Passionate digital marker and author of the SEOsandwitch blog.
Computers are the most integral part of office work today. Machines have become a constant competition to outsmart human beings since the Turing test of the 1950s.
Today, with the invention of machine learning and computer vision, the job sector is becoming even more competitive. Many duties and tasks that were initially assigned to human beings are now automated and done by computers.
Way back in the 1950s, Alan Turing had a preposition that machines would have the ability to think and reason like human beings. Nobody could believe computers, for instance, robots, could get instructions and execute tasks without human help.
When Turing came up with his preposition that machines could reason and work as human beings, nobody would believe this imagination. Today, it is no shock that Artificial intelligence (AI) and computer vision has become part of our daily lives.
From the way we communicate on social media platfoms, e-learning, chats to computerized parking systems, our lives are surrounded by AI.
Computer vision is AI’s invention that is taking the tech world by storm. AI computer vision technology came to the limelight back in the 1970s, changing different industries with its earliest forms. It is now taking various industries by storm following its unique capabilities.
Today, you may have encountered computer vision in many ways that you are probably not aware of. Computer vision is now living with us, from unlocking your smartphone with facial recognition to fingerprint key lock.
Computer vision is a new field of computer science that tries to copy the complexity of human vision. It allows machines to instantly identify objects and react to instant recommendations based on things or images on sight.
This field of science had not met its full potential in the early 1950s and had no complex algorithms like we witness today.
In recent years, computer vision has made an exponential leap because of video data and digital images that we now have in existence.
Research in neural networks and neuroscience has also led to the technology’s recent boom. The machine learning algorithms had to be undergoing some transition to mimic the neural paths that the human brain uses when viewing or processing information. This way, it would be possible for computer vision to replace human vision in all ways.
A lot is changing in the world of computer vision. It is surpassing human capabilities in most aspects. Right from the accuracy of just 50%, computer vision is now hitting 90% accuracy, making it even more convenient than before.
Since most people are likely to suffer from bias when processing images, computer vision has become more useful than before, boosting accuracy.
Human beings get tired, and this is likely to affect their accuracy; machines are always on the move, and the accuracy level is never compromised by overworking.
Today what remains a contention is whether computer vision can easily replace human vision in all industries.
By utilizing AI, the agricultural industry, for example, has made a significant shift towards high tech. This has increased all-around productivity. To streamline farming operations and increase productivity, farmers and growers can use aerial imagery. Also, some firms are already using computer vision to detect defects in commodity grading in yields, reducing the post-harvest losses.
Access to data on commodity grading helps farmers to achieve significant returns throughout the year and boost efficiency across the agricultural supply chain.
It is not easy to procure insights or data for streamlining agricultural operations without the help of AI. The success of this kind of product depends on human beings.
The growth of the agricultural industry is heavily reliant on input from farmers, food enterprises, and traders. There are still some major flaws in the AI industry that we all have to take care of to minimize machine errors.
There will be a need to guide machines on some tasks even if they are capable of doing some human tasks. As AI technology keeps advancing, we can expect more research in this field to correct the flaws.
AI is doing a lot in the retail industry by making work easier for entrepreneurs. It is slowly transforming the traditional old-school brick and mortar into the digital age or e-commerce.
It is helping people to create personalized shopping experiences and retailers to improve customer services. Customer experience is changing in 2021, and improving CX is central to boosting sales and brand recognition in the digital space.
Some grocery stores are applying AI differently. They have developed automated checkout systems that will mount automatically on the customer basket and carts. Computer vision makes it easier to scan checkout systems and improve customer experience in the long run.
It is also affordable to install AI systems to small retailers compared to other installments that are pretty involved. AI makes shopping more convenient and enjoyable for most online shoppers.
AI for better healthcare
Computer vision and machine learning are not just enhancing efficiency in medical service delivery but also reducing fatality. It is saving lives by transforming the health care sector.
The technology is helping doctors to diagnose diseases, administer care to patients, and perform surgeries after collecting helpful information that the human eye could easily ignore. AI also helps in data protection and HIPAA risk assessment and compliance.
Human bias can always affect treatment if doctors do not have the latest equipment to support a diagnosis. To reduce the disparities in the way people are treated, automated care is the best solution for all.
Automated health care is a perfect example of how human beings and machines should work together to improve results.
There is a lot that is changing in the world of technology today. We continue to learn more about AI and computer vision as more data floods the online world.
We should expect computer vision to become even better and replace the human eye. Computers may lack common sense but still are effective because they are not prone to errors.
However, relying on machines alone for critical tasks like healthcare is not advisable. Human beings are more reliable compared to machines. Until machines can match human intuition, machines and human beings will still collaborate on various tasks to improve results.
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