ai Artificial intelligence|What is it and why is it important?
ai Artificial intelligence (AI) enables machines to learn from experience, adapt to new applications and perform human-like tasks. Many of the AI models you hear about today - from computers playing chess to self-driving cars - rely heavily on in-depth learning and natural language processing. Using this technology, computers can be trained to perform specific tasks by processing large amounts of data and identifying patterns in data.
AI History
The term ai artificial intelligence (AI) was coined in 1956, but AI is still very popular today due to increased data, advanced algorithms, and improved computer power and storage.
The first study of AI in the 1950's explored topics such as problem solving and metaphorical approaches. In the 1960's, the US Department of Defense became interested in this type of work and began training computers to imitate basic human thinking. For example, the Defense Advanced Research Projects Agency (DARPA) completed street map projects in the 1970's. And DARPA produced intelligent human helpers in 2003, long before Siri, Alexa, or Cortana became household names.
This first work paved the way for automation and the legitimate reasons we see on computers today, including intelligent decision-making systems and search systems that can be built to assist and enhance human capabilities.
While Hollywood movies and science fiction novels portray AI as robots that take in different world, the current emergence of AI technology is not that scary - or so clever. Instead, AI has evolved to provide many more specific benefits across the industry. Continue to learn about modern examples of artificial intelligence in health care, marketing and more.
1950s - 1970s
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Early work on neural networks arouses interest in “thinking machines.”
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1980s-2010s
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Machine learning is popular.
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Present day
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The achievements of in-depth learning drive the AI boom.
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"AI has been an integral part of SAS software for years. Today SAS are helping clients across the industry to make a profit from AI development, and SAS will continue to embed AI technologies such as machine learning and in-depth learning solutions across the SAS portfolio."
Jim Goodnight ( Chief Executive Officer of SAS)
ai Artificial intelligence (AI), machine learning and deep learning
Quickly, read this image to understand the relationship between AI, machine learning and deep learning. You’ll see how both of these technologies work, with examples and a few funny asides.
Also, this great image that you can share with friends and family to explain the ingenuity of the installation in a way that anyone will find.
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See image above
Why is ai artificial intelligence (AI) important?
1-AI automatically performs reading and mimics by obtaining data. But AI is different from hard drive. Instead of doing manual labor, AI performs normal, advanced, computer-based tasks reliably and without fatigue. In this type of automation, personal inquiries are still required to set up a system and ask appropriate questions.
2-AI adds ingenuity to existing products. In most cases, AI will not be marketed as a single program. Instead, the products you use will be enhanced with AI capabilities, just like Siri was added as a feature to the new generation of Apple products. Automation, chat platforms, bots and smart devices can be combined with a large amount of information to develop more technologies at home and at work, from security intelligence to investment analysis.
3-AI syncs with continuous learning capabilities to allow data to be programmed. AI detects the structure and normalization of data so that the algorithm acquires the ability: The algorithm becomes a separator or predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself which product to recommend online next. And models change when new data is presented. Back distribution is an AI approach that allows the model to adapt, with more training and detail, when the initial response is incorrect.
4-AI analyzes large and in-depth data using neural networks with multiple hidden layers. Creating a deceptive detection system with five layers was almost impossible a few years ago. All that has changed with amazing computer power and great detail. You need a lot of data to train in deep learning models because they learn directly from the data. The more you feed them, the more accurate they become.
5-AI achieves incredible accuracy through deep neural networks - which was not possible before. For example, your interactions with Alexa, Google Search and Google Images are all based on in-depth reading - and it remains more accurate when we use it extensively. In the field of medicine, AI techniques from in-depth study, image classification and object recognition can now be used to detect cancer in MRIs with the same accuracy as highly trained radiologists.
6-AI gets a lot of data. When algorithms read for themselves, the data itself can be intellectual property. The answers are in the news; you have to use AI to get them out. As the role of data is now more important than ever, it can create competitive advantage. If you have the best data in the competitive industry, even if everyone uses the same strategies, the best data will proven.
BENEFITS & RISKS OF AI
How to Use ai Artificial Intelligence (AI)
Every industry has a great need for AI skills - especially query systems that can be used for legal aid, patent search, risk information and medical research. Other AI applications include:
Health care
AI applications can provide personalized medicine and X-ray readings. Your health care providers can work as health coaches, reminding you to take your pills, exercise,eat or keep healthy.
Sales
AI offers real-time purchasing capabilities that offer customized recommendations and discuss purchase options with the buyer. Stock management and site planning technology will also be improved with AI.
Production
AI can analyze IoT (Internet of Things) factory data as it comes from connected machines to predict expected load and demand using repetitive networks, a type of deep learning network used with sequential data.
Banking
ai Artificial Intelligence (AI) improves the speed, accuracy and performance of human efforts. In financial institutions, AI strategies can be used to identify in which transactions are likely to be fraudulent, to receive fast and accurate credit scores, and to perform more powerful data management functions.
Working with AI
ai Artificial intelligence (AI) is not here to replace us. It enhances our skills and makes us better at what we do. Because AI algorithms learn differently than humans, they look at things differently. They can see relationships and escape patterns. For any person, AI collaboration offers many opportunities. It can:
1-Bring statistics to industries and domains where it can currently be used.
2-Improve the performance of existing analytics technologies, such as computer vision and time series analysis.
3-Break down economic barriers, including language and translation barriers.
4-Develop existing skills and make us better at what we do.
5-Give us a better idea, a better understanding, a better memory and much more.
What are the challenges of using ai artificial intelligence (AI)?
ai Artificial intelligence (AI) will change the whole industry, but we must understand its limitations.
The limit of the AI system is that it reads from data. There is no other way information can be included. That means any inaccuracies in the data will be reflected in the results. And any layers of prediction or analysis should be added separately.
Modern AI systems are trained to perform a clearly defined function. A system that plays poker cannot play solitaire or chess. A fraud detection system cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.
In other words, these programs are very special. They are focused on one job and far from being human.
Similarly, self-study programs are not independent programs. The imaginative AI technology you see in movies and TV is still a science fiction novel. However, computers can process complex data in order to learn and perform certain tasks and become familiar.
How ai Artificial Intelligence (AI) Works
AI works by combining large amounts of data with acceleration, iterative processing and intelligent algorithms, allowing software to automatically learn from patterns or data symbols. AI is a broad field of study that incorporates ideas, methods and technologies. To know that How the artificial intelligence works, the major areas:
1-Machine learning creates a model structure for analysis. It uses methods from neural networks, mathematics, performance research and physics to find hidden data in data without explicitly plotting where it looks or pattern.
2-A neural network is a type of machine learning made up of connected units (such as neurons) that process information in response to external inputs, sending information within each unit. The process requires a lot of data to get to the connection and get the meaning from the unspecified data.
3-In-depth learning uses large neural networks with multiple layers of processing units, taking advantage of computer power developments and advanced training techniques to learn complex patterns with large amounts of data. Typical applications include image recognition and speech.
4-Comprehensive computer is under AI that strives for natural, human and mechanical interactions. Using AI and a computer for understanding, the ultimate goal is for the machine to mimic human processes by being able to interpret images and speech - and then speak harmoniously in response.
5-Computer vision depends on pattern recognition and in-depth reading to see what is in the picture or video. When machines can process, analyze, and interpret images, they can take pictures or videos in real time and interpret the environment.
6-Indigenous language processing (NLP) is a computer's ability to analyze, understand and produce human language, including speech. The next phase of NLP is natural language communication, which allows people to communicate with computers using common, everyday language to perform tasks.
Main article: how the artificial intelligence works
Additionally, many technologies allow and support AI:
1-Graphics processing units are key in AI because they provide the heavy computing power required for iterative processing. Training neural networks requires big data and computer power.
2-The Internet of Things generates a huge amount of data from connected devices, most of which are disabled. Creating AI models will allow us to use most of them.
3-Advanced algorithms are developed and integrated with new ways to analyze additional data quickly and at multiple levels. This smart analysis is the key to identifying and predicting unusual events, understanding complex systems and improving different situations.
4-APIs, or interface programming application, portable code packages make it easy to add AI functionality to existing products and software packages. They can add visual acuity to home security systems and Q&A skills.
In summary, the purpose of AI is to provide software that can think about installation and explain the output. AI will provide human-made interaction with software and provide decision-making support for specific tasks, but it is not a human environment - and will not be available anytime soon.
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