In the COVID era, computational biology is having a heyday – and machine learning is playing a massive role. With billions upon billions of compounds to search through for any given therapeutic application, strictly brute-force simulations are wildly unfeasible, necessitating more artificially intelligent methods of whittling down the options. Now, researchers from IRB Barcelona's Structural Bioinformatics and Network Biology lab have developed a deep learning method that predicts the biological activity of any given molecule – even in the absence of experimental data. The researchers, led by Patrick Aloy, are applying deep machine learning to a massive dataset: the Chemical Checker, which provides processed, harmonized, and integrated bioactivity data on 800,000 small molecules and is also produced by the Structural Bioinformatics and Network Biology lab. In total, any given molecule has 25 bioactivity "spaces," but for most molecules, data on only a few are known – if that.
It's one thing to have your cabbage patch or running man shown up by Zoomers on TikTok, but it's another level of embarrassment to have a robot out dance you. That's exactly what Boston Dynamics' cohort of robots -- including its dog Spot and more human-like bot Atlas -- did in a video that resurfaced on Twitter this weekend. Swaying to the tune of the 1962 classic "Do You Love Me?" by the Contours, the robotic dance team inspired awe, disbelief, and dread in users. But while online lamenting over the robot apocalypse is nearly always tongue-in-cheek, the engineering achievement lurking behind Spot's dance moves means this reality could be much closer and darker than we realize. It is difficult to believe your eyes when you watch the Boston Dynamics robots bust a move -- albeit jerkily -- in the December 2020 video that made new Twitter rounds this weekend.
Technology is continuously updating at such a fast pace which it is might be quicker than light. A programming language that is making the rounds today might be obsolete by the next couple of days. As more money is invested in the development and research, professionals and computing scientists are continuously tweaking and enhancing current technologies to maximize them. Thus, new technologies and programming language, patch, library, and plug-in are released per hour. To maintain this fast pace of development, you need to keep on knowing the newest technology ideas.
Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.
Representing knowledge and the reasoning for the conclusions drawn has remained a cornerstone of artificial intelligence (AI) for decades. A knowledge graph (KG) is a powerful data structure that represents information in a graphical format. DBpedia, an open source knowledge graph defines a knowledge graph as "a special kind of database which stores knowledge in a machine-readable form and provides a means for information to be collected, organised, shared, searched and utilised." Formally, a KG is a directed labeled graph which represents relations between data points. A node of the KG represents a data point.
All the sessions from Transform 2021 are available on-demand now. There is a significant gap between an organization's ambitions for using artificial intelligence (AI) and the reality of how those projects turn out, Intel chief data scientist Dr. Melvin Greer said in a conversation with VentureBeat founder and CEO Matt Marshall at last week's Transf0rm 2021 virtual conference. One of the key areas is emotional intelligence and mindfulness. The pandemic highlighted this gap: The way people had to juggle home and work responsibilities meant their ability to stay focused and mindful could be compromised, Greer said. This could be a problem when AI is used in a cyberattack, like when someone is trying to use a chatbot or some other adversarial machine learning technique against us. "Our ability to get to the heart of what we're trying to achieve can be compromised when we are not in an emotional state and mindful and present," Greer said.
British artificial intelligence giant DeepMind has released a database of nearly all human protein structures that it amassed as part of its AlphaFold program. Last year, the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP) recognised AlphaFold as a solution to the grand challenge of figuring out what shapes proteins fold into. "We have been stuck on this one problem – how do proteins fold up – for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we'd ever get there, is a very special moment." AlphaFold is a major scientific advance that will play a crucial role in helping scientists to solve important problems such as the protein misfolding associated with diseases such as Alzheimer's, Parkinson's, cystic fibrosis and Huntington's disease.
The surging number of applications being deployed on the cloud in several industries, rapid improvements being made in the internet of things (IoT) domain, advancements in numerous smart applications, and growing popularity of AI software are the major factors driving the expansion of the global edge AI software market. Due to these factors, the market generated $600 million revenue in 2020, and it is expected to exhibit huge expansion during 2021–2030, according to P&S Intelligence. The imposition of lockdowns in several countries to mitigate the spread of the COVID-19 infection negatively impacted the operations of many businesses, but positively impacted the growth of the edge AI software market. The COVID-19 pandemic has facilitated the progress of the medical services sector, with many organizations making huge investments in edge AI software to increase its applications in this sector. Moreover, with the increasing digitalization rate in the medical care and training sectors, the demand for edge AI software is rising sharply.
Through the use of filters, these networks are able to generate simplified versions of the input image by creating feature maps that highlight the most relevant parts. These features are then used by a multi-layer perceptron to perform the desired classification. But recently this field has been incredibly revolutionized by the architecture of Vision Transformers (ViT), which through the mechanism of self-attention has proven to obtain excellent results on many tasks. In this article some basic aspects of Vision Transformers will be taken for granted, if you want to go deeper into the subject I suggest you read my previous overview of the architecture. Although Transformers have proven to be excellent replacements for CNNs, there is an important constraint that makes their application rather challenging, the need for large datasets.
The important job that SVM's perform is to find a decision boundary to classify our data. This decision boundary is also called the hyperplane. Lets start with an example to explain it. Visually, if you look at figure 1, you will see that it makes sense for purple line to be a better hyperplane than the black line. The black line will also do the job, but skates a little to close to one of the red points to make it a good decision line.