McKinsey Technology Trends Outlook 2024

AI Upgrades The Internet Of Things

Artificial Intelligence (AI) is renovating the fast-growing Internet of Things (IoT) by migrating AI innovations, including deep neural networks, Generative AI, and large language models (LLMs) from power-hungry datacenters to the low-power Artificial Intelligence of Things (AIoT). Located at the network's edge, there are already billions of connected devices today, plus a predicted trillion more connected devices by 2035 (according to Arm, which licenses many of their processors).

The emerging details of this AIoT development period got a boost from ACM Transactions on Sensor Networks, which recently accepted for publication "Artificial Intelligence of Things: A Survey," a paper authored by Mi Zhang of Ohio State University and collaborators at Michigan State University, the University of Southern California, and the University of California, Los Angeles. The survey is an in-depth reference to the latest AIoT research.

"This survey comprehensively explores all aspects of AIoT, from sensing and computing to communication. It highlights the latest advancements, explains the role of AI in enhancing IoT, details how AI can be integrated, and showcases the new applications this AI-IoT synergy enables. It is a must-read for those who want to work in the AIoT domain," said Longfei Shangguan, an assistant professor in the Department of Computer Science at the University of Pittsburgh, an AIoT expert who did not contribute to the survey.

The survey addresses the subject of AIoT with AI-empowered sensing modalities including motion, wireless, vision, acoustic, multi-modal, ear-bud, and GenAI-assisted sensing. The computing section covers on-device inference engines, on-device learning, methods of training by partitioning workloads among heterogeneous accelerators, offloading privacy functions, federated learning that distributes workloads while preserving anonymity, integration with LLMs, and AI-empowered agents. Connection technologies discussed include Internet over Wi-Fi and over cellular/mobile networks, visible light communication systems, LoRa (long-range chirp spread-spectrum connections), and wide-area networks.

A sampling of domain-specific AIoTs reviewed in the survey include AIoT systems for healthcare and well-being, for smart speakers, for video streaming, for video analytics, for autonomous driving, for drones, for satellites, for agriculture, for biology, and for artificial reality, virtual reality, and mixed reality.

A device is categorized as belonging to the AIoT if it contains sensors, a micro-computer running AI algorithms with or without an accelerator, and either a direct actuator connection or a communications channel to other nearby AIoTs and/or a cloud aggregator.

AIoT smartens up motion sensors for robotic arms by adding an on-device recurrent neural network, convolutional neural network, and a gated recurrent unit or a Bidirectional Long Short-Term Memory that can track two factors, such as the orientation and location of an arm simultaneously.Credit: "Artificial Intelligence of Things: A Survey"

AIoTs equipped as AI agents have the ability to combine different sorts of sensor data from separate sources into fused perceptions. AI agents also perform complicated multi-step tasks, make high-level plans, and reason-out complicated decisions. For instance, the Octopus v3 multimodal AI Agent is an AIoT device using the authentication, authorization, and secure storage of tokens. LLMs interpret user instructions from which Octopus identifies and locates useful elements of apps, then plans and executes the user's tasks by autonomously navigating these multiple apps.

Likewise, the AutoDroid LLM-powered task automation system uses AIoT Agents to execute multiple-step tasks automatically by combining dynamic app analysis with LLM task specification. The two-step process starts with an initial offline stage, where agents catalog how the user-interfaces (UIs) accomplish simulated tasks; during a second execution stage, it defines the order of a set of LLM-specified tasks from its previously acquired UI knowledge, which its agents then perform in sequence to accomplish a complicated task.

Complimenting the 70-page survey is a GitHub repository that archives the 353 papers on AIoT included in the survey, reviews them, summarizes them, and sorts them by application. Zhang also claims to have commitments from his co-authors, as well as outside experts in AIoT, to keep the GitHub repository actively updated with new AIoT results as they are peer-reviewed and published in the future.

"The genius of combining artificial intelligence with connected devices is that it fundamentally reshapes what's possible in networked systems" said Yaxiong Xie, an assistant professor in the Department of Computer Science and Engineering at the University at Buffalo, NY, who was not involved in the survey. "We're witnessing a shift from simple data collection to intelligent decision-making right where the information originates. This new architecture doesn't just save bandwidth; it creates an entirely new class of system that can understand its environment, make real-time decisions, and continuously improve its performance. I believe this fusion of intelligence and connectivity will define the next era of technology—opening doors to applications we haven't even imagined yet."

IoT began its migration to AIoT when it was discovered that inference engines could be slimmed down using low-power small-integer neural networks that consume a fraction of the power required by power-hungry floating-point deep neural networks used in datacenters, and thus can be run at the network edge (see the 2018 conference paper "Training and Inference with Integers in Deep Neural Networks"). Since then, researchers also have invented ways to update these tiny neural networks in the field (see "Design Principles for Lifelong Learning AI Accelerators"), greatly expanding their applications.

"As AI gets built into more and more IoT devices, they are addressing more diverse applications—eventually empowering billions of people with the latest breakthroughs brought to them by next-generation AI inventions," said Zhang. "For instance, today even the latest Gen AI and LLM inventions can be accessible as services provided by AIoT," as described in detail in the survey.

According to Bo Yuan, an associate professor in the Department of Electrical and Computer Engineering at Rutgers University, "Professor Zhang [et al.'s] work is a comprehensive review, analysis, and summary of the milestones and future projections for the research activities in AIoT. This survey also discusses the new opportunities for AIoT, making it very timely and important."

AIoT streamlines the collection and categorization of myriad sensor readings, images, videos, audio clips, text files, physiological signals, and environmental measurements, according to Zhang, while IoT began with simple individual sensor monitoring, which passed its raw sensor readings to datacenters. Adding AI to IoTs expanded the landscape of AIoT technological innovations. Said Zhang, "Now I deeply believe that eventually all future IoTs will be AIoTs, since together the two technologies more efficiently process the world's streams of raw data."

"In order to interact with the physical world and serve a purpose in any real-world application, any IoT system must have the ability to sense and make decisions. AI represents the most effective and efficient means to enhance and integrate these capabilities into an IoT. Indeed, I believe that all future IoT systems will incorporate AI," said Xiaoxi He, an assistant professor at the University of Macau. "Zhang [et.Al.'s] survey provides a detailed taxonomy of the ways and paradigms with which modern AI can aid IoT systems. More importantly, it highlights how AI has transformed IoT systems, enabling new applications and purposes that were previously unattainable."

The synergy between AI and IoT in AIoT devices already is fundamentally transforming how users perceive and interact with the world, according to Zhang. "AIoT enhances decision making, facilitates a new level of time efficiency, and improves every aspect of human-machine interactions—such as transforming a simple step-counting IoT into a personal-fitness AIoT, essentially a virtual 'fitness coach' which not only makes intelligent recommendations relevant to your physical needs, but enables you to become more aware of your own physical fitness. And as more AI breakthroughs like LLMs and Gen AI are made, not only will portable AIoTs become more useful, but they will also be better able to protect the privacy of their users."

R. Colin Johnson is a Kyoto Prize Fellow who ​​has worked as a technology journalist ​for two decades.

Best YouTube Channels To Learn AI In 2025

Artificial Intelligence (AI) is transforming businesses around the world and has become a key skill in today's market. For those looking to become data scientists or professionals seeking to enhance their skills, YouTube provides a wealth of free tutorial resources on AI.

But of all the channels out there, which ones truly provide high-quality content? In this article, we've handpicked the best YouTube channels to learn AI in 2025, covering machine learning (ML), deep learning, and artificial intelligence for all skill levels.

Sentdex – Hands-On AI and Python Tutorials

For hands-on, practical learning about the world of AI, turn to Sentdex. This site is run by Harrison Kinsley and consists of elaborate tutorials using Python that are concentrated primarily on AI, machine learning, and deep learning. The tutorials walk the learners through actual projects, for example, the construction of neural networks and the development of AI-based applications.

Ideal for: Newbies & Intermediate Students

Key Topics: Python for AI, Deep Learning, AI Project Development

DeepLearning.AI – Expert-Led AI Courses

Developed by Andrew Ng, one of the strongest advocates of AI learning, DeepLearning.AI provides structured, university-standard AI courses.

Ideal for: Appropriate for Intermediate and Advanced Level Students

Major Topics: Deep Learning, Neural Networks, Artificial Intelligence Ethics

Two-Minute Papers – Demystifying AI Research 

AI research doesn't have to be mysterious at all times, with Two Minute Papers demystifying the whole process and making it enjoyable and understandable. This channel condenses cutting-edge AI research papers into an entertaining, bite-sized format, keeping students abreast of the newest developments.

Ideal for: AI Researchers & Enthusiasts

Primary Subjects: Emerging AI Technologies, AI Research

CodeEmporium – AI & Machine Learning Projects

For practical learners and developers, CodeEmporium offers AI tutorials with an emphasis on practical projects. The channel discusses machine learning models, data science, and neural networks, enabling learners to create portfolio-worthy AI applications.

Ideal for: Practical Learners & Developers

Key Topics: AI Project Tutorials, Python & AI, Data Science

freeCodeCamp – Full AI & ML Courses

freeCodeCamp is renowned for providing in-depth, full-length courses absolutely free. Their machine learning and AI playlists contain complete university courses with theory as well as practice in coding incorporated.

Ideal for: All Levels

Main Topics: Machine Learning, Deep Learning, AI Theory & Coding

Yannic Kilcher – AI News & Deep Learning Insights

If you're looking for in-depth AI discussions and expert breakdowns of AI research, Yannic Kilcher is the go-to channel. His videos offer a critical overview of AI trends, models, and industry breakthroughs.

Ideal for: Intelligent AI Students and Scholars

Main Topics: Research Papers on AI, Deep Learning, and Industry Trends

Lex Fridman's Conversations and AI Interviews 

Unlike tutorial channels, Lex Fridman has interviews with AI researchers, experts, and technology leaders. If you wish to know about the concepts, challenges, and what the future of AI entails, this channel has deep and introspective conversations for you.

Best for: AI Professionals & Enthusiasts

Major Topics: AI Ethics, AI in Society, Expert Opinions

Simplilearn – AI & Data Science Certification Courses

For students who wish to achieve certifications in AI and data science, Simplilearn provides structured courses according to industry requirements. The material is structured to help learners build AI careers with theoretical as well as practical experience.

Ideal for: Career Changers & Job Seekers

Key Issues: Industry-Ready AI Skills, AI Certification

How to Maximize These AI YouTube Channels Knowing the most effective ways of learning is half the battle. Below are some tips on how to master AI efficiently:

Systematically do your studies, starting with materials for beginners before moving to more advanced issues.

Try – Code utilizing Python libraries TensorFlow, PyTorch, and Scikit-Learn to build AI models.

Stay up to date – AI technology changes so rapidly; sign up for a service like Two Minute Papers for the newest research.

Final Reflections

With the introduction of AI, many industries are experiencing a wonderful revolution, and studying this topic can give access to new worlds. You are a beginner who wishes to study the basics of AI or a professional who wishes to delve deeper into the world of neural networks, but the above-mentioned YouTube channels have plenty of free information.

Groundbreaking Study Reveals How Topology Drives Complexity In Brain, Climate, And AI

A groundbreaking study led by Professor Ginestra Bianconi from Queen Mary University of London, in collaboration with international researchers, has unveiled a transformative framework for understanding complex systems. Published in Nature Physics, this pioneering study establishes the new field of higher-order topological dynamics, revealing how the hidden geometry of networks shapes everything from brain activity to artificial intelligence.

"Complex systems like the brain, climate, and next-generation artificial intelligence rely on interactions that extend beyond simple pairwise relationships. Our study reveals the critical role of higher-order networks, structures that capture multi-body interactions, in shaping the dynamics of such systems," said Professor Bianconi.

By integrating discrete topology with non-linear dynamics, the research highlights how topological signals, dynamical variables defined on nodes, edges, triangles, and other higher-order structures, drive phenomena such as topological synchronization, pattern formation, and triadic percolation. These findings not only advance the understanding of the underlying mechanisms in neuroscience and climate science but also pave the way for revolutionary machine learning algorithms inspired by theoretical physics.

"The surprising result that emerges from this research" Professor Bianconi added, is that topological operators including the Topological Dirac operator, offer a common language for treating complexity, AI algorithms, and quantum physics. "

From the synchronised rhythms of brain activity to the dynamic patterns of the climate system, the study establishes a connection between topological structures and emergent behaviour. For instance, researchers demonstrate how higher-order holes in networks can localise dynamical states, offering potential applications in information storage and neural control. In artificial intelligence, this approach may lead to the development of algorithms that mimic the adaptability and efficiency of natural systems.

"The ability of topology to both structure and drive dynamics is a game-changer," Professor Bianconi added. This research sets the stage for further exploration of dynamic topological systems and their applications, from understanding brain research to formulate new AI algorithms. "

This study brings together leading minds from institutions across Europe, the United States, and Japan, showcasing the power of interdisciplinary research. "Our work demonstrates that the fusion of topology, higher-order networks, and non-linear dynamics can provide answers to some of the most pressing questions in science today," Professor Bianconi remarked.

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