What is going on in AI?

What is going with AI ? (2024)

Artificial Intelligence (AI) continues to evolve rapidly. Shaking industries with innovative technologies and methodologies.

However the main trend we can spot is a partial come back from hysteria to more realistic expectations (I say partial because we are not out of the water yet, but we can hear more and more voices that resonnate with the sound logic)

In the early stages of AI development, there was significant hype about its potential to revolutionize every aspect of life. Early successes with LLM like GPT and the likes gave an inflated sense of its capabilities.

However, practical challenges and setbacks, with mostly inaccuracies and hallucinations of these models, combined with difficulties in generalizing AI capabilities and managing data quality have tempered these expectations.

As AI systems have been deployed in more real-world applications, it has become evident that while AI excels in specific tasks (like image recognition or natural language processing), it struggles with generalizing across different domains without significant retraining and fine-tuning.

AI is now widely recognized for its effectiveness in automating routine tasks, enhancing data analysis, and improving decision-making processes, rather than being seen as a cure-all for every business problem.

Human-AI Collaboration: The shift is towards viewing AI as a tool that augments human capabilities rather than replacing them. This perspective emphasizes the strengths of both AI and human intelligence, leading to more effective and realistic integration of AI into various domains.

Generally, the evolution of expectations for AI reflects a maturation process, where initial over-optimism has given way to a more balanced and nuanced understanding of AI’s potential and limitations.

This shift is still ongoing as marketing efforts by some of the most power companies in the world are not helping to drive sanity in the board room but we can nonetheless see some more realistics considerations based on practical experiences that are more focused on a collaborative approach to human-AI interaction.

Now, let’s talk concretely about some the key points to pay attention to:

Smaller, More Efficient Models

The trend towards developing smaller and more efficient AI models is gaining momentum. This shift is driven by the rising costs of cloud computing and the need for more accessible hardware solutions.

Techniques like Low Rank Adaptation (LoRA) and quantization are enabling the fine-tuning of large models with significantly reduced computational resources.

This makes sophisticated AI capabilities more accessible to smaller organizations and startups.

Generative AI Applications

Generative AI continues to expand its influence across various sectors.

In the creative industry, generative AI tools are revolutionizing video production and special effects.

These advancements are also sparking debates about the ethical implications and potential job displacement in creative professions.

Actually generative AI is really the core driver of ethical challenges right now - both on the training data side of things (what data are you allowed to use and with whose consent?) and the end result side : what are you allowed to generate? (famouse actor voices, political or public persons, music creation, basically a lot of the old world who has been used by AI to demonstrate its potential is now hitting back in an attempt to stand its ground and curve the threat to their business model)

AI in Healthcare

AI’s role in healthcare is becoming more prominent, with significant investments aimed at improving patient care and diagnostic accuracy.

AI systems are being used for disease detection, personalized treatment plans, and enhancing patient communication.

For example, companies like Paige have developed AI tools that assist pathologists in identifying cancerous regions in tissue samples.

The potential could be huge, given that the cost of training healthcare workforce is a brake (one of many) to equal care for all.

The issue here are potential hallucination and the fact that you cannot yet 100% rely on AI but maybe it could help doctors to treat or diagnose people in less time.

AI in Education

The educational sector is leveraging AI to enhance learning experiences and administrative efficiency.

AI-powered tools like personalized tutors and educational content generation platforms are becoming commonplace.

These tools help tailor educational experiences to individual student needs, improving engagement and outcomes. Additionally, AI is being used to detect and prevent academic dishonesty, ensuring integrity in educational assessments.

This actually works really good for the point of view of delivering knowledge.

The pedagical, and human aspect of learning is still quite far from reach (and frankly, also far from reach for humans when we look at the current state of education)

AI for customer support

AI-driven chatbots and virtual assistants are increasingly used to handle routine inquiries and tasks.

These systems can provide instant responses to customer questions, guide users through troubleshooting processes, and even process transactions.

By automating repetitive tasks, AI frees human agents to focus on more complex issues.

AI significantly reduces response times by quickly analyzing queries and providing accurate answers.

This speed is crucial in customer support, where timely assistance can greatly enhance the customer experience.

For example, AI-powered systems can instantly retrieve information from a knowledge base to answer customer questions.

Multimodal and General-Purpose AI

Advancements in multimodal AI models, which can process and understand multiple types of data (text, images, videos), are paving the way for more versatile AI applications.

These models are being used in robotics to create general-purpose robots capable of performing a wide range of tasks, from household chores to complex industrial operations.

Personalization at Scale

AI is driving a shift towards personalized user experiences in various domains, including media, ecommerce, and customer service.

Advanced recommendation algorithms used by platforms like Netflix, Spotify, and TikTok are examples of how AI personalizes content to enhance user engagement.

This trend is expanding into other areas, enabling businesses to offer highly customized services at scale.

All in all, AI is still ongoing many changes and innovation so that’s what more a snapshot of the current state of affairs.

What is to remember is AI is useful took and can actually help with automation of many tasks, as long as it has some sort of supervision to ensure the end-result is correct.