How VIA TAC™-kles Sensor Data

The electric grid is not just filled with sensors. It is a giant sensor.

Smart meters collect data from millions of customers, and in some countries like Spain and Denmark, from over 90% of homes. In addition, every utility collects load data in near real-time. 

Load, SCADA, smart meter, and other sensor data on the grid are extremely powerful datasets for advanced analytics. However, to paraphrase the Spiderman franchise, with great power comes great responsibility.

Many utilities are highly concerned about the responsibility that comes with these datasets. As a result, the data gets trapped for one or more of these four reasons:

  1. Data is too big: After you install a sensor, you might go from a reading once a month to 30 times a second. Data volume becomes an issue.
  2. Data is too confidential: Something in the sensor reveals critical infrastructure or personally identifiable information (PII). Smart meters can tell you vacation schedules and household size, for example. Many of the analytics insights can be considered protected information by consumer privacy laws (think CCPA and GDPR).
  3. Data is too distributed: Data from sensors by definition is going to be in a lot of locations, perhaps even across regional jurisdictional boundaries. This can make it expensive or impossible to aggregate.
  4. Data is too messy: Different sensors or meters or subsidiaries of a utility may have different data formats. Cleaning data is a time intensive process when performed manually.

VIA’s software platform, TAC™ was designed to address all four of these issues in power sector data. TAC™ reduces risk and improves IT efficiency for data access and data analysis projects. Follow the latest section of our website to learn about TAC™ features (in our Let’s TAC™ About It blog series) as well as new customer and project announcements.

Let’s TAC™ About It: Homomorphic Encryption Algorithms on TAC™ (HEAT)

For the sixth installment of our blog series, we will cover homomorphic encryption algorithms on TAC™, known as HEAT. So, are you ready to TAC™ about it?

It’s standard practice to encrypt data at rest (e.g., data in a database) and in transit (e.g., data sent over the internet). When an analyst has to work with data, however, the data is usually unencrypted to perform any calculation. Homomorphic encryption (HE) is cryptography that holds the promise of allowing computations on data while it remains encrypted. 

Despite the increasing need for data security and privacy, HE is not widely used. There are at least two barriers to adoption:

  • Impracticality: NIST-compliant encryption schemes that power widely-used protocols such as RSA have well-known properties that make it possible to perform a subset of arithmetic operations on encrypted numbers. Many of these methods, however, require brute-force computation that limits their ability to add large numbers.
  • Lack of standardization: To overcome the limitations of NIST-compliant encryption schemes, many new HE protocols have been developed to perform operations on larger numbers with some powerful optimizations. Unfortunately, none have been proven reliable enough to be standardized. Even large projects such as Microsoft SEAL have recognized security challenges.

VIA’s HE algorithm overcomes both these barriers for addition. Our protocol uses NIST-compliant standard elliptic curve cryptography (ECC) algorithms to sum arbitrarily large numbers. The key innovation is to represent integers in a base of 2n and sum the numbers for each column in separate batches up to an overflow limit of 232. This resulting number (a vector of numbers less than 232) can thus be decoded using standard brute-force decoding algorithms. 

To avoid overflow limits, the protocol is limited to summing 2(32-n) numbers at a time. If, however, there is a need to sum more than 2(32-n) at a time, we can increase the overflow limit by increasing computation power available for decoding. We distribute the computation to multiple machines to execute the decoding in parallel. In practice, HE is normally used on numbers that are already aggregated, so the 2(32-n) number limit is not a significant barrier. 

As an example, imagine a decrypting system that could only handle numbers up to 1,000 in base 10. How would you add the numbers, 236 and 56,798? While such a decrypting system can only handle numbers up to 1,000, we are not hampered by this limitation. The number 236 can be represented as a vector of (6,3,2,0,0). The number 56,798 is represented as (8,9,7,6,5). Adding the two numbers, we get (14, 12, 9, 6, 5). We can decode each of the components of that vector because they are all less than 1,000. We can then reexpress this result as (4,3,0,7,5) and finally decode this as 57,034. With this approach, we can add up to 100 numbers at a time and be sure that none of the individual columns exceed 1,000 and thus our cryptosystem will work.

What if we need to add 200 numbers? In this case, we can choose a small base (e.g., from 216 to 24) and raise the limit beyond 1,000 by distributing the decoding in parallel. The size of the base determines the amount of brute-force computation required. By breaking the addition into a series of smaller problems, it now becomes possible to use standardized encryption algorithms and brute-force computation to solve the problem. We also leverage existing standards to make it practical to perform arbitrarily complex sum operations. 

VIA has incorporated this HE addition algorithm into its TAC™ platform, known as Homomorphic Encryption Algorithm on TAC™ or HEAT.

To benchmark the system, we compared execution times for HEAT versus Microsoft SEAL, a popular open source HE library. The simple benchmark consists of recording the execution time to encrypt, add, and decrypt up to 80,000 integers. The cryptographic parameters for the ECC were chosen to match the same level of protection as SEAL.

The graph above shows that HEAT is roughly twice as fast as SEAL.

At VIA, we’re excited to have found a “no-tradeoff” solution for HE addition that has wide applications. We are already using HEAT to enable encrypted benchmarking of data across utilities as part of our GDAC™ program. We are also looking into using HEAT for training a federated deep learning model.

HE is a rapidly developing field. VIA is increasing its dedicated resources to improve its HE implementation including exploring lattice cryptography to meet post-quantum computing requirements and extending HEAT to enable homomorphic multiplication.

Let’s TAC™ About It: K-Anonymity

For the fifth installment of our blog series, we will cover TAC™’s k-anonymity functionality. So, are you ready to TAC™ about it?

Regulators are increasingly demanding that utilities release their data to third parties to support a wide array of clean energy initiatives. At the same time, regulators are also mandating increased information privacy and security requirements like with CCPA and GDPR.

The data about a consumer’s energy behavior can provide enormous insights for efficiency but, of course, they can also reveal private details like vacation habits, income levels, family size, etc.

How can utilities balance these two reasonable but competing requirements? 

VIA has implemented a k-anonymity function to handle this use case. The essence of k-anonymity is to segment the data in such a way that similar consumers are in groups that are both big enough to hide an individual consumer’s behavior and small enough to be meaningful and useful for analysis and creating energy efficiency programs.

The above images show two normally distributed variables with 1,000 points. Each cross indicates one point. There can be several points at each location on top of each other. The second image shows the points grouped with at least 15 points within each group. The groups are smaller and more frequent in the middle because there are more points located in the middle due to the normal distributions.

K-anonymity algorithms are sometimes referred to as Mondrian algorithms due to the groupings resembling the paintings of Piet Mondrian.

Choosing an optimal grouping size is not just a challenge, it is actually an NP-hard mathematical problem. TAC™ now provides a simple function to allow utilities to implement k-anonymity groupings to meet both regulator constraints. When a utility chooses this function, any request of data must meet specific group size constraints. If not enough data exists to create a set of data that would maintain data privacy (e.g., only one consumer meets the specific request), then the utility does not provide the data. Similarly, if a data request is made where there is more than enough data to maintain an individual’s privacy, smaller groups of data will be created to allow for more targeted analysis.

Utilities facing regulatory constraints are excited by the opportunity to have an automated means of ensuring data privacy. As a sign of growing interest in the area, the utility non-profit EPRI (Electric Power Research Institute) is also facilitating a working group to test new solutions to the regulator dilemma.

Let’s TAC™ About It: Digital Watermarking

For the fourth installment of our blog series, we will cover TAC™’s digital watermarking functionality. So, are you ready to TAC™ about it?

For centuries, watermarks have been used to verify currencies and important documents and to discourage counterfeiting. Today, watermarks are used for the same purpose to protect music, photos, videos, software, and other digital data.

The main goal of our TAC™ platform is to keep data private by not transferring data. Having said that, there are times when it is necessary (e.g., for audit or regulatory purposes) to transfer some data between trusted parties. 

At VIA, we use watermarking to enable data auditability when data owners choose to transfer data. For example, two organizations can receive identical datasets. If it is later found that data has been leaked, the data owner can review the leaked data and trace it back to the original analyst that requested it.

TAC™ provides the option to digitally watermark a dataset.

TAC™ automates the process of maintaining the provenance of data. As long as the data was transferred using TAC™, no additional steps are required by the data owner and there is no noticeable difference to analysts.

Watermarking is a simple and automatic method to provide auditability to data.

Introducing SWEET: AI for Data Wrangling

For the third installment of our blog series, Let’s TAC™ About It, we’d like to introduce you to SWEET, a built-in function on VIA’s TAC™ platform. So, are you ready to TAC™ about it?


SWEET stands for Smart Wrangling Engine for Extraction and Transformation. Check out the video (or the transcription that follows) to learn how we use AI to wrangle data!

Below is a transcription of the “Introduction to SWEET: AI for Data Wrangling” video.

Analysts need to transfer data from a spreadsheet to a database, often known as data wrangling. The analyst usually identifies a rule or multiple rules (such as: column x is data, row 5 is a header and can be discarded, and so on). The analyst then writes code to execute that rule. This works well when rules are easily identifiable. In most cases, however, this is incredibly time consuming.

Data scientists spend more time wrangling and cleaning data than on analysis and AI. The problem with that is analytics insights are of the highest value, but get the least amount of resources. The big leap in AI is being able to process information without humans writing all the rules.

As an example, computer vision is used to identify a dog. Think of all the varieties of dogs and all the possible variations in context that those dogs could be in. There isn’t an army of people large enough to write rules to identify a random dog in a random photo.

And yet, AI can.

VIA’s approach to data wrangling is to use some of the exact same AI algorithms used in image recognition. This works across a much wider variety of contexts and spreadsheet or file formats. Let’s take a look at how SWEET works.

Here’s a spreadsheet. What SWEET is going to do is use a number of different machine learning algorithms to automate the process of getting the information into a database format.

The first model uses machine vision to map out the spreadsheet. Purple represents blank space, green is headings, yellow is actual data. Once that model has run, there’s a second algorithm that takes a look at the content.

The second algorithm skips over the purple. It looks at the green (which is the headers) to know which column to write where in the database. Finally, it would take a look at the yellow area to write the data to the database.

A third machine learning algorithm determines which column is derived from other parts of the sheet. For example, a total column is just the sum of the other columns and may not be necessary to write to the database. The third model separates these derived columns from the raw data.

In this example, the ACCOUNT column turns out to be the total of the other columns added up. It could be difficult for a human to understand immediately, but one of our models does this instantly.

SWEET’s approach works irrespective of the format. The model doesn’t have to be re-trained when it comes across spreadsheets that are new or in different formats.

So, what’s new and different here? AI algorithms have been evolving quickly. Many of the models that we implemented didn’t exist just a few years ago.

The other insight is that we broke the “convert this spreadsheet into a db” problem into multiple steps and have a different AI algorithm for each step.

Combined, SWEET, a built-in function in VIA’s TAC™ ingestion engine, helps make processes that used to take analysts days to do manually and makes them instantaneous.


Update: July 31, 2020

The image below shows the steps SWEET takes to transform raw files into a standard format.

Meet the Team: Ashley DaSilva, Team Leader, Product Development

Through a Q&A-style interview, you will hear from VIA team members about things like a typical day at the office and favorite foods.


What does a typical day at VIA look like for you?

Typically I start my morning with coffee and code review. At VIA, we have a strong culture of peer review to help us learn from each other and improve our work. Reviewing my colleagues’ code is a good way to see all the amazing work my colleagues are doing, and setting aside this time makes sure I provide them timely feedback.

Once I’ve finished my coffee, I like to take a morning walk (before we moved to remote work due to COVID-19, this was the time I spent commuting to the office.) The rest of the day varies depending on the project I’m working on: it could be writing a software design proposal for a new feature, wrangling data, or automating deployments.

I spend a lot of my day working with my colleagues to solve technical challenges. Often this is pair programming, but sometimes it’s designing the solution together on a (virtual) whiteboard. I really like having the opportunity to work on such a variety of tasks.

What’s something you have worked on at VIA that you are most proud of?

I was the technical lead on VIA’s 30-Minute Pilot and am really proud of how much of a difference it has made for our partners. They were able to get valuable data analysis in less than 30 minutes, while learning about VIA’s privacy-protecting software.

What’s your favorite VIA memory?

At our last company-wide All Hands event, we did a scavenger hunt in the underground city of Montreal. It really fit VIA’s culture: we were split into teams, and each team had to collaborate to solve the puzzles and at the same time, we were competing to solve more puzzles than the other teams! I got to know a little more about Montreal, and got to collaborate with colleagues that I don’t work with day-to-day.

If you were given an extra hour in your day, what would you spend it doing?

That’s a tough one. I would probably practice painting. I love creating things, and that passion is not limited to software. I love the bright colors and rich textures of oil paints, and the challenge of exercising my creativity in a totally different way than I do at work.

What’s your go-to food?

Tacos! Since moving to Montreal, I have learned how to make corn tortillas from scratch, which is actually much easier than I thought it would be.

What’s something everyone may not, but should know about working at VIA?

VIA’s twice yearly All Hands events are held in an “unconference” style: all team members submit and vote on topics just a couple days ahead of time. Because everyone has input into the topics, and the fact that there’s not much time for session leaders to prepare a lot of talking points, the sessions focus on discussion, collaboration, and brainstorming.

Let’s TAC™ About It: Models for Private Data Service

For the second installment of our blog series, we will cover VIA’s Models for Private Data (MPD) service. So, are you ready to TAC™ about it?

Companies are awash with data. To make sense of the growing data volume, AI models continue to proliferate in number and improve in performance. Even better, many of the best models are open sourced. Leading companies like Amazon, Google, Microsoft, and others have made freely available AI models for everything from facial recognition to text analysis. 

So, why aren’t more companies taking advantage of these models for analysis of their corporate data?

One of the top challenges is data privacy. Many companies are fearful of sending their data to the cloud or to an external AI provider. Data and AI regulations from Europe (GDPR) to California (CCPA) are complex to navigate. Even without government oversight, companies worry about the reputational cost of a real or perceived privacy violation. 

VIA’s TAC™ platform solves this with its Models for Private Data (MPD) service. 

TAC™’s MPD service makes machine learning models available for download and easy incorporation into data science workflows. This has the dual benefit of eliminating the need to send data outside the organization or to the cloud and also the need to have internal AI experts to run state of the art machine learning models. The result is that data is kept private and secure.

As an example, imagine a company capturing images about their equipment to identify corrosion. Through TAC™’s MPD service, a data scientist can choose a corrosion analysis model (e.g., a pre-trained TensorFlow algorithm) and easily incorporate that model into their workflow. The MPD service accomplishes this by using containers to make the models easily accessible through their Python scripts. The models use input from their local databases and return a list of predictions locally. Data remains on premise in their local VPC at all times. This is one way that we keep data private across our Global Data Asset Collaborative™ (GDAC™) with multiple energy utilities.

Stay tuned for a video demo of this service in the coming weeks!

Need a laugh?

We’re back at it – this time giving you some laughs from our remote team. See our video below for the funniest moments since working remotely.

 

 

Meet the Team: Annvie Nguyen, UX Designer

Through a Q&A-style interview, you will hear from VIA team members about things like a typical day at the office and favorite foods.


What does a typical day at VIA look like for you?

The only typical part about my days at VIA is that it changes every day! As the go-to media person, I’m lucky to have the opportunity to collaborate with different teams and work on a variety of projects. Some days, I’ll be working on filming and editing videos for VIA (check out the careers video produced by yours truly). Others, I’ll get to flex my design eye and work with our developers to create user interfaces for our products. Today, I’m updating our website and writing a fabulous blog.

What’s something you have worked on at VIA that you are most proud of?

There are so many cool things I want to talk about, it’s difficult to choose!

For one, I really enjoyed working on the interface for the GDAC™ Transformers  30-Minute Pilot. In that project, I designed the user experience for a data analysis demo. It presented an interesting challenge: how can we make complex analysis simple for others to understand, operate by themselves, and see the value? I was able to participate from the conceptualization stage all the way to the user interface and experience. It was so rewarding to see the pilot go from its baby stages to a full-fledged site.

What’s your favorite VIA memory?

My favorite VIA memory might have to be this past holiday party. We made gingerbread houses, ate some delicious desserts (essential to the VIA brand), and played a ridiculous reindeer ring toss game that had us crying with laughter. Hats off to Jackie for being our star party planner!

via GIPHY

If you were given an extra hour in your day, what would you spend it doing?

I can’t deny that I’d be playing Animal Crossing on Nintendo.

What’s your go-to food?

Nothing can outrank my comfort food of choice: a nice, warm bowl of pho. It’s Vietnam’s most famous noodle soup, light but also flavorful. But to me, it’s like getting a hug from Grandma.

What’s something everyone may not, but should know about working at VIA?

You should know that at VIA we’re always working on improving ourselves. With retrospectives at the end of each sprint, open ears during one-on-one’s, and offsites each year, being open to feedback is an essential part of VIA culture.

Working Remotely? Us too!

Along with people all over the world, the VIA team has adjusted to a new normal over these last few months. 

Given our distance apart, we asked some of our team members what they look forward to each day of working remotely.

Check out our video below to see what our team has to say (spoiler: this will give you all the feels!)

 

 

Meet the Team: Emma Fechney, Senior Lead, People and Operations

Through a Q&A-style interview, you will hear from VIA team members about things like a typical day at the office and favorite foods.


What does a typical day at VIA look like for you?

One of the things that I love about my job is that for me, there is no such thing as a “typical day.” Because my role encompasses the entire candidate and employee experience, there are often lots of different projects going on at any moment. Examples include planning recruitment events, working with the team on our professional development program, or even ordering a whole lot of pizza for our regular lunch and learn sessions.

I always try to start my day by consulting my to do’s and then from there, it can be a mixture of meetings, catch-ups with team members, interviews, or planning the next team event.

What motivates you to come to the office each day?

People! I am motivated by the idea of enhancing people’s work experience, whether it’s exploring ways to support professional development or strengthening the connection between our team and our vision. Plus, finding ways to celebrate and have fun together never gets old!

What’s something you have worked on at VIA that you are most proud of?

Introducing Lattice, our performance management platform, was a big win for me. Lattice is a tool that helps us plan and track goals, organise 1:1’s, and is an excellent vehicle for supporting a feedback-rich culture. We love the integration with our #shoutouts channel on Slack for constant praise and appreciation! (Not a paid plug, I promise).

What’s your favorite VIA memory?

My first official day is something that sticks out. Not only were lots of pastries involved, but the whole office pushed pause in whatever they were doing to welcome me over breakfast and to get to know more about me and my background. This really set the tone of inclusivity and respect that has not faded since!

If you were given an extra hour in your day, what would you spend it doing?

The angel on my shoulder says learning french, but the devil on the other says catching up on some z’s.

What’s your go-to food?

I can’t go past a really good Kiwi meat pie!

What’s something everyone may not, but should know about working at VIA?

In addition to being a kick-ass energy tech company, our team could have a side career in the food industry. Everyone is either an excellent home cook and/or just genuinely enjoys the ritual of eating great food.

Meet the Team: John Muddle, Team Lead, Data Science

Through a Q&A-style interview, you will hear from VIA team members about things like a typical day at the office and favorite foods.


What does a typical day at VIA look like for you?

My typical workday starts with a coffee, from either one of the local third-wave coffee shops or the machine at work. Once I’m settled, I look at my calendar and plan my day, this can be anything from looking at notes from a previous meeting to making sure I’ve booked a room. When that’s complete, I look for something I can quickly finish before Scrum of Scrums or continue with the task from the day before.

Each day at 10:30, I attend Scrum of Scrums. As a technical lead, I need to know how my epic is progressing, so typically, I will check in with my colleagues before the meeting for any updates and check that the JIRA board reflects our current progress. Typically, there are a few meetings to take a deeper dive on topics raised at Scrum of Scrums. These are a great way to make sure that we are aligned across our epics and to share knowledge. 

Having conditioned myself over many years that 12 o’clock means lunchtime, I take a break at 12 to either buy lunch or heat up some leftovers. Lunch is a great opportunity to socialize with my colleagues on different epics and to give my brain some time to process in the background. Conversations range from the latest computer game, movies, and hockey, but mostly food. 

After lunch, my afternoons consist of meetings, customer calls, and development time. My time tends to be split into hour-long chunks which I’ve planned out in the morning. A lot of my work revolves around supporting my colleagues by answering questions, problem-solving, and pair-programming. One of the favourite ways to tackle a problem is to jump into a spare room and rubber duck the problem. Basically, by describing a problem to a rubber duck, you verbalise the issues you are facing and through this process, you tend to have a better understanding of your problem. Luckily, VIA has hired people rather than rubber ducks, so in fact, you can also get feedback, questions, and suggestions from your colleagues. 

At the end of the day, which is typically signaled by the sending of GIFs on Slack that it’s home time, I make sure to leave my work in a state that allows me to continue the next day.

What’s something you have worked on at VIA that you are most proud of?

At VIA, I have had the opportunity to work on many different projects. I’m proud of every single one of them, but if I have to choose one, then I am most proud of the GDAC™ Transformers project. The reason I have chosen this project is because it is the first application built on top of TAC™. I’m proud to have helped develop TAC™, but it’s even better to use it for the benefit of our partners. The partnership aspect is another element that makes me proud to have worked on GDAC™ Transformers. I have worked closely with our partners for almost a year and to see how far we’ve come together is fantastic. Having had the opportunity to meet our partners in person, I was able to see how the work we have done together will benefit them going forward.

What’s your favorite VIA memory?

VIA is a great place to work, I have so many memories from my time here. One that stands out to me, in particular, was “Game Night”. Each offsite (a company-wide in-person event where we talk about wins, solve project challenges, and plan for the next big thing) typically has an activity where we have a chance to socialize such as bowling, mini-golf, and a scavenger hunt. But, nothing quite beats seeing your own office converted, in-secret, to the ultimate “Game Night” experience to the point where it is almost unrecognizable and something I will never forget.

If you were given an extra hour in your day, what would you spend it doing?

One thing I say to myself quite often is “If only I had more time”, but what would I do with that extra time. If I had an extra hour in my day, I would like to improve my French. Montreal is a fantastic bilingual city and very little French is actually necessary, but to really appreciate its culture and to get the full Quebecois experience a good handle on French is essential.

What’s your go-to food?

My go-to food has to be a sandwich, you can’t beat the convenience, variety, and taste of a good sandwich. In particular, I’m a big fan of a bacon butty, which, for those of you who are unfamiliar, is a very basic sandwich with bacon and butter filling. Bacon buttys tend to be eaten in the morning at home or out and about. They are an excellent way to start the day if camping, at a sports event, or just a day of gardening.

What’s something everyone may not, but should know about working at VIA?

If you have the pleasure of working at VIA then you should know that using GIFs in presentations and on Slack is encouraged. Communicating context and meaning using just text can be difficult at times, but GIFs can add so much more. A strong GIF game is essential to signal it’s time to go home, go to the pub, play badminton, get bread, or celebrate the latest prize that VIA has won.

 

 

The End of Pilot Purgatory

We’re excited to have Andrew Bright, former ABB executive and VIA’s newest advisor, contribute to our blog. Read on to hear his commentary on one of VIA’s latest products, GDAC™ Transformers: 30-Minute Pilot.


Many industrial digitalization projects suffer from “Pilot Purgatory.” The pilots seemingly take forever and never end because no one can decide if they are a success or a failure. Since the term Pilot Purgatory was first coined a few years ago, much has been written about how to avoid it. However, the vast majority of this advice seems to involve throwing more resources, money and scale at the pilot, until well it no longer looks like a pilot but a full-scale roll-out. The logic is clear if the monthly cost of a pilot project is high enough – no one can afford to let the pilot continue indefinitely. How refreshing then, that VIA has come up with a radically different and frankly opposing approach for avoiding Pilot Purgatory. 

Their new GDAC™ Transformers: 30-Minute Pilot takes just 30 minutes, and most of this time is allocated to data gathering. If this were a recipe, you would be allowed up to 27 minutes to source the ingredients and just 3 minutes to do the cooking. Resources, time and money are all minimized. After 30 minutes VIA hopes to have delivered a valuable summary of the health of one of your transformers. If this has proved insightful, the pilot has been a success, if not then GDAC™ may not be for you. Either way, the pilot will have been concluded.

With their 30-Minute Pilot, VIA aims to demonstrate three specific concepts:

  1. show valuable insights about the health of one transformer and that the math really works;
  2. show that valuable analysis can be conducted whilst keeping data private and confidential; and
  3. provide an educational component about how VIA does what it does. VIA does more than provide recommendations, it also explains why & how a particular recommendation was made.

All three of these components are embedded in the 30-Minute Pilot. If you are interested in performing a full fleet analysis going back say 20 years, that’s more of a project and not the goal of this pilot. VIA’s 30-Minute Pilot is true to the spirit and literal about the term “proof-of-concept.” This seems to be an industry first and given the simplicity and radical reduction in resources, I hope that it becomes an industry standard approach.

VIA note:

If you are interested in learning more about VIA’s GDAC™ Transformers: 30-Minute Pilot and perhaps want to give it a try, feel free to contact us.

 

 

Meet the Team: Antoine Dozois, Software Developer

We’re thrilled to launch a new blog series at VIA called “Meet the Team.” Through a Q&A-style interview, you will hear from VIA team members about things like a typical day at the office and favorite foods.


What does a typical day at VIA look like for you?

Like most startups, we are working on lots of exciting projects. Here are some of the challenges that I’m involved in on a daily basis.

  1. Infrastructure: This means having the right data storage and resources through AWS necessary for our workloads. This is in constant evolution because every new project or project phase has different needs. So, we always look for improvements ranging from storage capacity and ease of use to performance and access management.
  2. Data (management, cleaning, processing): To deliver the best AI solution, data needs to be widely available, in the proper format, with good workflow, and overall managed well. This part of the work is more around trying to structure the workflow and the processes involved in the team’s recurrent tasks. We also need to understand the data to manipulate it and find insights that are most relevant for our customers and internal teams.
  3. Building software to empower the team: Software can be built to access data, compute some metrics, build a machine learning model, or display some insight. We build libraries to help us automate and optimize our workflow. Overall, we are always trying to create efficiencies internally through streamlining and automating recurring tasks.
  4. Collaborating with the team: This relates to reviewing code, meeting to organize our work, and helping each other when needed.

What’s something you have worked on at VIA that you are most proud of?

It is hard to pick just one piece of work that I am the most proud of since we are tackling lots of exciting new projects and challenges. But, in the first months of being at VIA, I wrote code to help us better ingest and manage data. It was the first time that I was put in charge of developing some code to solve a problem and make tasks more efficient for the team. We still use part of the code today and are continuously improving it. I think it was a real issue and being able to solve that problem was really rewarding.

What’s your favorite VIA memory?

My first offsite was really special for me, it was the first time I saw the whole team in-person and was able to learn more about every member of the team. It was also really nice to learn about the next challenges we were going to take on as a company.

If you were given an extra hour in your day, what would you spend it doing?

I like to learn, so anything from reading, learning a new skill, watching a documentary, or building something new would be mostly what I would use that hour for.

What’s your go-to food?

As a proud Montrealer (born and raised) I must go with Poutine (from La Banquise for the best in Montreal). I wouldn’t eat it every day because it is not the healthiest food, but I never say no to Poutine when proposed to me.

What’s something everyone may not, but should know about working at VIA?

The different backgrounds and diversity of every person in the company makes it a unique and fun workplace.

 

 

Let’s TAC™ About It: The Bridge to AI Experts

This is the first installment of our new blog series, where we will cover the many capabilities and benefits of VIA’s Trusted Analytics Chain™ (TAC™). So, are you ready to TAC™ about it?

Data science roles have proliferated in the past decade. The growth numbers look flat, however, in comparison to other technology trends.

AI is embedded in our phones, our TVs, and our cars. With 67% of large companies projected to have AI initiatives in place by 2021, big data and machine learning are no longer relegated to long-term, R&D projects in mainstream businesses. These are now critical functions required for the long-term prosperity of business. 

Many organizations with vast amounts of data struggle to recruit, develop, and retain the data science talent they need to analyze their data and deliver actionable insights. A large ecosystem of big data companies, AI specialist companies, and tools have emerged to address this data glut and talent shortage. However, there are very real data security and privacy risks to consider for the firms that work with external AI companies. In addition, selecting and managing AI companies is costly and time consuming. These barriers can trap an organization’s data and limit its insights. 

TAC™ reduces the time, cost, and risks to data security and privacy of working with external analysts in multiple ways.

  • TAC™ leaves the data where it is. Instead of sending data to an analyst, TAC™ controls the code delivered to the data’s location (even in multiple locations) through federated learning.
  • TAC™ automates data anomaly checks, data cleaning and harmonization, and NLP PII checks.
  • TAC™ tracks and, when required, restricts what analysis can be performed at the data’s location, through smart contracts and other programmatic controls.
  • TAC™ provides data owners control over results returned to analysts through digital watermarking, k-anonymity, additive homomorphic encryption, and other mathematical safeguards.
  • TAC™ streamlines data access workflows so companies can even grant multiple AI experts access to the same dataset. This allows companies to leverage the wisdom of the crowd to select the best solution for their particular challenge. 

In short, TAC™ is a secure bridge between a company’s distributed data and expert analysts. VIA works with global electric utilities and government agencies to help them gain access to the AI specialists that can harness massive datasets, even across companies, to transition to a cleaner, safer, and greener energy industry.