Lifeboat Foundation

IBM quantum computer runs machine-learning algorithm to find Higgs events.

Researchers at UCSF have trained an algorithm to parse meaning from neural activity.

Ai-Da is the world’s first ultra-realistic artist robot powered by AI and named after Ada Lovelace, the first female computer programmer in the world. She is a humanoid with human facial features and a robotic body created by the Oxfordians, a group of cutting-edge art and technology experts. Embedded with a groundbreaking algorithm, she has taken the scientific and art world by surprise, now becoming an intense subject of conversation in over 900 publications worldwide. She has already collaborated with Tate Exchange and WIRED at the Barbican, Ars Electronica, and will be performing at the Louvre Abu-Dhabi later this year.

Here, she discusses what it means to identify as a creative without a consciousness with Futurist Geraldine Wharry.

Continue reading “Meet Ai-Da, the world’s first AI artist, who is almost human” »

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Imagine a weapon that creates sound that only you can hear. Science fiction? No, this is one area that has a very solid basis in reality. The Air Force has experimented with microwaves that create sounds in people’s head (which they’ve called a possible psychological warfare tool), and American Technologies can “beam” sounds to specific targets with their patented HyperSound (and yes, I’ve heard/seen them demonstrate the speakers, and they are shockingly effective).

Now the Defense Advanced Research Projects Agency is jumping on the bandwagon with their new “Sonic Projector” program:

Continue reading “A Voice Only You Can Hear: DARPA’s Sonic Projector” »

Quantum computers will revolutionize information technology, ushering in an era where certain types of calculations will be performed with almost unimaginable speed. Practical applications will include healthcare disciplines such as molecular biology and drug discovery; big data mining; financial services such as portfolio analysis and fraud detection; and artificial intelligence and machine learning.

The federal government is helping to create an environment in which quantum computing innovation and experimentation can flourish. The National Quantum Initiative Act puts $1.2 billion into the quantum research budgets of the Energy Department, the National Institute of Standards and Technology, NASA and the National Science Foundation. The law also outlines a 10-year plan to accelerate the development of quantum information science and technology applications.

Meanwhile, The White House’s Office of Science and Technology Policy is working to ensure that economic growth opportunities and opportunities for improving the world are baked into quantum policies and systems.

A new blood test that can detect methylation of DNA can accurately predict whether a person has any one of 50 cancers and where the tumour is growing.

The California-based healthcare company Grail, which developed the test, owns a large database of methylation patterns in cancerous and non-cancerous cell-free DNA. From that repository, a machine learning program was developed to analyse blood samples. The algorithm identified methylation changes that are classified as cancerous or non-cancerous, and it could even pinpoint the tissue of origin before the onset of symptoms.

Validation of the test was carried out by researchers from the US at the Mayo Clinic, Cleveland Clinic and Harvard medical school, working with colleagues at the Francis Crick Institute and University College London in the UK. In all, more than 15,000 volunteers from over 140 clinics in North America took part, and their samples revealed that this ‘liquid biopsy’ had a 0.7% false positive rate for cancer detection. The test was also able to predict the tissue that the cancer originated in with more than 90% accuracy. It performed best on 12 of the most common cancers, including ones that are most lethal and have no established screening paradigms such as pancreatic and ovarian cancers.

An artificial intelligence can accurately translate thoughts into sentences, at least for a limited vocabulary of 250 words. The system may bring us a step closer to restoring speech to people who have lost the ability because of paralysis.

Joseph Makin at the University of California, San Francisco, and his colleagues used deep learning algorithms to study the brain signals of four women as they spoke. The women, who all have epilepsy, already had electrodes attached to their brains to monitor seizures.

Scientists have taken a step forward in their ability to decode what a person is saying just by looking at their brainwaves when they speak.

They trained algorithms to transfer the brain patterns into sentences in real-time and with word error rates as low as 3%.

Previously, these so-called “brain-machine interfaces” have had limited success in decoding neural activity.

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but they’ve been limited in their ability to optimize across multiple goals, including the chip’s power draw, computational performance, and area.