Gary Marcus

Canadian physicist and Ursula Franklin wrote many years ago about how technology shapes the way we work. In her discussion of prescriptive vs. holistic technologies, she channeled what Albert Borgmann would later articulate in his device paradigm: technology often serves to commoditize and optimize particular narrow aspects of a complex process. This commoditizing effect is exactly what creates the prescriptive framework that Ursula writes about in her first lecture in her 1989 CBC Massey Lectures "The Real World of Technology." She writes: “It is the first kind of specialization, by product, that I call holistic technology, and it is important because it leaves the doer in total control of the process. The opposite is specialization by process; this I call prescriptive technology. It is based on a quite different division of labour. Here, the making or doing of something is broken down into clearly identifiable steps. Each step is carried out by a separate worker, or group of workers, who need to be familiar only with the skills of performing that one step." As we think about AI, we need to consider Franklin’s framing of prescriptive vs. holistic technology. In many ways, AI offers opportunities to strengthen either manner of work. Most naturally, AI will be yet another step along the prescriptive path, commoditizing certain parts of the creative process and making them iteratively more efficient and frictionless. On the other hand, this commoditizing effect of AI might also allow individual creators to retain more control over the artistic process (e.g., by enabling them to do certain parts of the process that they might otherwise have been unable to do by themselves.) Of one thing I am certain, AI will shape the way we work, whether we like it or not. The challenge that we face now is carefully thinking though the natural orientation of our AI tools and designing systems and structures to enable AI to allow us to more deeply align with our work. In some situations, this may mean we need to exclude AI from the process altogether. Whatever we choose to do, let’s spend the time to think through the implications and consequences.

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I'm excited that this week at the CAIAC - Canadian Artificial Intelligence Association conference George Wang will be presenting a paper Adaptive Learning Rates for Gradient Boosting Trees, based on work he did as an intern at Kinaxis last fall, in collaboration with Christopher Wang, Yunfei O., Behrouz Soleimani, PhD. There are 3 reasons this is both impressive and cool: 1. They combine heuristics, machine learning, and optimization to speed up the training for an important algorithm, Gradient Boosted Machines, widely used in both academia and industry. Faster training improves model performance. 2. This paper is just one of the many sparks of innovation in #artificialintelligence coming out of Kinaxis, where > 50% of our patents filed in the last several years have been in this space, because we continue to push the boundaries by inventing new solutions like this one to solve problems our customers face. We've been delivering AI to the market since 2018 and were the first to provide solutions powered by AI to address both demand and supply. We are rooted in Canada, which brought the world out of the last AI winter in 2012 when a team from the University of Toronto won the famous Imagenet competition by blowing the lid off previous results. Their submission was layers of neural networks called deep learning, which later became the foundation for the #generativeAI revolution we are seeing today. 3. We have an outstanding intern program in #machinelearning at Kinaxis, where interns work on real problems, guided by senior staff. As a testament to letting the students shine, when their paper was accepted they decided to have George present it. So check out the paper if you're at the conference, but if you miss the event you can't miss the fact that we are actively speaking and sparking on AI. In fact, just last week Chantal Bisson-Krol spoke in Edmonton at the #upperbound conference on AI-Powered Strategies for Resilient Supply Chains and I spoke at the Reuters Supply Chain USA event on Embedding AI in supply chain to deliver end-to-end value.

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Any talk about the growth of the AI chip business is usually driven by stories about how dominant Nvidia's market share is, or how companies like AMD and Intel are trying their best to catch up. So it might be easy to assume that there isn't a chance for startups to compete. But several Canadian startups are finding a place in the chip market, which has room for them to co-exist with and compliment what's being sold by the big multinationals (and, possibly, disrupt them). https://lnkd.in/gm_gs9m9

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From brain implant research in the labs to a new startup ... how do you do that?!?!? Learn how from MJ Antonini and Nicolette Driscoll -- fascinating and many lesson to learn. Lab-To-Market has never been a hotter topic and never seen so much progress being made in it, but we still have a long ways to go and Macauley Kenney is leading our efforts at the Martin Trust Center for MIT Entrepreneurship https://lnkd.in/eX_kTg_z

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We are at the dawn of a new era where artificial intelligence is revolutionizing scientific discovery. From predicting molecular structures to aiding in the identification of new materials, AI is accelerating the pace of innovation and contributing to breakthroughs that were once thought to be decades away. The recent Nobel Prizes highlight the importance of these advancements, proving that AI is becoming an indispensable tool in the scientist's toolkit. I'm excited to share our latest article, "An AI-infused Framework for Scientific Discovery," where Anand and I demystify the concept of AI for Science and provide a straightforward framework to understand its potential. Dive in to explore how AI is transforming research and enabling new frontiers in science. Read the full article here: https://lnkd.in/divhhTxh #AI #Science #Innovation #Research #ScientificDiscovery

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A must-read! Thanks, Tim Sanders for the recommendation and the book itself! Not just due to their Canada-connection, Ajay Agrawal, Joshua Gans, and Avi Goldfarb packed this ~200-page book with not just facts and figures, but frameworks and fascinating, fantastic food for thought. One key takeaway for me is to cut the use of 'insights' from my vocabulary when it comes to the impact that #AI brings to businesses (insights on the tech and trends are still great though, heck our team is called the Research Insights team). When we think about the power that AI is bringing to enterprises, it is all about its impact on 𝕡𝕣𝕖𝕕𝕚𝕔𝕥𝕚𝕠𝕟𝕤 and 𝕕𝕖𝕔𝕚𝕤𝕚𝕠𝕟𝕤. Saying that it helps businesses generate or gain insights is too surface level and not getting to the core of AI's capabilities. Business leaders, take note: focus on what predictions your businesses rely on and then figure out if, how, and when AI can come and improve them.

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In #uWaterloo, We sat around a table, And asked each other to think of #startup ideas I chimed in, "Let's just make a dollar" Thinking that was a good start. We thought of outlandish ideas Crazy things that others had already done But we never made that dollar Turns out startups aren't about flash That new thing, Crazy idea It's about money. Can you make that dollar? Can you actually sell a product or a service? We just crossed 1000 payments processed. #ZeroInbox crossed 1000 payments. I'm flabbergasted. How did I go from struggling to make a single dollar To processing thousands? I started out this journey wanting to be an entrepreneur. Like anything it took time, and hard work. Some crazy crazy luck, and really really nice people. I made my first dollar not through #zeroinbox But through Facebook Marketplace! Selling gold I brought from a country I'd rather not name. Start with a dollar. A penny. #Startsomewhere, #startsomething!

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Elon Musk's Neuralink announce Data Compression Challenge open to public.  The main idea of the invention is to provide efficient interface between brain and machine/computer. Implant in the brain is expected to transfer wirelessly huge amounts of data to computer and, possibly, back. It's clear that the system has many restrictions like: limited energy reserve and limited computational power. Even if theoretically compression at the 200/1 rate would be feasible (what is not), the limitations of energy resources and computation would not allow to implement that sophisticated and computationally intensive transformation in acceptable cost.  https://lnkd.in/eS-r5DuS

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QUEBEC.AI is gearing up for production-scale LLM deployment! Immense gratitude to Louis-François Bouchard and Louie Peters for the invaluable gift: "Building LLMs for Production" 📖✨ Your insights will drive our success—thank you! 🚀 #ArtificialIntelligence #DeepLearning #LLMs

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This is so exciting. For the Nobel body, for the scientific community, and for the world to acknowledge the tremendous work done to enable AI and ML as we know it today. The shift in paradigm that was created with Jeffrey's work (and the co-recipient's) has made work in this space, deep, philosophical and applicable at same time. https://lnkd.in/ggW-q45K #machinelearning #ai #artificialintelligence Congratulations form all of us at Mesh AI (collaborative scheduling to end clinician burnout).

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Exploring JPEG AI with Dr. Elena Alshina: Revolutionizing Image Compression In a recent interview, I had the privilege of chatting with Dr. Elena Alshinaabout JPEG AI. Dr. Alshina is a prominent figure in the field, serving as the Audio Visual Lab Director and Director of the Media Codec and Standardization Lab at Huawei. She also plays a leading role in various international standardization projects, including for JPEG AI. During the interview, Dr. Alshina provided an in-depth look at JPEG AI, a groundbreaking image compression technology driven by neural networks. She discussed the technical aspects at length, covering design principles, performance metrics, and the overall development process. She explained how JPEG AI is set to improve image compression efficiency and quality, offering significant advancements over classical codecs. She also touched on potential applications for this technology, including super-resolution, noise reduction, and image classification. This interview is a must-watch if you’re a professional or enthusiast in digital imaging, artificial intelligence, or media technology. Those involved in image and video processing, software development, or technology innovation will find Dr. Alshina’s insights particularly relevant. She highlights the technical advancements of JPEG AI and also explores its practical implications and future potential. By understanding the capabilities and benefits of JPEG AI, you can stay ahead of the curve in image compression technology. #JPEG #JPEGAI

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Canada has reaffirmed its leadership in the responsible development of artificial intelligence (AI) with the launch of the Canadian Artificial Intelligence Safety Institute (CAISI). This initiative aims to address emerging risks associated with AI while fostering trust and innovation in the technology. - Une excellente nouvelle pour le Canada!!

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Canada invests $10.7m in AI-enhanced image-guided therapy INOVAIT Canada and the Government of Canada have announced a $10.7m investment for AI-enhanced image-guided therapy under the second iteration of INOVAIT’s Focus Fund programme. Image-guided therapy (IGT) is the practice of using medical imaging to plan, perform, and evaluate medical interventions. The INOVAIT network supports Canadian innovators who are unleashing the power of AI through IGT’s precision therapy and treatment to streamline healthcare and improve healthcare outcomes. https://lnkd.in/esUuq85q #ai #investment #medicalimaging #health #canada

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(DataSphere Lab @ McGill University) Been reading up on design of experiments. Denys Linkov DoE may provide useful insights .... Thursday's development session is winding down. Hopefully by sometime in the weekend, I'll be in a position to start running "experiments." Initially things will be simple. I will alter only one factor: the number of examples in the prompt. In the future, I'll alter other factors. I'll give the LLM utterances and it will generate code. An routine will compare the LLM's code to the expected output and it will either pass or fail. I'll do this say, 100 times. I'll look at the accuracy. I'll keep on raising the number of examples. I'll plot out a graph. Most of today, I have been debugging. I have a source document that is annotated (nothing fancy), a tool (nothing fancy) that expands annotations, and the subsequent target document. I examine the output. If there is a mistake, I either alter the source document and or the generation tool. It is slow work. Even with tools I have to be careful .....

🏆Impressive work from all T-RIZE Labs team and T-RIZE Labs Chairman Professor Kaiwen Zhang ! The integration of AI Federated Learning and blockchain-based carbon credit tokenization is a testament to how decentralized technologies can unlock new levels of privacy, security, and sustainability. 💚By combining these advanced methodologies, T-RIZE GROUP | RWA Tokenization & Onchain Federated Learning is not only driving innovation but also pioneering a path for scalable, eco-friendly solutions that align with global carbon reduction goals. This is the kind of forward-thinking approach that we expect to see from the world’s leading research hubs—an inspiring example for the tech and sustainability sectors alike. Digital Asset Canton Network IEX Chainlink Chainlink Labs

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Amplifying this new technical guide from Accessibility Standards Canada on accessible and equitable #AI developed by a technical committee chaired by Jutta Treviranus of the Inclusive Design Research Centre at OCAD University. This valuable technical guide focuses on areas where people with #disabilities may face barriers to accessible participation in the design, development, and use of AI systems and equitable treatment by AI systems. Equity refers to fairness, justice, and freedom from discrimination. Equity recognizes that each person has different circumstances and focuses on enabling all individuals to achieve the same outcomes. People with disabilities experience the extremes of both the opportunities and risks brought about by the deployment of AI. This guide offers guidance and directives that support ethical practices when implementing AI, which includes providing proactive guidance to prevent harm for people with disabilities in emerging applications of AI. For instance, the guide recommends that organizations: • Recognize that people with disabilities should be able to participate fully as stakeholders in all roles in the AI lifecycle. • Recognize that AI systems need to be accessible to people with disabilities. • Design, develop, procure, and/or customize AI systems that are accessible and equitable. • Conduct ongoing impact assessments, ethics oversight and monitoring of potential harms • Train personnel in accessible and equitable AI • Provide transparency, accountability and consent mechanism • Provide access to equivalent alternative approaches • Provide feedback, complaints, redress and appeals mechanisms ... and more! Check out the full technical guide here: https://lnkd.in/eV_raH7H #accessibleAI #a11y #accessibility #equitableAI #deia #responsibleAI #inclusiveAi #disabilityInclusion #diversityEquityInclusion

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Good news 🇨🇦 Tech friends: AI Tinkerers Toronto Meetup is now live! Show don't tell. Join us to share your killer AI demo with fellow AI builders and researchers, on May 30th at Shopify HQ. No pitches! We want to see your messy WIP! Let's build 🇨🇦 / acc. Apply here: https://lnkd.in/g-C73dwm 💡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗜 𝗧𝗶𝗻𝗸𝗲𝗿𝗲𝗿𝘀? We are not just “AI enthusiasts”, we are AI tinkerers. AI Tinkerers is a meetup built around live demos, for tech entrepreneurs, hackers and those deeply passionate about creating LLM-enabled applications and have hands-on experience in building such systems. For those who remember... yes this is a lot like DemoCamp :) 🎤 𝗗𝗲𝗺𝗼𝘀 We are actively looking for presenters! A presentation/demo is limited to 5-6 minutes in length and demonstrates a work in progress. We’ll select 4 or 5 of the most interesting topics to present. Note, as capacity to the event is limited, we’ll be prioritizing those that can demo something interesting. 🚀 Pls RSVP and spread the word! We've needed an event built around actual Demos. This is it. Sponsored by Shopify and Shopify Ventures; brought to you by the Human Feedback Foundation AIT city organizers. Let's go 🚀

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