If you’re looking to start a new career in Artificial Intelligence (AI) or Machine Learning (ML), it’s important to stay on top of emerging AI and Machine Learning trends. AI and ML are terms that nearly everyone has heard of these days. Even those who aren’t familiar with these terms encounter these new technologies almost every day. Research shows that 77 percent of the devices that we currently use have AI built into them. From a bevy of “smart” devices to Netflix recommendations to products like Amazon’s Alexa and Google Home, AI is the force behind many modern technological comforts that are now part of our day-to-day lives.
There are numerous innovative uses for Artificial Intelligence and Machine Learning. IBM’s Chef Watson, for instance, could create a quintillion possible combinations from just four ingredients. Also, AI-powered virtual nurses such as “Molly” and “Angel” are already saving lives and costs, while robots are assisting with various processes, such as less invasive procedures to open-heart surgery.
With the surge in demand and interest in these technologies, many new trends are emerging in this space. If you’re a tech professional or involved with technology in some capacity, it’s exciting to see what’s next in the realm of Artificial Intelligence and Machine Learning trends.
Machine Learning and AI Trends
1. Generative Pre-trained Transformer 3 (GPT-3): GPT-3 is a state-of-the-art language model trained on a massive amount of data, enabling it to generate human-like text with remarkable accuracy.
2. Edge AI: Edge AI involves running AI algorithms and models directly on edge devices such as smartphones, IoT devices, and sensors. It reduces latency and provides faster and more efficient processing of data.
3. Explainable AI: Explainable AI (XAI) involves creating transparent AI systems that can provide clear explanations for their decisions and actions. This is important for building trust and accountability in AI systems.
4. AI and Cybersecurity: AI detects and prevents cyber-attacks, identifies vulnerabilities, and enhances security measures.
5. AI and Healthcare: AI is used in healthcare to develop new drugs, diagnose diseases, and provide personalized treatment plans.
6. AI and Robotics: AI is being integrated into robotics to create more intelligent and autonomous robots that can perform complex tasks.
7. Transparency Trends in AI: Despite becoming so ubiquitous, AI suffers from trust issues. As businesses plan to increase their use of AI systems, they will want to do so more confidently. After all, no one wants to trust the decisions of a system that they don’t understand.
Hence, there will be a bigger push for deploying AI in a transparent and clearly-defined manner in 2021. While companies will make efforts to understand how AI models and algorithms work, AI/ML software providers will need to make sophisticated ML solutions more explainable to users.
With transparency becoming a key conversation in the AI space, the roles of professionals who are in the trenches of programming and algorithm development will become more critical.
8. Rising Emphasis on Data Security and Regulations: Data is the new currency. In other words, it’s the most valuable resource that organizations need to protect. With AI and ML being thrown into the mix, it’s only going to increase the amount of data they handle and the risks associated with it. For example, today’s organizations back up and archive massive amounts of sensitive personal data, which is predicted to be an expanding privacy risk in 2022.
Regulations like GDPR have made privacy violations very expensive. As the pressure to meet these regulations mounts, companies will need to have data scientists and analysts on hand to stay compliant and stay ahead in AI and Machine learning trends.
9. The Overlap Between AI and IoT: The lines between AI and IoT are increasingly blurring. While both technologies have independent qualities, used together, they are opening up better and more unique opportunities. In fact, the confluence of AI and IoT is the reason we have smart voice assistants like Alexa and Siri.
So, why do these two technologies work so well together? You can think of IoT as the digital nervous system and AI as the brain that makes the decisions. AI’s ability to rapidly glean insights from data makes IoT systems more intelligent. Gartner predicts that by 2022, more than 80% of enterprise IoT projects will incorporate AI in some form, up from just 10% today.
This AI and Machine Learning trend gives software developers and embedded engineers one more reason to add AI/ML capabilities to their resume.
10. Augmented Intelligence is on the Rise: For those that may still be worried about AI cannibalizing their jobs, the rise of Augmented Intelligence should be a refreshing trend. It brings together the best capabilities of both humans and technology, giving organizations the ability to improve the efficiency and performance of their workforce.
By end of 2024, Gartner predicts that 40% of infrastructure and operations teams in large enterprises will use AI-augmented automation, resulting in higher productivity. Naturally, their employees should be skilled in data science and analytics or get the opportunity to upskill on the latest AI and ML technologies to achieve optimal results.
11. Hyper Automation: Another emerging AI and Machine Learning trend is hyper automation, which is an efficient way to improve customer service and speed up various processes. There are several advanced technologies that help to power hyper automation, including Machine Learning, Artificial Intelligence (AI), cognitive process automation, and more. Aside from improving the customer service experience, hyper automation can also help accomplish other important tasks at a faster rate, such as system integration and organization, as well as improving worker productivity.
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