"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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 Additionally, published models run the risk of becoming outdated if new data isn't regularly provided to retrain them.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.
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.
To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.
Data scientists are expected to know a lot -- machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? I scoured job listing websites to find which skills are most in demand for data scientists. I looked at general data science skills and at specific languages and tools separately.
More than half of the world's active volcanoes are not monitored instrumentally. Hence, even very serious eruptions occur with no warning for nearby populations of the upcoming disaster. As a first and early step toward a volcano early warning system, a research project headed by Sébastien Valade from the Technical University of Berlin (TU Berlin) and the GFZ German Research Centre for Geosciences in Potsdam led to a new volcano monitoring platform that analyses satellite images using artificial intelligence (AI). Through tests with data from recent events, Valade and his colleagues demonstrated that their platform, Monitoring Unrest from Space (MOUNTS) can integrate multiple sets of diverse types of data for a comprehensive monitoring of volcanoes. The team's results were published in the journal Remote Sensing.
We hear a lot about how connected devices can support smarter cities. What are you working on? "I look at how to co-ordinate connected devices using artificial intelligence, in order to make complex systems work more efficiently. In particular, I use machine learning on linked systems such as traffic lights to help keep transport and pedestrians moving in cities, and on household appliances to use electricity more sustainably." How do you apply artificial intelligence to traffic lights?
A daredevil retired pilot has been captured on camera performing loops, rolls and a dramatic dive while flying the'world's smallest' twin-jet aircraft. Bob Grimstead, 70, flew at an altitude of 5,000ft (1,524m) in the diminutive plane which has been described as a'bubble car with wings'. At just 13ft (4m) long, 4ft (1.2m) wide and weighing a mere 180lbs, Mr Grimstead, from West Sussex, was able to reach speeds of 140mph (225kmh). The former British Airways airline pilot used to fly 400 tonne jumbo jets and said he had no fear taking to the skies in the micro plane and said it was'superb fun'. Bob Grimstead, 70, (pictured) flew the diminutive jet at 5,000ft (1,524m).