Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science

Now DeepMind has set its sights on another grand challenge: bridging the worlds of deep learning and classical computer science to enable deep learning to do everything. If successful, this approach could revolutionize AI and software as we know them.

Petar Veličković is a senior research scientist at DeepMind. His entry into computer science came through algorithmic reasoning and algorithmic thinking using classical algorithms. Since he started doing deep learning research, he has wanted to reconcile deep learning with the classical algorithms that initially got him excited about computer science.

Meanwhile, Charles Blundell is a research lead at DeepMind who is interested in getting neural networks to make much better use of the huge quantities of data they’re exposed to. Examples include getting a network to tell us what it doesn’t know, to learn much more quickly, or to exceed expectations.

Read more | >

Circa 2012

Quantum ground-state problems are computationally hard problems for general many-body Hamiltonians; there is no classical or quantum algorithm known to be able to solve them efficiently. Nevertheless, if a trial wavefunction approximating the ground state is available, as often happens for many problems in physics and chemistry, a quantum computer could employ this trial wavefunction to project the ground state by means of the phase estimation algorithm (PEA). We performed an experimental realization of this idea by implementing a variational-wavefunction approach to solve the ground-state problem of the Heisenberg spin model with an NMR quantum simulator. Our iterative phase estimation procedure yields a high accuracy for the eigenenergies (to the 10–5 decimal digit).

Read more | >

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.

Read more | >

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 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 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 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.

Read more | >

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.

Along this journey, particle physicists adapted their methods to deal with ever growing data volumes and rates. To handle the large amount of data generated in collisions, they had to optimise real-time selection algorithms, or triggers. The field became an early adopter of artificial intelligence (AI) techniques, especially those falling under the umbrella of “supervised” machine learning. Verifying the SM’s predictions or exposing its shortcomings became the main goal of particle physics. But with the SM now apparently complete, and supervised studies incrementally excluding favoured models of new physics, “unsupervised” learning has the potential to lead the field into the uncharted waters beyond the SM.

Read more | >

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.

Read more | >

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).

Read more | >

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.

Read more | >

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.

Read more | >

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.

Segway says Navimow operates relatively quietly at 54 dB. There are offset blades to trim edges and corners, while the mower gradually cuts grass from above to reach the height you want (between three and six centimeters). The mower can handle 45-degree inclines and it has an IPX6 water resistance rating, according to Segway.

Read more | >