Implementation of artificial intelligence has risen significantly in the last year, impacting platforms, tools and applications around the world, in fields as diverse as healthcare, retail, agriculture and more.
As an increasing number of industries begin opting for solutions based on machine learning, here are seven of the top AI trends that we have seen in 2019 and a prediction of what we think will come in the near future:
- AgTech: Startups and incumbents are embracing crop monitoring AI to manage pesticides, detect and target weeds, and forecast how weather changes impact agriculture. With the help of AI, farmers can evaluate a variety of factors in real time, including weather conditions, temperature, water usage and soil collections, to better inform their decisions.Using drones, AI-enabled cameras capture images of an entire farm and analyze them to identify problems and areas for improvement. Unmanned drones are able to cover more land in a shorter period of time than humans on foot, allowing for large farms to be monitored more frequently.As the data is being gathered, we expect increasing focus on data governance, as well as monetization efforts. Companies will look to establish and drive strategic partnerships that include frameworks for the sharing and exploitation of aggregated data and analytics.
- HealthTech: Fast-track regulatory approval for AI software products has opened up new commercial pathways for AI imaging and diagnostics companies. On the consumer side, image recognition technology is helping turn smartphones into powerful at-home diagnostic tools. Navigating the requirements of HIPAA and business associate agreements will continue to complicate the requirements for companies looking to innovate in the “healthtech” area, especially startups that are still getting familiar with the regulatory framework.
- Autonomous vehicles: Self-driving cars are not a practical possibility without Internet of Things (IoT) technology working closely with AI. The cars use sensors enabled by IoT to collect data, and the programs used for decision-making are powered by AI models. AI algorithms then make decisions and take action using the data collected.Key features include path planning, which enables the car to find the best route from point A to point B; eye-tracking, which monitors where drivers are looking and warns those who are distracted or not paying attention to the road; and natural language processing, which enables the car to follow voice commands. Autonomous vehicles could also have the ability to communicate with each other, allowing for optimization of traffic as a whole.As state and federal regulation of self-driving cars continues to evolve across the regulatory spectrum, we expect the market and negotiating positions between suppliers and buyers of technology to evolve around issues such as warranties and liability.
- Customer service: The use of bots as customer service agents is revolutionizing relationships between companies and their clients. AI-powered chatbots can interact with customers, identify consumer needs and ensure that inquiries are directed appropriately. The relatively low cost of chatbots, compared to traditional customer support, makes implementation attractive to many companies.AI has also proven to be useful in customizing user experiences. Some companies are using AI to analyze customer data and tailor products to their users, as well as to provide interactions aimed at increasing customer engagement and driving up sales. Privacy and data security considerations continue to impact this space.As the opportunities to use AI to supplement or replace human interaction increase, the employment considerations are likely to be an increasing focus.
- Facial recognition: Facial recognition helps businesses provide more personalized services to their customers. Early commercial applications are taking off in security, retail and consumer electronics, and facial recognition is quickly becoming a dominant form of biometric authentication as the accuracy and reliability of the technology continues to increase. However, the regulation of facial recognition technology may impact widespread adoption.
- Fraud prevention: AI-based fraud prevention relies on a combination of supervised and unsupervised machine learning. The technology takes into account emerging activities, behaviors and trends in transaction anomalies, making it possible to thwart more sophisticated attacks. AI also provides fraud analysts with real-time risk scores and greater insight into where best to set threshold scores to maximize sales and minimize fraud losses. Where decisions are made based on these scores, being able to provide assurances that the decisions are being made without any indicia of bias will become increasingly important.
With the help of AI, digital businesses are able to gain greater control over chargeback rates, decline rates and operations costs. Businesses selling virtual goods, such as gaming companies, can provide a more consistent, high-quality user experience.