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Florida chemists have found a way to use gold in the synthesis of nanoparticles, opening the path for their use in biotech applications.
Researchers have discovered that voice recognition software Deep Speech 2 has improved to a point that it has become significantly faster and more accurate at producing text on a mobile device than humans are at typing on a keyboard.
HUMANS VS AI
Earlier this year, we watched a world-renowned Go mastermind get pummeled in a complex game by an artificial intelligence (AI). Now, humans are about to lose in yet another battle with the machines—and this time, it’s over typing.
There is a speech recognition software that has improved to the point that it is faster and more accurate at producing text than human typists. That’s according to researchers from Stanford University and the University of Washington, which ran a study on a new program developed by Chinese internet giant, Baidu.
Baidu’s Deep Speech 2 is a cloud-based voice recognition software based on a deep learning neural network. Essentially, the software is able to train itself by analyzing massive datasets of real speech.
“Speech recognition is something that’s been promised to us for decades, but it has never worked very well,” said James Landay, a professor of computer science at Stanford and co-author of the study. “But we were noticing that in the past two to three years, speech recognition was actually improving a lot, benefiting from big data and deep learning to train its neural networks to produce faster, more accurate results. So we decided to formally test it against humans.”
To test the software out, the team pitted Deep Speech 2 against 32 people between the ages of 19 and 32. The tests, which ran in both English and Mandarin Chinese, had the humans taking turns saying, and then typing, short phrases into an iPhone—phrases like “physics and chemistry are hard,” or “have a good weekend,” and “go out for some pizza and beer.” Half of the subjects typed using the QWERTY keyboard, while the other half conducted the test using iOS’ Pinyin keyboard.
In the end, machine triumphed over man. For English, the speech recognition software was three times faster with a 20.4 percent low error rate. For Mandarin Chinese, the software was 2.8 times faster with a 63.4 percent lower error rate compared to typing.
Researchers hope that this breakthrough will encourage engineers to design interfaces that will take better advantage of voice recognition technology. “Imagine an interface where you use speech to start and then it switches to a graphical interface that you can touch and control with your finger,” Landay said.
Researchers from the University of Warsaw have employed liquid crystalline elastomers and soft robotics techniques to make a small robot caterpillar that moves according to light conditions.
Soft robotics is a field that not too many are familiar with, but it has led to the creation of some stunning robots. Far from the large and rigid clunkers that are the public face of robots (think: Atlas), soft robotics focuses on bots with a lighter touch, mimicking the graceful movements of natural organisms.
And mimic them they do.
Case in point, a new soft robot created by researchers from the University of Warsaw moves and crawls like a real caterpillar, powered by light.
The robot, described in a paper in Advanced Optical Materials, is made from Liquid Crystalline Elastomers. These are light-sensitive materials that are aligned in a particular molecular pattern, and they change shape when subjected to light.
This robot is controlled by a spatially modulated laser beam, and can not only crawl, but also climb steep inclines, squeeze into minuscule spaces, and move objects ten times its size.
Following American Chemical Society research, three strains of fungus were found to extract up to 85 percent of the lithium and up to 48 percent of the cobalt from old batteries. If successful, this research could have a great impact on our environment—reducing the amount of e-waste which rapidly fills up the Earth.
HOPE IN FUNGI
Every day, tonnes of electronic waste is sent to developing countries at the expense of the people and their environment. For years, they are exposed to highly toxic chemicals when handling this kind of waste, which could damage their brains, reproductive systems, and kidneys. There is a dire need for change in the systems currently used to deal with this kind of waste.
Fortunately, a team from the American Chemical Society (ACS) led by Dr. Jeffrey A. Cunningham, has developed a way to extract some of the precious metals from discarded batteries by using a trio of naturally occurring species of fungus.
Over the years, reliance on lithium-ion batteries has increased dramatically. Although there have been attempts to lessen the amount of waste by extending battery-life, we have still yet to find an efficient way of recycling them. This is where fungi play a part.
Many species are good at dealing with exposure to metals like lithium. As Cunningham explains, fungi naturally generate organic acids that leach out metals. This has been observed for some time, and it’s one of the main reasons that fungi are used all over industry to extract metals.
The batteries are first taken apart and the cathodes are pulverized. Then, the three different strains of fungus—Aspergillus niger, Penicillium simplicissimum and Penicillium chrysogenum—take over. These fungi will then produce organic acids such as oxalic acid and citric acid, which, according to ExtremeTech “can extract up to 85 percent of the lithium and up to 48 percent of the cobalt from the cathodes of spent batteries.”
The team eagerly seeks to further this research. “We have ideas about how to remove [them], but at this point they remain ideas,” Cunningham says. “However, figuring out the initial extraction with fungi was a big step forward.”
If successful, this research could have a great impact on our environment—capable of reducing the amount of the toxic battery waste rapidly filling the Earth.
Harvard researchers were able to create the world’s first fully autonomous soft robot. Ultimately, it is controlled by a pneumatic system.
A group of Harvard engineers were able to create a completely autonomous robot using soft robotics. Notably, this is the first robot created that does not use any hard components. And if that’s not enough, it’s also the world’s first completely autonomous soft robot.
In case you didn’t notice, the robot is inspired by something a little more natural, which also doesn’t have any rigid parts. Looking at the bot, it’s pretty obvious what that is (an octopus).
Octobot may not be able to traverse the ocean floor with the speed and grace of a real octopus, but what it lacks in agility, it makes up in innovation.
The movements of its legs don’t propel it, its legs just kind of flop around controlled by a (completely autonomous) pneumatic system. This is all possible thanks to the power of chemistry. Gas from hydrogen peroxide pushes fluid through Octobot’s structure in a specific sequence.
While its not anywhere near as complex (or useful) an many other autonomous bots, it is a good step in the further development of soft robotics.
Chinese search engine and news outlet Toutiao is using an artificial intelligence known as Xiaomingbot to publish articles on the Olympics. The bot was able to write a total of 450 articles during the 15-day event.
With dwindling budgets that require big layoffs, you really can’t fault the news industry if it wants to catch a break. And to that end (although this is not so awesome for the news industry’s writers), a lot of media outlets are kind of going full AI.
Case in point, The Washington Post threw its hat in the AI game when setting it’s AI, “Heliograf,” to cover the Olympics, writing basic stories and keeping tallies of medal counts.
But one news outlet has them beat.
Search engine and news syndication service Toutiao has employed Xiaomingbot, an AI writing robot, to cover the Olympics. Get this: the robot was able to publish 450 articles over the course of the 15-day event.
Developed by Peking University and Toutiao, the robot was able to publish 30-40 articles a day. It does this by poring over the Olympics database searching for real-time results. Its articles regularly ran around 100 words, but its longest was 821.
Xiaomingbot was designed to be faster than other reporters/AI, able to post an accompanying photo, and have a more natural feel to the writing.
It succeeded, at least by most accounts—alas! Not even high tech AI are safe from internet trolls, and there are some issues. As Quartz reports, the AI still has a rather “mechanical” form of writing, needing improvement in some wording.
Still, this seems to be a bold and unprecedented new era in writing.
Researchers have developed injectable nanoparticles that speed up the blood clotting process anywhere in the body. If the research pans out, the silent-killing trauma of patients from blood loss could be prevented.
In dire situations, stopping excessive bleeding could mean the difference between life and death. Although there are many existing methods for controlling external bleeding, only surgery can halt internal blood loss.
New research from the University of Maryland, Baltimore County (UMBC) could change the way we deal with this situation.
The UMBC researchers aim to reduce patients’ trauma resulting from blood loss by using injectable nanoparticles that speed up the blood clotting process, either internally or externally.
The process involves the addition of a molecule (to the nanoparticles) capable of sticking to a glycoprotein found only on activated platelets. Then, the nanoparticles will bind to the activated platelets—acting as a bridge—helping the glycoprotein and platelets join together to form clots.
After achieving a 50% reduction in bleeding time for rodents, Lavik’s team tested the method on pig’s blood. The researchers were forced to tweak their nanoparticle storage solution, adding a slippery polymer to keep the nanoparticles from sticking to each other, after the method triggered an immune response.
The next challenge: human blood, and additional research to be sure any unwanted clotting doesn’t occur. Still, a future where we can quickly stop internal bleeding, doesn’t seem too far off.
Amylea, a sweet baby girl, was born in December, in Aurora, CO. Unfortunately, when her parents, Nicole and Ernie Nunez, brought their baby at home, things went seriously bad.
These parents are desperately seeking a way to save the life of their baby for 2 months and try to treat her specific condition. Since the doctors In Albuquerque, New Mexico, were not able to detect the cause and thus could not provide a proper treatment, the Nunez family decided to go to Colorado.
Nowadays, the mother stays at the hospital in Aurora, while Ernie travels all the time, as he also needs to take care of their other children at home.
In order to stop the seizures, doctors in the Aurora hospital continued to prescribe the same drug cocktail. Yet, it is a fact that these medications provide severe adverse effects as well.
Therefore, the couple decided to try cannabis oil. As Ernie says:
“The medication she’s on is hard for her liver, and so we’re trying to do something different that’s not so bad on her body.”
They heard about the impressive effects of the treatment with Charlotte’s Web, a strain of cannabis, and this made them hope that there is a cure for their daughter.
The name of Charlotte’s Web was given after a young girl, Charlotte Figi, who was treated with it. Namely, the girl had her first seizure when she had only three months, and in the following several months, her seizures were frequent and lasted from 2-4 hours. Due to all this, the girl was constantly hospitalized. Yet, her parents discovered this cannabis strain and managed to save their little girl. Since then, numerous other children were treated with the same plant.
“I sat for a good three weeks fighting with the doctors and trying to talk them into giving me the okay. I’ve been working with the case study team and the neurology team here at children’s and I’m hopeful this will work.”
Finally, the doctors of her daughter decided to treat Amylea with cannabis oil.
Nicole said: “For us to get the approval for us to administer it while she in the NICU while she’s a patient…it’s kind of like a miracle. Because they were completely against it saying, ‘No you can’t do it, you have to wait until she’s an out-patient.”
However, the doctors agreed to use the cannabis oil, but the parents needed to administer it to her. The girl received it in small amounts, but her nurses noticed great improvements. The family claims that their daughter is the youngest patient who has ever received cannabis oil in a hospital.
This case is evidence that the cannabis is a viable medical option, and it being classified as a Schedule 1 drug by the federal government is absurd. This miraculous plant has been shown to treat epilepsy, cancer, and numerous other health issues.
A study, whose findings were reported by The Free Thought Project, found that this oil effectively treats intractable epilepsy. The study involved 261 patients, and even the frequency of seizures was reduced in 45% of them.
Additionally, 9% were seizure-free at three months. Even after the end of the study, some children continued to experience the benefits of the treatment, months and even a year afterward.
Therefore, it is up to us to raise the awareness of the general public about the reality and the positive outcome of the treatments with cannabis oil, and do all we can to change things, as apparently, we have the cure for the deadliest illnesses of the modern era, and it has been banned due to immoral reasons.
- The TeraStructure algorithm can analyze genome sets much larger than current systems can efficiently handle, including those as big as 100,000 or 1 million genomes.
- Finding an efficient way to analyze genome databases would allow for personalized healthcare that takes into account any genetic mutations that could exist in a person’s DNA.
Ever since the first complete mapping of the human genome in 2003, biologists and other scientists have been hard at work making the process easier and faster. They’ve gotten so good at it, in fact, that they’ve now sequenced the genomes of more than a million people and believe that number could rise to nearly 2 billion by 2025.
However, our ability to make sense of all this data remains lackluster. Machine learning could change that, though, as a new algorithm has been developed that can read large genome data sets for the goal of personalizing healthcare.
Currently being used for this purpose is the STRUCTURE algorithm, which was first described in 2000. It looks at all the variants in each genome in a data set before updating its model to characterizing ancestral populations and how they affect an individual’s own genome. Then it moves on to the next genome.
The new algorithm, TeraStructure, looks at one variant in all of the genomes in a data set before it updates its model to produce a working estimate of population structure. That allows it to create ancestry models more accurately and quickly — two to three times faster, in fact, on a simulated data set of 10,000 genomes. It can even analyze sets as large as 100,000 or 1 million genomes.
Each mapped genome is several billion characters long, and given that we could have as many as two billion mapped within the next decade, we need to find a efficient way to make use of this data. Therefore, machine learning could be an invaluable tool in the field of genomics as it could be used for genetic diagnostics, refining drug targets, pharmaceutical development, and personalized medicine, according to Brandon Frey, founder of company Deep Genomics.
Knowing the ancestry of an individual enables doctors to see which variants in an individual genome are due to normal genetic variation in a population and which are due to disease-causing variants passed down from ancestors. This allows for personalized healthcare — if your doctor knows which diseases you are more susceptible to due to mutations in your DNA, they can better treat or prevent those diseases. Ultimately, machine learning may provide a way for us to tailor medicine to provide better and faster relief for individuals.