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Top 10 AI and Machine Learning Trends for 2024

What is the Current State of AI?

Artificial intelligence (AI) technologies are revolutionizing both business operations and society as a whole. What are the AI trends in 2024 that businesses should be paying attention to?

The success stories highlight the advancements and evolution of algorithms. ChatGPT represents a novel AI language model that has the potential to disrupt modern search engines.

Equally remarkable and deserving of corporate attention are the novel tools streamlining machine learning pipelines, significantly expediting the development process.

Furthermore, AI is penetrating various new domains such as conceptual design, smaller devices, and multi-modal applications. These innovations will broaden AI’s capabilities across numerous industries. It’s also crucial for companies to stay abreast of cutting-edge AI technologies that exhibit immense promise and are now accessible for experimentation through cloud platforms—quantum AI, for instance.

Read more: 10 Remarkable Artificial Intelligence Applications in 2024

What are AI and Machine Learning Trends for 2024?

To fully capitalize on the advantages offered by AI and machine learning trends, IT and business leaders must devise a strategy that aligns AI initiatives with both employee interests and business objectives. The following key issues should be prioritized:

  • Streamlining and democratizing access to AI resources.
  • Addressing mounting concerns surrounding ethical and responsible AI practices.
  • Aligning AI compensation with business goals to ensure that AI implementations deliver tangible outcomes.

Here are the top 10 trends in 2024 that IT leaders should prepare for:

1. Automated machine learning (AutoML)

According to Michael Mazur, CEO of AI Clearing—a software company leveraging AI for construction reporting—two promising aspects of automated machine learning include enhanced tools for data labeling and the automatic optimization of neural net architectures.

The necessity for labeled data has led to the emergence of a labeling industry relying on human annotators situated in cost-effective regions like India, Central Eastern Europe, and South America, Mazur noted. Concerns related to offshore labor have prompted the market to seek alternative methods to minimize or avoid this part of the process. Improvements in semi-supervised and self-supervised learning are aiding companies in reducing the need for manually labeled data.

By automating the selection and fine-tuning of neural network models, AI is expected to become more cost-effective, enabling quicker deployment of new solutions to the market.

Looking ahead, Gartner anticipates a concentrated effort on enhancing the diverse processes essential for operationalizing these models: PlatformOps, MLOps, and DataOps. Gartner collectively terms these new capabilities as XOps.

Read more: Boost the Software by Integrating AI into DevOps

2. AI-driven Conceptual Design

In the past, AI primarily focused on optimizing processes related to data analysis, image recognition, and linguistic comprehension.

This application has been particularly effective in industries like finance, retail, and healthcare, especially for clearly defined and repetitive tasks. However, OpenAI recently introduced two novel models called DALL·E and CLIP (Contrastive Language-Image Pre-training), which merge language and visual elements to generate new visual designs based on textual descriptions.

Early experiments demonstrate the models’ capability to create innovative designs. For instance, by providing the AI with the caption “avocado armchair,” it designed a chair resembling an avocado. Michael Mazur anticipates that these new models will facilitate the widespread integration of AI into creative sectors. “We can soon anticipate similar disruptions in fields like fashion, architecture, and other creative industries,” Mazur remarked.

3. Multi-modal Learning

AI is becoming increasingly adept at accommodating multiple modalities within a single machine learning model, encompassing text, visual, speech, and IoT sensor data. Google DeepMind gained attention with Gato, a multimodal AI approach capable of executing tasks involving visual perception, language comprehension, and robotic movements.

Concurrently, developers are discovering innovative methods to combine modalities to enhance common tasks such as document comprehension, as noted by David Talby, the founder and CTO of John Snow Labs, a provider of NLP tools.

For instance, healthcare systems collate patient data, including visual lab outcomes, genetic sequencing reports, clinical trial documents, and other scanned records. Presenting this information effectively can significantly aid doctors in their analysis. AI algorithms trained through multi-modal techniques, such as machine vision and optical character recognition, could optimize result presentation, ultimately enhancing medical diagnostics. Leveraging the full potential of multi-modal techniques will necessitate hiring or training data scientists proficient in cross-domain skills, such as natural language processing and machine vision techniques.

4. Models Capable of Addressing Multiple Objectives

Typically, AI models are tailored to a single objective, aiming at specific business metrics like maximizing revenue. Justin Silver, an AI strategist and the manager of data science at PROS, an AI-driven sales management platform, anticipates a shift towards multi-task models that consider multiple objectives as early efforts mature. It’s crucial to differentiate multi-task models from multi-modal learning, which seeks to understand various data types jointly.

Focusing solely on a single business metric without accounting for other objectives could lead to suboptimal outcomes. For instance, if a product recommendation engine solely prioritizes customer conversion rates, it might overlook revenue opportunities tied to novel or diverse products a customer hasn’t previously purchased. Moreover, the growing significance of environmental, social, and governance (ESG) goals demands models that strike a balance between sustainability objectives such as carbon reduction and circularity, alongside conventional business targets like inventory reduction, delivery efficiency, and cost reduction.

5. AI-Powered Cybersecurity

Emerging AI and machine learning techniques will increasingly contribute to identifying and responding to cybersecurity threats. According to Ed Bowen, advisory AI Leader and managing director at Deloitte, a significant driving force is adversaries leveraging AI and machine learning to exploit vulnerabilities.

Anticipate a rise in enterprises using AI defensively and proactively to detect unusual behavior and novel attack patterns. Organizations failing to incorporate AI risk lagging in security readiness, facing a higher likelihood of detrimental impacts.

Bowen underscores, “AI-supported cyber programs often demonstrate superior management of multifaceted, evolving risks by enhancing detection effectiveness and bolstering agility and resilience in the face of increased disruptions.”

6. Enhanced Language Modeling

ChatGPT has introduced a fresh approach to interacting with AI, offering an interactive experience suitable for diverse use cases spanning marketing, automated customer support, and user experiences across various industries.

In the coming year, 2023, there will likely be an increasing emphasis on enhancing the quality control measures associated with these advanced AI language models. We’ve already witnessed a negative response due to inaccuracies in coding results. In the upcoming months, organizations can anticipate criticism regarding erroneous product descriptions and potentially harmful advice. Consequently, there will be a heightened interest in discovering more effective methods to elucidate the occurrences and causes of errors stemming from these tools.

Read more: Coding with AI: How AI is Changing the Way Developers Work?

7. Computer Vision in Business Expands but ROI a Challenge

The upcoming year, 2023, anticipates a surge in computer vision utilization for analytics and automation, driven by the accessibility of inexpensive cameras and new AI technologies.

Scott Likens, PwC’s innovation and trust technology leader, highlighted the emergence of opportunities facilitated by access to computing resources, sensors, data, and cutting-edge vision models. These advancements enable the automation of tasks necessitating human visual inspection and interpretation of real-world objects.

In the realm of back-office operations, refined machine vision is expected to streamline document workflows, while on the front lines, the adoption of computer vision will digitize physical aspects of business operations.

Likens foresees a challenge for CIOs in attaining a return on investment (ROI) from these initiatives. It’s critical to identify suitable use cases, requiring a growing demand for individuals, termed “bilinguals,” capable of bridging the technical and business domains to pinpoint new opportunities in computer vision.

The implementation of computer vision demands specialized expertise. High-performing systems rely on thousands of labeled examples, often not readily available within a company and necessitating manual curation at a substantial cost, creating a financial entry barrier. Unlike deep learning models used for language tasks and forecasting, computer vision implementations present hurdles. Some applications may mandate specific camera hardware and edge compute capabilities tailored to the use case, necessitating new operational and infrastructure skills for organizations not actively managing this type of technology ecosystem.

8. Democratized AI

Advancements in AI tooling are simplifying the expertise required for AI model development. This accessibility facilitates the involvement of subject matter experts, streamlining AI development and enhancing accuracy, as noted by Talby. Frontline experts contribute insights into where new models generate the most value and identify potential challenges or workarounds.

Doug Rank, a data scientist at PS AI Labs, foresees a trajectory for this trend akin to the evolution of technologies like computers and networks, transitioning from exclusive use by experts to widespread enterprise adoption. However, a significant challenge lies in ensuring data cleanliness and providing suitable access with appropriate controls.

Pini Solomovitz, head of innovation at Run:ai, predicts that efforts to simplify AI tools could drive AI deployments beyond existing IT services, reminiscent of shadow IT driven by cost-effective cloud services.

Democratizing AI raises concerns about costs, ethics, and data privacy for enterprises. CIOs will increasingly need to audit new AI uses to consolidate costs, identify risks, and streamline AI workflows.

9. Bias Removal in Machine Learning (ML)

As AI adoption escalates in enterprises, addressing AI bias and fairness becomes a crucial concern. The objective is to ensure objective predictions, preventing discrimination in scenarios like loan applications, online purchases, or medical treatment.

Liran Hason, co-founder and CEO of Aporia, emphasized the criticality of bias mitigation for businesses to foster trust in their ML products. CIOs in 2023 will grapple with governing data science practices and ML models, owing to their intricate nature. Implementing responsible AI practices and deploying appropriate tooling will gain heightened importance. Hason predicts an increased interest in tools for monitoring and mitigating bias in production AI, aiming to identify and explain specific biased predictions’ underlying data points and features.

10. Digital Twins Drive the Industrial Metaverse

In recent times, leading vendors specializing in industrial design and AI have established connections between digital twins—virtual models simulating reality—and the metaverse. Collaborations such as Nvidia and Siemens crafting an industrial metaverse, alongside Bentley, a construction behemoth, adopting the term “infrastructure metaverse,” signify this trend.

Anand Rao, the global AI lead at PwC, suggests that these advancements might signify a pivotal moment for digital twins, shifting from a niche technology to a foundational element of IT strategies. While digital twins have seen deployment across diverse industry sectors in recent years, Rao anticipates their adoption to accelerate and broaden in 2023.

The complexity of digital twins has notably expanded. Initially simple, these twins have evolved from relying on synthetic or real data to asset-based models fueled by IoT technology. Additionally, they now encompass customer-based and ecosystem-based variants. According to experts, digital twins are increasingly used to model human behaviors and assess various future scenarios, paving the way for their convergence with traditional industrial simulations and AI-driven agent-based simulations.

Rao emphasizes the upcoming evolution’s direction, envisioning the fusion of scientific computing, industrial simulation, and artificial intelligence to establish simulation intelligence. This next stage aims to embed foundational simulation elements into operating systems.

The potential applications for digital twins are vast, Rao asserts, offering businesses novel ways to leverage and predict data. Enterprises equipped with more intricate and adaptable digital twins can employ simulation intelligence to forecast real-world scenarios, ranging from disease progression to customer behavior and the economic repercussions of pandemics. Digital twins are poised to become indispensable technologies for organizations venturing into or expanding their involvement in ESG modeling, smart cities, drug design, and various other applications.

Currently, pilots of digital twin projects are progressing towards scaling and operationalization. CIOs must consider their integration within the business’s broader analytics architecture and cloud/IT stack. Companies should furnish both development and production environments for simulation execution. These workloads are compute-intensive, necessitating on-demand computing resources on-premises or in the cloud.

Moreover, digital twins represent an important facet for CIOs to employ in upskilling their workforce. Establishing well-defined processes for scoping, constructing, calibrating, deploying, and monitoring digital twins is crucial. These tools possess transformative potential for businesses but necessitate adequate preparation and readiness from the organization and its employees.

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