Artificial Intelligence (AI) Trends in 2022

There were times in human history when innovations made a breakthrough like the printing press or paper currency. We are living in a time when another major innovation is changing history and that’s artificial intelligence. In this blog post, we will talk about some statistics and facts and also cover major AI trends for 2022. Read on to keep updated!

Artificial Intelligence Statistics and Facts for 2022

Here are some interesting statistics and facts that will determine the future of AI.

  • AI technology can boost business productivity and the rate can reach up to 40 %.

What does this mean? This means drones in agriculture, robots in warehouses, and doubled economic growth. The rate of development will be high as ever before. Naturally, we will see more businesses adopt AI technology.

Trend: More businesses will adopt AI technology, especially large-scale ones. At the moment AI apps are not very affordable but as cheaper and more affordable options are in the market, the technology is going to be massively applied.

  • 97 % of mobile users are already using AI-powered voice assistants.

According to a study by IDAP, only 2% of iPhone owners have never used Siri, and only 4% of Android owners have never leveraged the power of OK Google. We notice or not, voice assistants are becoming as usual as for example, a regular mobile phone.

Trend: Voice assistants will continue to be tools for everyday use.

  • Google Machine Learning Program is 89% accurate.

This means that Google’s deep learning program is 15% more effective than pathologists.

Trend: As the accuracy of AI is improving, it will be used in sectors like medical diagnosis and the military.

  • Netflix saved $1 billion by using machine learning.

This is another advantage of using AI technology. It is much more cost-effective than traditional methods of running a business.

Trend: AI technology market will offer more cost-saving options.

  • Intelligent robots could replace 30% of human labor globally by 2030.

Yep, some specialists need to think about changing professions. Does this mean that human employment is endangered? If you take no action today, yes, you may be out of work in some 10 years. But AI replaces human labor as much as it creates some other jobs. The secret is to be flexible.

Trend: AI will close some jobs but will also open up opportunities for new types of jobs.

The prediction for 2020 was that 250 million cars were to be connected to the internet.

Experts predicted that there will be about 250 million internet-enabled vehicles by the end of 2020.

Trend: We will not witness self-driving cars massively soon but the process is developing slowly.

  • Chatbots will replace 85% of customer relationships with business enterprises

Today, to get an answer from a robot is not from the realm of fantasy. Some 85% of customer relationships are managed by chatbots.How to make a chatbot is a question on the table of thousands of developers today.

Trend: chatbots will continue to be the major tool for first-contact customer relations.

  • Apple is developing facial recognition technology to focus on customers’ reactions to ads

Seems simple but it will have an enormous impact on user experience. Businesses can adjust their strategies and build new and better models.

Trend: AI is already detecting not only words but emotions. It goes so far that robots are able to serve in the bank by talking to customers on a personal level. So, don’t be surprised if a robot asks you, “Are you sad today?”

  • At the moment over 28% of retailers are already deploying AI and ML solutions which is a sevenfold increase from 2016 when the number was only 4%.

Trend: Companies will start using AI more and more in the retail market — both physical stores and e-commerce strategies to stay ahead of the competition. This especially relates to price predictions and warehouse management.

  • 31% of the businesses have fully automated at least one key business function.

Trend: Organizations are going to automate the entire workflow reducing transaction times from hours to minutes. This relates from data gathering to logging, updating, processing, and validating data.

Uncertainty with AI

Since AI is gaining such popularity, there is a valid question ‘’is there any level of uncertainty connected with AI?’’ The question is valid because, in fact, we trust the machines part of our lives. If you think that even medical diagnosis and treatment are conducted today with AI machines, then you really understand how costly an error might be.

And to tell you the truth, there are, in fact, uncertainties connected with AI. For example, it is known that AI works by processing data. But who collects the data? Who does the data input? Humans! So, AI results may be erroneous due to human failure to input accurate data.

Flaws may come up during data pre-processing whether during curation, cleaning, or labeling. And after all, models are relying on various simplifying assumptions. Are they 100% accurate? The answer is that they introduce various modeling and inferential uncertainties.

Machine Learning and AI

In this last part of the blog post, I would like to talk about Machine Learning as a subfield of AI. I will not go into the complicated mathematical analysis of the relationship between AI and ML. For an ordinary person, it’s enough to know that AI is a bigger family where Machine Learning and Deep Learning rest. One thing is clear that more and more industries will turn to Machine Learning apps. To give you a clearer picture of what ML is, let us turn to some examples.

Plant breeding: With the aid of machine learning, plant breeding is becoming more accurate and efficient. For example, computer simulations are used to decide how a variety may perform under different subclimates, soil types, weather patterns, and other factors.

Medical diagnosis: ML can detect patterns of certain diseases within patient electronic healthcare records and inform specialists of anomalies that are detected. The machines evaluate patient health based on millions of observations extracted from big data sets. In this sense, ML is the second doctor for the patient and sometimes a more accurate one.

Military: Huge amount of information is extracted from radars and autonomous identification systems that are processed with ML computation. AI can also help in building the strength of sensors. Soon, we may witness robot-soldiers and robotic assistance will go further than drone networks and automatic drill machines.

Conclusion

The future of AI is promising. With all its limitations like unavailability of quality data, lacking modern infrastructure, and lack of desired skills, Artificial intelligence is still gaining momentum together with Machine Learning and Deep Learning. In 2–3 years, we could see a marked difference between the current and the forecasted positions of AI trends.

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