Artificial intelligence (AI) has been around for a while now, increasingly impacting how both individuals and businesses approach their decision-making. AI has a wide range of possibilities across all industries, helping business leaders, entrepreneurs, and workers improve processes.
Our previous look at top trends saw a focus on big data. In this article, we’ll go over a few AI trends, including:
- Large language models
- AI-based security
- Generative AI
- Multimodel learning
- Quantum AI
- Conversational AI
- AI-driven scientific discoveries
- Explainable AI
- No-code and low-code tech
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1. Large language models
AI language models rely on machine learning to know how sentences or paragraphs are related to each other. The models learn and understand language by being fed large amounts of text and then building statistical models that understand the sentence or paragraph relationship probability.
And these language models are getting bigger while becoming more refined in the understanding of language itself. AI is capable of processing and generating human-like interactions with semantic techniques that improve result quality.
Needing only a few training examples, large language models easily improve models for new problems. AI solutions used to need large amounts of human-labeled data, something that can be difficult and expensive to generate. These large models, however, are capable of achieving the same - or even better - results with one or a few examples, which leads to lower AI costs.
2. AI-based cybersecurity
Fraud management is an example of why businesses are adopting AI. Cybercrime vulnerability is a major issue, as the Internet of Things (IoT) has connected almost every device, offering exploitable loophole opportunities.
AI can be a great tool for identifying suspicious activities, as it can analyze patterns of network traffic extremely quickly. With connection increasing all the time with technologies like fog computing and cloud services for storage systems and devices, having efficient cybersecurity systems in place is key.
3. Generative artificial intelligence
This branch of AI focuses on creating content such as images, written text, text-to-image, and music. Useful for a variety of purposes, generative language models create both grammatically correct and topic-appropriate text that sounds natural.
Generative AI models can also solve problems, generate more general intelligence, and easily adapt to a variety of situations.
4. Multimodel learning
This branch of machine learning allows for systems to learn from sensory input such as text, sound, images, speech, and video. Multimodel systems can combine sensory, like speech and images, input to learn which lets them better understand ideas. Machines can also work with data from various sources to generate more accurate results.
Multimodal learning is becoming vital as it helps machines to learn how they can understand the world better. Through various input formats, machines easily understand events and objects, which in turn will help us build even better AI models for better results.
5. Quantum artificial intelligence
Quantum AI uses quantum computing for the computation of machine learning algorithms, as it has computational advantages over traditional machines. Quantum computing can train machine learning algorithms extremely efficiently and quickly, creating optimized algorithms.
AI that’s powered by quantum computing will help solve complex problems much faster than traditional AI. Quantum AI provides quicker and more accurate pattern prediction and data analysis, which is key for businesses to identify challenges and solutions.
6. Conversational artificial intelligence
Enabling speech-based interactions across platforms and users to improve engagement with at scale, conversational AI makes use of machine learning, speech recognition, natural language processing, and speech synthesis.
The market size of conversational AI is projected to reach US$18.4 billion by 2026. The adaptation of omnichannel strategies, an increase in the demand for AI-enabled customer support services and chatbots, and continuous customer engagement are all behind the expansion.
7. AI-driven scientific discoveries
In our new series, News Roundup, we go over just a few of each month’s latest discoveries on AI, deep learning, machine learning, and more. There’s an increasing trend of AI-driven breakthroughs in industries like healthcare.
From spotting type 1 diabetes in children much earlier to streamlining pediatric emergency department visits, AI is helping scientists and researchers to improve work throughout various sectors, and in turn, improve our way of living. We can keep expecting to see more breakthroughs in the coming years.
Learn more about deep learning with our guide below:
8. Explainable artificial intelligence
Also known as interpretable AI, explainable AI refers to AI in which the solutions’ results can easily be understood by humans. Existing in direct contrast with machine learning’s concept of the “black box” - where even its designers are incapable of explaining how AI arrived at a particular solution - it refines mental models of AI-powered systems users to help them perform better.
These algorithms are thought to follow three main principles:
- Interpretability. The possibility of understanding the machine learning model and presenting the underlying basis for decision-making in such a way that humans can understand.
- Transparency. If the approach designer can describe and motivate the processes that extract model parameters from training data and generate labels from testing data.
- Explainability. Not yet completely defined, it broadly refers to the collection of interpretable domain features that contributed to given examples to generate decisions, such as regression or classification.
9. No-code and low-code technologies
There’s a scarcity of skilled AI engineers and developers, which can be a major issue for businesses in adopting AI technology. This is where no-code and low-code technologies often come into play, as they aim to provide simple interfaces that can, in theory, develop highly complex AI systems.
No-code and web design user interface tools allow users to create web pages extremely easily. No-code AI tech also lets developers create AI systems that are intelligent by combining different ready-made modules and then feeding them industrial domain-specific data.
The “democratization” of AI, data tech, and machine learning can result from these advancements, as natural language processing, no-code, and low-code technologies can allow us to instruct complex machines through written instruction or voice commands.
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