9 AI Trends That Will Shape Data Science in 2024

Artificial intelligence (AI) continues to be a critical factor for advancements across various sectors, such as finance, healthcare, logistics, and retail. Despite vast amounts of data in these fields, fully leveraging this data presents challenges. The field of data science involves complexities like large data volumes, inherent biases, and the need for near-real-time insights, which often require innovative solutions to navigate these challenges efficiently.

AI, with its ability to adapt, learn, and automate, can help overcome these challenges with promising solutions. The synergy of AI with data science provides businesses, technologists, and researchers with unparalleled tools to extract deeper insights and make more informed decisions faster. As a result, this integration enhances operational efficiency and provides the opportunity to address complex challenges.

Let’s look into the top 9 AI trends that have the potential to considerably reshape data science.

1. Generative AI

Generative AI refers to deep-learning models that are designed to create content based on the data they were trained on. These models primarily use neural networks to identify structures and patterns within existing data to generate new and original content.

Apart from neural networks, generative AI models can also utilize techniques, including machine learning, reinforcement learning, and natural language processing. It can produce diverse types of content, including images, text, music, and videos. With an AI image generator, users can create high-quality visuals quickly and efficiently, enhancing their creative projects. Similarly, an AI video generator allows users to produce professional-grade videos effortlessly, revolutionizing their content creation process.

Here are some benefits associated with generative AI in the realm of data science:

• By automating tasks that typically require human intervention, generative AI saves valuable time while also reducing operational costs.
  
• It can analyze diverse datasets and synthesize large amounts of data to generate valuable insights. This empowers organizations across various sectors to unlock actionable intelligence and gain a competitive edge.
  
• Generative AI can automatically organize and categorize vast amounts of knowledge assets, helping extract insights from a range of sources.
• Generative AI is transforming industries by creating diverse types of content, including images, text, and videos. AI photo editors, for instance, enable users to create high-quality visuals effortlessly.

Some popular examples of generative AI tools include GPT-4, ChatGPT, Bard, GitHub Copilot, and Cohere Generate.

2. Automated ML (AutoML)

Automated Machine Learning, or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development.

Traditional ML involves tasks like feature engineering, model selection, and hyperparameter tuning, which are tedious and require expertise. In contrast, AutoML simplifies these procedures for ML experts as well as non-experts, much like AI text to speech simplifies voice generation without requiring in-depth knowledge.

Today, data scientists can use AutoML frameworks to deploy models, visualize data, and understand models. The key innovation in this is hyperparameters search, which is used for preprocessing elements, choosing model types, and optimizing hyperparameters.

Some benefits associated with using AutoML include:

• Drastic reduction in the time to develop and deploy an ML model.
  
• Non-experts can build effective models.
  
• It finds better models or hyperparameter settings than manual processes.

By leveraging AutoML, data scientists can focus on more strategic tasks in the data science workflow without compromising on the performance, speed, and consistency of the model-building process. Popular AutoML tools include DataRobot, Google Cloud AutoML, and H2O.

3. Edge AI

Edge Artificial Intelligence, or AI at the edge, is the deployment of AI applications directly on endpoint devices rather than processing data in a centralized cloud computing facility or private data center.

The need to process data at its source—right at the edge—has driven the demand for Edge AI. In Edge AI, data is processed locally on hardware devices, allowing real-time data processing and decision-making.

The growing popularity of Edge AI is attributed to several key benefits:

• It reduces the latency involved in sending data to a remote server for analysis, enabling real-time decision-making. This is critical for applications requiring instant responses.
  
• Edge AI devices can operate independently without relying on a network connection. This is beneficial for scenarios where there is a lack of reliable internet.
  
• With local data processing, Edge AI is cost-saving, and devices can save power and extend battery life.

Analysts predict that by 2025, over 55% of all data analysis by deep neural networks will occur in an edge system, an increase from less than 10% in 2021.

Examples of Edge AI devices include smartphones, self-driven cars, laptops, robots, drones, and more.

4. Cloud Data Ecosystems

Traditional data ecosystems are undergoing a significant transformation, shifting from standalone software or blended deployments to full cloud-native solutions.

Gartner predicts that by 2025, 95% of new digital workloads will be deployed on cloud-native platforms, up from 30% in 2021. To adapt to this change, organizations should evaluate data ecosystems based on their ability to tackle distributed data challenges and seamlessly integrate with external data sources.

Here’s how cloud data ecosystems can shape data science:

• Most cloud providers have pay-as-you-go models, resulting in cost savings while allowing organizations to access advanced computational resources. Even smaller businesses can access these resources for improved analytics.
  
• Cloud ecosystems support real-time data processing and analytics, providing your business with valuable insights in near real-time for effective decision-making.
  
• Apart from infrastructure, cloud providers typically offer AI and ML services for businesses to implement and deploy models without requiring in-depth expertise.
  
• Cloud platforms often provide integration services and connectors, allowing you to import data from diverse sources, including traditional databases, social media, and third-party applications.

Examples of cloud data ecosystems include Amazon Web Services, Google Cloud Platform, IBM Cloud, and Oracle Cloud.

5. Low-Code/No-Code AI

While there is a prevailing crunch of data science talent in terms of required skills, low-code/no-code platforms permit even amateurs to get into the business of data analytics. These platforms enable people with limited or no coding knowledge to create AI systems.

Such AI platforms typically include graphical user interfaces (GUIs), simplifying the design, development, and deployment of AI/ML models. The low-code/no-code AI platforms offer pre-built components, templates, drag-and-drop functionalities, and more to simplify the AI development process.

Low-code/no-code AI platforms have the following benefits:

• Individuals and businesses without in-depth programming knowledge or data science expertise can develop and deploy AI solutions.
  
• Reduced development time since businesses can prototype, test, and deploy AI models quicker.
  
• Cost-savings with reduced development time and reduced need for hiring specialized talent.

These platforms are designed to make app development and model building more accessible and efficient. You can use low-code/no-code AI systems to get started with tasks such as optimizing workflows, suggesting recommendations, and predicting churn.

Some use cases of low-code/no-code AI platforms include:

• Data Collection: These platforms automatically integrate data from different databases, cloud services, or SaaS applications to help with data management and analysis. Estuary Flow is an example of such a platform that is used both for building AI pipelines for model training as well as model execution such as this example with Slack and ChatGPT.
  
• Analytics: Low-code/no-code solutions offer pre-built analytics models and visualization tools to simplify complex data analysis. Some examples of such platforms include Tableau, Microsoft Power BI, and Qlik Sense.
  
• Machine Learning: These platforms help you build and train ML models using pre-defined templates and a visual interface. Some examples include Google Cloud AutoML, Amazon SageMaker, and DataRobot.

6. Ethical AI

Ethical AI is artificial intelligence that adheres to well-defined ethical guidelines about fundamental values, such as privacy, individual rights, non-discrimination, and non-manipulation.

With the constantly evolving landscape of AI, ethical considerations are essential, particularly when it concerns applications that may threaten or infringe data protection and privacy rights. Ethical AI helps ensure that the application algorithms aren’t biased toward producing a certain type of outcome.

Here are some ways Ethical AI can shape data science:

• It ensures the application behaves neutrally, without any bias or preferences. This requires careful consideration of the collection and processing of training data and model evaluation. Any bias in data can cause unfair outcomes in decisions or predictions.
  
• AI systems prioritize data privacy and security. Ethical AI-designed systems provide proper data governance and model management systems. Such systems respect individual data rights, use data responsibly, and ensure robust measures to protect user privacy.
  
• There is an emphasis on developing models that prioritize the individual, society, and environment’s well-being. Ethical AI-designed systems will work for the benefit of humankind.

The advancement of data science mandates the incorporation of Ethical AI principles, such as fairness, transparency, privacy and data rights, security, and avoiding malicious use. Besides helping avoid any harmful consequences, it also helps in building systems that are understandable and beneficial for all sections of society.

7. Explainable AI

Explainable Artificial Intelligence, or XAI, is a set of techniques and methods that allow you to understand and trust the outcomes of machine learning models.

Many AI models, especially complex ones like deep neural networks, operate as “black-box” models, with their decision-making process not being transparent. Such black-box models are created directly from the data, and not even the data scientists who create the algorithm can understand or explain the decision-making process.

The goal of XAI is to describe an AI model, its expected impact, and potential biases. It is crucial for organizations to build trust and confidence when putting AI models into production. XAI also helps organizations adopt responsible approaches to AI development.

Among the different benefits of XAI that can help shape data science are:

• XAI provides clear explanations, helping build trust in AI systems. With transparency in the model’s decision-making process, users are more likely to trust and adopt AI technologies.
  
• Sectors like healthcare, finance, and criminal justice involve regulations that may require AI-made decisions to be explainable. XAI can help ensure AI solutions meet regulatory standards.
  
• Understanding how a model arrives at its decisions helps data scientists diagnose potential issues, correct biases, and improve model performance.

Popular examples of XAI packages in Python include SHAP, LIME, ELI5, Shapash, and Dalex.

8. Quantum AI

The increasing importance of swift and accurate analysis of vast volumes of data has given rise to Quantum AI, an intersection of quantum computing and artificial intelligence. Quantum AI involves the use of quantum computing techniques to improve ML algorithms.

Despite the rapid progress of AI over the years, it hasn’t yet overcome technological limitations. Quantum computing helps eliminate the obstacles to achieving Artificial General Intelligence (AGI). It can be used for rapid training of ML models and to create optimized algorithms.

The combination of quantum computing and AI can impact the field of data science with the following benefits:

• Unlike traditional computers that use bits for computation, quantum computers use qubits (quantum bits), which may be 0, 1, or any superposition of these states. This allows accelerated processing of vast amounts of information. Complex algorithms that take a prolonged time to run on standard computers are executed in a fraction of the time with quantum algorithms.
  
• Quantum data and quantum computing can help design ML models that operate in the quantum domain. These models can process and recognize patterns in quantum data more efficiently.
  
• Many data science problems, ranging from logistics and supply chains to processes found in manufacturing, revolve around finding the best solution from multiple possibilities. Quantum computers can help improve solutions to these optimization problems.

Some examples of Quantum AI systems include Google Quantum AI, Terra Quantum, Quantinuum, Xanadu, and Sandbox AQ.

9. AIOps

AIOps, or artificial intelligence for IT operations, is the application of AI capabilities, such as ML models and natural language processing, to automate and streamline operational workflows.

It involves AIOps platforms gathering vast amounts of operational data from multiple sources and using ML and analytics to process the information (often in real-time) for meaningful insights.

Here are the benefits associated with AIOps:

• It can detect issues early, identify root causes, and propose effective solutions faster and more accurately than humanly possible. This can help your organization achieve faster mean time to resolution (MTTR) for IT incidents.
  
• Proactive monitoring of environments and automatic identification of operational issues reduce operational costs, supporting better resource allocation. It also frees up resources for more innovative and complex work, leading to improved employee experience.
  
• AIOps solutions can correlate and analyze data for automated actions and better insights. This allows IT teams to maintain control over the increasingly complex IT environments while also assuring application performance.

Some examples of AIOps tools include IBM Instana, BigPanda, PagerDuty, and Dynatrace.

Summing It Up: The Impact of AI Trends on Data Science 

In the rapidly evolving technological landscape, the promising union of AI and data science holds transformative potential. From Generative AI and Edge AI to Ethical AI and Quantum AI, these AI trends introduce a range of possibilities in the realm of data science.

Industries and businesses are constantly looking for improved data-driven decision-making and contemplating ‘How to do the AI trend?’ It’s clear that these AI trends will not only shape data science but also redefine the technological landscape of the future.

Frequently Asked Questions (FAQs)

1. How will AI change data science?

AI has the potential to significantly enhance the capabilities of data scientists, including automating repetitive tasks, uncovering patterns in large datasets, and providing advanced analytics and predictive modeling techniques. Additionally, AI-powered tools can provide real-time analytics and improve data processing speeds.

2. What are future AI trends in data science?

Gartner predicts that by 2024, 60% of data (up from 1% in 2021) for AI will be synthetic to simulate future scenarios and reality and derisk AI. Future trends in data science include the rise of quantum computing, Explainable AI (XAI), AutoML, and a stronger focus on ethical AI and data privacy.

3. Can AI replace a data scientist?

While AI is capable of automating certain tasks, it cannot completely replace a data scientist, particularly since AI mostly utilizes an existing knowledge base. Instead, it supplements the process with extra information in a customized way based on the inputs and prompts. AI technologies and data science work hand-in-hand to make predictions, derive insights, and solve complex problems.