Tech aficionados and industry professionals have watched AI with great interest for decades. Now that AI technology is growing at breakneck speed, it’s more crucial than ever that everyone appreciates its evolution and possibilities. In this article, we examine AI’s evolution and evaluate the challenges it faces, as well as the trends that lie ahead.
AI and Gen AI – how are they different?
Generative AI is, essentially, a sub-domain of AI. That said, whereas most AI is centered on the automation and optimization of various tasks, generative AI is all about producing objects. Classical AI tasks such as creating agents that converse or make decisions, smart automation, visual identification, processing, and translation, among many others, can be improved by GenAI. This facilitates the process of creating various digital assets. Because of this, incorporating generative AI into daily operations has become more smooth and empowering.
One might question what is the most common type of data generation. The truth is that it is not that simple. Multimodal models allow you to generate data based on very different inputs. This is why, even with available usage data, it would be difficult to understand what the most common type of generated data is. That said, as the current business needs suggest, large language models (LLMs) are likely the most popular. They can work with text but also numbers, and they can be used for answering questions, performing translation, generating reports, and so on.
Using LLMs: from history to present days
Large language models are a form of deep learning model. In general, LLMs have 8 to 70 billion parameters and have been trained on copious amounts of data. Take, for example, Crowl, one of the largest datasets: it includes web pages and data from the past 10 years, which hold dozens of data petabytes.
For example, the Titanic dataset (which contains about 900 samples that have information about the passengers who survived the disaster) is under 1 Mb. In turn, the model that achieves the highest prediction accuracy may have 25 to 100 parameters.
LLMs have a long history. The first GPT model came out in 2018. But even that was not the first time that we’ve seen text generation models in practice. By that time, we had already found ways to generate text using other methods, such as:
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Generative adversarial networks, where the generator learns by feedback from another network.
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Autoencoders, where the model tries to recreate the initial input.
Efficient vector word embeddings, such as word2vec, were introduced as early as 2013. Still, there were various other probabilistic and pattern-based generation attempts as far back as the previous century, including the Eliza chatbot in 1964.
The discussion about AI regulation
The modern AI community is split between those who call for AI regulations and control and those who think this isn't necessary. For instance, Yann LeCun, Facebook’s Chief AI, criticized AI agents, claiming that such tools are not even as smart as a dog.
Officials, too, have voiced concerns about regulating AI use. Earlier this year, Emmanuel Macron (the President of France) said that new European legislation designed to curb the development of artificial intelligence could disadvantage European tech companies compared with rivals in the US, UK, and China.
In opposition to this view are the AI regulation proponents. For instance, Elon Musk, CEO of Tesla, has called AI ‘our greatest existential threat’.
What is the EU Artificial Intelligence Act all about?
The EU Artificial Intelligence Act is one of the first major attempts to regulate the use of AI technologies. It’s designed to ensure that all AI systems used in the EU are safe and under human control. The use of AI technologies should not come at the expense of EU citizens’ privacy or protection against discrimination or other harm.
This isn’t simply about the occasional slap on the wrist in the form of fines for big tech when things go wrong. The Act categorizes AI systems according to their risk, and the greater the danger, the stricter the compliance that is required. High-risk categories include autonomous machines used in critical infrastructures, AI used in employment decisions, and AI-powered systems in ‘important’ private or public services that could compromise people’s safety or fundamental rights. Thus, the EU is creating a plan to develop more trustworthy technology. By establishing these laws, they are protecting people and opening a path to investment in AI.
Difficulties and issues in setting up and applying AI models
Deploying AI models comes with its fair share of challenges, from data collection to real-world application. Initially, the hurdle lies in acquiring vast, high-quality data sets that are both relevant and unbiased. This data must then be painstakingly prepared and processed, which isn’t a small feat. On the technical side, the infrastructure needs to be robust – handling and analyzing big data requires significant computing power, often necessitating sophisticated hardware that isn’t cheap or readily available.
Integrating these AI models into existing systems poses its own set of complications. It's not always straightforward and can require extensive customization to ensure compatibility and functionality. And once everything is up and running, there’s the continuous task of maintenance. AI models aren't set-and-forget; they need ongoing monitoring to learn effectively and adjust to new data or variables. Each of these steps requires time, expertise, and a good deal of patience, making the whole process quite demanding but crucial for those looking to leverage AI technology effectively.
How to use LLMs for better outcomes
Increasing the efficacy of LLMs isn’t just a matter of applying more computational power; it’s about smarter, more strategic use. Fine-tuning particular data sets can increase relevance and accuracy. Feedback loops with users can help improve the tone of their responses, making them not only smart but sensitive. Regular updates and training on new data keep models sharp and up to date with changing language practices. Finally, cross-industry collaboration means new applications of LLMs can emerge, showing that sometimes it really does take a village to raise a robot.
New developments and future plans in the field of LLMs
The field of large language models is buzzing with innovation. As technology advances, these models are not just getting bigger; they're becoming smarter and more nuanced in how they understand and generate human language. A big focus now is on making them more efficient and less resource-hungry, which is crucial if we don’t want to “fry” our planet while training a computer to write poetry. Another exciting path is enhancing their ability to better grasp context. With improvements in safety and ethical responses, the future LLMs are gearing up to be not only more reliable but also more in tune with human values and norms. All in all, we must all keep an eye on this field because LLMs are rapidly becoming essential solutions in our digital toolkit.