When OpenAI demonstrated a powerful artificial intelligence algorithm capable of generating coherent text last June, its creators warned that the tool could potentially be wielded as a weapon of online misinformation. Now a team of disinformation experts has demonstrated how effectively that algorithm, called GPT-3, could be used to mislead and misinform. The results suggest that although AI may not be a match for the best Russian meme-making operative, it could amplify some forms of deception that would be especially difficult to spot. Over six months, a group at Georgetown University's Center for Security and Emerging Technology used GPT-3 to generate misinformation, including stories around a false narrative, news articles altered to push a bogus perspective, and tweets riffing on particular points of disinformation. "I don't think it's a coincidence that climate change is the new global warming," read a sample tweet composed by GPT-3 that aimed to stoke skepticism about climate change.

One of the most powerful tools in Deep Learning is that sometimes we can take the knowledge or parameters the neural network has learned from one task and apply that knowledge to a different task. So for example maybe we have a neural network model, learned to recognize objects like cats, dogs, and other animals. Then we use that knowledge or use a part of it to do a better job at reading X-ray scans. This is called Transfer Learning. To have a more concrete definition, in transfer learning we reuse a pre-trained model on a new problem.

In this piece, we cover how AI can help personalize the customer experience, leading to higher satisfaction rates and greater revenue growth. Customers are used to getting a personalized experience from each company they interact with. You can personalize the experience of your customers by building effective recommender systems. These are the systems that personalize product placement and search results for each consumer. When you recommend products or content that customers are more likely to purchase, this gives the customer a better sales experience while driving more revenue for businesses through cross-selling and up-selling.

Microsoft HPC continues to invest to deliver the broadest range of Accelerated and high-performance computing (HPC) capabilities in the public cloud. From InfiniBand-enabled Virtual Machine families for artificial intelligence and HPC, to Hyperscale services like Cray supercomputing, Azure enables customers to deliver the full spectrum of AI and machine learning applications. With Azure CycleCloud, we are making it even easier for everyone to deploy, use, and optimize HPC burst, hybrid, or cloud-only clusters. For users running traditional HPC clusters, using schedulers including SLURM, PBS Pro, Grid Engine, LSF, HPC Pack, or HTCondor, this will be the easiest way to get clusters up and running in the cloud, and manage the compute/data workflows, user access, and costs for their HPC workloads over time. With a few clicks, HPC IT administrators can deploy high-performance clusters of compute, storage, filesystem, and application capability in Azure.

I often meet students that start their journey towards data science with Keras, Tensorflow and, generally speaking, Deep Learning. They build tons of neural networks like crazy, but in the end they fail with their models because they don't know machine learning enough nor they are able to apply the necessary pre-processing techniques needed for making neural networks work. Here's why, if you start your career as a data scientist, you don't need to start with Deep Learning. Data Science is about data, not about models. So, focusing on Deep Learning is like focusing on the models and that is wrong.

A report released Wednesday outlines how impactful today's artificial intelligence and neural networks could be if programmed to automate disinformation campaigns. Conducted by Georgetown's Center for Security and Emerging Technology, the report studies how OpenAI's GPT-3--a powerful AI system that generates text based on prompts from humans--could automate the future generation of disinformation campaigns. Researchers looked into GPT-3's capabilities after it authored a September op-ed in The Guardian--the first article written entirely by AI. "If GPT-3 can write seemingly credible news stories, perhaps it can write compelling fake news stories; if it can draft op-eds, perhaps it can draft misleading tweets," the report states. "In light of this breakthrough, we consider a simple but important question: can automation generate content for disinformation campaigns?" Researchers evaluated GPT-3's performance on six tasks common to most disinformation campaigns, including the operation carried out by Russia's Internet Research Agency in 2016.

Lung cancer is one of the most common cancers worldwide. According to a study published in Nature called "Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography", researchers got to know that with the help of AI (artificial intelligence), lung cancer and cardiovascular health can be screened through the usage of low-dose computed tomography. This can help to reduce the risk of developing lung and cardiovascular diseases. The study was a result of a combined effort by Rensselaer Polytechnic Institute and Massachusetts General Hospital. Dr Colin Jacobs, Ph.D. assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen said "As it does not require manual interpretation of nodule imaging characteristics, the proposed algorithm may reduce the substantial interobserver variability in CT interpretation," .

Much of artificial intelligence, and particularly deep learning, is plagued by the … To promote human-machine interaction and enable partnership and …

The development of increasingly complex and powerful architectures has enabled deep learning (DL) to scale to large, heterogeneous, complex and multiclass problems. In step with the stunning successes, however, DL training algorithms have become very computationally expensive. With Moore's law faltering, the AI research community is seeking new solutions to solve this DL issue. Fortunately, exciting possibilities are expected to open up due to the emergence of quantum computing devices that can overcome the technological and thermodynamical limits of classical computation. Research on the exploitation of quantum computing devices to carry out DL however remains in its nascent phase.

Why is automated data extraction for Resumes is tough using traditional methods? Recruitment is a $200 Billion industry globally with millions of people uploading resumes and applying for jobs everyday on thousands of employment platforms. Businesses have their openings listed on these platforms and job seekers come apply. Every business has a dedicated recruitment department that manually goes through the applicant resumes and extract relevant data to see if they are a fit. As people get creative with their resumes in terms of style and presentation, automating data extraction from these resume is difficult and it is still mostly a manual job.