Top 4 Trends Shaping the AI Landscape in 2024

AI is revolutionizing almost every industry it touches. According to the IBM global AI adoption index 2022, many companies have already adopted AI in their business, and others are planning to do so (see Figure 1 below).

As business leaders adopt or plan to adopt AI-enabled solutions, keeping informed on the trends that are shaping the development and usage of AI  technology can be beneficial.

In this article, we highlight the top 4 AI trends that we believe can help business leaders stay ahead of the curve in developing, implementing, and using AI-powered solutions.

1. Striving for unbiasedness

AI usage and implementation have been met with high levels of scrutiny.  That is because users fear that the technology might become biased and further amplify societal issues such as racism, sexism, and gender inequality. 

This fear is not unsubstantiated. 

According to a recent article by the ACLU, bias and prejudice have been mankind’s problems for a long time. And because  AI is developed, trained, and implemented by fallible humans, it can inherit some of their (implicit) racist, sexist, and gender-biased beliefs.

Some examples of AI bias are:

• Amazon’s gender bias recruitment AI favored men over women. This project was, therefore, canceled in 2018.
• An AI algorithm developed in the U.S. to predict the likelihood of patients needing extra medical care heavily favored people of lighter skin color over darker.
• The COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) algorithm was used in US courts to identify the likelihood of a defendant becoming a recidivist. The system was overpredicting against racial minorities.

How to combat AI bias?

Combating AI bias starts with striving to create a more objective algorithm through:  

• Using larger and more diverse training datasets, And a more thorough training and testing process to increase neutrality. 

For more in-depth knowledge on data collection, feel free to download our whitepaper:

2. The rise of generative AI

Generative AI is a disruptive technology that can create creative content such as art. The technology is on Gartner’s list of top strategic technology trends for 2022. Gartner states that Generative AI will benefit businesses in two ways: (1) improving current creative workflows together with humans and (2) working as a creative content generator.

The following are some more specific applications of generative AI:

• Converting text input into images (text-to-image),
• Generating images from other images (e,g, colored images from back and white),
• Converting satellite images to Google Map views,
• Improving old and distorted videos to clear and high-definition ones.

Watch this video to learn how generative AI is changing how we make things:

The famous aerospace company Airbus used Generative AI to design a cabin partition for its airplanes which:

• Is around 30kg lighter than the previous design
• Is made through 3D printing to save raw material costs
• Reduced the overall airline weight, fuel costs, and carbon emissions.

The company plans to use generative AI in the future to design modern and efficient aircraft.

3. Growth of multimodal learning

Another trend that will be prominent in 2022 and beyond is the increasing use of multimodal learning. Multimodal learning is a branch of machine learning in which the model learns from multiple data types rather than a singular one. 

For instance, a model can learn from images, texts, and audio data something from different perspectives. This means that via Multimodal learning, machines can generate more accurate results by simultaneously processing different forms of data. 

For instance, patient records could include visual test results, results of clinical trials, genetic sequencing reports, and other medical documents. AI-enabled medical vision systems trained on multi-modal techniques will be able to analyze and process all this data to offer more accurate diagnoses.

• Other application areas of multimodal learning techniques include security and fraud detection in banking and payment to authenticate the user through various verification methods.
• Product recommendation systems that analyze more than just one data category to provide more accurate recommendations.

As the adoption of multimodal AI increases, we believe it can be worthwhile for business leaders and CIOs to learn more about how multimodal AI can be implemented in their businesses.

4. Usage of large language models

Another trend that is impacting the AI trajectory is the increasing use of large language models. A language model is a probabilistic statistical model that predicts the next word that should come in a sentence. These models are used in natural language processing (NLP) applications to enable AI to create more natural and human-like speech.

Developers now prefer large language models because they are easier and more efficient to train, which helps companies save data annotation and training costs.

GPT-3 and Gopher are 2 of the largest language models developed yet.

Watch the video below to see how Meta is trying to create a single language model which can translate 200 languages:

Further reading

• 4 Steps and Best Practices to Effectively Train AI
• 100+ AI Use Cases / Applications

If you need help finding a vendor or have any questions, feel free to contact us: