The latest AI tools can learn from data without being programmed by humans. Gartner predicts that generative artificial intelligence will be used in the majority of AI systems by 2025.
Examples of this include sifting through large data sets to detect fraud and creating profile information about individuals for antisocial behavior prediction. Other applications include improving customer service and reducing manufacturing costs by automating tasks.
1. Self-driving cars
Many of the advancements in AI over the years have been the result of big data, massive processing powers and affordable open-source libraries. These technologies have allowed the creation of sophisticated algorithms that can perform tasks that were previously impossible.
As the capabilities of AI continue to grow, more industries will begin using it to automate and optimize their operations. This will lead to job displacement in some sectors but also create new opportunities for those with the right skills to work alongside AI.
For example, autonomous cars will change the way people commute. It will save time and reduce traffic, while making the environment safer. It will also allow for more efficient use of space, since a parking lot would not need elevators and staircases. However, it will be challenging for some drivers to become accustomed to riding in a car without a human behind the wheel. Moreover, it’s important to ensure that the safety features are foolproof and accurate.
2. Robots
In recent years, extraordinary advances in machine learning and artificial intelligence have helped robots move from rote machines to collaborators with cognitive functions. This has been a boon for the robotics industry. Military robots are already in the field today, as are drones, driverless cars and telepresence robots that connect people halfway around the world.
Speech recognition is another common application of AI algorithms, enabling computers to translate spoken words into text or commands. This is the technology behind Siri and other mobile device speech-to-text capabilities.
Feature engineering involves manually selecting relevant data features to improve an algorithm’s performance. It’s a component of machine learning and can be used to improve performance for tasks such as predicting stock market trends or weather patterns.
3. Smart assistants
With the likes of Amazon’s Alexa, Google’s Assistant and Microsoft’s Cortana, AI-powered virtual assistants are already part of our lives. These tools help manage our daily calendars, take notes, set alarms in advance of meetings and start apps with voice commands. They can also answer simple questions.
The technology is also being applied to a wide range of tasks, such as improving customer service, where AI-powered chatbots can provide 24/7 support and personalized recommendations.
At a larger scale, intelligent systems can improve energy efficiency, for example, by analyzing data from smart meters to forecast demand. They can also improve the environment through real-time weather forecasting, disaster preparedness and climate change mitigation.
However, the use of AI comes with ethical concerns linked largely to its reliance on big data and a lack of self-imposed limitations. Privacy is one such concern, with many people worried about their data being used to develop products and services that affect their daily lives in ways they might not anticipate.
4. Predictions
In the past, automation technology mostly affected physical work activities. Gen AI is expected to have an impact on knowledge work activities that involve decisions and collaboration. This means that professional fields like education, law, and technology could see their jobs replaced by AI.
Gen AI tools can now create most types of written, image, audio and video content. Entertainment companies like YouTube and SoundCloud are already using them to offer personalised content for their customers.
With gen AI also capable of conducting surveillance and maintaining profiles of individuals, some fear the potential of an Orwellian state. It can be used to sift through huge data sets, monitor activity on social media and even detect antisocial behaviour. Businesses are catching up to technologists in recognizing the importance of responsible AI and understanding that investing in ethical AI will improve their business outcomes. They will also implement protocols for drift and regular tuning of their machine learning models.
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