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.

Back in the 1950s, the fathers of the field, Minsky and McCarthy, described artificial intelligence as any task performed by a machine that would have previously been considered to require human intelligence. That's obviously a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. Modern definitions of what it means to create intelligence are more specific. Francois Chollet, an AI researcher at Google and creator of the machine-learning software library Keras, has said intelligence is tied to a system's ability to adapt and improvise in a new environment, to generalise its knowledge and apply it to unfamiliar scenarios. "Intelligence is the efficiency with which you acquire new skills at tasks you didn't previously prepare for," he said.

The man behind the Google Search curtain is coming out to explain a few things. On Thursday, Google expanded the information that it attaches to search results to show users why they're getting the website recommendations they receive. This includes the "matching keywords" and "related terms" associated with your search that show up in the result, as well as whether other pages reference that link, and if it makes sense for your local area. Google doesn't make a secret of the factors that go into its search rank algorithm -- it spells everything out here. But showing how it applies that criteria to your specific query gives users a new, practical look under the Google hood.