- AI is a top priority for 83% of companies in their business plans.
- The global AI market is currently worth $136.55 billion and is expected to grow by 37.3% CAGR by 2030.
- Responsible AI is a significant trend, focusing on ethical and transparent AI usage to prevent societal damage.
- Responsible AI addresses concerns of data privacy, bias, and explainability, setting standards for ethical AI development.
- Multimodal Learning teaches AI to understand and process information from various sources, enhancing AI’s ability to decode text, images, sounds, and videos.
- AI augmented apps combine AI and Augmented Reality (AR) to provide enhanced real-world experiences.
- Enhanced regulations for AI aim to protect privacy, mitigate bias, and ensure transparency in AI operations.
- The augmented workforce trend involves collaboration between humans and AI to enhance productivity and efficiency.
- Businesses need to identify processes for automation and upskilling employees for effective collaboration with AI.
- AI is expected to create 133 million new jobs by 2030, making upskilling in AI an important consideration for the future.
Did you know 83% of companies say that AI is a top priority in their business plans? Artificial Intelligence (AI) is the part and parcel of today’s world. The global AI market is currently worth $136.55 billion. And what’s more fascinating is, it is expected to grow at a CAGR of 37.3% by 2030. The AI market is booming. With it comes numerous artificial intelligence trends. In fact, each and every upgrade in this industry promises a lot. So, what are the best artificial intelligence trends? Read ahead as we discuss the top 5 artificial intelligence trends in 2024. Let’s dive in!
Top 5 Artificial Intelligence Trends in 2024
Before we jump into the best emerging artificial intelligence, here’s a quick overview of what artificial intelligence is.
AI is about teaching machines to think and learn. Imagine your computer learning from lots of data, just like we learn from experiences. This data can be text, images, numbers, or anything else. AI algorithms study this data to find patterns and connections. After analyzing, AI understands how the data fits together. This helps AI make decisions and predictions, like how we predict the weather from clouds, wind speed, and temperature.
Now that we got the definition of artificial intelligence, let’s jump into the top 5 AI trends to watch out for in 2024:
In the rapidly evolving landscape of artificial intelligence, one trend that’s poised to make a significant impact in 2024 is Responsible AI. This isn’t just another buzzword. It’s a significant shift towards the ethical and transparent use of AI. The potential for societal damage is extensive and often unintentional. AI’s power can impact the livelihoods of thousands or even millions of people. If an AI tool shows consistent bias against certain groups, it can have profound consequences. Likewise, any violation of data privacy laws puts individuals at risk and burdens companies with hefty fines.
Accenture’s 2022 Tech Vision research revealed that only 35% of global consumers trust how AI is being implemented by organizations. An alarming 77% believe organizations must be held accountable for AI misuse. This trust deficit places significant pressure on organizations to scale up their AI usage responsibly. So what’s the way out?
The big three concerns—data privacy, bias, and explainability—must be addressed first. To do that, companies are embracing Responsible AI. It’s a framework that enables businesses to develop, deploy, and use AI with good intentions. Its key benefits include minimizing bias, ensuring transparency, creating opportunities for employees to voice concerns. It also protects data privacy and security.
Responsible AI is all about accountability. It sets the standards for ethical development and usage of AI systems. It’s the foundation that ensures developers, businesses, and stakeholders adhere to a code of ethics. This level of accountability removes ambiguity, making it clear who’s responsible if something goes awry.
Examples of Responsible AI
- Microsoft’s Responsible AI Standard sets a compelling example. It’s based on six principles: fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability. These principles are the bedrock of ethical AI deployment.
- Another example of Responsible AI is Google AI’s DeepMind Health. The team developed an AI system that can detect diabetic retinopathy. It is a leading cause of blindness. The AI system detected the disease at least as accurately as expert ophthalmologists. The system is now being used in the NHS in the UK to screen patients for diabetic retinopathy.
With the advent of artificial intelligence, the need for realistic AI-generated results is higher than ever. Leading AI companies are already working towards this. For example, OpenAI’s GPT-3.5 and GPT-4 models have been enabled to study images and analyze them in words. They now support speech synthesis in mobile apps. This allows full-fledged conversations with AI. How do they achieve this?
To mimic human cognition, we have to train AI with the diversity of how we absorb information. We have to teach it about a new thing with texts, images, sounds, videos and whatever resources are available.
This is where multimodal learning comes into play. Multimodal Learning empowers AI to learn from the richness of data. As mentioned earlier, this data includes text, images, sounds, and more. At its core, Multimodal Learning teaches AI to understand and process information from different sources. We can see its impact around us already. AI can now decode text, images, sounds, and videos. We now have AI systems that mimic human cognition by drawing meaning from various modalities.
Statistics and facts support the significance of Multimodal Learning. For example, the classification accuracy of Multimodal Deep Boltzmann Machines outperforms other models like support vector machines, latent Dirichlet allocation, and deep belief networks. When tested on data with both image-text modalities, Multimodal Deep Boltzmann Machines shine. These systems can also predict missing data with high precision.
Examples of Multimodal Learning in AI
- The real-world impact of this AI trend is already apparent. Take the Amazon Alexa virtual assistant for example. It can answer questions, play music, control smart home devices, and more. It is trained to handle voice, and text inputs. You can type a song for it to play and then control the playback with your voice. With Google’s AI assistant you can even hum a song and it will find the song for you. You can mimic an animal’s sound and it will accurately identify which animal you are referring to.
- OpenAI’s CLIP and DALL-E are two more examples of multimodal learning. CLIP (Contrastive Language-Image Pre-training) is a model that can learn to classify images and generate text descriptions of images. It is trained on a massive dataset of text-image pairs, and it learns to predict which text caption best matches an image. CLIP can also be used to perform zero-shot image classification. You can give it a list of potential classes in the form of text captions, and it can predict the most likely class for the image.
- Meanwhile, DALL-E can generate images from text descriptions. It is also trained on a massive dataset of text-image pairs. Further, it learns to generate images that match the text captions. DALL-E can generate images of a wide variety of objects and scenes, and it can even generate images that are not explicitly represented in its training data.
AI Augmented Apps
In the world of technology, few trends have sparked as much excitement as Artificial Intelligence (AI) and Augmented Reality (AR). But how do they come together to impact your daily life in 2024?
AI augmented apps are the real deal. They are mobile applications that use the power of AI to supercharge your Augmented Reality (AR) experience. So what is AR? AR, in simple terms, allows you to experience digital information in your real world. You can experience computer-generated things in your real life just like other things around you. AI, on the other hand, is all about teaching machines to learn and perform tasks that typically need human intelligence.
AI has millions of citations on Google Scholar, and AR isn’t far behind with more than 400,000. These numbers prove how the research around them is evolving. Both technologies have had their highs and lows, but they continue to evolve. AI is now flexing its muscles in data analysis, drug discovery, and art. Though we’re not there yet with true Artificial General Intelligence, narrowly focused AI is making impressive strides.
On the other hand, AR faces its own set of challenges.From fitting a wide field-of-view display into a compact device to dealing with battery life, and ensuring user comfort, the challenges are many. So, how do AI and AR join forces? They’ve already been working together in some impressive ways. AI augmented apps are mobile applications that use artificial intelligence (AI) to enhance the augmented reality (AR) experience. How? Let’s take some examples to understand.
Examples of AI Augmented Apps
- Google Lens is a prime example of how AI-augmented apps are making your daily life smarter. This app uses AI to recognize objects and provide you with relevant information. For instance, you can scan a product barcode to fetch reviews or compare prices. Or, you can scan a business card, and Google Lens will save the contact info to your phone.
- Now, think how great it would be if you could see how that new piece of furniture would look in your home before buying it? IKEA Place, an AR app, lets you do just that. Using augmented reality, it allows you to place virtual IKEA furniture in your living space. It helps you visualize how it would fit and look. Asian Paints have a similar feature that allows you to see how a color would look on your walls even before painting it. All you need to do is, upload an image of your wall on its app.
- Microsoft Mesh takes this concept to the next level. This platform enables people to interact with each other and digital objects in a shared virtual space. Then AI comes into play by creating realistic avatars and improving real-time collaboration.
Enhanced Regulations for AI
AI thrives on data, often including personal information. To protect privacy, regulations mandate user consent and data security. Notably, in 2023, data governance challenges were at the forefront for privacy professionals. AI’s role in data management is pivotal, and AI regulations aim to strike a balance between data utility and privacy.
AI systems can practice biases. And this leads to unfair outcomes. In 2024, AI regulations are expected to require AI developers to test and mitigate bias. Further, transparency in AI operations is vital. It ensures that users have a crystal clear idea on how these AI systems work and make decisions. So, the AI legislation will focus on transparency in AI’s operations. Developers will have to disclose training methods and data sources. They are also expected to be held accountable for the decisions their AI systems make.
While we talk about AI legislations, it is important to address the “black box” nature of AI. This means that it will be difficult to understand how AI systems make decisions. This is because AI systems are often trained on large amounts of data. So the internal workings of the AI systems can be complex and opaque. So, AI legislation in 2024 has to be made keeping this in mind too.
However, it is important to note that AI regulations aren’t uniform. For example, the European Union is actively developing comprehensive AI regulations. They are classifying AI systems based on their potential risks.
On the other hand, India sees AI as a “kinetic enabler” for better governance. The nation is focusing on sector-specific AI deployment. Transparency and interpretability are vital for addressing fairness, privacy, and accountability issues. Now, let’s look at how different countries are preparing their respective AI regulations:
Examples of Enhanced Regulations for AI
- European Union (EU): The EU has proposed the “Artificial Intelligence Act” (AIA) to regulate AI systems. The AIA classifies AI systems by risk. It mandates development and use requirements. Further, it prohibits high-risk AI that could threaten safety and fundamental rights. Non-compliance can result in hefty fines, with penalties reaching up to €30 million or 6% of global income in the EU.
- India: India is focused on leveraging AI for better governance, rather than enacting specific AI laws. Regulatory efforts in India are concentrated on issues like intellectual property, privacy, and cybersecurity.
- New York City: The American city has enacted the Automated Employment Decision System (AEDT) Bias Law. It prohibits employers from using automated decision systems for employment decisions unless they can demonstrate the system is not biased.
- Washington State: Washington has brought the Automated Decision Systems (ADS) Law. This law requires businesses using automated decision systems to provide consumers with explanations of how these systems work and how decisions are made.
- Australia: There is no specific AI legislation in Australia. However, the Privacy Act Review Report of 2022 recommends that entities should disclose how personal information is used in automated decisions.
In 2024, we’re witnessing a significant transformation in the way we work, and artificial intelligence (AI) is at the forefront of this change. This transformation isn’t about replacing human workers with machines, It is about augmenting human capabilities and creating a more efficient and safer work environment through collaboration with AI.
The traditional notion of work is evolving as businesses recognize the potential of integrating AI into their daily operations. This shift is often described as the move towards an “augmented workforce.” In this scenario, humans and intelligent digital assistants work together to enhance productivity and efficiency.
The importance of the augmented workforce goes beyond being a buzzword. It addresses the growing need for increased productivity, reduced downtime, and improved safety in various industries. Several factors, including an aging workforce and external challenges such as the impact of the COVID-19 pandemic, have reduced labor availability. To address these issues, enhancing worker productivity and job satisfaction through the augmented workforce has become a necessity.
Instead of viewing AI as a replacement for human workers, it’s seen as a collaborator. It improves the abilities of human employees to achieve higher levels of efficiency and innovation. The success of the augmented workforce depends on reimagining processes and employee roles.
Welcoming this transformation effectively involves several key steps. Businesses need to identify the processes that can be automated to enhance efficiency. They should consider a range of technologies, including AI and robotics, and determine which employees may need upskilling or retraining to work alongside these technologies. Collaboration among all stakeholders, from executives to end-users, is crucial. Furthermore, creating a long-term strategic plan is necessary to align with the evolving workforce landscape.
Examples of Augmented Workforce
- Schaeffler, a global automotive and industrial supplier, embarked on a journey to improve operations, communication, and collaboration across its 75 plants worldwide. The company faced several challenges, including reducing its carbon footprint, connecting global experts with on-site teams, and maintaining functional excellence during the COVID-19 pandemic. It decided to use Microsoft’s remote assistance software on the HoloLens 2. One key enabler of this effective adoption was the global distribution and support network of the technology provider. With an augmented workforce, the company experienced several positive outcomes. Machine downtimes were reduced, and the ad hoc availability of experts increased.
- Amazon has deployed an army of robots in its warehouses to assist workers in picking and packing items efficiently. Amazon Robotics developed robotic arms designed to work alongside human workers. These arms use computer vision and machine learning algorithms to identify and select items for shipping. They make the process significantly more efficient. Currently, around 750,000 Amazon robots are doing the heavy lifting in warehouses. They allow employees to focus on delivering for customers. At Amazon’s fulfillment centers, these robots, often in the form of pods, transport items to human workers. They follow the Goods-To-Person model. With this robot-human alliance, human workers can now pack around 300 to 400 products per hour instead of just 100.
- Salesforce, a technology giant, is also leveraging AI to augment its workforce. Their AI tools automate repetitive tasks such as lead qualification and data entry, liberating sales reps and customer service agents to focus on more critical activities. Furthermore, Salesforce’s AI system analyzes customer data and provides representatives with recommendations on how to resolve customer issues more swiftly and effectively. This adoption of AI has helped Salesforce reduce the average time it takes to resolve customer issues, ultimately enhancing the customer experience.
The list of emerging artificial intelligence (AI) trends doesn’t end here. With more advancements in this field, we can expect to see more and more AI trends contesting for the top spot. So, will AI replace humans as it grows? The simple answer is, no. In fact, AI is expected to create 133 million new jobs by 2030. Now that you know the top 5 artificial intelligence trends in 2024, consider upskilling yourself for the AI-powered future. Wondering how? Just enroll for an artificial intelligence certification by the Blockchain Council. These AI certifications will equip you with all the necessary skills and resources to be the most sought-after AI professional.
What is the latest trend in AI?
- Responsible AI is one of the latest trends in AI.
- It emphasizes ethical and transparent AI usage to prevent societal damage, addressing concerns about bias, data privacy, and explainability.
What are the 5 emerging trends in AI?
- Responsible AI: Focusing on ethical and transparent AI usage to avoid unintended societal consequences.
- Multimodal Learning: Enhancing AI’s ability to understand and process information from various sources, including text, images, sounds, and videos.
- AI Augmented Apps: Combining AI and Augmented Reality (AR) to provide enhanced real-world experiences.
- Enhanced Regulations for AI: Mandating user consent, data security, and transparency in AI operations to protect privacy.
- Augmented Workforce: Collaborative integration of AI with human workers to enhance productivity and efficiency.
What is the next AI technology?
- The article doesn’t specifically mention the next AI technology, but it discusses emerging trends like Responsible AI, Multimodal Learning, and AI Augmented Apps that are on the horizon.
Which AI can predict the future?
- There is no particular AI that can predict the future accurately.
- However, AI can make decisions and predictions based on data and patterns, similar to how we predict the weather from various indicators like clouds, wind speed, and temperature.
What is the AI market prediction?
- The global AI market is currently worth $136.55 billion.
- It is expected to grow at a compound annual growth rate (CAGR) of 37.3% by 2030, indicating a significant increase in its market value in the coming years.