Over the past decade, artificial intelligence (AI) has continued to transform the way we live and work: from voice-activated virtual assistants to image recognition systems, this technology is gradually making its way into our everyday life. has become part of our daily lives and becomes inevitable.
In industries like healthcare, finance, and transportation, AI is already revolutionizing the way things are done, and as the technology continues to evolve, we can expect to see it play an even bigger role with the potential to improve efficiency, accuracy, decision making. do processes.
In this article, we present six top AI trends in 2023 and what they mean for the future, with real-world examples and expert opinions.
6 AI trends in 2023
Generative AI
This trend involves the creation of new content or data based on models and insights drawn from existing data. In 2023, we can expect to see growing interest in generative AI, as it has the potential to have a huge impact on a wide range of industries, from art and design to manufacturing, and beyond.
Language models such as ChatGPT and GPT-3 have attracted attention and debate, due to their ability to generate human-like text and perform various linguistic tasks such as text summarization, question answering and the translation.
And even though ChatGPT is literally everywhere around us, experts hope it will inspire more similar products and services.
“Generative AI will begin to permeate more and more of the products we use every day. The biggest player will be Microsoft, whose massive partnership with OpenAI will allow them to integrate large language models into their products. OpenAI will make significant improvements by mitigating some of the biggest flaws in their language models, such as factual accuracy. Marin Smiljanicfounder and CEO of AI-powered search engine tool Omnisearch, told The Recursive.
Moreover, regional startups such as Codewell AI are already creating digital assistants capable of speaking any language at a human level, and this trend is expected to continue even more in the coming period.
Edge AI and Internet of Things (IoT)
The integration of AI with IoT devices will continue to grow, with AI used to process data at the edge of networks, where it can analyze and act on information in real time without the need for a cloud or a central server.
IoT devices can collect large amounts of data about their environment, but without AI, it is often difficult to make sense of this data and extract valuable insights. By integrating AI technologies, such as machine learning and deep learning, into IoT devices, this data can be analyzed in real time to make predictions, automate processes and improve decision-making.
In practical applications, the integration of AI and IoT can result in more efficient, cost-effective and intelligent smart homes, smart cities and industrial IoT solutions.
For example, AI algorithms can analyze data collected from IoT devices such as sensors and cameras to detect anomalies, prevent equipment failures, and optimize energy consumption. In healthcare, AI and IoT can be integrated to monitor patient health and predict potential problems before they become serious.
Advances in computer vision
AI algorithms will continue to improve their ability to interpret and analyze visual information, leading to increased use of computer vision in industries such as retail, healthcare and transportation.
Now, AI has the potential to make even more progress in computer vision by being integrated with other technologies, such as robotics, AR/VR, and sensor networks. This integration can lead to new and innovative applications that can leverage the strengths of both technologies.
Overall, AI has the potential to continue to make significant advances in the field of computer vision by integrating new algorithms, hardware, and data to build more sophisticated and capable systems.
Natural Language Processing (NLP) and Conversational AI
NLP technology will continue to advance, enabling more sophisticated and human interactions with AI-powered chatbots and virtual assistants.
If NLP models have already shown their ability to generate language, improvements are still possible in terms of coherence and naturalness of the generated text. Further developments in this area could lead to more sophisticated and better language generation models.
Additionally, NLP models are primarily trained on a limited number of languages, with a focus on English. However, by extending the coverage of NLP models to more local languages and dialects, NLP models can be more effective in serving a wider range of users.
When it comes to conversational AI models, most are limited in their ability to hold human-like conversations. Further developments in this area could lead to more sophisticated and capable conversational AI models capable of more natural and engaging interactions with users.
AI and blockchain
The combination of AI and blockchain the technology will provide new possibilities for secure and decentralized data processing and analysis.
Decentralized AI is one of the biggest upcoming trends, as blockchain technology can be used to create decentralized AI systems, in which data and computing power are distributed across a network of nodes, rather than centralized in a single entity. This in turn could lead to more secure and privacy-friendly AI systems.
Then there’s secure data sharing, as AI algorithms often require large amounts of data to train effectively. Blockchain technology can be used to create secure data sharing mechanisms to share data between different entities, while maintaining data privacy and security.
“At the macro level, we will see more advanced implementations between blockchain and artificial intelligence, to help keep up with the rapid development of AI solutions that is currently occurring. Using blockchain to store and distribute AI models will provide an advanced audit trail and improve data security for AI development,” said Vali Malinoiu, Head of Blockchain at Humans.ai , at The Recursive.
AI in new industries
AI will continue to expand into new sectors, such as healthcare, energy, agriculture and logistics, improving efficiency and decision-making processes.
In healthcare, AI can be used for tasks such as diagnosis, treatment planning, drug discovery, and image analysis. By using AI, healthcare professionals can make more accurate diagnoses and treatments can be tailored more effectively to each patient.
Another example is the financial sector, where AI is used for tasks such as fraud detection, risk assessment and portfolio optimization. By using AI, financial institutions can improve their risk management and make more informed decisions.
In sectors such as agriculture, AI is used for tasks such as precision farming, yield forecasting, livestock management and soil analysis. By using this technology, farmers can improve their crop yields, reduce waste, improve the welfare of their livestock and enjoy many other benefits.
And the trends won’t stop there: experts are adamant that AI will still bring a lot of progress.
“I also expect to see some interesting research results on the architecture of deep learning models. For example, Transformers, the current state-of-the-art model architecture, perform significantly better than vanilla recurrent neural networks, which were previously state-of-the-art. There’s really no reason to believe that transformer architecture marks the end of the story,” Smiljanic concludes.
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