Intro
As we step into the year 2024, Artificial Intelligence continues to transform industries and our daily lives. From chatbots to AI-automated processes, this technology is rapidly increasing the speed of operations while delivering more specified and data-driven decisions. In this volatile reality, trends and technological innovations emerge faster, reaching a point in which it is hard to keep track of new AI solutions.
In this blogpost, we will explore some of the top AI trends to watch for in 2024, discussing how these trends are shaping the future of industries.
1. Increased Processing Capacity
Over the past few years, AI has made remarkable strides in expanding its processing capabilities, and this trend shows no signs of slowing down. In this sense, new technologies are being developed to consolidate AI’s performance. By increasing AI’s processing capacities, complex tasks that took months to be completed may now take days or even hours. Within this ever-growing scenario, here are some tools that are contributing to AI’s enhanced processing:
Natural Language Processing
According to IBM, human language is filled with ambiguities that make it incredibly difficult to determine the intended meaning of text or voice data. However, the use of NLP allows computers to analyze different forms of texts, enabling them to identify the content’s semantic intention, precisely recognize voice speech, differentiate semantic ambiguities, and track improvement points in text forms of varied languages.
Beyond that, NLP has evolved to the point of crossing multiple languages in its database. This allows companies to internationalize their efforts without needing to hire a foreign language specialist. Generative AIs such as ChatGPT, Google’s Bard, and Microsoft’s Bing Chat already incorporated NLP tools into their scope.
Besides being a useful tool for texts and translations, NLP has been enhancing the recommendation algorithm’s performance. In this scenario, text information can store a large amount of data that cannot be covered by ordinary filters, such as sarcasm or colloquial speech. These language varieties are clear to NLP, which facilitates locating the consumer’s preferences among thousands of filters and subjective criteria that are specific to the user.
Cloud Computing
Cloud computing’s main role is to provide access to vast amounts of computing resources on demand. And, since cloud service providers offer massive servers with extensive amounts of datasets, the ordinary citizen’s need for physical data storage is excluded. As it may seem that this factor does not represent an innovation for 2024, cloud computing’s constant growth allows AI systems to train with increasingly more data from all over the world.
In this context, models rely on data for learning, and its adequate availability and leveraging to analyze from. For this reason, with the dissemination of cloud storage, AIs will have an extent of sources to improve their capacities. According to Marketsandmarkets, the global Cloud Computing Market size was valued at $545.8 billion in 2022 and it is projected to reach $1,240.9 billion by the end of 2027.
2. Real-Time Accurate Performance Monitoring
Performance monitoring is key to any kind of business. In this scenario, the combination of multiple AI tools such as Machine Vision and Deep Learning algorithms has been changing how companies safeguard their operations, especially in heavy asset industries. For this purpose, AI integration has become a powerful tool for pattern recognition, being able to detect minimum anomalies in extensive and difficult-to-reach areas.
According to Amazon, AI is changing the way performance management is performed using a neural network, so it can be applied in different fields. Neural networks applied to images and videos use inputs to be trained, filtering useful information into their structure that will be transformed into outputs at the end of their structure. With these trained neural networks we can perform object recognition, monitoring, classification, image reconstruction, create new images, voice recognition, facial recognition, among other things.
By adopting this advanced AI combination, industries will be able to automate quality inspection, anomaly detection, safety, and environmental improvements. In this context, more accurate performance monitoring guarantees maintenance cost-saving, data-driven decision-making, and enhanced risk management. This showcases how AI is enabling optimizing maintenance for sectors that used to struggle with this complex activity.
Furthermore, this abrasive AI integration is driving efficiency, safety, innovation, and sustainability across industries. This tool allows companies to identify and address potential risks before they turn into big problems. Indeed, by integrating these models, image recognition will be constantly improved by the other learning capacities, polishing its behavior based on occasional previous mistakes.
3. Generative AI as the predominant search method
The way we search for information on the internet has undergone several changes over the years. From forums, blogs, and googling, to Generative AIs, the precision of search engines has evolved significantly, being more personalized and consumer-directed at each step. However, in this new context, the search method provides new information based on what the user is looking for, being able to generate text, audio, images, or videos.
The most consolidated platform for this service, OpenAI’s ChatGPT, functions by integrating different learning methods, including unsupervised or semi-supervised learning for training. With this technique, AI developers can use extensive sets of unlabeled data for building models. The outcome is a highly analytic search tool that can process different types of data, delivering more specific results that can be re-generated if the response doesn’t match the question.
Different from GenAI, traditional search engines rely heavily on keywords and search intention, leaving room for ambiguity and misunderstanding. In contrast, with the help of the previously mentioned NLP, Generative AI can comprehend the nuances of natural language, decipher intent, and provide answers that are tailored to the user’s specific needs. In this sense, this powerful tool is helping its users to solve complex problems that used to be more effort-requiring.
Moreover, this system has the potential to bridge language barriers and facilitate global communication. With the ability to translate text and generate content in multiple languages, it can open up a world of knowledge and culture that was previously less accessible. Besides that, GenAI undoubtedly is responsible for AI’s latest boom.
4. Deep Reasoning
Deep reasoning represents a step of AI development that still may be too far down the road. However, it consists of the system’s ability to perform logical inference and reasoning on complex and abstract problems. It’s a technology that goes beyond mere data processing and rule-based decision-making. In essence, the goal of deep reasoning is to create AI systems that can mimic human deduction without human interference.
AI’s deep reasoning differentiates from other AI models by understanding implied relationships between things and considering abstract concepts and common sense. This AI capability has far-reaching implications, from predicting stock market trends to allowing computers to develop human-like reasoning. However, it may be too early to make any type of allegation on this tool’s impact.
Indeed, the concept’s extent still remains uncertain to researchers. According to a University of Edinburgh study by Kwabena Nuamah, today’s models cannot, on their own, successfully perform algorithmic reasoning, especially if the task spans multiple domains. This includes, for instance, choosing which algorithms to use, when and how to combine them, which still requires a human perspective to properly execute.
AI’s democratization and regulation
No innovation has ever spread as quickly as Artificial Intelligence, and it has no intent to stop. According to a 2022 IBM research, more than a third of companies (35%) report the use of AI in their business, a four-point increase from 2021. In this context, AI has broken free from being a futuristic concept and passed to be a consolidated reality in less than 30 years.
Considering its high speed evolution, AI started to raise important questions. These concerns span a wide range of areas, from perpetuating bias, discrimination, and misinformation to replacing human work. Beyond those harmful prejudices, data privacy stands as another key obstacle to be addressed. For this reason, properly monitoring and regulating AI’s capacities seems to be the next step to be taken.
In this new reality, striking the right balance between fostering innovation and ensuring ethical use is a complex challenge that society must grapple with. According to Axios, in the US, AI-related bills have been introduced nationwide in state legislatures so far in 2023, but only 14 became laws. It may seem like a step forward, but it’s safe to say that these regulatory advancements won’t be able to follow AI’s rapid evolution.
In conclusion, as we embark on the journey of 2024, Artificial Intelligence continues to evolve at a rapid pace. Multiple AI advancements are reaching the hands of the ordinary citizen, generating the need for proper monitoring and responsible use while skilling up with such useful tools. The future of AI is both promising and uncertain, and it’s a space that will continue to captivate our imagination and drive innovation.
About the Author: Jorge Kawano
