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CERN Courier

Jennifer Ngadiuba and Maurizio Pierini describe how ‘unsupervised’ machine learning could keep watch for signs of new physics at the LHC that have not yet been dreamt up by physicists.

In the 1970s, the robust mathematical framework of the Standard Model ℠ replaced data observation as the dominant starting point for scientific inquiry in particle physics. Decades-long physics programmes were put together based on its predictions. Physicists built complex and highly successful experiments at particle colliders, culminating in the discovery of the Higgs boson at the LHC in 2012.

Continue reading “Hunting anomalies with an AI trigger” »

A three-qubit entangled state has been realized in a fully controllable array of spin qubits in silicon.

An all-RIKEN team has increased the number of silicon-based spin qubits that can be entangled from two to three, highlighting the potential of spin qubits for realizing multi-qubit quantum algorithms.

Quantum computers have the potential to leave conventional computers in the dust when performing certain types of calculations. They are based on quantum bits, or qubits, the quantum equivalent of the bits that conventional computers use.

Reinforcement learning (RL) is the most widely used machine learning algorithm, besides supervised and unsupervised learning and the less common self-supervised and semi-supervised learning. RL focuses on the controlled learning process, where a machine learning algorithm is provided with a set of actions, parameters, and end values. It teaches the machine trial and error.

From a data efficiency perspective, several methods have been proposed, including online setting, reply buffer, storing experience in a transition memory, etc. In recent years, off-policy actor-critic algorithms have been gaining prominence, where RL algorithms can learn from limited data sets entirely without interaction (offline RL).

Summary: Findings could advance the development of deep learning networks based on real neurons that will enable them to perform more complex and more efficient learning processes.

Source: Hebrew University of Jerusalem.

We are in the midst of a scientific and technological revolution. The computers of today use artificial intelligence to learn from example and to execute sophisticated functions that, until recently, were thought impossible. These smart algorithms can recognize faces and even drive autonomous vehicles.

Summary: Novel AI technology allows researchers to understand which brain regions directly interact with each other, which helps guide the placement of electrodes for DBS to treat neurological diseases.

Source: Mayo Clinic.

For millions of people with epilepsy and movement disorders such as Parkinson’s disease, electrical stimulation of the brain already is widening treatment possibilities. In the future, electrical stimulation may help people with psychiatric illness and direct brain injuries, such as stroke.

There’s no need to install a perimeter wire with the Navimow.

Is moving into the robot mower market with the Navimow. What sets this model apart from many others is that you don’t need to install a boundary wire. Instead, Navimow uses GPS and other sensors to stay within the perimeter of your lawn.

A so-called Exact Fusion Locating System can maintain Navimow’s position accurate to within two centimeters, according to Segway. If the GPS signal ever dips, the company says the device’s array of sensors and data ensure it will still work. You can tell Navimow where to mow, define the boundaries and instruct it to avoid certain parts of your garden via an app. Segway claims Navimow uses an algorithm to figure out a mowing path so it doesn’t have to criss-cross.

Continue reading “Segway’s robot mower uses GPS to stay on your lawn” »

Summary: A new AI algorithm can predict the onset of Alzheimer’s disease with an accuracy of over 99% by analyzing fMRI brain scans.

Source: Kaunas University of Technology.

Researchers from Kaunas University, Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer’s disease from brain images with an accuracy of over 99 percent. The method was developed while analyzing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity, and specificity than previously developed methods.

The cashierless technology shift continues apace with today’s news that Zippin has raised $30 million in a series B round of funding. The San Francisco-based company is one of several players in the space to gain traction for a technology that seeks to not only make supermarket queues obsolete, but also generate big data insights for retailers.

Founded in 2,018 Zippin leverages AI, cameras, and smart shelf sensors to enable shoppers to place items in their cart and walk out without waiting. The company opened its first checkout-free store in San Francisco back in 2018, and it has since entered into partnerships with the likes of Aramark, Sberbank, and the Sacramento Kings’ Golden 1 Center to power cashierless stores globally.

Zippin had previously raised around $15 million, and with another $30 million from SAP, Maven Ventures, Evolv Ventures, and OurCrowd, the company is well-financed to capitalize on the retail industry’s continued push toward automation-powered efficiency. The company said its ultimate goal is to retrofit stores with the required technology inside a day, with minimal downtime for retailers.

Integrated Information Theory is one of the leading models of consciousness. It aims to describe both the quality and quantity of the conscious experience of a physical system, such as the brain, in a particular state. In this contribution, we propound the mathematical structure of the theory, separating the essentials from auxiliary formal tools. We provide a definition of a generalized IIT which has IIT 3.0 of Tononi et al., as well as the Quantum IIT introduced by Zanardi et al. as special cases. This provides an axiomatic definition of the theory which may serve as the starting point for future formal investigations and as an introduction suitable for researchers with a formal background.

Integrated Information Theory (IIT), developed by Giulio Tononi and collaborators [5, 45–47], has emerged as one of the leading scientific theories of consciousness. At the heart of the latest version of the theory [19, 25 26, 31 40] is an algorithm which, based on the level of integration of the internal functional relationships of a physical system in a given state, aims to determine both the quality and quantity (‘Φ value’) of its conscious experience.