Scientists at the Massachusetts Institute of Technology (MIT) trained an artificial intelligence algorithm dubbed "Norman" to become a psychopath by only exposing it to macabre Reddit images of gruesome deaths and violence, according to a new study. Nicknamed Norman after Anthony Perkins' character in Alfred Hitchcock's 1960 film Psycho, the artificial intelligence was fed only a continuous stream of violent images from various pernicious subreddits before being tested with Rorschach inkblot tests. The imagery detected by Norman produced spooky interpretations of electrocutions and speeding car deaths where a standard AI would only see umbrellas and wedding cakes. Scientists at the Massachusetts Institute of Technology (MIT) trained an AI algorithm, "Norman," to become a psychopath by only exposing it to macabre Reddit images of gruesome deaths and violence. MIT scientists Pinar Yanardag, Manuel Cebrian and Iyad Rahwan specifically trained the AI to perform image captioning, a "deep learning method" for artificial intelligence to cull through images and produce corresponding descriptions in writing.

The widespread, deeply integrated use of artificial intelligence (AI) tools throughout all of radiology is still years--perhaps decades--away. But, the initial steps to using this technology are already in place. According to neuroradiology leaders at this year's American Society of Neuroradiology annual meeting in Vancouver, both patients and providers are already experiencing positive impacts even if the technology still has a way to go. "Convolutional neural networks (CNN) are not quite ready for prime time at this point," says Michael Lev, MD, director of emergency radiology and emergency neuroradiology at Massachusetts General Hospital. "It will not replace, but supplement radiology jobs. It will be a tool that radiologists will use for niche applications that can be done for certain detections."

Putting together bits of information dropped during AMD's PC-heavy hour-and-a-half presentation, it becomes apparent that Vega 7nm is finally aimed at high performance deep learning (DL) and machine learning (ML) applications – artificial intelligence (AI), in other words. AMD's EPYC successes may be paving the way for Vega 7nm in cloud AI training and inference applications. AMD claims that the 7nm process node it has co-developed with its fab partners will yield twice the transistor density, twice the power efficiency and about a third more performance than its 14nm process node. An educated guess says that not all Vega 7nm products will sport this high-end memory configuration – I think that showing off 32GB was a pointed message to AMD's cloud customers. AMD's Infinity Fabric interface will enable high bandwidth, coherent memory communications between Vega 7nm chips and AMD Zen processor chips, such as AMD's Zen2 7nm server chips.

Nvidia's latest cloud server platform is intended as a "building block," in the reference design sense, to support AI training and inference along with HPC workloads such as simulations. The GPU vendor (NASDAQ: NVDA) introduced its latest server platform dubbed HGX-2 on Wednesday (May 30) during a company roadshow in Taipei, Taiwan. Nvidia said the cloud server can be throttled up or down to support precision HPC calculations from 32-bits for single-precision floating point format, or FP32, up to double-precision FP64. Meanwhile, AI training and inference workloads are supported with FP16, or half precision, along with Int8 data. The combination is designed for varying processing requirements for a growing number of enterprise applications that combine AI with HPC, the company noted.

This article is a comprehensive overview of Topic Modeling and its associated techniques. In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning -- from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling. In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.

The two major tasks in medical imaging that appear to be naturally predestined to be solved with AI algorithms are segmentation and classification. Most of techniques used in medical imaging were conventional image processing, or more widely formulated computer vision algorithms. One can find many works with artificial neural networks, the backbone of deep learning. However, most works were focused on conventional computer vision which focused, and still does, on "handcrafted" features, techniques that were the results of manual design to extract useful and differentiating information from medical images. Some progress was visible in the late 90s and early 2000s (for instance, the SIFT method in 1999, or visual dictionaries in early 2000s) but there were no breakthroughs.

Machine learning is a complex discipline. But implementing machine learning models is far less daunting and difficult than it used to be, thanks to machine learning frameworks--such as Google's TensorFlow--that ease the process of acquiring data, training models, serving predictions, and refining future results. Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. It uses Python to provide a convenient front-end API for building applications with the framework, while executing those applications in high-performance C .

For the longest time, chips were a little bit boring. But the revolution in deep learning has now opened the market for startups that build specialty chips to accelerate deep learning and model evaluation. Among those is Israel-based Hailo, which is building deep learning chips for embedded devices. The company today announced that it has raised a $12 million Series A round. Investors include Israeli crowdfunding platform OurCrowd, Maniv Mobility, Next Gear, and a number of angel investors, including Hailo's own chairman Zohar Zisapel and Delek Motors' Gil Agmon.

Healthcare continued to be a lucrative target for hackers in 2017 with weaponized ransomware, misconfigured cloud storage buckets and phishing emails dominating the year. In 2018, these threats will continue and cybercriminals will likely get more creative despite better awareness among healthcare organizations at the executive level for the funding needed to protect themselves.

Artificial intelligence is the future. Generally artificial intelligence is portrayed as a supreme entity powerful to influence the technology. Machine learning is one of the science behind the supreme power of artificial intelligence and deep learning is the engine that propels the science. One of the most promising application of deep learning is to enhance interaction with the computers. Deep learning is particularly tailored for this purpose since it has the intuitive ability similar to biological brains.