Who invented deep residual learning? Modern AI is based on deep artificial neural networks (NNs). As of 2025, the most cited scientific article of the 21st century is an NN paper on deep residual learning with residual connections . Here is the timeline of the evolution of deep residual learning: 1991: recurrent residual connections (weight 1.0) solve the vanishing gradient problem 1997 LSTM: plain recurrent residual connections (weight 1.0) 1999 LSTM: gated recurrent residual connections (gates initially open: 1.0) 2005: unfolding LSTM--from recurrent to feedforward residual NNs May 2015: very deep Highway Net--gated feedforward residual connections (initially 1.0) Dec 2015: ResNet--like an open-gated Highway Net (or an unfolded 1997 LSTM) His recurrent residual connection was mathematically derived from first principles to overcome the fundamental deep learning problem of vanishing or exploding gradients, first identified and analyzed in the very same thesis. That is, at every time step of information processing, this unit just adds its current input to its previous activation value. The invariant residual connections transport error signals back to typically highly nonlinear adaptive parts of the NN where they can cause appropriate weight changes.

No one knows exactly what the future holds, but that isn't stopping artificial intelligence from trying to predict it. AI has been making some pretty impressive predictions lately, and if it continues on this trajectory, we're in for an exciting future. So what does AI foresee for the years to come? Read on to find out! If you had asked me this question a few years ago, I would have given you a very different answer than the one I would give today. Technology is evolving at an unprecedented rate, and it's hard to predict where it will go next.

What would Albert Einstein think of AI? We may never know for sure, but it's fascinating to imagine. Some believe that he would have been a strong advocate for the technology, while others contend that he would have been more cautious about its implementation. No matter where you stand on this debate, one thing is for sure: AI is here to stay. And with its ever-growing presence in our lives, it's important to consider Einstein's potential thoughts on the matter.

The debate over whether artificial intelligence is good or bad for humanity is one that has been ongoing since the inception of AI itself. On one side, proponents argue that AI can help us to solve some of the world's most pressing problems, such as climate change, disease and poverty. On the other side, detractors claim that AI will eventually surpass human intelligence, leading to a future in which machines rule the world. So far, there is no clear answer as to which side is right. However, what is certain is that AI presents both risks and opportunities for humanity.

Artificial intelligence systems powered by large language models today are transforming how people work and create, from generating lines of code for software developers to sketches for graphic designers. Kevin Scott, Microsoft's chief technology officer, expects these AI systems to continue to grow in sophistication and scale--from helping address global challenges such as climate change and childhood education to revolutionizing fields from healthcare and law to materials science and science fiction. Scott recently shared his thoughts with us on the impact of AI for knowledge workers and what's next in AI. In your mind, what were some of the most important advancements in AI this year? When we were heading into 2022, I think just about everybody in AI was anticipating really impressive things to take place over the next twelve or so months.

The first image is the original image of a robot head. We started with this picture and set out to create something amazing. And we did just that! Take a look at the final product. We modified this seemingly innocent image to make it look a little more sinister.

Action selection is the process of choosing which action to take in any given situation. This process is carried out by the brain, and it involves a complex interplay of various cognitive and emotional factors. In some cases, the decision may be made purely on the basis of logic; however, in other cases, emotions such as fear or excitement may also come into play. The specific algorithms used by the brain to choose an action are still not fully understood; however, it is clear that action selection is a vitally important part of human cognition. Action selection is what allows us to navigate the complex landscape of our lives and make choices that help us to achieve our goals.

Action model learning is a subfield of machine learning that focuses on learning how to perform actions in the world. This can include learning how to control robots, navigate in virtual environments, and much more. Action model learning focuses on giving machines the ability to learn from and predict actions. This can be done by observing and interactively exploring the environment, or by using reinforcement learning methods. Action model learning is an important area of research because it has the potential to create systems that can autonomously learn how to perform tasks by imitating human or animal behavior.

Computer science is the study of computation and information processing. Computation is any type of calculation or use of computing machinery and technology. Information processing is the manipulation of data by a computer. Computer scientists design algorithms, which are step-by-step sets of instructions that tell a computer what to do, as well as write and test code that will make computers do what they are supposed to do. They also work on applications that range from artificial intelligence to web browsers.

Data mining is the process of extracting valuable information from large data sets. By sifting through massive amounts of data, businesses and organizations can identify trends and patterns that they may not have otherwise noticed. This information can then be used to make better decisions, improve marketing strategies, and increase operational efficiency. Data mining requires both strong analytical skills and powerful computer software. Experienced data miners must be able to effectively identify relevant information and interpret it in a meaningful way.