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The global artificial intelligence (AI) market is growing at a tremendous rate, projected to reach $1.8 trillion by 2030 with a CAGR of 32.9% from 2022. Major trends like chatbots, image generation, and mobile AI applications are propelling rapid advancement. As we look ahead to 2024, some key trends are emerging that will shape the future of AI and its real-world impacts.
Larger language models, on-device AI, automated machine learning, and AI for chip design are four areas expected to see significant progress and converge to enable the next generation of AI capabilities. With skilled software developers to implement these advanced technologies responsibly, AI is poised to become an even more integral part of our tools and applications in 2024 and beyond.
One major trend is the development of ever-larger language models like GPT-3. These models can understand and generate human-like text and power applications like chatbots. In 2024, we will see models that are 10 to 100 times larger than GPT-3, with over a trillion parameters. This will enable them to be more conversational, knowledgeable, and creative. Large models will power applications from search to customer service to content creation. However, concerns around bias, misinformation, and computation costs remain challenges.
Another trend is the proliferation of on-device AI. Rather than sending data to the cloud, models will run locally on devices like smartphones, cars, and IoT devices. This reduces latency, increases privacy, and allows AI to work offline. Companies like Google, Apple, and Tesla are already working on putting ML models directly on their hardware. As specialised AI chips and techniques like model compression advance, more operations will happen on-device rather than in the cloud. This will enable emerging applications like autonomous driving, AR/VR, and smart assistants.
Automated machine learning, or AutoML, aims to make AI more accessible to non-experts. In 2024, AutoML tools will become more advanced, allowing users with limited data science knowledge to train, optimise, and deploy models quickly. AutoML can automate repetitive and time-consuming tasks like data preprocessing, model selection, hyperparameter tuning, and model optimisation.
This democratisation of AI can expand its applications in business and research. Some key use cases for AutoML include predictive maintenance, customer churn prevention, personalised marketing, and medical image analysis.
However, there are challenges to address. Guardrails will be needed to prevent misuse by novices without an understanding of data biases or project goals. Interpretability remains difficult, as AutoML may generate black-box models. Compute resource management and collaboration tools for large enterprises are still emerging.
While AutoML won't replace data scientists entirely, it can take over routine tasks to allow them to focus on high-value responsibilities. A thoughtful application of AutoML while addressing its ethical and technical limitations will be key.
Powerful and efficient hardware is fundamental to running advanced AI/ML models. In 2024, we will see new AI accelerator chips that provide dramatic speedups for neural networks compared to general-purpose GPUs or CPUs. McKinsey & Company predicts most of the growth from AI-related semiconductors will come in the next five years. The research firm projects annual growth of about 18% between now and 2025, when AI chips could account for almost 20% of all semiconductor demand, translating into about $67 billion in revenue.
At the same time, AI itself is being applied to aid in chip design. Using ML techniques like reinforcement learning and generative adversarial networks, AI can help automate and optimise parts of the notoriously complex chip design process.
This can reduce costs and time-to-market while creating better, more efficient chips specialised for ML workloads. A number of AI chip startups are active in this space, with impacts on the hardware running future AI systems. The synergies between AI and chip design will enable new use cases requiring powerful but efficient on-device inference.
To realise the potential of these trends, skilled software developers will be crucial. System software to manage on-device AI and interface with hardware, frameworks to build and deploy large language models, AutoML tools, and AI-enabled chip design toolchains will need coding to become usable products.
Software engineering best practices like version control, testing, and continuous integration will ensure the reliability and safety of these AI systems as they grow in scale and complexity. So while AI is gaining more capabilities, human software developers remain essential to translating these technologies into real-world value.
In 2024 and beyond, AI and ML will become an even more integral part of our tools and applications. As models grow, AI moves to devices, automation increases, and new application areas like chip design emerge, software developers have an exciting yet responsible task ahead to shape the progress of AI for the betterment of society. With thoughtful leadership and collaboration, the future looks bright for beneficial and broadly shared AI.
