One of the key aspects to consider is the training process of AI models, which largely determines the quality and reliability of their responses.
In this article, we will analyze the current problems faced by generative artificial intelligence systems and see how a careful and methodical approach to training can be the key to ensuring that their results are accurate, relevant, and ethical.
Óscar Barba
Co-founder & CTO of Coinscrap Finance
The difficulties of artificial intelligence in accessing unlimited data
One of the main problems of generative AI is the imminent shortage of data available for training. Experts warn that models like ChatGPT and Gemini will run out of the necessary information for their development in a matter of time.
The reason is simple: the exponential growth of AI has generated an unprecedented demand for digital information to feed on. Companies like OpenAI, Google, and Meta, leaders in this field, face the problems of nurturing their generative AI models. This could drastically slow down their progress.
According to The New York Times, when one of these tech giants ran out of information to train with, they created a voice recognition tool and transcribed no less than a million hours of YouTube videos, clearly violating the platform’s rules.
💡 Did you know that…
Before Chat GPT, there was a chess game in 1997 that was considered the beginning of artificial intelligence.
The importance of responsible training of generative AI
Given this scenario, it is more relevant than ever to pay special attention to how artificial intelligence models are trained. Here are some aspects to consider when developing a responsible and methodical approach to ensure quality responses:
Diversity and representativeness of data
One of the pillars of responsible training is to ensure that the data used is diverse and representative of reality. This involves including a wide range of perspectives, experiences, and contexts, avoiding biases and limitations that may be reflected in the system’s outputs.
Continuous validation and checking
Moreover, it is crucial to implement rigorous validation processes and continuously monitor the generated responses. This allows for the detection and correction of AI errors, as well as unwanted inconsistencies or trends as the model evolves.
Incorporation of ethics into artificial intelligence
Another fundamental aspect is the integration of ethical principles into the training of generative AI.
This involves establishing red flags and protection mechanisms to prevent systems from generating harmful, discriminatory content or infringing on human rights.
Innovative approaches to AI training
To address these and other challenges, artificial intelligence experts are exploring various strategies for training models. Given the limited human capacity to generate content, other options are being considered to help “feed the beast.”
Continuous and adaptive learning
One of these proposals is the development of continuous and adaptive learning systems, capable of updating and improving their capabilities as they interact with new data and users.
This would allow the relevance and accuracy of the generated answers to be maintained, solving some of the problems of generative AI.
Use of synthetic data
Another alternative is the generation of artificial information created specifically to complement and enrich existing data sets. This means that the AI itself, with its knowledge, generates more information as it learns.
Collaboration and transparency
Additionally, there is a need for greater collaboration and transparency among organizations developing generative AI. Sharing knowledge, best practices, and resources can help improve training processes and ensure more robust and reliable models.
The role of COCO in responsible AI training
At Coinscrap Finance, we have developed our own artificial intelligence engine, COCO, which stands out for its high precision in analyzing, categorizing, and enriching bank transactional data. This achievement is largely due to our rigorous and responsible approach to model training.
Data quality
One of COCO’s pillars is the careful selection and cleansing of training data. We ensure a wide variety of sources, covering different user profiles, which translates into greater accuracy and relevance of the generated categories and enrichments.
Continuous improvement
COCO’s outputs undergo a permanent validation process that allows us to detect anomalies or unwanted trends. In this way, we correct and improve the model quickly. This ensures that our AI engine remains updated and aligned with our customers’ needs.
Importance of security
When handling banking data, at Coinscrap Finance we maintain extreme security protocols.
Of course, the entities we work with, leaders in their sector, impose very high demands on us in this aspect, but we ensure to add additional layers of encryption. Data security is the most important thing for us.
Use of AI in banking: An intermediate layer between the user and generative artificial intelligence
Our role as technology providers is to offer a deep level of knowledge to banking customers. I like to think that we are that intermediate layer between the bank and GenAI. A preliminary step that enriches the information the entity has available and nourishes its relationship with digital banking users.
Due to our tools, the financial sector is able to offer its customer base curated and structured information that helps them make the best economic decisions. With this data, banks are able to gain valuable insights and offer recommendations.
Increasing customer engagement and loyalty of financial entities with AI
Thanks to this responsible and rigorous approach in COCO’s training, financial entities receive very positive feedback from their customers, who are more satisfied with the experience provided by their digital platforms. They feel that their individual needs are being addressed and that they are being listened to.
Moreover, consumers are also more open to receiving advice when they perceive that their bank cares about their well-being, which means greater recurrence and increased use time of electronic banking. As Pronix Inc. indicated in a recent report, 72% of banking users rate personalization as “crucial.”
People expect any interaction to consider a complete historical context, which includes their identity, specific needs, and transaction history with the company. These are data that banks have at their disposal, and it is necessary to take full advantage of them.
A final reflection on the problems of generative AI
The emergence of GenAI is causing great controversy across all sectors, from the scientific community, industry and citizens, to regulators. Misuse of these tools can be fatal.
However, if factors such as data supervision and continuous validation are taken into account, it is possible to ensure that the generated responses are of high quality, reliable, and aligned with the values that one wishes to convey.
At Coinscrap Finance, we are aware of this and design our intelligent tools following these premises. We are convinced that, thanks to our innovative and responsible approach, the financial sector has a more transparent conversation with its community and is able to help its users by offering valuable services for their daily lives.
About the Autor
Óscar Barba is co-founder and CTO of Coinscrap Finance. He is an expert Scrum Manager with more than 6 years of experience in the collection and semantic analysis of data in the financial sector, classification of bank transactions, deep learning applied to stock market sentiment analysis systems and the measurement of the carbon footprint associated with transactional data.
With extensive experience in the banking and insurance sector, Óscar is finishing his PhD in Information Technology right now. He is an Engineer and Master in Computer Engineering from the University of Vigo and Master in Electronic Commerce from the University of Salamanca. In addition, Scrum Manager and Project Management Certificate from the CNTG, SOA Architecture and Web Services Certificate from the University of Salamanca. He recently obtained the ITIL Fundamentals certification, a recognition of good practices in IT service management.
