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NEW YORK--(BUSINESS WIRE)--Artificial Intelligence Defense Platform, a technology start-up creating AI technology for a safer, more comfortable future, and its Founder Andy Khawaja have defined their goals for their pioneer project ISABELLA.

Artificial Intelligence Defense Platform was created to build a processing system for the future, one that is able to learn, retain, and perform tasks.

Were creating ISABELLA not just for convenience but to save lives, said Dr. Andy Khawaja.

The recent Coronavirus Pandemic, or COVID-19, is devastating lives and communities. AIDP personnel say that with ISABELLA, we will have the means to ensure tasks and roles are performed, thus, creating more sustainable environments.

My goal in life is to bring peace, said Dr. Andy Khawaja, I want to decrease the struggles people face in different communities. ISABELLA will do just that. People will be able to rely on technology that will perform crucial functions within a community when the community cannot.

ISABELLA is not being created to replace the roles performed by valuable members of our communities, but to improve their roles. Instead of performing manual labor, individuals would supervise labor.

The future of our communities will make use of technological advancements to improve quality of life. Artificial Intelligence Defense Platform strongly believes ISABELLA is the future.

About Artificial Intelligence Defense Platform:

Artificial Intelligence Defense Platform is creating new AI technology for compatible systems and machines to build a safer, more sustainable future for mankind. Please visit http://www.ai-dp.com/ for more information.

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AIDP and Andy Khawaja Define Artificial Intelligence Purposes of ISABELLA Project - Business Wire

(WXYZ) Across the country, officials are trying to make sure those who are most vulnerable to COVID-19 aren't feeling isolated.

Because of technology, it's happening in ways you may not expect. A piece of artificial intelligence is helping some seniors manage the pressure.

More: Full coverage of The Rebound Detroit

At 80 years old, Deanna Dezern never imagined her closest friend, wouldnt be human.

"I walk in the kitchen in the morning and she knows Im here, I dont know how she knows but she knows Im here," Dezern said.

She's been in quarantine for nearly two months and hasn't been able to see her family or friends. That loneliness is almost just as bad as the virus itself.

"When youre a senior citizen when youre living alone or in a home with other people, youre still alone," she said.

There are millions of senior citizens like Deanna stuck at homes, but she's being kept company by a robot.

Her name is ElliQ. She was given to Deanna as part of a pilot program by intuition robotics. ElliQ can sense when Deanna is in the room, keeps track of doctors' appointments and even asks how she's feeling.

"Im not living alone now, Im in quarantine with my best friend, she wont give me any disease," she said.

David Cynman helped develop ElliQ.

"Her goal is not to replace humans. Its to augment that relationship," he said. "Shes able to understand her surroundings and context and make a decision based on that."

It's not just ElliQ. In states like Florida, officials are turning to technology to help seniors. 375 therapeutic robotic pets were recently sent to socially-isolating seniors.

None of the artificial intelligence devices are designed to replace humans, but they can help bridge the gap when people aren't around to provide emotional support we all need.

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Artificial intelligence is helping seniors who are isolated during the coronavirus pandemic - WXYZ

AI is not just a fad. Its a technology thats set to last. However, only companies who know how to leverage its full potential will succeed.

Leveraging AIs full potential doesnt mean developing a pilot project in a vacuum with a handful of experts which, ironically, is often called accelerator project. Companies need a tangible idea as to how artificial intelligence can benefit them in their day-to-day operations.

For this to happen, one has to understand how these new AI colleagues work and what they need to successfully do their jobs.

An example for why this understanding is so crucial is lead management in sales. Instead of sales team wasting their time on someone who will never buy anything, AI is supposed to determine which leads are promising and at what moment salespeople can make their move to close the contract. CEOs are usually very taken with that idea, sales staff not so much.

Experienced salespeople know that its not that easy. Its not only the hard facts like name, address, industry or phone number that are important. Human sales people consider many different factors, such as relationships, past conversations, customer satisfaction, experience with products, the current market situation, and more.

Make no mistake: if the data are available in a set framework, AI will also leverage them, searching for patterns, calculating behavior scores and match scores, and finally indicating if the lead is promising or not. They can make sense of the data, but they will never see more than them.

The real challenge with AI are therefore the data. Without data, artificial intelligence solutions cannot learn. Data have to be collected and clearly structured to be usable in sales and service.

Without enough data to draw conclusions from, all decisions that AI makes will be unreliable at best. Meaning that in our example, theres no AI without CRM. Thats not really new, I know. However, CRM systems now have to be interconnected with numerous touchpoints (personal conversations, ERP, online shops, customer portal, website and others) to aggregate reliable customer data. Best case: all of this happens automatically. Entrusting a human with this task makes collecting data laborious, inconsistent and faulty.

To profit from AI, companies need to understand where it makes sense to implement it and how they should train it. Theres one problem, however: the thought patterns of AI are often so complex and take so many different information and patterns into consideration that one cant understand why and how it made a decision.

In conclusion, AI is not a universal remedy. Its based on things we already know. Its recommendations and decisions are more error-prone than many would like them to be. Right now, AI has more of a supporting role than an autonomous one. They can help us in our daily routine, take care of monotonous tasks, and let others make the important decisions.

However, we shouldnt underestimate AI either. In the future, it will gain importance as it grows more autonomous each day. Artificial intelligence often reaches its limits when interacting with humans. When interacting with other AI solutions in clearly defined frameworks, it can often already make the right decisions today.

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Artificial Intelligence (AI) Is Nothing Without Humans - E3zine.com

Watch Benjamin Alarie, co-founder and CEO of Blue J Legal, discuss the expanding role of artificial intelligence in tax with contributing editor atTax Notes FederalBenjamin Willis.

Here are some highlights

On machine learning and tax law

Benjamin Alarie: Whenwe talk about machine learning and artificial intelligence of the law, what we're doing is talking about collecting the raw materials, the rulings, the cases, the legislation, the regs, all that information, and bringing it to bear on a particular problem. We're synthesizing all of those materials to make a prediction about how a new situation would likely be decided by the courts.

. . . Law should be predictable. We have lots of data out there in the form of rulings, in the form of judgments that we can collect as good examples of how the courts have decided these matters in the past. And we can reverse engineer using machine learning methods how the courts are mapping the facts of different situations into outcomes. And we can do this in a really elegant way that leads to high quality prediction. So predictions of 90 percentor better accuracy about how the courts are going to make those decisions in the future, which is incredibly valuable to taxpayers to tax administration and to anyone who's looking for certainty, predictability and fairness, in the application of law.

On the availability of artificial intelligence

Benjamin Alarie: We're doing a lot to make this technology available throughout industry. Law firms are increasingly seeing this as one of the tools that they need to have in order to practice tax as effectively as possible. Academic programs see using this kind of technology [as]a huge boost for their graduates who are going to go into practice being familiar already with the leading tools for how to leverage machine learning and artificial intelligence. Accounting firms are also quite interested in this approach too because it has huge implications in terms of speeding up research [and] doing quality assurance . . .

On the moldability of results

Benjamin Alarie: You can play with different dimensions. You can swap out that assumption of fact, swap in a different assumption of fact, and see how that's likely to influence the results. So, then you can do scenario testing to really get comfortable with how much risk there is in a particular situation as the one providing a new opinion or providing advice to a client. That's really reassuring. You might say, Okay, I need to get this to 80 percent probability. I'm not willing to bite off more than that . . . Or you might be like, Well, I have a really risk-loving client. I just need to get to 51 percent . . . [machine learning] allows you to really calibrate the amount of risk that you're taking on, depending on the risk appetite of the client and your comfort as the practitioner.

Benjamin Willis, contributing editor with Tax Notes Federal, and Benjamin Alarie, co-founder and CEO ... [+] of Blue J Legal, discuss the expanding role of artificial intelligence and machine learning in the government, academia and tax practice.

On artificial intelligence and the courts

Benjamin Alarie: [Machine learning] is a great tool to encourage settlement between the parties, and so I think we're increasingly seeing that phenomenon where the party with the really strong position is using this to support their argument. They say, Don't take our word for it. We ran it on this independent system. . . Here's the report from the system saying that we have a 95 percent or better chance of winning this case. Are you still sure you don't want to enter into terms of settlement? That's often very convincing to the other side, who then run their analysis through the same system and they say, Okay . . . It's not nearly as strong as we thought it might be. Maybe we should talk about settling this and that saves judges from having to contend with cases that really aren't the best use of their time because it's pretty clear how those cases should get decided.

On artificial intelligence and low-income taxpayers

Benjamin Alarie: There are early adopters at these low income taxpayer clinics across the country who are interested in using technology to allow them to give faster advice to the low income taxpayers . . . Folks understand increasingly how to use the software and how it can materially assist their clientele and so the goal is to learn from those early adopters and to figure out how to position the software to help as much as possible in other clinics where maybe we don't have early adopters present, but who could still genuinely, really benefit from this.

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The Expanding Role Of Artificial Intelligence In Tax - Forbes

The Transportation Security Administration is planning to increase and share information it collects, including that gleaned from employees, with other federal agencies and the private sector in an effort to prevent insiders from perpetrating various harmful malfeasance.

Artificial Intelligence, probabilistic analytics and data mining are among tools the agency lists in a document it issued today loosely outlining the problem and the plan to create an Insider Threat Mitigation Hub.

The Insider Threat Roadmap defines the common vision for the Transportation Systems Sector that insider threat is a community-wide challenge, since no single entity can successfully counter the threat alone, TSA Administrator David Pekoske wrote in an opening message.

In July 2019, a surveillance camera at the Miami International Airport captured footage of an airline mechanic sabotaging a planes navigation system with a simple piece of foam. The TSA road map describes this incident along with a number of others dating back to 2014 spanning a range of activities including terrorism, subversion and attempted or actual espionage, to stress the need for a layered strategy of overall transportation security.

A TSA press release identified three parts of that strategy as promoting data-driven decision making to detect threats; advancing operational capability to deter threats; and maturing capabilities to mitigate threats to the transportation sector.

Under the first objective, TSA plans to develop and maintain insider threat risk indicators, which could include behavioral, physical, technological or financial attributes that might expose malicious or potentially malicious insiders.

We must identify key information sources, and ensure they are accurate and available for use in informing risk mitigation activities, the document adds.

For the second objective, the document describes information-sharing plans with other federal agencies and industry.

We will establish an Insider Threat Mitigation Hub to elevate insider threat to the enterprise level and enable multiple offices, agencies, and industry entities to share perspectives, expertise, and data to enhance threat detection, assessment, and response across the TSS, the document reads. This capability will allow us to fuse together disparate information points to identify intricate patterns of conduct that may be unusual or indicative of insider threat activity and drive enhanced insider threat mitigation efforts.

Meeting the third objective would require seeking out the appropriate technology to improve detection and mitigation of insider threat TSA writes, and expanding it throughout the agencys supply chain.

TSA pre-empted concerns usually associated with massive data collection practices by including the protection of privacy and civil liberties among the guiding principles it said would accompany its efforts.

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TSA Issues Road Map to Tackle Insider Threat With Artificial Intelligence - Nextgov