Brain-machine interfaces are devices that enable direct communication between a brain’s electrical activity and an external device such as a computer or a robotic limb that allows people to control machines using their thoughts.
In the vast and ever-evolving landscape of technology, neuromorphic computing emerges as a groundbreaking frontier, reminiscent of uncharted territories awaiting exploration. This novel approach to computation, inspired by the intricate workings of the human brain, offers a path to traverse the complex terrains of artificial intelligence (AI) and advanced data processing with unprecedented efficiency and agility.
Neuromorphic computing, at its core, is an endeavor to mirror the human brain’s architecture and functionality within the realm of computer engineering. It represents a significant shift from traditional computing methods, charting a course towards a future where machines not only compute but also learn and adapt in ways that are strikingly similar to the human brain. This technology deploys artificial neurons and synapses, creating networks that process information in a manner akin to our cognitive processes. The ultimate objective is to develop systems capable of sophisticated tasks, with the agility and energy efficiency that our brain exemplifies.
The genesis of neuromorphic computing can be traced back to the late 20th century, rooted in the pioneering work of researchers who sought to bridge the gap between biological brain functions and electronic computing. The concept gained momentum in the 1980s, driven by the vision of Carver Mead, a physicist who proposed the use of analog circuits to mimic neural processes. Since then, the field has evolved, fueled by advancements in neuroscience and technology, growing from a theoretical concept to a tangible reality with vast potential.
These businesses are building tech that could exceed the abilities of today’s AI.
The field of artificial intelligence is still in its early years, yet several businesses are already working on technology that can become the foundation for AI’s future. These companies are developing quantum computing systems capable of processing mountains of data in seconds, which would take decades for a conventional computer.
Quantum machines can execute multiple computations simultaneously, accelerating processing time, while typical computers must process data in a linear fashion. This means quantum systems can evolve AI beyond the abilities of the most powerful supercomputers, enabling AI to drive cars and help find cures to diseases.
China’s first major sodium-ion battery energy storage station is now online, according to state-owned utility China Southern Power Grid Energy Storage.
The Fulin Sodium-ion Battery Energy Storage Station entered operation on May 11 in Nanning, the capital of the Guangxi Zhuang autonomous region in southern China. Its initial storage capacity is said to be 10 megawatt hours (MWh). Once fully developed, the Station is expected to reach a total capacity of 100 MWh.
The state utility says the 10 MWh sodium-ion battery energy storage station uses 210 Ah sodium-ion battery cells that charge to 90% in a mindblowing 12 minutes. The system comprises 22,000 cells.
Chatbots share limited information, reinforce ideologies, and, as a result, can lead to more polarized thinking when it comes to controversial issues, according to new Johns Hopkins University–led research.
Intel’s next-generation Falcon Shores GPU is going to be a power-hungry beast as revealed to Computerbase during ISC 24.
Intel Falcon Shores GPUs Are Arriving In 2025 & Will Feature The Most Power-Hungry Design In The AI Accelerator Race
During ISC 24, Intel and its partners happened to have teased the power consumption figures for the upcoming Falcon Shores GPUs which will be the follow-up to Gaudi 3. While the Gaudi line of accelerators has been dedicated to the AI segment, Intel seems to have taken a step back with its standard HPC & AI GPU offerings. Recently, we reported how Intel has ended the deployment of its first true HPC GPU, Ponte Vecchio.
The transcript features an interview with renowned science fiction author Isaac Asimov, discussing his predictions and visions for the future of space exploration, computers, robotics, and humanity’s role in shaping that future. It touches on concepts like permanent space settlements, harnessing solar power, the increasing importance of computers and AI, the impacts of robotics on jobs, and taking an optimistic yet cautionary view of technological progress. It also covers some earlier inaccurate and exaggerated predictions about robots replacing humans, as well as actual technological developments in 1982 like artificial hearts and fusion reactors. The overall theme is Asimov’s hopeful but measured outlook on future scientific and technological advancements.
Since the release of ChatGPT in November 2022, artificial intelligence (AI) has both entered the common lexicon and sparked substantial public intertest. A blunt yet clear example of this transition is the drastic increase in worldwide Google searches for ‘AI’ from late 2022, which reached a record high in February 2024.
You would therefore be forgiven for thinking that AI is suddenly and only recently a ‘big thing.’ Yet, the current hype was preceded by a decades-long history of AI research, a field of academic study which is widely considered to have been founded at the 1956 Dartmouth Summer Research Project on Artificial Intelligence.1 Since its beginning, a meandering trajectory of technical successes and ‘AI winters’ subsequently unfolded, which eventually led to the large language models (LLMs) that have nudged AI into today’s public conscience.
Alongside those who aim to develop transformational AI as quickly as possible – the so-called ‘Effective Accelerationism’ movement, or ‘e/acc’ – exist a smaller and often ridiculed group of scientists and philosophers who call attention to the inherent profound dangers of advanced AI – the ‘decels’ and ‘doomers.’2 One of the most prominent concerned figures is Nick Bostrom, the Oxford philosopher whose wide-ranging works include studies of the ethics of human enhancement,3 anthropic reasoning,4 the simulation argument,5 and existential risk.6 I first read his 2014 book Superintelligence: Paths, Dangers, Strategies7 five years ago, which convinced me that the risks which would be posed to humanity by a highly capable AI system (a ‘superintelligence’) ought to be taken very seriously before such a system is brought into existence. These threats are of a different kind and scale to those posed by the AIs in existence today, including those developed for use in medicine and healthcare (such as the consequences of training set bias,8 uncertainties over clinical accountability, and problems regarding data privacy, transparency and explainability),9 and are of a truly existential nature. In light of the recent advancements in AI, I recently revisited the book to reconsider its arguments in the context of today’s digital technology landscape.
To be clear, humans are not the pinnacle of evolution. We are confronted with difficult choices and cannot sustain our current trajectory. No rational person can expect the human population to continue its parabolic growth of the last 200 years, along with an ever-increasing rate of natural resource extraction. This is socio-economically unsustainable. While space colonization might offer temporary relief, it won’t resolve the underlying issues. If we are to preserve our blue planet and ensure the survival and flourishing of our human-machine civilization, humans must merge with synthetic intelligence, transcend our biological limitations, and eventually evolve into superintelligent beings, independent of material substrates—advanced informational beings, or ‘infomorphs.’ In time, we will shed the human condition and upload humanity into a meticulously engineered inner cosmos of our own creation.
Much like the origin of the Universe, the nature of consciousness may appear to be a philosophical enigma that remains perpetually elusive within the current scientific paradigm. However, I emphasize the term “current.” These issues are not beyond the reach of alternative investigative methods, ones that the next scientific paradigm will inevitably incorporate with the arrival of Artificial Superintelligence.
The era of traditional, human-centric theoretical modeling and problem-solving—developing hypotheses, uncovering principles, and validating them through deduction, logic, and repeatable experimentation—may be nearing the end. A confluence of factors—Big Data, algorithms, and computational resources—are steering us towards a new type of discovery, one that transcends the limitations of human-like logic and decision-making— the one driven solely by AI superintelligence, nestled in quantum neo-empiricism and a fluidity of solutions. These novel scientific methodologies may encompass, but are not limited to, computing supercomplex abstractions, creating simulated realities, and manipulating matter-energy and the space-time continuum itself.
