Artificial general intelligence is closer than expected

The concept of Artificial General Intelligence (AGI) has long been the pinnacle of artificial intelligence (AI), representing machines that can perform any intellectual task a human can. Yet, in the blink of an eye. The rapid growth and acceptance we have seen of AI thus far has mainly been due to the public release of ChatGPT. Whilst the technology is certainly impressive, it does have its limitations and is defined as a very narrow form of AI, due to its inability to think outside of anything other than predefined tasks and questions being asked by the end user. 

Shifting investment priorities

Whilst the timeline for the move towards AGI has often been projected as 2030 onwards, several emerging factors suggest that AGI might actually be closer than anticipated. Amongst these are the shifting investment priorities from the metaverse towards AI. Not least of which the one caused by the adjusted target of net zero. This has caused a widespread pivot in investment from immediate carbon savings to emerging technologies – powered by AI – that path a sustainable road to carbon neutrality and net zero. 

Global efforts to combat climate change have accelerated the demand for more efficient, intelligent systems capable of optimising energy use and reducing carbon footprints. Many countries and organisations have set ambitious carbon-neutral targets, with a significant shift towards achieving net-zero emissions by 2050. A much lengthier target than the originally planned 2030. This urgency necessitates the development of advanced AI systems capable of managing complex energy grids, optimising resource distribution, and innovating in green technologies whilst being considerate of rising costs.

Remaining at the forefront

Traditional AI systems such as the ever-popular Chat GPT, whilst powerful, are often limited to specific tasks and lack the adaptive intelligence required to manage the multifaceted challenges facing us. AGI, with its broader understanding and vast learning capabilities, presents a far more promising solution to a number of global challenges. Its ability to integrate and analyse vast amounts of data across various domains could drive unprecedented advancements in renewable energy management, smart grid technology, and sustainable urban planning. 

This will lead to a number of government-led investments in the form of grants and research funding to ensure that their country is at the forefront of AI technologies and able to not attract the world’s best and brightest, but avoid the risk of brain drain to other countries.

Keeping it open

The tech industry’s investment landscape is also evolving. The initial hype around the metaverse – a virtual reality space where users can interact with a computer-generated environment and other users – has begun to wane. Instead, there has been a noticeable shift towards AI. Mark Zuckerberg, a notable figure in this shift, has significantly redirected Meta’s focus from the metaverse towards AI. He is now investing heavily in building sophisticated AI systems for the company. One of his key strategies involves making these AI systems open source. By doing so, he aims to accelerate innovation and collaboration within the AI community, fostering a more rapid progression towards AGI. 

Open source initiatives are crucial in this context because they democratise access to advanced AI tools and datasets, allowing researchers and developers worldwide to contribute to AGI’s development. This collaborative approach not only speeds up technological advancements but also ensures a diversity of thought and application. 

The move towards smart environments

Another critical driver pushing us closer to AGI is the move towards smart buildings and environments. As urbanisation intensifies, there is an growing need for intelligent infrastructure that can efficiently manage resources, enhance occupant comfort, and reduce environmental impact. Smart buildings leverage AI to optimise heating, ventilation, air conditioning, lighting, and security systems. Often in real-time with machine learning algorithms being applied to make these buildings even more intelligent moving forward. 

The complexity and interconnectedness of these systems require intelligence that goes beyond current AI capabilities. Luckily, AGI’s potential to learn and adapt autonomously makes it an ideal candidate for managing such smart environments and help them reach carbon targets quicker. It can seamlessly integrate various subsystems, anticipate, and respond to changing conditions, and make decisions that balance efficiency with sustainability.

Smart environments extend beyond individual buildings to encompass entire cities. The concept of smart cities, where data from a number of sources such as traffic patterns, energy usage, waste management, and public services are integrated and analysed, demands a level of intelligence that AGI could provide. Currently limitations in AI are a key reason why we have not seen a true data driven smart city appear yet. There is no doubt that such intelligent systems could significantly improve urban living conditions, making cities more sustainable and resilient in the face of climate change. 

The future is bright

The convergence of urgent environmental goals, shifting tech investments, and the rise of smart environments suggests that AGI might be closer than previously thought. The pressing need for carbon neutrality by 2050 is driving innovation in AI, while the pivot from the metaverse to more immediate AI applications indicates a strategic realignment of resources towards technologies with tangible benefits. Mark Zuckerberg’s investment in open-source AI systems exemplifies the collaborative spirit needed to achieve AGI and is removing many of the current barriers to entry. By harnessing the collective intelligence of the global research community, we are likely to see rapid advancements in AI capabilities that far eclipse the narrow-focussed AI that we know of today. The future is bright.

AI expert Stuart Fenton

Stuart Fenton is a seasoned expert in the field of artificial intelligence (AI), with a rich history of leadership roles across academia, government, and private sectors. Most recently the managing director of the Smart Wireless Innovation Facility (SWIFt) at Nottingham Trent University, where he honed his expertise in the practical applications of AI technology and its real-world use cases.
Currently a distinguished member of the UK Telecom Innovation Network's Artificial Intelligence Expert Working Group, Fenton has also been the AI specialist for the Department for International Trade Smart Cities and Technology team. Where he contributed to assisting innovative companies to export their services internationally as well as working with businesses around the world interested in moving to the UK.
Prior to this, Fenton had extensive experience in the telecoms, finance and public sector.

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