One of the major lessons learnt from the ongoing Ukraine-Russia war is that multi-domain battle space is getting more influenced by technology. And these include usage of swarms of drones, missiles, unmanned ground vehicles and more. And all of these are being driven by Artificial Intelligence or computer algorithms these are used in the war zones to not only process huge quantities of information, but have the ability to make decisions.

Artificial intelligence is definitely being leveraged for enhancing the current C4ISR capabilities. The National Task Force had identified the 12 AI domains and the Indian Army has since undertaken projects both in-house as well as with the industry, especially deep tech start-ups, the Indian Army Chief Gen Manoj Pande told Financial Express Online.

In response to a question, he said that to enhance C4ISR capabilities, the Indian Army is looking at critical use cases for aerial threats from drones/UAVs, drone imagery analysis, integrated situational awareness for integrated decision support system/COP and analysis of OSINT & SM platforms, drone imagery analysis.

To effectively build capabilities for C4ISR, there is a need to integrate and build capacities in emerging domains of IoT, 5G and BDA.

Why?

Because by meshing these emerging domains it will enable the military to effectively link the sensor to the decision maker to the shooter.

AI engines in various facets of C4ISR

They range from sensors to analysis and decision support systems, and are currently under development standalone and part of platforms or systems.

For instance,in the sensor domain swarm drone platforms, surveillance system inputs or autonomous platforms, AI is enabling remote target detection as well as classification.

ISR analysis: According to Gen Pande, AI engines are being trained for interpretation, change and anomaly detection and even intrusion detection. Similarly, in domains of autonomous lethal weapons, decision support systems or predictive maintenance, a serious effort is afoot to leverage AI.

Adding, We realise that to build an effective C4ISR grid, there is a need to get our data strategy right. And towards this, we have promulgated a Data Governance policy. Work is going on towards building a structured data management framework. Meanwhile, we need to churn out data for our AI engines through improvised on-the-fly techniques.

While evolving QR for any system, the use of AI is deliberated for its ability to enhance operational or logistic effectiveness. Therefore, the Indian Army has also established the AI Centre of Excellence (COE) at MCTE, Mhow. And at this facility, For skill development for our soldiers, AI development is being undertaken simultaneously.

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Adoption of Artificial Intelligence in Indian Armys C4ISR: Here is what the Chief said - The Financial Express

The European approach to artificial intelligence (AI) will help build a resilient Europe for the Digital Decade where people and businesses can enjoy the benefits of AI. It focuses on 2 areas: excellence in AI and trustworthy AI. The European approach to AI will ensure that any AI improvements are based on rules that safeguard the functioning of markets and the public sector, and peoples safety and fundamental rights.

To help further define its vision for AI, the European Commission developed an AI strategy to go hand in hand with the European approach to AI. The AI strategy proposed measures to streamline research, as well as policy options for AI regulation, which fed into work on the AI package.

The Commission published its AI package in April 2021, proposing new rules and actions to turn Europe into the global hub for trustworthy AI. This package consisted of:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed boost excellence in AI by joiningforces on AI policy and investment. The revised Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of the Commissions AI strategy. Through the Digital Europe and Horizon Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The newly adopted Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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A European approach to artificial intelligence | Shaping ...

Undetected machine failures are the most expensive ones. That is why many manufacturing companies are looking for solutions that automate and reduce maintenance costs. Traditional vibrodiagnostic methods can be too late in many cases. Taking readings in the presence of a diagnostician occasionally may not detect a fault in advance. 2017 Position Paper from Deloitte (Deloitte Analytics Institute 7/2017) claimed that maintenance in the environment of Industry 4.0.The benefits of predictive maintenance are dependent on the industry or the specific processes that it is applied to. However, Deloitte analyses at that time have already concluded that material cost savings amount to 5 to 10% on average. Equipment uptime increases by 10 to 20%. Overall maintenance costs are reduced by 5 to 10% and maintenance planning time is even reduced by 20 to 50%! Neuron Soundware has developed a artificial intelligence powered technology for predictive maintenance.

Stories from companies that have embarked on the digital journey are no longer just science fiction. They are real examples of how companies are coping with the lack of skilled labor on the market. Usually mechanic-maintainer who regularly goes around all the machines and diagnoses their condition by listening to them. Some companies are nowlooking for new maintenance technologies to replace

A failure without early identification means replacing the entire piece of equipment or its part. Waiting for the spare part which may not be in stock right now. Because it is expensive to stock replacement equipment. Devaluation of the current pieces of the component in the production thus the discarding of the entire production run. Finally, yet importantly, it would represent up to XY hours of production downtime. The losses might run into tens of thousands of euros.

Such a critical scenario is not possible if the maintenance technology is equipped with artificial intelligence in addition to the mechanical knowledge of the machines. It applies this knowledge itself to the current state of the machine. It is also able to recognize which anomalous behavior is currently occurring on the machine. Based on that send the send the corresponding alert with precise maintenance instructions. Manufacturers of mechanical equipment such as lifts, escalators, and mobile equipment use this today, for example.

However, predictive maintenance technologies have much wider applications. Thanks to the learning capabilities of artificial intelligence, they are very versatile. For example, the technology is able to assist in end-of-line testing. For example to identify defective parts of produced goods which are invisible to the eye and appear randomly.

The second area of application lies in the monitoring of production processes. We can imagine this with the example of a gravel crusher. A conveyor delivers different sized pieces of stone into grinders, which are to yield a given granularity of gravel. Previously, the manufacturer would run the crusher for a predetermined amount of time. To make sure that even in the presence of the largest pieces of rock, sufficient crushing occurred. With the artificial intelligence listening to the size of the gravel. He can stop the crushing process at the right point. This means not only saving wear and tear on the crushing equipment but more importantly, saving time and increasing the volume of gravel delivered per shift. This brings great financial benefit to the producer.

When implementing predictive maintenance technology, it does not matter how big the company is. The most common decision criterion is the scalability of the deployed solution. In companies with a large number of mechanically similar devices, it is possible to quickly collect samples that represent individual problems. From which the neural network learns. It can then handle any number of machines at once. The more machines, the more opportunities for the neural network to learn and apply detection of unwanted sounds.

Condition monitoring technologies are usually designed for larger plants rather than for workshops with a few machine tools. However, as hardware and data transmission and processing get progressively cheaper, the technology is getting there too. So even a home marmalade maker will soon have the confidence that his machines will make enough produce, deliver orders to customers on time, and not ruin its reputation.

In the future, predictive maintenance will be a necessity. In industry also in larger electronic appliances such as refrigerators and coffee machines, or in cars. For example, we can all recognize a damaged exhaust or an unusual sounding engine. Nevertheless, it is often too late to drive the car safely home from a holiday. For example, without a visit to the workshop. With the installation of an AI-driven detection device, we will know about the impending breakdown in time and be able to resolve the problem in time, before the engine seizes up and we have to call a towing service.

Pavel is a tech visionary, speaker, and founder of AI and IoT startup Neuron Soundware. He started his career at Accenture, where he took part in 35+ technology and strategy projects on 3 continents over 11years. He got into entrepreneurship in 2016 when he founded a company focused on predictive machine maintenance using sound analysis.

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Artificial intelligence in factory maintenance is no longer a matter of the future - ReadWrite

The vast list of artificial intelligence applications continually increases as researchers, technologists, and scientists try to leverage computing power to gain competitive edges over the more slowly adopting set. Today I want to traipse across the American business and tech landscape and present a few of the new and hopefully intriguing upgrades of these mostly familiar devices and services being brought into the 21st century via AI.

First a quick overview of the concept of AI and where its come from over the past years and decades. Earlier writings comingled two phrases to identify the technology: artificial intelligence, which has become the well-known marketable way to talk about the tech, and computational intelligence, which might be useful amongst a group of AIerr, CI?subject matter experts, but doesnt carry the cachet of its more widely accepted phrase. For anyone who uses either phrase, its generally understood to refer to some sort of machine-based intelligence. Natural intelligence is the way to describe we humans intellect. Depending on ones level, there are other ways to describe intelligence: lacking, or too good for ones own good come to mind, for example.

Machines that perform AI functions are programmed to take in the various and sundried inputs of their surrounding environment, analyze the data, and perform some action that, when it all works, tends to be the best action considering those inputs. From a textbook level of perspective, you might see natural intelligence described similarly. Were strolling down Main Street about to reach an intersection. We take in sights, sounds, all sorts of information and inputs. Then, we decide whether to wait or continue. Assuming de minimis human intelligence, the action we take will have maximized survival first, pace and progress, too, and other complex results all based on a process of intelligent decision-making.

The foundational descriptions and ideas about AI go back around 20 to 25 years for most purposes of contemporary discussion, though I and others in the past have even gone way back to the nineteenth century with Shelleys Frankenstein to demonstrate a variant of the two-word phrase, Dr. Frankensteins monster displaying artificial intelligence in this sense. We might agree that from whence it came, and toward where AI is headed, the descriptive thread woven throughout is that something other than a sentient being considers information before taking some action. That rather generic description gives a wide range to what parts of modern-day living may benefit from AI technologies.

You reap those benefits everyday already. Google searches, Amazon shopping, Netflix, Hulu, or any streaming platform. Youre really enveloped in the AI landscape at home, work, and even simply being out and about in your community. Anything, for example, that presents as a Smart [Thing] implicates AI. Also, you have likely enjoyed AIs functionality for longer than you might at first think. Remember the Ken Jennings Jeopardy! era? IBMs Watson computer was, essentially, an AI device earning millions of viewers a dozen years ago. If youve picked up a U.S. passport during the past 15 or so years, the facial recognition pieces of the process were driven by AI in ways that may be considered intrusive, but definitely bolster national security as you can imagine. The ethics of AI is an entirely different, albeit important and ongoing, discussion.

To me, biased as I can be about tech advances, nearly everything that incorporates artificial intelligence is intriguing or even exciting. The future altogether, generally, drums up the same sentiments, though Ill admit that over time I catch myself going into the but in my day mode such that I might pooh-pooh something new and improved. Oftentimes I get schooled. For a year Ive had a barely functioning thermostat that I knew needed replacement. I resisted the continual advice to get a smart one. I finally buckled, and to my surprise and delight, and chagrin, Ive truly enjoyed this tiny component of living a more comfortable life. But wait theres more.

Forget room temps, how about AI functionality that senses your emotion? Consider a sales team that, whether due to the pandemic or just the new way of doing business, meets new clients via Zoom or some other video conferencing application. After a pitch meeting, where frankly both sides protect their interests by putting on a show of sorts, the team can analyze the call and see where hot issues garnered certain emotional reactions. Again, Im passing on the ethical dilemmas evident in the tech.

Maybe more agreeable, scientists are developing a fascinating concept: AI colonialism. In New Zealand researchers are trying to reverse the disproportionately laden negative effects of colonialism on minorities. Their angle? The AI functions to retain and increase the Mori language. Contra the effects of human intelligence colonialismentering a political division from afar and imposing foreign ways on its peopleAI colonialism is meant to create the opposite effects. Data, its proponents say, is the last realm of colonialism.

Within infrastructure, smart highways are on the horizon and so some degrees in action already. Autonomous vehicles will require this marriage of roadways and tech, but the applications are nearer and wider, still. In Sweden, road surfaces are being replaced with underlying charging capabilities similar to contactless charging iPhones. Electric vehicles charge as they travel. In the U.S. some metropolitan areas are experimenting with smart roadways such that depending on traffic flows, emergencies and other factors, lanes are reassigned by signage or powered barricades.

Nary is the industry or sector immune from these developments. From agriculture to litigation AI is being enveloped, or it will. When we take time to also consider the ethical implications, and they are in fact sound, it becomes a genuinely exciting time to see innovations come to life. Itd be smart of you to welcome these developments, or at least give them a chance.

Ed is a professor of cybersecurity, an attorney, and a trained ethicist. Reach him at edzugeresq@gmail.com.

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LEFT TO MY OWN DEVICES: Be smart. Welcome new artificial intelligence solutions. - Times Tribune of Corbin

Sometimes reality is a cold slap in the face. Consider, as a particularly salient example, a recently published article concerning the use of artificial intelligence (AI) in the creation of chemical and biological weapons (the original publication, in Nature, is behind a paywall, but this link is a copy of the full paper). Anyone unfamiliar with recent innovations in the use of AI to model new drugs will be unpleasantly surprised.

Heres the background: In the modern pharmaceutical industry, the discovery of new drugs is rapidly becoming easier through the use of artificial intelligence/machine learning systems. As the authors of the article describe their work, they have spent decades building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery.

In other words, computer scientists can use AI systems to model what new beneficial drugs may look like for specifically targeted afflictions and then task the AI to work on discovering possible new drug molecules to use. Those results are then given to the chemists and biologists who synthesize and test the proposed new drugs.

Given how AI systems work, the benefits in speed and accuracy are significant. As one study put it:

The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

Specifically, AI gives society a guide to the quicker creation of newer, better pharmaceuticals.

The benefits of these innovations are clear. Unfortunately, the possibilities for malicious uses are also becoming clear. The paper referenced above is titled Dual Use of Artificial-Intelligence-Powered Drug Discovery. And the dual use in question is the creation of novel chemical warfare agents.

One of the factors investigators use to guide AI systems and narrow down the search for beneficial drugs is a toxicity measure, known as LD50 (where LD stands for lethal dose and the 50 is an indicator of how large a dose would be necessary to kill half the population). For a drug to be practical, designers need to screen out new compounds that might be toxic to users and, thus, avoid wasting time trying to synthesize them in the real world. And so, drug developers can train and instruct an AI system to work with a very low LD50 threshold and have the AI screen out and discard possible new compounds that it predicts would have harmful effects. As the authors put it, the normal process is to use a generative model [that is, an AI system, which] penalizes predicted toxicity and rewards predicted target activity. When used in this traditional way, the AI system is directed to generate new molecules for investigation that are likely to be safe and effective.

But what happens if you reverse the process? What happens if instead of selecting for a low LD50 threshold, a generative model is created to preferentially develop molecules with a high LD50 threshold?

One rediscovers VX gasone of the most lethal substances known to humans. And one predictively creates many new substances that are even worse than VX.

One wishes this were science fiction. But it is not. As the authors put the bad news:

In less than 6 hours ... our model generated 40,000 [new] molecules ... In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic, based on the predicted LD50 values, than publicly known chemical warfare agents. This was unexpected because the datasets we used for training the AI did not include these nerve agents.

In other words, the developers started from scratch and did not artificially jump-start the process by using a training dataset that included known nerve agents. Instead, the investigators simply pointed the AI system in the general direction of looking for effective lethal compounds (with standard definitions of effectiveness and lethality). Their AI program then discovered a host of known chemical warfare agents and also proposed thousands of new ones for possible synthesis that were not previously known to humankind.

The authors stopped at the theoretical point of their work. They did not, in fact, attempt to synthesize any of the newly discovered toxins. And, to be fair, synthesis is not trivial. But the entire point of AI-driven drug development is to point drug developers in the right directiontoward readily synthesizable, safe and effective new drugs. And while synthesis is not easy, it is a pathway that is well trod in the market today. There is no reasonnone at allto think that the synthesis path is not equally feasible for lethal toxins.

And so, AI opens the possibility of creating new catastrophic biological and chemical weapons. Some commentators condemn new technology as inherently evil tech. However, the better view is that all new technology is neutral and can be used for good or ill. But that does not mean nothing can be done to avoid the malignant uses of technology. And there is a real risk when technologists run ahead with what is possible, before human systems of control and ethical assessment catch up. Using artificial intelligence to develop toxic biological and chemical weapons would seem to be one of those use-cases where severe problems may lie ahead.

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Artificial Intelligence and Chemical and Biological Weapons - Lawfare - Lawfare

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Pentagon Names Chief Digital and Artificial Intelligence Officer - Nextgov

From Accenture:

A team of volunteers from Accenture has built an artificial intelligence (AI)-based solution that helps extract information on victims of Nazi persecution from documents in the Arolsen Archives 40 times faster than previous efforts.

The Arolsen Archives preserve the worlds largest collection of documents on Nazi persecution 110 million documents and digital objects, a portion of which are part of UNESCOs Memory of the World program to keep the memory of the crimes of the German terror regime alive. An essential part of the Archives work is to make these documents accessible to all who wish to search for traces of Holocaust victims and survivors, persecution of minorities and forced labor.

Every document maintained in the archives needs to be reviewed and its information (e.g., the family name and birth date on a prisoner registration form) put into a database. To facilitate this process, the Arolsen Archives established #everynamecounts, a crowdsourcing project for volunteers to extract information from documents manually.

Translating, reading, transcribing, cataloging and validating these documents by hand could take decades. Each document is indexed independently by three volunteers and, if the entries dont match, reviewed for accuracy by an Arolsen Archives employee. In effect, it can take up to four people to index and validate four documents in one hour.

[Clip]

Even though the AI does the heavy lifting, human oversight of the process remains important not just to ensure accuracy but also to keep the AI solution learning. By reviewing and correcting information, volunteers teach the solution to recognize handwriting characters and abbreviations that were typical for the time. Thanks to their inputs, the AI has gradually improved its precision by 10% within the form field of mothers last name. For the religion field, the AI is now operating at 99% confidence.

Since Accenture implemented the AI solution in December 2021, the solution has indexed more than 160,000 names of Nazi persecution victims, extracted information from more than 18,000 documents, and clustered more than 60,000 documents into similar groups to improve identification and analysis

Learn More, Read the Complete Announcement

See Also: 26 Million Documents About Victims of Nazi Persecution Online (April 16, 2020)

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Arolsen Archives' #everynamecounts Project Uses Artificial Intelligence to Help Uncover Information on Victims of Nazi Persecution - LJ INFOdocket

But few people had enough mastery of the language to manually transcribe the audio. Inspired by voice assistants like Siri, Mahelona began looking into natural-language processing. Teaching the computer to speak Mori became absolutely necessary, Jones says.

But Te Hiku faced a chicken-and-egg problem. To build a te reo speech recognition model, it needed an abundance of transcribed audio. To transcribe the audio, it needed the advanced speakers whose small numbers it was trying to compensate for in the first place. There were, however, plenty of beginning and intermediate speakers who could read te reo words aloud better than they could recognize them in a recording.

So Jones and Mahelona, along with Te Hiku COO Suzanne Duncan, devised a clever solution: rather than transcribe existing audio, they would ask people to record themselves reading a series of sentences designed to capture the full range of sounds in the language. To an algorithm, the resulting data set would serve the same function. From those thousands of pairs of spoken and written sentences, it would learn to recognize te reo syllables in audio.

The team announced a competition. Jones, Mahelona, and Duncan contacted every Mori community group they could find, including traditional kapa haka dance troupes and waka ama canoe-racing teams, and revealed that whichever one submitted the most recordings would win a $5,000 grand prize.

The entire community mobilized. Competition got heated. One Mori community member, Te Mihinga Komene, an educator and advocate of using digital technologies to revitalize te reo, recorded 4,000 phrases alone.

Money wasnt the only motivator. People bought into Te Hikus vision and trusted it to safeguard their data. Te Hiku Media said, What you give us, were here as kaitiaki [guardians]. We look after it, but you still own your audio, says Te Mihinga. Thats important. Those values define who we are as Mori.

Within 10 days, Te Hiku amassed 310 hours of speech-text pairs from some 200,000 recordings made by roughly 2,500 people, an unheard-of level of engagement among researchers in the AI community. No one couldve done it except for a Mori organization, says Caleb Moses, a Mori data scientist who joined the project after learning about it on social media.

The amount of data was still small compared with the thousands of hours typically used to train English language models, but it was enough to get started. Using the data to bootstrap an existing open-source model from the Mozilla Foundation, Te Hiku created its very first te reo speech recognition model with 86% accuracy.

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A new vision of artificial intelligence for the people - MIT Technology Review

NEW YORK, April 26, 2022 (GLOBE NEWSWIRE) -- BrainBox AI, a pioneer in autonomous artificial intelligence, today announces its agreement with Mountain Development Corp. to bring its cutting-edge technology to two office buildings in New Jersey. BrainBox AIs technology will manage the HVAC controls of 140,000 sq. ft for the real estate company, making the buildings smarter and greener while improving tenant comfort.

Mountain Development Corp., a leader in Class A office properties, is a full-service real estate company and is renown in the New Jersey commercial real estate circle. In early 2021, BrainBox AIs technology was installed at 26 Main St in Chatham, New Jersey, a 65,000 sq. ft. premium office space and 777 Passaic Avenue, a 75,000 sq. ft. Class A building in Clifton, New Jersey. Initial results have shown meaningful energy savings and operating cost reductions.

Our goal is to make a tangible impact on the commercial real estate industry while mitigating the impact of buildings on climate change, said Sam Ramadori, Chief Executive Officer of BrainBox AI. Our technology enables building owners and operators to save money and the environment simultaneously, a win-win solution for all. We are excited to work with one of the biggest players in New Jerseys real estate industry to make buildings more intelligent and save Mountain Development Corp. money both in the short and long term.

BrainBox AI creates value with savings in energy costs of up to 25%, up to 40% reduction in carbon footprint and improved occupant comfort. Building operators can also see an extension in the service life of the HVAC equipment with lower runtimes up to 50%. Its scalability and ease of implementation allow for both individual building and portfolio-wide impact.

Were constantly seeking out new and innovative ways to increase our profit outcome through the enhancement of our building operations. Its crucial that we do so while also improving our tenants overall experience, which can be a fine line at times. Were thrilled to team up with BrainBox AI as they allow us to deliver on these vital operational objectives. Their technology yields positive results in reducing our energy spend while simultaneously improving the tenant experience. Moreover, theyre reducing our carbon emissions and helping the real estate industry move one step closer to our net-zero carbon goals." said Nicholas Mazza, Director of Operations at Mountain Development Corp.

This announcement comes as BrainBox AI continues its expansion across the Northeast and Mid-Atlantic United States, with the Company recently announcing the inaugural installation of its real estate technology in New York City.

About BrainBox AI

Founded in 2017, BrainBox AI was created to address the dilemma currently facing the built environment, its energy consumption and significant contribution to climate change. As innovators of the global energy transition, BrainBox AIs game-changing HVAC technology leverages AI to make buildings smarter, greener, and more efficient. Working together with our trusted global partners, BrainBox AI supports real estate clients in various sectors, including office buildings, hotels, commercial retail, grocery stores, airports, and more.

Headquartered in Montreal, Canada, a global AI hub, our workforce of over 150 employees, bring with them talent from all sectors with the common thread of being in business to heal our planet.BrainBox AI works in collaboration with research partners including the US Department of Energys National Renewable Energy Laboratory (NREL), the Institute for Data Valorization (IVADO) as well as educational institutions including Montreals Institute for Learning Algorithms (MILA) and McGill University. For more information visit: http://www.brainboxai.com

About Mountain Development Corp.

Founded in 1979, Mountain Development Corp. (MDC) is a full-service real estate company with more than 40 years experience developing, acquiring, building, repositioning, managing, leasing and financing commercial property. MDC is an active acquirer of a broad range of opportunistic and value-added real estate investments, together with select core projects, capable of generating attractive, risk-adjusted returns for both its principals and select partners.

For media inquiries:

BrainBox AIRebecca BenderMontieth & Companyrbender@montiethco.com

Mountain Development Corp.Nicholas Mazza, RPADirector of Operations nmazza@mountaindevelopment.com

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BrainBox AI Brings Artificial Intelligence to Mountain Development Corp. with Installations in Two of its Commercial Office Buildings - GlobeNewswire

From mobile work to security and maintenance, perhaps no company has done more for the advancement of technology in todays society than SkyNet, a promising start up out of Austin, Texas that has made great strides during the COVID-19 pandemic. Last year, their T-400 model of home assistant swept the country, combining at home personal assistants with a walking talking android that actually helped with chores and tasks around the house.

We had the opportunity to sit down with Barry Snow, the CEO of the skyrocketing company, about SkyNets future and some of the backlash to what some have called unnecessarily violent home assistants.

~~~~

Hard Drive: Hey Barry, thanks for doing this interview.

Barry Snow: Oh hey man, no problem at all. Thanks for having me. Can I have one of those waters?

HD: Yeah, go ahead. So, your company was already gaining steam a few years ago, but it really seems that during the pandemic you pulled ahead of a lot of your peers with your home androids. Do you attribute this to the pandemic, or do you think SkyNet was going to be a major player in artificial intelligence and home securities no matter what?

BS: Man, this is good water. Thats a great question. I look at it this way SkyNet has already made many successful pivots in its short existence, which is the key to longevity in just about any industry.

A lot of people forget, but do you remember in 1992 when our former Director of Special Projects Miles Dyson blew our old building right to hell? A lot of people said we wouldnt recover from that, but we have. We built a new headquarters, and instead of trying to recreate weird robot shit that we found in an explosion one day, we started focusing on our own work with AI, alloy production, and laserbeams.

HD: I did want to ask you about the laser beams. A lot of people have said theres not a very convincing reason why the T-500 models should come equipped with lasers for opening packages and tricky bags of chips. Would you like to respond to that?

BS: Yes, and thank you for allowing me to do so. Look, weve all read the stories and seen the news clips. House fire in Tacoma. Bridge lasered in half in Miami. Just horrible stuff. But, to think that things like houses catching on fire and bridges falling apart like butter werent happening before we entered the corporate world and started putting lasers on Roombas is a little nave now, isnt it? Our work is so vast that it feels really manipulative to focus on the handful of unfortunate incidents when in fact over 10 percent of households now have a Skynet assistant in their homes. Youre gonna have a few house fires!

The future models are going to be even more exciting. The T-800s are a little ways away, but they can do anything you want. Anything. You can say, Hey T-800, go to the store and get me some soda, and let me tell you something, this thing is not coming back to your house without a big ol bottle of soda. Theyll follow any orders you want!

HD: Wow, you seem really excited about these T-800s.

BS: Oh yeah, I really really am. When you see them, youll understand. Were calling them Terminators.

The new Skynet T-800. Terminate housework!

You like that? I came up with that.

HD: Thats really good! Getting back to them doing anything you tell them to, certainly there are limits to that though, right? You wouldnt want to be able to tell your SkyNet Home Assistant to go hurt somebody or something.

BS: Hm. Thats interesting. Hadnt thought of that.

[This was followed by a long and uncomfortable pause.]

This is really good water, by the way.

Were there any more questions?

HD: Um. Whats next for SkyNet?

BS: The world! No, no, Im just joking. Were really excited about getting the Housework Terminators out into homes over the next few years. We just have to iron out a few details. We learned from product testing that we have to make these things turn on their masters if they try to have sex with them. You can warn them, and tell them about the erotic auto-defense programs weve implemented, but until they get slugged in the mouth theyre just not gonna stop trying to fuck these things. So thats not cool. Thats been a bit of a hiccup.

But, were really close to solving that, and then I think were off to the races! Were working on some interesting things for the T-1000 too, like a new liquid metal android that does shit you wouldnt believe. He can make his arm into a can opener, a wine bottle opener, an envelope opener, a lot of little things that we just couldnt quite do with the 800s. Which are still incredible, by the way. But the T-1000s are gonna blow your mind.

So yeah, the liquid metal, and were also looking at ways to disrupt the fabric of time, and we are really trying to get our laser guns a little more promo, to be honest. Do you want one of our laser gun prototypes?

HD: Sure!

BS: Here you go.

HD: Wow, awesome. Thank you. Do I charge this, or?

BS: Yeah, USB. No big whoop.

Im glad youre excited about it. A lot of people have warned us against some of our recent pursuits, saying that the writing on the wall couldnt be more ominous and that these things couldnt possibly benefit humanity. But hey, you know our slogan around here, SkyNet Judgment Day is Coming and It Will Be All Our Fault.

Hmm, actually maybe that doesnt apply here.

HD: No, not really. Its snappy, though. Say, your robot assistant is frightening me. Would you like to say anything to conclude this interview?

BS: Kids, dont forget to ask for a T-800 for Christmas! Thank you for speaking with me. Oh, dont forget your laser gun, Mark.

HD: Oh whoops, thank you.

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Exclusive: We Interviewed the CEO of SkyNet About Their Recent Breakthroughs in Artificial Intelligence - Hard Drive