The Evolution of AI: What Does the Future Hold in the Next Two Years?

According to industry forecasts, the business value of AI is expected to reach a staggering $5.1 billion by 2025. This includes the value derived from AI applications, services, products, and the overall benefits that AI can bring to organizations in terms of cost savings, revenue generation, productivity enhancements, and innovation. However, we’re just scratching the surface of AI’s potential.

AI has undergone exponential evolution, transforming from a theoretical concept to a practical, impactful technology in a remarkably short time. Initially, AI was limited to rule-based systems and narrow applications. However, recent advancements in deep learning, neural networks, and big data analytics have propelled AI into new realms of capability.

Today, AI systems can process vast amounts of data, learn from patterns, make complex decisions, and even mimic human cognitive functions. As AI continues to evolve, its potential to drive innovation, improve efficiency, and solve complex problems only expands.

So, what does the future hold for AI? This blog will guide you through potential developments in AI tomorrow.

So, let’s dive right in.

AI at the Edge

Future AI systems won’t always need a cloud connection. They will live right on your devices.

Efforts are currently underway to optimize Large Language Models (LLMs), which are the foundational models behind systems like ChatGPT, so they can operate directly on our smartphones. The brilliant minds at Meta have put forward an idea—Mobile LLM or an Small Language Model (SLM). It’s a set of smaller, streamlined models designed specifically for on-device AI. The first of these models already set records for accuracy, surpassing similar models. The secret? It’s not just about the data and parameters; it’s about the model architecture.

What makes Mobile SLMs stand out? They offer advantages such as enhanced privacy, quicker response times, and reduced costs for cloud services.

But, like all things, deploying SLMs on mobile devices has its challenges: draining battery life, needing large storage space, higher latency, the necessity of model optimization, complex deployment and maintenance, and privacy concerns.

But here’s the exciting part. Research has proven that smarter, not bigger, models are the future. By tweaking the model’s architecture, we can significantly boost its accuracy. Methods like slim and deep architectures, sharing embeddings, and new techniques such as block-wise weight sharing can enhance accuracy without making the model bigger or slowing it down.

This means we can bring high-quality SLMs to a wide range of mobile devices for things like personal assistants and multi-party API integration.

Integrated AI

AI systems will not be limited to the cloud, laptops, or smartphones.

More devices will incorporate the technology, increasing inter-device communication and bio-device connections.

Improvements in mobile hardware and optimization techniques are opening the door for powerful AI models and LLMs to run directly on our smartphones and extended reality (XR) devices. The convergence of AI and XR technologies, like Qualcomm partnering with Meta to fine-tune Learning with Latent Multi-Attribute (LLaMA) models for XR devices, hints at a future where AI-powered virtual interactions become smoother and more widespread. Imagine that.

Beyond smartphones and XR, the growth of IoT and the increased computational capabilities of edge devices will allow for AI integration into a variety of products and systems. This will enhance inter-device communication and potentially enable bio-device connections. Imagine your devices talking to each other or even your devices connecting with you biologically. TinyML is pushing AI into industrial IoT devices as well as wearables. Now, isn’t that something?

As the trend of integrating AI across diverse devices and platforms continues, our interaction with technology is poised to undergo a transformative shift. This evolution will result in experiences that are more intelligent, adaptive, and personalized. These advancements will be particularly impactful in sectors like education and healthcare, where AI-driven solutions have the potential to revolutionize learning, enhance medical diagnoses, and improve patient care outcomes.

Proactive AI

AI systems of tomorrow will enable prediction with commensurate intervention.

Today’s AI systems are predictive, but they largely fail to take preventative action. If something is predicted to break, AI will automatically order a new one. If it seems like you’ll have to work late, your morning meetings will be moved automatically. In healthcare, instead of simply flagging diseases, the AI system will be able to recommend and trigger appropriate preventative measures, such as scheduling check-ups, adjusting medication, or providing personalized treatment plans.

The key enablers for this shift will be advances in areas like real-time data processing, autonomous decision-making, and closed-loop control systems that can seamlessly translate predictions into actions.

The key to AI advancement will be not just to predict, but to act.

Evolution Of Multimodality

Move over LLMs… we’re moving towards LMMs.

Today, we can see small elements of multimodality (text, image, video and numbers) in some Large Language Models (LLMs). In the future, LLMs will evolve into Large Multimodal Models (LMMs), where you can input and output numbers, text, tables, images, videos, audio, movement, sensory cues, and more.

The evolution towards LMMs will be driven by the need to create AI systems that can truly understand and interact with the world more human-like manner. LMMs will enable transformative applications across domains like healthcare, education, entertainment, and robotics, where seamless multimodal interaction is crucial.

This will represent a major step towards Artificial General Intelligence (AGI) and a future where AI can truly understand and engage with the world in a comprehensive, human-like manner. Exciting?

Goal Orientated AI

The future of AI will be characterised by a shift towards more goal-oriented features and to accommodate human-centric objectives.

We are seeing hints of this with AgentGPT. However, with OpenAI launching function calling, we can now lay out the steps in a more structured way. Instead of just saying ‘first do this, then do this,’ you can now lay out the goal, such as ‘I want a lilac bouquet by April 15th,’ and approve the steps it will take accordingly.

AI will work alongside humans, offering real-time feedback, suggesting course corrections, and proposing alternatives to optimize desired outcomes.

Enterprise-grade AI

The future of enterprise-grade AI will be defined by its ability to integrate business operations with predictive and prescriptive capabilities seamlessly.

Many such state-of-the-art systems are in research or beta. Recent releases from Databricks (DBRX) and Microsoft (Fabric) and what AWS and Google are working towards offer a glimpse of what’s to come for enterprises in the next few years.

Enterprises will need to develop robust data observability, ethical guidelines, and security protocols to harness the power of AI responsibly. Transparency and accountability will be critical to building trust in these systems.

What is the future of business AI? One where proactive intervention will replace reactive responses.

Value-oriented AI

In the future, you will be able to incorporate your values into your AI.

Anthropic is leading the way in constitutional AI. They’ve built a technology that lets us infuse AI systems with our values—personal, team, company, and even city or country. This paves the way for AI that’s customized to our unique values. Hence, it’s crucial to establish a clear value system for AI now. This isn’t just a nice-to-have; it’s a need-to-have to truly harness the power of AI.

The horizons of AI are expansive and filled with both potential and challenges. Given its current pace of development, two weeks in AI equates to two months in the real world.

As we dive into this exciting future, let’s make sure that we’re creating an advanced but also ethical, responsible, and equitable AI.

What do you think comes next for AI? I’d love to hear your thoughts.

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