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The Role of Knowledge Graphs in Artificial Intelligence

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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.


Equipping AI with emotional intelligence can improve outcomes

#artificialintelligence

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.


DeepMind releases AlphaFold database of nearly all human protein structures

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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.


Edge AI software market to expand significantly by 2030 - Help Net Security

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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.


What is AI? Here's everything you need to know about artificial intelligence

ZDNet

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.


Now Google will display why it's showing you its search results

Mashable

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.


Vision Transformers or Convolutional Neural Networks? Both!

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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.


Support Vector Machines -- the basics

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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.


Getting Started in Medical AI

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The course takes a top-down approach starting with a few lines of computer code, rather than establishing detailed mathematical notation. The first lesson will guide you through the construction and testing of your own deep learning model.


Why Are Self-Driving Cars the Future and How are They Created?

#artificialintelligence

Due to the recent adaptive quarantine measures imposed in virtually all parts of the world, air travel, public transportation, and many other sectors took a really big hit in 2020. However, the automotive world and autonomous vehicles, in particular, have shown increased resilience during this difficult time. In fact, companies like Ford have increased their investments in the development of electric and self-driving cars by allocating $29 billion dollars in the fourth quarter of last year. Specifically, $7 billion of that money will go towards the development of self-driving cars. So Ford is joining General Motors, Tesla, Baidu, and other automakers in heavily investing in autonomous vehicles.