If you consider using ML in your company, the following machine learning trends will be hot in 2022.
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ML involves computer algorithms learning patterns by seeing examples instead of programming them with rules, as in traditional software programming.
Some of the many applications of Machine Learning in 2022 and beyond include:
Automated Machine Learning (AutoML) shows a significant shift in how many large companies look at machine learning this year. The need for skilled machine learning engineers and programmers is rising, so demand is more significant than supply for these workers. That’s why there is significant growth in tools that provide greater access to ML.
Formerly manual ML processes, including data labeling, allow almost anyone to use this tool, but there is less chance of human error.
Most parts of the machine learning process can be automated today, even deployment.
A greater need for labeling data is making a new labeling industry involving humans in less expensive nations in eastern Europe and India.
Artificial intelligence has historically been used to streamline data processes, as well as in linguistic analytics. These are ideal retail, finance, and healthcare applications for repetitive tasks.
However, OpenAI has developed new models that combine images and language to create a visual design from text descriptions.
Preliminary work shows that models can learn how to make unique visual designs. One example is an armchair shaped like an avocado that was created by providing the AI with a caption that simply read ‘avocado chair.’
Some AI and ML experts think this type of modeling will allow AI to be used soon in creative fields. For example, it’s expected they may be used in architecture and fashion.
Tiny ML is a new method to develop ML and AI models. They use devices with hardware, such as the controllers that power vehicles, utility meters, and even refrigerators.
Algorithms in tiny ML may be used to analyze gesture and voice commands and to identify familiar sounds, such as a door slamming or a gunshot.
Machines are getting more complicated, so we expect cybersecurity to become even more relevant in 2022 and beyond. So it’s understandable that as technology advances, cybercriminals come up with new, devious ways to attack data security.
The internet is, obviously, the most frequent way hackers launch cyberattacks. But cybersecurity and machine learning are being leveraged in the cybersecurity field. How?
ML’s relevance depends on its ability to learn from a changing environment, so AI security measures are being created to improve data protection.
In a few years, some industry insiders predict AI algorithms may detect and stop changing cyber threats. These automated ML systems may be able to stop the threats before a human even needs to intervene.
ML is already underway for creating antivirus software that can pinpoint any malware or virus based on its unusual behavior. This means the smart antivirus software can see old viruses and use that information to anticipate what new viruses will do and look like.
A recent example is a smart cybersecurity company named Chronicle, operated by a Google affiliate.
ML and AI are on the rise, but where do ethics come into play during this evolution? It’s not difficult to create new technology that is ‘smart,’ but what happens when the technology makes an error?
For example, what would happen if a self-driving vehicle didn’t see an object on the road and killed someone? Or if a machine learning algorithm discriminated against a woman or person of color? These issues have already happened.
AI and ML ethics will be more in play as these systems advance in complexity.
If you are worried about AI stealing your job, Augmented Intelligence might reassure you. This trend merges the best attributes of technology and humans, providing organizations with the means to boost workforce performance.
The future of driving will be heavily based on automation. EV companies, like Tesla, have offered tantalizing glimpses of what automated driving may look like in a few years.
Ten years ago, self-driving vehicles were prototypes. These days, demonstrations in the real world ensure that self-driving will be happening soon. And ML will be a significant part of that transition.
Car companies will use ML to devise far more advanced algorithms for vehicles that drive themselves. The new algorithms will help the vehicle identify anomalies and rare events quickly. This will make engaging autopilot safer and more seamless.
In 2022, data is more valuable than ever. It’s viewed as the most critical resource that companies need to safeguard. With ML and AI becoming common, the amount of data they will process will only rise, and so will security risks.
For instance, companies today back up a lot of personal data and store it, but this can be a serious privacy risk.
State and federal regulations have made violations of privacy expensive for companies. For example, it’s estimated that the Information Commissioner’s Office fined British Airways and Mariott International $300 million for privacy violations.
Machine learning is making substantial advances in 2022, and more are on the horizon. However, ML is still in its infancy. If you are a technology professional who thirsts to learn valuable ML skills, please review our Amazon Web Services Machine Learning Certification.
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