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Table of Contents AI Fest Day 1

The Future of Communication: Thad Starner, Dimitri Kanevsky AI: The Good, The Bad, and the Beautiful: Jana Eggers Design Thinking for Cyborgs and Centaurs: Tyler Schnoebelen Shifting from Smart to Smarter; AI Through a Social Lens: Trishala Pillai, Erik von Stackelberg

AI Fest Day 2

The Future of Workforce Talent: Stephanie Lampkin You’re a Startup and Not Doing AI?: Parinaz Sobhani AMA: Fundraising for your AI Startup: Elizabeth Gore, Lauren Kunze, Craig Buntin How Big Business is Using AI: Martin Aubut The Last Mile: Challenges of Deployment: Benjamin Fels The Future of AI: Parinaz Sobhani Issues About AI from a Lawyer’s Perspective: Carole Piovesan

AI Fest Day 3

Talking Gender Bias in AI: Hanan Salam The Brilliant Future of AI and Healthcare: John Mattison AI for Good: Ritika Dutt Conversational Commerce: Andy Mauro, Ravi Raj, Lauren Kunze, Mike Gozzo The Future of Work: Hessie Jones The Future of Customer Service: David Furlong AI Ethics: Workshops Summary: Jana Eggers, Tyler Schnoebelen, Katy Yam Past, Present and Next Generation of AI: Karen Bennett

elementai element_ai elementai elementai 1

https://www.linkedin.com/company/elementai/

https://twitter.com/element_ai

https://www.facebook.com/elementai

https://www.instagram.com/elementai/

AI Fest Day 1 The Future of Communication

Thad Starner (Google Glass, Georgia Tech)

● How can a person who cannot hear get information from audio and be aware of sounds in the environment? Using alternative modes of communication.

○ Norbert Wiener: Speech to touch experiment (1950): Came from MIT and met with Helen Keller, created a glove with vibrations on each of the finger tips to help transcribe voice into touch.

○ Tactile sign language is possible. VR is now being coupled with haptic vest technology to make it more realistic. Why not translate audio into haptic as well? Hong Tan has been exploring this.

○ When Dimitri (below) was at IBM in 70s, he explored stenotype technology.

● Today, much has progressed:


https://www.microsoft.com/en-us/research/blog/beyond-tapping-sliding/

https://en.wikipedia.org/wiki/Stenotype





○ Google Glass: Captioning on glass + voice recognition, in real time.

○ Captioning is not the only thing you need when you’re deaf. You need ambient audio clues too. Honking cars? Growling dogs? Useful to know if you’re deaf. Also developing a system for these more subtle audio clues.

○ When developing these solutions, it’s hard to create a comprehensive bank of all sounds that need to be visualized. But researchers are finding alternative methods of allowing users to make sense of audio visualization for ambient audio.

Dimitri Kanevsky (Google), Sagar Salva (Google)

● Live demo: Wearable projector that transcribes what person is saying (+ ambient audio sounds) and projects onto the person who is speaking:

○ Transcript of spoken words is projected

○ On-device neural network that classifies sounds (whistles, barking, car sounds) + projects a visual representation of this as well


http://cog.gatech.edu/





Dimitri Kanevsky (Google)

● There are two situations in which speech recognition still does not work well:

○ 1) When there is bad audio or noise (during conferences, conference calls). Microphone is the first factor that you need to improve in order for speech recognition to work well.

○ 2) Another factor that affects audio quality is accent.

● When he started working on voice transcription at IBM, Dimitri wondered what human transcribers would think about potentially being replaced by speech recognition. But 20 years later, he has realized that many people who are hearing impaired will prefer sign interpreters over speech recognition/CART services.

○ Sign interpreters have emotion. They give information about environment sounds. The sign interpreter shows them who is speaking. Transcription has a three second delay so it can be hard to determine who is speaking.

Thad Starner (Google Glass, Georgia Tech)

● Use machine learning + computer vision to learn from audio waveform -- where are people putting their tongue? Compare speech to where tongue is positioned and improve data sets and help deliver queues to users that help guide and correct enunciation, volume, improve accent.

● Conclusion/TL;DR: automatic speech recognition (and wearable tech) can aid communication for the deaf in understanding a spoken language. But there is also a huge opportunity for continuous in-the-moment speech therapy to help the deaf in producing intelligible speech for others to hear.






AI: The Good, The Bad, and the Beautiful

Jana Eggers

● In April, art generated by AI (created by collective Obvious) was purchased for 15K CAD. The painting was signed with the function that generated it. Applications like these push the boundaries of how we think about AI.

● It was done using GANs (adversarial networks working on trial and error): one AI tries something, another AI says if it’s good (based on what we like today). GANs can be used for:

○ Image generation

○ Image resolution improvements

○ Writing the next Harry Potter

○ Drug discovery

○ Discovering cancer molecules, etc… and you can build a GAN in only 50 lines of code!


https://news.artnet.com/art-world/art-made-by-artificial-intelligence-1258745





● THE GOOD: AI is accessible to everyone: Kavya Kopparapu taught herself to code in junior high school. Her grandfather was having some trouble with his eyes so she developed an iPhone app, and can now spot health problems in remote places.

○ We shouldn’t anthropomorphize AI (think Sophia the robot) or compare it to humans. Sometimes we beat them, sometimes they beat us. A better metric is how are we collaborating? How do we help AI see things and how do they help us see things?

● THE BAD: Recent deaths as a result of autonomous vehicles weren’t an AI problem, they were a problem of human bias and intervention. Software engineers decided to override the AI because they thought it was buggy (car was jerking back and forth too much).

● THE BEAUTIFUL: Dream big. In the palm of your hand, you can hold the tech to get you a skin diagnosis. That’s beautiful. “Robots Without Borders” is looking at how we can bring AI into crisis situations (providing disaster relief, healthcare, education).

○ What are your dreams? We need you to keep dreaming.


https://twitter.com/kavyakopparapu?lang=en

https://www.robotswithoutborders.io/





Design Thinking for Cyborgs and Centaurs

Tyler Schnoebelen

● Human + machine is where the value is.

○ Cyborgs: people who are enhanced (think bionic woman). Centaurs: Half human, half horse. As designers, we’re turning people into cyborgs and centaurs.

○ Think of how accurate things like Alexa and Snap Glasses are. You ask it what time it is or the weather and they give you an accurate answer.

● AI is when you make a computer like a brain.

○ You help it learn by giving it a list of pictures and words (data).

○ Teaching computers to recognize what’s important and blur out what doesn’t matter.

○ YouTube will ask you what you like, learn from that, and then you generally don’t have to control it unless you want to.






○ Feedback loops are incredibly important for AI systems to learn. Try something, test, see if it works, learn, iterate.

● As designers, you have to understand your users deeply (acquire data) through conversations, user interviews, surveys, etc.

○ Personas are becoming obsolete. You have so much data to work with, so those typical descriptions (“Joanne the soccer mom”) are too static.

● Prior probability is the way AI learns over time. Priors let you update your expectations. Based on the data, you’re adjusting your expectations/probabilities. It’s not AI if it’s not learning.

● Sometimes in a system (like Amazon search results) designers will insert random results. We will skew people if we only show them what they want. As designers, we need to consider showing people some randomness or something completely unrelated so they can learn and grow and explore.

● We also need to be aware of and proactive about the fact that we are building biases into our algorithms.

● Designers need empathy, diversity and humanity top of mind in order to build great AI systems.






Shifting from Smart to Smarter; AI Through a Social Lens

Trishala Pillai, Erik von Stackelberg

● The average Canadian works roughly 40 hrs per week (1,920 hrs per year and 124,800 hrs in a lifetime). We spend an insane amount of time at work, but how many of us can say that we feel empowered, enabled, and happy at work?

● How might AI make that time more meaningful, safe and productive?

○ MyPlanet makes software for the workplace. They see AI as a way to empower employees, not replace them.

Principle 1: Understand Your People

● AI needs to accept human behavior the way it is now, not the way it should be.

● Common concerns from Fortune 50 companies: “loss of control, fear performance, disruption of habits/relationships.”






○ Yet organizations are doing a poor job of communicating their AI strategy to their employees. They should let employees look into the “black box” to see what’s coming and calm those fears.

○ 88% of Gen Z believe AI will improve their job. 70% of baby boomers feel the same way.

● Don’t focus on technology, focus on the employees needs.

○ Look at the workplace as an example, we went from cubicles to open spaces, how do we marry the two? Look at opportunities like remote working.

● Use design thinking and user-centered design principles with employees to understand their needs.

Principle 2: Explore New Problem Spaces

● You don’t want to have cognitive bias where you don’t look at new possibilities.

● Look for the intersections between human needs and technology. Do this by understanding what types of problems AI can solve. From there, go back to the research you did with employees and see where the intersection is.

● As an example, employee onboarding can now be personalized with AI (predict onboarding needs and adjust the process)

● Remember to layer in data to help direct your efforts. In a workplace, you’ll have much richer data since the context is all focused around work.

Principle 3: If the Tech Exists, Pick a Good Partner

● Most organizations would rather buy embedded/packaged solutions versus building their own. Leverage what already exists, you can benefit from the knowledge that industry partners have within their respective industries. Partners also have pre-trained models and infrastructure, making it faster and cheaper for you to get started.

● In today’s world, even off-the-shelf products can be customized for your organization’s needs.

● Build your AI partner ecosystem, that includes things like AI labs, technology partners, governance, training, delivery, retraining for employees, change management, etc. Make sure your partner values the same things you do.

Principle 4: Start Small and Involve Your People

● Run small pilots (could be parallel), and try to bring employees along for the entire journey to build that employee experience and trust.






● Apply Lean Startup thinking: Start with a collection of experiments and MVPs. Then measure. Look at how employees are interacting with “the machine” at key touch points (like onboarding, setting up an account, etc.).

● Build on top of those successful pilots to rollout (and to figure out where/what to rollout).

● Get buy-in, manage the change, and don’t stop listening. Demystify AI for your employees (and use clear language).

● Once things go live, gather feedback continuously.






AI Fest Day 2 The Future of Workforce Talent

Stephanie Lampkin, CEO at Blendoor

● Human bias: Experiment in which they sent the same resume with two different names -- male name had 2x response rate.

○ Affinity bias: I like people who are like me

○ Halo effect: Correlating two qualities that are not related (he’s tall so he must be smart)

○ Confirmation bias: Tendency to interpret new evidence as confirmation of one’s existing beliefs/theories

● Machine bias: Now there are systems that empower HR to help hire employees, but there is still algorithmic bias that goes into decision making processes.


https://twitter.com/stephaneurial

http://blendoor.com/

https://cos.gatech.edu/facultyres/Diversity_Studies/Bertrand_LakishaJamal.pdf





○ For example, some platforms measure things like how quickly you’re promoted in an existing company and use that as a metric. But studies show that women are not promoted as quickly in organizations, so using that as a KPI in your algorithm is problematic.

○ Another consideration: How diverse are the teams and engineers who are creating these algorithms?

● Bloomberg: Correlating bias reduction (hiring, promotion, compensation, performance reviews) with business metrics and performance over time. There is a huge opportunity cost of not addressing bias.

○ We must build machine learning algorithms to help determine who is a good fit for a role using data other than which school you went to, how quickly you’ve been promoted (these are all biased metrics).

○ Removing age, name, race, sexual orientation, disability. Focusing attention on things that are relevant, integrating with HR systems.

● How can we hold ourselves more accountable? HBR: Why Diversity Programs Fail.

○ Focus energy less on unconscious bias programs, focus more on the business case, how we drive accountability for board representation. Because there is ultimately a huge opportunity cost of not addressing bias.

○ Think of Steve Jobs, Mark Zuckerberg. These men are seen as some of the most innovative and disruptive humans, but they don’t have business degrees. Yet the companies they went on to found predominantly hire white, educated males. What’s the opportunity cost of hiring people from other backgrounds who may be the next Jobs?

■ Further reading: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy


https://hbr.org/2016/07/why-diversity-programs-fail

https://www.amazon.ca/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

https://www.amazon.ca/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815





You’re a Startup and Not Doing AI?

Parinaz Sobhani

Background in deep learning/NLP, company's mission is to remove barriers for AI startups. Works for an accelerator, helping startups adopt machine learning.

● What's the difference between AI and analytics?

○ Before, it was a bottom-up approach: What kind of insights can you get from this data?

○ Now, it has shifted to be top-down: What are the opportunities to use AI and to create AI-enabled products?

● We need humans in the loop to help mitigate bias and error in the algorithms

○ Feedback loops are the key to machine learning. You need to train models based on both input and output (expected outcome and actual outcome). Over time, you’ll improve the models based on that delta and feedback loop






○ In almost every situation that involves AI, you need humans in the loop. Even if you think it can be automated from end to end, there’s usually a human involved at some point (creating an account, etc.)

○ Monitor the performance of your model continuously to spot things like bias, errors, and opportunities for improvements

● Do you want the future to be the same as the past? Especially when the past was biased?

○ There are techniques to remove (or limit bias). First step is to identify the bias, then you can think of ways to improve it.

○ Be transparent with your users to earn their trust.

● Don’t have enough data?

○ You can solve the limitations of not enough data with transfer learning. If you have a massive data set in one topic and you train the model, it’s much easier to transfer the knowledge onto a smaller dataset.

○ Machine learning models can be reverse engineered to have access to the data. This is a very big issue. We need more advanced privacy preserving techniques to maintain and build trust.

● Key takeaways/TL;DR:

○ Find the right problem

○ Start with proven machine learning techniques but iterate and experiment

○ Constantly use feedback loops to monitor and improve

○ Don’t be discouraged by not having enough data






AMA: Fundraising for your AI Startup

Elizabeth Gore, Lauren Kunze, Craig Buntin. Moderated by Naomi Goldapple

Elizabeth Gore: seed round, got 3 nos today, but got a yes on a grant

Lauren Kunze: largest chatbot platform in the world, here to represent the bootstrapping perspective

Craig Buntin: former olympic athlete, now runs SPORTLOGiQ, has developed an app “AI coach” Naomi Goldapple: early employee at Element, used to have a startup, back in those days you wrote a business plan and went to the bank… things have changed!

NG: What surprised you the most?

● Elizabeth: Everything! I wasn’t prepared. Time was the biggest issue (personal time). It’s hard to balance working full time and meetings with investors. If you’re planning to raise, make sure you have a team to run the day-to-day






● Lauren: They get a ton of inbound from investors because of their numbers. Don’t let people waste your time, you can set your own rules and you want to maintain control. Once they hit the numbers the investors were originally looking for, they didn't need the money anymore

● Craig: The irrationality of fundraising; they had a great plan, good numbers, but none of it mattered, what mattered was the long-term vision.

NG: As an AI company, how do you articulate the value of your data?

● Elizabeth: Taylor your pitch to the investor you’re talking to. If you know they’re more into research, talk about the data and science, if it’s a product investor, talk about the SaaS

● Craig: Make sure that everyone on your exec team understands AI deeply (difference between unsupervised and reinforcement, difference between GAN and CNN, etc.)

● Lauren: Meet with many investors and tweak your pitch based on their feedback

NG: How many meetings do you have with investors in total?

● Craig: Every single day! Active fundraising is 100 hour a week job in itself. Impossible to estimate how many meetings in total, but a lot! You have to be ready for rejection

● Elizabeth: 190 nos to get to 6 yesses for their seed round (1.1 million). As soon as we get out of a meeting, I send the feedback to my team for us to iterate

NG: As an inventor, how do you attract the right investors that have the right expertise?

● Lauren: look at people’s past investments, find a good similar match (but not too similar, you don’t want competitors VC investing in you). Write more about what you’re doing and you’ll get the right people to come to you

● Craig: You need a business person on the team to help size the market, pitch, do marketing, etc.

NG: What is the one piece of advice you would give to people fundraising?

● Elizabeth: look at all your options before giving up equity (government grants, etc.). And only raise what you need to, not more.

● Lauren: Be resilient. Fundraising is really hard, especially if you’re in an area like SF or the valley.

● Craig: Don’t ever take advice from anybody






How Big Business is Using AI

Martin Aubut

L’Oreal is the #1 beauty brand in Canada, is in 150 countries across the world and operating 17 ecommerce boutiques. They operate 38 separate brands, which they see as having 38 intrapreneurs innovating at L’Oreal.

For L’Oreal, the digital revolution has long arrived, and it’s a consumer revolution, which is why 38% of their investment goes into digital tech + partnering with startups to explore how to innovate (with AI and beyond).

● The digital consumer journey has changed.

○ People don’t “go” on digital — they “live” in digital.

■ 1.6 billion people shop online

■ 2 billion people access Facebook every month

■ 3 billion are smartphone equipped.






○ What digital has done is it has fragmented and de-linearized the customer decision journey.

■ Do you know that it takes 32 touchpoints online and offline to buy a skin care product in Germany? Those would include reviews and ratings, search online, social media exposition, TV campaigns maybe, offline store visit, etc…

■ What is clear is that consumers are increasingly mixing online and offline when they discover and buy products.

● This is what Jack Ma the founder of Alibaba calls “Online to Offline” and which typically includes back and forth of:

○ “ROPO”: Research Online and Purchase Offline or

○ “Showrooming”: discover offline and purchase online.

● Take a look at Jack Ma’s or Jeff Bezos’ strategies:

○ They are dependent on the data created from these interactions.

○ The 20-50-100 concept: 20% of sales done via e-commerce, 50% of transactions enabled through direct contact, 100% of transactions are digitally influenced.

● How does L’Oreal keep up as such a large company?

○ The L’Oreal Open Innovation initiative is an effort to co-create startups and applications to add to their tool kit. For them there are two main categories of focus:

■ Services bridging online and offline

● Beauty Tech Accelerator: L’Oreal able to source startups that are developing services and have the potential to develop PoCs at scale. Personalized, innovative beauty products, devices and services.

■ Learning new models for the future

● Indie Brand Accelerator: Identification of early stage indie beauty brands which have potential to gain key learnings (new business models, new development frontiers, new distribution models). They’ve founded 10 brands this way, Ex: Nyx, Urban Decay…

○ To do this they are working with Station F, Founders Factory and Partech Incubators.






○ Why work with startups?

■ FOCUS: Start up can focus on complex problems

■ AGILITY: Start ups tend to be more agile

■ TALENT: Access to specific talent

■ SPEED: Rapidly validate concept (and ROI!) before massive investment

● How do they use AI?

○ Virtual Skincare Expert Online — conversational marketing fueled by natural language processing

■ Create brain of cosmetician to make all of the steps of a personal experience, online expertise 24/7

■ They can can also mash up services and bring together their data for more rich services for consumers

○ Modiface is a realtime magic mirror to show you a haircut or makeup, a visual “try on”

■ E-commerce is growing 35% a year so want to make it easier to try and buy online.

■ Can also identify faces and create a diagnostic of the consumer and recommend the right products across their different brands.

● They’re looking for more startups to work with — could your startup be a fit?

○ When looking at consumer applications of AI they are able to prioritize by value as a factor of complexity

○ They are always looking for more data. If you’re already data rich, you need to make sure it is well structured for what you want to do and drive feedback loops to get the magic of iterative learning

○ They are overcoming internal competition between brands to share info, can better make recommendations. They can now see what their consumers are buying across their brands or others’ brands.






The Last Mile: Challenges of Deployment

Benjamin Fels, macro-eyes

Benjanmin has been working with ML since 2008, interested most in prediction and predicting the future. This is not a technical conversation but more about the philosophy of technology.

● How we see and think about technology impacts how it is received and used, so it’s important to think about it in a broader view.

● If you’re interested in machine learning, the best place to begin is in financial markets:

○ Comes at you in real-time, 24 hours a day at sub-millisecond speed. It’s where competition is fiercest and most creative.

○ His job at a quant trading firm was very clear: predict funds faster and better than other competing firms.

○ Trained his algorithms on signals that others couldn’t see or weren’t trying out






○ Deploying wasn’t hard because they had no customers. If they came up with something they could try it out and see right away if it was successful, which is totally different than running a startup with customers

● Benjamin has founded his company with high-level experts and they have discovered how large healthcare organizations think about innovation and work with cutting-edge disruptive technology, and found it to be similar with smaller orgs too:

○ Don’t believe the hype: The enterprise has not embraced machine learning

○ There’s a lot of misunderstanding and concern, despite working with some of the most sophisticated organizations

■ He believes it boils down to transparency: explainability, too much choice, and an issue around trust and data security

■ How do you know that an ML solution is actually the best fit for your problem, and is it secure?

Story time: Centre Pompidou, the algorithm that explains itself

● Unveiled in 1970s. Was considered a “monster” by critics. Radical because it took the guts of a building and put them on the outside and made visible. This structure is a machine for interacting with culture. A machine that explains itself -- most effective version of an explainable algorithm.

● If you step inside a modern data center, you will seen much the same approach:

○ The innards are revealed and color-coded. Helps to tell a complex story, it reveals its inner logic

● Why have this explainability front and center?

○ No matter who you present in the enterprise and organization has to go and explain this to others, and they need a clear and simple story.

○ ML is destabilizing — this notion of a system that learns in real time and responds to shifts on the ground.

○ Many organizations strive for stability, and this goes against their grain.

○ Metrics in ML are hard to describe and understand. There aren’t established metrics/terminology that is clear to a consumer

○ The Coastline Paradox: We cannot accurately measure a coastline because depending on our perspective certain features are visible and others are not. Are






you looking from a plane or a few feet? The features change depending on your vantage point and you will get a different measurement.

■ This is true in ML when it comes to measuring performance. It really depends on your vantage point and it's enormously complex

● SIBYL: Software for Intelligent Patient Scheduling

○ If we incorporate sensitivity to a cancellation or a no-show. They quickly get into 9 categories of states for scheduling.

○ Customer just wants to know how often are you right, wrong and how does it impact your solution.

○ So need to compress 9 possibilities into a binary measurement of success for the customer.

● The last “last mile problem”: Operationalizing insight

○ Orgs are full of insight, and we don’t want to just add paper to the pile. You want to solve a problem with your insight If you just give more insight you aren’t really helping

○ As an example: operationalizing mortality prediction

■ How would they change their product or reality if they could change the future?

■ Vast majority of cost of care are in the last weeks of life

■ If we could imagine the technology that can do this, predict the likelihood of someone expiring, what are you going to do with that information? This is why it’s so difficult. Nothing is a simple decision.

● Deploying ML is not a tech problem, it is a human problem of storytelling, presenting something in clear enough terms for them to take it with them and share with others






The Future of AI

Parinaz Sobhani

● Georgian Partners is trying to de-risk innovation for their startups by using machine learning and AI. By adopting machine learning, the portfolio companies can scale faster.

○ Companies like Slack, Dropbox, etc. use machine learning to scale at an insane rate.

○ Most narrow tasks will become automated, given that we have enough data for them.

● Data has become a business-critical asset. Now, there’s a huge opportunity for companies to leverage this data and unlock the value in the data to derive meaningful insights for your business.

○ How can AI help startups differentiate themselves from competitors? How can we create a moat to completely protect ourselves from competitors? What prevents our competitors from taking our market share?


https://georgianpartners.com/





○ We’ll shift from a data analysis (big data) mindset to an AI/machine learning mindset.

○ It used to be clear rules (if this then that), but modern machine learning now allows us to do the same thing but for more complex tasks where you’re not able to describe them with simple rules.

○ You need to have intelligent integrations and interfaces to add value to your data.

● Feedback loops are so important. We need to be able to capture what we expected the model to do vs what actually happened. The difference between those two will make you tweak your model to create a better dataset and better user experience.

○ Imagine if you’re a doctor using machine learning to help with diagnosis. You need to be able to spot the errors that the machine makes and retrain. You need access to a patient’s lifetime of data to be able to spot those inaccuracies.

● Great dataset = great competitive advantage

○ Google is a great example of a company with a data moat. They have a ton of data, a ton of outcome learning (was this search result correct?).

○ Facebook is an example of a company that built an empire from a data moat. Images, text, check-ins, etc. and then they use that for ads and recommended content to learn and get better.

○ This data moat model might not work anymore, because the world has changed. Users are much more concerned now with privacy and data privacy, so it’s getting harder to convince them to provide it to you. You need to build trust with users to get them to give you data. You have to be so careful now about managing your users data and sharing them with third parties.

● The companies that implement Trust within AI will be the leaders of the future

○ Communication, explainability, fairness, transparency, privacy/security are all ways you build trust.

○ Having to agree to a 20-page TOS document is a dumb idea. We need to have a simple way of explaining to users what they’re agreeing to. GDPR is a good example. We need to enable users to make those choices.

● Do you want the future to be the same as the past? Especially when the past was biased?






○ There are techniques to remove (or limit bias). First step is to identify the bias, then you can think of ways to improve it.

○ Be transparent with your users to earn their trust.

○ It’s important to test your model against biases

Issues About AI from a Lawyer’s Perspective

Carole Piovesan, Lawyer from Mccarthy Tetrault

We’re not in an AI-first world, but we will be soon. For now, businesses and their lawyers need to focus in on the data that will power the AI of the future.

● AI is a computer system that can analyze massive datasets, learn from analysis and execute action in predictable ways without human intervention

○ Key legal features

■ Not organic, but an evolutionary learning process






■ Action-oriented

■ Unpredictable (think of the surprising moves carried out by AlphaGO)

■ No human intervention

● When there is no human intervention, it creates a question of liability

○ Who is on the hook?

■ If I deploy it?

■ How much of an interaction do I need to maintain control of it?

● Research: Reshaping Business with Artificial Intelligence: Closing the Gap Between Ambition and Action (MIT Sloan: September 06, 2017)

○ Does AI has a large effect on business today? 15% yes

○ In 5 years? 60% yes

■ In summary: Companies and executives are getting ready to incorporate it more in a meaningful way. And they have lots of questions about what it means and the risks

● What are the obstacles to greater integration?

○ Data readiness

■ Key obstacle across studies of executives looking at information of AI

■ It’s not just data structure, but also regulatory certainty

■ What does it mean to properly collect use and store data?

● Legal and a reputational risk

● You have to understand this to pitch properly

○ Solution identification

○ Talent and skills gap

■ External and internal

■ Struggling to figure out how to train their people to use the tech

○ Trust in the tech


https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/

https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/





■ About liability at its heart

■ Will it do something it shouldn’t do?

■ Who is responsible if it goes off path?

○ Lack of a clear regulatory environment:

■ What is the regulation that will govern and provide certainty for organizations that crave that certainty?

● What are the main areas AI will bump up against in legal areas?

○ Privacy: Risk management strategies for training AI systems, protecting AI systems from cyber attacks

○ Competition: Amassing vast amounts of data can cross the line of data monopolizing/abuse of dominance

○ IP: Patents for AI-created products

○ Employment: Tensions between low-skilled labour market and efficient AI systems

○ Contract: Analysis of contract breaches caused by AI systems

○ Tort: Liability for and by the AI systems

● Privacy

○ Access to data is subject to legal restriction under various legislation regimes depending on health, personal, or non personal data

○ Issues of consent, de-identification, Internationalization

● Liability

○ Most AI systems will fit within some existing legal regime

○ A chatbot could do or say something that could trigger a class action lawsuit

○ Need to be able to talk to your clients about these risks

○ Be clear about the parameters of your system

■ Define what your system is supposed to do

■ It can protect you if your system is misused






○ Be clear about your contracts

■ Look at data and IP ownership

■ Be clear about who is in control as it will affect liability

○ Be clear about what you can attest to and what is not clear in your model, unexplainable and needs to be hedged

● Legality is the biggest hurdle to saying yes






AI Fest Day 3 Talking Gender Bias in AI

Hanan Salam

● We live in a biased world, we make biased decisions

○ When we’re building models, we need to make sure we’re replicating existing biases and not making new ones.

○ AI is the extension of its creator.

● Examples of bias appearing in AI:

○ Personalized ads, high-paying jobs were shown to more males than females (gender bias).

○ Google translate from Turkish to English: the Turkish word was gender neutral, but the English translation showed gender bias: “He is a doctor, She is a nurse”






○ Voice apps (Alexa, Siri, etc.) with female voices — most of the companies creating them are run by white males.

● It’s important to have more women in AI to help combat some of these issues, Only 18% of high-paying ML positions are filled by women.

● Women often choose literature/art over science when they’re in high school, which lowers the rate of them getting into this later in life (and it gets lower and lower as women get older)

● We need to take action on gender bias in AI. We’ll only reinforce those biases that exist in our society if we don’t act fast.

○ Women have better “approval” rates of the code they write when looking at GitHub commits.

○ If we want our technology to be representative, we need to have a diverse group of people at the table.






The Brilliant Future of AI and Healthcare

John Mattison

● Such an exciting time for healthcare because of how fast the technology/research is growing

● Stanford Virtual Lab Body Transfer: Physically transfer your image into this virtual world and let you see yourself in this world, and then you can change things to test things (like change yourself from a man to a woman, change your skin color, etc.). The hope is to increase awareness of bias and develop empathy.

● Data sharing in the medical community (through blockchain) will enable us to help fight disease mich better because we’ll be able to have matches of genomes to be able to spot patterns

● Reading suggestion: The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t, Nate Silver.

● There’s more bacteria in our gut than there are cells in our body. The regulation of this in one way or another regulates how our bodies work.


https://www.kqed.org/futureofyou/144683/stanfords-virtual-reality-lab-turned-me-into-a-cow-then-sent-me-to-the-slaughterhouse

https://www.amazon.ca/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087

https://www.amazon.ca/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087





○ The way it relates to AI is that we have a massive data set inside of us.

○ AI will be indispensable for understanding our bodies in ways we don’t fully understand yet.

● We need to be mindful when mining text to accommodate diversity, dialects, slang, etc.

● The future is “meshed thinking”: thinking between humans and machine. We’re obsessed with implicit bias in ML, but we must not lose sight of our own biases (cognitive bias).

● When Google came out with their ethical frameworks (8 points), employees demanded that they tie which projects are related to those values and see how those values can be enforced.

● Building an AI startup, the most important thing is to pay attention to diverse groups/underrepresented groups to have a more complete view.

AI for Good

Ritika Dutt - Botler AI


https://blog.google/technology/ai/ai-principles/





● Depending on who you speak to and context, some people will frame AI as a positive force. Others write headlines about existential risk and terminator-like visions — but these headlines are clickbait. What we really should be thinking about is its impact on jobs.

○ 800M jobs will be dislodged globally by 2030 (McKinsey)

○ Over 75% believe economy won’t create new jobs in response

○ So does this mean AI is all bad? Jobs will change, but how much?

○ 63% of people aren’t actually aware they use AI tech (HubSpot) so it’s hard to communicate its benefits.

○ But many people are using AI for good.

● AI for Good is broad and means a lot of things. For Ritika, it’s anywhere it’s providing a benefit in our lives.

○ UN AI For Good summit announced it would determine 17 goals for good applications of AI (not yet released it seems)

○ There are 600 million results on google for AI for Good

○ Every link just talks about proposals, but it’s very difficult to find concrete applications that are actually in use. If it’s going to be for good, people need to be able to see how they can use it and different ways it might benefit them.

● For instance, AI is being used all across the healthcare industry, and that impacts everyone

○ There’s a chatbot that combines personal medical history with an interactive app that can analyze what you tell it to give a diagnosis and a prescription of what to do. This can save a huge number of emergency room visits

○ Radiology apps for detecting minute differences that even the most experienced doctors can miss, with a 10% improvement over the best humans in some cases.

○ SkinVision in the Netherlands: you take a picture of a mole and analyze it in real time, and it can track your moles over time. If it starts to look concerning, it can give you a warning to go see the doctor and get it treated.

● Other positive applications:

○ We’re going from 7B to 10B people in 2050, and we need to feed everyone. But there are many environmental risks to navigate. An app that takes an aerial picture of crops and identifies high risk areas for carrying diseases to treat before they spread to the rest of the crop.


https://www.itu.int/en/ITU-T/AI/2018/Pages/default.aspx

https://www.skinvision.com/





○ How much water does it take to produce various food items? We can now use AI to continuously tune the light and water just when it's needed using real-time data. They can use zero pesticides, less land because it’s hydroponics, 395x more productive, and 95% less water.

○ Legal applications for a very complicated system that few people understand who aren’t lawyers. It’s expensive and hard to see cases all the way through. Her app Botler is a chatbot that can help navigate this journey for people.

■ One application is looking into sexual harassment and assault laws. It will ask you questions and assess your situation and recommend the next step.

● Why don’t we seemore AI for good around? 3 main reasons:

1. It’s very expensive. The average salary of an AI engineer is $250k.

2. AI is very data hungry, which is why we see a lot of proposals but no concrete applications. It can take years to gather the necessary data

3. Regulation is a very contentious issue. Regulations are badly needed and should protect consumers, but some think they can also stifle innovation.

a. A lot of existing innovation just isn’t clear for AI startups.

b. It’s also very reactive rather than proactive, exacerbating the deficit of policy.

c. There’s lots of discussion, so it’s coming, but builders should get more involved.


https://botler.ai/





Conversational Commerce

Andy Mauro, Ravi Raj, Lauren Kunze, Mike Gozzo

Mike Gozzo: What is conversational commerce?

● Ravi Raj: Anything that is conversational. For example, walking into a store and talking to an associate and then they can recommend products based on the conversation.

● Lauren Kunze: It’s too narrow of a term. Conversational reflects every point in the customer journey, it’s not just about commerce.

MG: How many useful bots are out there? Chris Messina coined the term conversational commerce. What will get people to really adopt conversational commerce?

● LK: We don’t really know, it’s still early. There are use cases where you don’t know what you want, but it’s still very early. It’s been driven a lot by the business side.

● RR: Users will want it if they see value. If they get good recommendations then they’ll see value.


https://medium.com/chris-messina/conversational-commerce-92e0bccfc3ff





● AM: Skincare is a great example. It’s expensive and hard to find the right product. They did a skincare solution where the chatbot sent a bunch of questions. For those who didn’t complete the questionnaire, they were sent a notification for a free trial and 24% of people took the free sample and engaged with the brand.

MG: Besides free stuff, what can brands do to engage with users?

● LK: Surveys are great to collect data from your users. People like talking about themselves and they’ll happily give you info if there is value for them

● RR: Consider this example: If you walk into a store looking for jeans, and the associate throws 20 pairs of jeans at you, that’s not going to be a good experience

MG: There are two ways to do bots. Should people look to real ML or a more rule-based approach?

● AM: It doesn’t matter. If you’re building an NLP platform, focus on the behind the scenes stuff (finding the right products to recommend, etc.).

● LK: The future is a hybrid approach. PandoraBots is more rule-based/pattern matching. ML requires a lot of data, hard to debug (black box). You need humans in the loop, so rule-based often makes sense

● RR: It’s all about solving real-world problems. Doesn’t matter which solution you use, just focus on solving problems.

MG: Can brands do this without using big technologies?

● AM: You shouldn’t say it could do this, it could do that, you need to show real examples. It’s kind of like email marketing in the past, people thought no one wanted to receive emails from companies, now it’s a regular thing. Early market means doing non-scalable stuff.

● LK: www.pandorabots.com ;) The market is growing a little slowly, but it is growing.

● RR: If you don’t have the right tech, might as well not do it. Focus on your core competency (shipping, supply chain, etc.) It might be better to partner with someone.

MG: A close friend of mine recoiled at the term “Customer Intimacy.” Should customers become that close with brands?

● AM: You can’t fake intimacy, brands need to be authentic. It’s a better, more transparent model to be clear with your users. You can’t fake it anymore, you can’t collect data secretly anymore, people are smarter these days


https://home.pandorabots.com/en/

http://www.pandorabots.com/





● RR: Trust is something you earn, and brands can definitely earn that trust if they work hard to earn it over time. Brands have to be very careful about how they use customer data

MG: What’s involved in bringing a bot to market?

● LK: Most of the work in chatbot development happens after launch. You need to put it into production to see what people are saying and how they’re interacting, then iterate rapidly and continuously. The goal is to add new features over time to keep the system growing. You need a cross-functional team of designers, researchers, copywriters, legal, and much more.

● RR: It’s a combination of humans and machines. On day one, you won’t have enough data to teach it intelligently. It’s better to have a bot hand-off to a live agent in the early days to make the bot more intelligent

The Future of Work of Work is Being Written

Hessie Jones - Salsa AI, Author, Strategist






Salsa is a platform for the masses, an end-to-end R&D platform for ingesting data, cleaning it, and then using it to solve problems with AI/ML

● Hessie has been in Big Data and AI for 18 years. In that time, she’s seen a dismantling of the things she believed to be true.

● Social media changed consumer behavior and made obsolete the marketing assumptions and frameworks she once used.

● We are seeing huge shifts from the digital world:

○ 21 major retail brands have gone bankrupt in the last year

○ 15% of sales were digital, growing to 17%

○ Rising dominance of digital platforms: top market cap companies from 2000 to today are tech companies and platform companies.

○ Travel tourism industry is 17T dollars, being disrupted by Uber and Airbnb (40M rides per month Uber, 100M nights booked per year Airbnb)

○ This is thanks to the consumer now being digital.

■ How long did the landline take to get to 50M users? 75 years, Radio 38 years, TV took 14 years.

■ Disruption now happens every 2 years

■ 2 years for Facebook, 10 months for YouTube, 35 days for Angry Birds, 19 days for Pokemon Go

● If we use old frameworks and ways of operating to plan for the future, we’ll keep chasing our tails.

● The environment is dictating how businesses organize:

○ Urbanization: by 2030 3 out of 5 people will live in urban areas

○ Cities are just in 2.6% of the earth’s surface. 50% of the population lives there

○ Need higher productivity, higher tech adoption, higher demand for services, higher expectation of convenience.

● Gig economy: 43% of the workforce will freelance by 2020

○ Workforce permanence is going away

○ A liquid workforce where people need to be continuously trained






○ Managing high service quality with a liquid workforce, innovate continuously, hiring for tasks vs. people, lower costs and boost productivity

● Social responsibility: 64% of CEOs are committed to investing in CSR programs

○ We vote with our pocketbooks, making for a stronger correlation between reputation and revenue

○ Companies that have high trust will better retain employees, improve partnerships, increase investment, and drive greater revenue

○ Expectation for business to do better, trust among customers, employees and stakeholders, improved value creation using technology

○ Transparency:

■ Consumers are better informed than ever. Privacy paradox: “Businesses must use more data to enrich their customers’ experiences without betraying their trust.” - Mary Meeker

■ Companies will be heavily scrutinized on how they use customer information

■ Innovate while respecting user information, adhere to evolving regulation, privacy by design, strengthened information security.

● The Connected Company

○ Business requires a dismantling of its precious infrastructure. We have organic growth and evolution happening all the time. Business needs to become more adaptable and evolutionary and flexible

○ A divided Company: Hierarchy created disseminated accountability and the ability for people to say “not my job” and reinforced silos, slowing down decision making and innovation

○ Business 3.0 must be empathetic ethical nimble

■ A connected company is self-organizing, has fractal work units, an uncertain environment, autonomy, flexibility, and is adaptive. It functions more like an organism.

■ Some people have proposed a thing called wholocracy as a way to be a flexible evolving company, which came out of Agile methodologies.

■ Podular organization of self-organizing groups or “pods” pairing the company's organizing principles; its core functions, protocols, standards






and services; and other pods in the org to deliver value and quickly adapt to the shifting market.

● Business has to design for ambiguity, complexity, and uncertainty. Three strategies for handling this:

1. Be a perpetual learning organization

a. Focus on employees

b. Enable individual initiative to optimize learning for themselves and eams

c. Adapt to dynamic markets

d. Continuous training to close the skills gap

2. Embedded design thinking as a strategic practice

a. Explore human-centred offerings

b. Collaborate cross-functionally

c. Be rigorous in data gathering

d. Run measured experiments

e. Build ownership through internal engagement

f. Create accountability at the edges

3. Data drives everything

a. Data is the new “gold”

b. The right to be forgotten vs. the right ot be remembered

c. Heightened business responsibility

d. Regulate for uncertainty

i. GDPR is telling us to slow down and make sure we’re careful about what we’re doing with Data

ii. Creating “privacy by design” - an idea from the 1990s about requiring companies to explain their use of the data and their results to prove they are being “fair and moral”, depending on what we mean by that






The Future of Customer Service

David Furlong

● Challenge in customer service: how many things do you put up with because “that’s the way it’s been done”? We’re at a point now where AI has he power to completely reshape the way CS works. What AI will do is allow the human component of CS to be better, faster, more accurate

○ Faster: Companies like Amazon have changed the way we expect to be serviced.

○ Always on : No more retail store hours

○ More accurate : How is it that you can go to a bank and they can’t answer a question for you when they have so much data? This is where AI will step in and add value






● In CS, what happens when you take a human out of the loop? How do you create value and a good experience for humans when you remove humans? In certain sensitive situations (think of the military) you might want to spend more time on the phone

○ It’s not about taking humans out of the service, it’s about using AI to aument human capacity

● At National Bank, they use bots to initiate the conversation, but then hand off to humans later. They have experts in the field train bots to have the bots have the right answers

● They went from couple of 100s of trades a day to 100,000 trades per day with the help of AI

● Because humans are programming and training the AI, the bias will be embedded into the AI.

○ Algorithm creation: Algorithms designed by humans, some bias will be transferred

○ Interpretation: Confirmation bias, availability bias

○ Complexity: Makes AI opaque, companies don’t want their proprietary algorithms scrutinized

● Potential impacts: millions of people in jobs ripe for social dislocations (millions of drivers, millions of call center employees, etc.). How do we make sure we’re thinking about those people as AI transforms those industries?

● We’ll also see an enormous amount of AI job creation, so it will hopefully offset the damage done by the transformation of AI. AI is a huge opportunity to create high-paying jobs

○ AI Scientists, data engineers, process people, IT coders, business people to see the value = designing, building, maintaining and using AI systems requires a hard-to-come by blend of skills.

● You have to be patient. AI takes a long time to get better, requires a lot of data, and is still relatively early on in its development.






AI Ethics: Workshops Summary

Jana Eggers, Tyler Schnoebelen, Katy Yam (moderator)

On Day 1 and Day 2, Jana Eggers and Tyler Schnoebelen led the audience through a collaborative presentation about AI ethics. After keynotes on key questions and frameworks, attendees were given scenarios as jumping off points for ethical debate and discussion.

● Jana Eggers is the CEO of Nara Logics, a neuroscience-based AI company focused on turning big data into smart actions. Jana has frequent discussions with her team about ethics + how to develop frameworks.

○ In her keynote, Jana shared some of the scenarios that sparked debate amongst her team, and shared a call to action:

■ Data scientists are hard at work developing algorithms: ethicists, entrepreneurs, teachers, philosophers — these are the people who also must get involved in the discussion.

● Tyler Schnoebelen is the principal product manager at Integrate AI. He’s an expert in data science, NLP and user experience.






○ Tyler shared ethical frameworks that can help frame thinking and spark debate. But his main takeaway:

■ “Regardless of your preferred ethical framework, data scientists and AI practitioners must consider the goals of the people affected by the systems they design and build.”

○ Workshops: Attendees were given one of two scenarios to discuss:

■ Scenario 1: “Toxicity and free speech: You’re an executive at Reddit. A team has created a product that scores comments for toxicity, constructiveness, and health- promotion. Several of your senior engineers have voiced strong concerns that this will destroy free speech. What do you do?”

● Group takeaways/action items:

○ Could we match influential healthy users with problematic users for coaching/helping?

○ Could we use this to build tools that help protect others? Racist/sexist/etc comments don’t affect everyone equally

○ Can we detect earlier that a thread was headed in a bad direction?

■ Scenario 2: "You have been collecting lots of data about when humans expect self-driving cars to save pedestrians versus passengers. You've built in heuristics that, more or less, try to minimize harm. But you have a competitor who is going to sell cars that minimize harm to the passengers--that is, the people who purchase the cars. You are seeing that their strategy seems to be working in the market. What do you do?"

● Group takeaways/action items:

○ Focus on improving non-collision AI versus what to do in a collision

○ Learn from other fields like medical

○ Focus on inclusivity and who should be in these discussions

○ Possibility of allowing more ethics control by the customer?






On Day 3 of the ethics workshops, Katy Yam summed up key points and shared action items for the audience.

● Another great take away from the workshops was a list of action items for encouraging these types of debate in one’s own organization:

○ Have regular, recurring discussion

○ Watch for who is talking/participating in these discussions and who isn’t

○ Check for diversity and inclusion

○ Include academics, government lawmakers, regulators, philosophers

○ Learn from other fields (but never forget that AI is something we’ve never encountered before)

○ Give people frameworks to help spark debate and frame discussions

○ Publish results! Turn agreed ideas into written policies and procedures

○ Tell folks it’s okay to have disagreements and encourage diverging POVs






Past, Present and Next Generation of AI

Karen Bennett

● Been in AI for over 25 years, one of her first projects was Deep Blue with IBM, working on chess and then Jeopardy

○ They moved really quickly to build a product (Deep Blue and Watson) but it was too soon and we’re seeing the side effects of that

○ Karen has brought approaches like DevOps to IBM and open source to RedHat, and cloud solutions for the enterprise

● Now she is back in AI for the last 5 years and she has had to re-learn what AI is, as when she was last in it, it was known as Expert Systems. She hadn’t heard of data scientist or a machine learning engineer. It’s been a huge learning curve with all the progress

● For anyone moving back into it, you really need to take courses






● A lot of hype, and for now all the research research, and good applications are taking care of the plumbing: the basic infrastructure applications of AI that the rest will be built on.

○ There’s still lots of work to be done on the deployment and clear explanations of what it is for it to reach its potential

○ The full potential of AI will transform businesses and the new plumbing itself will force us to start changing the rules of the game. But we need to explain much better what it does to allay fears

● Explaining what AI is going to do raises all these questions for society, economy, privacy, and governance, and how to solve for them.

○ But the solutions we have are not all that developed.

○ Tesla Paying truck drivers to get re-skilled $5k per year, but they’re not doing anything with it.

○ We have to be able to figure out how to explain the results and impacts in visuals and plain English before we can go forward with AI and deploy it widely

○ Most people expect the machine to be perfect. Can we put out a narrative that the AI is like a toddler it is still making many mistakes but it will get much better than humans at particular tasks?

● AI in the present:

○ Classification: image and speech recognition, senses

○ Prediction: incredible recommendation systems. She is Beta testing Amazon service that automatically ships products it predicts we want or need. She’s kept all five deliveries despite wanting to find a reason to send it back

○ Visualization: we have some good work from Data scientists to transform insights into rich visuals for communication.

○ Validation is a problem area: right now our only way to test is comparing to humans, but machine automation has different implications at scale and so need better validation mechanisms

● We’re seeing AI in production in many areas where we’ve seen performance better than humans, but that narrow validation doesn’t really tell us it is better. Do we want the teacher not grading the test and seeing the work? What are they doing instead for a net benefit?






○ In image recognition:

■ Acquisitions processing analytics and understanding applications

■ China’s face++ has been used for stopping petty crime with significant impact, but that still has its limitations

■ Can only process 1000s of images at a time an there are millions upon millions to look at.

○ In voice recognition:

■ Help find useful info, book appointments, language translation, bots

■ Duplex can sound like a human and have a natural conversation and we have excellent live translation for free!

○ Video recognition:

■ Search, coaching with playback, targeted marketing, and security

■ But the limitation is it all has to be digital in format (paper and film conversion)

○ Recommendation:

■ Retain and upgrade conversations

■ Give optimized offerings

■ Micro categorize customers

■ Real-time Net Promoter Score

○ Personalized marketing

■ Optimize pricing, advertising, and marketing cost and real-time A/B testing

■ Actually you don’t really have to do A/B testing as much any more, you can predict results and iterate 1000s of possibilities

● Is AI ready for enterprise prime time?

○ If you miss the boat you can miss out on a ton of revenue

○ The challenge of training requiring lots of data keeps out smaller players, even if they’re massive by other standards






○ There are some programs for open data or extrapolating the training with less data

○ But many organizations are still resistant to sharing data, in which case everybody loses including them

○ How do we also keep our data clean? The data flows are only going to increase and will soon be directed into a feedback loop of machine learning, can we really manage all of it?

○ We’re expecting GDPR type regulation in Canada by 2020 and those who are building the basic plumbing are not all ready to comply with the decisions we will need to make about data

○ The change in operations will be massive and difficult for organizations

○ Devops is a now an archaic example of the kind of shift we will need to undertake. This relates to all the new rules we need to come up with or update with the new capabilities but also risks like bias.

● Validation

○ How do you explain what happened when mistakes happened. But also are we catching them early, how can we input other validation measures to catch them earlier so that they aren’t so wrongly publicized. And how can we? Publicize explanations that are not so easily screwed or just misunderstood

○ We’re used to go from training to production, but we have a cycle of new data after it goes I to production that can change but we no longer have the parameters we keep for it when it is in training. How to you keep an eye on that cycle of new data and how it can change the model?

○ A new C-suite type role of Chief Data Officer

○ They’re definitely going to try and make the most money, but are they going to look after ethics? Not all do them, which is why we need external governing bodies to build on what gdpr is trying to lay out

● What’s next?

○ The blended mind perhaps

○ More certainly we will see self driving cars as a way of life, improved health care, and explainable AI

○ The last one is what will allow for the rest of the potential to be realized.






○ Getting an explainable model in insurance was massively difficult. It’s taken a year to just explain the inner workings to management and it’s still not live because they’re not ready yet.

● Enterprise readiness challenges:

○ Data: Bias, cleaning, data access/privacy

■ Old data actually doesn’t work all that well for predicting the future, mainly just stuff from the last 3-5 years

○ Diversity: explainable ai for everyone, new models of digital education and ai workforce development

■ Starting a consortium of academics to study the impacts of diversity on the development of ai technology specifically to further show the best ways forward

○ Validation: model management on training, in-test, and production

○ Governance: penalize malicious ai behavior, mechanisms for human oversight and control.

● There are the leaders, early adopters, and many potential big winners. The big leaders look clearly ready to gobble up the many promising potential winners, but there are many startups in Canada showing real promise and have sworn of being acquired. Could the next big tech leader be Canadian?






Love, The team at Element AI <3






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