It´s not easy to project trends in a market evolving as rapidly as AI. However, through analysis of cross-industry data and experience with a diverse client-base, we’re willing to make some bets. From automating mundane daily tasks to leveraging computer vision for more accurate medical diagnoses, here are 5 trends in AI we expect to emerge in 2019.
TREND 1: “EDGY” AI
Edge AI refers to processing AI algorithms locally instead of relying on cloud services or data centers.
Smartphones, cars, and wearable devices are examples of devices that need to make faster and more accurate real-time decisions. Autonomous vehicles, for instance, need to make hundreds of decisions per second – brake, accelerate, turn on lights, identify and interpret traffic patterns, signals, and speed limits – all while simultaneously responding to the driver’s voice commands. These decisions must take place in a fraction of a second, and they need to be independent of the connectivity issues that come with cloud computing. This means that autonomous vehicles need powerful chips to process all this information rapidly and accurately.
Tech leaders like Nvidia, Qualcomm, Apple, AMD, and ARM are investing in developing and delivering chips that can handle these kinds of workloads.
In 2019 we’ll see more models being deployed at the edge as well as specialized chips allowing AI models to operate independently from the centralized cloud, or on the “edge” if you prefer.
TREND 2: AI IN HEALTHCARE
Last year the FDA (U.S. Food and Drug Administration) approved IDx-DR, an AI-enabled software that can independently diagnose diabetic retinopathy before severe complications (such as blindness) emerge.
The FDA also cleared Dip.io, a product developed by startup Healthy.io, as a class II medical device. This diagnostic tool can monitor urinary tract infections and track pregnancy-related complications by analyzing photos of dipstick urine tests. It’s as simple as uploading a photo, the model takes it from there.
2019 will be a remarkable year for AI in healthcare.
TREND 3: PREDICTIVE MAINTENANCE
Equipment failure is one of the main causes of production downtime, a huge line-item for any asset-intensive business. However, today maintenance teams spend 80% of their time collecting data but only 20% analyzing it.
Factory and field equipment generate mountains of unleveraged data that could go a long way to solving these issues. Alongside cameras and sensors, ML-driven algorithms can learn to check assets’ “vital signs,” catch small irregularities (a loose screw) before they turn into larger ones (a damaged turbine) and provide productivity predictions, allowing firms to plan accordingly.
With sensors becoming more affordable, and edge computing gaining momentum, machine learning will become even more heavily incorporated in industrial processes in 2019.
TREND 4: CONVERSATIONAL AI
We say conversational AI, what pops into your head? If it’s chatbots, you’re not alone. While that’s certainly a huge part, the technology is much broader as it is integrated across messaging apps and voice-enabled virtual assistants who go far beyond the scope of chatbots.
In 2019 we can expect to see even more AI deployed to handle routine customer service interactions. Whether you’re booking a flight, searching for a new restaurant or requesting the arrival date of your next purchase, AI can assist you.
Research from eMarketer shows that this year 66.6 million Americans are expected to use speech or voice recognition technology. Banking and retail are great examples of industries already using conversational AI initiatives, and as the technology continues to mature in 2019, we expect to see even more use cases in even more industries.
TREND 5: RPA / BACK OFFICE AUTOMATION
RPA (Robot Process Automation) covers a variety of back-office tasks that can be automated by bots. It’s not a new concept, nor is it AI. But here are some interesting facts:
- According to McKinsey, RPA will have an economic impact of around $6.7 trillion by 2025.
- Forrester Research also mentioned that RPA market is estimated to grow to $2.1 billion by 2021.
Although RPA is not considered AI – since it’s rule-based and can’t learn anything on its own – there’s been a collaboration between RPA and AI. Due to its capacity of automating repetitive and time-consuming tasks, RPA can save employees tons of time, at the same time it can ensure processes are running smoothly and precisely. On the other hand, AI can enhance RPA.
For instance, take a bank that’s onboarding a new client and needs to adhere to Know Your Customer/Anti Money Laundering Compliance Regulation. RPA is great for doing a lot of manual work. What AI can do is analyze the data the RPA’s pull in a more sophisticated manner, and arm a Compliance officer with more useful information.
Whether there is a need to automate processes or implement solutions in this field, RPA has been mainly leveraged by large companies – until now. In 2019 we can expect to see small and medium-size businesses starting to adopt RPA, due to its clear benefits and increased popularity.