Since the first AI computer program completed a whole game of checkers in 1951, the technology has grown by leaps and bounds. From sequencing RNA for vaccines to multiplication algorithms, the capabilities of AI are astounding.

But these code-driven tools are not easily regulated or reined in. And when values and ethics aren’t baked into these tools, people’s rights are at risk.

Machine Learning

Machine learning is a form of AI that allows computers to improve without being explicitly programmed. It’s used in Internet search engines, email filters to sort spam, websites that make personalized recommendations, and lots of apps on our smartphones, such as voice recognition.

Unlike older, rule-based algorithms that could only perform limited tasks like recognizing faces or converting words to numbers, newer AI software can understand language and predict human intent. It can learn and adapt over time, boosting productivity and augmenting human capabilities.

The most popular business use of AI is better product recommendations based on data, such as the ones offered by e-commerce sites or chatbots that serve customers in off-hours and peak times. But, in addition to improved efficiency, many companies are leveraging AI for predictive maintenance and to monitor employee productivity and safety. These uses have a direct impact on the bottom line. AI could help cities become less congested and polluted on a grander scale. It’s also likely to transform the way we work and learn. For example, generative AI developments can empower users to create digital content, such as images and videos, without extensive coding or technical skills.

Deep Learning

AI is an efficient data processing system used across industries to optimize business processes and services. This is because the technology can quickly and effectively analyze large amounts of information and make decisions or take action based on that analysis. Some examples of this include:

They use machine learning to reduce equipment downtime and spot production defects, shorten design phases, lower operational costs, and improve overall productivity. In the healthcare industry, AI is used to help with medical diagnostics, reducing operating expenses and making it easier for doctors to understand complex patient data.

This technology can also be used to make better product recommendations, such as what you see on every other e-commerce website, and improve customer service by analyzing customer feedback and using NLP to build better chatbots that can understand what customers are saying. This is a huge benefit for companies, allowing them to focus on other business operations instead of answering basic questions. Another example is generative AI, which can enable people to create different digital content without extensive coding knowledge.

Natural Language Processing

There’s hardly a significant industry that modern AI hasn’t touched. Narrow systems that execute specific functions give way to broad AI that can work across different domains and problems. ML algorithms trained on large sets of unlabeled data and fine-tuned for other use cases are driving this trend.

Personal assistants like Siri, Alexa, and Cortana are famous examples of this type of AI. They use NLP to receive instructions from users and respond with relevant information. They also learn from their interactions with users and improve over time.

AI can be used to streamline administrative tasks for companies to reduce human error and improve efficiency. It can also help with medical diagnostics, as it can read MRI scans more quickly than doctors can and detect anomalies at an early stage. It can also be used in the supply chain to predict demand and optimize product placement. And in the workplace, it can be used to analyze employee data, match employees with the right jobs, and even screen job applicants without bias.

Machine Vision

With companies spending billions on AI products and services, universities making it a more significant part of the curriculum, and the U.S. Department of Defense upping its game, big things are on the horizon. That includes automated vehicles, which could reshape how we live and travel, as well as the impact on jobs, with robots taking over many more mundane tasks.

In a warehouse at online giant Amazon, workers work alongside tens of thousands of robots performing picking and packing functions. But the company says that while removing some jobs, those who lose them will find other positions or learn new skills.

Manufacturers use AI to spot quality issues, reduce equipment downtime and shorten design times. One such company is Lennox International, which uses image recognition to calculate how much money it should spend on warranty claims and other expenses. It says it expects to cut costs by 10 percent. The technology is also reducing the risk of human error and boosting productivity. for example, has a machine learning algorithm that identifies possible defects in MRI scans and biopsy images, saving it time, effort, and potentially lives.

Speech Recognition

Using acoustic analysis of pitch, tempo, and different accents, speech recognition technology transforms spoken words into readable written text. This is common daily, from dictation and transcription software to the voice command features of smartphones and home assistants like Siri, Alexa, and Cortana.

Some people have been wary of AI, with the doomsday scenarios sensationalized by movie-makers causing anxiety and doubts about whether these advancements are genuinely beneficial. However, there are several ways in which AI is revolutionizing technology, from increasing productivity and efficiency to reducing human error.

In healthcare, AI is already widely used in telemedicine and for assisted diagnosis. For example, AI programs can analyze MRI scans to spot cancerous growths faster and with a smaller margin of error than radiologists can. Furthermore, AI can interpret and categorize patient data to identify patterns that may signal a medical emergency or indicate the effectiveness of a treatment regimen. Moreover, it can help doctors and nurses make better decisions by providing more accurate patient histories.

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