Lifeboat Foundation

Tesla and CEO Elon Musk say many Teslas will soon drive themselves, and that up to 1 million private Model 3s will form an autonomous taxi service.

For some time now, we’ve seen robotic surgical devices that can be remotely guided within the human body. And while they do make surgery more precise and less invasive, they still have to be continuously operated by a surgeon. Recently, however, a robotic catheter successfully navigated beating pig hearts on its own.

An army of tiny robots scuttling about inside your mouth cleaning your teeth. It’s a disquieting thought, and yet it might be one of the most effective ways to deal with the sticky bacterial biofilms that coat our choppers – as well as water pipes, catheters and other tough-to-clean items.

In a focus section published in the journal Seismological Research Letters, researchers describe how they are using machine learning methods to hone predictions of seismic activity, identify earthquake centers, characterize different types of seismic waves and distinguish seismic activity from other kinds of ground “noise.”

Machine learning refers to a set of algorithms and models that allow computers to identify and extract patterns of information from large data sets. Machine learning methods often discover these patterns from the data themselves, without reference to the real-world, physical mechanisms represented by the data. The methods have been used successfully on problems such as digital image and speech recognition, among other applications.

More seismologists are using the methods, driven by “the increasing size of seismic data sets, improvements in computational power, new algorithms and architecture and the availability of easy-to-use open source machine learning frameworks,” write focus section editors Karianne Bergen of Harvard University, Ting Cheng of Los Alamos National Laboratory, and Zefeng Li of Caltech.

This particular version of Dadabots has been trained on real death metal band Archspire, and Carr and Zukowski have previously trained the neural network on other real bands like Room For A Ghost, Meshuggah, and Krallice. In the past, they’ve released albums made by these algorithms for free on Dadabots’ Bandcamp — but having a 24/7 algorithmic death metal livestream is something new.

Carr and Zukowski published an abstract about their work in 2017, explaining that “most style-specific generative music experiments have explored artists commonly found in harmony textbooks,” meaning mostly classical music, and have largely ignored smaller genres like black metal. In the paper, the duo said the goal was to have the AI “achieve a realistic recreation” of the audio fed into it, but it ultimately gave them something perfectly imperfect. “Solo vocalists become a lush choir of ghostly voices,” they write. “Rock bands become crunchy cubist-jazz, and cross-breeds of multiple recordings become a surrealist chimera of sound.”

Continue reading “This live stream plays endless death metal produced by an AI” »

UK-based design agency Layer has teamed up with Chinese electric car maker Nio to create a smart scooter that can learn where you want to go.

Once “Pal” learns your preferred routes, the smart scooter can autonomously take you to your destination. On its website, Layer calls the scooter a “near-future prototype” that “embraces AI and machine learning to offer flexible and convenient ‘last mile’ travel.”

It’s a stunning example of industrial design that could make short-distance travel much more convenient — whether it will ever actually be sold to the public or not.

Continue reading “Autonomous Scooter Uses AI to Learn Your Favorite Routes” »

We need more than engineers and computer scientists shaping the future of AI.

Just as the modern computer transformed our relationship with bits and information, AI will redefine and revolutionize our relationship with molecules and materials. AI is currently being used to discover new materials for clean-tech innovations, such as solar panels, batteries, and devices that can now conduct artificial photosynthesis.

Today, it takes about 15 to 20 years to create a single new material, according to industry experts. But as AI design systems skyrocket in capacity, these will vastly accelerate the materials discovery process, allowing us to address pressing issues like climate change at record rates. Companies like Kebotix are already on their way to streamlining the creation of chemistries and materials at the click of a button.

Atomically precise manufacturing will enable us to produce the previously unimaginable.

Continue reading “5 AI Breakthroughs We’ll Likely See in the Next 5 Years” »

Aging is by far the dominant risk factor for the development of cardiovascular diseases, whose prevalence dramatically increases with increasing age reaching epidemic proportions. In the elderly, pathologic cellular and molecular changes in cardiac tissue homeostasis and response to injury result in progressive deteriorations in the structure and function of the heart. Although the phenotypes of cardiac aging have been the subject of intense study, the recent discovery that cardiac homeostasis during mammalian lifespan is maintained and regulated by regenerative events associated with endogenous cardiac stem cell (CSC) activation has produced a crucial reconsideration of the biology of the adult and aged mammalian myocardium. The classical notion of the adult heart as a static organ, in terms of cell turnover and renewal, has now been replaced by a dynamic model in which cardiac cells continuously die and are then replaced by CSC progeny differentiation. However, CSCs are not immortal. They undergo cellular senescence characterized by increased ROS production and oxidative stress and loss of telomere/telomerase integrity in response to a variety of physiological and pathological demands with aging. Nevertheless, the old myocardium preserves an endogenous functionally competent CSC cohort which appears to be resistant to the senescent phenotype occurring with aging. The latter envisions the phenomenon of CSC ageing as a result of a stochastic and therefore reversible cell autonomous process. However, CSC aging could be a programmed cell cycle-dependent process, which affects all or most of the endogenous CSC population. The latter would infer that the loss of CSC regenerative capacity with aging is an inevitable phenomenon that cannot be rescued by stimulating their growth, which would only speed their progressive exhaustion. The resolution of these two biological views will be crucial to design and develop effective CSC-based interventions to counteract cardiac aging not only improving health span of the elderly but also extending lifespan by delaying cardiovascular disease-related deaths.

Over the last decades, average life expectancy has significantly increased worldwide although several chronic diseases continue to grow, with aging as their main risk factor [1]. Aging is a natural and inevitable degenerative process of biological functions characterized by the progressive decline in tissue and organ homeostasis and function. Despite the significant improvements in diagnosis and treatment, the majority of individuals older than 65 years of age suffer from an elevated risk to develop cardiovascular diseases (CVDs), with a decline in the quality of life and in the ability to perform the normal activities of daily living [1]. Aging produces numerous changes in the human heart at structural, molecular, and functional levels [2].