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How machine learning is different from artificial intelligence

Machine Learning and Artificial Intelligence

These technologies, collectively known as machine learning (ML) or artificial intelligence (AI), are engendering new revolutionary technologies and new approaches to solve difficult contemporary problems. Our faculty and students are actively involved in this process of revolutionary creation with projects in areas that include:

  • Neuromorphic devices and circuits, and brain-inspired architectures and algorithms for energy-efficient AI.
  • Tensor-methods for deep learning and tensor analysis of big and multi-modal data.
  • Applications of machine learning to wireless communications, network management, and dynamic spectrum access, sharing and sensing.
  • Reliable learning in adversarial environments and trustworthy AI hardware.
  • Deepfake detection.
  • Self-driving vehicles.
  • Smart warehouses.
  • Computer vision, object recognition and tracking.
  • Human-Robot interaction and collaboration.
  • Deep learning algorithms for machine intelligence and AI applications.
  • Biologically inspired learning models for multi-agent and complex systems.
  • Object classification and localization via quantized neural networks.
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Artificial Intelligence – A Danger to Patient Privacy?

Industries worldwide have integrated artificial intelligence (AI) into their systems as it promotes efficiency, increases productivity, and quickens decision-making. ChatGPT certainly raised eyebrows as it demonstrated similar characteristics at the start of its debut back in November 2022. 

The healthcare sector alone, according to Insider Intelligence, has experienced significant improvements in its medical diagnoses, mental health assessments, and faster treatment discoveries after the deployment of AI.  

Risks of AI in Healthcare 

As more healthcare software systems include AI-based features, the necessity for gathering more data increases. It’s important to assess potential privacy and security issues in AI. Using artificial intelligence in healthcare poses a risk to privacy and compliance within regulatory frameworks, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Security Rule.  

In this article, we highlight protocols that will aid in combating these risks to ensure artificial intelligence systems remain compliant with HIPAA and maintain patient trust. 

The Difference Between Artificial Intelligence and Machine Learning 

When talking about artificial intelligence, oftentimes machine learning is synonymously referenced. Artificial intelligence is an umbrella term that covers a wide variety of specific technological mechanisms and algorithms. Machine learning sits under that umbrella as one of the major subfields, similar to robotics and natural language processing.  

Hence, it’s important for us to highlight the nuances in this area. When we refer to artificial intelligence in this article, we’ll be referring to it generally and encompassing both artificial intelligence and machine learning.  

What is Artificial Intelligence? 

Artificial intelligence is a set of technologies that enable computers to learn to perform tasks traditionally performed by humans.  

What is Machine Learning? 

Machine learning is a type of AI application that automatically learns insights and recognizes patterns from data used in the past via algorithms. It then applies that knowledge to make increasingly complex decisions with almost zero programming additives

HIPAA and Patient Trust Using AI in Healthcare  

Let’s briefly review the three main requirements of HIPAA:  

  • Appropriate safeguarded mechanisms must be in place to protect the privacy of protected health information and must only be accessed by authorized parties.  
  • The confidentiality, integrity, and security of ePHI must be protected via administrative, physical, and technical defenses.  
  • Notification must be provided as the result of a breach of any unsecured ePHI.  

Currently, HIPAA does not have specific language that target artificial intelligence, however, it’s important to remain compliant to each HIPAA control, as each control is applicable even in the light of this relatively new technology.  

Arguing the Pros and Cons of Artificial Intelligence in Healthcare 

As mentioned above, artificial intelligence could provide a whirlwind of possibilities, including quickly diagnosing diseases, recommending treatment options, and decreasing surgery errors.  

However, some people when surveyed, felt indifferent to the uses of AI in health and medicine. According to the Pew Research Center, approximately three-quarters of Americans were concerned that healthcare providers would move too quickly to implement AI into medical systems without understanding the full scope of risks it could bring to patients.  

The Pew Research Center conducted a survey between December 12-18, 2022, with 11,004 US adults, which demonstrated 38% believed AI would lead to better health outcomes, 33% felt it would lead to worse outcomes and 27% remained neutral – presenting no changes at all.