Artificial Intelligence (AI) has, over the past few years, transformed the world as we know it; a recent survey by Forbes Advisor revealed that 56% of the interviewed companies are already using AI in customer service, while 51% have deployed AI technology for fraud management and cybersecurity.

Even more intriguing, we’re now seeing the integration of AI with knowledge-focused efforts in academia and research fields. Gone are the days when one would peruse hundreds of papers or run several medical trials with little to no precise outcome. With AI now in the picture, researchers and academics are doing less heavy lifting than before.

But like any nascent innovation, there are still some hurdles; AI, in its current state, is mostly centralized, which introduces the danger of biased research findings. The computational cost of training and running AI programs is also through the roof, which explains why only big tech, ‘the magnificent seven,’ is able to keep up.

In the next section of this article, we will highlight how AI is being integrated with academia and research, the hurdles, and what the future holds. The piece will also feature the rise of decentralized AI solutions, such as the blockchain-built Aigarth, and the potential to solve some of the existing challenges.

How AI Is Advancing Academia & Research Labs

If you’ve done a research paper or any form of academic research, you probably understand how stressful the process can be. That’s no longer entirely the case, at least for AI-savvy academics who know how to leverage the existing AI applications.

There are several applications that have been launched to make academic research a more seamless undertaking.

Data Analysis and Visualization: Advanced AI models such as GPT-4 have proven to be equal, if not better, than traditional machine learning models. This LLM model revolutionizes how researchers and academia can process big data, allowing even those with minimal data science knowledge to work on complex data sets and efficiently identify any correlations.

Literature Review: This tedious research phase is where most people tend to give up. Today, there are AI applications such as Jenni, SciSpace, and Elicit, which are designed to leverage Natural Language Processing (NLP) to make the process much easier. Instead of going through tons of papers, these applications can produce summaries, identify related research, or generate an initial framework to approach the research.

Medical Research: Besides academia, medical research labs are also tapping into the power of AI. Some of the key areas where AI is being used include clinical trials, drug discovery, diagnostics, and precision medicine. A good example of AI being used in medical research is in epidemiology where the University of Southern California Viterbi School of Engineering developed a predictive model that can slow the spread of communicable diseases.

The Challenge in AI Adoption

As mentioned in the introduction, current AI advancements are not foolproof. There are quite a number of issues that are yet to be tackled for everyone to be comfortable with integrating AI innovations. Some of the pertinent challenges include the lack of clear regulations, privacy breaching in AI training, and the cost of computation.

For context, it would cost a whopping $10,000 to acquire Nvidia’s A100, which is currently one of the most efficient GPUs for artificial neural network (ANN) training. Alternatively, one would have to rent this type of hardware for $3.06 on AWS or $1.14 on Google.

It can also be argued that AI applications could be biased to some extent given that the training data is obtained from centralized databases. What this means is that malicious parties can easily compromise research outcomes without anyone being able to audit them and ultimately push their agenda.

AI Meets Blockchain Technology

The future of AI is bright; fundamentals keep on improving, and more capital is finding its way into this burgeoning market.

But what’s more remarkable is the intersection of AI with blockchain technology, which solves some of the current challenges, including transparency and computational cost. The Qubic L1 blockchain is one example of a project that’s using the power of blockchain technology to enhance the state of AI innovation.

This Layer 1 blockchain is host to the Aigarth software; one of the few AI projects currently building towards singularity (self-improving artificial neural networks – ANN). Aigarth’s AI ecosystem will leverage extra computational power generated from Qubic’s Useful Proof of Work (uPoW) consensus and the blockchain’s public ledger. This decentralized approach reduces the cost of training ANNs while also enhancing transparency in AI training.

With such integrations, it is likely that the AI industry will gravitate towards an ecosystem where innovation is not restricted to big tech and early entrants such as OpenAI, which is currently the case. Instead, the decentralized and permissionless nature of blockchain networks opens room for a more collaborative environment. This is especially important in the research realm, which has long been a focal pillar of advancements in the human race.

Moreover, decentralized networks reduce the risk of single points of failure, making it harder for malicious actors to manipulate data or algorithms. The transparency provided by blockchain also means that the development and deployment of AI research models can be scrutinized and audited more effectively, promoting ethical practices and trust, which is fundamental in academia or any other form of research.

Conclusion

It’s now been over a year and a half since ChatGPT launched; the most recent stats show that this AI software enjoys close to 200 million monthly users. This figure is indicative of how fast the world is adopting AI but, at the same time, highlights the risk of centralization.

On the brighter side, AI innovations do not have to function in solitude. Combining their transformative power with other 4IR technologies like blockchain could be the much-needed strategy to unlock more trustworthy and AI use cases.

It had to happen.  As society has become more and more intertwined with social media, and finance has become more and more tech-savvy, the intersection of pop culture and finance was bound to intersect.  Instead of a nice synergy of two ideas, it had more the feel of a head on collision, and meme coins were born.  While the first meme coin of large-scale fame is Dogecoin (DOGE), many have followed in its wake.  In the case of Dogecoin, what first started as a joke to make crypto sound ridiculous turned into a cult following, which turned into the same heartfelt community that raised funds so the Jamaican bobsled team could attend the 2014 Olympics.  From this surprise success, many others have followed, with the trend even backflowing into traditional finance with the “meme stock” that was Gamestop. But why have meme coins taken on a life of their own, why have they created such controversy, and what role can they play in legitimate finance? Let’s find out.

Why Are Meme Coins Part of Today’s Society?

The idea of a meme coin is indeed preposterous, and at the same time more honest than many financial instruments and collectibles. To create value out of what is essentially a meme, or a short lived joke, is similar to any other fad.  The Tickle-Me-Elmo’s, the POG’s, the fast fashion trends. All of these are money making ideas born out of a spike in popular interest, and nothing more. What is interesting about meme coins is the intersection with legitimate financial tools. The world of cryptocurrency has democratized our ability to make a digital, fungible currency with a set mint volume and circulation. Instead of someone having to quickly design and mass produce a product that leverages a fast moving meme, team’s can make a meme coin overnight to quickly capitalize on its popularity, and at very little cost if the team has the right talent. At the other end of this trend is the element of collectables. Meme coins can act as a type of currency, yes, but they also behave as a limited edition collectible, and this is what can help to drive prices up, especially in the short term as popularity rises. Everybody wants to jump in, so prices on limited supplies go up. However, it’s what the development team does after that can determine whether that stock crashes and disappears forever, or stabilizes and moves past a meme to become a legitimate cryptocurrency with a supportive, long-term community. This is perhaps the most frustrating part of meme coins in today’s environment. Teams with ill-intent could purposefully launch a coin for a pump-and-dump scheme, which will inevitably hurt those who want to be involved. However, in addition to being bad form and smearing the crypto industry, more and more teams are seeing that this is a short sighted play for meme coins because they have a place in the longer life cycle of the crypto markets. With proper TGE guidance and MEV market making tools (organizations like Peanut Trade specialize in these services, even for meme coins), meme coins can launch for long term success.

Scams and Scandals

Meme coins have long been, and still are, a source of speculative investing and potential pump-and-dump scheming when they launch. There are a number of signs to watch out for to determine if a meme coin is a scam, though there is no guarantee either way. Coins with little community presence that spike suddenly in popularity, especially from a few influencers, may be on their way up just to crash soon after. Market volume and liquidity irregularity are bad signs, as is a lack of transparency and online presence of the development team itself. Whitepapers and detailed roadmaps, on the other hand, show a purpose for the coin and indicate a longer term plan. Still, with any meme coin as with any investment, you never put in more than you are willing to lose.

Strategies for Platforms and Investors Align

For development teams who are aiming to launch a legitimate meme coin, you need to be aware that there are predators looking to take advantage of your token launch even if you have the best intentions. Being able to combat those looking to artificially spike prices when the coin is launched in order to dump it is critical. Teams must have tools in place to safeguard liquidity from third-party arbitrage bots, but must design the tokenomics and smart contracts to be sturdy and long-term focused from the beginning. As mentioned above, there are organizations like Peanut Trade who offer services from start to finish on guiding this process, and teams may find a benefit if they are looking to build a longer term community focused on stability and a passive profit for their project’s treasury.

Successfully long-term launching any token, but especially a meme coin, takes strategy and effort long before the launch, but also requires a strong hand (and automated tools) on the day of launch, with management tools working to keep things smooth until the coin can stabilize on its own. Once it does, the few that are well managed can last for years and grow their communities into global forces.

This is the oddity that is our modern world. The ability to create your own cryptocurrency, where things like Dogecoin started as a joke and became a wealth fund of billions, where a borderless community across the globe can band together around any idea they’d like and develop something together for the long term. It’s a strange world, but it’s pretty amazing.