行业动态

Apps Of AI In Life

2018/07/02

applications of AI in health care
applications of AI in finance,AI in education

 

Artificial Intelligence, or AI as it is commonly known, is the technology of today. AI is no longer the realm of science fiction, for it is almost everywhere these days. Some of you may know about its applications in our daily life, while the others might not be aware that you are using it in some way or the other. And we do not blame it on you for AI is a vast field, and it encompasses many subsets and types. We have been discussing the difference between AI, machine learning, and deep learning. And then we also made you familiar with the 3 types of AI. How about discussing the applications of Artificial Intelligence this time? Since AI has a very bright future, you need to be familiar with the applications of AI in different fields of life. Read through to quench your spirit of inquiry.

 

Applications of Artificial Intelligence

 

Below are the different fields and areas where AI has successfully found its utilization and eminence.

 

AI In Healthcare & Medicine

 

AI has the power to turn doctors into superhumans. And this may come true in the coming times for artificial intelligence has changed the face of healthcare industry already. Important software and technology like EMR and Concept Processing are based on artificial neural networks (known as ANNs), which are deployed as clinical decision support systems for various medical diagnoses.

 

Other important applications of Artificial intelligence in healthcare include

 

•    Heart Sound Analysis

 

•    Computer-Aided Interpretation Of Medical Images

 

•    Design Treatment Plans

 

•    Design And Development Of Companion Robots For Taking Care Of The Elderly

 

•    Drug Creation

 

•    Design Treatment Plans

 

•    Predicting HIV Progression

 

•    Providing Consultations

 

•    Predicting The Possibility Of Death From The Procedure Of Surgery

 

•    Providing More Useful Information By Mining Medical Records

 

•    Performing Clinical Training By Using Avatars Instead Of Patients

 

•    Helping In Repetitive Jobs Such As Medical Management.

 

AI In Finance

 

Finance is one of the important sectors where AI finds its application. Whether it is data mining & market analysis, algorithmic trading, underwriting, portfolio management, or personal finance; AI is used almost everywhere in finance.

 

When it comes to algorithmic trading, it makes use of complex AI systems that have the capability of making millions of trades in a day sans any human intervention. Large institutional investors usually make use of these automated trading systems.

 

Speaking of market analysis and data mining, AI engines have been successfully used by various large financial institutions in order to assist in the investment practices. Some examples of such AI engines are Aladdin, Kensho, Sqreem, etc.

 

For personal finance, there are tools like Digit which is an application based on AI in order to help people optimize their savings and spending on the basis of their goals and habits. Another example is Wallet.AI.

 

Organizations have already started making use of AI-based platforms for credit underwriting. Such platforms include names like Upstart, and Zest Automated Machine Learning (ZAML).

 

Lastly, moving on to portfolio management, robo-advisors are being used in the investment management industry. These robo-advisors are capable of performing their jobs without any human intervention.

 

AI In Education

 

Making teaching innovative and fun, AI is changing the face of the education sector in the contemporary. Various companies have already started developing robots that are capable of teaching a wide array of subjects like computer science, biology, etc. Although these robotic teachers are not widespread, they may become an integral part of the education system in the coming years.

 

In higher education, there has also been a rise of ITS: Intelligent Tutoring Systems. Various organizations like DARPA have been deploying AI to train its Navy recruits in technical skills in a shorter span of time via digital tutors. Another example is SHERLOCK which is a type of ITS, designed and developed to teach the technicians of the Air Force to detect various electrical systems issues in aircraft.

 

Automatic grading of assignments and data-driven understanding of the learning needs and requirements of individual students have come into play with the help of the development of natural language processing integrated with machine learning. This is also the reason why Massive Open Online Courses or MOOCs have seen such an explosion in their popularity in the recent times. Sets of data accumulated from these large-scale online learning systems have given rise to learning analytics, which in turn has helped in enhancing the quality of learning at scale.

 

AI In Heavy Industry

 

While most of the people fear the power of robots, there are various industries where bots have already been appointed for jobs that are considered hazardous to humans. It has been found that robots are very effective in performing jobs that are very repetitive which may cause accidents or mistakes due to a slip in concentration.

 

The United States, Japan, China, Germany, and the Republic of Korea together constituted to the 70% of the total sales volumes of robots in the year 2014. Japan is the leading country out of all with the highest density of industrial robots. The ratio was found to be 1,414 robots per 10,000 employees.

 

These are just some of the fields and industries where AI finds it applications. Stay tuned to Ant PC for Applications of AI In Different Fields of Life.

 

Artificial Intelligence begins with Data Analytics

 

Radiologists should learn about data science to promote effective development of AI in imaging.

 

It's said that "data is the new oil."1 Radiologists should agree — our bread and butter is interpreting complex visual data. Enter artificial intelligence (AI), with its predictions of doom for human radiologists.

 

Hype aside, AI and machine learning  analytics are advanced techniques used in data science , a hybrid field of computer science and statistics. These are not the statistics we remember from medical school. Newer statistical techniques are increasingly concerned with classification , or grouping, of data. For example, if we take the given Data Set X, with three different classes A, B, and C, and we introduce a new data point, which group does it belong to?

 

To get started with data science, the venerable spreadsheet is a good entry point. But to truly understand data science, radiologists must become familiar with programming languages, functions, and analysis tools that many probably haven’t encountered before. Powerful apps like Tableau and Microstrategy  provide descriptive reporting, basic statistics, and even some predictive analytics for the non-coder. But data scientists tend to use R or Python  programming languages for analytics.2

 

R and Python allow for sophisticated analysis of complex datasets. R is an open-source programming language, with many available statistical, financial, and scientific analysis packages. Functions like Extreme Gradient boosting , LASSO , random forests , support vector machines , and k-nearest neighbors  are available in R.3 Python is object-oriented and well-suited for large datasets and machine learning using Tensorflow  and Keras . Hardware requirements and challenging software implementation for GPU accelerated high performance  deep learning can be as lofty as the impressive results.

 

If radiologists treat AI algorithms as a 'black box ' and surrender our sound clinical judgment, we risk further commoditization. By doing the groundwork to understand classification statistics, data analytics, and the computing innovations associated with AI, we can earn a seat at the table — and lend our voice to safeguard our patients and ensure that the practice of radiology is improved, not hurt, by AI.

 

As the title suggests, this article aims the newbie developers, like me, interested to be a part of this digital revolution, Data Science, who possess minimal knowledge on machine learning and Python.

 

What is Machine Learning?

 

Machine learning is the field of computational sciences and mathematics that often uses statistical techniques to give computers the ability to "learn" with data, without being programmed explicitly. It's an application of Artificial Intelligence(AI). Practically, it means, we need to feed data into an algorithm, and use it to make predictions about what might happen in the future.

 

The name 'machine learning' was coined in 1959 by Arthur Samuel.

 

In 1997, Tom Mitchell gave a definition that has proven more useful to engineering types :

 

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” 

 

So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully “learned”, it will then do better at predicting future traffic patterns.

 

Among the different types of ML tasks, a crucial distinction is drawn between supervised and unsupervised learning:

 

Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.

 

Unsupervised machine learning: The bunch of data is fed to the program and it should find patterns and relationships among the features/attributes therein.

There is a really vast range of applications which involves domains such as,

Healthcare(e.g. personalized treatments and medications, drug manufacturing)Finance(e.g. fraud detection)Retail(e.g. product recommendations, improved customer service)Travel(e.g. dynamic pricing like, how does Uber determine the price of your ride, and sentimental analysis, like, TripAdvisor collects information of the travellers from social media when we share photos and reviews, and tries on improvising its service based on the reviews)Media(e.g. facebook, from personalizing news feed to rendering targeted ads, machine learning is the heart of all social media platforms for their own and user benefits)

On the other hand, Unlike R, Python is a complete language and platform that you can use for both research and development and developing production systems. It can feel overwhelming to choose from multiple libraries and modules.

 

So, let's start with the step by step procedure to be followed by beginners to start with machine learning using Python.

 

Our first step shall be to learn Python.

 

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language which was created by Guido van Rossum during 1985- 1990 . Python source code is available under the GNU General Public License (GPL).

换一张