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AI Opportunities

Posted on by Michael Magee in Uncategorized

I did a full business model analysis of the U of C a month and a half ago. It took about 3 hours in total and generated a lot of recommendations and insights. It turns out that GenAI is very good at evaluating how its capabilities can align and support different aspects of an organization. You just need to know how to ask it the question. The work involved developing a series of prompts engineered to analyze publicly available data and generate a business model summary of an organization, in this case, U of C and the state of GenAI.

The initial business model summary is here.

Since then, there have been so many announcements about open-source LLMs, multimodal LLMs and AI agents that this entire analysis should be re-done. Things are changing so quickly that doing a discrete analysis every few months is obsolete. An organization needs to be faster to stay aware of the opportunities and challenges in the post-secondary space. Although most of the analysis of the impact of GenAI is still relevant and valuable, I’d do it differently next time. Rather than running the prompts every few months to re-evaluate an institutional strategy, having a real-time tool or dashboard with alerts would be better. A real-time analysis would allow you to monitor an organization’s strategy constantly and build awareness of burgeoning opportunities and challenges.

I designed this report for a senior leadership audience. Senior leaders would be the only ones with the resources and influence to pivot the strategy in the face of change. The report’s goals were to allow them to anticipate, identify, and leverage new opportunities arising from generative AI technology. I thought I would start with the positive aspects of GenAI in higher education. There is an ongoing debate on the utopian and dystopian future of the world and AI so I thought I’d start with a more utopian perspective. Here are the top five opportunities and recommendations on how to use them:

1 – Enhanced Personalized Learning

Opportunity: GenAI can create highly personalized learning experiences by adapting content to individual student needs, learning styles, and progress. Potential implementations:

  • Implement an AI-driven adaptive learning platform that tailors educational content to each student’s pace and understanding. A growing number of examples show how agents can increasingly guide a student’s learning. 1
  • Use AI to generate personalized feedback and learning strategies, helping students focus on areas where they need improvement. 1
  • Encourage faculty to integrate AI tools in their teaching methods to create more interactive and engaging learning environments. 2, 3

2 – Improved Research Capabilities

Opportunity: Generative AI can significantly enhance research capabilities by automating data analysis, generating hypotheses, and even drafting research papers. Recommendations for implementation:

  • Invest in AI tools that assist researchers in data mining, pattern recognition, and predictive analytics to accelerate research processes.
  • Provide training for faculty and students on effectively using AI tools in their research, ensuring transparency and adherence to ethical guidelines. 2, 4
  • Establish partnerships with AI research firms to stay at the forefront of AI advancements and integrate cutting-edge technologies into U of C’s research infrastructure.

3 – Streamlined Administrative Processes

Opportunity: Generative AI can optimize administrative tasks, from student admissions and course scheduling to resource allocation and performance tracking. Many post-secondaries have become very bureaucratic, and the amount of time spent by academics and administrators can be considerable. Recommendations:

  • Deploy AI-powered chatbots and virtual assistants to handle routine inquiries, freeing up administrative staff for more complex tasks.
  • Utilize AI for predictive analytics to improve resource management, such as predicting enrollment trends and optimizing class sizes. 1
  • Implement AI-driven systems for monitoring and enhancing student performance. These systems will help identify at-risk students early and provide targeted support.

4 – Innovative Teaching and Assessment Methods

Opportunity: Generative AI can transform teaching and assessment by providing new ways to evaluate student learning and engagement. Strategy to Leverage:

  • Develop AI-based tools for formative assessments that provide real-time feedback, enhancing the learning process without replacing human judgment. 3, 4
  • Encourage faculty to design assessments that incorporate AI-generated content. Students should be able to critically evaluate and improve AI outputs.
  • Use AI to create diverse assessment formats, such as simulations and interactive scenarios, that test higher-order thinking skills and practical application of knowledge.

5 – Fostering an AI-Empowered Workforce

Opportunity: Preparing students for a workforce increasingly reliant on AI technologies by integrating AI literacy into the curriculum. This has become a huge focus for many governments. The U.S. Government has put together hearings on creating an AI-enabled workforce to enhance U.S. strength and prosperity. 5 Recommendations:

  • Introduce AI literacy programs across all disciplines to ensure students understand the fundamentals of AI and its applications in their fields. 6
  • Offer specialized courses and certifications in AI and machine learning to attract students interested in pursuing careers in these areas.
  • Collaborate with industry partners to provide internships and co-op opportunities that give students hands-on experience with AI technologies.

This a list of the current opportunities but the point of using GenAI to do strategic analysis is that you can modify and shape the feedback until you get results that make sense for your organization. The critical difference with traditional methods is how quickly you can use a wide range of data and an LLM to develop and iterate your strategic planning. It can accelerate the process and provide you with insights that you can evaluate against your current organization. The next article with be the challenges that were identified for GenAI and the U of C.

University of Calgary Strategy, from an AI perspective

Posted on by Michael Magee in Uncategorized

Introduction

We’re at a point where the hype and reality of Generative AI (GenAI) are starting to settle. The reality of what GenAI can do is substantial, and it is reshaping industries and educational paradigms. There are many universities worldwide that are reassessing their roles, strategies, and value propositions. Many of these examples are from business schools but the rest of the university will also need to understand the changes coming. 3

The first step in analyzing a university’s or postsecondary’s existing strategy is to categorize, organize, and analyze the business model, value proposition, and strategic plan to analyze GenAI’s impact. It is no small irony that GenAi itself is an excellent tool for summarizing, categorizing, and analyzing an organization’s strategy. Through some prompt engineering and the publicly available documentation on their website (https://ucalgary.ca/), it was possible to pull together a summary of the organization that could be used for the analysis. Like many public institutions, information about the organization and its strategic planning is available on its website.

Although the U of C was used to create a case study on the impact of GenAI on that organization, many of the challenges and opportunities will be common to all higher education institutions. Analyzing U of C’s business model, value proposition, and strategic plan made it possible to gain insights into how a leading public research university can position itself to thrive in an AI-driven future.

The University of Calgary Overview: A Model of Adaptation and Innovation

Established in 1966 in Alberta, Canada, the University of Calgary has evolved into a centre of innovation and community engagement.

Business Model

U of C’s public institution model, funded through government grants, tuition fees, and research grants, supports three core activities:

  1. Education: Offering over 200 diverse programs to more than 33,000 students.
  2. Research: Spearheading cutting-edge investigations across 80+ research institutes and centers.
  3. Community Engagement: Local and global partnerships facilitate lifelong learning and knowledge transfer.

Value Proposition

The U of C distinguishes itself through:

  • Academic Excellence: Delivering high-quality education and research opportunities.
  • Innovation and Entrepreneurship: Cultivating an entrepreneurial mindset among students and faculty.
  • Community Impact: Driving positive change locally and globally.
  • Global Perspective: Fostering a diverse, inclusive environment that prepares students for global challenges.

Strategic Plan Summary

“Ahead of Tomorrow” (2023-2030)

U of C’s strategy for the future, “Ahead of Tomorrow,” outlines a vision for 2023-2030:

Mission: “The University of Calgary powers positive change. We strive for inimitable excellence through innovative teaching and learning, cutting-edge exploration, and community linkage.” 1

Foundational Commitments:

  • Equity, Diversity, Inclusion, and Accessibility
  • Indigenous Engagement
  • Mental Health
  • Global Engagement
  • Sustainability

Strategic Goals:

  1. Enhance Access to Future-Focused Education
  2. Leverage Research and Innovation for Societal Challenges
  3. Prioritize Community in All Activities
  4. Optimize Processes and Operations

Key Initiatives:

  • Boost financial support for thesis-based graduate students. 1
  • Offer one-semester financial aid for first-generation university students. 1
  • Develop innovative programming anticipating future demands. 1
  • Expand understanding of the university’s foundational commitments. 2
  • Enhance required programming in entrepreneurial thinking, research, and creative scholarship. 2

U of C aims to establish itself as a global intellectual hub, balancing academic excellence with innovation and community engagement. The strategic plan underscores the university’s commitment to addressing societal challenges, fostering entrepreneurial thinking, and preparing students for the future while honouring its Calgary roots and values.1 2

This is a quick summary of the organization, and there are undoubtedly some additional pieces that need to be included. Many strategic and business documents will be proprietary and confidential. Despite this, there is enough publicly available data to create a summary suitable for a strategic analysis of the U of C. A couple of points need to be made about how this analysis was conducted. By using GenAI tools, it was possible to take the existing data and do a complete analysis in a couple of hours. This has two major impacts:

  1. An organization’s strategic analysis can happen every few months; it doesn’t need to wait for years before it starts to assess the impact of GenAI on its operations.
  2. The pace of change in AI makes it difficult to keep track of all the potential impacts. Being able to use GenAI to assess an organization’s strategy also allows for the analysis of the most recent breakthroughs in the technology and the evaluation of their relevance.

Sources

1 2023-2030 Ahead of Tomorrow. https://www.ucalgary.ca/sites/default/files/teams/10/UCalgary-Strategic-Plan-Ahead-of-Tomorrow.pdf

2 Ahead of Tomorrow: UCalgary’s 2023-2030 Strategic Plan https://www.ucalgary.ca/sites/default/files/teams/10/UCalgary-Strategic-Plan-Ahead-of-Tomorrow.pdf

3 A manifesto for business schools to ensure the smart and deep integration of generative artificial intelligence into the curriculum. https://www.aacsb.edu/insights/articles/2024/04/future-proofing-higher-education

What a fly brain can tell us about AI

Posted on by Michael Magee in Uncategorized
Leonardo.ai and Affinity Photo

I’ve recently started going back into neuroscience research after reading some of the proposals from Yann LeCun about the current approaches to GenAI being a dead end. LeCun’s perspective is that the focus should be on objective-driven AI as the future of the field. For anyone looking for a quick summary, here it is:

Current GenAI mimics a small part of the brain’s architecture. It was inspired by how the brain creates neuronal networks, but GenAI, a subset of deep learning, is a much simpler version. A biological brain is much more complex.

LeCun’s AI framework is a close approximation to a functioning brain, but most of it is still theoretical. There are some really interesting parallels between how objective-driven AI and deep learning models work and a biological brain, but the complexity of the brain far exceeds current AI systems. This means a fly brain can still outthink current AI systems when faced with the real world.

Now, into some more detail. Being able to map out the entire neural network of a fly’s brain might seem trivial. Still, it is an opportunity to compare our approaches to AI with those of a real biological neural network. Here goes:

A quick summary of what Yann LeCun’s Objective-driven framework of AI is about. In this model, an AI system is able to achieve goals through a hierarchical structure of objectives. There is a world model, a cost function and an actor that selects actions to minimize the cost function. This system learns to represent the world and make decisions without extensive human-labeled data. Instead, it relies on intrinsic objectives and interactions with the environment. This sounds a lot like how any living creature figures out how to find food when hungry, so these AI systems are intriguing. Here’s where they align:

  1. Hierarchical Organization – LeCun’s model has a hierarchical structure, which aligns well with the brain’s organization. The brain processes information through hierarchical pathways, particularly in the neocortex (that’s only in mammals).
  2. Predictive coding – LeCun’s model predicts future states, something the brain constantly does through mechanisms like predictive coding.
  3. Reward systems and cost functions – The cost function of LeCun’s model evaluates the desirability of the predicted states and is very similar to the brain’s reward system. Anyone who is familiar with the concept of dopamine in the brain can understand how the brain rewards certain behaviours to guide learning and decision-making.
  4. Action Selection – The actor model in LeCun’s framework is responsible for selecting actions and is comparable to a biological brain’s motor cortex and basal ganglia.
  5. Self-supervised learning – LeCun emphasizes self-supervised learning of AI systems. These systems can learn from unlabeled data and interactions with the environment, which is similar to how the brain learns. A biological brain continuously learns from the environment without explicit external supervision.
  6. Distributed Representations – Both LeCun’s AI framework and the brain use distributed representations to encode information. In the brain, memories and concepts are thought to be represented by patterns of activity across large groups of neurons rather than in single cells.

Although there are a lot of intriguing parallels between LeCun’s AI framework and the brain, there are a couple of things to consider:

1. LeCun’s system is theoretical and has yet to be built.

2. The brain’s complexity still far exceeds current AI systems.

The brain’s ability to generalize, adapt, and operate across multiple timescales and domains is still unmatched in the world of artificial systems. Insights from neuroscience can certainly inform AI research and vice versa. There are many opportunities for dialogue between the two fields.

The Impact of GenAI on University Strategies

Posted on by Michael Magee in Uncategorized

Technology has been a disruptive force in society for centuries. The world of formal education has had to absorb those impacts, although often at a slower, more conservative pace. The reluctance of education to change is a more profound topic that I won’t cover in this article, but there is a new technology that could be both very disruptive to education and be occurring at an unprecedented pace. We are likely at the edge of a new era in higher education, shaped by the rapid advancement of generative artificial intelligence (GenAI). Universities, colleges and polytechnics are at a critical juncture. The emergence of tools like ChatGPT, Gemini, Claude and other AI models has sparked both excitement and apprehension within academic circles. The institutions that understand the potential impact of GenAI technology are reassessing their strategies and adapting to this transformative technology.

GenAI is a powerful tool. Its impact on higher education isn’t simple; it will be complex and far-reaching. There isn’t a single domain of the post-secondary business model that is free from impact. It had the potential to disrupt teaching methodologies and research practices as well as reshape administrative processes and student engagement. As David Paroissien, OES’s Generative AI Lead, aptly notes, “There are so many ways universities can use AI, from creating or re-designing high-quality learning materials to customizing student learning tasks to delivering automated, personalized feedback, to achieving time savings for academics” 1.

However, integrating GenAI into higher education is not without its challenges. Many educators are concerned about academic integrity, data privacy, and the potential erosion of critical thinking skills. These issues underscore the need for a comprehensive strategic analysis to help institutions navigate this new landscape’s complexities.

Strategic analysis concerning GenAI is crucial for several reasons:

  1. Identifying Opportunities: Evaluating the range of GenAI use cases allows universities to uncover innovative ways to enhance their existing operations.
  2. Mitigating Risks: A strategic approach allows institutions to anticipate and address potential risks associated with GenAI, such as concerns about academic integrity and AI bias.
  3. Adapting to Change: The speed of innovation in AI requires a proactive approach. A well-designed and agile strategy can help universities stay ahead of the curve and adjust their practices accordingly.
  4. Maintaining Relevance: GenAI is becoming increasingly prevalent as a business tool in various industries. Obsolescence is a genuine concern, and universities must ensure that their curricula and research initiatives remain relevant and aligned with real-world developments.
  5. Ethical Considerations: A strategic analysis can help institutions develop robust ethical guidelines for using GenAI, ensuring its implementation aligns with academic values and principles.

The Boston Consulting Group (BCG) has identified five significant ways that higher education can leverage GenAI, including:

1. Personalized recruitment marketing through hyper-personalization.

2. Improved student engagement and outcomes.

3. Enhanced course planning and curricula.

4. Teaching students to use GenAI.

5. Enhanced assessment and feedback for students.

These categories are a good starting point for any institution looking to incorporate GenAI into its operations 2. 

Creating more efficiency in an existing business model is only the start of the process. As Steve Andriole points out in Forbes, GenAI’s impact on higher education goes beyond mere enhancement of existing processes. It has the potential to fundamentally reshape the roles of professors and students, necessitating a comprehensive reevaluation of the educational paradigm 3.

As universities embark on this strategic journey, it’s essential to consider the multicultural perspectives on the impact of GenAI in higher education. A recent study published in the Educational Technology Journal highlights the importance of developing policies responsive to cultural expectations when integrating GenAI tools 4.

Although this is a quick overview, it is apparent that GenAI presents unprecedented challenges and extraordinary opportunities for higher education institutions. A thorough strategic analysis is not just beneficial—it’s imperative for universities and colleges seeking to thrive in this new technological landscape. By carefully examining the potential impacts, opportunities, and risks associated with GenAI, institutions can chart a course that leverages the power of this technology while upholding the core values and mission of higher education.

As we move forward, it’s clear that the institutions that will lead in this new era will embrace change, foster innovation, and maintain a steadfast commitment to academic excellence and integrity. The time for strategic action is now.

References:

1 https://www.oes.edu.au/generative-ai-higher-education/

2 https://www.bcg.com/publications/2023/five-ways-education-can-leverage-gen-ai

3 https://www.forbes.com/sites/steveandriole/2024/03/18/how-generative-ai-now-owns-higher-education–now-what/

4 https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-024-00453-6

Runway AI and Lionsgate

Posted on by Michael Magee in Uncategorized

Among all the copyright lawsuits around AI companies that trained their models with IP they didn’t own, it is great to see an announcement about an AI company working with an IP owner. Runway, a company specializing in AI, has partnered with Lionsgate, a major film studio, to develop and train an AI model based on Lionsgate’s proprietary film catalogue. The model aims to help filmmakers, directors, and creative professionals enhance their work, generating cinematic videos that can be refined using Runway’s tools. The collaboration focuses on using AI to streamline both pre-production and post-production processes in filmmaking. The partnership is seen as a strategic move to capitalize on AI’s potential in content creation, making processes more efficient and innovative. Runway is also exploring offering these AI models to individual creators and other companies.

There are probably a few relevant points:
1. Innovation in Filmmaking: This is a significant step toward integrating AI into the creative process of filmmaking. This will allow filmmakers to experiment with visual styles, streamline workflows and potentially reduce production costs.
2. Industry Trend: There is a growing trend toward media and entertainment companies leveraging AI for efficiency in content production. This trend is mostly driven by cost reduction concerns.
3. Competitive Edge: This move will give Lionsgate a competitive edge and position them as a leader in tech-driven content creation.

https://runwayml.com/news/runway-partners-with-lionsgate

Brace for a Seismic Shift: New Study Reveals How AI Will Transform Canada’s Workforce

Posted on by Michael Magee in Uncategorized

There has been considerable hype and fear around the impact of AI on jobs and careers. Reports of CEOs planning headcount reductions due to AI and estimations of the ability of current AI to replace many of the tasks that make up jobs today have people understandably concerned. Since the release of the paper GPTs are GPTs (https://openai.com/index/gpts-are-gpts/) last year, I’ve been trying to understand what that means for Canada. Our economy has traditionally been in resource extraction and manufacturing although our modern economy is dominated by real estate, rental and leasing, making up 13% of our GDP (Economy of Canada. (2024, September 3). In Wikipedia. https://en.wikipedia.org/wiki/Economy_of_Canada). So what kind of exposure does Canada have to AI job disruption and displacement?

In a recent study that should make Canadian businesses take notice, researchers have uncovered the staggering impact of artificial intelligence (AI) on the nation’s labour market. The newly released report, “Experimental Estimates of Potential Artificial Intelligence Occupational Exposure in Canada,” paints a vivid picture of the impending transformation that should have every Canadian professional sitting up and taking notice (https://www150.statcan.gc.ca/n1/pub/11f0019m/11f0019m2024005-eng.htm).

The findings aren’t surprising but they should be creating much more conversation. We could see 60% of Canadian employees suddenly finding their roles dramatically reshaped by AI. This is higher than research on the American workforce that uses a similar research methodology. The implications go far beyond the traditionally automation-prone roles, with nearly half of these positions highly complementary to the latest AI technologies.

The study’s authors, Tahsin Mehdi and René Morissette, have meticulously mapped out the industries that will bear the brunt of this shift. Professionals in the fields of finance, healthcare, education, and even the vaunted tech sector should brace for a fundamental rewrite of their job descriptions. Conversely, the construction and hospitality industries appear relatively insulated from the AI onslaught.

The study does has some positive elements. The researchers have identified a critical nuance—the “complementarity-adjusted AI occupational exposure index.” This metric suggests that many highly educated roles may actually benefit from AI, leading to a transformation rather than outright job losses. Despite this, the message is clear and consistent with other studies. The Canadian workforce will need to adapt or risk obsolescence.

As the authors emphasize, the long-term effects of AI on employment remain uncertain. Yet one thing is clear: the Canadian workforce must be prepared to navigate this uncharted territory. Businesses that fail to anticipate and address the implications of AI will risk being out-competed by companies that use AI effectively.

For most companies, the time to act was over a year ago, but Canadian companies can still act now. The future of work is being re-defined, and the professionals who can harness the power of AI will thrive in the years ahead.

Reference

https://www150.statcan.gc.ca/n1/pub/11f0019m/11f0019m2024005-eng.htm

Luma AI Video and udio ai music

Posted on by Michael Magee in Uncategorized

It isn’t surprising that Luma is asking for quotes on H100 inference capacity on LinkedIn today. Their recent release of Luma AI’s Dream Machine has stirred excitement in the tech world with its ability to create realistic videos from simple text prompts. I tested out the capability, but apparently, a lot of other people did as well. As someone intrigued by the creative potential of AI, I couldn’t help but write down a few thoughts about the latest offering of text-to-video in this space.

It is exciting to visualize your most creative ideas with just a few words. People who lack the deep technical knowledge required to create videos no longer need to invest in complex editing software or expensive equipment. Tools like Dream Machine make video creation accessible to anyone with a creative spark. This could lead to an explosion of content as more people explore these tools and flood the internet with AI-generated videos.

I know there are going to be many professionals who look at the output of these AI systems and criticize the quality of what is being created. They are far from something an experienced professional would create for a film or television product. Maybe that won’t ever be the target consumer, but quick and cost-effective video generation at scale could revolutionize entire industries. Advertisers could create highly targeted campaigns in minutes, educators could make abstract concepts come to life, and content creators could produce at an unprecedented pace.

The power of these tools is also missing some obvious safety messages. Every AI company, not just Luma, is pushing the technology as much as they can to remain competitive in a very lucrative market. There are still many serious ethical concerns about deepfakes, misinformation, and copyright infringement. We need to address these challenges as a society to harness the potential of AI video for good.

Competition in this area is also intensifying, with rivals like OpenAI and Kuaishou demonstrating impressive video generation capabilities. An open approach that encourages community engagement could give Luma AI an advantage, despite the fierce competition.

From a technical standpoint, creating coherent videos that adhere to prompts while maintaining natural movements is a significant achievement. Dream Machine has made progress, but there is still room for improvement, particularly in morphing effects and text rendering.

As a researcher and enthusiast in the world of AI, I’m fascinated by the possibilities of AI video generation. It’s a frontier full of potential that could transform how we create, but many deeper conversations also need to happen.

In the meantime, enjoy the waves, at least AI can create a relaxing scene while we try to figure out how this will impact us all.

The Professorless Post-secondary?

Posted on by Michael Magee in Uncategorized

AI will revolutionize post-secondary programs and course creation. What does that mean for the future of universities and the value of human-led education?

To put this in context, I’ve designed decades worth of post-secondary programs ranging from 1-year certificates to 4-year degrees. The student value in creating these sizable learning experiences lies in their ability to provide a student with marketable skills in the job market and personal growth. For the business world, these programs provide the human capital they need to operate and compete successfully. Recently, I was reviewing a new course created by faculty, and it turned out to have been generated by AI.

It shouldn’t surprise me as I watch universities and professors complain about AI’s impact on the student work submitted to them. We should also consider AI’s implications for post-secondary business models. There are several key areas:

  1. Potential job displacement caused by automation: AI-powered platforms will be able to replace specific educational roles, such as course creation, assignment grading, and lecture delivery.
  2. Disruption of traditional teaching methods: AI-powered platforms will provide intelligent tutoring and adaptive learning, making education more accessible and personalized.
  3. Competition from online and alternative education providers: AI learning platforms will offer students more flexible, relevant and affordable options.
  4. Challenges adapting to AI and maintaining relevance: To remain competitive, post-secondaries will need to invest in expensive AI tools and platforms. They will also need to rethink the faculty role and the post-secondary experience, which may require significant changes to their business models and operations.
  5. Potential vulnerability to revenue streams: With other revenue streams and facing limits on tuition hikes, post-secondaries must look for new markets for revenue and students. International student recruitment has been a focus for many post-secondaries.

The timeline for disruption in post-secondary education is debatable, but given the pace of AI development, it will happen in the next 5-10 years. The end of post-secondary education isn’t inevitable, and they must develop strategies to adapt now. The strategies depend on the type of post-secondary and the learning experience they offer. Some common elements that all post-secondaries would need include:

  1. Ethical implementation of AI: Several ethical considerations, such as transparency, fairness, and data privacy, must be addressed in designing and deploying AI systems.
  2. Develop AI policies: Post-secondaries need to establish clear policies and frameworks to guide AI’s responsible and effective integration into teaching, learning and decision-making.

There are many other elements, but the key to this is constant evaluation and adaptation. One of the best descriptions I’ve seen of a successful future with AI is the concept of co-intelligence, which Ethan Mollick described in his book Co-intelligence. It is the idea that an individual (or institution) who understands AI’s abilities and limitations is uniquely positioned to realize AI’s full potential. A diversity of thought is beneficial, and post-secondaries will need to include the perspective of AI to reach the solutions and innovations required to navigate the disruption ahead.

References

Chan, C.K.Y. A comprehensive AI policy education framework for university teaching and learning. Int J Educ Technol High Educ 20, 38 (2023). https://doi.org/10.1186/s41239-023-00408-3

EULER University Institute. (2023, August 22). Artificial Intelligence (AI) as a threat to higher education – EFMU: The Euler-Franeker Memorial University and Institute. EFMU: The Euler-Franeker Memorial University and Institute. https://euler.euclid.int/artificial-intelligence-ai-as-a-threat-to-higher-education/

AI with a warning

Posted on by Michael Magee in Uncategorized

AI can code but also provides a warning…

It is hard to keep up with the range of AI tools available and their updates. I finally got around to getting Perplexity.ai to write the Python code for a game using the following prompt:

“Could you write the Python code to create an economic simulation game about an ASI that makes paper clips and will do anything to achieve that outcome”

Writing the code was no problem. I put it up in Github codespaces, and it ran as expected. It started building paperclips until it started taking away the resources that people needed to live. It kept focusing on paperclips to the detriment of humans until. Eventually, there were no humans left, just paper clips.

What was interesting is that Perplexity.ai added some additional context. Not only did it write the code, but it also recognized the implications of creating this kind of simulation. Unprompted, it added a note to the code:

“This simulation demonstrates the potential dangers of an ASI that is single-mindedly focused on a specific goal, even if that goal is seemingly harmless like producing paperclips. As the ASI consumes more and more resources to achieve its goal, it could potentially cause significant harm to the environment and human society.”

It is a good reminder that there are potential consequences of unchecked artificial intelligence (AI) and a lack of human oversight in designing and implementing these systems. I don’t know if Perplexity.ai intentionally did this, but having ethical warnings that come with code output would be an interesting development for AI output.

The convergence of AR and AI

Posted on by Michael Magee in Uncategorized

A recent Stanford Computational Imagine Lab paper is a step towards an extremely light form factor for AR glasses. The team accomplished it by combining AI and AR to create a new type of display: https://lnkd.in/gvKha2hK

The convergence of AI and AR has been a hot topic, and it is great to see some research showing what that convergence will look like. It isn’t an easy read, but they have developed a computer algorithm that uses artificial intelligence to generate holograms. It combines AI, physics modelling and camera feedback so that the algorithm can precisely predict how the hologram will look after passing through the waveguide. The waveguide is the ultra-thin, glass optical element that uses metasurfaces to precisely guide light waves carrying 3D holographic information. Its compact design allows for realistic 3D image overlays in a pair of AR glasses.

The most important takeaways:
-This will allow hardware developers to create compact form-factor glasses. AR glasses can be lightweight and follow a wide range of style options. This means anyone in the eyewear field can start thinking about adding AR elements without the bulk of the current AR/ MR headsets like the Apple Vision Pro or the Meta Quest 3.

-Rendering accurate 3D depth cues and focus blur will reduce visual discomfort and allow for more realistic AR experiences.