artificial intelligence

Weird new type of magnetic liquid could be used to control soft robots

New Scientist

Not all magnets have to be solids – a new kind of liquid magnet may eventually help control wireless soft robots. Liquid magnets of a sort already exist. These ferrofluids are a mixture of a non-magnetic liquid and solid magnetic nanoparticles, but they only work when under the influence of an external magnetic field. Thomas Russell at the University of Massachusetts Amherst and his colleagues were able to turn a ferrofluid into a truly magnetic fluid that retains its magnetic properties.

Japan 'underdeveloped' in use of AI technology, says SoftBank's Masayoshi Son

The Japan Times

SoftBank Group Corp. Chairman and CEO Masayoshi Son said Thursday that Japan has become an "underdeveloped" country in the use of artificial intelligence in businesses, lagging behind China, India and Southeast Asian countries that have fast-growing technology companies. "Japan once was a leader in technology but has become an underdeveloped country in AI. It is in a pretty bad situation so Japan needs to awaken," Son told an audience at a company event in Tokyo. Among the audience were Ritesh Agarwal, CEO of Indian hotel operator Oyo Hotels & Homes, and Anthony Tan, CEO of ride-hailing company Grab Holdings Inc. of Singapore. Oyo and Grab are among over 80 AI startups in which SoftBank Group's $100 billion Vision Fund has invested.

Is FaceApp an evil plot by 'the Russians' to steal your data? Not quite Arwa Mahdawi

The Guardian

Over the last few days the #faceappchallenge has taken over social media. This "challenge" involves downloading a selfie-editing tool called FaceApp and using one of its filters to digitally age your face. You then post the photo of your wizened old self on the internet and everyone laughs uproariously. You get a small surge of dopamine from gathering a few online likes before existential ennui sets in once again. On Monday, as the #faceappchallenge went viral, Joshua Nozzi, a software developer, warned people to "BE CAREFUL WITH FACEAPP….it Some media outlets picked this claim up and privacy concerns about the app began to mount. Concern escalated further when people started to point out that FaceApp is Russian. "The app that you're willingly giving all your facial data to says the company's location is in Saint-Petersburg, Russia," tweeted the New York Times's Charlie Warzel. And we all know what those Russians are like, don't we? They want to harvest your data for nefarious ...

Deepfake AI: Researchers Have Finally Found A 'Good' Way To Use It


Deepfakes have gained a lot of negative attention recently. Be it the hugely criticized DeepNude AI app which removes clothing from pictures of women or the FakeApp that swaps the faces of celebrities with porn stars in videos. Deep-learning algorithms are excellent at detecting matching patterns in images. This capability can be used to train neural nets to detect different types of cancer in a CT scan, identify diseases in MRIs, and spot abnormalities in an x-ray. While the idea of implementing deepfake AI for medical purposes sounds great, researchers don't have enough data to train a model -- simply because of privacy concerns.

Intel's new AI chips can crunch data 1,000 times faster than normal ones


The hardware is already being used to improve the performance of things like prosthetic limbs. The news: Intel has just unveiled Pohoiki Beach, a system that contains 64 of its Loihi AI processors. These are so-called neuromorphic chips that seek to imitate the learning ability and energy efficiency of human brains. Although the technology is still in its infancy, it's proving popular with researchers training various kinds of AI applications. A silicon leg up: Pohoiki Beach can perform certain data-crunching tasks up to 1,000 times faster than more general-purpose processors such as CPUs and GPUs, while using much less power.

Leveraging blockchain to make machine learning models more accessible 7wData


Significant advances are being made in artificial intelligence, but accessing and taking advantage of the Machine Learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn't regularly provided to retrain them. We envision a slightly different paradigm, one in which people will be able to easily and cost-effectively run Machine Learning models with technology they already have, such as browsers and apps on their phones and other devices. Through this new framework, participants can collaboratively and continually train and maintain models, as well as build datasets, on public blockchains, where models are generally free to use for evaluating predictions.

Running An Artifical Neural Network On An Arduino Uno The DIY Life


The best way to learn and understand how the code works is to run it and see on the Serial monitor how the solution to the training data is developed.

Beware the hype around AI. It has fooled many


By Ariel Procaccia Last March, McDonald's Corp. acquired the startup Dynamic Yield for $300 million, in the hope of employing machine learning to personalize customer experience. In the age of artificial intelligence, this was a no-brainer for McDonald's, since Dynamic Yield is widely recognized for its AI-powered technology and recently even landed a spot in a prestigious list of top AI startups. Neural McNetworks are upon us. Trouble is, Dynamic Yield's platform has nothing to do with AI, according to an article posted on Medium last month by the company's former head of content, Mike Mallazzo. It was a heartfelt takedown of phony AI, which was itself taken down by the author but remains engraved in the collective memory of the internet.

Eta's Ultra Low-Power Machine Learning Platform


Eta Compute has developed a high-efficiency ASIC and new artificial intelligence (AI) software based on neural networks to solve the problems of edge and mobile devices without the use of cloud resources. Future mobile devices, which are constantly active in the IoT ecosystem, require a disruptive solution that offers processing power to enable machine intelligence with low power consumption for applications such as speech recognition and imaging. These are the types of applications for which Eta Compute designed its ECM3531. The IC is based on the ARM Cortex-M3 and NXP Coolflux DSP processors. It uses a tightly integrated DSP processor and a microcontroller architecture for a significant reduction in power for the intelligence of embedded machines.