AI Adoption Among Small Businesses
A significant portion of small businesses have adopted artificial intelligence, finding it beneficial for saving both time and costs. There’s a common confusion between automation and AI, though. While both relate to technology, they tackle different objectives. Automation focuses on minimizing manual labor for routine tasks, whereas AI, in its essence, aims to replicate human intelligence to perform tasks independently.
Gregory Allen, a senior advisor at the Wadwani AI Center, points out, “Artificial intelligence aims to enhance worker productivity. However, whether this leads to increased work varies by industry.” He cites agriculture as an example: in the 1920s, a large number of American workers were employed on farms. Fast forward to today, and, even with a population exceeding 300 million, less than 1% work in that field.
A similar pattern emerged in the manufacturing sector. As reported by the U.S. Bureau of Labor Statistics, there were over 17 million manufacturing jobs at the close of 2000, but as of this June, that number has dropped to 12.7 million. A study from the University of Chicago indicated that while automation hasn’t significantly affected overall employment, it has changed the manufacturing landscape.
“Tractors increased farm productivity without necessarily creating more farming jobs,” Allen added.
AI Innovations and Electric Infrastructure
Polling data reveals interesting insights about public perception of AI. When responding to concerns about job security due to AI, only 3% of voters expressed fear, while 43% viewed it negatively and 26% positively.
Robots are now designed to collaborate with humans—some tackle household chores, while others help address labor shortages and even compete in robotic sports.
The International Federation of Robots reported that over 4 million robots were active in factories worldwide by 2023. Interestingly, around 70% of the newly deployed robots this year were developed to work alongside humans, particularly in Asia, integrating AI features for enhanced productivity.
“We’re seeing labor shortages across various sectors, like automotive and transportation,” explains Allen. “The older generations are leaving these jobs, younger ones aren’t interested, and the middle generation seems to be opting out too.”
Hexagon is advancing the development of a robot named Eon, designed for dynamic industrial environments. With AI systems integrated, Eon can navigate four times faster than a standard human walk and is equipped with 22 sensors to map its environment.
AI in Sports Technology
AI also plays a significant role in professional sports. For example, researchers at the University of Waterloo are utilizing AI algorithms to assess pitching performances in Major League Baseball. The Baltimore Orioles contributed to a project named Pitchernet that aims to refine player techniques and minimize injury risks. By employing advanced camera systems and smartphone footage, they have created a 3D model of pitchers, providing athletes and coaches with clearer insights into player dynamics.
Interestingly, technology like Pitchernet is also being tapped into across different sports disciplines, enhancing training practices.
Elon Musk even predicts that robots could outperform top surgeons within five years.
In baseball, what’s known as the automatic ball-strike system has been tested in minor leagues. Triple-A teams have experimented with this approach, assessing both fully automated and challenge-based systems. The major leagues have begun trials of their own, with a focus on improving the accuracy of calls while maintaining the judges’ roles.
Every team initiates the game with two challenges. If a call made by the referee stands after review, the challenging team loses that opportunity. While incorporating the automated system has demonstrated a slight uptick in call accuracy, human referees continue to handle most decisions.
Opinions on AI management vary. For instance, Brad Smith from Microsoft states, “We should ensure AI remains under human oversight.” He suggests that AI systems should always have a failsafe, akin to emergency brakes on trains, ensuring they can be controlled when necessary.
