When the dust finally settles, the rationalization and ethical democratization of GenAI will hopefully bring benefits and not generative cyber risks…

The new year brings forth an era of unprecedented transformation in the industrial sector as AI takes center stage, reshaping the way businesses operate, innovate, and contribute to societal progress. 

Here is our outlook on some of the current areas of progress in data science and AI in 2024 and how they will impact the industrial sector.

Going beyond do-it-all chatbots

Over the last year, GenAI-powered chatbots have engaged with customers in real-time, providing instant assistance by comprehending complex scenarios, offering personalized solutions and improving the overall customer experience by assist in tasks like order tracking, troubleshooting, and guiding users through product features.

In 2024, the use of GenAI will optimize a spectrum of industrial processes, from supply chain management to inventory control and production scheduling. When correctly deployed and customized, GenAI will transform factories and warehouses with newfound efficiency and cost-effectiveness, and overall productivity and safety. Predictive maintenance is also expected to reach new heights. 

Dimitry Fisher, Senior Vice President of Data Science, Aicadium

As GenAI proliferates, we foresee the emergence of more intelligent and autonomous robots possessing tremendous potential to revolutionize industrial operations’ efficiency and safety standards.

In the realm of computer vision, GenAI will improve the way systems are designed and deployed. Generative models can generate synthetic data to augment training datasets for computer vision models, enabling more robust and diverse training and improving the model’s ability to generalize to various conditions. 

Synthetic or augmented data is particularly useful in domains and scenarios where high-quality images are crucial, or if sufficient volumes of data are unavailable. This will prove particularly useful in applications like detecting defects in manufacturing, or identifying events of interest in CCTV footage.

Raising the bar for ethical AI 

In the last few years the heightened global focus on sustainability has also increased expectations for firms to address pressing challenges across social, environmental, and humanitarian realms. 

AI-driven solutions will play an ever-expanding role in monitoring and mitigating the far-reaching impact of climate change. As we think about the industrial carbon footprint, we also need to make sure that AI itself reduces rather than increases it. 

To make the proliferation of AI more sustainable, technologies that reduce the carbon footprint of AI systems will begin to take hold in the coming year, leading to more efficient training and inference beyond 2024. 

The current obsession with ever-larger AI models will begin to give way to smaller, more specialized, and computationally efficient AI models and solutions. Simultaneously, a spotlight will be cast on firms that embrace ethical AI practices: 2024 will witness an elevated commitment to fostering AI systems that are transparent, accountable, and devoid of bias. 

Ethical AI necessitates a concerted, collaborative effort involving industry players, academia and policy makers, ensuring responsible AI development and deployment become the norm. As AI agents help humans in more and more areas of work and life, the bar for ethical behavior is and must be, far higher for AI than it is for individual humans.

Efficient democratization needed

The strategic integration of AI into pre-existing infrastructures emerges as an imperative for competitiveness and adaptability. A prime example lies in the repurposing of security or monitoring cameras, capturing images or video data for analysis by computer vision AI algorithms. 

Firms will begin to find that the time it takes to roll out new generations of hardware can be mitigated by advanced algorithms that can overcome the limitations of older-generation cameras. Additionally, tapping into existing on-premises servers or leveraging existing cloud infrastructure to process and store data generated by AI systems will prove to be a judicious and cost-effective avenue. 

The democratization of AI integration will be further accelerated in the era of open-source and free-use software libraries and frameworks, and the growing availability of AI-driven enterprise software products. This progression will empower firms to deploy AI applications without shouldering the financial burden and vendor lock-in associated with proprietary software investments.

On the other hand, in the upcoming year, businesses will be more driven to forge strategic partnerships with specialized technology vendors or service providers. Specifically, smaller vendors and consultants with specialized experience and skill sets in the industrial deployment of AI will be at the forefront. 

The speed of progress will make it difficult for firms to hire and train the kind of talent needed to deploy AI systems, so collaboration will be key to success. These collaborations will serve as invaluable conduits for expertise and support in areas including data collection, model training, and system integration. 

By harnessing existing infrastructure and third-party expertise, businesses can accelerate the implementation of AI solutions, ensuring a nimble and efficient transition into the era of AI-driven operations.

Retraining the workforce

In 2024, forward-thinking firms will begin to invest in training and education programs to enable workers to interact with and utilize the technology effectively and responsibly. This will involve a combination of classroom instruction, on-the-job training, and online courses. Hiring new talent with specialized skills in data analysis, machine learning, and software development will continue to be challenging, so retraining current employees to use advanced AI tools will be critical. 

Beyond the technical skills needed, firms will need to ensure that their employees are comfortable working with and trusting AI, which may require a cultural shift in some organizations. 

Overall, firms that invest in training and education programs for their employees will be better positioned to reap the benefits of AI in the industrial sector.  

Charting the future

Another emerging feature in the AI landscape is the prominence of domain-specific models. The once-exclusive domain of tech giants like OpenAI has expanded, allowing the fine-tuning of foundation models for specific domains using parameter-efficient methods on publicly available pre-trained models. 

This democratization foreshadows a future where AI/ML providers and their clients will develop proprietary, deployable foundation models finely tuned for precise application domains.

In the future of AI, interactivity and explainability will stand out as crucial threads as the technology becomes increasingly intertwined with the daily functioning of enterprises. The emphasis on creating models that are not only interactive but also explainable will becomes essential. This commitment to transparency and user understanding marks a conscientious step toward building AI systems that align with ethical and user-centric principles to safeguard our future.