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Published: 29/12/2022

What are the biggest AI trends for 2023?

What are the biggest AI trends for 2023?

Artificial intelligence has significantly evolved in the last decade. It is increasingly making a noticeable impact on people’s everyday lives and can make daily tasks easier. AI and Machine Learning can be found everywhere, from chatbots, through AI-powered virtual assistants like Amazon Alexa or Google Assistant, to self-driving cars.

AI technology can contribute to various aspects of life, whether it be an everyday reality or business processes and purposes. More and more applications will emerge for AI in the future. `What will be its influence, and what can we expect? Let’s take a deep dive into future AI trends.

Three main types of artificial intelligence

Artificial narrow intelligence (ANI)

Also called “weak AI”, it focuses primarily on performing a single narrow task with a limited range of abilities. Currently, the ANI is the only one present in our life. However, scientists are getting closer to the next stage of AI development, general AI.

Artificial general intelligence (AGI)

This stage of AI development would be on the level of the human mind. Since we have yet to learn about the human mind, the successful development of general AI can still take some time. However, if it were possible at this stage to create artificial intelligence at this level, it would be capable of thinking like a human, much like the robot Sonny from the “I, Robot” film.

Artificial superintelligence (ASI)

This stage is still primarily theoretical and can be scary when considering dystopian fiction. It would surpass the human mind. For the technology to be considered ASI, it would need to be more capable than the human being in every way possible. This AI could carry out tasks and have relationships and emotions. It raises concerns about AI ethics and potential dangers.

How is AI developed?

Artificial intelligence is a complex concept that covers multiple fields, disciplines, and technologies. Let’s break down the disciplines involved in AI development.

Machine learning

Machine learning is the most common technology used to achieve AI. It allows computer systems, programs, and applications to learn automatically and develop better results without being programmed. As mentioned before, machine learning enables AI to find patterns in data, uncover insights, and improve the outcomes of whatever task the system has set out to achieve.

Deep learning

This specific type of machine learning allows AI to learn and improve by processing data. It uses neural networks mimicking biological neural networks in the human brain to process data, find connections, and produce inferences or results based on positive and negative reinforcement. It’s currently the most advanced AI technology, and you can read about it in greater detail later in the article.

Neural networks

Neural networks work like networks of neurons in the human brain to analyze the data many times over and over to find associations and interpret the meaning. They allow AI systems to take in large data sets, uncover patterns, and answer questions about the provided data.

Cognitive computing

Cognitive computing allows AI to mimic the way a human brain works when performing a complex task. For example, it can be analyzing text, speech, or images. It can simulate the thought process in complex situations where the answers may be ambiguous and uncertain.

Natural language processing

It is considered one of the critical pieces of the AI process. It allows artificial intelligence to recognize, analyze, interpret, and genuinely understand written and spoken human language. This function is essential for any AI-driven system that interacts with humans in some way, either via text or spoken inputs.

Computer vision

The ability to review and interpret the content of an image via pattern recognition and deep learning is considered one of the critical competencies of artificial intelligence. Computer vision enables AI systems to identify the components of visual data to help them learn to identify specific objects like cars or bicycles.

What technologies are essential for AI development?

Extensive, accessible data sets: artificial intelligence thrives on data and grows increasingly essential alongside the rapid increase of data and access to it. Potential applications of AI are growing thanks to developments like the Internet of Things, which produces a massive amount of data from connected devices.

Graphical Processing Units (GPUs): they are one of the essential factors increasing the value of AI. They are vital to Graphical Processing Units. GPUs provide the computing power needed for AI to process and interpret big data rapidly.

Intelligent data processing: AI requires data, and better data processing is needed to allow the creation of more advanced software algorithms to analyze data faster and at multiple levels simultaneously. Thus, understanding complex systems and predicting rare events more quickly.

Application Programming Interfaces (APIs): these interfaces are essential as they allow to integrate the artificial intelligence into traditional computer programs and software applications, allowing them to become more intelligent by enhancing their ability to identify and understand patterns in data.

What is the most advanced AI technology right now in 2022?

The most advanced software algorithms designed so far for artificial intelligence come from the deep learning technique. These AI models are trained by being fed a large amount of data which helps them recognize the features, patterns, and anomalies. Over time, the machine can make decisions, solve problems, and perform other tasks over the provided data sets.

It’s an impressive technique for finding defects or even diagnosing diseases, among many other uses. Deep Patient is an example of such. Trained data scientists fed data from over 700,000 individuals, including doctors’ visits and patients’ test results. The AI algorithm war pretty accurate at predicting diseases based on patients’ records. Moreover, it can predict whether a person is prone to schizophrenia or other psychiatric disorders in the future.

Deep learning still shows much promise for future development in the health department and others, like art or business. There are still many uses of deep learning that are yet to be discovered and further improved.

Most advanced AI in 2022

Why is artificial intelligence development significant?

We can see examples of how AI makes our everyday lives easier. It can be something as simple yet valuable as predictive text suggestions reducing the time used to type a message or e-mail or something more complex, like tools for translating languages – the prime example being Google Translate.

Over the last decade, progress has gone even further. Artificial intelligence helps us adopt the smart, like a more energy-efficient way to control our environment at home, for example, app-controlled lighting. AI models can better create text, graphics, audio tracks, and images than humans can. It opens opportunities to optimize and modernize the common workflows in marketing for better user experience and engagement.

Moreover, due to its data and analytics capabilities, artificial intelligence is finding more and more use in the health industry, as it can contribute to a quick and efficient diagnosis of diseases. Putting AI-powered solutions as support to human efforts can augment the work, thus making it more efficient and often more manageable.

What are the main concerns associated with AI?

Many works of fiction have already manifested the most prominent fears regarding the development of artificial intelligence. However, we’re not even halfway into realizing the dystopian nightmares, as AI is not yet so advanced. For now, software engineers want to achieve AI that would be as helpful to humans as possible.

However, some concerns are much more valid and are still subject to debate. One of the biggest ones is the use of data. When we talk about using AI for public services, the programs and algorithms may collect very sensitive data, like health or financial information, which means personal data, not everyone wants to share.

Although this may help management and leadership teams access real-time dashboards and reporting, giving an instant up-to-the-minute overview of operational effectiveness, people still have doubts about the safety of their data.

A whole different topic on the use of AI is AI ethics. It’s hard to ensure that data scientists and AI developers can explain how the AI makes the decisions and what information it uses to arrive at them. Organizations are already trying to eliminate bias and unfairness from their AI-powered solutions and automated decision-making systems.

A prominent example of possible bias can be this AI-judged beauty contest. Some argue that the algorithm was biased by only choosing white men and women for the winners. And although some agree with the picks, others say that it wasn’t fair for the algorithm to select people from only one race and, thus, could be biased. Removing bias from AI-enabled systems and creating explainable AI models no one would have to fear is, therefore, one of the most critical tasks for AI software engineers.

Main concerns associated with AI

How is AI improving businesses?

Putting AI systems to business use is not a new concept anymore. Simple drag-and-drop or wizard-based interfaces and generative AI algorithms exist in many business sectors. From predictive text suggestions through autonomous systems and smart machines specifically designed for a single task.

There are many advanced software algorithms designed to improve everyday work. An example of an AI that can help develop enterprise ai applications can be SwayAI. Another platform, Akkio, might also help create prediction- and decision-making tools.

AI can also enable businesses to create automated industrial machinery that can be easier operated. Companies can deploy AI-powered solutions for augmented reality or virtual assistants to convey and visualise their ideas more easily. AI can even contribute to creating renewable energy-powered infrastructure. The possibilities are endless, and AI development is becoming an increasingly indispensable work skill.

Challenges posed by the AI skills gap created by the shortage of skilled data scientists and AI software engineers

There is an increasing number of developing and new artificial intelligence – AI trends. Thus, there’s a specific AI skills gap in acquiring talent. As we can read in various publications, there is a considerable talent with a technical skill shortage in all segments – starters, skilled and seasoned AI specialists.

Some solutions imply covering the AI skills gap created by using AI to maximise what’s already there and reduce the burden on the AI team whenever possible. It is also sensible to build skills around sourcing the best AI technologies and suppliers and leveraging cloud-based platforms with pre-built solutions and accelerators. It is also essential to look at experienced hires, university hires and how partners and vendors can help fill the gaps in all the infrastructure.

AI skills gap

There are already some interesting and biggest artificial intelligence – AI trends that we can already see and experience. But what is there? AI already achieved mainstream exposure decades ago, but what realistic trends could we expect in the coming year? Let’s take a look at a few examples.

The rise of the quantum machine learning

Quantum computing could contribute to developing more advanced software algorithms and learning AI models. Although the prospect seems quite distant, some companies, like Microsoft or IBM, are continuously developing cloud-based quantum computing tools and simulators. Combining machine learning with quantum computing could improve their business process, and tackle challenges not yet tractable.

Machine learning with Automation (AutoML)

The aspects of AutoML looking promising are enhanced tools for labelling data and the automatic tweaking of neural net structures. With time, AI can become more democratised and cheaper, thus allowing us to create new solutions when selecting and refining a neural network model is automated.

Improving Predictive Analytics

Advancements in predictive analysis emerged as an exciting direction in artificial intelligence – AI trends. Predictive analysis AI models have their place in many fields of study and business. It uses data, statistical algorithms, and machine learning approaches to use past data to predict future outcomes.

The objective of predictive analysis is to use historical data to predict future outcomes accurately. It can prove especially useful for business analysts and market specialists from different sectors.

Higher diagnostic accuracy in healthcare

Given the use of AI in all kinds of automation processes, it becomes apparent that many initiatives aim at improving the healthcare system. It can be essential, given that, unlike humans, artificial intelligence can’t get tired and can simultaneously process vast amounts of existing data. Its use in the healthcare industry and ability to create a correct prediction might prove invaluable.

New AI models significantly speed up diagnosing and allow doctors to focus more on the treatment. Although AI diagnostic tools are already pretty accurate, with the growth and development of artificial intelligence capabilities, we can expect that accuracy to rise in the coming months and years. It may become one of the most important trends in the future.

See how we helped create a health wearable for dogs.

View the case study

AI ethics and principles for compliance

Artificial intelligence is a powerful technological invention that can help address pressing global issues and promote economic growth and diversity in the workplace. The topic of AI ethics is becoming more prominent in the business world. Issues like data transparency, biased data and algorithm fairness are drawing increased attention.

The term “AI ethics” doesn’t have an established universal definition. However, we know that it refers to a set of principles that promotes the development of ethical AI by bringing together the following essential elements:

  • protection of people
  • protection of the environment
  • safety of people from harm caused by the AI

AI ethics aim to ensure that AI applications are ethical. It involves safeguards against AI bias, privacy concerns, blunders, and environmental effect management. Preparing the organisation’s AI systems for regulatory purposes can be accomplished through transparency in disclosing AI uses to customers, creating explainable AI, fairness and accountability by publishing the internal governance policies, and engagement with the regulatory and legislative processes.

AI ethics

We’ve covered some more important artificial intelligence trends we might expect in the coming year. However, there are some more entertaining uses of AI. Let’s have a look at the funnier side of AI.

Fortune-telling

It may seem like this is an easy thing to do and a piece of cake for an AI-powered program. However, fortune-telling coming from AI is getting increasingly popular among the artificial intelligence trends. An Android and iOS app called Faladdin gives users tarot readings, horoscopes, and even AI-powered coffee cup readings. Some users are even surprised and say that the readings are scaringly spot-on.

Making perfumes

Even though artificial intelligence doesn’t have a sense of smell, it doesn’t keep it from creating signature fragrances. In a partnership between IBM Research and Symrise, a global producer of aromas and flavours based in Germany, customers in Brazil can now buy perfume made by Philyra – an AI perfume-making apprentice. Out of three fragrances, one was created solely by AI. Philyra’s and Apel’s creations for O Boticário, Egeo ON Me and Egeo ON You came out for Brazil’s Valentine’s Day on June 12.

Brewing beer

At first, brewery companies, like many others, started to deploy AI-powered solutions for supply chain management and marketing purposes. However, artificial intelligence proved useful also for creating recipes, as it can help effectively incorporate customer feedback. Brewers can use it to create an optimal formula based on the data from the received feedback. Artificial intelligence can also monitor the quality of the beer by, for example, monitoring the CO2 levels in real-time.

AI algorithms have become increasingly advanced, and machine learning allows us to use AI functionality for all manners of creation. Artificial intelligence even wrote a book on its own. However, recently, a platform for creating new images from written prompts, namely DALL·E 2. Anyone can write a prompt, and the artificial intelligence behind it will do its magic and create a new image. We can request specific things to appear in the picture and a style we want it to imitate. Want a picture of Shrek in the style of Zdzisław Beksiński’s art? It really can be done.

There is an ongoing debate about whether AI can replace people in artistic professions like graphic design. However, it’s still far from perfect, and AI cannot imitate emotion and perception easily without human touch.

Generative AI art

Listen to eye square Michael Schießl’s presentation on the role of AI in art.

Watch the video

What is the future of artificial intelligence in business?

Whether we use it to develop enterprise applications, augmented reality or an entirely new virtual world, the significance of artificial intelligence is ever-growing. Businesses are already building ai tools and even autonomous systems to create prediction and boost their work. For office workers, it can be simple drag-and-drop wizard-based interfaces and predictive text suggestions reducing the time spent composing a text (which we used to create this article). It can also control automated industrial machinery and self-driving cars.

Artificial intelligence can be both a blessing and a curse. However, there is no denying that it can significantly enhance workflow or customer experience. Important trends show that AI is creating multiple opportunities for all industries. In the 2023 forecast of artificial intelligence – AI trends, we will see it used increasingly frequently to create synthetic data that businesses can use for all purposes. Many initiatives already enable businesses to improve work and efficiency. Virtual assistants are already a great help in the everyday environment.

Although some fear that it may make some positions and jobs obsolete, we would think that it will be more of an extension of the human experience rather than its replacement. At least, these are the AI trends and prospects for the coming decades.

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