The Future of AI: Trends to Watch in 2024

2022 was a breakout year for generative AI in the public eye, and by 2023, it had entered the commercial mainstream. After the major steps taken, 2024 surely is going to be the year of further realization for AI as scientists and business persons come together to find the best ways to incorporate this new transformational technology into our lives.

With a far faster pace of development, generative AI has followed the same trend as computers. Businesses and research institutes got smaller, better machines. They replaced the large, centrally controlled mainframes made by a select few. Moreover, increase in developments led to the home computers that amateurs could play around in the next coming decades. Smart no-code interfaces on powerful personal computers eventually spread throughout the world.

Also, like computers, generative AI has moved past its "hobbyist" stage. It is now moving toward smaller, more powerful packages. 2023 saw the release of Meta's LlaMa family of large language models (LLMs), which was followed by the likes of StableLM, Falcon, Mistral, and Llama 2, leading to an explosion of increasingly effective foundation models with open licenses. Relative similarity between DeepFloyd and Stable Diffusion and top proprietary models was achieved.

In other words, AI is expected to grow this year, by becoming the most sophisticated, user-friendly, and built into products that will streamline daily operations and help with solving some of the most difficult issues humanity is facing. These are the top three AI trends to watch in 2024.

Small Language Models: If you have used Copilot for finding difficult answers, you must have encountered the power of large language models, or LLMs. So, small language models (SLMs) are becoming more popular. This is big because the models are so massive that they need a lot of processing power to run. Despite having several billion parameters, SLMs are still rather huge compared to LLMs, which have hundreds of billions of parameters. However, SLMs are tiny enough to operate offline on a phone. The variables, or modifiable components, known as parameters control how a model behaves.

Furthermore, Sebastien Bubeck of Microsoft Research's Machine Learning Foundations group says, "Small language models can democratize AI. This is due to their size and cost."" He also said, at the same time, though, we're discovering new ways to make them more powerful like huge language models.

The smaller models use carefully chosen, high-quality training data, in contrast to LLMs that are trained on massive volumes of internet data. Researchers have discovered new performance and size thresholds for these models. You might see better models this year that are intended to encourage more research and creativity.

Multimodal AI: Multimodal models are able to comprehend information from several data types, including text, images, audio, and video, while most LLMs are limited to processing only texts. It brings richer, more accurate, and seamless technology. It does this for everything from search tools to creative apps. In addition, a multimodal model that can handle photos, natural language, and Bing search data allows you to retrieve information from Copilot about what's happening in an uploaded image. One example of pertinent information that Copilot can produce is the historical relevance of a landmark in your photo.

Similarly, Microsoft Designer is a program that uses multimodal AI to create pictures based on a description of what the user wants. It also makes personalized neural voices possible. These are natural-sounding voices for text readers and devices. They are for those who are voice-impaired. According to Jennifer Marsman, a principal engineer at Microsoft, "Multimodality can create more human-like experiences. It can better use the range of senses we use, such as sight, speech, and hearing.""

AI in Science: Experts are expecting some major developments in AI technologies which are aimed to accelerate scientific findings, which are focused on addressing global issues such as climate change, energy crises, as well as diseases. Also, the Microsoft researchers use artificial intelligence to make better weather forecasts and carbon estimators. They are also making other sustainable agriculture tools. They are doing this to reduce climate change and help farmers work better.

They're also working on AI solutions that will help farmers in the field. For example, a chatbot can help a farmer identify a puzzling weed. It can also assess the efficacy of different irrigation methods using farm-specific data. Also, life science researchers are working on the world's largest image-based AI model to fight cancer. They are also using advanced AI to find new medications for infectious diseases and chemicals for breakthrough medicines. The technology is condensing scientific trial and error, which might take years, into a few weeks or months.

AI is also revolutionizing materials science. It is a vast discipline concerned with making new materials with certain features. One recent accomplishment demonstrated the ability of artificial intelligence and high-performance computation to speed the hunt for less harmful battery materials.

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