Artificial intelligence has been a hot topic for the last decade or so. More and more companies are devoting resources to the development of solutions, and it’s easier for specialists to earn top dollar at these companies.
Copyright by Alex Arsic
For instance, statistics show that the median salary for managers and senior computer scientists in is north of $127,000. 84% of company representatives trust to provide competitive advantages. Their bet will pay off well because can alter fundamental aspects of society. This alteration will impact the way we work, predicting behavior, advertising, and more.
Let’s have a look at how is influencing our daily lives.
7 Modern Applications of Artificial Intelligence in Our Everyday Lives
#1 Autonomous Vehicles
Multiple ridesharing app companies such as Didi Chuxing, Lyft, and Uber are taking huge strides in designing autonomous vehicles. Self-driving and parking cars use , a subspace of , to recognize the space around a vehicle.
According to its website, Nvidia, a technology company, uses to give cars “the power to see, think, and learn so that they can navigate a nearly infinite range of possible driving scenarios.”
There are installations of the company’s -powered technology in cars by Audi, Mercedes-Benz, Tesla, Toyota, and Volvo. It is sure to change how people drive while allowing vehicles to drive themselves.
#2 Predicting User Demand
This application is also common in ridesharing applications. Uber uses to predict periods of high driver demand, meaning surge pricing could end soon.
The on-demand entertainment company uses to predict movies that users might like. The predictions usually depend on their reactions to previous movies they’ve watched on the platform.
#4 Intelligent Digital Assistants
Almost everyone knows – an assistant that can help with many things, such as shopping, playing music, and searching for information. ’s potential remains largely untapped.
Digital assistants such as Amazon’s , Apple’s Siri, Google Now, and Microsoft Cortana help users perform various tasks. These tasks may include searching for information online, checking their schedule, or sending commands to an app.
is essential as these apps learn user interactions. They then get better at recognizing patterns and serving correct results. According to Microsoft, Cortana continues to learn about the user and will eventually correctly anticipate user needs.
#5 Vehicle Recognition Identification
A growing number of cities around the world now use -powered traffic cameras to read license plates. IntelliVision, PlateSmart, and Sighthound are some companies using the so-called computer vision to turn conventional surveillance into vehicle monitoring.
Computer vision is an implementation that can see and understand images. This tool is essential as authorities search for specific plate numbers. Don’t run that red light!
You’ve probably used vacuum cleaners your whole life, but have you heard of the Roomba 980 model vacuum? It cleans the floor by itself using . It scans the size of the living area, identifies objects that might be in the way, and remembers the best routes for cleaning the carpet.
The vacuum can also tell how much cleaning a room needs depending on the size of the room. It can also do this after cleaning a small room three times or a medium-sized room two times.
Some of the magic in apps like Uber depend on another subset of called . Uber explains that and are critical to supporting its mission to develop reliable transportation solutions for people everywhere.
The company uses to enable efficient ridesharing, identify fraudulent or suspicious accounts, suggest optimal drop-off or pick-up points, and make better UberEATS deliveries. For the latter, it recommends restaurants, predicting wait times so your food can reach you when you need it.
Image Source: Epsiloneg
Computers can now “see” and identify elements or scenarios or even stories in images and videos. One can scan any image or video against familiar entities, including cars and houses, using context-specific logic.
But, algorithms can ascertain additional attributes of the image and characteristics of the various entities in it. These properties may include the number of persons in the picture, their age, gender, or even emotions. If they are vehicles, their types, and brands can be recognized.
Computer vision has plenty of potential. Autonomous cars can “see” everything around them and understand their environment.
Microsoft’s Seeing helps the blind or visually impaired to appreciate their environment. The user can request a detailed description of the surroundings, and updates on any changes. The response could come as a description in natural language through a synthesized voice.
Medical examination, navigation systems, online content management, , and security systems highlight some of the areas Computer Vision is currently active.
Once you encounter Amazon , Cortana, Google Assistant, and Siri, you see how robust Neuro-linguistic programming is. IBM and Microsoft say their technologies are either as good as or comparable to professional transcribers in processing discussions of various topics.
While algorithms may still struggle with innumerable accents and noisy environments, their general performance keeps getting better.
Interaction with digital assistants now goes beyond mechanical Q&A sessions to more natural dialogues. Note that digital assistants are getting increasingly smart, thanks to the massive amounts of data users possess.
Digital assistants will surely act autonomously soon. They should be able to initiate meaningful discussions from non-obvious logic on triggers in the user’s environment. A digital assistant will soon ask random yet meaningful questions. It’ll make relevant suggestions about educational, informational, social, scheduling, and traveling activities that interest you.
Assessing Innovations Trends and Opportunities
The Value and the Concerns
Advancements in technologies, and the expanding capabilities of sorting and processing massive data volumes drives . The internet of things (IoT) will interconnect billions of devices. They will send events, operational, and other data, which they’ll store and process using advanced big data, , and technologies.
This wealth of data and the increasing ability to automatically make sense of it will provide significant opportunities for improvement across education, health, lifestyle, transportation, and nearly every human activity.
However, there are telling concerns and timely questions concerning the ethical, political, and social implications of . For example, we can achieve “intelligent automation” at scale through the use of . Experts expect it to transform how we work and the skills in demand.
As tasks grow in automation, roles will pale in significance, and grow in redundancy. Some professions will eventually disappear. There are also issues about access and control over the derived knowledge, and the power that provides.
Preparing for the Future
We are at the dawn of the technological revolution, and it’s transforming our world and how we live already. It’s important to be ready.
First, an understanding of the technology should be acquired, along with knowledge of its potential and associated risks. Then, people must enter a life-learning mode to gain new skills and assess new talents relevant to the markets.
Under these new dynamics, states will have to adapt by modernizing laws, social programs, frameworks, and the education system. Thought leaders need to introduce proper rules and global agreements to avoid the centralization of power and control over technology and data.