Understanding Environmental Conservation Technologies

Environmental conservation technologies encompass a broad spectrum of innovative solutions to mitigate environmental impact, reduce waste, conserve natural resources, and promote sustainable practices. These technologies can be applied across various sectors, including energy, agriculture, manufacturing, transportation, and construction, to name a few. Here are some key areas where businesses can make a significant impact:

Renewable Energy:

Transitioning to clean and renewable energy sources like solar, wind, and hydropower can reduce greenhouse gas emissions and reliance on fossil fuels. Installing solar panels, wind turbines, and energy-efficient systems can help businesses reduce their carbon footprint.

Waste Reduction and Recycling:

Implementing efficient waste management practices and promoting recycling within your organisation can significantly reduce landfill waste and save valuable resources. Consider reusing materials, reducing packaging, and recycling programs.

Sustainable Supply Chain:

Collaborating with suppliers, prioritising sustainability, and responsible sourcing are crucial. Tracking and reducing the carbon footprint of your supply chain can enhance your overall sustainability efforts.

Green Building Technologies:

Incorporating energy-efficient building designs, materials, and technologies can lower energy consumption and operational costs. This is especially relevant for companies in the construction and real estate sectors.

Water Conservation:

Efficient water use and wastewater treatment technologies can help conserve this precious resource. Installing water-saving fixtures and recycling water in manufacturing processes can be beneficial.

Transportation Solutions:

Reducing emissions from company vehicles or offering incentives for employees to use public transportation or carpooling are ways to contribute to cleaner air and reduced congestion.

Practical Steps for Businesses to Lead in Environmental Conservation Technologies

Conduct an Environmental Audit:

Assess your current environmental impact by conducting a comprehensive environmental audit. This will help identify areas where improvements can be made.

Set clear sustainability goals and targets for your organisation. Make them measurable, time-bound, and aligned with global sustainability initiatives like the United Nations Sustainable Development Goals (SDGs).

Embrace Renewable Energy:

Invest in on-site renewable energy sources or purchase Renewable Energy Certificates (RECs) to offset your energy consumption from non-renewable sources.

Implement energy-efficient technologies, such as LED lighting and smart HVAC systems, to reduce energy consumption.

Sustainable Procurement:

Collaborate with suppliers who share your commitment to sustainability—Prioritise suppliers with responsible sourcing practices and eco-friendly products.

Consider circular economy principles, such as product design for longevity and ease of recycling.

Waste Management and Recycling:

Implement waste reduction strategies within your organisation. Encourage employees to reduce, reuse, and recycle.

Partner with recycling companies to ensure proper disposal of electronic waste (e-waste) and hazardous materials.

Employee Engagement:

Foster a culture of sustainability within your workforce. Educate employees about the importance of environmental conservation and encourage their active participation.

Offer incentives for sustainable commuting, such as public transportation subsidies or telecommuting options.

Technology Adoption:

Embrace cutting-edge environmental conservation technologies that align with your industry. This may include investing in energy-efficient machinery, adopting IoT-based ecological monitoring systems, or utilising predictive analytics for resource management.

Publicise Your Commitment:

Communicate your sustainability efforts transparently to customers, investors, and the public. Highlight your achievements and progress towards sustainability goals.

Showcase eco-friendly certifications, such as LEED (Leadership in Energy and Environmental Design) or B Corp certification, if applicable.

Collaborate and Innovate:

Seek partnerships and collaborations with other businesses, research institutions, and government agencies to share knowledge and drive innovation in environmental conservation technologies.

Explore emerging technologies like carbon capture and utilisation, sustainable biofuels, and advanced recycling processes.

Conclusion

Environmental conservation technologies are no longer just a choice but a necessity for businesses looking to thrive in a world increasingly focused on sustainability. By adopting these technologies and implementing practical steps, companies can reduce their environmental impact, cut operational costs, and position themselves as leaders in the transition toward a more sustainable future. Embracing these innovations benefits the environment, enhances a company’s reputation, attracts environmentally-conscious customers, and ensures long-term success in an eco-friendly world.

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Critics have doubted the appeal of the face-worn computer, which “seamlessly blends digital content with the physical world”, but Apple has pre-sold as many as 180,000 of the US$3,500 gizmos.

What does Apple think people will do with these pricey peripherals? While uses will evolve, Apple is focusing attention on watching TV and movies, editing and reliving “memories”, and – perhaps most importantly for the product’s success – having its customers not look like total weirdos.

Apple hopes the new device will redefine personal computing, like the iPhone did 16 years ago, and Macintosh did 40 years ago. But if it succeeds, it will also redefine concerns about privacy, as it captures enormous amounts of data about users and their environments, creating an unprecedented kind of “biospatial surveillance”.

Spatial computing

Apple is careful about its brand and how it packages and describes its products. In an extensive set of rules for developers, the company insists the new headset is not to be referred to as a “headset”. What’s more, the Apple Vision Pro does not do “augmented reality (AR), virtual reality (VR), extended reality (XR), or mixed reality (MR)” – it is a gateway to “spatial computing”.

Spatial computing, as sketched out in the 2003 PhD thesis of US software engineer Simon Greenwold, is: “human interaction with a machine in which the machine retains and manipulates referents to real objects and spaces”. In other words, the computer can interact with things in the user’s physical surroundings in real time to provide new types of experiences.

The Vision Pro has big shoes to fill for new user experiences. The iPhone’s initial “killer apps” were clear: the internet in your pocket (including portable access to Google Maps), all your music on a touch screen, and “visual voicemail”.

Sixteen years later, all three of these seem unremarkable. Apple has sold billions of iPhones, and some 80% of humans now use a smartphone. Their success has all but killed off earlier tools like paper maps and music CDs (and the ubiquity of text, image and video messaging has largely done away with voicemail itself).

Killer apps

We don’t yet know what the killer apps of spatial computing might be – if any – but here is where Apple is pointing our attention.

The first is entertainment: the Vision Pro promises “the ultimate personal theatre”.

The second is an attempt to solve the social problem of walking around with a weird headset covering half your face. An external screen on the goggles shows a constantly updated representation of your eyes to offer important social cues about your gaze to those around you. Admittedly, this looks weird. But Apple hopes it is less weird and more useful than trying to interact with humans wearing blank aluminium ski goggles.

The third is the ability to capture and and relive “memories”: recording and playback of 3D visual and audio from real events. Reviewers have found it striking:

this was stuff from my own life, my own memories. I was playing back experiences I had already lived.

Apple has patented tools to select, store, and annotate digital “memories”. These memories are files, and potentially products, to be shared in “spatial videos” recorded on the latest iPhones.

Biospatial surveillance

There is already a large infrastructure devoted to helping tech companies track our behaviour in order to sell us things. Recent research found Facebook, for example, receives data from an average of around 2,300 companies on each individual user.

Spatial computing offers a step change to this tracking. In order to function, spatial computing records and uses vast amounts of intimate data about our bodies and surroundings.

One study on headset design noted no fewer than 64 different streams of biometric and physiological data, from eye tracking and pupil response to subtle changes in the body’s electromagnetic field.

Your face tomorrow

This is not “consumer” data like the brand of toothpaste you buy. It is more akin to medical data.

For instance, analysing a person’s unconscious movements can reveal their emotional state or even predict neurodegenerative disease. This is called “biometrically inferred data” as users are unaware their bodies are giving it up.

Apple suggests it won’t share this type of data with anyone, and Apple has proven better than most companies on privacy. But biospatial surveillance puts more of ourselves in use for spatial computing, in ways that are expanding.

It starts simply enough in the pre-order process, where you need to scan your facial features with your iPhone (to ensure a snug fit). But that’s not the end of it.

Apple’s patent about memories is also about how to “guide and direct a user with attention, memory, and cognition” through feedback loops that monitor “facial recognition, eye tracking, user mood detection, user emotion detection, voice detection, etc. [from a] bio-sensor for tracking biometric characteristics, such as health and activity metrics […] and other health-related information”.

Social questions

Biospatial surveillance is also the key to Apple’s attempt to solve the social problems created by wearing a headset in public. The external screen showing a simulated approximation of the user’s gaze relies on constant measurement of the user’s expression and eye movement with multiple sensors.

Your face is constantly mapped so others can see it – or rather see Apple’s vision of it. Likewise, as passersby come into range of the Apple Vision Pro’s sensors, Apple’s vision of them is automagically rendered into your experience, whether they like it or not.

Apple’s new vision of us – and those that surround us – shows how the requirements and benefits of spatial computing will pose new privacy concerns and social questions. The extensive biospatial surveillance that captures intimate biometric and environmental data redefines what personal data and social interactions are possible for exploitation.

Luke Heemsbergen, Senior Lecturer, Digital, Political, Media, Deakin University

This article is republished from The Conversation under a Creative Commons license.

His company Neuralink aims to facilitate this convergence so that humans won’t be “left behind” as technology advances in the future. While people with disabilities would be near-term recipients of these innovations, some believe technologies like this could be used to enhance abilities in everyone.

These aims are inspired by an idea called transhumanism, the belief that we should use science and technology to radically enhance human capabilities and seek to direct our own evolutionary path. Disease, aging and death are all realities transhumanists wish to end, alongside dramatically increasing our cognitive, emotional and physical capacities.

Transhumanists often advocate for the three “supers” of superintelligence, superlongevity and superhappiness, the last referring to ways of achieving lasting happiness. There are many different views among the transhumanist community of what our ongoing evolution should look like.

For example, some advocate uploading the mind into digital form and settling the cosmos. Others think we should remain organic beings but rewire or upgrade our biology through genetic engineering and other methods. A future of designer babies, artificial wombs and anti-aging therapies appeal to these thinkers.

This may all sound futuristic and fantastical, but rapid developments in artificial intelligence (AI) and synthetic biology have led some to argue we are on the cusp of creating such possibilities.

God-like role

Tech billionaires are among the biggest promoters of transhumanist thinking. It is not hard to understand why: they could be the central protagonists in the most important moment in history.

Creating so-called artificial general intelligence (AGI) – that is, an AI system that can do all the cognitive tasks a human can do and more – is a current focus within Silicon Valley. AGI is seen as vital to enabling us to take on the God-like role of designing our own evolutionary futures.

That is why companies like OpenAI, DeepMind and Anthropic are racing towards the development of AGI, despite some experts warning that it could lead to human extinction.

In the short term, the promises and the perils are probably overstated. After all, these companies have a lot to gain by making us think they are on the verge of engineering a divine power that can create utopia or destroy the world. Meanwhile, AI has played a role in fuelling our polarised political landscape, with disinformation and more complex forms of manipulation made more effective by generative AI.

Indeed, AI systems are already causing many other forms of social and environmental harm. AI companies rarely wish to address these harms though. If they can make governments focus on long-term potential “safety” issues relating to possible existential risks instead of actual social and environmental injustices, they stand to benefit from the resulting regulatory framework.

But if we lack the capacity and determination to address these real world harms, it’s hard to believe that we will be able to mitigate larger-scale risks that AI may hypothetically enable. If there really is a threat that AGI could pose an existential risk, for example, everyone would shoulder that cost, but the profits would be very much private.

A familiar story

This issue within AI development can be seen as a microcosm of why the wider
transhumanist imagination may appeal to billionaire elites in an age of multiple crises. It speaks to the refusal to engage in grounded ethics, injustices and challenges and offers a grandiose narrative of a resplendent future to distract from the current moment.

Our misuse of the planet’s resources has set in train a sixth mass extinction of species and a climate crisis. In addition, ongoing wars with increasingly potent weapons remain a part of our technological evolution.

There’s also the pressing question of whose future will be transhuman. We currently live in a very unequal world. Transhumanism, if developed in
anything like our existing context, is likely to greatly increase inequality, and
may have catastrophic consequences for the majority of humans.

Perhaps transhumanism itself is a symptom of the kind of thinking that has created our parlous social reality. It is a narrative that encourages us to hit the gas, expropriate nature even more, keep growing and not look back at the devastation in the rear-view mirror.

If we’re really on the verge of creating an enhanced version of humanity, we should start to ask some big questions about what being human should mean, and therefore what an enhancement of humanity should entail.

If the human is an aspiring God, then it lays claim to dominion over nature and the body, making all amenable to its desires. But if the human is an animal embedded in complex relations with other species and nature at large, then “enhancement” is contingent on the health and sustainability of its relations.

If the human is conceived of as an environmental threat, then enhancement is surely that which redirects its exploitative lifeways. Perhaps becoming more-than-human should constitute a much more responsible humanity.

One that shows compassion to and awareness of other forms of life in this rich and wondrous planet. That would be preferable to colonising and extending ourselves, with great hubris, at the expense of everything, and everyone, else.

Authors: Alexander Thomas, Programme Leader, Media, Fashion & Communications, University of East London

This article is republished from The Conversation under a Creative Commons license.

Image Credit: Kotin / Shutterstock

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2023 was an inflection point in the evolution of artificial intelligence and its role in society. The year saw the emergence of generative AI, which moved the technology from the shadows to center stage in the public imagination. It also saw boardroom drama in an AI startup dominate the news cycle for several days. And it saw the Biden administration issue an executive order and the European Union pass a law aimed at regulating AI, moves perhaps best described as attempting to bridle a horse that’s already galloping along.

We’ve assembled a panel of AI scholars to look ahead to 2024 and describe the issues AI developers, regulators and everyday people are likely to face, and to give their hopes and recommendations.

Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder

2023 was the year of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though I think that anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so – in ways that often do more harm than good.

However, as the year went on, there was a recognition that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. I don’t think we should be revamping education to put AI at the center of everything, but if students don’t learn about how AI works, they won’t understand its limitations – and therefore how it is useful and appropriate to use and how it’s not. This isn’t just true for students. The more people understand how AI works, the more empowered they are to use it and to critique it.

So my prediction, or perhaps my hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative artificial intelligence is that, in contrast to ELIZA’s very basic pattern matching and substitution methodology, it is much more difficult to find language “sufficiently plain” to make the AI magic crumble away.

I think it’s possible to make this happen. I hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. I hope that media outlets help cut through the hype. I hope that everyone reflects on their own uses of this technology and its consequences. And I hope that tech companies listen to informed critiques in considering what choices continue to shape the future.

Kentaro Toyama, Professor of Community Information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years we will have a machine with the general intelligence of an average human being.” With the singularity, the moment artificial intelligence matches and begins to exceed human intelligence – not quite here yet – it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024? First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory and global supremacy. Expect more powerful AI, in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning – what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will it require a fundamentally different approach, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so I expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire – comically, tragically or both. Deepfakes, AI-generated images and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI – like Elon Musk and Sam Altman – can’t seem to stop themselves from building ever more powerful AI. I expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, what I most hope for 2024 – though it seems slow in coming – is stronger AI regulation, at national and international levels.

Anjana Susarla, Professor of Information Systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multi-modal, meaning the data used to train them comes not only from textual sources such as Wikipedia and Reddit, but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multi-modal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed on a variety of hardware and in a variety of applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open source LLMs could usher in a world of autonomous AI agents – a world that society is not necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. My chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as pose new types of algorithmic harms.

The deluge of synthetic content produced by generative AI could unleash a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy and serendipity provided by search engines, social media platforms and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosure of AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies like the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it is clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes and society.

Authors: Anjana Susarla, Professor of Information Systems, Michigan State University; Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder, and Kentaro Toyama, Professor of Community Information, University of Michigan

This article is republished from The Conversation under a Creative Commons license.

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A simple internet search reveals several reported cases where the cars have made errors in identifying objects on the road. For instance, a Tesla car mistook an image of a stop sign on a billboard for the real thing and confused the yellow moon with a yellow traffic light.

There have also been numerous recent examples of problems with the “robotaxis” operating in San Francisco. It raises questions about whether the technology that enables vehicles to operate autonomously is ready for the real world.

The driving force behind self-driving vehicles is artificial intelligence (AI), yet current algorithms lack the human-like understanding and reasoning necessary for context when driving. This includes advanced contextual reasoning for interpreting complex visual cues such as obscured objects, and inferring unseen elements in the environment.

Social interaction

Furthermore, these vehicles must be capable of counterfactual reasoning –evaluating hypothetical scenarios and predicting potential outcomes. This is a crucial skill for decision making in dynamic driving situations.

For instance, when an autonomous vehicle (AV) approaches a busy intersection with traffic lights, it must not only obey the current traffic signals but also predict the actions of other road users and consider how those might change under different circumstances.

An example of this scenario is provided by a 2017 accident in which an Uber robotaxi drove through a yellow light in Arizona in 2017 and collided with another car. At the time, there were questions about whether a human driver would have approached the situation differently.

Additionally, social interaction – an area where humans excel and robots falter – is essential. For example, on urban roads with cars parked along both sides, it’s not always clear who has the right of way and we use social skills to negotiate a fair way to proceed.

At roundabouts, it’s common for several cars to arrive at once, making it unclear who has right of way. Again, social skills allow drivers to safely pull onto the roundabout.

To ensure seamless coexistence with AI-driven cars, we urgently need to develop groundbreaking algorithms capable of human-like thinking, social interaction, adaptation to new situations and learning with experience. Such algorithms would enable AI systems to comprehend nuanced human driver behaviour, react to unforeseen road conditions, prioritise decision making that factors in human values, and interact socially with other road users.

As we integrate AI-driven vehicles into existing traffic, the kinds of standards we’ve been using to assess and validate the success of autonomous driving systems will become insufficient. There is a pressing need for new standards and mechanisms to assess the capabilities of these driverless cars.

Tesla Car Vacuum Cleaner Cordless 

Specific uses

These new protocols should provide more rigorous testing and validation methods, ensuring that AI-driven vehicles meet the highest standards of safety, performance and interoperability (where AI systems from different manufacturers can work “understand” and work together). In doing so, they will establish a foundation for a safer, more harmonious traffic environment where driverless and human-driven cars mix.

It would be a mistake to write off fully self-driving cars, even without the developments which are needed. There is still a place for them, albeit not as ubiquitously as the rapid spread of Tesla vehicles might indicate. We’ll initially need them for specific uses such as autonomous shuttles and highway driving. Alternatively, they could be used in special environments with their own dedicated infrastructure.

For instance, autonomous buses could drive a predefined route with a dedicated lane. Autonomous trucks could also have a separate lane on motorways. However, it’s crucial that uses focus on benefiting the entire community, not just a specific – usually wealthy – group in society.

To ensure autonomous vehicles are well integrated on our roads, we’ll need a diverse groups of experts to enter into a dialogue. These include car manufacturers, policymakers, computer scientists, human and social behaviour scientists and engineers and governmental bodies, among others.

They must come together to address the current challenges. This collaboration should aim to create a robust framework that accounts for the complexity and variability of real-world driving scenarios.

It would involve developing industry-wide safety protocols and standards, shaped by input from all people with a stake in the matter and ensuring these standards can evolve as the technology advances.

The collaborative effort would also need to create open channels for sharing data and insights from real-world testing and simulations. It must also foster public trust through transparency and demonstrate the reliability and safety of AI systems in autonomous vehicles.

Author: Prof Saber Fallah, Director of Connected Autonomous Vehicles Lab at the University of Surrey, University of Surrey

This article is republished from The Conversation under a Creative Commons license.

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We are currently working on a research project focusing on conversations with specialists within the field of AI. We are questioning them about their thinking and values, as well as what ethical considerations they consider most important – and why. The norms and values of developers can become embedded in the AI systems they engineer. However, they – and we – are often unaware of this, along with its consequences.

It’s vital to understand as much as possible about AI’s development because the technology is already changing us in ways we don’t seem to realise. For example, research published in 2017 showed that social media algorithms create outcomes based on assumptions about the users, but users also adapt to these outcomes, such as which stories show in their feeds, also changing the logic of the algorithm. Our daily interactions with AI are making us increasingly reliant on it, but the power dynamic in this relationship greatly favours the AI systems. This is a technology whose inner workings aren’t even fully understood by its creators.

Too heavy a human reliance on technology can reduce creative and critical thinking. AI has already led to job displacements and unemployment. And, while the warnings that it could lead to human extinction shouldn’t be taken at face value, we can’t afford to completely dismiss them either.

Algorithms have been shown to contain discriminatory tendencies towards race, gender and other protected characteristics. We need to understand how these and other problems with AI development arise.

Some commentators have drawn attention to what they say is a failure to consider security and privacy by the companies developing AI. There is also a lack of transparency and accountability regarding AI projects. While this is not unusual in the competitive world of big tech, we surely need to adopt a more rational approach with technology that’s capable of exerting such power over our lives.

Identity crisis?

What has been neglected in the discourse about AI is how our sense of meaning, identity and reality will increasingly rely on engaging with the services it facilitates. AI may not have consciousness, but it exercises power in ways that affect our sense of who we are. This is because we freely identify with – and participate in – the pursuits enabled by its presence.

In this sense, AI is not some great conspiracy designed to control the world and all its inhabitants but more like a force, neither necessarily good nor bad. However, while extinction is unlikely in the near term, a much more present danger is that our reliance on the technology leads to humans effectively serving the technology. This is not a situation any of us would want, even less so when the technology incorporates human norms many would consider to be less than ideal.

For an example of what we’re talking about here, let’s take the performance guidelines for, and monitoring of, delivery drivers, which is facilitated by automated systems with AI. They have been described by a UK all-party parliamentary group as negatively affecting the mental and physical wellbeing of workers as “they experience the extreme pressure of constant, real-time micro-management and automated assessment”.

Another example was highlighted by Erik Brynjolfsson, a Stanford economist who has raised the possibility of something called the “Turing trap”. This refers to concerns that the automation of human activities could leave wealth and power in fewer and fewer hands. In his book The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence, Brynjolfsson writes: “With that concentration (of power) comes the peril of being trapped in an equilibrium in which those without power have no way to improve their outcomes.”

More recently, Jeremy Howard, an AI researcher, described how he introduced ChatGPT to his seven-year-old daughter after she asked several questions about it. He concluded that it could become a new kind of personal tutor, teaching her maths, science, English and other subjects. Clearly, this would involve a displacement of the role of teachers. However, Howard also warned his daughter that she should not believe everything it said. This aspect poses a real risk for learning. And even if ChatGPT was conveying accurate knowledge, would his daughter retain this information as readily as when it was communicated through “embodied” speech – in other words, by a human?

What the algorithm sees

These real world cases demonstrate the way that AI can transform the way we view the world and ourselves. They indicate that there is a power dynamic between users and AI where the machine exercises authority over those who interact with it.

As Taina Bucher, assistant professor in communication and IT at the University of Copenhagen, reported in a 2016 research paper carried out with the help of consumers: “It is not just that the categories and classifications that algorithms rely on match our own sense of self, but to what extent we come to see ourselves through the ‘eyes’ of the algorithm.”

AI is often simply accessed through our computer screens or other more abstract mediums, it is not embodied except in the most limited sense. As such, its effect is often restricted to the cognitive level of identity, bereft of a “soul”, or the emotional sensibility or what’s sometimes known as affective energy. This is a description of the natural ways that humans interact and spur reactions from each other.

If you asked ChatGPT whether AI can be embodied, the answer would only be concerned with the embodiment of machine intelligence in a physical form, such as in a robot. But embodiment is also about emotions, sentiment and empathy. It cannot be reduced to linear sequences of instructions.

That’s not to say that AI doesn’t affect our feelings and emotions. But machines can never replicate the rich emotional life inherent in the interactions between two or more human beings. As our lives seem to be ever more entwined with AI, maybe we should slow the relationship down, especially as it’s clear this is far from an equal partnership.

Authors: David Knights, Professor of Organisation Studies, Lancaster University and Guy Huber, Senior Lecturer, Oxford Brookes Business School, Oxford Brookes University

This article is republished from The Conversation under a Creative Commons license.

Image Credit: cono0430 / Shutterstock

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The track record of AI development over the last 20 years provides a range of evidence of government misuse of the technology around the world. This includes excessive surveillance practices, the harnessing of AI for the spread of disinformation.

Although recent focus has been on private companies that develop AI products, governments are not the impartial arbiters they might seem to be at this AI summit. Instead, they have played a role that’s just as integral to the precise way that AI has developed – and they will continue to.

Militarising AI

There are continual reports that the leading technological nations are entering into an AI arms race. No one state really started this race. Its development has been complex, and many groups – from inside and outside governments – have played a role.

During the cold war, US intelligence agencies became interested in the use of artificial intelligence for surveillance, nuclear defence and for the automated interrogation of spies. It is therefore not surprising that in more recent years, the integration of AI into military capabilities has proceeded apace in other countries, such as the UK.

Automated technologies developed for use in the war on terror have fed into the development of powerful AI-based military capabilities, including AI-powered drones (unmanned aerial vehicles) that are being deployed in current conflict zones.

Russia’s president, Vladimir Putin, has declared that the country that leads in AI technology will rule the world. China has also declared its own intent to become an AI superpower.

Surveillance states

The other major concern here is the use of AI by governments in surveillance of their own societies. As governments have seen domestic threats to security develop, including from terrorism, they have increasingly deployed AI domestically to enhance the security of the state.

In China, this has been taken to extreme degrees, with the use of facial recognition technologies, social media algorithms, and internet censorship to control and surveil populations, including in Xinjiang where AI forms an integral part of the oppression of the Uyghur population.

But the west’s track record isn’t great either. In 2013, it was revealed that the US government had developed autonomous tools to collect and sift through huge amounts of data on people’s internet usage, ostensibly for counter terrorism. It was also reported that the UK government had access to these tools. As AI develops, its use in surveillance by governments is a major concern to privacy campaigners.

Meanwhile, borders are policed by algorithms and facial recognition technologies, which are increasingly being deployed by domestic police forces. There are also wider concerns about “predictive policing”, the use of algorithms to predict crime hotspots (often in ethnic minority communities) which are then subject to extra policing effort.

These recent and current trends suggest governments may not be able to resist the temptation to use increasingly sophisticated AI in ways that create concerns around surveillance.

Governing AI?

Despite the good intentions of the UK government to convene its safety summit and to become a world leader in the safe and responsible use of AI, the technology will require serious and sustained efforts at the international level for any kind of regulation to be effective.

Governance mechanisms are beginning to emerge, with the US and EU recently introducing significant new regulation of AI.

But governing AI at the international level is fraught with difficulties. There will of course be states that sign up to AI regulation and then ignore them in practice.

Western governments are also faced with arguments that overly strict regulation of AI will allow authoritarian states to fulfil their aspirations to take the lead on the technology. But allowing companies to “rush to release” new products risk unleashing systems that could have huge unforeseen consequences on society. Just look at how advanced text-generating AI such as ChatGPT could increase misinformation and propaganda.

And not even the developers themselves understand exactly how advanced algorithms work. Puncturing this “black box” of AI technology will require sophisticated and sustained investment in testing and verification capabilities by national authorities. But the capabilities or the authorities don’t exist at the present time.

The politics of fear

We’re used to hearing from the news about a super-intelligent form of AI threatening human civilisation. But there are reasons to be wary of such a mindset.

As my own research highlights, the “securitisation” of AI – that is, presenting technology as an existential threat – could be used as an excuse by governments to grab power, to misuse it themselves, or to take narrow self-interested approaches to AI that don’t harness the potential benefits it could confer on all people.

Rishi Sunak’s AI summit would be a good opportunity to highlight that governments should keep the politics of fear out of efforts to bring AI under control.

Author: Joe Burton, Professor of International Security (Security and Protection Science), Lancaster University

This article is republished from The Conversation under a Creative Commons license.

Image Credit: Deemerwha studio / Shutterstock

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Some of these tasks are now becoming automated. This could benefit gender equality, but we also need to monitor some of the risks.

Women continue to do more unpaid domestic work than men in most households. Yet the extent of gender inequality when it comes to domestic work varies between societies.

Time spent on household tasks can come with a price: doing more than your equal share of parenting, for example, is associated with a “caregiver penalty” of lost wages, slower career progression, and lower lifelong earnings.

Historically, technological advances –- such as the rise of domestic appliances in the 1950s –- have been associated with women playing a bigger role in the labour market. In fact, female employment and family responsibilities – especially parenting – have both increased. This means that there is a large unmet demand for help with domestic work.

Existing household robots, such as robotic vacuum cleaners, floor mops and lawn mowers, have outnumbered all other types of robot in terms of units sold from as early as 2010. Sales of household robots have since accelerated dramatically.

Other devices that can step in and take over care work are also on the way. These include automated cots that can respond to a baby’s cries by rocking them to sleep and chatbots designed to combat loneliness that are able to mimic human conversation.

A gift of time?

With the rise of smart technologies, AI experts see the potential for a further transformation of unpaid domestic work -– increasing discretionary time (time not spent on work, or necessary rest and personal care) and perhaps bringing about greater equality in the home.

Earlier this year, our team published a study examining the future of unpaid work in the home, based on predictions from 65 AI experts in the UK and Japan. This showed that around 39% of domestic work could be automated in the next decade.

Of course, the type of domestic work is key here. Some 44% of typical housework, including cooking, cleaning, and shopping, is expected to be automated. In the study, grocery shopping had the highest expected potential for automation at 59%. Care work, on the other hand, is harder to automate, with only about 28% of domestic care tasks expected to be suitable for automation within the timeframe of a decade.

In the UK, working-age men spend around half as much time on domestic unpaid work as working-age women. In Japan, the difference in time spent on domestic tasks is much more striking, with Japanese men spending just a fifth of the time spent by women on domestic tasks.

In the best-case scenario for the future, the rise of domestic automation could address gender inequality in domestic work by increasing the time available for women to carry out paid work and leisure. Our recent simulation suggests that the time freed up by domestic automation might enable an additional 5.8% of women in the UK, and 9.3% of women in Japan to join the labour market.

Of course, not everyone will choose to spend this time on paid work, but may rather study, rest, or sleep. In any case, an overall increase of “discretionary” time – time left over once a person has finished their paid work, household responsibilities, took time for sleep and basic personal care – could result in greater wellbeing.

These benefits, however, are not a foregone conclusion. In most countries, people on low incomes do more housework than those on high incomes. Given that AI-powered technologies are likely to carry a substantial price tag when they hit the market, they could exacerbate existing inequalities in available time between rich and poor.

A darker side?

Automation of domestic work also carries with it certain risks, as many domestic tasks require knowledge about household members in order to be performed effectively. A cooking robot would need to know not only about everyone’s food preferences, but also allergies, intolerances and underlying health conditions. Management of the data the technology collects and uses – and the protection of this sensitive information – is an important issue that needs to be addressed.

Technologies used to help care for other people, in particular, raise a multitude of ethical concerns. Care work might involve the monitoring of children or vulnerable older people to ensure their physical safety. While technology can take on some of this work – examples include baby cameras and location tracking apps – this raises concerns about surveillance and who has access to the monitoring data.

Time spent on caring for family members strengthens family bonds. Can a robot helper really replace the kind of nurturing interaction a human can provide? And if a robot or a chatbot does become the focal carer -— at least in terms of the time spent interacting – could those being cared for get emotionally attached to the technology?

These bigger societal questions need to be taken into consideration in the drive towards greater automation in the home.

Authors: Ekaterina Hertog, Associate Professor in AI and Society, Oxford Internet Institute and Institute for Ethcis in AI, University of Oxford and Lulu Shi, Lecturer, Department of Education and Research Associate, Oxford Internet Institute, University of Oxford

This article is republished from The Conversation under a Creative Commons license.

Image Credit: Robot vacuum cleaners are already a feature in some homes. Daniel Krason / Shutterstock

Robotics

The animal kingdom has inspired the development of robotics, particularly in biomimetics. Biomimetics studies biological systems and processes and how they can be used to create new technologies. Many animals have unique features that have inspired the development of robots that can perform similar functions.

Researchers have studied the movements and behaviours of animals to create robots that can perform similar tasks. This approach is beneficial for creating robots that can operate in environments that are difficult for humans to access.

For example, researchers at the University of California, Berkeley, have developed a robot called Salto that can jump like a grasshopper. The grasshopper’s ability to jump long distances with little energy inspired the robot’s design. In addition, the robot’s ability to jump allows it to navigate over obstacles and uneven terrain.

Similarly, how insects fly has inspired the development of robots that can fly with incredible agility and precision. Likewise, insects can fly with great speed and control. This ability has been replicated in flying robots that can be used for various applications, from search and rescue to crop monitoring.

Another example is the development of robotic snakes. The movement of real snakes inspired the design of the snake robot. The snake robot can slither through tight spaces and climb obstacles like a real snake. These robots have potential applications in search and rescue operations, as they can navigate small areas difficult for humans to access.

Aviation

Animals have greatly influenced the aviation industry. Birds, in particular, have inspired many designs in aviation. The Wright Brothers, credited with inventing the first successful aeroplane, were inspired by birds. They studied the wings of birds to design their aircraft, they observed that birds had a curved wing shape that allowed them to generate lift and control their flight. They also looked at how birds used their tails to control their flight and used that knowledge to create the first aeroplane control surfaces.

Birds are not the only animals that have inspired flight technology. Bats, for example, have inspired the creation of ultrasonic sensors used in aviation to detect obstacles. These sensors emit high-frequency sounds that bounce off objects and return to the sensor. This technology has been used in aircraft to detect objects that are not visible to the naked eye, making air travel safer.

Another example of aviation technology inspired by animals is the development of drones. Drones have been designed to mimic the flight patterns of birds. Researchers have studied the movements of birds and developed drones that can fly and manoeuvre like them. These drones have potential applications in fields such as agriculture and wildlife monitoring.

Energy

Animals have also inspired advancements in energy technology. Researchers have studied the behaviour and structure of animals to develop new technologies that can generate and store energy. For example, researchers at the University of Michigan have developed a new type of solar panel inspired by butterflies’ wings. The solar panel has a design that mimics the scales on the butterfly’s wings, enhancing sunlight absorption and increasing energy efficiency.

Another example of energy technology inspired by animals is the development of wind turbines. Wind turbines have been designed to mimic the movement of bird wings. The turbine blades are shaped like birds’ wings and placed at a specific angle to generate energy efficiently.

Conservation

Finally, animals have also inspired advancements in conservation technology. Researchers have developed new technologies to monitor and protect endangered species and their habitats. For example, researchers have developed camera traps that can be used to monitor wildlife populations. These cameras are designed to mimic the eyes of predators, which can help to capture natural behaviour without disturbing the animals.

Another example is the development of GPS collars for animals. These collars can track the movements of animals in the wild, which can help to monitor their populations and protect their habitats. These technologies have been instrumental in conservation efforts and have helped to protect many species from extinction.

Conclusion

Animals have inspired many technological advancements in a wide range of fields. From biomimicry to robotics, aviation, medical technology, energy, materials science, and conservation, animals have inspired new technologies that improve our lives and the world. As we continue to study and learn from animals, they will continue to inspire recent technological advancements. However, it is essential to consider the ethical implications of these technologies and ensure that they benefit both humans and animals.

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Biomimicry

Biomimicry is a design approach that involves studying nature’s systems, processes, and materials to create new technologies that mimic natural designs. This approach has been applied to various fields, including engineering, architecture, and product design.

One example of biomimicry in engineering is the bullet train development in Japan. The design of the bullet train was inspired by the beak of a kingfisher bird, which allows the bird to dive into the water without creating a splash. The bullet train has a similar design, which enables it to travel at high speeds without creating sonic booms.

Another example of biomimicry is the development of Velcro. Swiss engineer George de Mestral was inspired by the burrs that stuck to his dog’s fur during a walk. He studied the burrs under a microscope and noticed that they had tiny hooks that stuck to the dog’s fur. He then created a hook-and-loop fastener that mimicked the burrs’ design and created Velcro.

Military Technology

Animals have inspired military technology, particularly in the field of camouflage. Many animals have developed unique ways of hiding and blending into their environment to avoid detection by predators or prey. The military has used these principles to develop camouflage technology that can conceal soldiers and equipment in various environments.

For example, how chameleons change colour has inspired the development of camouflage technology that can change colour to match its surroundings. This technology has been used in military uniforms and equipment to help soldiers blend in with their environment and avoid detection by enemies.

Similarly, how octopuses can change their skin’s texture has inspired the development of camouflage technology that can mimic the texture of its surroundings. This technology has been used in military uniforms and equipment to help soldiers blend in with various environments, from rocky terrain to sandy beaches.

Medical Technology

Animals have inspired many advancements in medical technology. Researchers have studied the biological processes of animals to develop new medical treatments and technologies. For example, researchers at Harvard University have studied the adhesive properties of gecko feet to build a new type of surgical adhesive. The gecko’s ability to stick to surfaces without leaving any residue inspires the adhesive. The adhesive has potential applications in surgical procedures where traditional adhesives may need to be improved.

Animals have also inspired medical technology, particularly in the field of prosthetics. Researchers have looked to animals such as elephants and whales to develop prosthetics that can replicate their unique features. For example, an elephant’s trunk is incredibly flexible and robust, allowing it to perform complex tasks such as grasping objects and lifting heavy weights. Scientists have used this inspiration to develop prosthetic limbs that perform similar functions.

Similarly, the fins of whales have inspired the development of prosthetic limbs used in water. The fins of whales are incredibly efficient and allow them to move through the water quickly and with minimal effort. By studying the fins of whales, scientists have created prosthetic limbs that are more hydrodynamic and efficient in the water.

Another example is the development of artificial limbs. Engineers have studied the movement and flexibility of animal limbs to develop artificial limbs that can replicate natural movements. For example, a team of engineers at MIT has developed a prosthetic ankle that mimics the movement of a natural ankle. These advancements have greatly improved the lives of individuals who have lost limbs.

Materials Science

Materials science is another field that animals have greatly influenced. Researchers have studied the properties of animal materials, such as spider silk and mussel adhesive, to develop new materials with similar properties.

Spider silk, for example, is one of the strongest materials known to man. Researchers have studied the structure and composition of spider silk to develop new materials that are similarly strong and flexible. These materials have potential applications in fields such as construction and aerospace.

Mussel adhesive is another material that has inspired advancements in materials science. Researchers have studied the adhesive properties of mussel glue to develop new adhesives that can stick to wet surfaces. These adhesives have potential applications in fields such as medicine and underwater construction.

Architecture

The animal world has also inspired architecture, particularly in sustainable design. Many animals have evolved to live in harsh environments and have developed unique ways of conserving energy and resources. For example, termites build towering mounds that regulate temperature and humidity, allowing the colony to survive in harsh conditions. These mounds have inspired architects to create buildings that use similar principles to regulate temperature and conserve energy.

Similarly, the shape of a seashell has inspired architects to create more energy-efficient buildings. The spiral shape of a seashell allows it to move water efficiently, which has inspired architects to design buildings that use a similar shape to move air more efficiently. This can help reduce the energy needed to heat and cool buildings, making them more sustainable.

Conclusion

Animals have played a significant role in inspiring technological advancement. From flight to architecture, robotics to military technology, animals have provided many ideas and designs that have helped us create innovative technologies. By studying the unique features of animals and how they have evolved to survive in their environment, we can continue to develop new technologies that improve our lives and advance our civilisation. The natural world will continue to inspire us for years to come, and we should always be open to the lessons it has to teach us.

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