Collaborating Authors

* to NOW

GPT-3 Finally Correctly Nailed


GPT-2 was a great success. OpenAI didn't want to publish the most enormous and mightiest version, with 1.5B parameters. At least, claiming that they were afraid of misusing it for less ethical purposes. Lately, they claimed that they didn't found shreds of evidence of such. All of this is legit, considering the volume of the false "news" generated using it. And the truth is that it can be very successful in developing false news/stories.

Rank and File


Try the Evanston RoundTable's free daily and weekend email newsletters – sign up now! By subscribing, you agree to share your email address with us and Mailchimp to receive marketing, updates, and other emails from us. Use the unsubscribe link in those emails to opt out at any time. Championship tournaments for computer chess engines moved from onsite competition to online well before many human tournaments made the move last year in response to the COVID-19 pandemic. In recent years the Top Engine Chess Competition, which has been played virtually since 2010, has become the unofficial world computer chess championship.

NLP Natural Language Processing Fundamentals in Python


Welcome to your first step into the Natural Language Processing and Text Mining world! This is your risk-free approach (30-day refund policy) to delve deep into the fundamentals which Google, Amazon and Microsoft base themselves on when working with text data. Natural Language Processing is one of the most exciting fields in Data Science and Analytics nowadays. The ability to make a computer understand words and phrases is a technological innovation that brought a huge transformation to tasks such as Information Retrieval, Translation or Text Classification. In this course we are going to learn the fundamentals of working with Text data in Python and discuss the most important techniques that you should know to start your journey in Natural Language Processing.

Sinergies between automation and robotics


In this IEEE ICRA 2021 Plenary Panel aimed at the younger generation of roboticists and automation experts, panelists Seth Hutchinson, Maria Pia Fanti, Peter B. Luh, Pieter Abbeel, Kaneko Harada, Michael Y. Wang, Kevin Lynch, Chinwe Ekenna, Animesh Garg and Frank Park, under the moderation of Ken Goldberg, discussed about how to close the gap between both disciplines, which have many topics in common. The panel was organised by the Ad Hoc Committee to Explore Synergies in Automation and Robotics (CESAR). As the IEEE Robotics and Automation Society (IEEE RAS) explain, "robotics and automation have always been siblings. They are similar in many ways and have substantial overlap in topics and research communities, but there are also differences–many RAS members view them as disjoint and consider themselves purely in robotics or purely in automation. This committee's goal is to reconsider these perceptions and think about ways we can bring these communities closer."

Gradient Descent: Taking a Different View


I had my first encounter with the Gradient Descent algorithm when I was learning about Linear Regression for the very first time. I devoured information about Gradient Descent as much as I could. Scouring through the internet looking for an explanation that would satisfy me. The most common explanation I found was analogous to the "going downhill on a cliff" experience. While this was really intuitive and easily comprehensible.

Become A Thought Leader In AI And ML


Are you an experienced professional or mid/senior-level manager looking for an exponential career growth? This may be the most important career transforming information you have come across in a long time. As AI has gotten extremely powerful in the last two decades. What used to take days then now take minutes. What used to cost millions now cost cents.

It's time to establish a quantum computing strategy, study suggests


A study of IT leadership suggests that not only are businesses ready for quantum computing, many have already allocated a budget for future Quantum computing projects. What this means, the report concludes, is that competitive organizations can't afford to ignore quantum computing any longer. For those unfamiliar with the term, quantum computing is an emerging field of computer science that ditches binary data bits (able to be either 0 or 1) in favor of qubits, which can be both 0 and 1 at the same time, have values between 0 and 1 and even use superdense coding to encode two separate bits of binary data onto one qubit. SEE: The CIO's guide to quantum computing (free PDF) (TechRepublic) Quantum computers are designed to solve problems that standard computers would be unable to solve in a practical manner. It's debatable whether or not quantum computers have become practical tools for most modern businesses, but vendors like Honeywell and IBM are making regular progress toward quantum supremacy (the point at which quantum computers are able to solve problems regular computers cannot).

Artificial Intelligence-Based Battle Management Training Rolled Out


The system, called Battle Management Training NEXT (BMTN), provides command and control battle management operators sustained, high quality, low cost training repetitions. "BMTN was developed in partnership with Vectrona, Breakaway Games and Sentrana to provide a host of first-ever combined artificial intelligence, machine learning, biometric, and natural language processing capabilities consolidated into one command and control training system," explained Lt. Col. Kip Trausch, Western Air Defense Sector chief innovation officer. "BMTN will be the fulcrum for the Battle Control Center to break the negative training feedback loop and enable consistent and meaningful wartime preparation." BMTN, which was also rolled-out to the Air National Guard Battle Control Center enterprise, solves the negative feedback loop generated from an ever-present and high operations tempo coupled with training that can only be conducted internally that results in a lack of time, instructors, and system resources to conduct comprehensive wartime readiness on pace with friendly capability and enemy threat evolution. "BMTN is a direct tactical-level answer to CSAF Brown's Accelerate Change or Lose and systems like this have the flexibility baked in to allow headquarters, commanders, and end-users to create the latest training content to drive familiarity, proficiency, and, potentially for the first time, fluency," Trausch said.

Intersectional Group Fairness in Machine Learning


At the ML Fairness Summit, we welcomed Fiddler Data Scientist, Léa Genuit to discuss intersectional group fairness. As more companies adopt AI, more people question the impact AI creates on society, especially on algorithmic fairness. Instead, they hold a binary view of fairness, e.g., protected vs. unprotected groups. In the below blog, Lea covers the latest research in research on intersectional group fairness. Before explaining why, the first question should be how do you detect and mitigate bias in European models to avoid a bad experience?

Classifying Food with Computer Vision 🍞


Here's an article on convolutional neural networks (used for computer vision). All the code is available on my GitHub repository here. Without further ado, let's get into it! I found a good food data set on Kaggle: It contains 101 different foods ranging from baklava to deviled eggs, and sashimi. The first thing you need to do is select the images folder then click the download button (circled in red on the top right). This will download the images as a zip that you'll have to extract. Once everything is extracted, copy it over to the jupyter notebook directory where you're planning to create the rest of your project.