The trend of artificial intelligence technology is driving humanity forward. Digital transformation has spread across all industrial sectors, and artificial intelligence is realizing what scientists have always dreamed of. Currently, artificial intelligence and machine learning technologies are being used in several real-world applications that consumers encounter every day.
While some dystopian stories warn of the dangers of sentient machines, most AI applications implemented today have dramatically enhanced the human experience, enabling us to achieve more success, save lives, and even make the world a better place. That said, here are 10 AI technology trends you really need to know:
1. Low-code and no-code solutions
Automated machine learning is nothing new, but this year we've realized how autoML can enable the building of high-quality AI models without deep programming knowledge. Historically, AutoML's functionality has focused on finding the optimal solution for certain datasets. Now, there are numerous low-level and no-code API solutions that allow businesses to create production-ready AI-driven applications without any data science knowledge.
2. Chatbot
Chatbots have made significant strides over the past decade and are now transforming how customers and businesses interact. Outbound sales are being replaced by conversational AI that can not only recommend products and answer questions related to product features, but also resolve many customer issues without needing to contact a real person at the company. However, if you need the help of a living person, they'd be happy to assist you.
Voice robots are another branch of conversational AI, capable of communicating by listening to and responding to a person's voice rather than typing words. This AI-driven technology can also use data collected from customer interactions to create more personalized customer experiences and enable businesses to gain insights from those interactions.
3. Market intelligence
AI and ML help businesses transform massive amounts of data into actionable business intelligence. AI-driven analytics improve every aspect of the sales cycle, from lead generation to customer support. In 2021, businesses were using market intelligence applications that leveraged AI capabilities to make more accurate predictions, make smarter decisions, and create more efficient sales processes. These applications provide real-time analytics so businesses can better communicate with both B2C and B2B customers.
4. Environmental sustainability
Artificial intelligence (AI) is being used in a variety of ways to support the environment and improve sustainable development practices in sectors such as agriculture, water, energy, and transportation. The use of AI applications in these areas can have a significant impact on emissions and contribute $5.2 trillion to the global economy by 2030. AI is being used to monitor environmental conditions, crop yields, help reduce and mitigate the impact of waste, and predict weather to improve water use.
AI-driven applications are also helping to manage the use of renewable energy through the use of deep learning, predictive capabilities, and even AI-powered power grid systems. AI enables self-driving cars, which can reduce vehicle emissions and minimize damage. In the future, we may see how AI uses location data to improve traffic congestion and even the efficiency of freight transport.
5. Robot-assisted surgery
In healthcare, the potential applications of artificial intelligence are virtually limitless. While the industry is notorious for its slow adaptation to the growing influence of technology, it has made significant progress since the outbreak of the pandemic. In fact, 43% of primary care visits are conducted via telemedicine, demonstrating that both healthcare providers and patients are willing to adopt medical technologies when necessary.
Would you trust a robot to perform your surgery? Predictive analytics and machine learning AI can identify patterns in patients and surgeries for adjustments. For example, these robots can learn from a patient's surgical history to avoid potential problems during surgery in real time. While many hospitals use robotic assistants in surgical applications, we don't yet have a full team of robotic surgeons.
6. Natural Language Processing
In e-commerce, computational linguistics, text analytics, and AI-driven NLP (Natural Language Processing) are used to better serve their consumer base. Sentiment analysis and brand perception analysis help companies better understand their customers to improve their products and services.
The feedback collected from users can then be processed by a machine that can distinguish nuances in language to extract qualitative and quantitative data that businesses can take action on.
7. Earthquake detection and prediction
Machine learning algorithms used to detect and analyze seismic wave patterns are transforming how we react to earthquakes. In fact, these AI-driven algorithms are able to detect twice as many earthquakes as scientists can. This helps shorten earthquake response times, saving lives, and allows scientists to better understand how tectonic plates move. Hopefully, these algorithms will become smart enough to predict future earthquakes.
8. Artificial Intelligence Engineering
While this trend is near the end of our list, it forms the basis of most other AI trends. Beyond the development of AI tools and processes, there are many complex aspects, including security, transparency, ethics, and compliance. AI engineering is a strategy that makes AI a natural component of DevOps processes, rather than a separate department or an afterthought.
This fragmentation between AI and DevOps can lead to compliance and cybersecurity issues, slowing down the entire process. A cohesive planning, development, and implementation workflow, including AI, developed by experienced network administrators, streamlines a company's ability to bring products from idea to market.
9. Insurance Forecasting Analysis
More and more insurance companies are using artificial intelligence predictive analytics across multiple different areas. They use predictive analytics to identify fraud, calculate new customer risk and pricing, optimize products, and improve user experience.
Predictive analytics also allows companies to personalize and expand insurance coverage so that individuals can access the coverage they need at an affordable price. Furthermore, insurers that have integrated predictive analytics into their processes are growing 7% faster than those that don't, indicating that customers are also benefiting from ML in the insurance industry.
10. AIOT
With the number of IoT devices projected to reach 3.5 billion by 2023, the shift towards AIoT solutions is a natural progression. AI-powered smartphones, voice assistants, and other IoT devices can create intelligent machines that support decision-making with minimal human interaction.
Conclusion: The trend of artificial intelligence technology has driven a $13 trillion market.
These ten AI technology trends give us a glimpse into what the future of AI and machine learning might look like: robot doctors, intelligent virtual assistants, real-time weather data, and sophisticated market forecasting.
By 2030, the value of artificial intelligence is projected to reach $13 trillion. While most AI technologies are currently generated in software, we can expect to see AI applications in sectors such as tourism, manufacturing, and retail. We'll soon find out what the world of intelligent machines will look like.
