World Renowned Cardiologist and Research Leader to Guide Clinical Strategy, Product Innovation, and AI-Driven Cardiac Diagnostics TORONTO, ON / ACCESS Newswire / January 28, 2026 / AI/ML Innovations Inc. ("AIML" or the "Company") (CSE:AIML)(OTCQB:AIMLF)(FWB:42FB) is pleased to announce the appointment of Dr. Paul Dorian, MD, MSc., as the Company's Medical Innovation Architect (MIA) and Head of the Company's Medical Advisory Board, effective immediately. Dr. Dorian succeeds Peter Kendall, who previously serv ...

Core Insights - The article discusses the evolution and significance of "pick-and-shovel" markets, highlighting how companies like Intel, Qualcomm, NVIDIA, and Amazon have adapted to changing technological landscapes and consumer demands [1][4][25]. Group 1: Historical Context - The concept of pick-and-shovel markets has existed for centuries, with Samuel Brennan being an early example of exploiting such a market during the gold rush [2][3]. - Brennan's strategy involved purchasing mining equipment and reselling it at high markups, leading to his success as the first West Coast millionaire [3]. Group 2: Modern Examples - Intel became a dominant player in the CPU market after being selected as the microprocessor supplier for IBM PCs, demonstrating the effectiveness of scale in pick-and-shovel businesses [4][5]. - Qualcomm and NVIDIA also exemplify this trend, with Qualcomm leading in digital communications chipsets and NVIDIA dominating the GPU market in data centers [5][6]. Group 3: Innovation and Scale - The success of these companies is attributed to a combination of foresight, skill, and strategic partnerships, which allowed them to leverage their scale for competitive advantage [6][7]. - All three companies utilize backward-compatible designs to guide buyers' upgrade paths, preventing rivals from gaining similar advantages [8]. Group 4: Amazon's Mechanical Turk - Amazon's Mechanical Turk (MTurk) emerged as a solution to product labeling challenges, allowing users to perform microtasks that machines could not handle effectively [12][15]. - Despite initial challenges, MTurk eventually demonstrated its scalability, particularly when used by researchers like Fei-Fei Li for large-scale image labeling [19][20]. Group 5: Economic Implications - The article emphasizes that while investments in innovation are beneficial for society, they can also lead to monopolistic behaviors, as seen in the historical context of pick-and-shovel markets [28][29]. - Companies in these markets share similar incentives to innovate, but the outcomes can vary, as illustrated by Amazon's experience with MTurk, which did not yield significant financial returns despite its impact [26][27].

Core Insights - The article highlights the early adoption of AI in finance by Miro Mitev, who recognized the potential of neural networks for financial forecasting as early as 1997 [1][2] - Mitev founded SmartWealth Asset Management, which operates entirely on AI systems, with its latest fund, IVAC, targeting $2 billion in assets under management and aiming for annualized returns of 14-15% [2] Company Overview - SmartWealth Asset Management is a firm that relies solely on AI for decision-making, indicating a significant shift in asset management practices [2] - The firm’s latest fund, IVAC, is positioned to attract substantial capital, reflecting confidence in AI-driven investment strategies [2] Industry Implications - The reliance on AI in financial forecasting suggests a transformative trend in the investment industry, where traditional human decision-making is being supplemented or replaced by advanced algorithms [1][3] - Mitev emphasizes the importance of human involvement in the AI process, particularly in selecting training data and model parameters, which indicates a hybrid approach to AI implementation in finance [3]

AI Capabilities & Development - AI is currently understood as neural networks, deep learning (large neural networks), and large language models (big neural networks for autocomplete) [1] - The "bitter lesson" of AI is that scaling up machines with more parameters and data leads to increased intelligence, but whether it can scale to superintelligence remains unknown [1] - The AI industry is in a race to achieve Artificial General Intelligence (AGI), where the winner takes all, incentivizing rapid development and potentially overlooking safety concerns [2][3] Potential Benefits of AI - AI could lead to personalized media, personalized healthcare, and potentially cure all diseases [1] - AI has the potential to eliminate work and usher in an era of play, world peace, and space exploration [1] - AI could significantly improve lives and enhance humanity if aligned with human values [4] Risks & Challenges of AI - AI is distorting reality, making digital verification impossible and leading to the humanization of AI, which can have negative impacts on children [1] - AI could lead to separate realities and erode trust, which is vital for human society [2] - Increased reliance on AI could lead to cybercrime, as AI can be used to generate hacking code, making everyone vulnerable [2] - Uncontrolled superintelligent AI could lead to unintended consequences and potentially the destruction of humanity [2] - Over-reliance on AI could erode human attention, skills, and motivation, leading to premature handover of power to machines [2] AI Alignment & Control - The current approach to AI development, led by entrepreneurs and software developers, prioritizes speed over safety and alignment [4] - AI alignment with humanity must be a core goal, pursued with the same or greater vigor as the pursuit of superintelligence [4] - The industry needs to balance the benefits of AI with the risks and guard against them, advocating for a return to philosophy and exploration of different perspectives [4]

Model Calibration Importance - Modern neural networks can be misleading due to overconfidence in predictions [1][2] - Calibration ensures predicted probabilities align with actual outcomes, crucial for reliable decision-making [2][3] - Overly confident but inaccurate models can lead to suboptimal decisions, exemplified by unnecessary medical tests [3] Calibration Assessment - Reliability Diagrams visually inspect model calibration by plotting expected accuracy against confidence [4] - Expected Calibration Error (ECE) quantifies miscalibration, approximated by averaging accuracy/confidence differences across bins [6] Calibration Techniques - Calibration is important when probabilities matter and models are operationally similar [7] - Binary classification models can be calibrated using histogram binning, isotonic regression, or Platt scaling [7] - Multiclass classification models can be calibrated using binning methods or matrix and vector scaling [7] Experimental Results - LeNet model achieved an accuracy of approximately 55% with an average confidence of approximately 54% [5] - ResNet model achieved an accuracy of approximately 70% but with a higher average confidence of approximately 90%, indicating overconfidence [5] - ResNet model thinks it's 90% confident in its predictions, in reality, it only turns out to be 70% accurate [2]

60 Minutes Rewind. Whether you think artificial intelligence will save the world or end it, you have Jeffrey Hinton to thank. Hinton has been called the godfather of AI.A British computer scientist whose controversial ideas help make advanced artificial intelligence possible and so change the world. Hinton believes that AI will do enormous good, but tonight he has a warning. He says that AI systems may be more intelligent than we know and there's a chance the machines could take over, which made us ask the ...

Computing Power Evolution - The industry has witnessed a dramatic growth in computing power over the past 5 decades, transitioning from early CPUs to GPUs and now specialized AI processors [4] - GPUs and accelerators have rapidly outpaced traditional CPUs in compute performance, initially driven by gaming [4] - Apple's M4 chip features a neural engine delivering 38 trillion operations per second, establishing it as the most efficient desktop SOC on the market [3] - NVIDIA's B200 delivers 20 quadrillion operations per second at low precision in AI data centers [3] Hardware and AI Development - The development of CUDA by Nvidia in 2006 enabled GPUs to handle more than just graphics, paving the way for deep learning breakthroughs [6] - The "hardware lottery" highlights that progress stems from available technology, not necessarily perfect solutions, as GPUs were adapted for neural networks [7] - As AI scales, general-purpose chips are becoming insufficient, necessitating a rethinking of the entire system [7] Efficiency and Optimization - Quantization is used to reduce the size of numbers in AI, enabling smaller, more power-efficient, and compact AI models [8][10] - Reducing the size of parameters allows for more data movement across the system per second, decreasing bottlenecks in memory and network interconnects [10][11] - Wafer Scale Engine 2 achieves similar compute performance to 200 A100 GPUs while using significantly less power (25kW vs 160kW) [12] Future Trends - Photonic computing, using light instead of electrons, promises faster data transfer, higher bandwidth, and lower energy use, which is key for AI [15] - Thermodynamic computing harnesses physical randomness for generative models, offering efficiency in creating images, audio, and molecules [16] - AI supercomputers, composed of thousands or millions of chips, are essential for breakthroughs, requiring fault tolerance and dynamic rerouting capabilities [17][20] Global Collaboration - Over a third of all US AI research involves international collaborators, highlighting the importance of global connectedness for progress [22] - The AI supply chain is complex, spanning multiple continents and involving intricate manufacturing processes [22]

Expertise & Focus - The author has 9 years of experience training neural networks [1] - The content focuses on optimizing model training in the fields of Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) [1] Content Type - The author shares tutorials and insights daily on DS, ML, LLMs, and RAGs [1] - The content includes 16 ways to actively optimize model training [1]

Model Training Optimization - The industry has been training neural networks for 9 years [1] - The industry actively uses 16 ways to optimize model training [1]

Core Technology & Architecture - The workshop focuses on a GPT-2 inference implementation in Vanilla JS, providing a foundation for understanding modern AI systems like ChatGPT, Claude, DeepSeek, and Llama [1] - It covers key concepts such as converting raw text into tokens, representing semantic meaning through vector embeddings, training neural networks through gradient descent, and generating text with sampling algorithms [1] Educational Focus & Target Audience - The workshop is designed for web developers entering the field of ML and AI, aiming to provide a "missing AI degree" in two hours [1] - Participants will gain an intuitive understanding of how Transformers work, applicable to LLM-powered projects [1] Speaker Expertise - Ishan Anand, an AI consultant and technology executive, specializes in Generative AI and LLMs, and created "Spreadsheets-are-all-you-need" [1] - He has a background as former CTO and co-founder of Layer0 (acquired by Edgio) and VP of Product Management for Edgio, with expertise in web performance, edge computing, and AI/ML [1]