Vehicles are evolving as AI, automation, connectivity networks and advanced safety systems reshape how people drive.
AI and automation are transforming accident response, claims and repair, making recovery faster and more efficient.
Investment in AI and automation is accelerating, pointing to a future where safer journeys and smoother post-accident experiences define a new era of driving.
Across the world, summer often signals a surge in travel – especially by car. Whether it’s a weekend getaway, vacation or simply commuting to work, our roads are visibly more congested. It’s just not in markets such as the USA. Globally, reports show that driving volumes have not only returned to pre-pandemic levels but are surpassing them. According to the OECD, vehicle miles travelled are returning to or exceeding 2019 levels across much of North America, Europe and the Asia-Pacific.
At the same time, both the experience of driving and what happens after an accident have changed dramatically. Technology is not only reshaping how people get from point A to point B but also changing how people are supported when things go wrong.
The rise of smarter, more complex vehicles
Today’s vehicles are far more advanced than they once were. They function as complex digital platforms with embedded safety systems, real-time connectivity and advanced automation. Features like Automatic Emergency Braking (AEB) and Advanced Driver Assistance Systems (ADAS) are now standard in many new vehicles. AEB is, in fact, mandated in the EU and required in all new light-duty vehicles in the USA as of 2029.
These technologies are designed to help drivers avoid accidents or reduce their severity, particularly in congested urban settings and lower-speed environments.
Global traffic accident rates, however, remain high. According to theWHO, road traffic crashes kill 1.3 million people each year and injure 20-50 million more.
Part of the paradox here lies in the human element of how today’s drivers interact with technology. Many drivers are still adjusting to semi-autonomous features, while a growing number may overestimate the capabilities of their vehicles. For example, they might believe ADAS can replace their need to be attentive on the road. Others may disable these systems entirely due to discomfort or lack of trust.
When safer tech drives higher repair complexity
Even when accidents are less severe, the cost and complexity of repairing vehicles with advanced technology have grown significantly. Modern cars can contain over 1,400 semiconductor chips and tens of thousands of parts. In terms of vehicle pricing, electronic components now account for roughly 40% of a vehicle’s cost, driven by innovations in infotainment, sensors, connectivity and automation.
Repairing these vehicles often requires specialized recalibration of sensors and replacement of costly modules. It’s no surprise, then, that repair costs are high and are likely to increase. For example, in the USA, the average cost to repair an internal combustion engine (ICE) vehicle is nearly 40% more than it was in 2020. Research also shows that electric vehicle repairs require nearly four additional labour hours than traditional ICE vehicles, resulting in labour costs averaging 30% higher.
A network that responds
The connection between vehicle platforms with accident response and the claims and repair management teams is also becoming much stronger. From the moment a crash occurs, for example, a series of systems and technologies may trigger:
Telematics-based crash alerts that notify emergency responders
Real-time dispatch of roadside assistance or towing.
Automated claims intake, powered by AI, directly from a mobile device.
Simultaneous scheduling of repair appointments at a network of digitally connected repair shops.
AI and digitally powered claims and repair workflows that surface insights, inform decisions and speed best actions to improve the claims and repair experience.
Instant digital payment processing and automated parts ordering.
This interconnected ecosystem relies on cloud-based integrations between automakers, insurers, collision repairers, tow providers and more. When these connections come together, intelligent experiences (IX) become possible for everyone involved. These experiences are designed to reduce friction and deliver faster recovery. They also improve transparency for consumers navigating post-accident services and for employees working to bring a resolution. The goal is to return drivers to the road quickly and restore their health after accident-related injuries, ensuring recovery is handled with speed, care and efficiency.
AI and automation are reshaping the claims process
In the USA, there is a clear shift toward tech-enabled, AI-powered claims and repair management. Today, nearly 30% of auto insurance claims are initiated using digital photo capture. This enables insurers and collision repairers to apply AI across the post-accident response. Such tools allow for more efficient processes, with AI assessing vehicle damage to generate estimates ready for review within seconds.
As vehicles continue to grow in complexity, the scale and speed of AI-driven claims and subsequent repair coordination are expected to grow along with them. Whether a driver is navigating city streets or major roadways, the driver’s expectation will be the same – if something goes wrong, the recovery will be smooth and fast.
A cross-sector challenge: Affordability and access
As the industry grows more complex, the focus must shift to tackling key challenges: controlling rising costs, meeting demand for skilled labour, and keeping new technologies accessible and efficient. Success depends not just on adoption but on responsible application.
The road ahead
The insurance and collision repair industry is embracing AI. We continue to see companies investing generously in R&D to streamline the historically fragmented post-accident process, optimize customer engagements and bolster workforce proficiency. Investment is expected to accelerate in the coming years. Financial services firms invested $35 billion in AI in 2023 and that number is projected to grow to $97 billion by 2027, with anticipated investments across insurance, banking and payments.
As investments in this space grow and organizations across the industry collaborate and focus on AI governance, we can expect to see a rapid shift from early adoption to enterprise value. By strengthening governance and upholding standards like bias mitigation and privacy protection, the industry can accelerate innovation while building the trust and confidence needed to deploy new technologies at scale.
There is no doubt that the modern road trip has evolved into a new digital experience. As drivers move toward more connected cars, post-accident processes will increasingly mirror the same innovation and technology built into the vehicles. This will ensure drivers benefit from advanced tools both in daily travel and when unexpected incidents occur.
A team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.
Rachel Gordon | MIT CSAIL
July 16, 2025
Image: Alex Shipps/MIT CSAIL, using assets from Shutterstock and Pixabay
A new paper by MIT CSAIL researchers maps the many software-engineering tasks beyond code generation, identifies bottlenecks, and highlights research directions to overcome them. The goal: to let humans focus on high-level design, while routine work is automated.
Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine’s reach. Recent advances appear to have nudged that future tantalizingly close, but a new paper by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and several collaborating institutions argues that this potential future reality demands a hard look at present-day challenges.
Titled “Challenges and Paths Towards AI for Software Engineering,” the work maps the many software-engineering tasks beyond code generation, identifies current bottlenecks, and highlights research directions to overcome them, aiming to let humans focus on high-level design while routine work is automated.
“Everyone is talking about how we don’t need programmers anymore, and there’s all this automation now available,” says Armando Solar‑Lezama, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and senior author of the study. “On the one hand, the field has made tremendous progress. We have tools that are way more powerful than any we’ve seen before. But there’s also a long way to go toward really getting the full promise of automation that we would expect.”
Solar-Lezama argues that popular narratives often shrink software engineering to “the undergrad programming part: someone hands you a spec for a little function and you implement it, or solving LeetCode-style programming interviews.” Real practice is far broader. It includes everyday refactors that polish design, plus sweeping migrations that move millions of lines from COBOL to Java and reshape entire businesses. It requires nonstop testing and analysis — fuzzing, property-based testing, and other methods — to catch concurrency bugs, or patch zero-day flaws. And it involves the maintenance grind: documenting decade-old code, summarizing change histories for new teammates, and reviewing pull requests for style, performance, and security.
Industry-scale code optimization — think re-tuning GPU kernels or the relentless, multi-layered refinements behind Chrome’s V8 engine — remains stubbornly hard to evaluate. Today’s headline metrics were designed for short, self-contained problems, and while multiple-choice tests still dominate natural-language research, they were never the norm in AI-for-code. The field’s de facto yardstick, SWE-Bench, simply asks a model to patch a GitHub issue: useful, but still akin to the “undergrad programming exercise” paradigm. It touches only a few hundred lines of code, risks data leakage from public repositories, and ignores other real-world contexts — AI-assisted refactors, human–AI pair programming, or performance-critical rewrites that span millions of lines. Until benchmarks expand to capture those higher-stakes scenarios, measuring progress — and thus accelerating it — will remain an open challenge.
If measurement is one obstacle, human‑machine communication is another. First author Alex Gu, an MIT graduate student in electrical engineering and computer science, sees today’s interaction as “a thin line of communication.” When he asks a system to generate code, he often receives a large, unstructured file and even a set of unit tests, yet those tests tend to be superficial. This gap extends to the AI’s ability to effectively use the wider suite of software engineering tools, from debuggers to static analyzers, that humans rely on for precise control and deeper understanding. “I don’t really have much control over what the model writes,” he says. “Without a channel for the AI to expose its own confidence — ‘this part’s correct … this part, maybe double‑check’ — developers risk blindly trusting hallucinated logic that compiles, but collapses in production. Another critical aspect is having the AI know when to defer to the user for clarification.”
Scale compounds these difficulties. Current AI models struggle profoundly with large code bases, often spanning millions of lines. Foundation models learn from public GitHub, but “every company’s code base is kind of different and unique,” Gu says, making proprietary coding conventions and specification requirements fundamentally out of distribution. The result is code that looks plausible yet calls non‑existent functions, violates internal style rules, or fails continuous‑integration pipelines. This often leads to AI-generated code that “hallucinates,” meaning it creates content that looks plausible but doesn’t align with the specific internal conventions, helper functions, or architectural patterns of a given company.
Models will also often retrieve incorrectly, because it retrieves code with a similar name (syntax) rather than functionality and logic, which is what a model might need to know how to write the function. “Standard retrieval techniques are very easily fooled by pieces of code that are doing the same thing but look different,” says Solar‑Lezama.
The authors mention that since there is no silver bullet to these issues, they’re calling instead for community‑scale efforts: richer, having data that captures the process of developers writing code (for example, which code developers keep versus throw away, how code gets refactored over time, etc.), shared evaluation suites that measure progress on refactor quality, bug‑fix longevity, and migration correctness; and transparent tooling that lets models expose uncertainty and invite human steering rather than passive acceptance. Gu frames the agenda as a “call to action” for larger open‑source collaborations that no single lab could muster alone. Solar‑Lezama imagines incremental advances—“research results taking bites out of each one of these challenges separately”—that feed back into commercial tools and gradually move AI from autocomplete sidekick toward genuine engineering partner.
“Why does any of this matter? Software already underpins finance, transportation, health care, and the minutiae of daily life, and the human effort required to build and maintain it safely is becoming a bottleneck. An AI that can shoulder the grunt work — and do so without introducing hidden failures — would free developers to focus on creativity, strategy, and ethics” says Gu. “But that future depends on acknowledging that code completion is the easy part; the hard part is everything else. Our goal isn’t to replace programmers. It’s to amplify them. When AI can tackle the tedious and the terrifying, human engineers can finally spend their time on what only humans can do.”
“With so many new works emerging in AI for coding, and the community often chasing the latest trends, it can be hard to step back and reflect on which problems are most important to tackle,” says Baptiste Rozière, an AI scientist at Mistral AI, who wasn’t involved in the paper. “I enjoyed reading this paper because it offers a clear overview of the key tasks and challenges in AI for software engineering. It also outlines promising directions for future research in the field.”
Gu and Solar-Lezama wrote the paper with University of California at Berkeley Professor Koushik Sen and PhD students Naman Jain and Manish Shetty, Cornell University Assistant Professor Kevin Ellis and PhD student Wen-Ding Li, Stanford University Assistant Professor Diyi Yang and PhD student Yijia Shao, and incoming Johns Hopkins University assistant professor Ziyang Li. Their work was supported, in part, by the National Science Foundation (NSF), SKY Lab industrial sponsors and affiliates, Intel Corp. through an NSF grant, and the Office of Naval Research.
The researchers are presenting their work at the International Conference on Machine Learning (ICML).
In the global battle against climate change and to be more sustainable, the quest for energy efficiency has taken centre-stage. The focus on sustainability is an increasing emphasis on humanity’s finite resources and the effect of our energy-consumption habits on the world around us. This heightened awareness is leading to a radical rethinking of how energy is consumed and saved, at home and in our daily lives.
This is where smart home technologies come in: by harnessing the power of connection, automation and design, smart home systems promise to use energy far more efficiently. What’s more, smart home technologies are not just a way for households to save on their energy bills, they are also at the leading edge of the broader movement towards environmental sustainability.
This article sheds light on how smart technology influences houses and explains how smart home innovation is creating the path to a future of significant energy saving, helping to preserve our planet by minimising carbon footprints. Smart homes rely on gadgets, from smart thermostats with self-regulating temperature control depending on occupancy to smart lighting systems that automatically dim when people are not in a room or are automatically switched off when nobody is present. These gadgets and the innovative tech that lies behind them illustrate a number of ways we can achieve a greener and a more sustainable world filled with smart homes.
The evolution of smart home technology
Smart home technology had early predecessors in the home automation work of the late 20th century. Early systems were crude, with the primary focus on home security and control of home functions such as lights and appliances with timers and remote controls. They were a precursor to the connected, smart spaces of today.
There is a real inflection point that occurred with smart homes when the internet and wireless evolved. Wi-Fi became a thing and broadband became available, as did short range connectivity alternatives such as Zigbeeand Z-Wave. All these developments allowed devices to talk to others, not just within the home, but beyond it.
The availability of user-friendly smartphone interfaces and mobile applications also helped transform users’ interactions with their home environments. Much of the data provided by smart home control systems could be monitored by the homeowners remotely, regardless of their location, in nearreal-time.
Photo credit: Stanisic Vladimir/Shutterstock.com
Energy efficiency takes centre stage
As environmental worries increased, the smart home began to focus on energy usage. New products such as smart thermostats that learn owners’ habits and automatically adjust heating and cooling depending on energy use, and smart lighting systems that use low-energy LED bulbs with automated control began to dominate the smart home discourse. Such solutions offered convenience while at the same time significantly reducing energy usage and associated costs.
The current state and adoption
As smart home technology begins its next phase of mass adoption, driven by energy savings, home security and household convenience, the market is flooded with an array of smart devices, ranging from energy monitors to automated blinds, and including smart appliances that can learn from their users’ energy specifications to make more energy saving adjustments, while utilising better energy efficiency along the way. The addition of artificial intelligence and machine learning to smart energy systems has significantly improved their ability to learn and then adjust their energy savings based on a home’s needs.
As consumers become increasingly aware of the importance of living a life that doesn’t hurt the planet, a demand for smart home technology has also materialised; energy efficiency is achievable and cost-effective, ultimately helping to conserve and utilise an energy supply that does not harm the environment. In other words, companies now have reason to innovate.
And in the future, as smart home technology advances to increase efficiency, we will see a wave of new smart homes that will truly define sustainable living, bringing us one step closer to global environmental targets.
Definition of energy efficiency in smart homes
Energy efficiency in the context of the smart home addresses the use of energy to perform different household functions in an optimal way, minimising the overall energy consumption used to support those functions, without foregoing comfort or convenience. In other words, energy efficiency is not simply about using less energy, it relates to the smart application of technology to ensure that energy is used in an optimal and efficient way. One of the most pressing contemporary challenges of our planet is the appraisal of how current modes of energy production and usage are affecting the environment, the concept of energy efficiency holds an immense global significance and relevance. Energy efficiency in smart homes represents the usage of advanced technologies to monitor, control and manage the energy consumption of appliances and systems in order to provide a sustainable living environment.
How smart homes achieve energy efficiency
Energy use is further reduced through automation, monitoring and control systems intended to work together in smart homes, which can also use information technology – such as being able to access your home’s control system from your phone – to reduce consumption. Here are some ways this is achieved:
Automation: Smart homes utilise automation to manage energy consumption proactively. For example, smart thermostats can automatically adjust the heating and cooling of a home based on the time of day, occupancy patterns, and even weather forecasts. Similarly, smart lighting systems can turn off lights in unoccupied rooms or adjust brightness based on the natural light available, thus saving energy.
Monitoring: The ability to monitor energy usage in real-time is a cornerstone of smart home energy efficiency. Homeowners can use apps to track the energy consumption of various devices and appliances. This visibility enables users to identify patterns of high usage and take steps to mitigate waste, such as adjusting the settings of energy-hungry devices or scheduling their operation during off-peak hours.
Control: Smart homes provide unparalleled control over home systems and appliances, even remotely. Homeowners can use smartphones or voice commands to control lighting, heating, cooling and appliances, allowing for on-the-fly adjustments to reduce energy use. For instance, turning off heating in an empty home or starting a dishwasher during off-peak energy hours can lead to significant savings.
The role of IoT devices in enhancing energy efficiency
Thanks to the Internet of Things (IoT), smart homes are becoming more energy efficient. IoT – or Internet of Things – refers to devices that are connected to each other and to the internet; when they work together, they can offer levels of interactivity and control never before possible. The IoT is comprised of various devices – smart meters, smart thermostats and energy-saving smart appliances – that can communicate with and control each other and integrate with other devices in the home and beyond.
IoT devices contribute to energy efficiency by:
Collecting data: They continuously gather data on energy usage, environmental conditions and user behaviour. This data is crucial for understanding and optimising energy use patterns.
Learning and adapting: Many smart home devices are equipped with AI and machine learning capabilities, allowing them to learn from user habits and adjust their operations to maximise efficiency. For example, a smart thermostat can learn the preferred temperature of the household and adjust itself to maintain that temperature in the most energy-efficient way possible.
Integrating systems: IoT devices can integrate various home systems, such as heating, ventilation and air conditioning (HVAC), lighting and security, into a cohesive, energy-efficient operation. This integration ensures that all systems are working together in the most energy-efficient manner, reducing redundancy and waste.
Key components of a smart energy-efficient home
Building a smart eco-home involves harnessing a number of components designed to work together to minimise energy consumption and maximise sustainability, both saving homeowners money on their energy bills and leaving a lighter carbon footprint. Let’s examine the key components and how they operate within the ecosystem of the smart home.
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Smart thermostats
The smart thermostat and the connected home are inextricably linked to energy efficiency in the home. These devices control the heating and cooling equipment, which boast a high fuel propensity. Sitting in the sweet spot between precision and comfort, smart thermostats ensure that a home is neither overly heated nor too cool, saving energy and money. These thermostats learn the habits of a household and its occupants, and accordingly adjust heating and cooling accordingly, for example, reducing it when no one’s home and bringing it back to a comfortable level by the time people return home. Optimising heating, ventilation and air conditioning (HVAC) operations delivers the lion’s share of energy savings in homes.
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LED lighting and smart light controls
Without a doubt, LED lighting is one of the pillars of energy-reducing lighting technologies. If compared with a conventional incandescent bulb, LED bulbs utilise at least 75% less energy. While the introduction of LED lights already saves a significant amount of energy, when combined with smart light controls, such as automated dimmers, motion sensors and programmable schedules, these products contribute to even greater energy savings. Dimmers reduce light usage at times when lighting may not be needed; motion sensors turn lights on when people are present and turn them off when spaces are empty; and programmable schedules ensure lights are automatically shut off at times when no one is using the space.
According to global technology intelligence firm ABI Research, 138 million smart light components, which include luminaires, sensors, controllers, and switches, will be shipped in 2030 and installed in smart buildings.
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Energy monitoring systems
Energy-monitoring systems are allowing owners to get a big picture of their home’s energy use. In real-time, they show where energy is being consumed and when it is being wasted. They can even show whether an appliance is using a large amount of energy. This means people can make informed choices over how they use energy and what steps to take in order to waste less.
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Smart appliances and plugs
Smart fridges, washing machines and dishwashers provide the best energy use possible without impacting performance. They can be controlled by a user to run at off-peak times when energy rates are low or when there are times when there is a high presence of renewable energy in the grid. Similar opportunities exist for non-smart appliances through smart plugs that provide remote control and scheduling ability to turn off appliances when not in use, thus saving power.
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Renewable energy integration
Adding renewable energy sources to a smart home improves its efficiency and sustainability greatly. The use of solar panels produces clean, green electricity that can be used to run the home. In some cases it might be possible to swap the electricity generated back into the grid and receive money for it. It is also possible to have batteries that can receive the energy being generated by the solar panels. This allows you to have a steady stream of green energy coming into the house, rather than using other forms of energy that aren’t renewable like fossil fuels. The use of energy can be managed by the smart system and use the best electricity types available at the time, which in the examples mentioned earlier – would be the electricity generated by the solar system and recycled back from the batteries.
These components, when combined, constitute the building blocks of a smart, energy-efficient home. As smart home technology improves, energy savings and environmental benefits will only increase exponentially. And we will have set ourselves on the path for a greener world.
Real-world examples and success stories
Despite the number of specific case studies with the relevant figures always increasing, some of them certainly highlight the impact of smart home technologies on energy efficiency and, consequently, on sustainability in a way that is both quantifiable and exemplary.
One of the most often held up as a certifiable smart home energy efficiency triumph is the adoption of smart thermostats by Nest, which has written in its whitepaper that early users were cutting 10-12% off heating, and 15% off cooling, per year – estimated average savings of about US$131 to US$145 per home. That cuts energy use in half; heating and cooling account for about half of a home’s energy use.
Another example is a survey on energy efficiency in smart homes that highlights how Europe’s adoption of smart grids, integrating novel information and communication technologies like sensors and high-performance digital communication systems, has redefined energy distribution. These technologies enable precise, real-time measurement and monitoring of energy parameters and facilitate remote operation and optimisation of distribution. The transformation to smart grids has introduced the concept of prosumers—users who both produce and consume energy. By using big data with advanced analytics and semantic technologies, these smart environments aim to enhance energy efficiency significantly. Challenges such as information security remain, but the push towards microgrids and data-driven solutions promises a future where smart homes can efficiently manage and reduce energy consumption.
Smart lighting has the potential to save 7–27% of a home’s lighting energy use
Because household plug loads can theoretically include an almost infinite number of electronics and electric devices, the potential for plug load energy savings is substantial—up to 50% in some households
Smart appliances can reduce energy costs for a typical household by 2–9%
Smart heating, ventilation and air-conditioning (HVAC) systems can save up to 10% of energy
Reduction in carbon footprint: a study from Finnish researchers concluded that home automation saves 12.78% of original emissions
Utility bill reduction: Homeowners utilising smart thermostats and energy monitoring systems often see a reduction in utility bills ranging from 5% to 22%
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Challenges and considerations
Adopting smart home technology brings numerous benefits in terms of energy efficiency and convenience, yet it also introduces several challenges and considerations for homeowners:
Financial outlay: The upfront costs involved in acquiring and installation of smart home devices can be significant. Often, high-end smart thermostats, lighting systems, security devices, and other IoT (Internet of Things) equipment tend to be expensive, averaging at around US$1,000-3,500. But certain smart home appliances can cost up to US$10,000, such as a smart fridge.
Return on investment (ROI): While smart home tech has the potential to save energy, leading to potentially lower utility bills over the long term, it will take years for some homeowners to recover their initial investment. Long-term savings versus cost of installation.
Data privacy: Smart home devices will monitor and transmit user data, such as patterns of operation. The methods of use of this data and who can have access to it raises privacy concerns. Understanding the privacy policies and data handling practices of device manufacturers and service providers is therefore essential.
Security vulnerabilities:IoT devices are susceptible to hackers and cyberattack, which could compromise personal information, or even allow someone into your home. Devices should be secure and regularly updated, with passwords kept secure.
Ecosystem fragmentation: The smart home market encompasses many manufacturers and platforms, some devices might not work properly with each other, potentially leading to degraded functionality and experience.
Depending on the user’s resources and determination, these challenges might not be enough to outweigh the benefits of integrating smart home technology, especially if one researches the system well, buys from brands with a good reputation for security and privacy, and lays a foundation for a scalable system. While tackling the impediments which come with integrating smart home technology and the gadgets that are tied to the system might seem daunting at first, in the long run the advantages of creating an energy-efficient, convenient and well-connected home environment can outweigh the initial challenges of installing and maintaining smart appliances.
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When it comes to climate change and sustainability, a crucial step towards using less energy without making our everyday lives less comfortable is enabling smart homes. Starting from the early experiments with home automation, the idea of smart technological homes is becoming more and more elaborate nowadays. Smart homes pair the energy saving possibilities of smart thermostats, LED lights and IoT devices to adjust home energy use properly, reducing electricity consumption and recommending the most energy-efficient appliances. While the adoption of a smart home might be challenging for some in terms of cost and data privacy, the goal of creating a more sustainable planet with smart homes brings many benefits, including energy efficiency, lower emissions, smaller carbon footprint, and better control over a home environment. Smart technology, as it develops and becomes more common, is poised to become an important step towards achieving global environmental targets.
A new policy document outlines China’s plan to create an internationally competitive BCI industry within five years, and proposes developing devices for both health and consumer uses.
In a policy document released this month, China has signaled its ambition to become a world leader in brain-computer interfaces, the same technology that Elon Musk’s Neuralink and other US startups are developing.
Brain-computer interfaces, or BCIs, read and decode neural activity to translate it into commands. Because they provide a direct link between the brain and an external device, such as a computer or robotic arm, BCIs have tremendous potential as assistive devices for people with severe physical disabilities.
In the US, Neuralink, Synchron, Paradromics, and others have sprung up in recent years to commercialize BCIs. Now, China boasts several homegrown BCI companies, and its government is making the development of the technology a priority.
Jointly authored in July by seven departments within the Chinese government—including the Ministry of Industry and Information Technology, the National Health Commission, and the Chinese Academy of Sciences—the new policy document lays out a road map for China to achieve breakthroughs in BCI technology by 2027 and build an internationally competitive industry by 2030.
“We know that China is strong at translating basic research into practical uses and commercialization. We’ve seen that in other industries, such as photovoltaics and electric cars. Now BCI is another area where that’s going to be critical,” says Max Riesenhuber, a professor of neuroscience and codirector of the Center for Neuroengineering at Georgetown University Medical Center, who has published research on China’s BCI developments.
“The document really lays out this whole umbrella of activities needed to become a leader in BCI, not just in research, but in actually getting it into the mainstream,” he says.
Research on BCIs dates back to the 1970s, but for decades, the technology was too cumbersome and unreliable for practical applications. Neuralink and its US competitors are all aiming to improve on the design and performance of early BCIs to make useful products for patients.
China’s foray into BCI research came later, but the country is quickly catching up to the US. Several companies and research institutions in China have successfully tested BCI implants in patients, showing that people with paralysis can move a cursor on a computer screen, operate a robotic arm, and type out their thoughts. Last year, the Chinese government released ethical guidelines for the use of BCIs. Now, its policy document lays out a road map for speeding up the development of these devices. It outlines 17 specific steps, which include creating better chips to capture brain signals, improving software to decode those signals, standardizing BCI technology, and establishing manufacturing capabilities.
“The Chinese government has always been supportive of disruptive technologies,” says Phoenix Peng, cofounder and CEO of NeuroXess, a BCI company based in Shanghai. “I think, from the government’s point of view, this policy means that BCI technology has already passed from a concept level into the product level.”
He says NeuroXess has implanted six paralyzed patients with its device. In three of those patients, it was used to accurately decode Chinese speech. For the others, it allowed thought control of digital devices.
Given these and other recent milestones, Peng says it’s realistic to think that at least one BCI system could gain approval in China by 2027.
Minmin Luo, director of the Chinese Institute for Brain Research (CIBR) in Beijing, agrees that the country is well on its way to meeting the goals set out by the new policy document. “It is basically an engineering project, with not so ambitious goals. Already, there are so many people working on it,” he says.
Luo is the chief scientist at NeuCyber NeuroTech, a spinoff of CBIR, which has developed a coin-sized brain chip called Beinao-1 and so far implanted it in five people. “We have observed excellent safety and stability in our clinical assessments,” he says.
The recipients, who are paralyzed, are now able to move a computer cursor and navigate to smartphone apps, Luo says. The team plans to implant a sixth patient by the end of August.
“We believe there is a significant unmet need for assistive BCI technology in China,” he says. He estimates that at least 1 to 2 million patients in the country could benefit from BCIs for assistive and rehabilitative purposes.
Beyond those uses, the policy document lays out other medical applications. It says BCIs could be used to monitor and analyze brain activity in real time to potentially prevent or reduce the risk of certain brain diseases. It also endorses consumer applications, such as monitoring driver alertness. The document says a wearable BCI could provide timely alerts for drowsiness, lack of attention, and slow reaction times, helping to reduce the probability of traffic accidents.
“I think noninvasive BCI products will get a huge market boost in China, because China is the biggest consumer electronics manufacturing country,” Peng says.
A few US companies, including Emotiv and Neurable, have started selling consumer wearables that use electroencephalography, or EEG, to capture brain waves through the scalp. But the devices are still expensive and have yet to take off more broadly.
China’s policy document, meanwhile, is promoting the mass production of non-implantable devices in various forms—forehead-mounted, head-mounted, ear-mounted, ear buds, and helmets, glasses, and headphones. It also proposes piloting BCIs in certain industries for safety management, such as hazardous materials handling, nuclear energy, mining, and electricity. The document suggests that BCIs could provide early warnings for workplace events such as low oxygen levels, poisoning, and fainting.
While the new policy guidance sets up a China-US rivalry in the BCI space, Peng sees room for cross-country collaboration among entrepreneurs. “We can cooperate as a society to build something for the patients, because they are desperate for this technology to have a better life,” he says. “We don’t want to be involved in any geopolitical issues. We just want to build something useful for patients.”
Technology is evolving at a rapid pace to pave the way for growth and progress. The use of trending technologies can help businesses lower costs, improve customer experience, and boost profits. Being familiar with the latest technologies is crucial for exploring better opportunities and building a stable career. Dive into this article to learn about the top technology trends that will dominate every industry in 2025.
12 Latest IT Technology Trends in 2025
When it comes to technology trends, a lot of new technology is emerging every day to make our life better, simpler, more advanced, and better for everyone. Let’s look some of the Latest Emerging Technology Trends 2025.
1. Robotic Process Automation (RPA)
Robotic Process Automation is one of the trending technologies that use various software and applications to automate business processes. It can aid with various repetitive tasks like data collection, analysis, and customer service. RPA is one of the most prominent technology trends in different industries.
Career Options:
RPA Tester
RPA Consultant
RPA Support Engineer
RPA Business Analyst
RPA Operations Manager
2. Computing Power
Computing power is one of the most important trending technologies because every device and appliance is getting computerized. As the computing infrastructure we use today will transform for the better, it will continue to be one of the best IT technologies.
Career Options:
Data Scientist
Robotics Researcher
Robotics Designer
AI Engineer
AI Architect
3. Datafication
Datafication is the process of using data to convert different parts of our lives into devices and software. As data-driven technologies continue to take over manual tasks, datafication will continue to be one of the trending technologies.
Career Options:
Data Analyst
Data Engineer
Data Scientist
Database Administrator
Data Visualization Specialist
4. Artificial Intelligence (AI) and Machine Learning
Artificial intelligence and machine learning continue to be highly trending technologies in the world. As AI continues to evolve, newer applications of the latest technology will come to the surface. It is one of the best technology trends with applications like image and speech recognition, voice assistants, navigation programs, and more.
Machine learning is a part of AI, and it can learn new functions with the help of supervised learning. Since it is one of the latest technology trends, the demand for skilled AI and ML professionals has increased considerably. Enrol in an AI & ML course to learn about trending technologies.
Career Options:
Chatbot Developer
AI Consultant
AI Product Manager
Deep Learning Engineer
Business Intelligence Analyst
5. Internet of Things (IoT)
IoT is one of the most promising trending technologies of the decade. IoT can be used to track remote activity using smart devices connected to your phone, remotely monitor home doors, switch applications on and off, and more. Businesses also use IoT for multiple purposes, including tracking remote location activities from a single hub or predicting issues with applications to fix them faster.
It is one of the potential emerging technology trends that may rise in 2025. Blockchain became immensely popular in the context of cryptocurrency to offer security. Since blockchain data can neither be removed nor modified, it is an extremely secure technology. The consensus-driven nature of blockchain ensures that no single person or organization has control over the data, and no third party is present to supervise transactions.
Career Options:
Blockchain Marketing Manager
Blockchain Consultant
Blockchain Technical Writer
Blockchain Security Specialist
Blockchain Business Analyst
7. Quantum Computing
Quantum computing is one of the latest technologies dependents on quantum theory principles. It is one of the trending IT technologies for querying, analyzing, and taking initiatives according to the present data.
Career Options:
Quantum Computing Engineer
Quantum Systems Administrator
Quantum Algorithm Researcher
Quantum Computing Scientist
Quantum Information Theorist
8. Full Stack Web Development
Fullstack development is one of the latest technologies in the software domain. Full stack development requires knowledge of web development and server-side programming. Having knowledge of Python libraries is essential for full stack development professionals.
Career Options:
JavaScript Developer
Front-End Developer
Backend Developer
Angular Developer
Full Stack Engineer
9. Cyber Security
Cybersecurity is not one of the latest technologies, but the rising number of security threats has made it one of the trending technologies. Cybersecurity professionals improve security protocols and strengthen systems to fight against malicious attacks.
DevOps is one of the trending IT services that combine the operations and development teams of organizations. DevOps can offer the following benefits to an organization:
Shorten the software delivery cycle
Improve the overall quality of products
DevOps can also help reduce errors in software development and support faster software upgrades.
Learning about List in Python can help you with a DevOps job role.
Career Options:
Automation Engineer
Configuration Engineer
Release Engineer
Cloud Engineer
Security Engineer
11. 5G
5G is one of the latest technologies that hold the potential to change the online world. When 3G and 4G were trending technologies, they transformed the way we interacted with mobile devices. They enabled faster internet browsing with the help of data-driven services and increased the bandwidth for live streaming.
5G is one of the latest technologies that aim to revolutionize virtual interactions by combining AR and VR. 5G will also be able to offer better cloud-based gaming experiences. Moreover, 5G is one of the trending technologies for tracking and streamlining operations in factories and businesses.
Career Options:
5G Software Developer
5G Testing and Validation Engineer
5G Operations Engineer
5G Network Architect
5G Integration Engineer
12. Virtual Reality and Augmented Reality
VR and AR will be the top trending technologies in 2025. VR can immerse the user in a new environment, while AR can improve the existing environment of users. VR and AR are the latest technologies particularly used for gaming and filters on social media.
Career Options:
VR UI/ UX Designer
VR Sound Designer
VR Animator
VR Marketing Manager
VR Project Manager
Conclusion
The emergence of the latest technology trends in data science and other fields is creating multiple job opportunities. You can enroll in the right courses to learn about all the latest technology trends and build a stable career path. So enroll in a course today to work on your skills!
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