Billy Basso was glued to his computer. It was launch day for the Chicago developer's debut solo game, a surreal Metroidvania called Animal Well, and he couldn't stop reading reviews online and watching people play the game. He'd pulled off the impossible: breaking through a turbulent industry to create a hit game that would grow to be a critical and commercial success. He just didn't realize how big of one it would be quite yet. Most successful video games are made by teams of people that vary in size from a half dozen to somewhere in the hundreds.

One of the most common dramas experienced by data scientists is the uncertainty that the available volume of data will suffice for model building. Challenging such insecurity, I've decided to make a Machine Learning algorithm fitted with the lowest possible amount of instances. After many reflections, I've found that working with a binary classification task would be an interesting idea. Since I wished to use the minimum amount of data, the final model needed to be fed with only two instances, each one belonging to a specific category. The data points selection would be performed with K-Means.

The consortium of SPATIAL Project (Security and Privacy Accountable Technology Innovations, Algorithms, and Machine Learning) announces the official start of this joint European initiative funded by the European Commission under the Horizon 2020 Research & Innovation programme. Get to know about the project, the partners and the use cases. SPATIAL planning to participate at the IoT Week 2022. Learn more about the partners through the intereviews and get to know how SPATIAL participated at the EuCNC in Grenoble (June 2022). "Digital Services Act and Digital Markets Act set a new cornerstone for digital in Europe" article.

On the surface, it may not appear that Dr Dolittle and artificial intelligence (AI) have much in common. One belongs in 1900s children's literature, while the other is firmly rooted in the 21st century. One is a physician turned vet who can talk to animals, and the other a computerized technology that cannot. AI has already given us the ability to bark instructions at robots like Siri and Alexa – could its potential be extended to the animal kingdom? Could it help us decipher some of the mysteries of the natural world and maybe one day allow us to "talk" to animals? There are certainly some who think so.

In this blog post, I'll summarize my understanding of attention used in natural language processing (NLP). As a machine learning and NLP self-learner, when I initially got exposed to the idea of attention, I felt overwhelmed by its whole bunch of different variations and all the nitty-gritties involved in the implementations. Now, after reading articles, blogs and code, watching YouTube videos and also implementing it myself in several projects, I found it actually not that hard to understand when looking back. Hopefully by sharing what I learned along the journey, I could help some of those who are also going though that learning process, especially beginners like who I was a couple of months ago, speed up their progress and make it a bit more enjoyable. The concept of attention was firstly widely spread because of its use in the sequence-to-sequence (seq2seq) model for neural machine translation.

Machine learning is becoming increasingly important in the world of technology. As computers become more advanced and powerful, they can process data faster and more accurately than ever. Recent developments in machine learning have increased interest in using coordinate-based neural networks that parametrize the physical properties of scenes or objects across space and time to solve visual computing problems. These methods, known as neural fields, have been used successfully for synthesizing 3D shapes, human body animation, 3D reconstruction, and pose estimation. The Neural Radiance Fields (NeRF) model, which learns to represent the local opacity and view-dependent radiance of a static scene from sparse calibrated images, is one of the most recent works using neural fields.

Model development and experimentation is part of any machine learning lifecycle. However, without careful planning, keeping track of experiments can become tedious and challenging; especially given the number of configurations we typically deal with. MLflow is a machine learning lifecycle framework that allows ML engineers and teams to keep track of their experiments. In PART 1 of the series, we are going to focus on the first two steps -- tracking experiments and sharing code. PART 2 will be dedicated to model packaging, while PART 3 will show how the concepts outlined in the previous parts can be used in a React web application. For now, let's try to understand what MLflow is, and what it can do for us!

In the Approval Participatory Budgeting problem an agent prefers a set of projects $W'$ over $W$ if she approves strictly more projects in $W'$. A set of projects $W$ is in the core, if there is no other set of projects $W'$ and set of agents $K$ that both prefer $W'$ over $W$ and can fund $W'$. It is an open problem whether the core can be empty, even when project costs are uniform. the latter case is known as the multiwinner voting core. We show that in any instance with uniform costs or with at most four projects (and any number of agents), the core is nonempty.

From Deep Blue's victory over chess grandmaster Garry Kasparov to AlphaGo being the first computer program to defeat a Go World Champion, unbeatable superhuman agents have paved a new path for remarkable advancements made in AI. However, the primary question remains whether AI can create agents that can use language to negotiate and collaborate with others to achieve strategic goals in a manner comparable to humans. As it involves players mastering the art of understanding other people's perspectives and devising methods appropriately to persuade them to make agreements and form alliances with others, Diplomacy has long been considered a near-impossible challenge in AI. The complexity of human emotions makes it simple to learn these diplomatic skills. Nevertheless, the question remains: can artificially intelligent machines achieve this level of understanding and persuasion skills?

Welcome to the first advanced and project-based Pandas Data Science Course! No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! Efficiently import and merge Data from many text/CSV files. Clean, handle and flatten nested and stringified Data in DataFrames. Know how to handle and normalize Unicode strings.