Birth Of The AI Exchange: Sharing Is Caring

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The idea around participating in an AI Exchange that facilitates shared learnings and collaboration isn’t far off. As we hear of breakthrough innovation in AI, from machine comprehension to fake celebrities generated by GANs (Generative Adversarial Networks), one quickly forgets we are still in the very early days of AI adoption – especially in the enterprise space.

And as with every breakthrough in innovation, AI has its fair share of teething problems, one of which is the lack of collaboration. Most data scientists begin building models from scratch. Data curation and wrangling consumes a whopping 80 percent of their time.

It is normal for Neural Networks (or Deep Learning) to take days or sometimes even weeks to complete a single training run. Simply optimizing a model may entail tuning the model’s hyperparameters and rerunning the training process. This is like baking bread: you bake the first loaf (model training), sample a few bites (model testing), tweak the ingredients (hyperparameter tuning) and then shove new dough back into the oven and repeat this over and over again, until you get the perfect loaf.

It is expensive and time consuming without even mentioning the sizeable computer power needed to accomplish this task.

The Model Menace

While the industry is converging on Python as the defacto programming language for machine learning, programmers are spoiled for choice when it comes to libraries. However, “more is less” as Barry Schwartz postulated in his book The Paradox of Choice.

The vast majority of programming frameworks are open source and some of the early frameworks are either losing steam or will simply be usurped. Your choice of framework may soon cease to exist. A safe bet in this regard, is to pick a framework that is backed by a well known brand name (no endorsements here).

The prized jewel – the model – is nothing more than a collection of harmless looking files. However, thanks to the abovementioned choices these models are NOT interoperable between programming frameworks. So should you choose to build in TensorFlow, the model files are saved as ‘data’ and ‘meta’ files, and only TensorFlow applications can use them. Should you choose Caffe2 tomorrow, the previously built TensorFlow models are rendered useless.

The same can be said of the myriad of Cognitive Services provided by the AI giants. You cannot port your models between DialogFlow, Microsoft LUIS, and IBM Watson. For example, should you build your AI application around DialogFlow the value of your AI is trapped inside DialogFlow. Porting to Microsoft LUIS will entail dumping your training data and starting on LUIS from scratch.

One could compare this situation to the early days of computer programming without libraries. In the absence of libraries, every trivial task – from reading a file to printing an output – would be incredibly painful and time consuming. Libraries have significantly accelerated the speed at which new applications are rolled out.

“Alone we can do so little; together we can do so much” – Helen Keller

As enterprises seek to double down on AI as a key pillar in their transformation journeys, collaboration and reuse through an AI exchange will be key to preventing the reinvention of the wheel and possessing the ability to port AI assets between applications and organization silos.

These AI assets span everything; from training data that has been cleansed and normalized, to models that have been genericized such that they are rapidly localized to a given industry, enterprise and use case on hand. In effect, once the barriers surrounding AI reusability are addressed, AI will become the new vehicle of collaboration between organizations small and large, without data ever having to change hands.

To field a practical example, consider a facial recognition model that can readily spot human faces. However, the model cannot pick human faces in helmets. Rather than train a model to now recognize human faces in helmets from scratch, the facial recognition model can be repurposed and localized, to recognize faces in helmets at a fraction of the computer resources and time.

Sunlight on the horizon

Here’s the great news: the industry is slowly but surely formalizing this idea around an AI exchange with various avenues that enable such reuse and facilitate collaboration. Needless to mention is that terms like collaboration and reuse raise the elephant in the room – security.

What if someone out there is able to reverse engineer the training data the model trained on, simply by observing the model’s outputs through repeated runs? And should the model itself be compromised, is it possible to infer the training data from the models weights? The training data after all, may represent actual customer data that the model observed.

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As an Implementation Engineer you will be responsible for the solution integration of an enterprise grade conversational AI experience from a technical perspective. You will work closely with the lead solutions architect and be the technical face of the implementation team and lead the customer through the entire implementation cycle. You will work with one of the most diverse teams of linguists, data scientists, and innovators to deliver the best AI enabled customer experience.

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Cognitive Data Specialist

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As a Conversational Experience Designer, you will be responsible for the designs of the overall customer experience, including the end-to-end dialog flows & journeys of the solution ensuring  design leverages  UxD best practices for optimal customer experience.

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Responsible for the overall success for the client, including the end-to-end delivery and optimization of the solution, you will manage the sales process from pillar to post, including technical and commercial proposals, pipeline management, sales forecasting, and contractual documentation.

David Trotter, Wysdom

David Trotter

SVP, Sales & Marketing

David has 30 years of global sales leadership experience as a collaborative leader who believes in a strong team concept within sales and marketing organizations. David has spent many years working with growth companies and enjoys being face to face with customers and partners to create solutions that have a lasting effect on the customer’s business environment. 

Prior to joining Wysdom, David was the Vice President, Sales at Scalepad, and previously spent 11 years as Vice President for Latin America and Asia Pacific for Absolute Software. He also held senior sales management positions at GE Capital and Clevest.  

Michel Benitah

VP, Optimization & Delivery

Michel has 20 years of experience in leading the successful delivery of Conversational AI and Natural Language Customer Care solutions to some of the largest financial, telco, healthcare, utilities, and retail enterprises throughout North America. 


Prior to joining Wysdom, Michel spent 20 years at Nuance Communications, holding senior management and leadership positions within the enterprise division, most recently as director of the Toronto office and professional services team.

Frederic Lam

SVP, Sales

Fred brings in 25 years of international experience in sales and business development across North America, the Caribbean, Asia-Pacific, Europe, and the Middle-East.


Prior to Wysdom.AI, he held sales leadership positions at Oracle, Redknee, and Movius/Glenayre, successfully growing revenues in both large and small organizations. Fred has also been involved in the start-up community in the earlier stages of his career as an Investment Manager with SP Capital and was an alternate director on a few investee companies.

Karen Chan

Chief Engineering Officer, Co-Founder

With 20 years of experience in software and mobile, Karen has held senior technical roles at 5 startups, including Wysdom.AI, Clickfree, Mobile Diagnostix (HP), Teamatic, and Virtualthere.

Karthik Balakrishnan

Chief Technology Officer

Karthik has over a decade of hands-on, proven global expertise in emerging technologies and implementing intricate platforms and solutions for telecommunications and enterprise during his time at Amdocs, with senior positions in their India, Cyprus, America, and Canada offices.

Nitin Singhal

Chief Operating Officer

Nitin has over 20 years of success in global executions of business technology, driving operational efficiency and digital scalability for some of the world’s largest enterprise clients. 


Nitin spent 16 years at Redknee holding executive positions in Research and Development, Customer Operations, Partner Alliances, and most recently as COO.

Jeff Brunet​

President, Co-Founder

Jeff has more than 20 years of experience in the startup world, founding and growing 4 software companies: AracNet, Mobile Diagnostix (HP), ClickFree, and Wysdom.AI. 


His in-depth understanding of software development and the challenges in making new technologies successful in the startup world prove invaluable as he serves on the boards of XMG, SurfEasy (Opera), Locationary (Apple), Groupie, and as an advisor to Pushlife (Google), LogMeIn (IPO) and HP. 


Jeff holds 23 issued patents in the wireless and consumer electronics spaces and is the lead inventor on 30+ pending patents.

Ian Collins​

CEO, Co-Founder

Ian has founded and grown 6 technology companies over the past 20 years, primarily in the enterprise software space including Wyrex, Mobile Diagnostix (HP), Clickfree, and most recently Wysdom.AI. 


Ian invests, mentors, and sits on the boards of several startups in the Toronto area.