Top 8 AI and Machine Learning Trends to Watch in 2023

AI and Machine Learning have moved from a trendy discipline to one of the drivers of modern IT. Since 2018, the industry has been steadily growing at a rate of 40-50% per year: in projects, market volume and the number of jobs.
Let’s have a look at top trends in this area in 2023:

1. Natural Language Processing

Human work is costly, especially in support. That's why text and speech generation technologies are becoming increasingly popular. Many of us use voice assistants (like Apple's Siri or Amazon's Alexa) every day. Voice assistants play music, tell us the weather, and Alisa from Yandex can even make up and read aloud children's stories.

Now such solutions work in banks and retail and save millions of man-hours every year. The robot learns to understand human speech by responding contextually.

Another example is RASA, one of the most popular platforms for creating conversational AI assistants. On the accepted AI quality scale, RASA reaches levels 3 and 4. It means that the "robot" not only understands humans with high accuracy in a given contextual field but also learns to recognize contradictions and ulterior motives.

It should come as no surprise that the natural language processing (NLP) market will reach $341.7 billion with a 27.6 per cent CAGR by 2030

2. Deepfakes

Technology for creating images and sound indistinguishable from raw footage or audio recordings is becoming an essential commercial field. “Dessa”, an AI startup, is learning to copy celebrities' voices from a corpus of their speech to "speak" them into text, thanks to machine-learning technology.

Their "voice clone" of famous podcaster Joe Rogan is no different for most listeners than the original. So "Joe Rogan" was even able to record an episode with "Steve Jobs"!

What's the trend? I'm sure the technology will become super popular in movies and commercials. The headline news about Bruce Willis selling the rights to his voice and images for deep fake use in commercials is just the first sign.

In a few years, deepfakes of famous actors will squeeze real people into blockbusters. To see a star on the screen who has already passed away, forever young and almost as real, is magic that becomes a real thing in our eyes.

One example is the 2020 film "Star Wars: The Rise of Skywalker," in which deepfake technology was used to recreate the character of Emperor Palpatine, who had previously been played by actor Ian McDiarmid. The character was brought back to life using a combination of archival footage of McDiarmid and deepfake technology to create a realistic-looking and sounding digital version of the character.

A big concern surrounding deepfake technology, however, is its potential use to spread misinformation. Deepfakes can be used to create fake videos or audio recordings of individuals saying or doing things that they did not actually say or do.

3. Personalization in Retail

Large retailers are already using machine learning technology in sales. It has been already 10 years since Target was able to use artificial intelligence and machine learning technologies to predict the pregnancy of a teenage girl simply by analyzing her interests in product selection.

And now AI has great potential to revolutionize the way retailers personalize their products and services for individual customers.

Here are a few trends:

• Product recommendations: Machine learning algorithms help to analyze customer data and make personalized product recommendations in real time. This can be done through email marketing, social media, or on the retailer's website or app.

• Dynamic pricing: Some retailers are using AI to dynamically adjust prices based on factors such as demand, competition, and the customer's purchase history.

• Customer service: Retailers are using chatbots and virtual assistants powered by AI to provide personalized customer service, such as answering questions and resolving issues.

• Marketing campaigns: AI can be used to analyze customer data and create personalized marketing campaigns that are more targeted and effective

4. Small and Wide Data

As computers and their programs have become powerful enough, scientists and mathematicians have been able to use them to study and analyze very large amounts of data (“big data”) to learn new things and make important discoveries.

And now there is a counter term — the rejection of reckless accumulation of "big data", and the growing need for conventionally "small but enriched" data. Gartner says that by 2025, up to 70% of companies will change their data tactics.

Instead of the expensive accumulation and processing of "raw" information about people, companies will move to X-analytics and self-learning models that will also self-check the accuracy and quality of predictions. Fewer data and fewer people, faster processing, higher quality — and higher applicability in marketing.

5. Content Generation

Right now, ChatGPT is blowing up the market — a neural network has learned to generate clear, detailed, coherent texts that are often impossible to recognize from those written by humans. StackOverflow, for example, has even banned ChatGPT for answering popular developer questions. An inexperienced reader might not even realize that the text was computer-generated. Read more about it in my previous article https://dev.to/ermakovnv/chatgpt-the-most-impressive-use-cases-of-this-mind-blowing-ai-chatbot-3jk0

Illustrators are being pushed by AI technologies like DALL-e or Midjourney — these solutions are turning from toys into billion-dollar commercial products. Now we already have to deal with the issue of licensing the content created by AI. Another question is how to deal with prohibited content that is not captured on camera but created by an ML.

Also, we will see websites and code generators based on natural language understanding. Moreover, such solutions are already out there (e.g. Copilot https://github.com/features/copilot )— and they can create the simplest working examples.

6. AI in Cybersecurity

As cyber-attacks increase in number and complexity, artificial intelligence helps security professionals stay ahead of threats, especially when they lack resources. Gathering threat intelligence from millions of academic articles, blogs and news stories, artificial intelligence technologies such as machine learning and natural language processing quickly provide useful intelligence for protection.

Bots make up a huge portion of Internet traffic today and they can be dangerous. Bots can do many things, from hijacking accounts with stolen credentials to data fraud.

By studying behavioural patterns, companies will get answers to the questions "what does a normal user journey look like" and "what does a risky and unusual user journey look like." From there, companies can get ahead of the bots.

The coming years will see a lot of focus on AI/ML to combat the dangers of cyberattacks. The market for AI/ML in cybersecurity is expected to reach $38.2 billion by 2026.

7. Hyper Automation

It is said that the best inventions in the history of mankind came from laziness. But there is another side to this joke: the introduction of new technologies (especially in IT) frees up a huge amount of human resources currently engaged in tedious and low-productivity labour.

Hyper automation is a trend in which organizations use a combination of tools, including artificial intelligence and machine learning, to automate processes and tasks at a scale and speed that goes beyond what is possible with traditional automation methods. This includes automating both simple and complex tasks, as well as decision-making processes that involve analyzing and interpreting data. Hyper automation often involves the integration of multiple tools and technologies, and may also involve the automation of entire business processes or value chains.

Here are some examples:

• Using robotic process automation (RPA) to automate data entry and other repetitive tasks in an accounting department.
• Implementing natural language processing (NLP) to automatically classify and route customer service inquiries.
• Understanding documents using optical character recognition (OCR)
• Using machine learning algorithms to predict maintenance needs and schedule preventative maintenance for equipment.
• Using AI to analyze customer behavior and make personalized product recommendations in an e-commerce platform.

8. Self-Driving Cars

The future of the automotive market is not just about electric cars. It has to do with self-driving cars. According to a recent Renub Research report, the market for autonomous cars in the U.S. will grow to $186 billion by 2030, up from $4 billion in 2021.

Along with the convenience of driving, autonomous cars are said to provide a safer ride. In other words, if human error accounts for 94 per cent of all traffic accidents, according to the National Highway Traffic Safety Administration, it may make sense to rely more on technology to keep us safe.

Alphabet (through its subsidiary Waymo) and GM are among the big players pushing commercial operations in the U.S. Another big market for these services is China, where Baidu and Didi are releasing driverless fleets on the roads of cities such as Shanghai and Beijing.

Conclusion

In summary we are at a unique crossroads in human history at a time when the ongoing digital revolution is taking us into a data-driven world where AI and Machine Learning will continue to gain momentum. We are entering into a world of inter-connected devices where AI will play a key role in machine to machine and machine to human communications.

Whether we like it or not, we will have to live in this new world and it is better to be prepared a little in advance.

And last but not least It is a multibillion-dollar market, which could multiply by 2030. It's an excellent time to take a closer look and ride the trends!