Artificial intelligence and machine learning are changing society. It is still too early to tell whether these changes are for better or worse since the concept of ML and AI is still relatively new.
However, there is no denying that the role of both is growing with each passing year. What does 2022 have in store for machine learning? Let’s take a look at some of the trends that have been shaping up different industries recently and are likely to do so in the near future.
Internet of Things
The Internet of Things is one of the standout trends right now, and this applies not just to ML and AI. We live in times when digital devices dictate our lives. It is hard to imagine going outside without a smartphone, and it is more than likely that you have multiple other devices at your home that are part of those things that make IoT.
Reliance is understandable because digital devices make our lives easier. And as artificial intelligence and machine learning advance, we can expect to see even bigger improvements.
Take laptops and smartphones, for example. You no longer need to bother entering a password on some laptop models to access it. No, instead, you can sync a smartwatch with a laptop. Figuring out how to use apple watch to unlock mac is a piece of cake, and the same thing applies to other brands that offer such and other similar features.
From the perspective of progress, it makes sense to connect machines and create a monolith network. The more devices are in the network, the more data they can exchange, which speeds up the results.
Cybersecurity Improvements
Due to the constant rise in cybersecurity threats, it is crucial to utilize the benefits of AI and protect devices from constant attacks.
As we connect more machines to the internet, it also means creating more targets for hackers to consider. An unprotected device is easy prey, and unless you disconnect it from the World Wide Web, there are no guarantees to prevent a cybersecurity threat.
Technology experts, including the ones working in machine learning, are putting a lot of effort into creating algorithms and antivirus models to minimize or eliminate the cybersecurity risks where possible. And given the prominence of both cybersecurity threats and the growth of IoT, this trend is likely to continue for the foreseeable future.
AI Ethics
Ethics are not the first thing that comes to mind when we are talking about AI and ML. Having said that, there are concerns that AI ethics cannot keep up with the development.
It is important to emphasize that artificial intelligence mimics human behavior, and machines need to collect vast amounts of data to develop insights.
If a project starts with poor fundamentals or develops unintended consequences later on, the whole thing can snowball out of control and cause harm.
Since algorithms in machine learning are developing rapidly, it is crucial to keep an eye. It is often unclear how a learning machine reaches its conclusions and what it reaches.
Potential harm to society is not out of the question, and there are odds that someone is developing AI with the intent to misuse it.
As such, AI ethics should not be taken for granted because they define how an AI should be used and what the consequences for misusing it would be.
Speech Recognition
When it comes to the everyday usage of AI in our lives, speech recognition is one of the first things that we think of.
Ever since the introduction of digital home assistants like Alexa or Siri, it became pretty clear that carrying out simple day-to-day tasks via an assistant that recognizes speech patterns is beneficial to society.
The future seems quite bright for speech recognition as well. One of the most recent trends in voice shopping. More and more online stores are adopting the technology to accommodate the needs of the shoppers who wish to have a simpler shopping experience.
It is also worth mentioning people with disabilities. With the help of machine learning and speech recognition patterns, this demographic will also have an easier time carrying out various tasks in their lives, including shopping.
Automation
Automation also referred to as hyper-automation, is what businesses have been utilizing in customer service. Chatbots that can respond to customer queries instantly or algorithms that process data are a couple of examples.
No-Code Machine Learning
Due to the lack of highly-skilled AI specialists, developers are looking to simplify machine learning and make it more accessible so that the technology can progress faster.
No-code machine learning is about simplicity. Interested parties can implement ML without any code and get the results.
Take website development, for instance. There are platforms that offer drag-and-drop tools. Ready-to-use templates mean that a developer has to make minimal adjustments, and they hardly require technical knowledge.
