Phil and Stephen discuss a number of future developments that they have been predicting for years that now appear to be coming true.
Parrish, then 44, took a myostatin inhibitor, a drug being tested as a treatment for muscle loss and a telomerase gene therapy which lengthens telomeres. Telomeres are located at the end of every chromosome, and they get shorter over time as the body ages.
Telomeres also allow the cell and its DNA to divide until it experiences cell death, which scientists believe contributes to ageing. This also shortens telomeres. The gene therapy that Parrish took encourages cells to produce telomerase, a protein that repairs them.
The Marsh McLennan report warns that the cost of elderly health care in Asia over the next 15 years will snowball to approximately US$20 trillion, which is unsustainable. Current health systems aren’t prepared to handle the wave of unmet demands that will occur from societal ageing – such as whether there will be enough doctors, specialists and hospital beds. It forces a focus on the future workforce – who will care for old people?, and infrastructural capacity – where will we care for them?
Since her personal experiment, Parrish says people have contacted her to ask if they can try her anti-ageing gene therapy. She admits this is not enough to expedite the official sanction for use of such therapies in humans. Instead, she is interested in asking countries to re-regulate.
Alibaba on Monday said its artificial research outperformed mere mortals in a global reading comprehension test that seeks answers to such pressing questions as “what was Nikola Tesla’s ethnicity?” and “how big is the Amazon rainforest?”
Luo Si, chief scientist of natural language processing at Alibaba’s research arm, the Institute of Data Science of Technologies, dubbed the machines’ victory “a milestone”. He said the technology has many uses, from customer service to museum tutorials to medical enquiries — some of which are already being handled by chatbots globally.
A team from Kyoto University used a deep neural network to read and interpret people’s thoughts. Sound crazy? This actually isn’t the first time it’s been done. The difference is that previous methods—and results—were simpler, deconstructing images based on their pixels and basic shapes. The new technique, dubbed “deep image reconstruction,” moves beyond binary pixels, giving researchers the ability to decode images that have multiple layers of color and structure.
“In August last year, the FDA finally approved CAR-T cell therapy treatment after an additional 50 more patients who had been subjected to the trial went into remission.
CAR-T cell therapy is a type of cancer treatment that involves the process of using a patient’s T cells, a type of immune system cell, and altering them in the laboratory so that they can attack the cancer cells. These cells come from the extracted blood of a patient, and a certain protein in these cells bind to a gene for a special receptor referred to as chimeric antigen receptor (CAR).”
Or maybe it’s not happening — update on computer program that can tell if you’re gay.
we can immediately see that some of these differences are more superficial. For example, the “average” straight woman appears to wear eyeshadow, while the “average” lesbian does not. Glasses are clearly visible on the gay man, and to a lesser extent on the lesbian, while they seem absent in the heterosexual composites. Might it be the case that the algorithm’s ability to detect orientation has little to do with facial structure, but is due rather to patterns in grooming, presentation and lifestyle?