Artificial intelligence (AI) is a wide branch of computer science is the intelligence demonstrated by machines. It's basically refer to building smart machines which are capable of performing tasks without any manual effort. It is the simulation of human intelligence processes by machines.
In our daily life, we have to deal with huge amount of data and for human brain its quite difficult to keep all that in the mind accurately. That is why we need to technologies like AI.
Basically it's simply means that to give machines power to think and learn.
As AI and Machine Learning are being expanding across various industries, Large companies investing in this, and the demand of persons who are expert in AI and ML are increasing drastically.
AI is generally divide into two categories--
- Artificial General Intelligence (AGI):It is referred as "Strong AI," is the type of artificial intelligence which you generally seen in movies like The TERMINATOR and ROBOT.Narrow AI:It is referred as "Weak AI," this type of artificial intelligence operates within a limited context; it is well good in some particular tasks but not as advance as AGI.
1. Reduce in Human Errors
2.It can work 24 hours
3. Human life risk on an event reduce
4. Take Fast Decisions
5. Complete the task in low time etc.
2. Make Human Lazy
4.Lack of New Ideas
1. Why is artificial intelligence important?
AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
AI analyses more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
According to great Professor Stephen Hawking: