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.
DisClamier: This content is informational and should not be considered financial advice. The views expressed in this article may include the author's personal opinions and do not reflect The Crypto Basic opinion. Readers are encouraged to do thorough research before making any investment decisions. The Crypto Basic is not responsible for any financial losses.
