Artificial intelligence is broadly described as a computer program’s ability to learn and think. It was in 1950 that John McCarthy first penned down the term ‘Artificial Intelligence.’
McCarthy expressed that all features of learning and intelligence can be described with such precision that a machine can start simulating it. Machines can then form abstraction and concepts. They can resolve problems that are presently reserved for human beings.
It is difficult to define Artificial Intelligence. Andrew Moore is the dean of Computer Science at Carnegie Mellon University. In a 2017 interview with Forbes, Moore expressed that Artificial Intelligence is the science and engineering that makes computers behave in specific ways. Human intelligence was until recently believed to be a prerequisite for these behaviors.
AI has essentially made progress over time. In the 1950s, AI used to find chess and checkers to be challenging. Nowadays, we seldom consider chess, playing games to be AI.
Computers nowadays carry out more complicated tasks using AI. This includes processing voice commands, driving cars, and detecting cancer.
Strong AI or Artificial General Intelligence (AGI) is the human-level AI. Generalizing knowledge comes easily to human beings. When we learn concepts in one field and apply them to the other. In contradiction with perceptions made earlier, human-level AI may not be around the corner.
Narrow AI does not attempt to replicate the human brain’s functionality. Instead, Narrow AI works towards optimizing a single task.
Narrow AI hence finds a plethora of real-world applications. A few of the top applications of Narrow AI include displaying personalized content in FB NewsFeed, recommending videos on YouTube, converting audio to text and recognizing faces.
Many scientists believe that narrow AI can help with repetitive tasks to increase human efficiency. AI algorithms can help doctors scan X-rays quicker, letting them see more patients. Similarly, Narrow AI can help to fight cyber threats.
Machine Learning (ML)
ML engineers do not manually develop rules for AI. They train the models by feeding in a significant amount of sample data. ML algorithms find patterns in the training data. It then comes up with its own behavior.
As an example, an ML algorithm can train over a company’s vast sales data. It can then make a sales forecast.
Applications of AI
AI is bringing about a significant change across domains in the world that we live in.
Autonomous driving may be a reality in the future. AI can enable prime components in a car to understand their surroundings. One of the ways the cars achieve the same is by taking feed from cameras installed around the vehicles. They detect objects, such as traffic signals, people, and other cars.
Cortana and Alexa are digital assistants. They code the human voice to text and follow instructions. They can gauge separate nuances in spoken language, and synthesize human voice as well.
AI simplifies translation using deep learning. AI may not master human language anytime soon but has made commendable progress.
Facial recognition may be used to unlock smartphones and detecting intruders at home. It is a technology that comes with civil liabilities.
AI helps analyze MRI scans and provide personalized healthcare tips. They are used in wearable technology devices. Historical data may help predict epidemic outbreaks and optimize medical care.
Banks feed in data regarding fraudulent and non-fraudulent transactions. AI correspondingly uses the data to make predictions.
Future of AI
Recent changes in technology have made AI more adaptable than earlier. In the future, AI will play a more significant role in our lives. We can expect a lot of changes in our lives with AI. AI and robotics will combine to render strength and longevity. Artificial limbs will be faster and more efficient. We will receive better medical care. Our faces will become our IDs, and virtual assistants like Alexa will be more advanced.