"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
It's another graph neural networks survey paper today! Clearly, this covers much of the same territory as we looked at earlier in the week, but when we're lucky enough to get two surveys published in short succession it can add a lot to compare the two different perspectives and sense of what's important. In particular here, Zhou et al., have a different formulation for describing the core GNN problem, and a nice approach to splitting out the various components. Rather than make this a standalone write-up, I'm going to lean heavily on the Graph neural network survey we looked at on Wednesday and try to enrich my understanding starting from there. For this survey, the GNN problem is framed based on the formulation in the original GNN paper, 'The graph neural network model,' Scarselli 2009.
A study by German scientists from Jena and Hamburg, published today in the journal Nature, shows that artificial intelligence (AI) can substantially improve our understanding of the climate and the Earth system. Especially the potential of deep learning has only partially been exhausted so far. In particular, complex dynamic processes such as hurricanes, fire propagation, and vegetation dynamics can be better described with the help of AI. As a result, climate and Earth system models will be improved, with new models combining artificial intelligence and physical modeling. In the past decades mainly static attributes have been investigated using machine learning approaches, such as the distribution of soil properties from the local to the global scale.
Stuart McClure is on a personal mission. After more than two decades in the anti-malware industry, he firmly believes that ninety percent of malware attacks today can be prevented by not clicking on this, not clicking on that, and not opening that attachment either. While he's not the first nor alone in suggesting the user bears at least some responsibility, the anti-malware industry up until now hasn't yet produced an effective alternative to signature-based solutions based on known attacks. McClure's company, Cylance, thinks it has the answer with its first-generation AI-driven anti-malware products for both enterprises and consumers. "Why couldn't we simply train a computer to think like a cybersecurity professional to know what to do and not to do based on the characteristics and features of known attacks?" asked McClure.
When it comes to deep learning frameworks, TensorFlow is one of the most preferred toolkits. However, one framework that is fast becoming the favorite of developers and data scientists is PyTorch. PyTorch is an open source project from Facebook which is used extensively within the company. For a long time, Facebook developers used another homegrown framework called Caffe2, which was adopted by academia and researchers. Last year, Facebook announced that it is merging the efforts of developing Caffe2 and PyTorch to focus on creating a unified framework that is accessible to the community.
When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence. Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity. During this same period, the FDA has given 70 AI healthcare tools and devices'fast-tracked approval' because of their ability to save both lives and money. The pace of AI-augmented healthcare innovation is only accelerating. In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.
Artificial Neural Networks are the computational models inspired by the human brain. Many of the recent advancements have been made in the field of Artificial Intelligence, including Voice Recognition, Image Recognition, Robotics using Artificial Neural Networks. These biological methods of computing are considered to be the next major advancement in the Computing Industry. The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. It receives signals from other neurons. It sums all the incoming signals to generate input.
With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges.
"Sparsity, that's the direction where deep learning should expand," says Gopi Prashanth, who is vice president of engineering at AI-startup Landing AI, run by former Google AI luminary Andrew Ng. In an interview with ZDNet, Prashanth reflected on the challenge of taking something built for really big data, the machine learning approach called deep learning, and re-engineering it for very little data, perhaps just one single sample at a time. It is not an academic concern. The mandate of Ng and his team is to put AI to work for business. That requires using techniques such as machine learning in some settings where there my be very few good examples of a problem to use to train the machine.
The ability of AI to generate fake visuals is not yet mainstream knowledge, but a new website -- ThisPersonDoesNotExist.com -- offers a quick and persuasive education. The site is the creation of Philip Wang, a software engineer at Uber, and uses research released last year by chip designer Nvidia to create an endless stream of fake portraits. The algorithm behind it is trained on a huge dataset of real images, then uses a type of neural network known as a generative adversarial network (or GAN) to fabricate new examples. "Each time you refresh the site, the network will generate a new facial image from scratch," wrote Wang in a Facebook post. He added in a statement to Motherboard: "Most people do not understand how good AIs will be at synthesizing images in the future."
If you're reading these words, rest assured, they were written by a human being. Whether they amount to intelligence, that's for you to say. The age of writing by a machine that can pass muster with human readers is not quite upon us, at least, not if one reads closely. Scientists at the not-for-profit OpenAI this week released a neural network model that not only gobbles tons of human writing -- 40 gigabytes worth of Web-scraped data -- it also discovers what kind of task it should perform, from answering questions to writing essays to performing translation, all without being explicitly told to do so, what's known as "zero-shot" learning of tasks. The debut set off a swarm of headlines about new and dangerous forms of "deep fakes."