Kuwait initiated a major crackdown on crypto mining, citing growing strains on the electric grid.

Kuwait shuts down on crypto miners as nations struggles with blackouts

## What is AGI?
_When the lines blur between man and machine, you &#8216;re looking at artificial 
general intelligence (AGI). Unlike its counterpart, artificial narrow 
intelligence (ANI), which is the use of AI for solving individual problem 
statements, AGI represents artificial intelligence that can understand, learn 
and apply knowledge in a way that is indistinguishable from human cognition._
AGI is still theoretical, but the prospect of artificial intelligence being 
able to holistically replace human input and judgment has naturally attracted 
plenty of interest, with researchers, technologists and academics alike 
seeking to bring the concept of AGI to reality.
Yet another strand of prevailing research seeks to explore the feasibility and 
implications of AGI vs. ANI in a world increasingly shaped by AI capabilities.
Indeed, while ANI has already transformed various industries, AGI&#8217;s potential 
goes far beyond. Imagine a world where machines can not only assist humans in 
their tasks but also proactively understand the drivers behind specific tasks, 
predict outcomes, and autonomously create innovative solutions to achieve 
optimal results. This paradigm shift could revolutionize healthcare, 
education, transportation and countless other fields.
## Why is AGI so powerful?
_Unlike ANI, AGI is not confined to pre-programmed tasks or predefined 
responses within a limited domain. Instead, it has the potential to generate 
and apply knowledge across various contexts._
Imagine a self-driving car powered by AGI. It can collect a passenger from a 
train station but also personalize the journey with custom recommendations for 
pit stops, sightseeing avenues or navigating unfamiliar roads to arrive at the 
desired destination. And because it&#8217;s a machine, AGI would not experience 
fatigue and would continue learning and improving at exponential speeds.
Here&#8217;s a definition of AGI by Vitalik Buterin, who highlights the sheer 
potential of AGI:
The example highlights some interesting features of AGI, which include:
 * **Learning capability:** AGI can learn from experiences and improve its performance over time without a concerted effort by human programmers to perform additional data set training. This learning is not limited to specific tasks and instead encompasses a broad spectrum of activities. 
 * **Problem-solving skills:** AGI can solve complex problems by applying logical reasoning just as a human would. This includes consideration of non-traditional variables, such as emotional impact, which can highlight an even wider range of potential outcomes. 
 * **Adaptability:** AGI can adjust to new situations and environments without explicit programming, which means it can thrive in dynamic and unpredictable settings. 
 * **Understanding and interpretation:** AGI is equipped to comprehend natural language, abstract concepts and emotional nuance, allowing for sophisticated human-machine interactions.
_**Did you know?** Blockchain timestamps could serve as a legal memory for AGI 
systems, allowing future audits to determine exactly what an AGI knew &#8212; and 
when._
## The pursuit of AGI: Where does it stand as of April 2025?
_AGI is currently the science-fiction version of AI. However, while still 
theoretical, the sheer potential of the concept makes AGI the science fiction 
equivalent of artificial intelligence. _
While existing models, such as ChatGPT, are constantly evolving and improving 
with each day, the journey to bringing AGI to life involves overcoming 
significant technical challenges, such as:
 * **Defining the tech stack:** The purely hypothetical nature of AGI makes it exceedingly difficult, if not altogether impossible, to determine the precise nature of the technological stack required for practical implementation. 
 * **Neural networks:** Advances in deep learning have propelled this field forward, but AGI would also require specialist neural networks that mimic the human brain&#8217;s structure to process information and introduce a layer of emotion and nuance. 
 * **Natural language processing (NLP):** Significant advances are required in the field of NLP to enable machines to better understand and generate human language, incorporating nuance, emotion and complexities. This includes a more complex analysis of language syntax, semantics and context, which is still evolving in traditional machine learning models that leverage NLP. 
 * **Reinforcement learning:** Using reward-based mechanisms to teach machines to make decisions would allow AGI to learn optimal behaviors through trial and error.
Despite advancements, creating AGI that can truly think like a human remains 
an elusive goal.
_**Did you know?** DeepMind warns that not all AI risks come from the machines 
themselves &#8212; some start with humans misusing them. In its paper titled &#8216;An 
Approach to Technical AGI Safety and Security&#8217;, DeepMind identifies four key 
threats: misuse (bad actors using AI for harm), misalignment (AI knowingly 
going against its developer&#8217;s intent), mistakes (AI causes harm without 
realizing it), and structural risks (failures that emerge from complex 
interactions between people, organizations, or systems)._
## Can AGI think like a human?
_The question of whether AGI can think like a human delves into the very core 
of human cognition. Human thinking is characterized by consciousness, 
emotional depth, creativity and subjectivity. While AGI can simulate certain 
aspects of human thought, replicating the full spectrum of human cognition is 
a formidable challenge._
Several dimensions of human cognition are particularly difficult to emulate:
 * **Consciousness and self-awareness:** One of the defining traits of human thinking is consciousness, the awareness of oneself and one&#8217;s surroundings. AGI, as sophisticated as it may become, lacks the intrinsic human ability to introspect. AGI operates on an underlying set of algorithms and complex, learned patterns, without any subjectivity or genuine emotion. 
 * **Emotional intelligence:** Humans experience a wide range of emotions that influence their decisions, behaviors and interactions. While AGI can be trained to recognize and respond to such emotions, the lack of genuine emotional experience means that it cannot wholly replicate these emotions. Emotional intelligence in humans involves empathy, compassion and moral considerations, elements that are challenging to encode into machines. 
 * **Creativity and innovation:** Creativity involves generating novel ideas and solutions, often through intuitive leaps and imaginative thinking. AGI can mimic creativity by combining existing knowledge in new ways, but it lacks the intrinsic motivation and subjective insight that drive human innovation. True creativity stems from emotional experiences, personal reflections and cultural contexts, which AGI cannot authentically replicate.
## Key benefits of AGI
_The litmus test for AGI lies in its ability to holistically replicate a human 
experience. When realized, the potential benefits are enormous and stretch 
across various industries, spawning various aspects of daily life._
Despite its limitations, AGI is increasingly viewed as a force for good across 
a range of industries, including:
 * **Healthcare:** AGI can assist in diagnosing diseases, developing personalized treatment plans and predicting customized health outcomes, leveraging a vast body of underlying training data. 
 * **Education:** It can provide customized learning experiences, tutoring and academic research support. AGI can adapt to individual learning styles and pace, enhancing educational outcomes. 
 * **Economics:** It can optimize financial models, predict market trends, and enhance productivity. It can analyze economic data to forecast market trends and guide investment decisions. 
 * **Environmental Science:** AGI can analyze climate data, model ecological impacts, and propose sustainable solutions.
Additionally, AGI&#8217;s potential extends to areas such as transportation, 
communication and entertainment, offering new frontiers for innovation.
_**Did you know?** Some futurists believe AGI systems could eventually 
negotiate with each other autonomously using blockchain-based smart contracts 
&#8212; forming agreements, trading data or even co-developing solutions without 
human intervention._
## Ethical and societal considerations
_The rise of AGI raises significant ethical and societal questions. _
While powerful, AGI requires careful consideration for safe usage, which has 
prompted the creation of nonprofit societies, such as the AGI Society, as 
shown in the image below.
Fundamentally, it is crucial to address concerns such as:
 * **Safety:** Ensuring AGI operates within safe and controlled parameters to prevent unintended consequences. This includes robust testing and the introduction of regulatory frameworks to oversee AGI deployment. 
 * **Privacy:** Protecting personal data from misuse by AGI systems. As AGI can process vast amounts of data, safeguarding privacy is paramount. 
 * **Bias and fairness:**Preventing discriminatory practices and ensuring equitable access to AGI benefits. Developers must ensure that AGI systems are free from biases that could lead to unfair treatment. 
 * **Employment:** Addressing the impact of AGI on job displacement and workforce dynamics. As AGI automates tasks, there is a need to consider its impact on employment and provide support for affected workers.
The integration of AGI into society requires a thoughtful approach to its 
governance, ensuring that it serves the common good and respects social 
values.
## Can blockchain power AGI?
_AGI could create computers as smart as humans, revolutionizing fields 
likecryptocurrency trading or market analysis. But AGI needs trust and 
fairness to work for everyone. Blockchain, the tech behind Bitcoin and 
Ethereum, offers a secure, transparent way to make this happen. _
Here&#8217;s how blockchain can supercharge AGI with crypto-inspired solutions:
 * **Clear training records:** Blockchain works like Bitcoin&#8217;s open transaction log, recording every piece of data (e.g., crypto trading patterns) used to train AGI. This helps ensure the system is fair and free from hidden biases. 
 * **Shared decision-making:** Similar to Ethereum&#8217;s smart contracts, blockchain will allow developers, traders and users to vote on AGI&#8217;s rules, ensuring no single company controls it. 
 * **Safe data sharing:** Like crypto wallets safeguarding funds, blockchain could protect sensitive data from crypto exchanges, allowing secure sharing for AGI training without leaks. 
 * **Rewards for fairness:** Developers who build unbiased AGI, such as accurate trading predictors, could earn digital tokens, just like crypto mining rewards, encouraging ethical work.
However, ongoing challenges such as blockchain&#8217;s slow speed, delays in crypto 
transactions and limited storage capacity could make it hard for AGI to 
process data quickly or handle large datasets.
To make blockchain AGI-ready, researchers are already exploring:
 * **Offchain storage:** Decentralized systems like InterPlanetary File System (IPFS) are used to store large files offchain, while the blockchain keeps only verifiable hashes, reducing congestion. 
 * **Sharding and danksharding:** Like Ethereum&#8217;s scalability upgrades, sharding splits data across multiple nodes, allowing AGI to process more information without slowing down the network. Also, danksharding, an advanced form of sharding being developed for Ethereum, combines rollups and data availability sampling to scale data access efficiently &#8212; ideal for real-time AGI applications. 
 * **Data pruning:** Advanced blockchain models like Decentralized Artificial Intelligent Blockchain-based Computing Network (DAIBCN) prune old or irrelevant data, keeping the system lean and optimized for high-demand tasks like AGI. DAIBCN also enables secure, distributed AI computing &#8212; blending blockchain trust with AI performance.
## The future of AGI
_Artificial general intelligence represents the pinnacle of AI development, 
promising capabilities that rival human intellect. _
While AGI can simulate aspects of human thinking, achieving true human-like 
cognition remains a distant goal. Consciousness, emotional depth and 
creativity are intrinsic to human experience and pose significant challenges 
for AGI.
Nevertheless, the pursuit of AGI continues to drive innovation and reshape our 
understanding of intelligence. As we advance toward this frontier, it is 
imperative to navigate ethical considerations and societal impacts to 
responsibly harness AGI&#8217;s potential.
Ongoing research, identifying practical opportunities and technical 
requirements, and initiating dialogue across society are all essential steps 
to address the challenges and opportunities posed by AGI.
The future of AGI holds promise, but it requires a balanced approach to ensure 
that its eventual integration into society enhances human well-being and 
respects ethical standards.

Artificial general intelligence (AGI): Can it really think like a human?

Ripple moved 500 million XRP, valued at around $1.1 billion, sparking fresh 
speculation across the crypto space. The transfer, flagged by blockchain 
monitoring service Whale Alert on May 2, initially sparked speculation of a 
large-scale dump. On-chain data, however, indicates…

Ripple moves 500M XRP worth $1.1B as market eyes $2.30 resistance

HAEDAL surged by as much as 50% today ahead of its listing on South Korea’s 
Bithumb exchange. According to CoinGecko data, the liquid staking protocol 
Haedal (HAEDAL) rallied to an intraday high of $0.174 during Asia morning 
hours on May…

OKX Ventures-backed Haedal Protocol rallies 50% ahead of Bithumb listing

Bubblemaps has visualized the token distribution for Boop crypto, a fairly new 
token that stems from the new Solana-based meme coin launchpad Boop.fun. BOOP 
has gone up more than 200%. On May 2, the on-chain analysis platform 
Bubblemaps showed traders…

Boop crypto rises more than 200% after Bubblemaps reveals visualization

The on-chain analytics firm Glassnode has revealed in a report how sell-side 
pressure may see an uptick from Bitcoin long-term holders around this price 
level. Bitcoin Long-Term Holder Profit Will Hit 350% At 99,900 In its latest 
weekly report, Glassnode has talked about the latest trend related to the 
Bitcoin long-term holders. The &#8220;long-term holders&#8221; (LTHs) refer to the Bitcoin 
investors who have been holding onto their coins since more than 155 days ago. 
The LTHs make up for one of the two main BTC holder cohorts divided on the 
basis of holding time, with the other side being known as the &#8220;short-term 
holders&#8221; (STHs). Related Reading: Bitcoin Sentiment Nearly Back To 
Neutral—Green Sign For Rally? Statistically, the longer an investor holds onto 
their coins, the less likely they become to sell them at any point, so the 
LTHs with their long holding times can be considered more resolute than the 
STHs. Below is the chart shared by the analytics firm that shows how the total 
supply held by these HODLers has changed during the last couple of years. As 
is visible in the graph, the Bitcoin LTH Supply has witnessed an increase over 
the past two months or so, a sign that some STHs have been maturing into the 
cohort. In total, the metric has gone up by 254,000 BTC during this period. 
While the LTHs tend to be resolute entities, it&#8217;s not as if they never sell at 
all. From the chart, it&#8217;s apparent that these diamond hands took profits 
during both the major rallies from last year. Recently, BTC has once again 
been trying to put together bullish momentum, but the continued uptrend in the 
LTH Supply implies these HODLers haven&#8217;t decided to sell yet. As for when this 
cohort would become enticed enough to sell, perhaps history can provide a hint 
or two. In the chart, Glassnode has attached the data for the average cost 
basis or Realized Price of the LTHs and some price lines that correspond to a 
specific degree of profit/loss for this cohort. At present, the BTC price is 
far above the LTH Realized Price, so the members of this group would be 
carrying a significant amount of profit. Despite this fact, these HODLers 
don&#8217;t seem to be willing to part with their coins just yet. As for when their 
tune might change, the report has explained: Historically speaking, the Long- 
Term Holder cohort typically ramps up their spending pressure when the average 
member is holding a +350% unrealized profit margin. Related Reading: Old 
Bitcoin Whales Resurface With $760M Move—Brace For Impact? Based on their 
current cost basis, the LTHs can be expected to reach this 350% profit 
threshold when Bitcoin hits the $99,900 mark. Thus, if BTC&#8217;s recovery run 
continues this far, it&#8217;s possible that the cryptocurrency might find some 
trouble due to the HODLer selling. BTC Price At the time of writing, Bitcoin 
is trading around $96,500, up around 4% in the last seven days. Featured image 
from Dall-E, Glassnode.com, chart from TradingView.com