Một số kiến thức AI cần được update
To deploy a machine learning inference environment, you need three main components in addition to the model:
- One or more data sources
- A system to host the ML model
- One or more data destinations
AI Application in E-Commerce
Personalized Shopping Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty towards your brand.
AI-powered Assistants Virtual shopping assistants and chatbots help improve the user experience while shopping online. Natural Language Processing is used to make the conversation sound as human and personal as possible. Moreover, these assistants can have real-time engagement with your customers. Did you know that on amazon.com, soon, customer service could be handled by chatbots?
Fraud Prevention Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with. By considering the usage patterns, AI can help reduce the possibility of credit card frauds taking place. Many customers prefer to buy a product or service based on customer reviews. AI can help identify and handle fake reviews.
Applications Of Artificial Intelligence in Education
Although the education sector is the one most influenced by humans, Artificial Intelligence has slowly begun to seep its roots in the education sector as well. Even in the education sector, this slow transition of Artificial Intelligence has helped increase productivity among faculties and helped them concentrate more on students than office or administration work.
Some of these applications in this sector include:
Administrative Tasks Automated to Aid Educators Artificial Intelligence can help educators with non-educational tasks like task-related duties like facilitating and automating personalized messages to students, back-office tasks like grading paperwork, arranging and facilitating parent and guardian interactions, routine issue feedback facilitating, managing enrollment, courses, and HR-related topics.
Creating Smart Content Digitization of content like video lectures, conferences, and text book guides can be made using Artificial Intelligence. We can apply different interfaces like animations and learning content through customization for students from different grades.
Artificial Intelligence helps create a rich learning experience by generating and providing audio and video summaries and integral lesson plans.
Voice Assistants Without even the direct involvement of the lecturer or the teacher, a student can access extra learning material or assistance through Voice Assistants. Through this, printing costs of temporary handbooks and also provide answers to very common questions easily.
Personalized Learning Using top AI technologies, hyper-personalization techniques can be used to monitor students’ data thoroughly, and habits, lesson plans, reminders, study guides, flash notes, frequency or revision, etc., can be easily generated.
Applications of Artificial Intelligence in Lifestyle
Artificial Intelligence has a lot of influence on our lifestyle. Let us discuss a few of them.
Autonomous Vehicles Automobile manufacturing companies like Toyota, Audi, Volvo, and Tesla use machine learning to train computers to think and evolve like humans when it comes to driving in any environment and object detection to avoid accidents.
Spam Filters The email that we use in our day-to-day lives has AI that filters out spam emails sending them to spam or trash folders, letting us see the filtered content only. The popular email provider, Gmail, has managed to reach a filtration capacity of approximately 99.9%.
Facial Recognition Our favorite devices like our phones, laptops, and PCs use facial recognition techniques by using face filters to detect and identify in order to provide secure access. Apart from personal usage, facial recognition is a widely used Artificial Intelligence application even in high security-related areas in several industries.
Recommendation System Various platforms that we use in our daily lives like e-commerce, entertainment websites, social media, video sharing platforms, like youtube, etc., all use the recommendation system to get user data and provide customized recommendations to users to increase engagement. This is a very widely used Artificial Intelligence application in almost all industries.
Applications of Artificial intelligence in Navigation
Based on research from MIT, GPS technology can provide users with accurate, timely, and detailed information to improve safety. The technology uses a combination of Convolutional Neural Network and Graph Neural Network, which makes lives easier for users by automatically detecting the number of lanes and road types behind obstructions on the roads. AI is heavily used by Uber and many logistics companies to improve operational efficiency, analyze road traffic, and optimize routes.
Applications of Artificial Intelligence in Robotics
Robotics is another field where artificial intelligence applications are commonly used. Robots powered by AI use real-time updates to sense obstacles in its path and pre-plan its journey instantly.
It can be used for -
Carrying goods in hospitals, factories, and warehouses Cleaning offices and large equipment Inventory management
Applications of Artificial Intelligence in Human Resource
Did you know that companies use intelligent software to ease the hiring process?
Artificial Intelligence helps with blind hiring. Using machine learning software, you can examine applications based on specific parameters. AI drive systems can scan job candidates’ profiles, and resumes to provide recruiters an understanding of the talent pool they must choose from.
Applying AI in Healthcare
Let’s not kid ourselves. AI is going to be huge in healthcare. According to Acumen Research and Consulting, the global market will hit $8 billion by 2026. Other tech giants like IBM, Oracle, and AMD already have industry-specific solutions, too. While there are dozens of ways organizations can harness AI in healthcare, let’s look at a few.
In-Patient Mobility Monitoring The clinical staff is busy people. Take intensive care unit (ICU) nurses, for example, who often have multiple patients in critical condition under their watch. Limited mobility and cognition during long-term treatments can adversely affect the patients’ overall recovery. Monitoring their activity is vital. To improve outcomes, researchers at Stanford University and Intermountain LDS Hospital installed depth sensors equipped with ML algorithms in patients’ rooms to keep track of their mobility. The technology accurately identified movements 87 percent of the time. Eventually, the researchers aim to provide ICU staff with notifications when patients are in trouble.
Clinical Trials for Drug Development One of the biggest challenges in drug development is conducting successful clinical trials. As it stands now, it can take up to 15 years to bring a new – and potentially life-saving – a drug to market, according to a report published in Trends in Pharmacological Sciences. It can also cost between $1.5 and $2 billion. Around half of that time is spent in clinical trials, many of which fail. Using AI technology, however, researchers can identify the right patients to participate in the experiments. Further, they can monitor their medical responses more efficiently and accurately — saving time and money along the way.
Quality of Electronic Health Records (EHR) Ask any healthcare professional what the bane of their existence is, and undoubtedly cumbersome EHR systems will come up. Traditionally, clinicians would manually write down or type observations and patient information, and no two did it the same. Often, they would do it after the patient visit, inviting human error. With AI- and deep learning-backed speech recognition technology, however, interactions with patients, clinical diagnoses, and potential treatments can be augmented and documented more accurately and in near real-time.
Industry Challenges Persist While the potential benefits, AI, and machine learning bring to the healthcare table are quite clear, there are many challenges to overcome. Long-ingrained institutional practices and different cultures in organizations cannot be optimized by merely slapping an algorithm on them, after all. Legacy EHR and Electronic Medical Systems that run on-premises don’t necessarily play well with other organizations’ ones either. Organizations also need to consider strict government regulations that are always changing. Making sense of the sheer volumes of data being generated today — which is primarily unstructured — isn’t an easy thing to do, either. That’s why data scientists, trained in the latest technologies and techniques, are in such high demand in the healthcare industry today.
We’ve only scratched the surface on the potential impact of AI in healthcare here, but one thing is clear — Data Science and AI are critical to the industry’s future. Collaborated with IBM, Simplilearn’s Artificial Intelligence Course gives aspiring professionals everything they need to know to advance their careers and make a real and lasting impact. From Python programming, Machine Learning, to Natural Language Processing — our Data Science and Artificial Intelligence Master’s programs, offered with the unique blended learning model provides students with the path that works for them.
As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of these growing technologies and hands-on experience. Keeping the innate need in mind, Simplilearn has launched the AI and Machine Learning certification courses with Purdue University in collaboration with IBM that will help you gain expertise in various industry skills and technologies from Python, NLP, speech recognition, to advanced deep learning. This Post Graduate program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning and deep learning.
Một số model cần được tham khảo
Model 1
Chi tiết mô hình Dùng trong trường hợp nào? Nội dung chi tiết Tài liệu tham khảo Process của mô hình và các kiến thức toán liên quan
Model 2
Tài liệu tham khảo
Internet
Hết.
