Japanese scientists have developed a "slightly sweaty" robotic finger covered in living skin in an advance they say brings truly human-like robots a step closer. The finger, which was shown to be able to heal itself, is seen as an impressive technical feat that blurs the line between living flesh and machine. But scientists were divided on whether people would warm to its lifelike anatomy or find it creepy. "We are surprised by how well the skin tissue conforms to the robot's surface," said Shoji Takeuchi, a professor at the University of Tokyo, who led the work. "But this work is just the first step toward creating robots covered with living skin."

In the first millionths of a second after the Big Bang, the universe was a roiling, trillion-degree plasma of quarks and gluons -- elementary particles that briefly glommed together in countless combinations before cooling and settling into more stable configurations to make the neutrons and protons of ordinary matter. In the chaos before cooling, a fraction of these quarks and gluons collided randomly to form short-lived "X" particles, so named for their mysterious, unknown structures. Today, X particles are extremely rare, though physicists have theorized that they may be created in particle accelerators through quark coalescence, where high-energy collisions can generate similar flashes of quark-gluon plasma. Now physicists at MIT's Laboratory for Nuclear Science and elsewhere have found evidence of X particles in the quark-gluon plasma produced in the Large Hadron Collider (LHC) at CERN, the European Organization for Nuclear Research, based near Geneva, Switzerland. The team used machine-learning techniques to sift through more than 13 billion heavy ion collisions, each of which produced tens of thousands of charged particles.

One of the main challenges in the design of soft robotic grippers is integrating traditional sensors onto the robot's fingers. Ideally, a soft gripper should have what's known as proprioception -- a sense of its own movements and position -- to be able to safely execute varied tasks. However, traditional sensors are rigid and compromise the mechanical characteristics of the soft parts. Moreover, existing soft grippers are usually designed with a single type of proprioceptive sensation; either pressure or finger curvature. To overcome these limitations, scientists at Ritsumeikan University, Japan, have been working on novel soft gripper designs under the lead of Associate Professor Mengying Xie.

In recent years, AI researchers have developed and fine-tuned deep-learning networks -- layered programs that can come up with novel solutions to achieve their assigned goal. For example, a deep-learning network can be told which face to identify in a series of different photos, and through several rounds of training, can tune its algorithms until it spots the right face virtually every time.

This humorous clip, Biologist talks to statistician, featured in the newsletter of the American Statistical Association, Amstat News, takes aim at the statistical ignorance of scientists. Statistics Done Wrong (Reinhart) is a pithy book which digs more deeply into confusion about statistics. Uncertainty: The Soul of Modeling, Probability & Statistics (Briggs) is hard-hitting and takes on both scientists and statisticians. Vital Statistics You Never Learned...Because They're Never Taught is a short interview with highly-regarded statistician Frank Harrell, author of the influential book Regression Modeling Strategies. I would urge any marketing researcher, statistician or data scientist to take a few minutes to read this interview.

Many people have built brain-like neural networks that can learn on their own, but they're typically using plain old silicon to do it. Wouldn't it be better if the chips themselves were brain-like? A mix of Italian and Russian researchers might help. They've created a neural network based on plastic memristors, or resistors that remember their previous electrical resistance. Since they effectively work like brain synapses, they're ideal for creating "true" neural networks where signal transfers create long-lasting effects.