Vishing, a fusion of "voice" and "phishing," represents a sophisticated social engineering tactic that leverages telephonic communication to extract sensitive personal or administrative information. Though not a novel concept, historical instances underscore the enduring efficacy of vishing in breaching security barriers.
MGM Cyber Attack Analysis
Against the backdrop of historical precedents, the MGM Resorts cyberattack in September 2023, orchestrated by the Scattered Spider group utilizing ALPHV/BlackCat ransomware, stands out as a poignant example. Employing vishing as a pivotal element, the assailants adeptly simulated an MGM employee during a call to the IT help desk, successfully obtaining credentials that were then used to disrupt critical services such as card payments, knock out reservations sites, shut down ATMs and locked guests out of their hotel rooms. The ensuing compromise of customer data prompted MGM Resorts to implement comprehensive measures, including free credit monitoring.
In today's fast-paced world, the Internet of Things (IoT) has become a ubiquitous presence, connecting everyday devices and providing real-time data insights. Within the IoT ecosystem, one of the most exciting developments is the integration of artificial intelligence (AI) and machine learning (ML) at the edge. This article explores the challenges and solutions in implementing machine learning models on resource-constrained IoT devices, with a focus on software engineering considerations for model optimization and deployment.
Introduction
The convergence of IoT and AI has opened up a realm of possibilities, from autonomous drones to smart home devices. However, IoT devices, often located at the edge of the network, typically have limited computational resources, making the deployment of resource-intensive machine learning models a significant challenge. Nevertheless, this challenge can be overcome through efficient software engineering practices.
With a rapidly increasing reliance on online networks, cloud computing, and online data storage, companies must strengthen their cybersecurity procedures. As the cyber terrain grows, so does the onslaught of cyber threats that put companies at risk of data breaches, loss of sensitive data, and other ever-evolving cyber threats. Organizations must change their security posture, expand beyond perimeter-based security techniques, and adopt new machine-learning cybersecurity techniques that enhance network security.
A subset of artificial intelligence, machine learning uses algorithms from previous datasets and statistical analysis to make assumptions about a computer’s behavior. The computer can then adjust its actions, even performing functions it wasn’t programmed to do. These abilities have made machine learning a crucial cybersecurity asset.
In the evergrowing realm of Artificial Intelligence (AI) and Machine Learning (ML), the existing methods to acquire and utilize data are undergoing a significant transformation. As the demand for more optimized and sophisticated algorithms continues to rise, the need for high-quality datasets to train the AI/ML modules also keeps increasing. However, using real-world data to train comes with its complexities, such as privacy and regulatory concerns and the limitations of available datasets. These limitations have paved the way for a counter approach: synthetic data generation. This article navigates through this groundbreaking paradigm shift as the popularity and demand for synthetic data keep growing exponentially, exhibiting great potential in reshaping the future of intelligent technologies.
The Need for Synthetic Data Generation
The need for synthetic data in AI and ML stems from several challenges associated with real-world data. For instance, obtaining large and diverse datasets to train the intelligent machine is a formidable task, especially for industries where data is limited or subjected to privacy and regulatory restrictions. Synthetic data helps generate artificial datasets that replicate the characteristics of the original dataset.
AWS SageMaker and Google Cloud AI emerge as titans in the rapidly evolving landscape of cloud-based machine learning services, offering powerful tools and frameworks to drive innovation. As organizations navigate the realm of AI and seek the ideal platform to meet their machine learning needs, a comprehensive comparison of AWS SageMaker and Google Cloud AI becomes imperative. In this article, we dissect the strengths and capabilities of each, aiming to provide clarity for decision-makers in the ever-expanding domain of artificial intelligence.
1. Ease of Use and Integration
AWS SageMaker
AWS SageMaker boasts a user-friendly interface with a focus on simplifying the machine learning workflow. It seamlessly integrates with other AWS services, offering a cohesive environment for data preparation, model training, and deployment. The platform's managed services reduce the complexity associated with setting up and configuring infrastructure.
If you are a site reliability engineer (SRE) for a large Kubernetes-powered application, optimizing resources and performance is a daunting job. Some spikes, like a busy shopping day, are things you can broadly schedule, but, if done right, would require painstakingly understanding the behavior of hundreds of microservices and their interdependence that has to be re-evaluated with each new release — not a very scalable approach, let alone the monotony and resulting stress to the SRE. Moreover, there will always be unexpected peaks to respond to. Continually keeping tabs on performance and putting the optimal amount of resources in the right place is essentially impossible.
The way this is being solved now is through gross overprovisioning, or a combination of guesswork and endless alerts — requiring support teams to review and intervene. It’s simply not sustainable or practical, and certainly not scalable. But it’s just the kind of problem that machine learning and AI thrives on. We have spent the last decade dealing with such problems, and the arrival of the latest generation of AI tools such as generative AI has opened the possibility of applying machine learning to the real problems of the SRE to realize the promise of AIOps.
The Industrial Internet of Things (IIoT) has revolutionized the industrial landscape, providing organizations with unprecedented access to real-time data from connected devices and machines. This wealth of data holds the key to improving operational efficiency, reducing downtime, and ensuring the longevity of industrial assets. One of the most transformative applications of IIoT is predictive maintenance and anomaly detection, made possible by the integration of Machine Learning (ML) and Artificial Intelligence (AI) technologies. In this article, we will delve into the pivotal role that ML and AI play in IIoT monitoring, highlighting their contribution to predictive maintenance and early anomaly detection.
The Significance of Predictive Maintenance in IIoT
Predictive maintenance is a proactive approach to equipment maintenance that leverages data and analytics to predict when machines are likely to fail. Unlike traditional reactive or preventive maintenance, which relies on predefined schedules or breakdowns, predictive maintenance allows organizations to address issues before they escalate, reducing unplanned downtime and maintenance costs.
As technology continues to reshape traditional practices, financial institutions are confronted with the question: Is incorporating machine learning (ML) worth the potential challenges it brings? From enhanced predictive analytics to improved risk management, the promises of machine learning are enticing.
According to Havard University, ML can analyze historical data to understand the demand, supply, and inventory, then forecast the future's demand, supply, and inventory. ML can forecast the client's budgets and several other economic indicators, thus helping the business improve its performance.
Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. The coronavirus was just a piece of the challenge. This post explores how Lufthansa leverages data streaming powered by Apache Kafka as cloud-native middleware for mission-critical data integration projects and as data fabric for AI/machine learning scenarios such as real-time predictions in fleet management. An interactive conversation with Lufthansa as an on-demand video is added at the end as a highlight if you want to learn more.
Data Streaming in the Aviation Industry
The future business of airlines and airports will be digitally integrated into the ecosystem of partners and suppliers. Companies will provide more personalized customer experiences and be enabled by a new suite of the latest technologies, including automation, robotics, and biometrics.
Machine learning can save developers time and resources when implemented with patterns that have been proven successful. However, it is crucial to avoid anti-patterns that will interfere with the performance of machine learning models. This Refcard covers common machine learning challenges — such as data quality, reproducibility, and data scalability — as well as key patterns and anti-patterns, how to avoid MLOps mistakes, and strategies to detect anti-patterns.
Think about how testing might change if software had the ability to learn and adjust. That's what machine learning in software testing can do for you. Ensuring everything in programming functions flawlessly may be like looking for a needle in a haystack. However, computers can now learn from a vast amount of data owing to machine learning. They can also develop intelligence, see issues, and offer solutions.
We will go into great depth about machine learning in software testing in this blog. We'll learn more about its significance, use cases, and more. Therefore, let's begin.
What Are Artificial Intelligence and Machine Learning?
Artificial Intelligence (AI) and Machine Learning (ML) are closely related fields within the broader domain of computer science and data science. While they are related, they have distinct definitions and purposes:
Artificial Intelligence (AI)
AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks encompass a wide range of activities, including problem-solving, learning, reasoning, perception, language understanding, and decision-making. AI aims to create machines or software that can mimic or simulate human cognitive functions.
Machine learning is now a critical tool for businesses and researchers aiming to glean meaningful insights from their data. Amazon SageMaker, a fully managed service from Amazon Web Services (AWS), stands as a robust platform for constructing, training, and deploying machine learning models on a large scale.
In this article, I will delve into using Amazon SageMaker to maximize the benefits of machine learning in your projects.
The text you enter into the chat window is called a ‘prompt’. Writing a descriptive and well-thought prompt helps you get a more detailed and informative answer.
Bard is Google’s AI tool based on their LaMDA (Language Model for Dialogue Applications) technology. Similar to ChatGPT, Bard can be used to get answers using an interactive chatbox.
However, it is a little different than ChatGPT. For instance, it is trained on a more extensive data set consisting of code and text. It also has internet access, allowing it to find and use more up-to-date information than ChatGPT.
It also shows relevant Google searches, which enables you to further extend your research. Instead of typing, you can also enter your prompt using voice.
Bard is currently available as beta in select countries and languages. However, in terms of capabilities, it is on par, if not better, than ChatGPT.
Bing Chat is based on the newer ChatGPT (version 4), making it much better than the free ChatGPT, which currently uses GPT-3.
It comes integrated into the Microsoft Edge browser and can be used in the sidebar. This sidebar chat can also be used to research the topics displayed on the web page you are currently viewing.
However, a more extensive version of Bing Chat is available on Bing search. You can start a new conversation from here in the full browser tab.
Bing Chat uses a newer dataset, which makes it faster and more accurate than ChatGPT’s free version.
Powered by the You.com AI search engine, YouChat provides an alternative to ChatGPT and Bard.
It is trained on large datasets and uses open-source technologies with in-house enhancements to create the AI bot chat experience.
One distinguishing feature that YouChat applies to the conversation is adding material from popular sources on the internet. It also adds citations and mentions the sources below its responses.
The popular sources include only English language content from websites in the United States, such as Wikipedia, StackExchange, Amazon, and more. You can add these sources to your preferred apps, and YouChat will prioritize responses from preferred sources whenever possible.
YouChat can also write code based on the text input and will also include a description to help you extend or study the code.
Currently, it is free to use and is a good alternative for users who want to use something faster than ChatGPT.
Need to generate images instead of text? Midjourney is the best AI-powered visual images and graphics generator.
Like ChatGPT, Midjourney is powered by generative AI and machine learning technologies. It allows you to provide instructions in natural language to generate images as the response.
Midjourney requires you to join their Discord server and uses Discord to input commands to the Midjourney bot.
Jasper is a collaborative AI writing assistant for businesses, marketers, and brands.
It can adapt to your brand’s voice and style to create marketing content, initial drafts, landing pages, and ad copy.
Jasper is trained on GPT 3.5 and its custom Natural Language Understanding models. It is designed for marketers and writers, and it can help businesses generate AI-powered marketing material on demand.
It also offers tools to generate AI images, translations for content in over 30 languages, a template library, and brand memory and style training.
Copilot by GitHub is a code-writing assistant powered by ChatGPT. You enter a prompt in plain language, and it will write code to match your instructions.
It can write code in the most popular coding languages and libraries. You can also integrate it into your existing code editor to generate on-demand code when needed.
CoPilot is trained on OpenAI Codex to suggest code and entire functions in real time. This allows developers to confidently troubleshoot code, look up examples, get real-time assistance, and more.
A free trial is available, after which monthly plans start at $10.
Character AI is a fun and useful ChatGPT alternative. It allows you to create different personas for the AI chatbot and interact with those characters.
The responses of the AI will be tailored to match the character you created. Users can make characters using the quick mode or select a more advanced mode that includes example chats and sample messages.
The platform also allows you to define character attributes and train your character based on chats. You can choose a voice and style for the character and keep it private or publicly available.
However, Character AI warns you that these characters may lie and confidently make things up, so their output should not be taken as factual.
Character AI can be very helpful for generating customer personas, writing with different tones and styles, developing dialogues, creating scripts, and more.
Replilka AI is a chatbot you can train to be your personal trainer, advisor, motivational coach, friend, listening partner, and more.
You can create an AI character, choose an appearance, and start talking. You can then coach the character and earn points by interacting with it.
Replika provides AI-based companionship that can be taken to the next level by joining the AI persona in augmented reality (AR). You can request the character to send you selfies too.
The AI doesn’t have access to the internet and is not trained on large datasets like some other AI tools. However, it is trained on human behavior and can provide friendly advice or act as a listening post.
It is trained on billions of lines of code and can help you write code faster and better. It works with the most popular programming languages, the AWS platform, and several popular integrated development environments (IDEs).
It is free to use for individuals and offers unlimited code generation. You also get up to 50 free security scans of your code each month.
Even though it is optimized to work with the AWS platform, it is not limited to just Amazon services. You can take your code and use it anywhere you like.
CodeWhisperer provides an excellent resource for individual developers to boost productivity and write code confidently.
Bonus Tools
ChatGPT is a generative text-based AI that uses Natural Language Processing. However, AI can be used for many more cases than just generating text responses.
Here are a few tools that can help you use AI in more ways than just plain text.
11. Bing Image Creator
Powered by Dall-E, the Bing Image Creator tool allows you to generate images using AI by providing simple text input.
You can use it for free to generate images. Each user gets 25 boosts each day, which allow you to create images more quickly. Once you run out of the boosts, the image generation will become slower.
Bing Image Creator is much faster than Dall-E, even when you run out of credits.
The quality of images depends on how descriptive your prompt is. Like Dall-E prompts, you can choose a style, provide a detailed scenario, set up the scene, and choose colors and artistic themes for the image.
Artificial intelligence doesn’t only help you come up with blog post ideas, create article outlines, and do research. It can also help you write better.
Grammarly is an AI-powered writing assistant that works everywhere, including inside the WordPress post editor.
It proofreads your content as you write, checks grammar, and autocorrects spelling. More importantly, it helps you adjust your writing style for different voices and emotions.
Grammarly also recommends changes to make your content more readable, avoid cliches, and make it sound more natural or conversational.
It has a forever free plan, and you can also upgrade anytime to unlock more powerful features.
As technology continues to evolve at an unprecedented pace, the field of programming is undergoing constant transformation. As a programming expert, it is crucial to stay ahead of the curve and be prepared for the future. In this article, we will explore the top five future trends in programming that every expert needs to be ready for. From emerging languages to new paradigms, these trends will shape the programming landscape in the coming years.
Artificial Intelligence and Machine Learning: Artificial Intelligence (AI) and Machine Learning (ML) have become integral parts of various industries, and programming experts need to be well-versed in these technologies. AI-powered systems are revolutionizing areas such as natural language processing, image recognition, and data analysis. As a programmer, you should familiarize yourself with popular ML frameworks like TensorFlow and PyTorch and learn how to develop and deploy AI models. Understanding concepts like neural networks, deep learning, and reinforcement learning will be essential for creating intelligent software solutions.
Internet of Things (IoT) Programming: The Internet of Things is rapidly expanding, connecting everyday devices and enabling them to communicate and share data. From smart homes to industrial automation, IoT programming is becoming increasingly important. Programmers will need to specialize in working with IoT platforms and protocols such as MQTT and CoAP. Furthermore, expertise in developing low-power, resource-constrained applications and ensuring security and privacy in IoT ecosystems will be in high demand.
Cross-Platform and Mobile Development: The dominance of mobile devices continues to grow, and programming experts must adapt to this shift. Cross-platform development frameworks like React Native and Flutter are gaining popularity, allowing developers to create mobile apps for multiple platforms using a single codebase. Learning these frameworks and understanding mobile-specific design patterns and user experience considerations will be crucial for building successful mobile applications.
Blockchain and Smart Contracts:Blockchain technology is not limited to cryptocurrencies; it has the potential to transform various industries by providing secure, transparent, and decentralized solutions. Programming experts should understand the fundamentals of blockchain and how to develop decentralized applications (DApps) using platforms like Ethereum. Additionally, smart contracts, which are self-executing contracts with the terms of the agreement directly written into the code, will require expertise in languages such as Solidity. Acquiring knowledge of blockchain and smart contract development will open up new avenues for programmers in sectors like finance, supply chain management, and healthcare.
Quantum Computing: While still in its early stages, quantum computing has the potential to revolutionize computation by solving problems that are currently infeasible for classical computers. As quantum computing continues to advance, programming experts will need to familiarize themselves with quantum programming languages like Q# and learn to develop algorithms for quantum computers. Quantum cryptography, quantum simulation, and optimization are some areas where quantum computing will have a significant impact.
Conclusion
As programming continues to shape the world around us, it is crucial for experts to stay updated with emerging trends. The future of programming lies in artificial intelligence, machine learning, IoT, cross-platform mobile development, blockchain, smart contracts, and quantum computing. By embracing these trends and acquiring the necessary skills, programmers can position themselves at the forefront of technological innovation. Adaptability, continuous learning, and a passion for exploration will be key to thriving in the ever-evolving field of programming.
The use of machine learning in the medical industry has gained significant traction in recent years thanks to its ability to improve patient outcomes, reduce costs, and streamline clinical workflows. While there are several programming languages available for machine learning, Python and R are the two most popular languages in the medical industry. This article discusses the use of Python and R in machine learning in the medical industry and argues why Python is considered the superior language in this field.
Python in the Medical Industry
Python is a high-level programming language that is easy to learn and use, making it a popular choice among data scientists and machine learning engineers in the medical industry. The following are some of the key reasons why Python is the preferred language in this field:
Entrepreneurial ideas take advantage of the range of opportunities this field opens up, thanks to what is engineered by scientific profiles such as mathematicians or programmers.
ALGORITHM. In Computer Science, an algorithm is a set of steps to perform a task. In other words, a logical sequence and instructions form a mathematical or statistical formula to perform data analysis.
SENTIMENT ANALYSIS. Sentiment analysis refers to the different methods of computational linguistics that help to identify and extract subjective information from existing content in the digital world. Thanks to sentiment analysis, we can be able to extract a tangible and direct value, such as determining if a text extracted from the Internet contains positive or negative connotations.
PREDICTIVE ANALYSIS. Predictive analysis belongs to the area of Business Analytics. It is about using data to determine what can happen in the future. The AP makes it possible to determine the probability associated with future events from the analysis of the available information (present and past). It also allows the discovery of relationships between the data that are normally not detected with less sophisticated analysis. Techniques such as data mining and predictive models are used.
BUSINESS ANALYTICS. Business Analytics encompasses the methods and techniques used to collect, analyze, and investigate an organization's data set, generating insights that are transformed into business opportunities and improving business strategy. AE allows an improvement in decision-making since these are based on obtaining real data and real-time and allows business objectives to be achieved from the analysis of this data.
BIG DATA. We are currently in an environment where trillions of bytes of information are generated every day. We call this enormous amount of data produced every day Big Data. The growth of data caused by the Internet and other areas (e.g., genomics) makes new techniques necessary to access and use this data. At the same time, these large volumes of data offer new knowledge possibilities and new business models. In particular, on the Internet, this growth begins with the multiplication in the number of websites, beginning search engines (e.g., Google) to find new ways to store and access these large volumes of data. This trend (blogs, social networks, IoT…) is causing the appearance of new Big Data tools and the generalization of their use.
BUSINESS ANALYTICS (Business Analytics). Business Analytics or Business Analytics allows you to achieve business objectives based on data analysis. Basically, it allows us to detect trends and make forecasts from predictive models and use these models to optimize business processes.
BUSINESS INTELLIGENCE Another concept related to EA is Business Intelligence (IE) focused on the use of a company's data to also facilitate decision-making and anticipate business actions. The difference with EA is that EI is a broader concept, it is not only focused on data analysis, but this is an area within EI. In other words, EI is a set of strategies, applications, data, technology, and technical architecture, among which is EA, and all this focus on the creation of new knowledge through the company's existing data.
DATA MINING or data mining. Data Mining is also known as Knowledge Discovery in Database (KDD). It is commonly defined as the process of discovering useful patterns or knowledge from data sources such as databases, texts, images, the web, etc. Patterns must be valid, potentially useful, and understandable. Data mining is a multidisciplinary field that includes machine learning, statistics, database systems, artificial intelligence, Information Retrieval, and information visualization, ... The general objective of the data mining process is to extract information from set data and transform it into an understandable structure for later use.
DATA SCIENCE. The opportunity that data offers to generate new knowledge requires sophisticated techniques for preparing this data (structuring) and analyzing it. Thus, on the Internet, recommendation systems, machine translation, and other Artificial Intelligence systems are based on Data Science techniques.
DATA SCIENTIST. The data scientist, as his name indicates, is an expert in Data Science (Data Science). His work focuses on extracting knowledge from large volumes of data (Big Data) extracted from various sources and multiple formats to answer the questions that arise.
DEEP LEARNING is a technique within machine learning based on neural architectures. A deep learning-based model can learn to perform classification tasks directly from images, text, sound, etc. Without the need for human intervention for feature selection, this can be considered the main feature and advantage of deep learning, called “feature discovery.” They can also have a precision that surpasses the human being.
GEO MARKETING. The joint analysis of demographic, economic, and geographic data enables market studies to make marketing strategies profitable. The analysis of this type of data can be carried out through Geo marketing. As its name indicates, Geo marketing is a confluence between geography and marketing. It is an integrated information system -data of various kinds-, statistical methods, and graphic representations aimed at providing answers to marketing questions quickly and easily.
ARTIFICIAL INTELLIGENCE. In computing, these are programs or bots designed to perform certain operations that are considered typical of human intelligence. It is about making them as intelligent as humans. The idea is that they perceive their environment and act based on it, focused on self-learning, and being able to react to new situations.
ELECTION INTELLIGENCE. This new term, "Electoral Intelligence (IE)," is the adaptation of mathematical models and Artificial Intelligence to the peculiarities of an electoral campaign. The objective of this intelligence is to obtain a competitive advantage in electoral processes. Do you know how it works?
INTERNET OF THINGS (IoT). This concept, the Internet of Things, was created by Kevin Ashton and refers to the ecosystem in which everyday objects are interconnected through the Internet.
MACHINE LEARNING (Machine Learning). This term refers to the creation of systems through Artificial Intelligence, where what really learns is an algorithm, which monitors the data with the intention of being able to predict future behavior.
WEB MINING. Web mining aims to discover useful information or knowledge (KNOWLEDGE) from the web hyperlink structure, page content, and user data. Although Web mining uses many data mining techniques, it is not merely an application of traditional data mining techniques due to the heterogeneity and semi-structured or unstructured nature of web data. Web mining or web mining comprises a series of techniques aimed at obtaining intelligence from data from the web. Although the techniques used have their roots in data mining or data mining techniques, they present their own characteristics due to the particularities that web pages present.
OPEN DATA.Open Data is a practice that intends to have some types of data freely available to everyone, without restrictions of copyright, patents, or other mechanisms. Its objective is that this data can be freely consulted, redistributed, and reused by anyone, always respecting the privacy and security of the information.
NATURAL LANGUAGE PROCESSING (NLP). From the joint processing of computational science and applied linguistics, Natural Language Processing (PLN or NLP in English) is born, whose objective is none other than to make possible the compression and processing aided by a computer of information expressed in human language, or what is the same, make communication between people and machines possible.
PRODUCT MATCHING. Product Matching is an area belonging to Data Matching or Record Linkage in charge of automatically identifying those offers, products, or entities in general that appear on the web from various sources, apparently in a different and independent way, but that refers to the same actual entity. In other words, the Product Matching process consists of relating to different sources those products that are the same.
Conclusion
Today there are numerous data science and AI tools to process massive amounts of data. And this offers many opportunities: performing predictive and advanced maintenance, product development, machine learning, data mining, and improving operational efficiency and customer experience.
AI is catapulting every sector into innovation and efficiency as machine learning provides invaluable insights humans never previously conceived. However, because AI adoption is widespread, threat actors see opportunities to manipulate data sets to their advantage. Data poisoning is a novel risk that jeopardizes any organization’s AI advancement. So is it worth getting on the bandwagon to gain benefits now, or should companies wait until the danger is more controlled?
What Is Data Poisoning?
Humans curate AI data constantly sets to ensure accurate determinations. Oversight manages inaccurate, outdated, or unbalanced information. It also checks for outliers that could skew things unreasonably. Unfortunately, hackers use data poisoning to render these efforts void by meddling with the input provided to machine learning algorithms in order to produce unreliable outcomes.
