In the data economy, data is king. Today, any business -- small, medium, or large -- thrives on its data assets. The recent trend of offering data-driven insights as a service to the outside world has opened up a profitable revenue channel for businesses. Cloud computing and hosted analytics have brought data-as-a-service to the desktops of ordinary business users, which was unheard of even a few years ago. As the global business environment is fast moving toward "all things digital," as predicted by Gartner a while back, artificial intelligence (AI), machine learning (ML), and deep learning (DL) will play as important roles as Data Science in reshaping businesses across the world.

New intelligent applications and smart devices built using GE Healthcare's Edison intelligence platform help improve radiology efficiency and enhance patient care. It's no secret that Artificial Intelligence (AI) is poised to make a powerful impact on patient care and healthcare operations. At the 2019 annual meeting of the Radiological Society of North America (RSNA) in Chicago, AI is again the hottest topic at the show. An entire floor is dedicated to the ever-expanding AI vendors and sessions feature a full slate of speakers showcasing emerging AI technologies and trends. This year, GE Healthcare introduced several new intelligent applications and smart devices built using Edison, our secure intelligence platform unveiled at RSNA 2018.

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

So in Part-I I gave a simple introduction on what linear regression is and how we can find the equation of the best fit line for our data. In this post, I will show you how to implement the task we worked on in Part-I in PyTorch. The input size is set to 1 since our inputs to the model are scalars h (hour in the day). The output is also set to 1 since we will get only one value returned for r (number of pages being read). So, basically, we will leave our program to find the best values for B_0 and B_1 that we calculated in the previous part of this tutorial.

In GTC China yesterday, NVIDIA made a series of announcements. Some had to do with local partners and related achievements, such as powering the likes of Alibaba and Baidu. Partners of this magnitude are bound to generate impressive numbers and turn some heads. Another part of the announcements had to do with new hardware. NVIDIA unveiled Orin, a new system-on-a-chip (SoC) designed for autonomous vehicles and robots, as well as a new software-defined platform powered by the SoC, called Nvidia Drive AGX Orin.

Abstract: Human pose estimation and action recognition are related tasks since both problems are strongly dependent on the human body representation and analysis. Nonetheless, most recent methods in the literature handle the two problems separately. In this work, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences. We show that a single architecture can be used to solve both problems in an efficient way and still achieves state-of-the-art or comparable results at each task while running at more than 100 frames per second. The proposed method benefits from high parameters sharing between the two tasks by unifying still images and video clips processing in a single pipeline, allowing the model to be trained with data from different categories simultaneously and in a seamlessly way.

There is a benchmark of desktop and laptop GPU cards for deep learning: AI Benchmark. You can run these tests yourself, see https://pypi.org/project/ai-benchmark/. Take note that some GPUs are good for games but not for deep learning (for games 1660 Ti would be good enough and much, much cheaper, vide this and that). For general benchmarks, I recommend UserBenchmark (my Lenovo Y740 with Nvidia RTX 2080 Max-Q here.) For comparison of different cards between frameworks, see Performance in: Keras or PyTorch as your first deep learning framework (June 2018), based on Comparing Deep Learning Frameworks: A Rosetta Stone Approach.

The ICO has published information on its review of the Royal Free NHS Foundation Trust and the Trust's use of the Google DeepMind AI project to analyse patient data. This follows up the case from a couple of years back whereby the Trust were found to be in breach of data protection law as the patients hadn't been given enough information about the sharing of their medical data. Most of the latest information from the ICO reflects on it's review on the measures enforced upon the Trust, namely: But whilst Royal Free appears to have done everything the ICO asked for, it has raised further issues around clarity relating to how the duty of confidentiality (common law which is outside the purview of the ICO) fits with data protection. This appears to be the issue of balancing a clinicians "conscience" against patient's expectations. The ICO say they will consider this further by working with the National Data Guardian and Health Research Authority to ensure guidance is clear to help the healthcare implement data-drive technology safely and legally.

Intel Corporation this week announced that it has acquired Habana Labs for approximately $2 billion. Habana is an Israel-based company that develops programmable deep learning accelerators for the data centre. This acquisition is aimed at strengthening Intel's artificial intelligence portfolio and accelerate its efforts in the AI silicon market, which Intel expects to be greater than $25 billion by 2024. "This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data centre," said Navin Shenoy, executive VP at Intel, in a press release. In July, Habana announced its Gaudi AI training processor, which the Tel Aviv startup promised was capable of beating GPU-based systems by 4x.

The development of artificial intelligence technologies has brought forth the era of autonomous cars, realistic robots, intelligent chatbots, and AI YouTubers. And now, researchers have created an artificial intelligence tool that utilizes language models to identify'fake news'. This tool has been developed to stop the spread of misinformation through stance detection. Based on deep-learning, the AI tool can verify the information provided in posts made on various platforms by comparing it to other posts available on the subject. With the tool, researchers want to eliminate the deceptive posts that have plagued the internet.