2024 promises to be a game-changer in the AI/ML world. So, buckle up, because we're about to dive into the top 10 trends that will shape the year ahead: 1. AI Goes Multimodal: Imagine a robot that understands not just your words, but also your facial expressions and even the tone of your voice.
But although generative AI continues to captivate the tech world, attitudes are becoming more nuanced and mature as organizations shift their focus from experimentation to real-world initiatives. This year's trends reflect a deepening sophistication and caution in AI development and deployment strategies, with an eye to ethics, safety and the evolving regulatory landscape.
Here are the top 10 AI and machine learning trends to prepare for in 2024.
1. Multimodal AI
Multimodal AI goes beyond traditional single-mode data processing to encompass multiple input types, such as text, images and sound -- a step toward mimicking the human ability to process diverse sensory information.
"The interfaces of the world are multimodal," said Mark Chen, head of frontiers research at OpenAI, in a November 2023 presentation at the conference EmTech MIT. "We want our models to see what we see and hear what we hear, and we want them to also generate content that appeals to more than one of our senses."
2. Agentic AI
Agentic AI marks a significant shift from reactive to proactive AI. AI agents are advanced systems that exhibit autonomy, proactivity and the ability to act independently. Unlike traditional AI systems, which mainly respond to user inputs and follow predetermined programming, AI agents are designed to understand their environment, set goals and act to achieve those objectives without direct human intervention.
For example, in environmental monitoring, an AI agent could be trained to collect data, analyze patterns and initiate preventive actions in response to hazards such as early signs of a forest fire. Likewise, a financial AI agent could actively manage an investment portfolio using adaptive strategies that react to changing market conditions in real time.
3. Open source AI
Building large language models and other powerful generative AI systems is an expensive process that requires enormous amounts of compute and data. But using an open source model enables developers to build on top of others' work, reducing costs and expanding AI access. Open source AI is publicly available, typically for free, enabling organizations and researchers to contribute to and build on existing code.
GitHub data from the past year shows a remarkable increase in developer engagement with AI, particularly generative AI. In 2023, generative AI projects entered the top 10 most popular projects across the code hosting platform for the first time, with projects such as Stable Diffusion and AutoGPT pulling in thousands of first-time contributors.
4. Retrieval-augmented generation
Although generative AI tools were widely adopted in 2023, they continue to be plagued by the problem of hallucinations: plausible-sounding but incorrect responses to users' queries. This limitation has presented a roadblock to enterprise adoption, where hallucinations in business-critical or customer-facing scenarios could be catastrophic. Retrieval-augmented generation (RAG) has emerged as a technique for reducing hallucinations, with potentially profound implications for enterprise AI adoption.
RAG blends text generation with information retrieval to enhance the accuracy and relevance of AI-generated content. It enables LLMs to access external information, helping them produce more accurate and contextually aware responses. Bypassing the need to store all knowledge directly in the LLM also reduces model size, which increases speed and lowers costs.
5. Customized enterprise generative AI models
Massive, general-purpose tools such as Midjourney and ChatGPT have attracted the most attention among consumers exploring generative AI. But for business use cases, smaller, narrow-purpose models could prove to have the most staying power, driven by the growing demand for AI systems that can meet niche requirements.
While creating a new model from scratch is a possibility, it's a resource-intensive proposition that will be out of reach for many organizations. To build customized generative AI, most organizations instead modify existing AI models -- for example, tweaking their architecture or fine-tuning on a domain-specific data set. This can be cheaper than either building a new model from the ground up or relying on API calls to a public LLM.
6. Need for AI and machine learning talent
Designing, training and testing a machine learning model is no easy feat -- much less pushing it to production and maintaining it in a complex organizational IT environment. It's no surprise, then, that the growing need for AI and machine learning talent is expected to continue into 2024 and beyond.
"The market is still really hot around talent," Luke said. "It's very easy to get a job in this space."
In particular, as AI and machine learning become more integrated into business operations, there's a growing need for professionals who can bridge the gap between theory and practice. This requires the ability to deploy, monitor and maintain AI systems in real-world settings -- a discipline often referred to as MLOps, short for machine learning operations.
7. Shadow AI
As employees across job functions become interested in generative AI, organizations are facing the issue of shadow AI: use of AI within an organization without explicit approval or oversight from the IT department. This trend is becoming increasingly prevalent as AI becomes more accessible, enabling even nontechnical workers to use it independently.
Shadow AI typically arises when employees need quick solutions to a problem or want to explore new technology faster than official channels allow. This is especially common for easy-to-use AI chatbots, which employees can try out in their web browsers with little difficulty -- without going through IT review and approval processes.
8. A generative AI reality check
As organizations progress from the initial excitement surrounding generative AI to actual adoption and integration, they're likely to face a reality check in 2024 -- a phase often referred to as the "trough of disillusionment" in the Gartner Hype Cycle.
"We're definitely seeing a rapid shift from what we've been calling this experimentation phase into [asking], 'How do I run this at scale across my enterprise?'" Barrington said.
9. Increased attention to AI ethics and security risks
The proliferation of deepfakes and sophisticated AI-generated content is raising alarms about the potential for misinformation and manipulation in media and politics, as well as identity theft and other types of fraud. AI can also enhance the efficacy of ransomware and phishing attacks, making them more convincing, more adaptable and harder to detect.
Although efforts are underway to develop technologies for detecting AI-generated content, doing so remains challenging. Current AI watermarking techniques are relatively easy to circumvent, and existing AI detection software can be prone to false positives.
10. Evolving AI regulation
Unsurprisingly, given these ethics and security concerns, 2024 is shaping up to be a pivotal year for AI regulation, with laws, policies and industry frameworks rapidly evolving in the U.S. and globally. Organizations will need to stay informed and adaptable in the coming year, as shifting compliance requirements could have significant implications for global operations and AI development strategies.
Compiled by Bhumika Sharma
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