You have probably heard that artificial intelligence could be used to do lots of impressive tasks and jobs. AI can help designers and artists make quick tweaks to visuals. AI can also help researchers identify "fake" images or connect touch and sense. AI is being used to program websites and apps by combining symbolic reasoning and deep learning. Basically, artificial intelligence goes beyond deep learning.

Another wave of enthusiasm hit the media in recent weeks unfolding the endless opportunities for artificial intelligence (AI) in service for medicine. This time the "new AI neural network approach detected heart failure from a single heartbeat with 100% diagnostic accuracy" – an energetic and simple message - was shared and reshared across social media and beyond. Lay readers fueled the rolling snowball with comments and views suggesting that Goliath of medicine has been successfully defeated by magic capabilities of AI. But, are we really done with heart failure? The paper, which gave grounds for these discussions was published online in the Biomedical Signal Processing and Control by Mihaela Porumb et al (1).The authors have implemented in a very elegant manner a new approach for analysis of electrocardiogram (ECG) using the hierarchical neural networks that mimic the human visual system called Convolutional Neural Network (CNN or ConvNets) (2). This method being a class of deep neural networks, allows for image recognition and classification, is used for object or face recognition.

In this episode I have a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI and deep learning in particular. He doesn't seem to share the vision of folks like Elon Musk who claimed that we are going to see an exponential improvement in self driving cars among other things (he actually said that before a Tesla drove over a pedestrian). I have a somewhat complex love and hate relationship with deep learning.

Matt DeCamp, associate professor with the Center for Bioethics and Humanities from CU Anschutz, framed the AI landscape: Up to $6 billion anticipated for AI investment into biomedical research by 2021 At least 14 recent AI-related FDA approvals in past two years, mostly in imaging, ophthalmology and pathology 55 active or pending clinical trials using the term "deep learning" 141 startup biotech companies using AI Insurance companies actively using AI to review records and optimize care for chronic conditions

Researchers have found a way to marry human creativity and artificial intelligence (AI) creativity to dramatically boost the performance of deep learning. A team led by Alexander Wong, a Canada Research Chair in the area of AI and a professor of systems design engineering at the University of Waterloo, developed a new type of compact family of neural networks that could run on smartphones, tablets, and other embedded and mobile devices. The networks, called AttoNets, are being used for image classification and object segmentation, but can also act as the building blocks for video action recognition, video pose estimation, image generation, and other visual perception tasks. "The problem with current neural networks is they are being built by hand and incredibly large and complex and difficult to run in any real-world situation," said Wong, who also co-founded a startup named DarwinAI to commercialize the technology. "These on-the-edge networks are small and agile and could have huge implications for the automotive, aerospace, agriculture, finance, and consumer electronics sectors."

The traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in ten of these molecules pass clinical trials in human patients. Insilico was able to ideate and generate a novel molecule from start to finish in 21 days. In a similar technique used by DeepMind to outcompete human GO players, GENTRL -- powered by generative chemistry that utilizes modern AI techniques -- can rapidly generate novel molecular structures with specified properties. Insilico has made GENTRL's source code available as open source. "The development of these first six molecules as an experimental validation is just the start," said Alex Zhavoronkov, CEO of Insilico Medicine.

Machine learning plays a key role in powering Twitter and our purpose of serving the public conversation. To continually advance the state of machine learning, inside and outside Twitter, we are building out a research group at Twitter, led by Sandeep Pandey, to focus on a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning. We are excited to announce that, to help us get there, we have acquired Fabula AI (Fabula), a London-based start-up, with a world-class team of machine learning researchers who employ graph deep learning to detect network manipulation. Graph deep learning is a novel method for applying powerful ML techniques to network-structured data. The result is the ability to analyze very large and complex datasets describing relations and interactions, and to extract signals in ways that traditional ML techniques are not capable of doing.

Integrating a deep neural network accelerator, vector digital signal processor (DSP) and vector floating point unit (FPU), Synopsys explains that the DesignWare EV7x Vision Processors' heterogeneous architecture delivers 35 Tera operations per second (TOPS) for artificial intelligence system on chips (AI SoCs). The DesignWare ARC EV7x Embedded Vision processors, with deep neural network (DNN) accelerator provide sufficient performance for AI-intensive edge applications. The ARC EV7x Vision Processors integrate up to four enhanced vector processing units (VPUs) and a DNN accelerator with up to 14,080 MACs to deliver up to 35 TOPS performance in 16-nm FinFET process technologies under typical conditions, which is four times the performance of the ARC EV6x processors, reports Synopsys. Each EV7x VPU includes a 32-bit scalar unit and a 512-bit-wide vector DSP and can be configured for 8-, 16-, or 32-bit operations to perform simultaneous multiply-accumulates on different streams of data. The optional DNN accelerator scales from 880 to 14,080 MACs and employs a specialized architecture for faster memory access, higher performance, and better power efficiency than alternative neural network IP.

The Milky Way is on a collision course with the neighboring Andromeda galaxy. But no need to revise your will -- the two star systems won't meet for around 4 billion years. "At some point in every galaxy's life, it'll undergo one of these mergers," said William Pearson, Ph.D. student at the Netherlands Institute for Space Research and the University of Groningen, Netherlands. "It's part of our understanding of how we think the universe works. These galaxies tend to find and crash into each other."

Billions of birds in North America make the trek south each fall, migrating in pursuit of warmer winter temperatures. Many of these migratory birds fly under the cover of night, making it challenging for birdwatchers and ornithologists to observe them and track long-term trends. But the need to monitor avian population levels is critical. Recent research estimates that the number of birds in North America has fallen by 3 billion in the past 50 years, impacted by climate change, habitat loss, hunting and pesticides. Spring migration has declined by 14 percent in the last decade.