Collaborating Authors

Ethics in Ai -- Current issues, existing precautions, and probable solutions


Introduction- Most of the Artificial Intelligent (Ai) Systems are developed as black boxes, especially Machine Learning and Deep Learning-based systems. Nowadays, these Machine and Deep Learning-based systems make decisions for our daily life, and should be explainable and should not be taken for granted to the end-users. The implication of such systems is rarely explored for the efficiency in the public usage (i.e., usage in -- Agriculture, Air Combat, Military Training, Education, Finance, Health Care, Human Resources, Customer Service, Autonomous Vehicles, Social Media, and several others[1]-[9]). Not only these, but the future might also be relying on Ai based system that will do our laundry, mow our lawn, fight wars [9]. Thus, there is so much room to improve the transparency of the systems along with fairness and accountability. There are some works that already stated the necessity of guidelines and governance of the Ai based systems, but more exposure is required in each area of application.

Report: How AI and ML optimize the diagnosis process in health care


Did you miss a session from the Future of Work Summit? A new report by CSA reveals that rapid developments in AI, ML, and data mining have allowed technology and health care innovators to create intelligent systems in order to optimize and improve the diagnosis process, quickly capturing unforeseen patterns within complex and large datasets. According to the Agency for Healthcare Research and Quality, 10% of patient deaths are a direct consequence of misdiagnosis. By using AI and ML, health care service providers can improve the precision of each diagnosis. Medical diagnostics using AI and ML are rapidly expanding, and automation is increasingly helping to detect life-threatening conditions in their earliest stages.

ETL Tool Apache Hop Graduates Incubator


Apache Hop, a metadata-driven data orchestration tool used to design and build pipelines, today emerged from incubator status and was named a Top-Level Project at the Apache Software Foundation, clearing the way for more intensive production use. Apache Hop, which stands for Hop Orchestration Platform, is a Java-based product designed to help data professionals manage a variety of data and metadata orchestration and integration needs. The software sports a visual design environment that allows users to create ETL pipelines, as well as an execution engine that can run by itself or embedded into Spark, Flink, Google Dataflow, or on AWS EMR via Apache Beam. "Hop is entirely metadata driven," it states on the Apache Hop website. "Every object type in Hop describes how data is read, manipulated or written, or how workflows and pipelines need to be orchestrated. Metadata is what drives Hop internally as well. Hop uses a kernel architecture with a robust engine. Plugins add functionality to the engine through their own metadata."

5 Exciting Artificial Intelligence Trends to Watch for in 2022


In the past few years, Artificial Intelligence has become a crucial part of many small as well as large organizations. Ai witnessed massive advancements and is being readily adopted across various industries. So, what are some exciting developments or Biggest Ai Trends of 2022 that you should watch out for? Read on to find out! One of the most talked about things in the IT industry is the menace of cybercrime. Be it individuals or organizations, everyone wants their data to be safe and secure.But continued malware/virus attacks on various kinds of devices owned byboth individuals and organizations has prompted even the World Economic Forum to identify cybercrimeas a critical threat.

Create chatbot using bot framework sdk and LUIS -- Part 1


Source code for the bot is available here. You can download the code from the git directly or you can follow this blog to code along with me. We will develop the bot in multiple parts. Create python script files Microsoft app id and password are required for Bot framework SDK.

Controlling complex systems with artificial intelligence


Researchers at ETH Zurich and the Frankfurt School have developed an artificial neural network that can solve challenging control problems. The self-learning system can be used for the optimization of supply chains and production processes as well as for smart grids or traffic control systems. Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes--and can also be used to develop new business models. Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis--both from the Chair of Computational Social Science--have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states.

Microsoft will buy Activision Blizzard, betting $70 billion on the future of games

The Japan Times

SEATTLE – Microsoft plans to buy the powerhouse but troubled video game company Activision Blizzard for nearly $70 billion, its biggest deal ever and one that places a major bet that people will spend more and more time in the digital world. The blockbuster acquisition, announced Tuesday, would catapult the company into a leading spot in the $175 billion gaming industry. Games on virtually every kind of device, from bulky consoles to smartphones, have gained even greater popularity during the pandemic. Technology companies are swarming around the industry, looking for a bigger share of attention and money from the world's 3 billion gamers. In an industry driven by big franchises, Activision makes some of the most popular titles, including Call of Duty and Candy Crush.

Edge processing research takes discovery closer to use in artificial intelligence networks


The MMT, first reported by Surrey researchers in 2020, overcomes long-standing challenges associated with transistors and can perform the same operations as more complex circuits. This latest research, published in the peer-reviewed journal Scientific Reports, uses mathematical modelling to prove the concept of using MMTs in artificial intelligence systems, which is a vital step towards manufacturing. Using measured and simulated transistor data, the researchers show that well-designed multimodal transistors could operate robustly as rectified linear unit-type (ReLU) activations in artificial neural networks, achieving practically identical classification accuracy as pure ReLU implementations. They used both measured and simulated MMT data to train an artificial neural network to identify handwritten numbers and compared the results with the built-in ReLU of the software. The results confirmed the potential of MMT devices for thin-film decision and classification circuits.

This is how AI bias really happens--and why it's so hard to fix


Over the past few months, we've documented how the vast majority of AI's applications today are based on the category of algorithms known as deep learning, and how deep-learning algorithms find patterns in data. We've also covered how these technologies affect people's lives: how they can perpetuate injustice in hiring, retail, and security and may already be doing so in the criminal legal system. But it's not enough just to know that this bias exists. If we want to be able to fix it, we need to understand the mechanics of how it arises in the first place. We often shorthand our explanation of AI bias by blaming it on biased training data. The reality is more nuanced: bias can creep in long before the data is collected as well as at many other stages of the deep-learning process.