This paper describes our system for Task 4 of SemEval-2021: Reading Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks where the main goal was to predict an abstract word missing from a statement. We fine-tuned the pre-trained masked language models namely BERT and ALBERT and used an Ensemble of these as our submitted system on Subtask 1 (ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity). For Subtask 3 (ReCAM-Intersection), we submitted the ALBERT model as it gives the best results. We tried multiple approaches and found that Masked Language Modeling(MLM) based approach works the best.

One of the most common problems of data science professionals is to avoid over-fitting. Have you come across a situation when your model is performing very well on the training data but is unable to predict the test data accurately. The reason is your model is overfitting. The solution to such a problem is regularization. Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below.

Let's first consider the causal open dynamical system model (1). If for the transition function we take an activation function applied to a linear state transition and add an output equation you may recognize a standard recurrent neural network (RNN), which is very well suited for the dynamical system modelling by construction and widely used to handle sequential data, e.g. It's quite a complicated model which requires taking into account both internal autonomous system state and external inputs. So let's simplify this complicated model by reformulating the problem to a more complex one-- consider the closed dynamical system. For that, we have to expand our internal system space by adding an external subsystem containing dynamics of the external variables. This tradeoff between the model complication and the problem complexity is not cheap and will cost us later when we teach the model to understand new variables' relations of the expanded internal space.

Human intelligence reflects our brain's ability to learn. Computer systems that act like humans use artificial intelligence. That means these systems are under the control of computer programs that can learn. Just as people do, computers can learn to use data and then make decisions or assessments from what they've learned. Called machine learning, it's part of the larger field of artificial intelligence.

Although beef is one of the most consumed foods around the world, eating it when it's past its prime is not only unsavory, but also poses some serious health risks. Unfortunately, available methods to check for beef freshness have various disadvantages that keep them from being useful to the public. For example, chemical analysis or microbial population evaluations take too much time and require the skills of a professional. On the other hand, non-destructive approaches based on near-infrared spectroscopy require expensive and sophisticated equipment. Could artificial intelligence be the key to a more cost-effective way to assess the freshness of beef?

Since the dawn of the computer age, humans have viewed the approach of artificial intelligence (AI) with some degree of apprehension. Popular AI depictions often involve killer robots or all-knowing, all-seeing systems bent on destroying the human race. These sentiments have similarly pervaded the news media, which tends to greet breakthroughs in AI with more alarm or hype than measured analysis. In reality, the true concern should be whether these overly-dramatized, dystopian visions pull our attention away from the more nuanced -- yet equally dangerous -- risks posed by the misuse of AI applications that are already available or being developed today. AI permeates our everyday lives, influencing which media we consume, what we buy, where and how we work, and more. AI technologies are sure to continue disrupting our world, from automating routine office tasks to solving urgent challenges like climate change and hunger.

The graph represents a network of 1,425 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 02 April 2021 at 10:24 UTC. The requested start date was Friday, 02 April 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 9-hour, 52-minute period from Tuesday, 30 March 2021 at 00:38 UTC to Thursday, 01 April 2021 at 10:30 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Artificial Intelligence is now capable of generating novel, functionally active proteins. Researchers are now able to demonstrate offers fantastic potential for a number of future applications, such as faster and more cost-efficient development of the protein-based drug. The new results from the Chalmers researchers represent a breakthrough in the field of synthetic proteins. They have developed an AI-based approach called ProteinGAN, which uses a generative deep learning approach. In essence, the AI is provided with a large amount of data from well-studied proteins; it studies this data and attempts to create new proteins based on it. At the same time, another part of the AI tries to figure out if the synthetic proteins are fake or not. The proteins are sent back and forth in the system until the AI cannot tell apart from natural and synthetic proteins anymore. This method is well known for creating photos and videos of people who do not exist, but in this study, it was used for producing highly diverse protein variants with naturalistic-like physical properties that could be tested for their functions.

There are many great articles about Artificial Intelligence (AI) and its benefits for business and society. However, many of these articles are too technical for the average reader. I love reading about AI, but I sometimes think to myself, 'Gee, I wish the author had explained this in simple English.' I will try and explain AI and its related technologies in simple terms, using real-life examples, as though I were talking to someone at a party. Your colleagues or your (close) friends may tolerate your endless and complex ramblings, but I guarantee you that people at parties are far less forgiving.

This is an intermediate-level free artificial intelligence course. This course will teach the basics of modern AI as well as some of the representative applications of AI including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. To understand this course, you should have some previous understanding of probability theory and linear algebra.