2021 was staggering—not only to escalate core artificial intelligence (AI) capabilities like natural language modeling and self-supervised learning (SSL), but also for scientific discoveries like Proteins structure prediction and development tools like Copilot. (Also read: A Primer on Natural Language Understanding (NLU) Technologies.)
These jaw-dropping developments gave rise to expectations from AI and made many curious about upcoming trends and advances in the field. Thus, this article will highlight some of the key forthcoming developments in AI, poised to make it more potent and impactful.
Here are the developments you should look forward and consider incorporating into your work:
More Power to Language Modeling
Language modeling is 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.
Since OpenAI released GPT-3, the most powerful language model ever built, it has been in the limelight due to its breathtaking language capabilities. For example, it has been demonstrated that—with proper human priming—GPT-3 can generate creative fiction, working 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 will bring more breakthroughs in language modeling and applications like automatic generation of computer programs.
SSL for Image Modeling
In the last year, large scale text data’s SSL abilities have grown to the extent that we can learn complex tasks such as machine translation, text classification, question answering and many others using few-labelled examples.
Comparably, the progress on images’ and videos’ SSL capabilities is far behind, mainly due to the non-discrete nature of the data, which makes it difficult to learn in a huge continuous data space.
Although this field progressed in 2021, it has not matured to the extent of text data. As many research groups are working to tackle this challenge, we can expect some breakthrough in this area. (Also read: Understanding Self-Supervised Learning in Machine Learning.)
Conversational AI is technology to enable speech-based interaction across users and platforms especially to better engage with users at scale. Building it requires utilities such as speech recognition, speech synthesis, natural language processing and machine learning.
In late 2021, ReportLinker announced the size of the conversational AI market will grow from $6.8 billion USD to $18.4 billion USD by 2026. The key factors giving rise to this phenomenon are an increase demand for AI-enabled customer support services, the adaptation of omni-channel strategies, continuous engagement with customers and the increasing demand for chatbots during COVID-19 restrictions.
Given the rising demand for conversational AI systems, we can expect to see advances in these endeavors.
As we depend more on machines everyday, we are becoming more vulnerable to cybercrimes because every device connected to the internet gives attacker an opportunity to exploit its loopholes. And since connected devices are becoming increasingly complicated, it’s increasingly difficult to pick out and address the existing loopholes. AI can play a vital role in identifying suspicious activities by analyzing patterns of network traffic.
Therefore, we can expect some significant developments in using AI in cybersecurity in 2022.
Computer Vision Technology in Businesses
Computer vision is the most intended investment among organizations that have already put money into AI, according to a recent Gartner survey. The same survey found each of these companies is planning to invest an average of $679,000 over next two years.
Computer vision is a field of AI that deals with enabling machines to understand and interpret images and videos. AI’s machine learning algorithms are usually trained on images to recognize patterns, enabling them to identify and classify objects. It has a wide spectrum of use cases in many fields such as:
- Autonomous vehicles—to detect obstacles, tracks and pedestrians.
- Healthcare—to analyze medical scans such as X-rays, CTs and MRIs.
- Manufacturing—to visually inspect equipment.
- Agriculture—to use drones to monitor the conditions in fields and farms. (Also read: The 6 Most Amazing AI Advances in Agriculture.)
More AI-driven Scientific Discoveries
The AI-driven prediction of proteins’ 3D structure, a Deepmind discovery, is “Science” magazine’s 2021 “Breakthrough of the Year” because of its potential to solve a longstanding challenge in biology. “Science Focus” also named a humanoid robot, which can lip-sync with speech, in their list of 2021’s best scientific discoveries.
The last year was also a breakthrough year in weather forecasting, where Google and the University of Exeter joined forces to develop an AI-driven short-time weather forecasting system called “nowcasting.” Nowcasting can predict weather in two hours—compared to previous systems, which forecast it in anywhere from six hours to two weeks.
Given AI’s potential to address scientific challenges, we can expect more such breakthroughs in the coming years.
Explainable Artificial Intelligence
The rising interest in data regulations as well as AI transparency and fairness is making explainable AI (XAI) more and more pivotal. XAI deals with enabling, understanding and articulating the decision making process of black-boxed AI systems. (Also read: Why Does Explainable AI Matter Anyway?)
Besides empowering algorithmic capabilities, AI will help improve programmers’ and developers’ productivity this year.
In the past few years, AI has been used in tools like Amazon Code Guru to help developers improve their codes’ quality and find their most expensive lines of code. Github collaborated with OpenAI to build Copilot, which is a tool to assist developers in writing efficient code. And recently, Salesforce announced its CodeT5 project to assist Apex developers with coding.
Some other examples of recently developed AI-driven tools for developers are Tabnine and Ponicode. Further, code generation from natural language description is a popular application of language modeling; and recent advances in language modeling have made it a topic of interest. Codex from OpenAI is an example of this—and we can expect more such outcomes in this year.
The last year saw some incredible breakthroughs in the field of artificial intelligence. Building on them, corporations and the developers who work for them are poised to spearhead equally impressive advances in 2022.