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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

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

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

Signal AI, an artificial intelligence startup that trawls the vast sea of internet and other publicly available data to provide organizations with sentiment insights and other information to make better business decisions, has raised $50 million. It plans to use the funds to continue building out its AI platform to bring in more diversified data sources, in order to extract insights across an ever-wider range of business questions that a person might ask.

Organizations still dont have an effective radar to get ahead of threats and opportunities, and turning challenges into opportunities, said David Benigson, the startups CEO, in an interview. The aggregate hundreds of sources of data from social and news media through to 25,000 podcasts, regulatory filings and other public records into a single platform.

It then applies machine learning and other AI techniques to extract insights from it all based on natural language questions posed by Signal AI customers. Weve been diversifying the data that we inject in the platform, he added. Signal AI currently works across some 100 languages.

The funding is coming in the form of a Series D, and it is being led by Highland Europe, with new backer abrdn plus previous backers Redline (which led Signal AIs Series C in 2019), MMC, and strategic backers Hearst and Guardian Media Group Ventures also participating. London-based Signal AI has now raised $100 million. It is not disclosing its valuation, but Benigson who co-founded the company with Miguel Martinez, the companys chief data scientist said it grew 100% over the last round.

PItchBook estimates that valuation was around $100 million at that point (in 2019), which would put it at $200 million now, if that figure is accurate. In any case, Signal AI itself has definitely grown. Benigson said that the startup now works with 40% of the Fortune 500, with its customer base including Deloitte, Bank of America and Google.

The challenge that Signal has identified and is building to fix is one that we all encounter every day, but feels particularly acute when it involves businesses navigating tricky issues with potentially billions of dollars of investment at stake if they take the wrong turn.

The internet has equipped us with a vast trove of information, but not always the best keys and maps for unlocking and getting around it, especially when the answers we are looking for are not straightforward, as they are in the case of more fluffy questions around sentiment analysis, or answers that are actually a collation of information from a number of sources.

There are a number of companies that have also identified this gap and are building to solve it, including Dataminr (which raised a huge round this year at $4.1 billion valuation), Meltwater (publicly traded and also acquiring businesses to build out its technology muscle), and Cision (now privately held and also making big acquisitions to grow).

Much of the emphasis and impetus for these companies started and still sits squarely in the area of media monitoring a huge business tapped not just by other media sources but by companies themselves. Indeed, Signal AI itself used to be called Signal Media and focused mainly on this area as well.

That in itself is still a big market that is worth disrupting the old school way of approaching this was to collate media clippings, provided to clients; the new approach is not just to collate mentions but to deliver more summarized information and insights gleaned from those clips. The more the internet grows, the more clips there will be, and so in fact simply getting a pile of them becomes untenable for even the most enthusiastic teams of communications specialists.

Even so, that model set up for more intelligent media monitoring also paves the way for applying the same format and algorithms to a much wider set of use cases, which is the premise Signal AI has been building upon.

A large part of its work, thus, still remains focused on providing insights to people working around communications strategy, but its deepening the types of information it can provide to them by way of its AIQ platform (as Signal calls it).

Benigson notes that this now includes, for example, more information and insights for a company on potential business partners; getting up to speed on an aspect of diversity and inclusion and how its being approached by others; a pending decision on environmental strategy; and data to inform a companys strategy on regulatory compliance in areas like taxes or data protection.

While comparisons to companies like Dataminr are fair, Benigson said, Signal AI differs from these as it provides more context both in the kinds of queries that can be asked by users, and in the responses that are given. Alongside its business growth, that richer experience is another reason why investors are interested in seeing how the startup will grow.

Signal AI is a stand out category defining business said Tony Zappal, a partner at Highland Europe, in a statement. We are excited to be involved with the next chapter of the companys innovative growth. David and the leadership team have a clear vision for the decision augmentation category they are helping to define and as Gartners research has shown, the opportunity is huge.

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Signal AI, a decision augmentation startup, raises $50M for a platform that extracts insights from the internet and other public content - TechCrunch

When it comes to answering difficult questions,well-builtartificial intelligencewill always haveus beat.

That'was a keytakeaway from a conversation betweeneconomist Daniel Kahneman and MIT professor of brain and cognitive science Josh Tenenbaumat the Conference on Neural Information Processing Systems (NeurIPS)last week. The pairspoke during the virtual event about the shortcomings of humans and what we can learn from them while building AI.

Kahneman,aNobel Prize winner in Economic Sciences and the author of Thinking, Fast and Slow, noted an instancein which humansuse judgment heuristics--shortcuts, essentially--to answer questions they don't know the answer to. In the example, people are given a small amount of informationabout a student: She's about to graduate, and she was reading fluently when she was four years old. From that, they're asked to estimate her grade point average.

Based on this information, many people will estimate the student's GPA to be 3.7 or 3.8. To arrive there, Kahneman explained, they assign her a percentile on the intelligence scale--usually very high, given what they know about her reading ability at a young age. Then theyassign her a GPA in what they estimate to be the corresponding percentile.

Of course, the person answering the question doesn't consciously realize that they're following this process. "It's automatic," saidKahneman."It's not deliberate. It's something that happens to you."

And the guess they offer isn'tlikely to be a particularly good one."The answer that came to your mind is ridiculous, statistically," saidKahneman. "The information that you've received is very,very uninformative."

A student's reading ability at age four, in other words, doesn't have a high correlation with their GPA fourteen years later. But when we're faced with a question we can'tanswer, saidKahneman, we tend to answer a simpler one instead.

"We're rarely stumped," he said."The answer to a related questionwill come to our mind, and we may not be fully aware of the fact that we're substituting one questionfor another."

In reality, the best way to estimate the student'sGPA would be to start with an average GPA--say, 3.0 or slightly higher--and make a minorupward adjustment based on what we know about the girl. But research shows that most people don't think this way. They tend to lean too heavily on the information theyhave (in this case, the girl's reading ability at a young age)andnot realize how much information they don't have.

A soundly engineered AI system, on the other hand, isn't likely to make the same mistake.Properly build AIwill use all the data it has andwon't over-adjust based on one piece of new information.

Engineers should keep this in mind when building AI, saidTenenbaum. "If there are ways in which human thinking is a model to be followed, we should be following it," he said."If there are ways in which human thinking is flawed, we should be figuring out how to avoid those in the AIs we build."

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A Nobel Prize-Winning Economist Explains Why Good AI Will Always Outsmart Humans - Inc.

Tuesday, December 14, 2021

The use of artificial intelligence (AI) and machine learning in the workplace is growing exponentially and specifically in hiring. Over the last two decades, web-based applications and questionnaires have made paper applications nearly obsolete. As employers seek to streamline recruitment and control costs, they have jumped to use computer-based screening tools such as chatbots to communicate with job applicants, to schedule interviews, ask screening questions, and even conduct video conference interviews and presentations in the selection process. Employers of all sizes are creating their own systems, or hiring vendors who will design and implement keyword searches, predictive algorithms and even facial recognition algorithms to find the best-suited candidates. The algorithms in these computer models make inferences from data about people, including their identities, their demographic attributes, their preferences, and their likely future behaviors. Now faced with the impact of COVID-19 and the growing talent shortage, employers may see the use of AI as a more efficient way through the hiring process.

However, the use of AI technology is controversial. Researchers have voiced increasing concerns about the inherent risk of automating systemic bias. Because algorithms rely on historical datasets and human inputs, the technology can generate bias, or exacerbate existing bias. AI systems can generate unfounded conclusions that may be based on protected characteristics.

In 2016, recognizing these concerns over bias, the U.S. Equal Employment Opportunity Commission (the Commission or EEOC) started to examine the impact of AI, people analytics and big data on employment. Big data was characterized as the use of algorithms, data scraping of the internet, and other technology-based methods of evaluating huge amounts of information about individuals. The Commission acknowledged in its 2016 systemic program review, that [h]iring or nonselection remains one of the most difficult issues for workers to challenge in a private action, as an applicant is unlikely to know about the effect of hiring tests or assessments, or have the resources to challenge them. Despite this and some reports that the agency reportedly investigated the use of an algorithm in hiring in two cases in 2019, it took little further action until 2021.

Meanwhile, some high-profile examples of AI bias flagged the risk. In 2018, Amazon stopped using an algorithmic-based resume review program when its results showed that the program resulted in bias against female applicants. In 2019, the Electronic Privacy Information Center filed an FTC complaint against recruiting company HireVue arguing that its face-scanning technology constituted an unfair and deceptive trade practice. HireVues AI-driven assessments, use video interviews to analyze hundreds of thousands of data points related to a persons speaking voice, word selection and facial movements. With a lack of federal focus on the issue, in 2019 states began to generate legislation or resolutions. During 2021 at least seventeen states have introduced bills or resolutions, or enacted or implemented laws related to the use of AI.

Finally, on December 8, 2020, ten U.S. Senators sent a letter to then-EEOC Chair Janet Dhillon, calling on the agency to exercise its oversight authority regarding hiring technologies. The timing of the letter reflected the Senators concerns that businesses reopening after pandemic closures might seek to hire quickly with the potential upswing in use by employers who turn to technology to manage and screen large numbers of applicants to support a physically distant hiring process.

The Senators placed responsibility for addressing the risk of bias and discrimination from the use of hiring technologies squarely with the Commission, stating, The Commission is responsible for ensuring that hiring technologies do not act as built-in headwinds for minority groups. Effective oversight of hiring technologies requires proactively investigating and auditing their effects on protected classes, enforcing against discriminatory hiring assessments or processes, and providing guidance for employers on designing and auditing equitable hiring processes.

Among other questions, the Senators asked the Commission to provide information about its use of its authority to investigate and/or enforce against discrimination related to the use of hiring technologies. The letter asked the Commission to identify what authority it could or has used to study and investigate the development and design, use, and impacts of hiring technologiesabsent an individual charge of discrimination? Notably, the letter also queried the Commissions intention to issue guidance, regulations, and to conduct research.

Re-enter the EEOC. On October 28, 2021, EEOC Chair Charlotte A. Burrows announced a new agency initiative focused on ensuring that the use of AI by employers at all employment stages complies with federal anti-discrimination law. A companion press release outlined the EEOCs five initial goals for its initiative, which were notably reflective of the type of action queried by the Senators:

Establish an internal working group to coordinate the agencys work on the initiative;

Launch a series of listening sessions with key stakeholders regarding use of algorithmic tools and their employment ramifications;

Gather information about the adoption, design, and impact of hiring and other employment-related technologies;

Identify promising practices; and,

Issue technical assistance to provide guidance on algorithmic fairness and the use of AI in employment decisions.

In making the announcement Burrows stated, The bottom line here, really, is despite this aura of neutrality and objectivity around artificial intelligence and predictive tools that incorporate algorithms, they can end up reproducing human biases if were not careful and aware that we need to check for that. She characterized the risk-benefit analysis that must attach to use of AI saying, Artificial intelligence and algorithmic decision-making tools have great potential to improve our lives, including in the area of employment. At the same time, the EEOC is keenly aware that these tools may mask and perpetuate bias or create new discriminatory barriers to jobs. We must work to ensure that these new technologies do not become a high-tech pathway to discrimination.

Burrows comments follow 2021 comments and articles issued by fellow EEOC Commissioner Keith Sonderling, beginning in early Spring 2021. In September, Sonderling signaled that EEOC may use Commissioner charges agency-initiated investigations unconnected to a discrimination charge to ensure employers are not using AI unlawfully. Sonderling explained that using Commissioner charges would assist enforcement because job applicants and employees are often unaware that they have been excluded from a certain job because of flawed or improperly designed AI software or bias. In 2021 and prior to the agencys announcement of the AI initiative, Commission investigators participated in AI training.

The public comments by the Commissioners clearly signal that the agency has brought its focus to the use of hiring and employment technologies as an area of systemic discrimination. When the agency does this, employers using AI or other technologies should exercise caution. Despite the initiative announcement characterizing the Commission as initially concentrating on information collection, education and guidance, the investigator training points to a companion focus of enforcement. Burrows signaled this, saying, Bias in employment arising from the use of algorithms and AI falls squarely within the Commissions priority to address systemic discrimination. Employers subject to a Charge investigation should be alert to Commission inquiries into their use of technology.

In light of the Commissions statements of priority, employers using hiring and employment technology systems, such as artificial intelligence or algorithmic decision-making systems, should evaluate the systems and their use to ensure there is no ensuing bias. Regular auditing should occur to ensure bias is not introduced at a later stage. Additionally, if an employer has used or is considering use of an AI technology vendor, the employer should: (1) ensure the vendor understands the employers EEO obligations; (2) ask the vendor to explain how it proactively avoids bias in its process and the results; and, (3) consider making the avoidance of bias a material term of the vendor contract.

Moreover, employers using hiring and employment technology systems should implement policies related to such use including requiring any managers using such technology to report any biased results and to prohibit inappropriate or discriminatory use of such systems.

Finally, given the relative volume of Commission activity in this area, it is anticipated that the agency may be issuing Guidance on use of hiring and employment technologies soon. Employers are advised to keep informed.

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EEOC Initiative Ensuring AI in Hiring Complies with Anti-Discrimination Law - The National Law Review