an airport, for example, Ottobot creates a software simulation of the floor plan. We create a digital twin and then navigate within that, CEO Ritukar Vijay said. The arrangement of sensors also needs to be different to navigate through crowds and see glass barriers.

While automobiles and robots are capturing increasing attention at CES, the show is best known for showcasing smaller gadgets.

When device manufacturers talk about embedding AI, typically that doesnt mean expecting big AI models to run on the device. A gadget may or not embed some modest machine learning capabilities, but typically the training of the model occurs in the cloud while what gets installed on the device is a much more compact inferencing model for interpreting and acting on sensor data. Even with that simplification, optimizing software to run within the size, power, and processing constraints of a given device can be a steep challenge.

For example, OrCams assistive technologies for the blind and visually impaired are in form factors the size of magic or a clip-on camera for a pair of glasses. So while the vice president of R&D, Oren Tadmor, respects the AI processors from companies like Nvidia, theyre not computers that we can ever dream of fitting into our devices, he said. Instead, the company winds up working with specialized chipsets for vision processing.

At the same time, Tamdor says, OrCam has been able to take advantage of big advances in the state of the art for deep learning as it applies to computer vision, which has made problems like face recognition much easier to solve. OrCam is an Israeli company whose cofounders Amnon Shashua and Ziv Aviram also founded Mobileye, a leader in computer vision for collision avoidance and self-driving car technology.

For computer vision, we can do anything, or almost anything, that a person can, Tamdor said. And its just a matter of finding, what are the features that our users can use?

Hardware-specific optimizations may sometimes be necessary, but that isnt stopping software tool makers from trying to promote a more standardized approach to device programmability. I think one of the exciting things here is the interplay between these two types of optimizations, said Davis Sawyer, cofounder, and chief product officer at Deeplite. Where the two meet up, thats where we see 400 to 500% increases over one or the other on their own.

At CES, Deeplite announced the Deeplite Runtime software development kit for creating efficient deep learning models based on Pytorch, particularly for computer vision applications. Where the companys previous Deeplite Neutrino product worked with GPUs and other types of processors, the new Deeplite Runtime is specifically for compiling applications to run on ARM processors, which are among the most popular on smart devices.

Given the prevalence of things like ARM CPUs, the familiarity with developers, and also the low power profile for battery-powered devices, thats where I think there [are] a lot of opportunit[ies] created, Sawyer said.

Fluent.ai, a device software player focused on voice command systems, aims to be as hardware agnostic as possible, CEO Probal Lala said. However, some hardware partners prove to be easier to work with than others. At CES, Fluent.ai is announcing a partnership with audio tech specialist Knowles, and theyll be jointly demoing voice-controlled earbuds.

For Knowles, the attraction is that Fluent.ais software operates efficiently, without being dependent on cloud services or the power and network capacity required to access them. They offer a large command set, the largest Ive ever seen thats completely offline, said Raj Senguttuvan, director of strategic marketing for audio and sensing solutions at Knowles. That opens up a wide range of entertainment and business application opportunities, he said.

Fluents key optimization is that it shortcuts the common voice application pattern of translating voice to text and then doing further processing on the text. Instead, the software does its pattern matching by working with the audio data directly.

The increasing variety of base technologies, including AI capabilities, ought to get you thinking about business opportunities.

Im a big believer that the technology doesnt mean anything to the end-user without a little imagination as to how it is going to improve their lives, Richard Browning, chief sales and marketing officer at NextBase, a maker of car dash cams.

For NextBase, that means re-imagining how the dashcam can move beyond being just a mobile security camera you can use to share crash footage with your insurance company. Just the challenge of producing good video under conditions that can range from glaring daylight to rainy daylight is steep enough and requires some AI image processing power, Browning says. The NextBase IQ product being announced at the show and readied to ship in September takes that capability further to also provide driver assistance (recognizing when other drivers are behaving badly) and spatial awareness (anticipating accidents so they can be recorded more completely).

The addition of an inside-facing camera allows the system to detect and warn drowsy or distracted drivers, but it also allows for capturing video evidence that wouldnt be captured by a front-facing camera such as road rage or aggressive road stop incidents. With a voice command, the device can be toggled into witness mode to record exactly how you behave when a cop walks up to the vehicle and asks for your license and insurance.

When in witness mode, whether triggered by voice command or sensors detecting that an accident has occurred, the video is transmitted to a cloud account for later review. Previous versions of NextBases products required the driver to manually download video data to their phone.

With these and other features, the NextBase IQ has almost outgrown the dashcam category as it was previously defined except the company cant figure out what else to call it, other than a smart dashcam, Browning says. People understand what smart is these days theyve got [a] smart home, smart security, smart health its a product that is connected and intelligent.

That will be a large part of what CES 2022 is about.

Excerpt from:

CES 2022: AI is driving innovation in smart tech - VentureBeat

In an increasingly digitized world, the artificial intelligence (AI) boom is only getting started. But could the risks of artificial intelligence outweigh the potential benefits these technologies might lend to society in the years ahead? In this segment of Backstage Pass, recorded on Dec. 14, Fool contributors Asit Sharma, Rachel Warren, and Demitri Kalogeropoulos discuss.

Asit Sharma: We had two questions that we were going to debate. Well, I'll have to choose one. Let me do the virtual coin toss really quick here. We're going with B, artificial intelligence has the potential to be more harmful than beneficial to society. Rachel Warren, agree or disagree?

Rachel Warren: Gosh. [laughs] This may seem like a bit of a cop out, but I don't really feel like it's a yes or no answer. I think that technology in and of itself is an amoral construct.

I think it can be used for good, I think it can be used for bad. I think you think of all the benefits that artificial intelligence are providing to how the way that companies run, how software runs, how accompanies monetize their products, how you think of companies that are using AI to power more democratized insurance algorithms, for example.

I think artificial intelligence is going to continue to provide both benefits as well as detriment to society. You think of all the positives of artificial intelligence.

But then you look at how it can be used, for example, by law enforcement agencies to find criminals. That can be a really great thing. It's empowering these law enforcement agencies to have a more efficient way of tracking down criminals, keeping people safer.

But at the same time, how fair are these algorithms? Are these algorithms judging people equally or are they including certain things that single out certain individuals that may or may not be fair in the long run and may, in fact, result in less justice?

That's just an example. For me, I think personally, artificial intelligence can do great things, I think it can be used as well for very harmful things, and I think it ultimately is something that people need to view with caution and not just automatically view it as good or evil. That's just my quick take. [laughs]

Sharma: Love it. Very well said in a short amount of time. Demitri, reaction to what Rachel said.

Demitri Kalogeropoulos: Asit, if it scares Elon Musk, it should scare me. [laughs].

Sharma: Great.

Warren: True. [laughs]

Kalogeropoulos: I would just say, yeah, I agree with a lot of what Rachel said. I think it's interesting. I mean, it clearly has the potential to be harmful in ways. I was just thinking about just in the last couple of weeks where we're hearing all these changes in Instagram and Facebook. Rachel mentioned the way these algorithms are working. We're clearly finding. Remember maybe a couple of years ago that there was an issue with YouTube that was driving users.

The algorithm is there to maximize engagement, for example, in all these cases. It's getting smarter at doing that. It's got all this content that can do that. It knows it's using the millions and billions of us as little testing machines to kind of tweak that. But they've had to make adjustments to these because they were harmful in a lot of ways just without being programmed that way.

If you did a chart on Facebook in terms of if you ranked engagement level up to the level of prohibited content, engagement rises as you get closer to prohibited, and goes to infinity if you got the prohibited, that's just human nature, I guess. Bad news travels faster than good news and conspiracy theories travel a lot faster than the truth. These are all just weaknesses, I guess, you could say in human psychology that algorithms can be ruthless at cashing in on or if you want to say, or monetizing.

That's clearly something I think we need to watch out for. Most cases, thankfully, it seems like we're finding these in time, but I think we have to be really careful that we're watching out because sometimes, who knows which ones we're not finding and years later, we find out that we were being manipulated in these ways.

Sharma: I love both of those comments. I mean, personally for me, I feel that this is a space that has enormous potential to do good. But without some type of oversight or regulation, we open the doors to really deleterious effects. Palantir is an example of a company that I won't invest in because I don't think that they really care that much about the detriment they can do.

Rachel mentioned the inadvertent. Well, I mean, this may have been reading between the lines, but this has been shown with some of their technologies, inadvertent racial profiling that comes from the tech they're using to help law enforcement.

Warren: Yes. Like mass surveillance, yes.

Sharma: It's interesting, governments have been a little bit slower to think about the regulation of AI. We can vote with our pocketbooks, we can buy companies that are using AI to good effect, and we can be a little bit of activist shareholders as a society to point to how we want companies to behave to the level of seriousness that we want them to take a look at what their algorithms are arriving at. I'll stop here so I can give the two of you the last word. We've got about a minute left.

Warren: I agree with what you're saying. I think that this is also something to remember. As investors, we look at all of the investment opportunities within the artificial intelligence space. These opportunities are only going to grow. I think if there's aspects of this technology that concern you or bother, it's OK to say, "This looks like a really great business, but I personally, I don't feel comfortable, ethically speaking, to invest in it."

That's OK. There are no shortage of fantastic investment opportunities available within the broader technology space. I think it's definitely something where you look at this area, there are so many potential benefits, I agree with what you were saying, there's so much potential here as well. For businesses, for companies, there's obviously a lot of profits to be made, but I think it's something to be wary of as well.

What Demitri was saying about Facebook algorithms. My timeline might look very, very different from my good friend's timeline based on I click on a couple of articles and then my entire feed changes in a certain direction, and then you go deeper down the rabbit hole.

I think just the nature of how these algorithms work, it makes it extremely difficult to regulate. With that knowledge, I think it's important to approach this area and investing in it with just a bit of caution.

Sharma: Demitri, you get the last word and then we'll sign off for the night. [laughs]

Kalogeropoulos: I don't have much to add to that for sure.

Warren: [laughs]

Sharma: I know. Rachel is on fire tonight, everything is sounding so persuasive and succinct and eloquent.

Kalogeropoulos: You just nailed it. [laughs] I would just say, yeah. I mean, you can look for companies that maybe don't have looked for incentives there. I like a company, for example, like Netflix.

If you're just evaluating something like that, if you're comparing a Facebook to a Netflix, Netflix made the decision not to advertise on their service, for example, because they don't want to get into a lot of these sticky subjects, whereas Facebook has to monetize.

It's a free service so they have to find a way to monetize it in different ways. That's just another thing to think about when you're comparing these companies.

Sharma: That's a great point, think about the business model. Sometimes, that causes behavior that you don't want to see.

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Could Artificial Intelligence Do More Harm Than Good to Society? - The Motley Fool

Ransomware has become a multibillion-dollar industry, and roughly 15% of its business goes through a single group called Wizard Spider. This group who are thought to work closely with the Russian government and remain under investigation by the FBI and Interpol have used the Conti ransomware strain in more than 400 known attacks. While the media refers to the group as the "Conti Ransomware Gang," the group doesnt view itself as a gang. The group would rather be viewed as a business.

A Booming BusinessAs they become larger and more profitable, criminal groups such as Wizard Spider often mimic legitimate business practices. Victim organizations are rebranded as "customers," extortion attempts become "negotiations," and criminal peers are called "affiliates." Their dedicated site on the Dark Web even has a collection of "press releases."

The groups "business model" involves training independent affiliates in how to deploy the ransomware and then taking a 30% cut of the profits themselves. However, because exact profits are revealed to Wizard Spider and not their affiliates, this percentage is normally much higher.

One underpaid affiliate caught wind of the gangs practices in August 2021 and began leaking their resources, declaring in protest, they recruit suckers and divide the money among themselves.

Meanwhile, the US government has taken measures to obstruct groups like Wizard Spider; beginning this year it will impose sanctions on cryptocurrency exchanges facilitating ransomware transactions.

However, these setbacks havent perturbed Wizard Spider, whose profits have continued to soar. Conventional cyber defenses have consistently failed to keep up with the groups innovations in attack techniques and so the organizations that employ them remain firmly in Wizard Spiders target market.

How Wizard Spider Gets InOne of the groups recent targets was a transportation company in the US. It took a single missed Microsoft patch and resulting ProxyShell vulnerabilities to leave the company open to attack. This is a relatively new exploit for Wizard Spider, who previously relied on phishing attacks and firewall exploits.

Two weeks after the initial breach, rare connections were made to an unusual endpoint in Finland using an SSL client that appeared innocuous. The endpoint was not known to threat intelligence tools at the time, meaning rules and signature-based security tools didnt know what to detect.

Going Public With Conti NewsIf you refuse to pay its ransom, Wizard Spider will not only take your most important files from you, but the group will also exfiltrate and publish them using its dedicated "Conti News" website or sell them directly to your competitors. This is double extortion ransomware, and its the Conti gangs favorite new sales tactic.

In the transportation company's case, three terabytes of company data was uploaded over four days, and then rapidly encrypted. Encryption began at almost midnight, meaning human security teams werent available to organize a response the ransomware "business" never respects business hours. The next morning, the company was met with a ransom note.

The company was able to investigate and connect the dots of the attack using Darktraces security AI tool. The security tools natural-language report brings disparate events into a cohesive attack narrative

How Ransomware Attackers Evade Cyber IntelligenceIts all too easy for threat actors to alter the infrastructure of their attacks, and in this case something as simple as a new endpoint was enough to beat threat intelligence. This is how Wizard Spider continues to thrive, and its a problem that governmental sanctions and defecting insiders are fundamentally unable to address.

Organizations need to take matters into their own hands with a new approach. By using AI that learns what normal business operations look like, anomalous behavior that inevitably arises from a ransomware attack can be identified at every stage, even when its using never-before-seen attack methods.

And in an era of fast-moving cyberattacks and threat actors deliberately striking when security teams are out of the office, AI technologieshave become essential in taking targeted action to contain threats, without interrupting normal business.

If leaks or legislation were to bring down Wizard Spider, other groups would simply rise up to fill the gap in the market. Ultimately, ransomware must be made unprofitable if its to be stopped. One way to do that is to use AI to stopransomware attacks at every stage of their attacks, weeks before human analysts can.

Read more from the original source:

Putting Ransomware Gangs Out of Business With AI - DARKReading

When it comes to training machine vision and AI-powered retail checkout systems, packaged goods and locally created food items are treated very differently.

Thats at least according to Mashgin, a maker of touchless checkout systems. Company spokesperson Toby Awalt said thats because another store on the network has likely already added that bag of chips or candy bar to their 10 thousand plus item database.

Not so when it comes to locally made food items.

CPG items, we have to do less and less because theres enough overlap, said Awalt, who gave us a walkthrough of the system at CES 2022. But for dishes, well do every time.

According to Awalt, adding a new food menu item for a restaurant doesnt take that since most cafeterias or restaurants only serve between 15 and 50 items.

You can do that relatively quickly, he said.

Still, a new packaged good has to be entered into the system now and then. Whenever that happens, the operator has to position the package in several different positions to give the system enough info to recognize the product whenever it shows up under the camera.

I actually do dice rolls with the product, said Awalt, throwing a Hagen-Dazs ice cream bar onto the tray.

According to Mashgin, the company recommends the system capture 20 to 50 total positions of a product so it can recognize the product from various angles and also distinguish between different variations within the same product line (such as two different flavors of ice cream or potato chips).

You can watch a walkthrough of the Mashgin system below.

Related

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How Do You Train AI-Powered Checkout To Recognize A Product? In Vegas (& Elsewhere), You Throw It Like Dice - The Spoon

Police departments need to leverage artificial intelligence in policing to reduce crimes

Artificial Intelligence touches almost every aspect of human lives, from mobile banking and online shopping to social media and real-time traffic maps. Around the country, police departments and courtrooms are turning to artificial intelligence algorithms to help them decide everything from where to deploy police officers to whether to release defendants on bail. Supporters believe that the technology will lead to increased objectivity, ultimately creating safer communities. Others however, say that the data fed into these algorithms is encoded with human bias, meaning the tech will simply reinforce historical disparities. In this case, according to the survey research, 52% responded saying that maybe AI can potentially help make policing fairer, whereas 40% said that yes, this advanced technology can eliminate biasness, and another 8% were in denial.

Dependability is one of the most question when it comes to maintaining law and order. Increasingly law enforcement has turned to artificial intelligence to augment their officers and agencies. In this case, according to the survey research, 34% believe that AI cops cannot be more dependable than human cops, whereas 34% said that maybe you can depend on AI cops when it comes to maintaining law enforcement, and another 31.9% does have strong feeling that AI cops can be more dependable.

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Analytics Insight Survey on Artificial Intelligence in Policing in 2022 - Analytics Insight

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How to spark productivity through technology in the digital age? Recently, XKool Technology, a leader in AI applications for the architecture industry, held a new product launch of "Plan Deep" to kick off a new round of evolution in architecture technology capabilities.

Information about XKool's newly released intelligent design products is included inthis article, as well as the upcoming DigitalFUTURES Tutorial Session to be presented by XKool on January 8, 2022, where you're invited to experience the collaboration between architecture and technology up close.

When Rem Koolhaas received the Pritzker Architecture Prize in 2000, he made the following acceptance speech: "Only wearchitects dont benefit from this redefinition marooned in our own Dead Sea of mortar. Unless we break our dependency on the real and recognized architecture as a way of thinking aboutall issues, from the most political to the most practical, liberate ourselves from eternity to speculateabout compelling and immediate new issues, such as poverty, the disappearance of nature,architecture will maybe not make the year two-thousand-fifty." [1]

Global science and technology innovation are developing rapidly, new technologies, new business models and new modes are popping up with deep integration of cutting-edge technologies into industries. In the architecture field, the problems of sloppy development, slow penetration of information technology and low efficiency have been holding back industrial upgrading."Technology" has become oneofthemostimportant issuesfor the architecture industry to regain its vitality in the present digital era.

Since its founding in 2016, XKool Technology has had a clear mission - architecture intelligence. XKool Technology was born in response to the trend of the digital era and reflected the founder's profound understanding of "Revolution of Architecture". With background of research studies at Belgrade Institute of Delft University of Technology and seven years of practical experience at OMA under Rem Koolhaas, Wanyu He has paved the way from architecture to architecture intelligence.

Wanyu He focuses on Associative Design, which emphasizes systematic logical thinking rather than relying on inspiration, and uses computer algorithms to achieve the overall design outcome. She believes that this design logic and operational decision-making is the real core of architecture. It is on the core and essential logic that XKool Technology addresses the macro vision of the future of architecture, combined with the use of the most advanced computer technology to design a systemic solution mechanism.

In 2016, the year of AlphaGo's victory over Lee Sedol, XKool Technology was officially established, starting from the issue of "how to reduce the repetitive and inefficient work of designers", hoping to create greater value for design with AI solutions.

As early as before its establishment, XKool had a clear plan for the prototype: "First of all, it must be cloud-based, which can be flexibly called and efficiently coordinated; secondly, it should realize the interaction amongst data, model and regulations, without the designer drawing and modeling one stroke at a time; Furthermore, it is very intelligent, which can update information, deposit data and iterate continuously. "

XKool Technology focuses on the vertical application of artificial intelligence and cutting-edge technology in architecture, assisting urban planning, architectural design and development decisions with AI design engines, developing the world's first artificial intelligence architectural design engine, and creating an intelligent design and management platform covering the whole cycle in the industry.

The launch focused on the intelligent development of the whole design cycle, the continuous evolution of the underlying technology of XKool, and the comprehensive upgrade of intelligent design capabilities.

XKool Design Cloud, a cloud-based one-stop intelligent design product for the architectural design, is also the core product of XKool Technology.

In the past year, XKool Design Cloud has implemented 359 user requirements, and all these functions have been integrated into XKool's special six design steps "Investigate/import data and analysis - AI-assisted generate qualified design - Intelligent edit on cloud - Schemes' real-time compliance and quality check - Multi-party collaboration of design outcomessynchronously- Export multi-format files and applied to different scales and stages. The update mainly includes three modules: MasterPlanner, BuildingCreator and ColorMaster.

Facing the difficulties in the initial stage of real estate development, such as heavy workload and many possibilities of plot concept planning, this module focuses on obtaining relatively optimal solutions for plot-scale zone planning and building layout. As the initial product of XKool, MasterPlanner has already undergone four major versions and completed nearly 100,000 effective solutions in total.

1. Informatization of land use conditions CAD upload: online reading and recognition of drawings, fast transformation into information model on cloud. Indicator setting: support multi-site indicator setting, calculate the gap in real-time.

2. Multi-dimensional analysis of the site Surrounding data visualization: view multiple types of data and analysis of the surrounding to get a comprehensive understanding of the site and assist in quick initial judgment of land value.

3. Smart building count estimationAutomatic calculation: set the parameters of building type and calculate the matching of building type to meet the target according to the site conditions.Strategic schemes output: provide a variety of estimation strategies such as maximum profit value, minimum building type, closest target housing type allocation ratio, etc.

4. Layout solution generationOverall layout: automatic mode, according to land use indicators, estimation result and generation strategy setting, a preliminary building layout solution is generated for the selected land with one click.Lightning sketch: semi-automatic mode, according to the land division scope and target building type specified by the architect, the building layout solution is generated by rapid feedback.

5. Intelligent assisted editingAssisted editing: improving the editing efficiency of the building layout by with directional distance moving, spacing ruler and snap.CAD coordination: CAD + plugins, one-click synchronization between local and cloud.

6. Real-time reviewReal-time feedback on indicators: editing schemes and real-time feedback on indicators make design adjustments more targeted.Daylighting and design codes checking: real-time daylighting and design code checking to help improve design compliance.

7. Input and outputMulti-format downloadable output: CAD 2D drawing, DAE/OBJ/IFC model files that support lossless edit in 3D modelling and BIM software, housing layout, daylight analysis, economic and technical index tables, and PPT.Schemesreceiving and sharing: supports cross-project schemes receiving, realizing online asynchronous collaboration and project management; non-registered users can directly view the shared schemes through QR code or link address.

8. Cloud-based collaborative managementOnline project management: it provides cloud-based digital management tools by linking the whole project business process.Management of product library of standard housing andbuilding types: it automatically identifies the features of housing products and adds labels to support efficient search applications. Flexible control of permissions and quick updates of housing product.Schemes positioning comments: supports adding comments to specific locations in the scheme to improve the efficiency of design communication.

As the most common scale in architectural design, building itself often has a large number of modification needs. This product module focuses on making room and floor design simple, allowing designers to focus on the core of the design. This module is also the core of the launch. The XKool AI capability takes it a step further and provides more accurate, faster and smarter design interactions in the solution and initial design stages.

1. Building type importCAD recognition: it connects with common CAD software for quick uploads standardized building floorplans, supports dwg/dxf format, automatically identifies objects in standardized drawing.

2. Intelligent designSketch mode: it starts from scratch to explorethe housing type, it supports rapid housing plan generation from single-line draft to detailed housing type via editing with space block.Intelligent generation: it replaces the repetitive work of designers with intelligent generation, such as generating doors, windows, furniture, load-bearing walls, grids layout, etc. with one click.

3. Efficient editingEditing on cloud: supports cloud-based editing in terms of component objects.Localized shortcut keys: supports Chinese input methods: Pinyin shortcut keys to enhance the editing experience.

4. Evaluation and reviewIndicator specifications: experts interpret the regulations, built-in rules for calculating area indicators under the Chinas standard, real-time statistics of various indicators.Compliance assistance: "interaction of data, model and regulations as a whole" is realized in the process of design, providing real-time indicators, real-time component error reporting, real-time dimensioning and other assistance functions.Plan evaluation: 7 scoring dimensions are built by experts to evaluate the quality of housing design to enhance housings living quality.

5. Collaborative linkageMulti-professional collaboration: standard format ".xkool" can be used to interface with partners in ecosystem to achieve multi-discipline collaboration.Linkage with MasterPlanner: redirects to BuildingCreator for adjusting building type details before updating to MasterPlanner in real time. It also participates in checking and table calculation to ensure the consistency of the data and model of the planning scheme.

6. Results exportCAD drawing: promptly generate CAD plan drawings that meet drawing standards.Colorize scheme diagram: promptly generate colorized building floorplan and housing plan in raster graphics and PSD file required for presentation and promotion.

ColorMaster can realize intelligent and fast generation for site plan coloring that is time consuming and repetitive. The landscape information can be generated intelligently by simply inputting CAD plan drawing; at the same time, supports a variety of color scheme styles to switch with one click and can output high-resolution layered PSD format.

1. Object digitization in site planCAD recognition support for dwg/dxf format, automatic recognition of the semantics of each object.Properties editing online to edit object properties and support minor modification of recognized outcomes.

2. Colored drawing generation with one clickAutomatic obtaining of surrounding information: automatic obtaining of surrounding information within a certain range based on the project location.One-click generation of site plan elements: one-minute generation of visual elements such as landscape, building shadows, and annotation.

3. Customized style switchingColor style switching: four built-in color styles for quick testing.Customized texture mapping: replaceable visual object and texture with support for style file import and sharing.

4. Layered PSD format export It automatically separates landscape, building, shadow, road and other elements by layers to maximize support for further editing.

ARP is a lightweight architectural intelligence research platform released by XKool in June of this year. It enables rapid integration of big data, AI algorithms, cloud computing and other technologies to quickly experiment and iterate on various design scenarios and issues in the architectural industry at multi- scales.

In response to the current needs such as apartment renovation, urban planning and building operation projects, ARP derived X series tools - X-FlatTool, an apartment generator for mass apartment renovation, and X-UrbanTool, an urban designer for urban planning and building operation.

For instance, in X-UrbanTool can achieves optimized urban planning schemes by entering the target site and adjusting the planning objectives and restraint parameters such as plot ratio, building density, and building height limit.

Kooltect, a new business line explored by XKool Technology for the downstream of the industry chain. It is a extension of XKool towards the design, measurement, manufacturing, and construction of building industrialization, starting from modular box assembly to designing and building houses like building Lego blocks, increasing the efficiency of BIM in prefabrication design, achieving the connection of design with factories.

At present, Kooltect can support models equivalent to LOD 300~350, which supports the collaboration of major professions and further design stage.

In the four major professions of HVAC, plumbing, M&E, and firefighting, the supported components includes pipes, bend pipe, T-pipe and connections in detail. While in structure it introduces in detail the components such as columns, beams, beam-column connections and corrugated plates, so that it could be as detailed as the construction drawing details of LOD 350.

In addition, the platform also supports the export of IFC model, which can be further edited in software such as Revit BIM software, and export plan, elevation, sectional and detail drawing to realize the connection from conceptual design, schematic design, design development and to construction documentation.

XKool is realizing the intelligent design of construction documentation phase especially in prefabricated modular assembly, which proves the feasibility of BIM design from the earliest stage.

As the digital revolution gradually takes over the industry, XKool is taking the lead and stepping into the "architecture + AI" field. XKool summarizes the complex design process into 6 major tasks and integrate them into one platform, adapting to the design thinking and habits of architects, continuously exploring possibilities of intelligent design, and iterating it in terms of intelligent design software products and solutions.

Want to learn more about XKool's intelligent design products? You can experience the new version of XKool Design Cloud 2022 by visiting XKool official website (www.xkool.ai) on your web browser (Chrome is highly recommended).

Meanwhile, Wanyu He, founder and CEO of XKool Technology, is invited by DigitalFUTURES to share the theme of "XKool AI Design Could Tutorial" at 9 am EST/3 pm CET/10 PM China on January 8, 2022, introducing the technical capabilities, core functions and case studies of XKool intelligent design products.

The presentation will be livestreamed on YouTube and BiliBili, please visit the following link for reservation:

https://www.youtube.com/c/DigitalFUTURESworld/live

https://live.bilibili.com/22290623

[1]https://www.pritzkerprize.com/sites/default/files/inline-files/Rem_Koolhaas_Acceptance_Speech_2000.pdf

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Solution Generation, Cloud-based Editing, Automatic Drawing... the Junior AI Assistant for Architects! - ArchDaily

Artificial Intelligence Technology Solutions, Inc.

AITX CEO Steve Reinharz poses with a poster that states the company's commitment not to engage in a reverse stock split of its common stock before January 1, 2024.

Detroit, Michigan, Jan. 06, 2022 (GLOBE NEWSWIRE) -- Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), today filed a Form 8-K with the Securities and Exchange Commission that provides details on the companys corporate charter amendment that has been filed with the Nevada Secretary of State. The charter amendment formalizes the companys commitment not to engage in a reverse stock split of its Common Stock before January 1, 2024, unless the Company is uplisting the Company to NASDAQ or the NYSE.

The AITX investor communitys support of our growth, progress and success is remarkable and much appreciated. Although Ive shared this exact statement about not planning any reverse stock splits on Twitter, weekly videos, and other interactions, Im happy to prove this beyond a doubt by locking it in with a corporate charter amendment and associated Form 8-K filing, said Steve Reinharz, CEO of AITX. I will continue to balance the considerations of our clients, our retail investors, our team members, our dealers and our institutional investors to ensure we remain focused on our mission of building AITX into a powerhouse of human-aiding AI-enabled machines.

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTSThis release contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are "forward-looking statements" that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, to meet business and financial goals including projections and forecasts, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.

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About Artificial Intelligence Technology Solutions (AITX)AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITXs RAD, RAD-M and RAD-G companies help organizations streamline operations, increase ROI, and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staffs and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education, and healthcare. To learn more, visit http://www.aitx.ai, http://www.radsecurity.com and http://www.radlightmyway.com, or follow Steve Reinharz on Twitter @SteveReinharz.

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Steve Reinharz949-636-7060@SteveReinharz

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Artificial Intelligence Technology Solutions Files 8-K Detailing Commitment Not to Engage in a Reverse Stock Split of its Common Stock - Yahoo Finance