Mara Cairo is captivated with utilizing AI for good. She has a Bachelor of Science in Electrical Engineering from the College of Alberta and holds her P.Eng. and PMP designations. Earlier than becoming a member of Amii, she labored within the {hardware} growth house, the place she helped shoppers take their merchandise to market, with a deal with micro and nano-fabrication.

As Product Proprietor of Superior Expertise at Amii, Mara leads a technical group that helps {industry} companions construct machine studying capability inside their group by offering steerage and experience to develop predictive fashions. Her group works with shoppers who’re dedicated to advancing alongside the AI adoption spectrum by making use of machine studying to their most difficult enterprise issues.

Amii (Alberta Machine Intelligence Institute) is one in all Canada’s preeminent facilities for AI, they associate with corporations of all sizes, throughout industries, to drive innovation technique and supply sensible steerage and recommendation, company coaching and expertise recruitment providers.

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

What initially attracted you to electrical engineering?

As a child, I simply actually appreciated constructing issues. My mother would convey house a fan when it was sizzling in summer season, and I’d wish to construct it. I bear in mind rising up as a youngster, I had a cellphone, a type of Nokia’s that you can take aside and I’d take it aside and put bejewels throughout it on the within and the antenna. However once I opened it up, it was like, “Holy crap, what’s in right here? What is going on on?” It was actually attention-grabbing to me.

I all the time excelled in math. So, placing all of these collectively, my mother and father additionally pushed me within the engineering course as a result of I used to be good at math, I had only a basic curiosity in electronics and needed to know extra about it, that is type of what drew me in to start with.

Additionally, in engineering, I simply actually appreciated the concept of making use of math to real-world issues. Yeah, okay, cool, math is nice and thrilling and enjoyable for me, however with engineering you may apply it to resolve arduous issues. It appeared type of the right mesh of issues that will result in an attention-grabbing profession.

Your mother and father sounded very proactive in supporting your pursuits.

Yeah. My dad particularly. He says he noticed it in me from a younger age and simply all the time pushed me in that course. I used to be at a Ladies in AI occasion final night time too and we talked about eradicating some limitations and making it a extra approachable discipline for girls. And I did not actually see that as a barrier as a result of, once more, my mother and father have been like, “That is what you must do. It is not a query of your gender or something. It is simply it is a ability you’ve got. It’s best to naturally type of comply with it and nurture it.” I by no means felt prefer it wasn’t for me, which helped clearly.

Earlier than becoming a member of Amii you labored within the {hardware} growth house to deal with micro and nanofabrication. May you outline these phrases?

Undoubtedly. So, in electrical engineering, I took the nanoengineering choice. It was the specialty round designing and manufacturing on the micro and nanoscale. Once we speak about a nanometer, we’re speaking a couple of millimeter divided in one million is a nanometer. A really, very small scale. And that is cool. These items are so small you may’t even see them with the bare eye. However I may take this specialization to discover ways to manufacture on that scale and design issues on that scale.

We reside in a really linked world. There’s electronics throughout us and we want to have the ability to design electronics for the packaging and house constraints. We’re always attempting to make issues smaller and smaller. You’re taking one thing cumbersome, a prototype, and also you want to have the ability to make it reproducible and scalable. Nanofabrication is actually concerning the instruments and the strategies that you just use to design and manufacture on that type of stage.

That is from manufacturing microchips to taking these two completely different chips and connecting them electrically to the ultimate packaging. Doing all of that on the microscale requires a special method than constructing one thing on our human scale. The micro and nanofabrication are simply across the chemical processes that you just use and {the electrical} processes, the packaging that you want to make sure that these are hermetically sealed and protected against their atmosphere.

Outdoors of microchips, what could be one other software or use case?

We labored on a whole lot of initiatives like fiber optics. Once more, all of it finally should come to some form of processing unit that is taking in alerts or producing alerts. We did work within the telecom {industry}, optics, cameras, all of that stuff. However the brains of it are usually some form of microchip within the center. However there’s additionally the sensors which can be feeding their alerts into no matter processing unit you are utilizing. So numerous manufacturing strategies for constructing no matter kind of sensor or enter or output machine that we want.

What are a number of the challenges behind engaged on this sort of nanoscale?

One piece of mud can spoil your entire day. Belongings you’re engaged on are the identical measurement because the mud within the air. So, you fabricate in a clear room. The clear room is actually an atmosphere that is defending what you are engaged on from you as a human, as a result of we’re very soiled as people, we’re always type of spitting out particulates, our garments are particulating, the make-up that we’re sporting it is making the air soiled. We have to remove as a lot of that as doable in order that the issues that we’re constructing are clear and clear of that form of contaminant.

One other problem, there’s nice methods to construct these clear rooms and there is a entire type of examine and science behind that, however the different problem is taking it out of the lab as a result of finally this stuff are going for use in our very soiled world. That is when the packaging turns into essential. We nonetheless want to have the ability to entry these gadgets, however we have to do it in such a method that we’re not contaminating the atmosphere, the packaging. So hermetically sealing issues, ensuring it is fully sealed, nothing’s getting in or out. That is one other set of challenges that I noticed. We might have one thing that works nice on a lab bench in a managed setting, however usually many of the issues that we’re constructing are supposed to be introduced out into our soiled world. That was difficult as effectively.

Once more, from manufacturing all the best way to taking it to its last vacation spot, it is simply very particular type of concerns and environmental issues once you’re coping with issues that small. Additionally, issues do not all the time behave as anticipated on that small of a scale. In our bodily world, we anticipate issues to work a sure method, however once you get right down to the micro and nanoscale, the bodily world turns into somewhat bit completely different, and you’ll’t all the time anticipate the outcomes. That is an entire different discipline of examine.

What could be some examples of being completely different than the common bodily world?

Passing present by way of a wire. Now we have our chargers and our telephones and we’re passing present by way of it. Whenever you’re passing present by way of a wire that is sized like a strand of hair, there’s clearly warmth concerns and issues will simply begin behaving in a different way as a result of, once more, the house and the scale constraints.

What’s your present function at Amii, and the way does your group assist {industry} companions?

My present function at Amii is vastly completely different from the world of micro and nanotechnology.

I am Product Proprietor of the Superior Expertise Workforce at Amii. I lead a group of principally machine studying scientists and undertaking managers who’re all working with our completely different {industry} companions to resolve their enterprise issues by way of the appliance of machine studying.

We’re very industry-focused, all about bridging the hole between what’s occurring in academia, the entire actually nice breakthroughs with machine studying and AI however making use of them to our {industry} companions largest wants. We reply to these wants by primarily serving to our shoppers discover the talents and the experience that they want to have the ability to transfer the work ahead.

We run our internships and residencies program by way of the superior know-how group. So, I am hiring lots. Recruitment will not be my background, but it surely’s one thing I do lots now. And it is all about type of matchmaking, discovering the precise ML expertise to position on our consumer’s undertaking. We rent these people as Amii workers for a set time period and provides them a whole lot of help and mentorship, however actually, they’re devoted to work on the consumer’s undertaking and transfer that ahead. It is a method for our shoppers to get entry to expertise with out having to do the recruitment themselves. Amii has some fairly good model recognition, we’re capable of convey actually nice expertise in after which place them on these {industry} initiatives.

A possible advantage of the system is the consumer having the chance to rent these people after the time period with us is completed. We wish this expertise to remain right here. We do not need mind drain. We’re giving the consumer a little bit of a leg up in order that they’ll attempt the expertise out, check out the undertaking, get a really feel for what machine studying truly is, what do we have to make it profitable, after which ideally putting the expertise inside these corporations in a long term in order that these corporations actually change into AI corporations and are capable of transfer their very own initiatives ahead sooner or later.

How lengthy is the time period that they join usually?

Typically, 4 to 12 months.

It’s one thing we work out initially, relying on the complexity of the undertaking and what number of issues we’re attempting to resolve. We discover the longer, the higher. Machine studying initiatives to do in 4 months could be difficult. There’s much more to it than simply constructing ML fashions. Closely reliant on the information that is collected from the consumer that is handed over to us, that helps us construct the fashions. The longer we’ve got, the higher it’s to iterate and cycle by way of the entire alternatives.

The work is experimental and exploratory in nature. Amii is a analysis institute; we will not all the time assure the result. An extended runway simply provides us extra time to do this analysis and make it possible for we have exhausted our choices and pursued as many issues as doable as a result of it is arduous for us to say, “That is the tactic that is going to work finest.” It’s a must to attempt it and see.

What are some examples of difficult enterprise issues that your group has labored on with these corporations?

I alluded to it, undoubtedly information preparedness is an enormous problem. Ongoing {industry} notion of information preparedness is completely different than what a machine studying scientist would assume is prepared for a machine studying mannequin. And entry. How straightforward is it for the consumer handy over the information to us in a method that’s consumable for our ML fashions. That is why we do like longer initiatives as a result of it provides our group time to work with our shoppers by way of these kinds of information preparedness challenges and set them up for fulfillment.

Rubbish in is rubbish out, for those who hand us rubbish information, we will create a rubbish mannequin. We actually want high quality information. And there is a little little bit of a studying curve for shoppers. Trade notion, once more, of what high quality information is, what are the examples that we have to see to have the ability to predict issues sooner or later. It is only a literacy factor, ensuring that we’re talking the identical language, they perceive the constraints primarily based off of no matter information they’ve entry to once they perceive what is going on to set us up for fulfillment.

You want examples of what you are attempting to foretell in your dataset. If an occasion is actually uncommon, it should be arduous for us to ever anticipate it occurring. We may construct a very correct mannequin of one thing that simply say 99% of the time correct as a result of it is by no means predicting the 1% time that one thing does happen. Once more, simply ensuring that the consumer understands what we have to construct correct fashions.

We have seen even seemingly easy issues could be extremely complicated relying on their dataset. On the outset, having an preliminary discovery name with a consumer, we do should anticipate the size of time that we’ll want. However generally once we begin peeling again the layers of the onion, we understand, no, that is rather more complicated than we thought due to these information complexities.

Different challenges, lack of dedication from subject material consultants wanted. Once we associate with our {industry} companions, we actually want them to proceed to come back to the desk as a result of they’re the area consultants and often the information consultants too. We’re not like a dev store the place we are able to simply take the information, construct the mannequin, and hand it over to them in the long run. It is very, very collaborative. And the extra that our {industry} companions put in, the extra that they’re going to get out as a result of they’re going to be capable to information us in the precise course, make it possible for the predictions that we’re making make sense to them from a enterprise perspective, that we’re concentrating on the precise metrics, we perceive what success is for them.

We do want a multidisciplinary group round us to help the initiatives and it takes greater than only one machine studying scientist to construct a profitable mannequin that is going to influence a enterprise positively. There’s numerous challenges. These are those that got here to thoughts.

You personally imagine that AI needs to be a power for good. What are some ways in which you assume AI can positively change the longer term?

The factor I like most about my job is we work with shoppers from throughout all industries, fixing very completely different issues, however all of them are actually getting used for some form of optimistic change. And Amii has our principled AI framework that ensures that we’re doing simply that. From the contracting stage, we’re ensuring that the initiatives that we’re engaged on with our {industry} companions are getting used for that optimistic change in an moral method. All of the initiatives I get to see are getting used for good and positively altering the longer term.

One factor that involves thoughts, in Alberta as a rule now we’re coping with wildfire conditions in the summertime. This 12 months particularly, even in April, it was dangerous. We not too long ago partnered with Canada Wildfire. It is a analysis group out of the College of Alberta. 40 years of climate information tied to extreme wildfire occasions. Working with them to higher predict these occasions sooner or later so we are able to higher put together the sources that may be wanted, have the groups go in and mood the environments earlier than it will get to a stage the place the wildfires are raging. I feel that is simply being in Edmonton, I do not know for those who have been right here final week, but it surely was very smoky.

Once I arrived Sunday night time (Might 21, 2023) it was fairly smoky.

It is devastating. It ruins communities. It takes individuals’s properties away. Having to breathe particulate within the air is not nice, however the devastation could be very immense. That is one attention-grabbing (undertaking) that is near all of our hearts.

One other space we’re working in is the agriculture house. How are we going to feed our rising inhabitants? We’re working with the Nationwide Analysis Council on a protein abundance drawback. Attempting to ensure the vegetation that we’re rising have greater protein content material to feed our rising inhabitants and utilizing machine studying to have the ability to make these predictions.

Decreasing emissions is one other very talked-about one. Working with corporations within the oil and fuel sector to make it possible for the processes and methods and instruments which can be used are as environment friendly as doable. We’re working with a water remedy plant out of Drayton Valley, which is a small city in Alberta, ensuring that that water remedy plant is operating as effectively as doable and that we’re creating as a lot clear water for the group as doable. Precision drugs as effectively.

The listing goes on. Actually, each firm we work on its these kinds of initiatives, these kinds of causes. It is arduous for me to select a favourite as a result of when you consider it, all of them have the chance to have a extremely optimistic influence on the longer term.

What’s your imaginative and prescient for the way forward for AI or robotics?

My publicity to robotics has actually been within the provide chain. It is the place robotics are already getting used, but it surely’s additionally how can we improve them with AI to construct on current methods and automation, once more, by way of extra environment friendly processes? The availability chain is clearly fascinated by rising throughput, fulfilling extra orders extra shortly, and extra environment friendly decision-making. On the robotics aspect of issues, once more, my publicity has been constructing on high of current robots to make them smarter and higher.

I feel extra usually, the longer term from what I see {industry} doing remains to be very human-centric. Robotics are used as a instrument, as an augmentation to people. Possibly robotics being deployed in situations which can be harmful to people the place we should not be uncovered to the environments. Robotics are an ideal alternative for us in that case to maintain us safer. There’s additionally actually cool analysis being executed by our fellows and bionic limbs, so simpler management and motion of people that do want that help. All very a lot nonetheless tied to people and their use of those instruments however making it simpler for them to make use of and making their lives simpler by way of these new methods.

By way of the way forward for AI generally, that is simply such an attention-grabbing time to be on this house. Trade is lastly getting it that AI is right here and it will change all the things and you’ll both lead or be led. I feel one in all Amii’s visions is to have each firm snug with the know-how, conscious of what it could possibly and can’t do, and actually prepared to experiment and iterate on implementing it of their enterprise to resolve a few of their hardest issues.

Up till now, I feel perhaps there was a notion that it was simply tech corporations that have been AI and ML customers, however now it is turning into extra obvious that ML could be deployed in primarily each group. It is not all the time the precise reply, however there’s often a use case for it. I am hopeful that the longer term is corporations turning into pure AI corporations themselves by getting extra literate and acquainted with the know-how and conscious of how they’ll use it for his or her enterprise.

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