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AI Data Collection Services For Improve Machine Learning

The ability to collect, share, and deliver data is becoming an important priority as digitalization has disrupted every industry, even healthcare.  Machine learning, big-data and artificial intelligence can be used to solve the many challenges presented by large amounts of data.  Machine learning can be used to help healthcare organizations address growing medical needs, improve operations, and reduce costs.  Healthcare professionals can use machine learning to detect and treat disease with more efficiency and personalized care.

Examining machine learning in healthcare reveals that technology innovation can lead more effective and holistic care strategies that could increase patient outcomes.


What is Machine Learning?  An Overview


Machine Learning is one the most widely used forms of AI.  It finds patterns in large data sets and processes them to allow for decision-making. Allgorithms is a collection instructions for performing a particular set of tasks.  The algorithms are programmed to work independently of human intervention and learn from the data. Machine learning algorithms are able to improve their prediction accuracy over time without programming. Three key components of machine learning are revealed in a deep dive: representation, evaluation, and optimization. Representation refers to data being classified in a way that computers can understand.  This is the first component of evaluation. It determines whether data classifications are useful.


To overcome AI Development Blockades, More Reliable Data is the Key


Today's average person has millions more computing power than NASA required to successfully land the moon in 1969.  This ubiquitous device, which conveniently displays an abundance in computing power, also fulfills another prerequisite for AI's golden age: an abundance data.  Information Overload Research Group's insights show that 90% of the world's data was created over the past two year.  With the rapid increase in computing power resulting in equally explosive growth in data generation, AI data innovations have been so prolific that experts believe they will spark a Fourth Industrial Revolution.

The National Venture Capital Association has data that shows that the AI industry saw record investments of $6.9B in the first quarter. AI tools are everywhere, so it's easy for us to see the potential. One of the most prominent uses for AI products is the recommendation engines behind popular apps such as Spotify or Netflix. While it's great to find new music or a new TV series to watch, these applications are very low-stakes. Other algorithms can also grade test scores and determine where students will be accepted to college. Others scan through resumes to decide which candidates get the job. AI Data Collection tools have the potential to make a difference in your life, like the AI model for screening for breast cancer. This AI tool outperforms doctors.




Machine learning allows machines go through a learning process. It accomplishes this by building foundational models to solve problems. Machine learning algorithms alter the model each time they scan through data and find new patterns. This method allows learning and delivers more precise outputs.

The algorithm does this without any programming. Machine learning can either be supervised, unsupervised or semisupervised.

  • Supervised Learning. Gartner says that supervised Learning, which involves the input and classification of historical and classified data to machine learning algorithms, will continue being the most widely used type of machine learning through 2022.
  • Unsupervised learning. Unsupervised learning allows algorithms to recognize patterns in data by themselves without any prior classification. It has many applications. For example, in industrial sectors, it can identify faults in factory systems before they happen through predictive maintenance.
  • Semisupervised education. semisupervised learning is somewhere in the middle of supervised and unsupervised. Semisupervised learning algorithms may use both classified and unclassified information to solve problems. A semisupervised learning model for drug discovery
  •  has been demonstrated in a recent study.
  • Reinforcementlearning. Reward system. Enhancement learning teaches algorithms via a rewards system. Algorithms create different outputs and learn which ones to select. They get rewarded and penalized for undesirable actions. Recent research shows that reinforcement learning is possible in many applications, including autonomous robotic process


Inconsistent Quality of Data in AI Solutions: A Challenge


1. Relevant

Data that is of high quality must provide value to the decision making process. Do you see a correlation in the performance of job applicants who are state champion pole vaulters and their status as job applicants? It is possible but seems highly unlikely. A computer algorithm can filter out irrelevant data and focus on information that will have an impact on outcomes. 


 2. Accuracy

Data you use must accurately represent your ideas. If it doesn't, it's not worth it. Amazon used 10 years' worth of applicant resumes as a training ground for its hiring algorithm, though it's not clear whether they verified the information in those resumes. Checkster, a reference checking company, has found that 78% lied or would consider lying when applying for a job. For example, if an algorithm makes recommendation decisions based on a candidate's GPA it is a good idea for you to confirm those numbers. Although this process will take some time and cost money, it will ensure that your results are more accurate.


3. Properly organized, annotated

Annotation is very easy for a hiring model that is based on resumes. Although a resume is already pre-annotated in some ways, there may be exceptions. Most applicants list job experience under "Experience" and relevant skills under “Skills." However data may be different in certain situations like cancer screening. This information could include medical imaging, physical screening results, and even conversations between doctor and patient about health history and possible cases of cancer. To make sure that the AI model can accurately predict the outcome of an inferential analysis, the information must be well organized and annotated.


4. Keep up-to date

Amazon was trying to create a tool which would allow people to quickly and easily make hiring decisions. Data must be up-to-date in order to make recommendations that are as accurate as possible. For example, if a company used to prefer candidates who could repair typewriters, this would probably not impact the fitness of current job applicants for any role. Therefore, it is wise to eliminate them.


5. Diverse and appropriate

Amazon engineers chose to train an algorithms with a large male pool.  This was a major error and made worse by the fact that the resumes they had at the time were not available to them.  Amazon engineers could have collaborated with respected organizations with similar job openings to compensate. It could have artificially reduced the number men's resumes to match the women, and trained and guided its algorithm to produce a more accurate representation.  It is important to recognize that data diversity is essential and biased outputs can prevail if there is not a concerted effort to eliminate bias in inputs.

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