From code to robots – the main AI trends changing business and life
Hello, this is Yulia Rogozina, business process analyst at Sherpa Robotics. Today I translated for you an article on trends in artificial intelligence. AI technologies are constantly evolving, and we need to keep an eye on them to keep up with changes and apply them effectively. This article covers multimodal AI, small models, agent-based AI, open source models, and more. Also, at the end, I will talk a little about the use of corporate neuro-employees, which I have already encountered in my work.
Artificial intelligence is no longer a concept in the distant future – it is already here, evolving at astonishing speed and changing the face of various industries before our eyes. From healthcare to the entertainment industry, the impact of AI is being felt everywhere, sparking innovation, increasing efficiency and sparking ethical debate. But with such a flurry of changes, which direction is the industry heading? To make sense of the chaos, we've compiled a list of the most significant trends that are not only making headlines, but also shaping the next stage in AI development. These trends highlight breakthrough advances that push the boundaries of what artificial intelligence can do.
In this article, we look at ten key trends shaping the future of AI, from the rise of multi-modal systems capable of processing text, images, video and audio, to the growing demand for smaller, more efficient models. We'll also delve into the growing importance of open source in AI, the emergence of autonomous agents, and the expanding role of AI in areas such as programming, gaming, and humanoid robotics. Buckle up and let's take an in-depth look at how AI is transforming our world—one step at a time.
Top 10 Artificial Intelligence Trends to Watch
As AI technologies evolve, key trends are emerging on the horizon, highlighting the most exciting and transformative trends in the industry. From innovations in model architecture to the application of AI in everyday technologies, these trends provide us with a glimpse into the future of AI capabilities. Let's look at ten current trends that are currently moving the industry forward.
1. Multimodal AI
Large language models (LLMs) earned their name because they were originally created to process text data. However, our world is by nature multimodal, so the next logical step was to create AI models that can process multiple types of data simultaneously. The move to multimodality has led to the development of models such as OpenAI's GPT-4, Anthropic's Claude-3.5, and Google's Gemini models, which were originally designed to be multimodal. These models are not only capable of understanding and generating text, but also interpreting images, analyzing audio, and even processing video, opening up new horizons of possibilities.
Multimodal AI enables a wide range of applications across a variety of industries. For example, such models can provide more dynamic customer support by interpreting images submitted by users; generate creative content, such as video scripts or music, based on a combination of visual and text data; or improve accessibility tools by converting text to audio and vice versa. Moreover, multimodal capabilities strengthen AI models by exposing them to a variety of data types, which enriches the learning process and improves overall accuracy and adaptability. This evolutionary direction towards multimodality lays the foundation for more powerful and versatile AI systems, opening new horizons in areas such as education, healthcare and entertainment.
2. Small models
With the ongoing race for leadership in AI, there is a significant trend towards developing smaller, more efficient models that can deliver high-quality results without the need for huge computing resources. Examples of such models include OpenAI's GPT-4o Mini, Microsoft Azure's Phi-3 models, Apple's On-Device models, Meta's LLaMA 3 8B, and Google's Gemma-7B. These compact models are designed to deliver reliable performance while using significantly fewer resources, making them suitable for a variety of applications, including those that can run directly on mobile devices or edge compute nodes.
The desire to create small models is fueled by several factors. First, they consume less power and require less computational effort, which is especially important for companies looking to implement AI solutions at scale while conserving energy. Second, some of these models, such as Apple's On-Device, are optimized to run directly on smartphones and other portable devices, allowing for AI features such as real-time translation, speech recognition, and improved user experience without dependency from cloud computing. By focusing on efficiency and affordability, these small models help democratize AI by making powerful technologies available to a wider range of users and industries while reducing the infrastructure burden typically associated with large models.
3. Open source models
Open source models have become the cornerstone of the democratization of AI, allowing unrestricted access and allowing developers from different sectors and skill levels to develop technologies. However, there is ongoing debate about what should actually be considered “open source”. Recently, the Open Source Initiative (OSI), the key organization that defines open source software standards, released a new definition that for an AI system to be considered open, it must allow anyone to use it for any purpose without the need for permission. Moreover, researchers must have full access to examine its components and understand the system's operation, including details about the training data. By this standard, many AI models that are commonly referred to as “open” may not fully meet the criteria, as they often do not provide transparency about their training data and impose some restrictions on commercial use. As a result, such models are better referred to as “open scale models,” which offer open access to their scales but with certain restrictions. Open weight models have made impressive strides forward, closing the performance gap with leading indoor models. The release of Meta LLaMA 3.1 405B has set a new standard, beating proprietary models such as GPT-4o and Claude 3.5 Sonnet in a number of key areas. Other notable models with open weights include the Mistral model, the Grok from Elon Musk's xAI, and the Gemma model from Google.
Open approaches play an important role in promoting transparency and ethical development of AI, as greater scrutiny of code can help identify biases, errors, and security vulnerabilities. However, there are legitimate concerns about the potential misuse of open AI to generate misinformation and other harmful content. The future challenge will be finding a balance between democratizing AI development and ensuring responsible and ethical use of these powerful technologies.
4. Agent AI
Agent-based AI represents a significant shift in artificial intelligence capabilities, moving from reactive systems to proactive, autonomous agents. Unlike traditional AI models, which operate by responding to specific user requests or following pre-established rules, agent-based systems are able to independently assess the environment, set goals, and execute actions without constant human supervision. This autonomy allows them to independently decide what steps to take to complete complex tasks that cannot be solved in one go or with a single tool. Essentially, agent-based AI is capable of making decisions and acting in pursuit of specific goals.
These advanced agents open the door to applications at incredibly high levels of performance. One impressive example is AI Scientist, an agent-based system that directs large language models to generate new ideas for AI research, write code to test those ideas, and even write scientific papers based on the results. Another interesting app is TransAgents, which uses a multi-user workflow to translate Chinese novels into English. Here, different LLMs (or instances of the same model) act as a translator or localization specialist, checking and editing each other's work. As a result, TransAgents achieves a level of translation quality comparable to that of professional translators.
As agent-based AI evolves, we are likely to see even more applications across multiple sectors, pushing the boundaries of what AI can achieve on its own.
5. Customized Enterprise AI Models
Although general-purpose large-scale models such as GPT-4 and Gemini have received much public attention, their usefulness for business applications may be limited. The future of AI in the enterprise space is increasingly heading towards smaller, more focused models designed to solve highly specialized problems. Companies are demanding AI systems that meet their specific needs, and these customized models are demonstrating greater sustainability and long-term value.
Building an entirely new AI model from scratch, while possible, is often prohibitively expensive and resource-intensive for most organizations. Instead, many choose to adapt existing models, either by modifying their architecture or customizing them with specialized data sets. This approach is more cost-effective than building a model from scratch and allows companies to avoid the ongoing costs of accessing a public LLM via an API.
Given this demand, general purpose model providers are adapting. For example, OpenAI now offers tuning options for GPT-4o, allowing companies to optimize the model for greater accuracy and performance in specific applications. Tuning allows you to adjust the tone, structure, and responsiveness of the model, making it more suitable for complex, subject-specific instructions.
Success stories related to this trend are already emerging. Cosine's Genie, a software engineering assistant built on a customized version of GPT-4o, delivers outstanding results in bug resolution, feature engineering, and code refactoring. A similar customized version of GPT-4o, Distyl, excelled at tasks such as query reformulation, intent classification, and SQL generation, demonstrating the power of customized AI for technical tasks. This is just the beginning—OpenAI and other companies are committed to expanding customization capabilities to meet growing demand from the enterprise sector.
Customized generative AI tools can be developed for virtually any business scenario, be it customer support, supply chain management, or legal document review. Industries such as healthcare, finance and law, with their unique terminology and workflows, can benefit enormously from these customized AI systems, which are quickly becoming essential for companies seeking accuracy and efficiency.
6. Generation with extraction support
One of the key problems that generative AI models face is “hallucinations”—situations where the AI generates answers that sound convincing but are factually incorrect. This represents a major hurdle for businesses looking to integrate AI into mission-critical or customer-facing processes where such errors could have serious consequences. Retrieval-assisted generation (RAG) has emerged as a promising solution to this problem, offering a way to improve the accuracy and reliability of AI inferences. By leveraging the ability to extract information in real time from external databases or knowledge sources, RAG allows models to provide factual and relevant answers rather than relying solely on pre-existing internal data.
RAG has profound implications for enterprise AI, especially in industries that require high precision and relevance. For example, in healthcare, AI systems using RAGs can extract the latest research or clinical recommendations, supporting healthcare professionals in decision making. In customer service, chatbots with RAG can access a company's knowledge base to accurately and relevantly resolve customer issues. Likewise, law firms can use RAG to improve document review by retrieving relevant case law or statutes in real time, reducing the risk of errors. RAG not only helps curb the problem of hallucinations, but also allows models to remain lightweight since they don't have to store all sorts of knowledge internally. This leads to faster performance and lower operational costs, making AI more scalable and reliable for enterprise applications.
7. Voice assistants
Generative AI is transforming the way we interact with voice assistants, making conversations more fluid, natural, and responsive. OpenAI's GPT-4o with voice capabilities, recently demonstrated, promises a significant leap forward in the field of conversational AI. With an average response speed close to that of a human, it supports more dynamic interactions, allowing users to have real-time conversations without awkward pauses. At the same time, Google is actively developing its Astra project, which integrates advanced voice features to create seamless, intuitive conversations between users and AI. These developments signal major changes in how voice assistants will function in the near future, moving from basic team interactions to rich, conversational exchanges.
Apple is also stepping up its game: Siri will soon be able to offer more natural responses, based on the company's latest presentation. The improvements are expected to make Siri more responsive and intuitive, bridging the gap between human communication and AI interaction. This evolution means that we will soon be communicating with voice assistants as if we were talking to a knowledgeable colleague. Voice assistants can transform the way we approach tasks ranging from scheduling meetings and answering emails to controlling smart homes and even assisting in healthcare by providing real-time symptom analysis. While we may not rely solely on voice commands, the ability to effortlessly switch to voice interactions will soon become standard, making AI assistants more adaptive and comfortable in different contexts.
8. AI in programming
The intersection of AI and software development is experiencing rapid growth, highlighted by increased funding highlighting the sector's potential. Recent investments in companies like Magic, a startup focused on code generation that raised a staggering $320 million, and Codeium, an AI-powered coding acceleration platform that raised a $150 million Series C round, indicate growing interest in this space. Cosine, previously known for its advanced GPT-4o model, has also secured $2.5 million in funding for its AI developer, which has demonstrated the ability to outperform human coders in tasks such as debugging and feature engineering. These investments indicate a surge in interest in AI solutions in the programming space as companies look for ways to improve the efficiency and effectiveness of their software development processes.
Generative AI is already transforming the coding process by automating tasks such as code generation, debugging, and refactoring, significantly reducing the time and effort required for developers to complete projects. For example, platforms like GitHub Copilot have proven their ability to increase developer productivity by up to 55% by suggesting code snippets, identifying bugs, and providing real-time help. The use of AI in programming goes beyond just writing code—AI can help optimize testing, automate documentation, and even improve performance. This increased speed and efficiency benefits not only individual developers, but also entire teams, allowing them to focus on more complex tasks while AI handles the mundane and time-consuming aspects of programming. With further advancements, AI-based coding tools are becoming an integral part of modern software development.
9. Humanoid robots
Humanoid robots are rapidly gaining popularity amid significant advances in robotics and artificial intelligence. Designed to emulate human physical capabilities, these machines are developing new functionality for applications in industries such as manufacturing, warehousing and logistics, where their flexibility allows them to perform tasks that require precision, dexterity and adaptability. Companies such as Tesla with its Optimus robot, Figure Robotics, Agility Robotics and 1X are leading this rapidly growing sector.
However, the use of humanoid robots is not limited to factories and warehouses. 1X's Neo and Weave's Isaac robots are designed to become home assistants. Thus, the recently introduced Weave device can help with everyday tasks such as cleaning and organizing space. These robots also show potential in the care sector, where they could help older people with daily activities or provide an element of companionship. With further development, humanoid robots are likely to become more common in both professional and personal spaces, supporting people in tasks that require physical interaction in everyday life.
10. Artificial intelligence in games
Artificial intelligence is revolutionizing the gaming industry, with generative AI leading the way by automatically creating complex assets such as 3D objects, characters, and even entire environments. Instead of manually designing each object or landscape, developers can now use AI models to generate realistic or fantastical elements at scale, speeding up the production process and expanding creative possibilities. For example, AI-based tools can design varied terrain, buildings, and non-playable characters (NPCs) that dynamically respond to player actions, making worlds more immersive and reducing the workload of game designers.
Particularly interesting is Google's new AI game engine, which has demonstrated the ability to recreate classic games like DOOM, and potentially any other game as well. This technology has the potential to revolutionize the game development and remastering process, offering new ways for developers and fans to experience their favorite titles. Using AI to recreate the mechanics, graphics, and even plots of iconic games, this technology not only preserves gaming history, but also opens the door to new iterations and modifications. The implications of this are enormous: generative AI could lead to personalized games, where players can influence everything from storylines to the design of the game world, creating unique and customized experiences.
With the advancement of these technologies, we can see a future in which AI helps both independent developers and large studios create detailed, engaging games faster and cheaper, while enabling unprecedented levels of creativity and customization.
Shaping the future of AI: what's next?
The rapid development of AI in various fields is redefining the boundaries of what is possible in both enterprise and personal applications. Each of the trends discussed—be it the rise of agent-based AI, refined enterprise models, or the expanding role of AI in software development—points to a future where AI becomes increasingly pervasive in our daily lives. As AI evolves, it will not only increase productivity and creativity, but will also open up new ethical questions and challenges, especially as more industries begin to adopt these technologies.
The future of AI is both exciting and challenging. Whether it's transforming industries like manufacturing, healthcare and gaming, or revolutionizing personal assistants and corporate work flows, AI is poised to take center stage in the way we live and work. As these trends evolve, a key challenge will be to ensure that AI development remains balanced, ethical and beneficial to society as a whole.
Comment
Soon, managing neuro-employees will become one of the basic management competencies, similar to how many managers already write in their resumes about the skills of managing both offline and online teams.
At Sherpa Robotics, we are creating neural employees. First of all, smart robots are penetrating into those areas where there is a lot of information that is required in different combinations by different people: technical support, sales, law, HR, etc.
In the coming years, all people will have to learn to work not only with other people, but also with robots. It's not as simple as it seems at first glance. Smart robots (neural employees) can already do a lot, but setting them up for each department will have to be done together. Feedback from users will be especially important – evaluation of the response will be a key indicator of the effectiveness of the neuroscientist’s work.
Also, the use of neural workers will require people to focus on truly complex and ambiguous tasks, which will make their workday more stressful and their work more responsible.
As an analyst, I can say that instantly obtaining information that previously took hours, days, weeks and even months to collect will significantly speed up business processes. But people will also have more time to make decisions based on them.
