What's In and Out for AI in 2024

In 2024, AI is rapidly evolving, and the global AI market size is expected to grow by 37% annually from 2023 to 2030. With over 40% of business leaders reporting a surge in productivity due to AI automation, it's clear that AI is a real driver of efficiency and innovation.

I'll dissect these trends to show you what's truly working in AI this year and what's not, providing you with a clear, concise guide to the AI strategies reshaping industries and those losing their edge.

The Best AI Trends to Look for in 2024

Check out some of the best AI trends that are for 2024:

1. The Ethical Use of AI with Responsible AI

The 2022 Tech Vision research of Accenture showed that around 77% of global consumers had a trust issue regarding AI misuse in organizations - especially regarding bias, explainability and data privacy.?

To address this issue and build a trustworthy relationship with the audience, more organizations in 2024 will be inclining toward Responsible AI.?

Responsible AI is a framework that encourages organizations to use AI transparently and ethically, ensuring every organization remains accountable for not harming or damaging individual or societal norms intentionally or unintentionally.

When such a practice is followed, it not only encourages developers and stakeholders to adhere to the ethical rules but also increases the trust of your audience, ensuring better customer loyalty in future.?

For example, have you read about Google AI’s DeepMind Health? Google’s research team used AI to create a framework that can detect the probability of diabetic retinopathy (a leading cause of blindness) in patients. Surprisingly, the AI’s capability was almost as accurate as that of expert ophthalmologists - and is now being used in the UK and NHS to screen patients for the same.?

2. Better Experience of AI Augmented Applications

AI-augmented mobile applications have been a conscious trend and will be even more in trend in 2024. With millions of citations on Google Scholar, these applications are widely used in art, entertainment, data analysis, and drug discovery.?

One of the most common examples of AI-augmented applications is Google Lens, which has saved much of our effort and time. Whether furniture or a jacket or making a transaction via card - it has shown us how to get so much information about a product just by scanning it.?

3. Encouraging Multiple Input Types with Multimodal AI

Multimodal AI offers multiple input types instead of just being restricted to single-mode data processing which will take us a step further and equip AI to mimic human-like abilities for processing diverse sensory information. In fact, Mark Chen, from Open AI, also said, “We want our models to see what we see and hear what we hear, and we want them also to generate content that appeals to more than one of our senses.” during his conference at the EmTech MIT.?

For example, Chat GPT 4's ability to identify audio, visual, and text and give relevant results shows the multimodal capabilities of AI. This is only the beginning, and the abilities of multimodal AI will only expand in the future in industries like education, healthcare, design, and more.

4. Deploy Open Source AI Models

Until recently, building large language models seemed to be very challenging, considering the amount of data and resources it required. However, leveraging open-source AI models has been quite a trend in 2023 and will continue to be so even in 2024.?

One of the major benefits of using it is that it’s almost free and allows researchers and developers to build on existing code.

For example, in the past year, GitHub data showed quite a remarkable increase in generative AI in 2023. Even though there were open source generative models which existed before, their performance often lagged. But in 2023, with the initiation of Meta's Llama 2 and Mistral AI's Mixtral models, 2024’s future seems flourishing.

5. Increased Hiring of Machine Learning and AI Talent

While designing and testing a model is easier, managing it in a complex IT environment isn’t. The lack of talent who can bridge the gap between theoretical and practical knowledge of AI is quite prevalent, and recruiting managers and business owners are realizing it every day as AI and machine learning become more integrated in day-to-day business operations.

As the market isn’t saturated with top talent yet, and organizations are still looking for individuals who can label, train, and identify data bias, there is a scope of better job opportunities for talent in this domain.?

6. More Attention to Security Risks of AI

With the increased use of deepfakes and AI content for negative means and spreading misinformation, more attention will be paid to managing AI security risks in 2024. Apart from social challenges, AI has been widely used for theft, phishing attacks, and fraudulent activities, making individuals lose a lot of resources. Even though there are technologies in place to identify AI content, it is quite easy to surpass the AI detection software, resulting in false positives.

AI Trends That Won’t Work in 2024

Check out the AI trends that are going to go downhill in 2024:

Even though 2024 will be quite progressive in certain AI domains, it will come with some conscious reality checks. Since the eve of 2023, there have been certain concerns about the complexities of generative AI, such as scaling an organization, scope of integrations, and more tasks like training and maintaining models, which will seem even more challenging in 2024.?

Here are some of the trends that will not be making the cut in 2024 anymore:

1. Preparing Improper Training Data

In AI, there is nothing as important as data - the better the data, the more accurate the results will be. In this case, developers might need to consider the legal and ethical sources relevant to your project and country.?

Besides, if your AI model happens to be complicated, collecting data from a larger dataset is ideal, but it can also be very expensive. When AI gains advanced features in 2024, you will eventually lag if you still use improper training data.?

The solution? Start by reviewing your data, focus on improving your data quality and use the latest data collection sources and methods.

2. Non-Integrations in Businesses?

When it comes to new ML models, integrating them with your business can be challenging - and, ultimately, won't deliver any results. And, in 2024, when every business’s day-to-day activities rely on AI, everything can fall apart without integration. The only way to sort it out is to plan effectively for better operations considering the data inputs, compatibility with business models, level of data security, and more in the same domain.

3. AI Bias

One of the major problems with ML models is that they can provide different results for different data sets - also known as AI bias. When there's so much confusion in results, it can significantly impact the performance of AI models. Especially in 2024, when data quality and precision make such a huge difference, inaccurate data can cost you a lot.

Final Thoughts to Consider

Now that you know quite a bit about what to expect and what not to expect from AI in 2024, it's crucial to approach emerging trends with both optimism and a healthy dose of skepticism. Surely, we will see great advancements in this space, but eventually, some technologies that have been in the spotlight for a long time will gradually decline.

The true value lies not in blindly following every new development but in understanding which innovations enhance efficiency, decision-making, and human experience.

As we move through 2024, let's aim to support AI developments that truly matter – those that solve real problems and improve everyday life. Let's commit to fostering AI advancements that are both technologically sound, ethically responsible, and socially beneficial. This mindful approach will ensure that we harness the full potential of AI to create a future that's not just technologically advanced but also human-centric and sustainable.