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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

We are an Instructor, Modern Full Stack Web Application Developers, Freelancers, Tech Bloggers, and Technical SEO Experts. We deliver a rich set of software applications for your business needs.

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Artificial Intelligence (AI)

WORM-Compliant Storage: Exploring Write Once Read Many (WORM) Functionality

This is sometimes referred to as write once, read many, or WORM, compatible storage. With a name like that, it’s hardly surprising that many need help understanding it.

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How does WORM storage work

Organizations generate an immense quantity of data, yet sometimes they need or want to preserve it in an unalterable format.

For legal reasons, a non-profit organization may desire to keep its financial information in this format regularly. An institution may desire to save graduation records in this manner in case the actual records are lost in an accident. Businesses may desire unalterable records to avoid tampering, which might cover up fraud.

This is sometimes referred to as write once, read many, or WORM, compatible storage. With a name like that, it’s hardly surprising that many need help understanding it.

We’re here to help you get a grasp on things. Let’s go!

1. What Exactly Is Write Once, Read Many?

The simplest explanation is that it is immutable storage. You may write data to the storage device or medium just once. Following that, no one may legally alter the data in any manner.

CD-R discs are a basic kind of WORM storage. You may add data to the blank disk, but it will remain in that state indefinitely. You may damage or destroy the disk to prevent someone from accessing it, but you cannot modify the data contained in it.

WORM storage allows for repeated reads of the data. Assuming the disk or drive isn’t destroyed, there’s no practical limit to how often you can access the data.

2. How does WORM storage work?

There are two options for implementing WORM storage in your business. The first technique is hardware, which uses tape or a similar form of media that permanently stores data, making physical destruction of the WORM storage device the sole way to delete it.

Nonetheless, with many solutions migrating to cloud and SaaS services, selecting particular hardware might be challenging. However, many of these service providers now provide software-defined WORM solutions, which combine the flexibility of software with the strictness, security, and indelibility of hardware-based WORM.

Whether you utilize software or hardware to achieve your compliance objectives, the idea is the same. When someone writes data to a WORM disk, it remains there eternally. The assumption that you cannot alter the data on a WORM drive only refers to anything that has already been saved there; the ability to add new data is always available as long as there is adequate storage space on the drive.

Do you need WORM-compliant storage

3. Do you need WORM-compliant storage?

Unless your company works in the securities or healthcare industries, which are subject to SEC or HIPAA laws, there is likely no legal need to adopt write-once, read-many (WORM) compliant storage solutions. However, legal requirements are only one motivator for using WORM storage systems.

WORM compliant storage provides a key role in addition to regulatory compliance. For example, if you want to keep a safe archive of historically important documents, WORM storage is a wise solution. This guarantees that once data is written, it cannot be changed or erased, protecting the integrity and validity of critical documents over time.

Furthermore, in situations where internal workers may tamper with corporate documents, WORM storage adds an extra degree of protection. Creating immutable copies of papers makes it easy to check the correctness and validity of records, reducing worries about possible manipulation.

Likewise, WORM storage may serve as a protection for proof of trade secrets or intellectual property, providing a snapshot of data at a given point in time and preserving valuable assets from illegal changes or access. In conclusion, although WORM-compliant storage is not legally required for all enterprises, it provides essential advantages for data integrity, security, and crucial information preservation.

4. What are the primary advantages of WORM storage?

WORM technology protects businesses against many of the usual difficulties associated with data corruption and loss. The primary advantages of deploying WORM storage include:

Compliance With Industry Regulations

Using WORM storage helps firms comply with recordkeeping rules and laws. More than simply archiving data is required. Businesses must store their data in the right, unalterable format to comply with regulatory requirements and avoid significant fines and penalties.

Risk Mitigation for Poorly Archived Data

Companies must have a robust procedure for archiving all data. If information is needed as part of an audit or lawsuit and the required data is lost or damaged, difficulties (and penalties) will arise.

Better Information Security

WORM Storage secures precious and sensitive data and, more critically, prevents it from being doctored or changed. It guards against occurrences such as data being accidentally or purposefully manipulated.

Better Data Governance

WORM storage contributes to the present business-wide practice of rigorous and well-planned data governance. It also enables you to better adhere to the Electronic Discovery Reference paradigm (EDRM), a paradigm that describes the steps of the eDiscovery process throughout an inquiry.

5. Use Cases of WORM Storage

Professional content distribution includes financial records, police investigations, court testimony, computerized voting, and other applications in which data files must be safeguarded against manipulation or deletion, particularly when material is accessed, relocated, or transferred. Organizations transfer all data given over to the data investigation business onto WORM disks, ensuring that nothing changes beyond that point.

Transferring a read-only file across a network using encryption and passwords does not ensure that the file is original or untouched. Furthermore, it is usually preferable to enforce something in hardware rather than software since when the program is not operating, the data may be tampered with.

  • Corporate records
  • Financial and Insurance
  • Intelligence collection
  • Law enforcement
  • Electronic Voting
  • Court Proceedings
  • Medical Records and Devices
  • Public Records
  • Artificial Intelligence
  • Cyber-attack protection
  • IT security and log files

Conclusion

Write once read many compliant storage enables enterprises to safeguard information in a method that no one can tamper with. This may occur with something as basic as a CD-R or with software-based cloud storage.

The size of the data typically influences the medium used. You can keep a few papers on physical media without issue. Petabytes of data need a strong in-house storage ecosystem or a cloud storage provider.

If you decide to use a cloud storage provider, be sure to inquire about the retention term choices and redundancies.

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