Artificial Intelligence Trends to Expect in 2022

2021 has been a staggering year, not only for increasing basic artificial intelligence (AI) capabilities like natural language modeling and self-supervised learning (SSL), but also for scientific discoveries like prediction. protein structure and development tools like Copilot. (Also read: A Primer on Natural Language Understanding (NLU) Technologies.)

These revolutionary developments have raised expectations on the part of AI and aroused many curiosities about future trends and advances in the field. So, this article will highlight some of the major upcoming developments in AI, poised to make it more powerful and impactful.

Here are the developments you should anticipate and consider integrating into your work:

More power for language modeling

Language modeling is the machine understanding and generation of natural languages, which is used in applications such as speech recognition, machine translation, handwriting recognition, question answering, and information retrieval.

Ever since OpenAI released GPT-3, the most powerful language model ever, it has been in the limelight due to its jaw-dropping linguistic capabilities. For example, it has been shown that with the right human priming, GPT-3 can generate creative fiction, work computer code, and compose introspective business memos.

Now that OpenAI is working on GPT-4 and other big companies are developing their own more powerful language models, you can expect 2022 to bring more breakthroughs in language modeling and applications like automatic generation of computer programs.

SSL for Image Modeling

Over the past year, the SSL capabilities of large-scale text data have grown to the point where we can learn complex tasks such as machine translation, text classification, question answering, and many more using unlabeled examples.

Comparatively, progress on image and video SSL capabilities lags far behind, mainly due to the non-discrete nature of the data, which makes it difficult to learn in a huge space of continuous data.

Although this field has progressed in 2021, it has not matured to the extent of textual data. As many research groups are working to address this challenge, we can expect a breakthrough in this area. (Also read: Understanding self-supervised learning in machine learning.)

Conversational AI

Conversational AI is a technology enabling speech-based interaction between users and platforms, especially to better interact with users at scale. Its construction requires utilities such as speech recognition, text-to-speech, natural language processing, and machine learning.

At the end of 2021, ReportLinker announced that the size of the conversational AI market will increase from $6.8 billion to $18.4 billion by 2026. The main factors driving this phenomenon are growing demand of AI-powered customer support services, adapting omni-channel strategies, continued engagement with customers, and growing demand for chatbots during COVID-19 restrictions.

Given the growing demand for conversational AI systems, we can expect to see progress in these efforts.

AI-based cybersecurity

The World Economic Forum recently recognized cybercrime as a major risk to global prosperity and urged the world to jointly address it.

As we depend more and more on machines every day, we become more and more vulnerable to cybercrimes, because each device connected to the Internet gives the attacker the opportunity to exploit its vulnerabilities. And as connected devices become more and more complicated, it is increasingly difficult to spot and close existing gaps. AI can play a vital role in identifying suspicious activity by analyzing network traffic patterns.

Therefore, we can expect significant developments in the use of AI in cybersecurity in 2022.

Computer vision technology in business

Computer vision is the most planned investment among organizations that have already invested in AI, according to a recent Gartner survey. The same survey revealed that each of these companies plans to invest an average of $679,000 over the next two years.

Computer vision is a field of AI that involves enabling machines to understand and interpret images and video. AI machine learning algorithms are typically trained on images to recognize patterns, which allows them to identify and classify objects. It has a wide range of use cases in many areas such as:

More scientific discoveries based on AI

AI-based prediction of the 3D structure of proteins, a discovery by Deepmind, is Science magazine’s “Breakthrough of the Year” in 2021 because of its potential to solve a long-standing challenge in biology. “Science Focus” also named a humanoid robot, which can lip-synch with speech, to its list of top science discoveries of 2021.

Last year was also a breakthrough year in weather forecasting, where Google and the University of Exeter joined forces to develop an AI-based short-term weather forecasting system called nowcasting. Nowcasting can predict weather in two hours, compared to previous systems, which predicted it in six hours to two weeks.

Given the potential of AI to address scientific challenges, we can expect more such advances in the years to come.

Explainable artificial intelligence

The growing interest in data regulation as well as AI transparency and fairness makes Explainable AI (XAI) increasingly crucial. XAI deals with enabling, understanding, and articulating the decision-making process of black-box AI systems. (Also read: Why is explainable AI important anyway?)

Developer Productivity

In addition to boosting algorithmic capabilities, AI will help improve the productivity of programmers and developers this year.

In recent years, AI has been used in tools like Amazon Code Guru to help developers improve the quality of their codes and find their most expensive lines of code. Github collaborated with OpenAI to build Copilot, which is a tool to help developers write efficient code. And recently, Salesforce announced its CodeT5 project to help Apex developers code.

Tabnine and Ponicode are other examples of recently developed AI-based tools for developers. Additionally, generating code from the natural language description is a popular application of language modeling; and recent advances in language modeling have made it a topic of interest. OpenAI’s codex is an example of this – and we can expect more such results this year.

Conclusion

The last year has seen incredible breakthroughs in the field of artificial intelligence. Building on them, companies and the developers working for them are poised to lead equally impressive advances in 2022.


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