Top AI Trends Every Data Scientists and Engineers Must Not Miss in 2020

As we’ve trodden the path of 2019 with much galore in AI and the big data realm, we look back on a year whose start already saw the AI revolution. But what does 2020 have in store for us?

You’ll be surprised to know that 2020 will be a crucial year for AI adoption.

The artificial intelligence market continues to rise at breakneck speed. As predicted by IDC, the global spending on AI systems is said to reach USD 97.9 billion in 2023. Simply put, the coming year will be critical to set foot for the next innovation to take place.

What follows is the top emerging trends every AI specialist, AI engineer, and data scientist must take heed:

Deployment to lower power gets easier in AI

AI that uses 32-bit floating-point math is available in high-computing systems as well as clusters, data centers, and GPUs. The purpose of this is to avail of relevant results and to have easy training of models. However, this ruled out the lower cost and low power devices that use fixed-point math.

Also, there have been recent advances in software tools that support AI interference models having different levels of fixed-point math. This advancement has helped the deployment of AI even on low power and has caused low-cost devices to open up new avenues for AI professionals like- AI engineers that help incorporate AI in their designs. For instance, low-cost electronic control units (ECUs) in vehicles are the best examples that are seen today.

Shortage of workforce skills have lessened

As AI takes the lead in the tech industry today, there will be more AI engineers and data scientists working toward the same goal. These sets of expert professionals will gain access to the existing deep learning models and research from the community. Providing them this privilege gives them an upper hand in taking up projects rather than starting from scratch.

While most of the AI models used to be image-based, they are now incorporating sensor data which also includes text, radar, and time-series data. There are tools such as automated labeling that helps curate large and high-quality datasets. There are higher chances of obtaining high-quality data and a higher likelihood to find better accuracy in AI modeling, thus a higher rate of success.

AI engineers and data scientists are likely to find success in taking up projects due to the level of expertise they possess.

Reinforcement learning has moved from gaming to real-world industrial applications

It is said that by 2020, reinforcement learning is going to shift from gaming to real-world applications especially in control design, automated driving, robotics, and autonomous systems. There will be a high success rate seen wherever reinforcement learning (RL) is used to improve a larger system.

Key enablers are said to be easy tools that AI engineers can use to build and train RL policies, integration of RL agents into systems, and generate simulation data for training purposes. The RL can be integrated into a fully autonomous driving system model which also includes environment model, vehicle dynamics model, image processing algorithms, and camera sensor models.

Simulation can lower the primary barrier and lead to successful AI adoption

The quality of data is the biggest barrier toward successful AI adoption according to a survey made by analysts. By 2020, the simulation will help lower this barrier. Training an accurate AI model generally needs a large amount of data. While there are a lot of data available for normal system operation, what you require is data that is obtained from critical failure condition or anomalies.

This is generally ideal for predictive maintenance applications that initiate predicting the remaining life for a pump on an industrial site. And because creating failure data taken from physical equipment is destructive and can be expensive, the best approach is to generate data from simulations that represent the failed behavior.

Increased in design-complexity due to the rise of AI-driven systems

AI has been trained to function properly with sensor types such as Lidar, Radar, and IMUs. AI engineers are now driving AI into several ranges of systems such as aircraft, wind turbines, industrial plants, and autonomous vehicles using different sensor types.