Learn out about the top AI and machine learning trends for 2024, such as multimodal applications, open-source AI, and custom corporate models, and how they may completely transform business.
Even while generative AI continues to excite the tech community, opinions are becoming more nuanced and mature as organizations start putting more of an emphasis on real-world operations rather than experimenting. This year’s trends demonstrate how, in order to balance safety, ethics, and the evolving legal landscape, AI development and deployment strategies are become increasingly complex and circumspect
Here are the top 10 AI and machine learning trends to prpare for in 2024
Multimodal AI
Multimodal AI goes beyond traditional single-mode data processing and approaches mimicking human comprehension of a range of sensory information by evaluating many input formats, such as text, pictures, and sound.
Head of Frontiers Research at OpenAI, Mark Chen, said, “The world’s interfaces are multimodal,” in a November 2023 talk at the Em Tech MIT conference Our models should be able to see and hear what we do, as well as provide material that appeals to a variety ofour senses.”
Thanks to multimodal characteristics, the OpenAI GPT-4 model can respond to both visual and auditory input. Using an image of a refrigerator’s contents as an example, Chen demonstrated how to utilize ChatGPT to suggest a recipe based on the components displayed during his presentation. Using ChatGPT’s voice mode, you can even record the conversation if you ask the question aloud.
Although most current generative AI initiatives are text-based, Matt Barrington, Americas emerging technologies leader at EY, stated that “the real power of these capabilities is going to be when you may combine dialogue and text with pictures and video, mix and match all three, and use them for a range of industries.”
Agentic AI
With the introduction of agentic AI, reactive AI is increasingly giving way to proactive AI. Agents of artificial intelligence (AI) are intelligent systems with proactivity, autonomy, and independent cognition. Unlike typical AI systems, artificial intelligence (AI) agents are designed to understand their environment, establish objectives, and take actions to achieve those objectives without requiring direct human intervention. Conventional AI systems mostly obey preprogrammed instructions and respond to user inputs.
In the context of environmental monitoring, for example, an AI agent may be trained to collect data, spot trends, and initiate preventive actions in response to threats like the first signs of a forest fire. In a similar vein, an AI financial agent may actively manage a portfolio of investments by utilizing adaptive strategies that react instantly to changing market circumstances.
“2023 was the year of being able to chat with an AI,” according to computer scientist Peter Norvig, a fellow at Stanford’s Human-Centered AI Institute, who wrote this in a recent blog post. “Agents will be able to finish things on your behalf by 2024. Plan ahead, book a holiday, and create links with other businesses.”
Open-source AI
Large language models and other powerful generative AI systems are expensive to develop and require large amounts of data and processing power. Nevertheless, by adopting an open-source philosophy and building upon the work of others, developers may reduce costs and broaden access to AI. Because open-source AI is made publicly available, generally at no cost, it enables researchers and companies to build upon and contribute to existing code.
GitHub data from the past year shows a notable increase in developer interest in artificial intelligence (AI), particularly generative AI. Generative AI projects first appeared in the top 10 most popular projects on the code hosting site in 2023, with projects like Stable Diffusion and Auto GPT drawing hundreds of new contributors
When open-source generative models were available at the beginning of the year, their performance was sometimes inferior to that of commercial solutions such as ChatGPT. But the field expanded significantly in 2023, and now there are formidable open-source rivals like Mistral AI’s Mixtral models and Meta’s Llama 2. By making cutting-edge AI models and technology available to smaller, less resource-rich firms, this has the potential to transform the AI landscape in 2024.
Retrieval-augmented generation
Even though generative AI technologies are widely employed in 2023, they are still plagued by the problem of hallucinations, which leads them to give users answers to their questions that appear reasonable but are actually incorrect. This limitation has prevented corporate adoption since it can lead to catastrophic hallucinations in scenarios involving customers or company criticality. The use of retrieval-augmented generation (RAG) to reduce hallucinations has gained popularity, and this might have a big influence on how AI is implemented in business environments.
RAG combines text synthesis and information retrieval to increase the accuracy and relevance of AI-generated content. It provides LLMs with access to external data, enabling them to respond with more precision and context awareness.
Customized enterprise generative AI models
Large, flexible tools like Midjourney and ChatGPT have attracted the greatest attention from users researching generative AI. But with the growing need for AI systems that can meet specific demands, smaller, more targeted models could end up being the most robust for commercial use cases.
Building a new model from scratch is doable, but because it requires a lot of resources, it will be too expensive for many businesses. Instead, to generate customized generative AI, most companies modify pre-existing AI models, either by optimizing on domain-specific data sets or fine-tuning their architecture. This might be less costly than starting from scratch with a new model or relying on API calls to a public LLM.
“Calls to GPT-4 as an API, just as an example, are very expensive, both in terms of cost and in terms of latency — how long it can actually take to return a result,” stated Shane Luke, vice president of AI and machine learning at Workday. “To reach the same capacity, we are optimizing really hard, but we are concentrating and focusing our efforts very well. It may thus be a much more manageable, smaller model.”
The primary advantage of customized generative AI models is their ability to satisfy specific markets and user expectations. For a variety of uses, such as supply chain management, customer support, and document analysis, customized generative AI systems may be created. This is especially crucial for sectors with extremely specialized terminology and procedures, including healthcare, banking, and law.
Talent in AI and machine learning is required
A machine learning model is difficult to create, train, test, and implement; it’s even more difficult to maintain once it’s in production within a convoluted corporate IT environment. Therefore, it shouldn’t be shocking that until 2024 and beyond, there will be a need for people with experience in AI and machine learning.
For example, when AI and machine learning are integrated into business processes, there is a growing need for professionals who can bridge the knowledge gap between theory and practice. This requires the know-how to implement, manage, and keep up AI systems in real-world settings. Machine learning operations, or MLOps, is the popular term for this area of study.
According to a recent O’Reilly poll, the top three skills that respondents’ companies needed for generative AI projects were operations for AI and machine learning, data analysis and statistics, and AI programming. However, these sorts of skills are scarce.
Shadow AI
As people from a range of job categories exhibit interest in generative AI, businesses are facing the issue of “shadow AI,” or the usage of AI within an organization without explicit IT department authorization or oversight. This trend is become more prevalent as AI becomes more accessible and even nontechnical staff may use it independently.
Shadow AI might arise from employees who need rapid remedies for problems or who want to learn about new technology faster than approved channels allow. This is especially common for staff workers who may try out AI chatbots on their web browsers without needing IT review and approval procedures because they are user-friendly.
Looking for ways to use this new technology is a positive sign of initiative and creativity. However, end users’ common lack of awareness about security, data privacy, and compliance poses a concern as well. Unaware that entering commercial secrets into a publicly accessible LLM exposes such sensitive information to third parties, for example, might occur.
A generative AI reality check
Businesses will likely get a reality check in 2024 as they transition from the initial excitement over generative AI to actual adoption and integration. According to the Gartner Hype Cycle, this is referred to as the “trough of disillusionment”.
After the initial euphoria fades, organizations are coming to terms with the limitations of generative AI, which include problems with output quality, security and ethical difficulties, and integration hurdles with existing workflows and systems. It may turn out to be more challenging than anticipated to handle problems like maintaining AI systems in use, training models, and ensuring data quality.
Increased attention to AI ethics and security risks
In light of the potential for misinformation and manipulation in politics and the media, worries about identity theft and other types of fraud, as well as the proliferation of deepfakes and sophisticated AI-generated content, are being voiced. Artificial intelligence (AI) have the capability to enhance the efficacy of ransomware and phishing attacks by enhancing their resilience, persuasiveness, and elusiveness.
Although technologies are being developed to recognize data produced by artificial intelligence, the process remains challenging. Current AI watermarking techniques are very easy to circumvent, and existing AI detection methods are prone to false positives.
Evolving AI regulation
It should not be surprising, given these moral and security concerns, that 2024 is proving to be a pivotal year for AI regulation, with laws, regulations, and industry frameworks rapidly shifting on a national and worldwide scale. The next year will need enterprises to be alert and adaptable since shifting compliance rules might significantly affect global operations and AI development strategies.
Members of the EU Parliament and Council have reached a provisional agreement on the EU’s AI Act, which is the first comprehensive AI law in the world. Should it become law, it would forbid the application of AI in some circumstances, impose obligations on individuals who develop high-risk AI systems, and require companies using generative AI to be transparent. There might be violations.
