AI tech trends are driving humanity forward. Digital transformation has reached every industrial sector, and AI is achieving things that scientists have only dreamed about. Now, AI and ML technology is being used in several real-world applications that consumers come into contact with on a daily basis.
While some dystopian tales warn of the dangers of sentient machines, most of the applications of AI that are being implemented today greatly augment the human experience so that we can achieve more success, save lives, and even make the world a better place to live. With that said, here are 10 AI tech trends that you really need to know about:
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Low-code and no-code solutions
Automated machine learning is not anything new, but this year we realize how autoML can enable high-quality AI models to be built without in-depth programming knowledge. In the past, AutoML’s capabilities were traditionally focused on finding the best solutions for certain sets of data. Now, there are a number of Low and no-code API solutions that allow businesses to create production-grade AI-powered apps without any data science knowledge whatsoever.
Chatbots have come a long way in the past decade, and now they are changing the way that customers and businesses interact. Outbound sales are being replaced with conversational AI that can not only recommend products and answer questions related to a product’s functionality but also solve a number of customer issues without ever having to contact a real live person from the company. Although, if you need help from a live person, they will gladly connect you with one.
Voice bots are another branch of conversational AI that is able to communicate by listening and responding to a person’s voice instead of typed-out words. This AI-powered technology also has the ability to use the data collected from customer interactions to create a more personalized customer experience and allow businesses to gain insights based on the interactions.
AI and ML help businesses boil down massive amounts of data into actionable business intelligence. AI-powered analytics improve all aspects of the sales cycle from lead generation to customer support. Businesses in 2021 are using market intelligence applications that use AI features to make more accurate predictions, more informed decisions, and create more efficient sales processes. These applications are able to provide real-time analytics so that businesses can communicate better with B2C and B2B customers.
AI is being leveraged in many ways to support the environment and improve sustainability practices across industries such as agriculture, water, energy, and transportation. Using AI applications in these areas can have a significant effect on emissions as well as contributing $5.2 trillion to the global economy by 2030. AI is being used to monitor environmental conditions, crop yields, help reduce and lessen the impact of waste, and forecast weather to improve water usage.
AI-driven applications are also helping to manage renewable energy usage by using deep learning, predictive capabilities, and even AI-powered grid systems. AI makes self-driving cars possible, which can lower the number of emissions from vehicles and cause less damage. In the future, we might see how AI can use location data to improve traffic congestion and even cargo transport efficiency.
AI has almost an unlimited amount of possible applications when it comes to healthcare. And although the industry is notorious for being slow to adapt to rising tech influence, there have been major strides since the onset of the pandemic. In fact, 43% of primary care visits were conducted through telehealth, showing that both healthcare organizations and patients are happy to adopt medical technology when necessary.
Would you trust a robot to perform surgery on you? Predictive analytics and machine learning AI can recognize patterns across patients and surgeries in order to allow up to the second adjustments to be made. For example, these robots can learn from patients’ history of surgeries to circumvent potential issues during surgery in real-time. While many hospitals utilize robot assistants for surgery applications, we don’t exactly have a fleet of robot surgeons quite yet.
Natural Language Processing
In e-commerce, computational linguistics, text analysis, and AI-powered NLP (natural language processing) are being used to serve their consumer base better. Sentiment analysis and brand image analytics help companies understand their customers better to improve their products and services.
The feedback collected from users can then be processed by a machine that can distinguish language nuances to extract qualitative and quantitative data that businesses can take action on.
Earthquake Detection and Prediction
Machine learning algorithms that are used to detect and analyze patterns of seismic waves are changing how we respond to earthquakes. In fact, these AI-powered algorithms are able to detect twice as many earthquakes as scientists can. This helps improve earthquake response time so that lives can be saved and scientists can gain a better understanding of how the Earth’s plates move. The hope is that these algorithms will become smart enough to predict future earthquakes.
Although this trend is toward the end of our list, it is the basis for most of the other AI trends. There are many complicated aspects outside of the development of AI tools and processes that include security, transparency, ethics, and compliance. AI engineering is a strategy that makes AI a natural part of the DevOps process instead of a separate department or afterthought.
This fragmentation between AI and DevOps can lead to issues with compliance and network vulnerabilities, slowing the overall process. Cohesive planning, development, and implementation workflow including AI as developed by an experienced network administrator streamlines the ability of companies to get a product from an idea to the market.
Insurance Predictive Analytics
More and more insurance companies are utilizing AIs predictive analytics in several different areas. Insurance companies use predictive analytics to identify fraud, calculate new customer risk and pricing, product optimization, and optimize user experiences.
Predictive analytics also allows companies to increase insurance personalization and coverage so that individuals have the coverage that they require at a price they can afford. Additionally, insurance companies that have been integrating predictive analytics into their processes have grown 7% faster than companies that don’t use predictive analytics, showing that customers are reaping the benefits of ML in insurance, too.
As the number of IoT devices is expected to increase to 3.5 billion in 2023, there is a natural evolution toward AIoT solutions. Smartphones, voice assistants, and other IoT devices powered by AI can create intelligent machines supporting decision-making behaviors with little to no human interaction.
Conclusion: AI Tech Trends Driving a $13 Trillion Market
These ten tech trends in AI give us a glimpse into what the future of AI and machine learning might look like. Robot doctors. Intelligent virtual assistants. Instant weather data. Refined market predictions.
By 2030, it is estimated that the value of AI will reach $13 trillion. And while right now, most AI technology is being generated in software, we can expect to see AI applications across sectors like travel, manufacturing, and retail. What does the world of intelligent machines look like? We’re about to find out.