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The power of AI: artificial intelligence examples for a modern world

Author: Adam Kimmel

Research and development are critical to sustained business growth. Companies that invest in R&D tend to outpace their competition by fueling a pipeline of products and services to stay ahead of margin erosion by commoditization. But because R&D carries an investment cost, budget constraints often dampen any enthusiasm for disruptive research.

Increasingly, companies are leveraging the power of AI to speed up research and increase its value. The monumental amount of data generated every second creates the opportunity to predict where to place research bets. Data is the new currency of business, but organizations cannot capitalize on it without a high-bandwidth, low-latency network and a robust infrastructure.

How is artificial intelligence changing the world?

Artificially intelligent computers perform tasks that are generally done by humans and that often demand sensory capabilities such as depth perception, adaptability and decision-making. Sensors collect massive amounts of data and process it at ever-increasing speeds, applying scripted codes to predict and execute an appropriate response. Machine learning, a recent extension of AI, analyzes patterns in AI responses to facilitate continuous improvement.

For research purposes, the most useful and applicable benefit of AI and machine learning is predictive capability. Research aims to generate new information or conclusions through methodical investigation, and AI can provide substantial support to research through the speed of analytics, the identification of patterns and the removal of human error.

Applying artificial intelligence examples to research

Programmers can look to real-world examples as they search for ways to optimize each of the areas mentioned above for a specific application.

  • Signal processing speed. The futuristic dream of controlling machines with the mind is becoming a reality through research. Brain-computer interfaces, which boast the processing power of microcomputers and the speed of analytics, are gaining traction in healthcare. Users send mental signals to a computer for processing and response. These interfaces could, for example, allow paralyzed limbs to move again—and the faster the data is processed, the more natural the movement.
  • Pattern identification. Research often generates enormous quantities of data that must be organized and scrutinized before any meaningful insights can be gleaned. AI's pattern recognition capabilities can dramatically speed up data analysis, freeing up more time and energy for interpretation.
  • Eliminating human error. Driver and pedestrian safety is an important factor driving the movement toward autonomous vehicles. Vehicle AI reacts to situations with a research-guided, preprogrammed response that is free from the distractions that plague human drivers.

Unlocking the power of AI

Given the varied nature of AI's applications and the increasing amounts of data needed to conduct its research, network designers must consider the network and hardware requirements to deploy AI solutions.

  • A high-bandwidth, low-latency network. The sheer volume of research data strains network bandwidth. In addition to having the bandwidth to store and process the data, the network should be low-latency to minimize delay from the process signal to the response.
  • Computing capacity. AI increases the number of operations that a computer processes, and the sheer amount of data processed by research demands that a network have sufficient computing capacity.
  • Data storage. More research data is moving to the cloud for security and convenience, and the network needs to be able to house all of it.
  • Security. Data must be secure to protect intellectual property or trade secrets developed by a company's research team. These items defend the company's long-term viability and market position, so security is always a consideration.

Setting up the right network for research AI

Every research application has unique network requirements. Benchmarking similar companies and applications is a traditional approach. You could also have your technical team determine the most critical research data and design a network to account for it. Your engineers can recommend a size, speed and capacity strategy, but their plan must be tested and tweaked to ensure optimal performance.

You will know that you are ready to deploy AI for research when the maximum number of planned users are on the network and its latency and processing time are still at acceptable levels. Conducting a small beta test before implementing AI at scale lets you optimize your capital outlay before making the full investment.

Learn more about how a 5G network can support AI for research.