1. Speech recognition
2. Learning
3. Planning
4. Problem-solving
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
Let’s go Little Deep:
Artificial intelligence aims to create intelligent machines. It has become an essential part of the technology industry. The core problems of artificial intelligence include programming computers for certain traits such as:
1. Knowledge
2. Reasoning
3. Problem-solving
4. Perception
5. Learning
6. Planning
7. Ability to manipulate and move objects
Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties, and relations between all of them. Incorporating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Why is artificial intelligence important?
A. AI automates repetitive learning and discovery through data.
AI is different from hardware-driven, robotic automation. It performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, a human inquiry is still essential to set up the system and ask the right questions.
B. AI adds intelligence
It adds intelligence to existing products. It improves the product that you are already using by imparting AI capabilities. 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.
C. AI Adopts through progressive learning algorithms
AI discovers structure and regularities in data so that the algorithm acquires a skill. The algorithm turns into 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. Backpropagation is an AI technique that allows the model to adjust, through training and added data when the first answer is not quite right.
D. AI analyzes more and deeper data
AI analyzes more and the deeper data using neural networks that have many hidden layers. A lot of data is required to train deep learning models as they learn directly from the data. The more data, the more accurate they become.
E. Incredible Accuracy
AI achieves incredible accuracy though 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.
F. 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.
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