Posted by & filed under Cyber Security.

How do hackers break into your technology environment? According to our penetration testing services team, it often comes down to one missing patch. How do you avoid this fate? Let’s step through a recent penetration test that illustrates the latest hacker techniques. We’ll share how the pentest team finds the security gaps and some critical steps that stop attackers in their tracks.

The target: a professional services firm. The IT team had recently updated their systems and said it was unlikely that the penetration testing services team would find anything, but they wanted to do their due diligence (turns out, that was a very wise move!)

The client provided a list of IP addresses which anyone on the Internet could scan—and nothing else. From there, it was game on.

Source: LMG Security

Date: November 18th, 2022

Link: https://www.lmgsecurity.com/penetration-testing-services-team-says-it-often-comes-down-to-one-missing-patch/

Discussion

  1. What is “a patch” when it comes to software and operating systems?
  2. Why is so hard for companies to keep up with “patches”?

Posted by & filed under AI/Artificial Intelligence, Career.

While nearly half of the executives surveyed by KPMG LLP (KPMG) at the start
of 2021 say that they are concerned that their overall industry may be moving
too fast with AI adoption, nearly all wish their own organization would move even
faster. Executives also harbor a nagging feeling that everyone else is doing better
than they are. Nearly eight in 10 say AI is functional in their organization, and a
majority using it say it is delivering value beyond what was promised. Yet threequarters believe the use of AI to help businesses is still more hype than reality,
and nearly two-thirds believe the U.S. is trailing other countries in taking advantage
of the technology.
Impossible contradictions? We see a coherent narrative behind these
findings. Faced with a stark reminder of what is possible with AI—COVID-19
vaccines developed in record time, for example—it is natural for many executives
to worry that their own organization may not be keeping pace. And trite as it
may seem, it is worth remembering that the grass usually looks greener on the
other side.

Source: KPMG

Date: November 15th, 2022

Discussion

1) Why are these percentages like this?

2. “

Executives widely believe in AI’s ability to deliver value. Ninety-two percent agree
AI would make their organization run more efficiently, and individual industries report
confidence in AI’s potential to solve some of their biggest challenges. Indeed, at
organizations where AI has been adopted, the majority of executives say it is adding
even more value than was promised. The retail industry is a leader on this front, with
69 percent of retail executives saying their organization’s AI initiatives are yielding more
value. But even among the industries that are not as far ahead in this area—life sciences
and government—at least half report similarly favorable results (50 percent and 54
percent, respectively).”
In what ways can MIS majors be involved in AI initiatives?

Posted by & filed under App Economy, Sensors.

Saskatchewan has become a hub for emerging ag tech, according to several industry experts. Plus, farmers in that province have a track record of adopting new technology and supporting local startups, says Sean O’Connor, managing director of Emmertech, a $60-million Conexus venture capital fund focused on Saskatchewan ag tech startups. 

“Farmers are the most innovative business owners in Canada as far as we’re concerned, and they’re looking for new solutions,” he says. 

“You can’t build ag tech companies on Bay Street. They belong in agriculture ecosystems, where you’re directly interacting with industry itself.”

Source: Canadian Broadcasting Corporation

Date: November 11th, 2022

Link: https://www.cbc.ca/news/canada/saskatoon/saskatchewan-agriculture-technology-fertilizer-emissions-1.6638165

Discussion

  1. “He has installed half a dozen weather stations across his property – third-generation land he farms with his family near Filmore, Sask., in the southeast part of the province – to make his operations more efficient. The stations track a variety of factors that affect crops, such as air and soil temperature, moisture levels and wind. The app, developed by a Saskatchewan agriculture technology company, helps him interpret the data.”
    The key here is not the “half a dozen weather stations”, it’s the “app, developed by a Saskatchewan agriculture technology company, [that] helps him interpret the data.”
    Data analytics is critical, but requires the correct data input.
    How can MIS be involved?
  2. What other ways could sensors be used in combination with an app to help?

Posted by & filed under AI/Artificial Intelligence, Machine Learning.

As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions’ choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right?
Sadly, what’s true in theory has not been borne out in practice. Lenders often find that artificial-intelligence-based engines exhibit many of the same biases as humans. They’ve often been fed on a diet of biased credit decision data, drawn from decades of inequities in housing and lending markets. Left unchecked, they threaten to perpetuate prejudice in financial decisions and extend the world’s wealth gaps.

Source: Forbes

Date: November 9th, 2022

Link: https://hbr.org/2020/11/ai-can-make-bank-loans-more-fair

Discussion

  1. “In our work with financial services companies, we find the key lies in building AI-driven systems designed to encourage less historic accuracy but greater equity. That means training and testing them not merely on the loans or mortgages issued in the past, but instead on how the money should have been lent in a more equitable world.”
    It is worth making sure that students know how AIs are trained. Typically it is “machine learning” which means you feed in information to the AI, let it do its work to find the answer, and then tell it whether or not it got the answer correct. Over time it learns how to get the correct answer from the data it is given.
  2. “by using AI, one lender discovered that, historically, women would need to earn 30% more than men on average for equivalent-sized loans to be approved. It used AI to retroactively balance the data that went into developing and testing its AI-driven credit decision model by shifting the female distribution, moving the proportion of loans previously made to women to be closer to the same amount as for men with an equivalent risk profile, while retaining the relative ranking. As a result of the fairer representation of how loan decisions should have been made, the algorithm developed was able to approve loans more in line with how the bank wished to extend credit more equitably in the future.”
    Use this example to explain how to correct for AI machine-learning inaccuracies

Posted by & filed under Cloud Computing, Low-code, SaaS Software as a Service.

Despite widespread automation in other industries, contact centers are still lagging. Humans largely handle everything from resolutions to logging call summaries. I recently asked a 30-year contact center executive how she compared her agents’ work today to the work at the same company 20 years ago. She said she used to handle six calls an hour, and her agents today handle exactly the same number of calls.
Call volumes have increased with unpredictable spikes. Contact center leaders have lost their ability to plan workforce assignments effectively, and call patterns continue to change with pandemic waves and hybrid work—leaving leaders with no baseline for planning.

Source: Forbes

Date: November 9th, 2022

Link: https://www.forbes.com/sites/forbestechcouncil/2022/07/20/the-brave-new-world-of-contact-center-automation/?sh=662ce33b68b6

Discussion

  1. “Many of today’s solutions are pretrained on customer service data, require little development effort and natively integrate with existing contact center software. This eliminates the need for contact centers to hire their own machine learning experts or accrue thousands of hours of training data themselves to deploy an effective automation solution.”
    This is Software as a Service (Saas), cloud computing and low-code programming all in one.
    Make sure students understand these three concepts, and how they are all coming together.
  2. What role can MIS majors play in the delivery of Software as a Service (Saas), cloud computing and low-code programming?

Posted by & filed under Robotic Process Automation (RPA), Robotics.

Lovely, intelligent and well-dressed.
That’s how 82-year-old Frances Greenberg describes Grace, the newest member at her long-term care home in Montreal’s Notre-Dame-De-Grâce neighbourhood.
Grace is a rosy-cheeked, young-looking woman with a layered bob haircut. Oh, and she’s also a robot. 
Designed by the GeriPARTy Laboratory team, Grace will be visiting Résidence Pearl & Theo twice a week for the next eight weeks as part of a study led by Montreal’s Jewish General Hospital.
Her goal during each 30-minute session will be to keep seniors living in nursing homes company and help break social isolation.
“It’s lovely to have something like this here,” said Greenberg, who is among three seniors taking part in the study. 

Source: Toronto Daily Star

Date: November 4th, 2022

Link: https://www.cbc.ca/news/canada/montreal/grace-humanoid-robot-montreal-seniors-study-1.6623292

Discussion

Discussion

  1. https://www.cbc.ca/i/caffeine/syndicate/?mediaId=2087939651989
    1 minute video
    The interaction looks very clunky. In what other circumstances might this sort of humanoid technology work?
  2. Robots in the workplace will look nothing like this. Robots in the workplace will be Robotic Process Automation, which is basically the same as Excel macros. They will complete a set of repetitive tasks such as doing all the accounting for an invoice that has been received by pdf. This is an important point to make with students.

Posted by & filed under AI/Artificial Intelligence.

AI plays a huge role in Amazon’s recommendation engine, which generates 35% of the company’s revenue. Using data from individual customer preferences and purchases, browsing history and items that are related and regularly bought together, Amazon can create a personalized list of products that customers actually want to buy.

Data from these three main pillars of the company work together to create a cohesive customer experience. A customer can visit the Amazon Go store to get a few items for dinner, ask Alexa to look up a recipe and the product recommendation engine can determine that the customer likely needs to purchase a certain type of sauce pan. Instead of fighting against each other, different divisions share their innovative knowledge to provide a customized and cohesive customer experience.

Source: Forbes

Date: November 2nd, 2022

Link: https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/?sh=7d5c6d977361

Discussion

  1. “Using data from individual customer preferences and purchases, browsing history and items that are related and regularly bought together, Amazon can create a personalized list of products that customers actually want to buy.”
    What other companies could use AI in this way (Netflix is a good example)
  2. The article is titled “How Amazon Has Organized Around Artificial Intelligence And Machine Learning“. Machine Learning is just a part of AI, and one way to get AI to work. Make sure students understand this. Machine learning is basically where you feed the AI a whole bunch of data that you already know the output or answer to, and you let the AI work with the data to come up with an answer, and then tell it whether it got it correct or not. This lets the AI “learn” so it will get to the correct outcome on its own.

Posted by & filed under AI/Artificial Intelligence, Career.

AI algorithms are increasingly responsible for a variety of today’s interactions and innovations—from personalized product recommendations and customer service experiences to banks’ lending decisions and even police response.

But for all the benefits they offer, AI algorithms come with big risks if they aren’t effectively monitored and evaluated for resilience, fairness, explainability and integrity. To assist business leaders with monitoring and evaluating AI, the study referenced above shows that a growing number of business leaders want the government to regulate AI in order to allow organizations to invest in the right technology and business processes. For the necessary support and oversight, it’s wise to consider external assessments offered by a service provider with experience in providing such services. 

Source: Forbes

Date: November 2nd, 2022

Link: https://www.forbes.com/sites/kpmg/2022/10/26/3-reasons-your-organization-will-need-external-algorithm-assessors/?sh=69dab8c8b517

Discussion

  1. “AI algorithms come with big risks if they aren’t effectively monitored and evaluated for resilience, fairness, explainability and integrity.”
    This is an excellent list of potential issues with AI algorithms and it would be worth discussing each of the four with class.
    Resilience = ability to work in different situations. What happens when bad data goes in?
    Fairness = shows no bias. I usually cover the example of Google, who used machine-learning AI to hire new software engineers. The machine-learning looked at how was a successful software engineer at Google and concluded (correctly, based on the past and data it was fed) that you needed to have software engineering degree from Stanford, and be a white male. Instead of reducing bias in hiring it actually increased bias in hiring.
    Explainability = we know how the AI came to its conclusion(s).
    Integrity = make sure you have validation of what you are doing
  2. What role can an MIS major play in being an “Algorithm assessor”?

Posted by & filed under Digital Transformation.

Source: Deloitte Canada

Date: October 28th, 2022

Link to 1 hour video. Start at 2 minutes 50 seconds in to skip the introductions: https://www.youtube.com/watch?v=TxB_iTCkJv8

Discussion

  1. “Digital transformation means different things to different people” (main presenter, 3 minutes in to the video). A discussion about the different aspects of “digital transformation” is very useful.
    5 minutes and 50 seconds in to the video it is discussed.
  2. 74% of CEOs will enact “digital transformation”. The presenter from the US also says that this is a “high stake, high reward activity”.
    How would MIS majors play in this transformation?

Posted by & filed under 3D Printing.

Israel’s Redefine Meat has struck a partnership with importer Giraudi Meats to drive European distribution of its ‘New Meat’ steak cuts produced on 3D printers.

Source: Reuters

Date: October 28th, 2022

Link to 3 minute 38 second video: https://www.youtube.com/watch?v=zQSCzHaMcTg

Discussion

  1. In what ways would an MIS major be involved with a company like “Redefine Meat”?
  2. Why is 3D printing so important to disrupting the supply chain?