Speaking at a recent media roundtable, Sebastian and other company executives discussed the many benefits the technology will have, not only on organizations, but for Canada as a whole. The company’s latest Economic Impact Report, released late last month, found that GenAI has the potential to increase the nation’s economy by $210 billion.

As for the comparison between it and a mobile phone, he said, “when all of a sudden users had a supercomputer in their pocket – it was also a phone, it was also a camera, it was also a map – that changed everything that consumers and businesses could do. The same thing is happening with GenAI, and it is unprecedented. It is going to transform how we interact with information and how businesses interact with each other.”

In a blog that highlighted the report’s findings, Sabrina Geremia, country managing director of Google Canada, said that as the company “celebrates its 25th birthday this year, we’re looking ahead to the next big shift for technology, which brings us to AI.

“Of course, Google’s core platforms have long been powered by AI. We were one of the first companies to use machine learning in our products and we became an “AI first” company in 2015. This technology offers radical potential for exponential growth, and Google is working to help Canada fully realize AI’s economic potential.”

Sebastian said Canada “has long been the leader in AI, Toronto has the largest concentration of AI startups of anywhere in the world. Montreal has the largest concentration of AI researchers of anywhere in the world. And Canada is uniquely positioned to take advantage of this opportunity. But we have to go after it, we have to be bold, we have to be responsible along the way.

“But I think it is a huge opportunity for this country. Because, number one, we are well positioned. And number two, the technology is here. We are 18th in productivity, Canada is, around the world. And that trend is not our friend. This is a huge opportunity to get us back leading on a global stage.”

As for what Canadian organizations want and need with GenAI, Craig Alleva, director of Google Cloud Customer Engineering Canada, said any discussion revolves around five key areas: “How do they use that technology to better understand their customers and consumers and others, and how to engage them better.

“They’re trying to find ways to attract and retain talent in Canada and within their organization using that tech, they are looking at ways to optimize the way they operate. They are also looking at ways of how to better collaborate, internally amongst themselves and their own employees, and third parties that they work with. And then lastly, trying to improve efficiencies and profitability for the organization.”

Alleva outlined three examples of GenAI in use by Google customers. The first was Geotab, a company that is headquartered in Oakville, Ont. and just happens to be one of the largest vehicle fleet tracking organizations in the world.

“They track about 1.4 million commercial vehicles at the moment. And what they do is they collect data from those vehicles, and they provide AI-powered recommendations to the operators of those fleets on how to better operate and manage those vehicles.

“They had a client that has about 91,000 vehicles in their fleet. And using AI-powered data from us, they were able to help them go green by using recommendations that converted 13 per cent of that fleet to electric vehicles. That had the effect of essentially saving them $33 million, it reduced carbon emissions by 194,000 tonnes, which is the effect of removing 42,000 vehicles from the road, using AI-powered recommendations.”

As for CN Rail, “we work closely with them and they’re building a digital supply chain platform for their customers that is intended to be AI-powered. And the goal there is to say, ‘how can we improve the supply chain through simplified ordering, automating tracking of shipments and really improving the overall flow of the supply chain goods, which has been a challenge for awhile.”

The third example is Shopify. “Most of their business runs on Google, and last year they engaged us to solve a problem known as search abandonment,” said Alleva. “Search abandonment is this pesky issue that retailers face whereas a consumer, if you go to the web site, you are looking for something to buy, if you do not find it, you leave the site.

“That costs retailers something like US$2 trillion annually and they wanted to solve that for their top merchants. They engaged us to bring our AI search capabilities that we have in- house to apply to some of their top merchants, ultimately driving down search abandonment, improving the customer experience, and converting more sales.”

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As a part of the three-year project, seven professors and 27 researchers from the École de technologie supérieure (ÉTS), Concordia University, and Polytechnique Montréal will work alongside Ericsson to develop more sustainable network technologies, which will be used in Ericsson’s future products. Environment and Climate Change Canada will also offer guidance and expertise.

The Swedish telecommunication company is no stranger to Canada. As a leading technology provider and the primary hardware supplier for Rogers’ 5G network, it has been partnering with Canadian academia since the 1980s. Today, their research focuses on cloud, security, quantum, photonics, and AI solutions. This latest research partnership in Montreal is just one of many that pivots toward sustainability.

Energy cost is already one of the highest costs on an operator’s bill. The Global System for Mobile Communications Association (GSMA) says it constitutes 20 to 40 per cent of a network’s operational expenditure. That figure is only set to rise as data traffic is expected to increase 1,000 times in the 5G era. With the imminent onset of massive Internet of Things (IoT), rich media, Metaverse, and emerging industrial use cases, operators are exploring all avenues to be more sustainable and drive down costs.

The new 5G networks are more efficient than 4G networks by design. The 3GPP’s 5G specification calls for a 90 per cent reduction in energy use compared to 4G, an aggressive target flanked by stricter green policies from governments.

When asked which part of the network the new research partnership targets, Erik Ekudden, chief technology officer at Ericsson, pointed out that advancements need to be made across the entire infrastructure stack.

“We are actually looking at most parts of the network when it comes to optimization here,” said Ekudden. “It goes to everything from energy-efficient handling of the radio network to using resources only when they are needed, all the way up to how you apply AI to the applications that run on top of it.”

A 2020 McKinsey report underscored that data transmission only accounts for around 15 per cent of a carrier network’s total energy use. The rest is lost from inefficient power controls, heat loss, cooling systems, and battery units.

Further, the report emphasized that operators can reduce energy consumption by as much as 15 to 20 per cent in just a year through structural and architectural transformations and sourcing green energy. As newer technologies become available, consumption can be lowered even more over time.

The telecom industry generally agrees that AI will be a key technology that will help it reach its carbon-neutral goals.

“AI today, which is a broad set of technologies, is very much about machine learning, meaning that we can use the operational data from the network to optimize the use of [radio wave] frequencies,” explained Ekudden. “We can optimize the use of resources, basically not wasting energy when it’s not needed.”

Beyond just managing radio wave broadcasting, Ekudden said a data driven approach also benefits the network at a hardware and software level, improving how data traffic is directed and lowering power consumption when the network is idle. Additionally, the results from today’s research can help build a stronger foundation for future networks like 6G.

Perhaps more importantly, Ekudden highlighted that these emerging technologies also benefit the networking hardware that has already been deployed.

“That’s the beauty of it. So much of the hardware that’s out there can be software upgraded continuously,” said Ekudden. “When we talk about AI capabilities, there is, of course, a natural learning cycle where performance improves over time. So yes, absolutely, it will improve over time, and there will be opportunities to upgrade with new AI software as well.”

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Announced at VMware Explore 2022 in San Francisco were the following:

• Project Northstar for multi-cloud networking, security and end-to-end visibility
• Expansion of network detection and visibility to the Carbon Black Cloud endpoint protection platform, with early access available now
• Project Trinidad, which extends and advances VMware’s API security and analytics
• Project Watch, which VMware described as a new approach to multi-cloud networking and security that provides advanced app-to-app policy controls.

“Enterprises are facing an unprecedented level of threat and complexity as they operate in today’s multi-cloud world,” said Tom Gillis, senior vice president and general manager of VMware’s networking and advanced security business group.

Project Northstar

According to the company, Project Northstar, which was announced as a technology preview, and will run on NSX – its network virtualization and security platform – will “transform how enterprises consume networking and security in a multi-cloud world.”

The SaaS-based network and security offering will contain network and security management capabilities, network detection and response (NDR), network visibility and analytics (NSX Intelligence), advanced load balancing (ALB), and workload mobility (HCX) for private cloud environments and VMware Cloud deployments.

Network detection and visibility

VMware said the “growth in laterally moving threats requires security teams to pay closer attention to east-west network traffic. Examining traffic through network taps is no longer sufficient. Modern distributed cloud architectures can further exacerbate blind spots.”

The company said it is strengthening its lateral security capabilities by embedding network detection and visibility into Carbon Black Cloud’s endpoint protection platform, which is now available to select customers in early access.

Project Trinidad

Project Trinidad, which is in tech preview, extends VMware’s API security and analytics by deploying sensors on Kubernetes clusters. It also uses machine learning with business logic inference to detect anomalous behavior in east-west traffic between microservices.

“Findings from VMware’s recent Global Incident Response Threat Report reveal that ransomware actors continue to evolve their cyber extortion strategies. Ransomware will continue to be a business reality,” the company said.

Project Watch

VMware said Project Watch, which is also in technology preview, “will help network security and compliance teams to continuously observe, asses and dynamically mitigate risk and compliance problems in composite multi-cloud applications.”

In a blog posted yesterday, Jacob Rapp, director and lead technologist, networking and security at VMware, and Sergio Pozo-Hidalgo, a senior product line manager with the firm, wrote that “Project Watch aims to introduce a new set of security metrics that convey a well-understood and industry-agreed level of risk and compliance.

“For example: Only end to end (user to app, or app to app) transactions with a risk score less than 73 is acceptable. This manner of assessing a risk threshold allows for a customizable set of mitigations as the risk approaches, hits, and surpasses the acceptable level: the end goal is to allow more transactions by remediating risks, not to block as much as possible.”

VMware Cross-Cloud

The company also made an announcement that revolved around how best to secure the edge for the distributed workforce. VMware said that in the past two years, enterprise customers have fundamentally changed how they build and deploy networks and access to support distributed workers.

“The market for secure access service edge (SASE) solutions is expected to nearly triple by 2026, with security being a key driver of growth as enterprises strategically invest for the new age of distributed applications and hybrid work, according to the Dell’Oro Group,” it said.

To address this, it launched VMware Cross-Cloud, a portfolio of services designed to help organizations navigate the multi-cloud era that contains five key pillars: App Platform, Cloud Management, Cloud & Edge Infrastructure, Security & Networking, and Anywhere Workspace.

Combined, they “deliver a unified and simplified way to build, operate, access, and better secure any application on any cloud from any device,” the company said.

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With these new releases, VMware seeks to drive digital workspaces, fueled by data science and proactive automation, across endpoint management, security and end-user experience. VMware says these new tools will deliver the following outcomes:

• Self-configuring – configures to a desired state and eliminates the need for constant monitoring for changes and reactive policies
• Self-healing – detects and isolates end-user experience incidents, accelerates change management, and automatically remediate issues to normal working state. 
• Self-securing –proactively secures workspace access, quarantines apps and devices, and fixes anomalies to accelerate return to desired state 

The four key technology areas delivered by VMware’s Anywhere Workspace platform are:

1. VDI  AND DaaS 

Building on Horizon Cloud on Microsoft Azure, VMware has announced a next generation of VMware Horizon Cloud to drive multi-cloud flexibility and simplify infrastructure needed to be deployed inside customer environments while increasing the scalability and reliability of VMware’s DaaS (Desktop as a Service) platform. In addition, this new release is API-driven, enabling customers, partners and Internet Service Providers (ISPs) to build tools, services, and automations.

Customers with Horizon subscription licenses are entitled to the new Horizon Cloud at no additional charge. 

VMware will also introduce VMware Horizon Managed Desktop– a cloud-hosted service that delivers fully managed virtual desktops and apps by providing the choice of deployment options; on-premises, hybrid, or public cloud. It seeks to enable secure access for a hybrid workforce and relieve IT teams from costly and time-consuming reactive operations, while proactively adapting to changing work demands.

Additionally, Google and VMware are expanding their partnership to provide the hybrid workforce access to any app on any device and allow organizations to use hundreds of certified endpoints to connect to Horizon virtual desktops and apps. 

2. Digital Employee Experience 

VMware’s Workspace ONE Unified Endpoint Management (UEM) will expand its Digital Employee Experience (DEX) solutions to include VMware Horizon and third-party managed and unmanaged devices, increasing support to remote, hybrid and frontline workers.

VMware is also introducing the Digital Employee Experience Management (DEEM) solutions pack, with dashboards spanning ROI, shared-device usage, device loss, critical application performance, and more, to measure and improve mission-critical frontline deployments.

Additionally, VMware’s Workspace One is adding DEEM Guided Root Cause Analysis capabilities to enable IT to solve issues more quickly and create automations through its machine-learning algorithms to prevent other users from experiencing the same issue.

3. Unified Endpoint Management (UEM)

VMware is expanding Workspace ONE Freestyle Orchestrator to mobile devices and third-party apps, in order to scale UEM capabilities. Designed to support multi-platform automation and orchestration, this expansion seeks to offer administrators the flexibility to create automation workflows to fit specific process requirements of organizations and help IT teams to eliminate complex and manual configuration tasks.

Additional UEM enhancements in this quarter include a multi-user mode for Windows OS, distribution-agnostic Linux endpoint management, XR Hub for AR/VR devices, Tech Previews of its next-gen Windows Update management, support for new Chrome OS APIs for Education, work profile on Android AMAPI, and a dynamic, data-driven UI for iOS and Windows day 1 new-policy support.

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This partnership aims to improve access to public transit with the help of contactless fare technology. Umo facilitates this type of payment with smart cards and on mobile devices, including multi-ride tickets and pass products that are securely validated using QR code technology. 

The solution chosen by Sault Ste. Marie consists of the Umo SaaS platform, which includes new onboard bus mobile validators and new Canadian-made TAG fareboxes, a cash fare collection system. The joint Umo and TAG solution will help improve customer convenience while reducing agency operation and maintenance costs.

The service will be deployed to 39 vehicles in Sault Ste. Marie, including 26 conventional buses, two community buses and 11 paratransit buses. 

“We will put validators, the things that go on the pole that you tap the card to, and it’s really exciting because it’s the full kind of gamut,” said Matt Newsome, senior vice president and general manager of Cubic Americas. “You can use your phone, you can use barcodes, you can use a card, you can go to retail agents to do it. You can go to a website to do it. So it’s kind of that whole gamut. So we focus on all of the customer riders, helping those who may have less income, and eventually we’ll even have students.” 

“People are so excited to wait to move away from cash, but we have to realize that for some people that’s still their only means,” he added, noting that cash payment will remain an option available to passengers through the new TAG fareboxes.

Sault Ste. Marie’s transit fare collection system project is being funded through the Investing in Canada Infrastructure Program (ICIP), with contributions from the Canadian government, the Province of Ontario, and the Corporation of the City of Sault Ste. Marie.

As well as the company’s Sault Ste. Marie announcement, Cubic has been making moves throughout the country.

Together with TransLink and Interac Corp., Cubic is working to integrate Interac Debit into the fare payment system in Vancouver, offering contactless payment options for riders. 

“In Vancouver, we’ve also launched open payments. You can just take your credit card, as long as it has that cool little wave symbol on the back, and you can tap that, or if you have your card loaded in your watch or your phone, you use your phone or your watch. And then we provide the services, we help with all of the data management, we provide the call centre, we do the card fulfillment and everything,” Newsome said.

Cubic is celebrating the one-year anniversary of its Umo platform launch by providing contactless and mobile fare payment services to 900 buses across 30 transit systems outside of Metro Vancouver as part of its contract with BC Transit. It will soon introduce this advanced payment technology to smaller towns around the major metropolis.

As of now, Cubic is still working with BC to eventually create a “regional interoperability system.” 

“Whether that would be through an app, through a phone, through a device, through a card, those kinds of discussions are starting now.”

In the greater Toronto area, Cubic is joining forces with McMaster University to launch the Centre of Excellence for Artificial Intelligence and Smart Mobility. This centre will work to develop the next generation of diverse engineers, scientists and leaders to deliver the future of public transport.

Newsome noted that right now in the centre, the McMaster team is focusing on virtual reality work related to traffic data, while also looking at how Cubic can enhance its products.

For example, engineers are looking into how cars move on roads and at intersections, analyzing the traffic data and looking for ways to make the roads safer through technology. 

“How do we detect when cars are doing the wrong kind of manoeuvre? And the more and more we learn through AI the more and more we can start to predict, we can kind of figure and make traffic flow and move better. This then kind of makes the streets safer for pedestrians crossing, So that’s some really cool stuff in that area,” Newsome said.

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The business intelligence development company has patented its cloud-based marketing analytics platform that is capable of monitoring marketing efforts across all paid, earned, and owned marketing channels. BAM uses technology to monitor marketing performance against real-time consumer behaviour, determining brand efficacy and marketing effectiveness within specified brand categories. 

The information system then distills the data into a single brand attraction score, as well as creating individual scores across metrics for sales performance, web attraction, paid media effectiveness and efficiencies along-side brand efficacy, ranking and analysis, brand portfolio management, and more.

This new move is being supported through a Series B funding round that will be used to automate and scale the user experience in desktop and mobile, with a lens on AI for predictive analytics reporting. 

Mass Minority said in a release that it brought together a team of data scientists, mathematicians, statisticians, and market analysis experts to invent and validate BAM’s marketing effectiveness and brand efficacy. 

“Our clients call it their unfair advantage that guides them to winning positions in their markets with comparable, easy-to-understand, actionable reporting,” said Mass Minority chief executive officer Brett Channer. “Coming from leadership roles in creative-based agencies, I could no longer look clients in the eye and tell them what was working and not working. This drove me to assemble the science team that helped invent an unbiased inflight contribution to the ROI platform that puts the control back into marketer’s hands. At Mass Minority, better ROI for clients has been our single-minded unit of value and our BAM technology is a result of that focus.”

According to the company, BAM has helped Mass Minority clients achieve significant double-digit growth and improved ROAS (return on ad spend), with better overall marketing ROI (return on investment).

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Features, time tracking and push notifications

After taking down its first marketing mobile app from the App Store in 2014, the company spearheaded the development of a second mobile app, launched in 2019, aimed at expanding the avenues of engagement with its candidate population, and simplifying the job search process. Candidates can search for jobs without an account or profile and if they create one, they are matched with the best opportunities based on their requirements.

The ‘hook’, however, Johnson said, lies in the app’s time tracking tool, allowing the company’s own employees to record their work hours, get paid and obtain their T4s using the app. “We have today more people using the app to record time than any other way”, he said. The company also updated its legacy payroll system, adding APIs to ensure connectivity via the app offered the rapid response time expected of a modern mobile app.

The app’s push notifications, which let candidates know if they are being considered for a position, have also been key to their success, Johnson said, in closing “the gray area in interactions we have with candidates”.

AI matching technology

Johnson said that the company worked to iteratively develop and improve their AI matching technology for the last seven years. He explained that there are three AI engines behind this matching technology:

1. Fit, matching the candidates’ past experiences and skills against the job requirements and to similar successful candidates previously placed by the company, resulting in a ‘double match’.
2. Engagement, determining how active the candidate is on their entire ecosystem (app, website, and partner platforms).
3. Progress, evaluating how far candidates are making it through the job search process; whether, for example, they talked to a client, have been interviewed or signed paperwork.

Additionally, the company abstains from capturing demographic data, writing styles or people’s education (for example, where they went to school) and developed a proprietary natural processing language (NLP) that allows them to “to take someone’s resume apart, remove the context, focusing instead on words or groups of words, and by doing that also remove bias”. 

Roadmap

In 2023, the company plans to implement the following features in the app:

• Onboarding documents in the application to make it easier for candidates to start the job
• Chat, in app messaging, and a chat bot to facilitate interactions
• A mechanism to determine users’ time zones to provide them with more targeted guidance 
• Features to engage, retain, and help candidates move from assignment to assignment
• Badges representing the skills candidates offer

“The combination of a simple user experience combined with both powerful job search and job matching capabilities, that’s the thing that really stands out, ” concluded Johnson. He added that the company is very proud and has “only just begun”.

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In a research paper, they wrote that they have invented something called Mega Portraits, which is short for megapixel portraits, based on a concept called neural head avatars, which, they said, “offer a new fascinating way of creating virtual head models. They bypass the complexity of realistic physics-based modeling of human avatars by learning the shape and appearance directly from the videos of talking people.”

Lou Steinberg, the founder of CTM Insights, a New York City-based cybersecurity research lab and incubator, said intentionally edited images, also known as deepfakes, are a growing and troubling issue with possibilities that include editing a picture of someone to cause reputational/brand damage, often with AI tools that are becoming more capable.

“We see this today with revenge porn images and videos, fraudulent payment audio and videos claiming to be from the CEO, fake-news pictures and videos posted to social media, and nation state disinformation campaigns (e.g., faked images have been circulating re the war in Ukraine),” he said.

There are also cases of editing an image to circumvent “fingerprinting” so it can’t be matched against a database of known images as well as faking images in medical and scientific research papers.

“We have seen attacks against information in places like municipal water systems, critical infrastructure – dam control systems where data values open or close valves – and voter registration database changes,” said Steinberg, the former chief technology officer (CTO) of TD Ameritrade.

In the case of Samsung, he said, researchers potentially made a mistake by publicly releasing their findings before reaching out to “people like us and others who build defences and suggesting ‘you guys might want to start thinking about this because it’s coming.’

“I had a similar, ‘oh my goodness reaction’ a few years back when cybersecurity researchers in Israel did a proof of exploit much like Samsung’s.”

The researchers from Ben-Gurion University of the Negev, located in Beersheba, Israel, had their findings published in the 2019 edition of the USENIX Security Symposium.

“In 2018, clinics and hospitals were hit with numerous attacks leading to significant data breaches and interruptions in medical services,” they wrote in it. “An attacker with access to medical records can do much more than hold the data for ransom or sell it on the black market.

“In this paper, we show how an attacker can use deep learning to add or remove evidence of medical conditions from volumetric (3D) medical scans. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder.”

According to Steinberg, the researchers’ proof of exploit was that they could hack MRIs and computerized tomography (CT) scan images and insert fake cancer using a generative adversarial network (GAN).

Of note, he added, is that the test was so successful that a radiologist would end up misdiagnosing cancer upwards of 95 per cent of the time: “Imagine what the ransomware attack would be against a hospital if they (the perpetrators) called up one day and said 90 per cent of your radiology reports have been faked and you don’t know which ones they are. We will tell you if you pay.

“You see the Mega Portraits from Samsung and say, ‘what happens if they get in the wrong hands?’ We said the same thing when we saw the Israeli cancer images that were successful in fooling radiologists and just said, we have to fix this, we can’t wait.”

CTM ended up creating a way to use “overlapping micro-fingerprints to not only detect that something has been changed, but to isolate where the untrustworthy change was.”

It is, said Steinberg, a mistake to assume that if the team of researchers from Ben Gurion University or Samsung had not discovered this then the “bad guys” would never have been able to figure it out: “Do you want to figure it out first? Probably, but the question is, what are you going to do with it, because you can’t put the genie back in the bottle. If you figure it out first, do something with the results.

“We know that technology can be used for good or bad – almost every technology has had that capability. I want the good guys to invent it first, if you are going to use it to build defences. If all they are going to do is give it to the bad guys, now we have a problem.

“We took the images from the Israelis and built our initial system around being able to detect fake cancer and expand it out from there and we proved that we could. But if you don’t take that critical step, now you are building weapons of mass disruption and that is a problem.”

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“The survey has shown that enterprises are shifting away from a purely tactical approach to AI and beginning to apply AI more strategically,” said Gartner analyst Erick Brethenoux. “For example, a third of organizations are applying AI across several business units, creating a stronger competitive differentiator by supporting decisions across business processes.”

The survey revealed that on average, 54 per cent of AI projects make it from pilot to production. This is virtually identical to the Gartner 2019 AI in Organizations Survey, which reported an average of 53 per cent of AI projects make it to production.

“Scaling AI continues to be a significant challenge,” said Gartner analyst Frances Karamouzis. “Organizations still struggle to connect the algorithms they are building to a business value proposition, which makes it difficult for IT and business leadership to justify the investment it requires to operationalize models.”

Upwards of 40 per cent of organizations surveyed indicated that they have thousands of AI models deployed. This creates governance complexity for the organization, further challenging data and analytics leaders’ ability to demonstrate return on investment from each model.

While talent shortages are often assumed to impact AI initiatives, the survey found it is not a significant barrier to AI adoption, as 72 per cent of executives reported that they have or can source the AI talent they need.

“The most successful organizations use a combination of in-house development and external hiring for AI talent,” said Brethenoux, who is responsible for managing Gartner’s research agenda for AI. “This ensures that the team renews itself continuously by learning new AI skills and techniques and considering new ideas from outside the organization.”

Interestingly, security and privacy concerns were not ranked as a top barrier to AI adoption, cited by just three per cent of executives surveyed. Yet, 41 per cent of organizations reported they have previously had a known AI privacy breach or security incident.

When asked which parties the organization was most worried about when it comes to AI security, 50 per cent of respondents cited concerns about competitors, partners, or other third parties, and 49 per cent were concerned about malicious hackers.

However, among organizations who have faced an AI security or privacy incident, 60 per cent reported data compromise by an internal party.

“Organizations’ AI security concerns are often misplaced, given that most AI breaches are caused by insiders,” said Brethenoux. “While attack detection and prevention are important, AI security efforts should equally focus on minimizing human risk.”

The survey was conducted online from October through December 2021 among 699 respondents in the U.S., Germany and the U.K., at organizations that have either deployed AI or intend to deploy it within three years.

Gartner analysts are currently discussing the keys to successful AI implementations and barriers to AI operationalization during this week’s Gartner Data & Analytics Summit, taking place in Orlando, Fla. through Wednesday.

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According to a release, augmenting the former’s machine learning algorithms which are focused on rooting out online brand impersonation attacks with the latter’s technology will further enhance (its) ability to find more fake web sites, social media accounts and mobile apps.

The release went on to say that consumers are often tricked by phishing emails that take them to a fake Web site that steals log-in credentials, payment data, or other personal information: “This can result in identity theft and fraud, costing billions of dollars at e-commerce sites and financial institutions around the globe.

“Images on the fake web sites sometimes have minuscule changes to confuse automated detection engines. The technology developed by CTM Insights showed 97 per cent accuracy in real-world testing, even with changed images.”

Lou Steinberg, the former chief technology officer at TD Ameritrade who founded the company five years ago, said that the patent-pending approach helps solve the growing problem of deepfakes that are becoming more disruptive to society

“We are able to check to see if a photo is a known ‘bad’ image even if the image was changed,” he said. “While Allure Security is licensing the software to detect fake web sites, the core of this technology can be used to combat other issues like child pornography and fake news.”

On its Web site, the company describes itself as a research lab combined with an early- stage technology studio and incubator, focused on new approaches to structural problems: “We provide seed funding and guidance to prototype ideas that can Change The Model (CTM) to some of the tech’s hardest problems in cybersecurity. Incubated ideas can result in IP licensing or as companies that continue on as independent entities.”

In an interview with IT World Canada, Steinberg said the core reason he launched the company was to solve the problem of fake data.

“That could be fake news or an edited image that gets uploaded. That could be manipulated data in voting records,” he noted.

He recalled that while at TD Ameritrade, what “terrified” him and other cyber security professionals on staff was not “ransomware where somebody would come in and encrypt data – we had good backups – but the fact someone might change data.

“If you change data, you can change positions (the amount of a security asset owned), balances, accounts payable, or accounts receivable. You can even change AI training data and start to teach AI to do the wrong thing.

“We started looking at the next generation of attack that we decided needed to be solved. And we said, ‘it’s not good enough to know if your data was changed. We have to know where your data was changed. We have to isolate the ‘where’ – whether it’s records in a database or part of an image.’ That was the beginning – the problem statement we went after was ‘how do you know where data should not be trusted?’”

As the CTO who oversaw cybersecurity and fraud detection, a common practice was to model the capabilities of threat actors and then project what they might be capable of doing three years into the future.

Steinberg cited the example of a study of cybercrime organization the Russian Business Network or RBN, in which “we know they can do this today; we think they will be able to do that. And then we would build defenses against future attacks, because you can’t wait for them to get good at something to start defending.

“We had a punch list of what happens when they acquire some new capability that we couldn’t defend against. And when I retired, I said, I’m tired of waiting for someone else to solve these. I’m going to start a research lab and we’re going to start solving problems. And that’s what we do.”

He likens it to being in a constant state of co-evolution: “Co-evolution says when the lions get faster, gazelles get faster because slow gazelles get eaten. We have this in cyber, in that we’re both ratcheting up our capabilities. But every once in a while, the lions get jet-packs. And it’s a bad day to be a gazelle. I’m in the anti jet-pack business.”

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