Lifeboat Foundation

What we need now is an expansion of public and private investment that does justice to the opportunity at hand. Such investments may have a longer time horizon, but their eventual impact is without parallel. I believe that net-energy gain is within reach in the next decade; commercialization, based on early prototypes, will follow in very short order.

But such timelines are heavily dependent on funding and the availability of resources. Considerable investment is being allocated to alternative energy sources — wind, solar, etc. — but fusion must have a place in the global energy equation. This is especially true as we approach the critical breakthrough moment.

If laser-driven nuclear fusion is perfected and commercialized, it has the potential to become the energy source of choice, displacing the many existing, less ideal energy sources. This is because fusion, if done correctly, offers energy that is in equal parts clean, safe and affordable. I am convinced that fusion power plants will eventually replace most conventional power plants and related large-scale energy infrastructure that are still so dominant today. There will be no need for coal or gas.

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn.

It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi’s The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy. This means that potentially 9 out of 10 companies are correctly assessed.

“This research shows how ensembles of non-linear machine-learning models applied to big data have huge potential to map large feature sets to business outcomes, something that is unachievable with traditional linear regression models,” explains co-author Sanjiv Das, Professor of Finance and Data Science at Santa Clara University’s Leavey School of Business in the US.

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.