Mara Cairo is captivated with using AI for good. She has a Bachelor of Science in Electrical Engineering from the University of Alberta and holds her P.Eng. and PMP designations. Before joining Amii, she worked within the hardware development space, where she helped clients take their products to market, with a deal with micro and nano-fabrication.

As Product Owner of Advanced Technology at Amii, Mara leads a technical team that helps industry partners construct machine learning capability inside their organization by providing guidance and expertise to develop predictive models. Her team works with clients who’re committed to advancing along the AI adoption spectrum by applying machine learning to their most difficult business problems.

Amii (Alberta Machine Intelligence Institute) is considered one of Canada’s preeminent centers for AI, they partner with firms of all sizes, across industries, to drive innovation strategy and supply practical guidance and advice, corporate training and talent recruitment services.

We sat down for an interview on the annual 2023 Upper Certain conference on AI that’s held in Edmonton, AB and hosted by Amii.

What initially attracted you to electrical engineering?

As a child, I just really liked constructing things. My mom would bring home a fan when it was hot in summer, and I might wish to construct it. I remember growing up as a teen, I had a cellular phone, considered one of those Nokia’s that you could possibly take apart and I might take it apart and put bejewels throughout it on the within and the antenna. But once I opened it up, it was like, “Holy crap, what’s in here? What is going on on?” It was really interesting to me.

I all the time excelled in math. So, putting all of those together, my parents also pushed me within the engineering direction because I used to be good at math, I had only a general interest in electronics and desired to know more about it, that is sort of what drew me in to start with.

Also, in engineering, I just really liked the concept of applying math to real-world problems. Yeah, okay, cool, math is great and exciting and fun for me, but with engineering you possibly can apply it to resolve hard problems. It seemed sort of the proper mesh of things that may result in an interesting profession.

Your parents sounded very proactive in supporting your interests.

Yeah. My dad especially. He says he saw it in me from a young age and just all the time pushed me in that direction. I used to be at a Women in AI event last night too and we talked about removing some barriers and making it a more approachable field for ladies. And I didn’t really see that as a barrier because, again, my parents were like, “That is what it is best to do. It isn’t a matter of your gender or anything. It’s just it is a skill you’ve. You must naturally sort of follow it and nurture it.” I never felt prefer it wasn’t for me, which helped obviously.

Before joining Amii you worked within the hardware development space to deal with micro and nanofabrication. Could you define those terms?

Definitely. So, in electrical engineering, I took the nanoengineering option. It was the specialty around designing and manufacturing on the micro and nanoscale. Once we speak about a nanometer, we’re talking a couple of millimeter divided in 1,000,000 is a nanometer. A really, very small scale. And that is cool. This stuff are so small you possibly can’t even see them with the naked eye. But I could take this specialization to learn tips on how to manufacture on that scale and design things on that scale.

We live in a really connected world. There’s electronics throughout us and we’d like to give you the option to design electronics for the packaging and space constraints. We’re continually attempting to make things smaller and smaller. You’re taking something bulky, a prototype, and you have to give you the option to make it reproducible and scalable. Nanofabrication is basically concerning the tools and the techniques that you simply use to design and manufacture on that sort of level.

That is from manufacturing microchips to taking those two different chips and connecting them electrically to the ultimate packaging. Doing all of that on the microscale requires a distinct technique than constructing something on our human scale. The micro and nanofabrication are only across the chemical processes that you simply use and the electrical processes, the packaging that you have to be sure these are hermetically sealed and protected against their environment.

Outside of microchips, what could be one other application or use case?

We worked on a number of projects like fiber optics. Again, all of it eventually must come to some form of processing unit that is taking in signals or generating signals. We did work within the telecom industry, optics, cameras, all of that stuff. However the brains of it are generally some form of microchip in the center. But there’s also the sensors which can be feeding their signals into whatever processing unit you are using. So diverse manufacturing techniques for constructing whatever form of sensor or input or output device that we’d like.

What are a few of the challenges behind working on this kind of nanoscale?

One piece of dust can wreck your whole day. Stuff you’re working on are the identical size because the dust within the air. So, you fabricate in a clean room. The clean room is basically an environment that is protecting what you are working on from you as a human, because we’re very dirty as humans, we’re continually sort of spitting out particulates, our clothes are particulating, the makeup that we’re wearing it’s making the air dirty. We’d like to eliminate as much of that as possible in order that the things that we’re constructing are clear and clean of that form of contaminant.

One other challenge, there’s great ways to construct these clean rooms and there is a whole sort of study and science behind that, but the opposite challenge is taking it out of the lab because eventually this stuff are going to be utilized in our very dirty world. That is when the packaging becomes necessary. We still have to give you the option to access these devices, but we’d like to do it in such a way that we’re not contaminating the environment, the packaging. So hermetically sealing things, ensuring it’s completely sealed, nothing’s getting in or out. That is one other set of challenges that I saw. We’d have something that works great on a lab bench in a controlled setting, but generally a lot of the things that we’re constructing are supposed to be brought out into our dirty world. That was difficult as well.

Again, from manufacturing all of the solution to taking it to its final destination, it’s just very special sort of considerations and environmental concerns if you’re coping with things that small. Also, things don’t all the time behave as expected on that small of a scale. In our physical world, we expect things to work a certain way, but if you get all the way down to the micro and nanoscale, the physical world becomes a little bit bit different, and you possibly can’t all the time anticipate the outcomes. That is an entire other field of study.

What could be some examples of being different than the regular physical world?

Passing current through a wire. We’ve our chargers and our phones and we’re passing current through it. If you’re passing current through a wire that is sized like a strand of hair, there’s obviously heat considerations and things will just start behaving otherwise because, again, the space and the scale constraints.

What’s your current role at Amii, and the way does your team help industry partners?

My current role at Amii is vastly different from the world of micro and nanotechnology.

I’m Product Owner of the Advanced Technology Team at Amii. I lead a team of mostly machine learning scientists and project managers who’re all working with our different industry partners to resolve their business problems through the applying of machine learning.

We’re very industry-focused, all about bridging the gap between what’s happening in academia, all the really great breakthroughs with machine learning and AI but applying them to our industry partners biggest needs. We reply to those needs by essentially helping our clients find the talents and the expertise that they should give you the option to maneuver the work forward.

We run our internships and residencies program through the advanced technology team. So, I’m hiring loads. Recruitment just isn’t my background, nevertheless it’s something I do loads now. And it’s all about sort of matchmaking, finding the correct ML talent to position on our client’s project. We hire these folks as Amii employees for a set term and provides them a number of support and mentorship, but really, they’re dedicated to work on the client’s project and move that forward. It is a way for our clients to get access to talent without having to do the recruitment themselves. Amii has some pretty good brand recognition, we’re capable of bring really great talent in after which place them on these industry projects.

A possible good thing about the system is the client having the chance to rent these folks after the term with us is completed. We wish this talent to remain here. We don’t desire brain drain. We’re giving the client a little bit of a leg up in order that they’ll try the talent out, check out the project, get a feel for what machine learning actually is, what do we’d like to make it successful, after which ideally placing the talent inside these firms in a long run in order that these firms really turn into AI firms and are capable of move their very own initiatives forward in the longer term.

How long is the term that they join for normally?

Generally, 4 to 12 months.

It’s something we determine in the beginning, depending on the complexity of the project and what number of problems we’re trying to resolve. We discover the longer, the higher. Machine learning projects to do in 4 months might be difficult. There’s loads more to it than simply constructing ML models. Heavily reliant on the info that is collected from the client that is handed over to us, that helps us construct the models. The longer we have now, the higher it’s to iterate and cycle through all the opportunities.

The work is experimental and exploratory in nature. Amii is a research institute; we won’t all the time guarantee the end result. An extended runway just gives us more time to do this research and be sure that we have exhausted our options and pursued as many things as possible since it’s hard for us to say, “That is the strategy that is going to work best.” You might have to try it and see.

What are some examples of difficult business problems that your team has worked on with these firms?

I alluded to it, definitely data preparedness is a giant challenge. Ongoing industry perception of knowledge preparedness is different than what a machine learning scientist would think is prepared for a machine learning model. And access. How easy is it for the client handy over the info to us in a way that’s consumable for our ML models. That is why we do like longer projects since it gives our team time to work with our clients through those varieties of data preparedness challenges and set them up for fulfillment.

Garbage in is garbage out, in case you hand us garbage data, we will create a garbage model. We actually need quality data. And there is a little bit little bit of a learning curve for clients. Industry perception, again, of what quality data is, what are the examples that we’d like to see to give you the option to predict things in the longer term. It’s only a literacy thing, ensuring that we’re speaking the identical language, they understand the restrictions based off of whatever data they’ve access to once they understand what is going on to set us up for fulfillment.

You would like examples of what you are attempting to predict in your dataset. If an event is basically rare, it should be hard for us to ever anticipate it happening. We could construct a extremely accurate model of something that just say 99% of the time accurate since it’s never predicting the 1% time that something does occur. Again, just ensuring that the client understands what we’d like to construct accurate models.

We have seen even seemingly easy problems might be highly complex depending on their dataset. On the outset, having an initial discovery call with a client, we do should anticipate the length of time that we are going to need. But sometimes once we start peeling back the layers of the onion, we realize, no, that is way more complex than we thought due to these data complexities.

Other challenges, lack of commitment from subject material experts needed. Once we partner with our industry partners, we actually need them to proceed to come back to the table because they’re the domain experts and frequently the info experts too. We’re not like a dev shop where we will just take the info, construct the model, and hand it over to them in the long run. It’s totally, very collaborative. And the more that our industry partners put in, the more that they will get out because they’ll give you the option to guide us in the correct direction, be sure that the predictions that we’re making make sense to them from a business perspective, that we’re targeting the correct metrics, we understand what success is for them.

We do need a multidisciplinary team around us to support the projects and it takes greater than only one machine learning scientist to construct a successful model that is going to affect a business positively. There’s numerous challenges. Those are those that got here to mind.

You personally imagine that AI must be a force for good. What are some ways in which you think that AI can positively change the longer term?

The thing I like most about my job is we work with clients from across all industries, solving very different problems, but all of them are really getting used for some form of positive change. And Amii has our principled AI framework that ensures that we’re doing just that. From the contracting stage, we’re ensuring that the projects that we’re working on with our industry partners are getting used for that positive change in an ethical way. All of the projects I get to see are getting used for good and positively changing the longer term.

One thing that involves mind, in Alberta as a rule now we’re coping with wildfire situations in the summertime. This yr especially, even in April, it was bad. We recently partnered with Canada Wildfire. It is a research group out of the University of Alberta. 40 years of weather data tied to severe wildfire events. Working with them to raised predict these events in the longer term so we will higher prepare the resources that could be needed, have the teams go in and temper the environments before it gets to a stage where the wildfires are raging. I feel that is just being in Edmonton, I do not know in case you were here last week, nevertheless it was very smoky.

Once I arrived Sunday night (May 21, 2023) it was quite smoky.

It’s devastating. It ruins communities. It takes people’s homes away. Having to breathe particulate within the air is not great, however the devastation may be very immense. That is one interesting (project) that is near all of our hearts.

One other area we’re working in is the agriculture space. How are we going to feed our growing population? We’re working with the National Research Council on a protein abundance problem. Attempting to be sure the plants that we’re growing have higher protein content to feed our growing population and using machine learning to give you the option to make those predictions.

Reducing emissions is one other very fashionable one. Working with firms within the oil and gas sector to be sure that the processes and systems and tools which can be used are as efficient as possible. We’re working with a water treatment plant out of Drayton Valley, which is a small town in Alberta, ensuring that that water treatment plant is running as efficiently as possible and that we’re creating as much clean water for the community as possible. Precision medicine as well.

The list goes on. Literally, every company we work on its these varieties of projects, these varieties of causes. It’s hard for me to choose a favourite because when you consider it, all of them have the chance to have a incredibly positive impact on the longer term.

What’s your vision for the longer term of AI or robotics?

My exposure to robotics has really been in the provision chain. It’s where robotics are already getting used, nevertheless it’s also how will we enhance them with AI to construct on existing systems and automation, again, through more efficient processes? The provision chain is clearly taken with increasing throughput, fulfilling more orders more quickly, and more efficient decision-making. On the robotics side of things, again, my exposure has been constructing on top of existing robots to make them smarter and higher.

I feel more generally, the longer term from what I see industry doing remains to be very human-centric. Robotics are used as a tool, as an augmentation to humans. Possibly robotics being deployed in conditions which can be dangerous to humans where we should not be exposed to the environments. Robotics are a terrific alternative for us in that case to maintain us safer. There’s also really cool research being done by our fellows and bionic limbs, so easier control and movement of people that do need that support. All very much still tied to humans and their use of those tools but making it easier for them to make use of and making their lives easier through these recent systems.

When it comes to the longer term of AI basically, that is just such an interesting time to be on this space. Industry is finally getting it that AI is here and it will change all the pieces and you possibly can either lead or be led. I feel considered one of Amii’s visions is to have every company comfortable with the technology, aware of what it might and can’t do, and really willing to experiment and iterate on implementing it of their business to resolve a few of their hardest problems.

Up until now, I feel possibly there was a perception that it was just tech firms that were AI and ML users, but now it’s becoming more apparent that ML might be deployed in essentially every organization. It isn’t all the time the correct answer, but there’s often a use case for it. I’m hopeful that the longer term is firms becoming natural AI firms themselves by getting more literate and acquainted with the technology and aware of how they’ll use it for his or her business.