• Consumerism on a Normal Day

    Rick Liu

    Cannon Editor-in-Chief

    In recent developments of the global economy, there is one driving factor that stands out: consumerism. This concept has not only established a death grip on markets worldwide but has also extended its roots into virtually every aspect of daily life. As engineers, consumerism has become a major player in the profession; whether this intrusion is an unwelcome one is something that is very controversial. One thing that is certain is that consumerism has brought about major changes in the engineering industry and will continue to do so for many years to come.

    Consumerism is the practice of selling ever-increasing amounts of goods, often characterized more by a display of status than any real necessity or functionality to the buyer. There is no concrete origin for the rise of consumerism, but many point to the rapid growth of the middle class in the 18th century as a possible starting point. The idea of a system of mass production to promote the consumer markets came to life in the Industrial Revolution with people such as Henry Ford. This later evolved into today’s culture of conspicuous consumption and emphasis on materialistic status.

    The engineering profession has become deeply affected by consumerism in recent years. One of the areas that we may encounter on a regular basis is cost cutting. This idea in of itself is not a fully detrimental one, but the methods that many corporations attempt to achieve it lead to a lackluster quality in products. This is often due to financial constraints on the engineering teams tasked with the manufacturing of these products. Some might say that the engineering profession has been misled by the dubious glories of consumerism. Instead of pursuing an optimal design, financial gain and consumer pressure often take precedence and become the defining factors in a project.

    On September 11, Apple revealed their newest addition to the iPhone family. With only select major upgrades, such as the triple camera setup, over the previous model, one might wonder if this was merely a lackluster effort to please those who expect a new model released annually. This was but the latest in a series of product by Apple that have demonstrated a decline in the engineering prowess of the company. Where once Apple stood at the forefront of innovation, it’s now too concentrated on making profit to achieve any real breakthroughs anymore. 

    In a similar fashion, the food industry has been excessively impacted by the idea of consumerism. With recent developments in technology, genetic manipulations and chemically induced growth have become commonplace. Corporations neglect to consider the side effects of such methods in favour of the cheap and drastic increase in production, which is often followed by an increase in profit. As a result, the people who have to suffer the consequences of these actions are the consumers themselves. 

    Professional integrity is an important part of the engineering industry. However, the current state of affairs in the global market will often put pressure on us to prioritize profit over anything else. It will be up to ourselves to decide whether to heed our inner compass or follow the lures of consumerism.

  • The Engineering Failures of the Boeing 737 MAX

    Rick Liu

    Cannon Editor-in-Chief

    In 2011, American Airlines signed a massive $38 billion order for the next generation A320neo with Airbus. Having lost a longtime customer, Boeing had a major dilemma on its hands. The company was working on a replacement aircraft to its Boeing 737 at the time, but the threat of even more customers defecting to the A320neo changed its decision making. After three generations, dating back to 1966, Boeing decided to design a fourth generation Boeing 737, named the Boeing 737 MAX, with brand new engines in order to compete with the newer Airbus plane in terms of fuel efficiency. 

     

    Nothing appeared particularly out of the ordinary until the Lion Air crash on October 29, 2018. Almost two weeks after the crash, speculation immediately turned to the MCAS system Boeing implemented on its plane. 

     

    The MCAS system was a software fix for changes in the engine that led to increased fuel economy, but also changed the way the plane flew in the air. The 4th generation plane had larger engines, which meant that the engines had to be shifted forward and higher on the wing than previous generations in order to provide sufficient ground clearance. However, this created different aerodynamic handling characteristics which could cause stalls to occur more often. MCAS was designed to fix that problem by taking over some control of the plane in order to prevent stalls. While this level of automation is not unprecedented since similar electronic handling existed in the previous generation 737 and A320, MCAS was more aggressive in “correcting” the plane’s handling, and was activated more often than other anti-stall measures. 

     

    While many in the industry pointed to MCAS, many others questioned the safety record of the airline. Lion Air had a questionable history of safety, and was previously banned from flying to the European Union and the United States until 2016. It was not until the Ethiopian Airlines crash on March 10, 2019 where it seemed that the fault was clearly on the aircraft.

     

    Even before the March 2019 crash, it was clear that a brand new aircraft crashing, especially with a new design that incorporated all of the latest innovations in the aircraft industry, meant that there was a high likelihood that this was not a fluke. Media outlets such as the New York Times and aviation analysts such as Leeham Group pointed out that the MCAS system was a likely cause in the aircraft undergoing a sharp and sudden descent. 

     

    Moreover, Boeing did not disclose the presence of the system to pilots in its manual, or the steps needed to dis-engage it. As a new feature, that only existed on the 4th generation 737, engineers at Boeing should have held “paramount the safety, health, and welfare of the public” as stated in the National Society of Professional Engineers in the United States. Many other professional engineering institutions have a similar clause, including the Professional Engineers of Ontario. 

     

    Not specific to engineering, the practice of informed consent also applies, where pilots should have had the right to know that the plane, even when auto-pilot was off, would suddenly take over the operation of the aircraft much more than existing “fly-by-wire” automation systems found in other aircrafts. 

     

    However, it was purely an economic decision by Boeing to not disclose the existence of the system. In advertising the plane to airlines, Boeing emphasized the plane’s commonality with the previous three generations. This was done so that airlines with large existing fleets of 737s did not face enormous expenses of re-stocking parts, training mechanics, and especially re-training pilots to fly the new plane. This trade-off clearly showed that engineers at Boeing prioritized economics over the safety of the public in not disclosing this feature to pilots.

     

    After the October crash, Boeing did disclose the existence of the system to pilots and ways to dis-engage the system. However, the March crash by a reputable and reliable carrier in Ethiopian Airlines raises more questions about the engineering failures. Black box data, released in the interim report, showed that the pilots did exactly as Boeing recommended to dis-engage MCAS when it forced the nose of the airplane down. However, the system kept re-enabling and eventually pushed the aircraft to enter a steep descent. 

     

    This brings into question the need for the system in the first place. The New York Times reported on June 1st that the original design for MCAS system was far less aggressive in its automated corrections. This was the original justification for removing any mention of MCAS from pilot training and the manual. But initial test flights and simulator runs brought up unsatisfactory maneuvering characteristics caused by the larger engines. On two occasions, engineers increased the aggressiveness and the level of control of MCAS to make the aircraft fly smoother. These actions highlight another common problem of engineering, where decisions are made in a vacuum.

     

    Engineers at Boeing were too fixated on the maneuvering characteristics of the plane, and wanting to minimize the difference between the 737 MAX and previous generations of 737s, that they did not realize how much bigger they were making the holes in the swiss cheese theory of safety, or how increasingly reliant the plane was on MCAS over manual control. Quick fixes that Boeing is considering now to get the 737 MAX in the air, such as making MCAS less aggressive in its corrections, or making it reliant on more than one airspeed and angle sensors (as is the case currently) should have been considered earlier.

     

    Moreover, there are further questions on how much  Boeing can safely optimize what is already a 53 year old design. Boeing focused too much on optimizing fuel burn with the larger engine and minimizing cost to itself, without considering more creative and radical approaches to lowering fuel economy. The 737’s ground clearance, by far the lowest among any modern airplane, has not changed since 1966, while Airbus in its A320, or Boeing in their 777, can easily add larger engines to improve fuel burn without shifting the positioning of the engines like on the 737, or modifying the maneuverability of the plane. 

     

    Many students probably remember one of the very first lectures in APS100: Orientation to Engineering. In it, Professor Stickel quotes an excerpt from the book “Educating the Engineer of 2020”. The book cites a number of attributes for the engineer of 2020 that engineers at Boeing could have shown more of.  However, this “engineering in the vacuum” shows that there was not good communication, and not all stakeholders with different perspectives were engaged. Engineers did not engage their creativity in finding ways to fit larger engines (or even designing a brand new aircraft instead of rehashing a 53 year old plane). And probably the most significant error was the lack of high ethical standards and professionalism in doing their full due diligence and proper disclosure in designing the plane. 

     

    With automation playing an increasing role in today’s society, engineers should not just be relied on for their technical and analytical skills, which machines can probably do a much better job of. Rather, it’s these skills described in the Engineer of 2020 (analytical skills, practical ingenuity, communication, business and management, leadership, high ethical standards, agility, and lifelong learning) that will truly make great engineers, and avoid engineering failures in the future.

  • Catalyzing Change at the WISE Conference

    NAIN HAIDER
    Cannon Contributor

    WISE Exec Team CREDIT: FANG SU

    It is 8 AM on the 26th of January; a cold Saturday morning in downtown Toronto. As I walk upstairs from the beautiful front lobby of The Westin Harbour Castle, I find that several of my colleagues are already setting up in the conference area, and have been doing so since 7 AM. Indeed, this dedicated team has been hard at work for the past several months to make this event a success. The group is Women in Science and Engineering UofT (WISE), currently preparing for its 7th Annual National Conference to begin. Since 1999, WISE has been developing various programs of outreach, professional development, and mentorship in order to support students at various points of their journey with advice and guidance. Its annual two-day conference brings together hundreds of delegates, speakers, and sponsors for a weekend of recognition and collaboration amongst peers.

    This year, the conference’s theme was Catalysts for Change, promoting the motto “be the change you wish to see in the world”. The healthcare and engineering case competitions were challenging, the workshops and panel sessions highlighted cutting-edge research and technology, and the continuous stream of networking sessions allowed vital connections to be built between students and industry professionals, all making the idea of a successful and fulfilling career seem both tangible and achievable.

    As a volunteer at this year’s conference, it was easy to see the value of such an event. There was an undeniable energy in the venue as attendees worked together to instantiate their shared goal of helping women share equal success in STEM fields. This was perhaps best exemplified by the joy felt across the auditorium at Sunday’s closing ceremonies, which saw the first prize for the engineering case competition being awarded to a group of first-year Engineering students: Taylor Faiczak, Catherine Guo, Smile Peng, and Donna Gao.

    Many of the conference’s speakers highlighted why proper representation is important. For Dr. Shawna Pandya, a keynote speaker (and a physician-surgeon and citizen-scientist astronaut candidate), women in STEM serve as a reminder that the exploring and fulfilling of potential lead to an individual realising just how much they are capable of. Another keynote speaker, Aheri Stanford-Asiyo (a software engineer at Microsoft) believes that it is important to have role models that you can identify with and relate to in the field(s) that you are hoping to pursue. Finally, for Aashni Shah (another software engineer passionate about philanthropy), women can bring their own flavour of interpersonal skills to the workplace, and help build supportive communities that encourage and mentor the next generation of female scientists, engineers, and entrepreneurs as well.

    My commute back home on the evening of Sunday the 27th takes me up University Avenue, past the hospitals of Mt Sinai and Princess Margaret, across from the MaRS Discovery District building, and along the beautiful university campus. For perhaps the umpteenth time since beginning my undergraduate degree, I remind myself how lucky I am to be a student here.

  • Is Amazon’s Marketplace Fair?

    CREDIT: FORTUNE

    Samuel Penner
    Cannon Senior Editor

    Amazon is a household name connecting people from around the world with nearly every kind of consumer product. Is the convenience of having low cost goods readily available at our fingertips a good thing in the long run? According to Statista, an online statistics and business intelligence portal, Amazon’s market share in 2017 for e-commerce retail was 37% and is expected to increase significantly by 2021. Projections for that same time frame predict that Amazon’s market share will be responsible for 50% of the Gross Merchandise Volume (GMV) for online sales: the dollar value of merchandise sold through a specific marketplace in a given time. In early September, it was briefly valued around one trillion dollars and has more revenue than most of the biggest tech companies, like Google and Microsoft, combined. Large retailers have often been in the business of offering in-house private label products on their shelves to compete with ‘name brand’ products, like Walmart’s George clothing or Costco’s Kirkland brand products.

    Similarly, Amazon has been steadily introducing its own private labels for a wide variety of products ranging from clothes to batteries. According to a 2018 article written by Julie Creswell in The New York Times, Amazon’s batteries, priced 30% lower than competing brands, now represent about a third of the online battery market and outsell Duracell and Energizer. Batteries, however, are only the tip of the iceberg since Amazon has near a hundred private label brands on their site.

    According to Creswell, the aim of Amazon in the early 90s was to democratize retail and facilitate small manufacturers in reaching the marketplace where traditionally well established brands reigned supreme. There are indications that the rapid shift toward private label brands indicates a growing bias.

    Big technology, big data, and the internet of things are shaping how people shop and interact with the world. With the power of big data, companies like Amazon can identify how consumers spend and target our spending habits to great effect. Creswell reports that Amazon is using the knowledge gained through its algorithms to guide online shoppers away from competitors and toward its own products. In addition, Alexa’s voice technology is capitalizing on further bias by only providing Amazon brands in response to a product search. Combined with their ever-increasing market share, this creates a landscape where competitors must nevertheless sell their products on Amazon’s online platform to reach a wide enough consumer base.

    The academic debate as to whether Amazon’s behavior is monopolistic is gaining traction in more circles thanks to an article written by Lina Khan. David Streitfeld, writing for The New York Times, described Lina Khan as reframing decades of monopoly law, where he explains how her article published in the Yale Law Journal is finding readership in Washington. Her thesis is essentially that antitrust laws have not changed to reflect the current realities of internet commerce, especially in the case of Amazon.

    According to Streitfeld, Khan’s argument goes against the consensus regarding antitrust regulation, and that it should be defined based on consumer welfare. Since Amazon has historically low prices, it would not constitute a target for intervention by the federal government. Khan argues that it is the structural power that Amazon is gathering which poses a threat to a fair marketplace. Khan reasons that regulating Amazon based on its structural influence could be sensible such as treating it like a utility or telephone network, where access to its infrastructure is non-discriminatory.

    Whatever the future holds for Amazon, our access to the consumer marketplace will more and more be dominated by platforms on the internet like Amazon in the same way that our consumption of information is curated by platforms like Google and Facebook. The 21st century could be defined by how we decide, as a society, to regulate the influence of big businesses and the private citizen.

  • Debate About the EngSci Machine Learning Program

    By Najah Hassan & Dale Gottlieb, Cannon Senior Editor & Cannon Editor-in-Chief

    Machine Learning or Machine Dumbing? Credit Muhammad Ali

    The “I’ll-Figure-It-Out” Computers: Najah Hassan

    Starting in September 2018, the Division of Engineering Science will introduce a new option in Machine Intelligence. While some students are excited for this new addition to the curriculum, others worry that this proposed program may be too far apart from the definition of ‘engineering’. Until about a century ago, an engineer was more commonly associated with the study of mechanical structures, civil infrastructure and material composites. However, today engineering also includes electrical engineers, software engineers, financial engineers and now, machine intelligence engineers.

     

    Machine intelligence or machine learning is defined as the study of developing machines that can think for themselves. New developments in the field have shown that this is a growing area of interest with a huge demand for graduates with this specialty. The new option through the Division of Engineering Sciences proposes to equip students with the ability to use multi-disciplinary wholesome thinking to solve complex problems in the world. Why is this so important? The amount of data that is being collected through our devices on a daily basis is growing and humans just are not able to keep up with the organization and analysis of this information. By teaching computers to find patterns in data on their own, we can solve some complex problems and find the answers to questions that we had not thought of previously.

     

    Take the example of 23 and Me’s new research towards discovering the cause of Parkinson’s disease. 23 and Me, a personal genomics company, is using data mining techniques to sift through around 2 million genetic samples that it received from its consumers. The company has used this information to find more than a dozen different mutations that are linked with the disease. Doing further analysis in this realm could lead to the cure for Parkinson’s, allowing us to break through to new domains of healthcare.

     

    The applications for machine learning are numerous. Toronto’s recently launched Vector Institute promises to use artificial intelligence to improve the lives of Canadians and establish economic growth by focusing on the principles and potential of machine learning. It aims to be Toronto’s new research hub and has a lot of opportunities for graduates in machine learning.

    Self-driving cars, machines that find cures, and future predictions based on past performances have all become a reality. The world is moving towards developing technologies that lead to faster, smarter and more efficient societies. As engineers who aspire to make a difference in the world, a knowledge of machine learning and artificial intelligence in this day and age will be a useful skill to have to adapt to this new change. Engineers can combine their problem-solving skills, mathematical background and engineering design strategies with machine learning to solve some of the world’s biggest problems. No doubt, there is a lot of work to be done in this field!

     

    The “I’ll-stop-you-from-figuring-it-out” computers

    By: Dale Gottlieb

    From the Stone Age to the Iron Age, the Bronze Age, and the Silicon Age, the technological development of society has always been measured by our capabilities to manipulate the materials around us. We need a fundamental understanding of nature if we ever wish to achieve the great strides in the quality of life that engineering rightfully takes credit in developing. Even the computer and the software that it runs only exists because of the knowledge to manipulate silicon on the atomic level and to tailor it to our needs. This is why I think it’s a shame that engineering is drifting away from the physical sciences and towards software.

    The new introduction of Machine Learning engineering in Engineering Science at the loss of infrastructure engineering I feel is a grave representation of the stagnation of progress. Engineering Science used to be defined by the broad choices in studies available. For old-school students, there was Infrastructure Engineering, for students interested in chemistry, there was Nanotechnology Engineering, and so on. Now there’s Aerospace Engineering, Robotics Engineering, Electrical Engineering, and Machine Learning…Engineering (?) all teaching a similar subject matter.

    Other disciplines are following suite. Materials Engineering, once called Metallurgical Engineering, changed its name to cover catchier fields of research like nanotechnology. Mechanical Engineering now focuses on circuit design for robotics, and Industrial Engineering focuses on computer optimizations.

    The uniqueness of engineering is being lost, and the ability for engineers to do what scientists can’t is fading. A condensed matter physicist is better at nanotechnology than a materials engineer, much like a Computer Scientist is better than a Machine Learning Engineer at programming. Soon, the only group of people with the knowledge and ability to progress the basic knowledge of science will be lost to the promise of high paying, immediate payoff jobs in Silicon Valley.

    At the present, especially as young millennials, it feels as though an app like Uber is revolutionary, but in reality, it adds nothing to society. The ability to hail a taxi a second faster using an app pales in its affect on society compared to the design of the car, or even a small but essential component of a car like the transmission. A field of engineering like Machine Learning Engineering will get many students high paying jobs in Silicon Valley, but will detract from the development of society engineering has held so dear since its inception.

    To some, my argument against the introduction of Machine Learning Engineering might be translated to ‘machine good, machine learning bad’, but I think it stems to a much bigger issue than we can predict today. With the increasing reliance on computers to solve our problems, and the decreasing number of people researching anything else, we’re destined to be stuck in the silicon age forever. As the top engineering school in the country, I feel UofT needs to think twice before setting an image for all other schools that the only thing an engineer is good for, is what a computer scientist is great for.

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