Condensed knowledge

A recent study (1) that appears to demonstrate mining entire body of articles than abstracts provides higher information content is obvious. The more important question is what the mining is for and how the results are expected to be used. Also interesting is how much more incremental knowledge can be extracted from full articles.
 
In a world of exponentially expanding data, the risk of getting caught in the details is high. The “detail oriented technician,” has been more useful than those who see “the big picture,” in a scientific context because discoveries came from pouring over the data in a detailed fashion. But now, data is arriving from every direction and time is not an affordable luxury for decision-making and advancing ideas, with half-lives tending toward days and weeks.
 
Humans have not been here before. For hundred thousand years, they banked on experience, accumulated over many generations. Now, experience is losing out to machine learning. Here, time is of the essence and understanding patterns reigns supreme. Patterns have a hierarchical structure, and if a pyramid needs to be built bottoms up, it may not add value if it takes too long. Hyped up technologies in the areas of deep learning and mind will likely find that brute force approaches to “artificial general intelligence,” are unlikely to yield interesting results. 
 
The early human survivor in the wild had an intuition that the leopard is behind the bush to the left of her and she relied on patterns at the highest levels. Data scientists toiling with oceans of data, statistical modeling platforms, machine learning, deep learning and even deeper mind, may have to look back and understand that throwing data, analytics and technology at a problem, never helped humanity.
 
(1) http://www.sciencemag.org/news/2017/07/want-analyze-millions-scientific-papers-all-once-here-s-best-way-do-it

 

Fusion deficient

Humans' ability to harness energy, propelled tactically by the Sun, remain meager. Unfortunately, they have not even been able to replicate the processes in the Sun that apparently throws of seemingly unlimited free energy. Fusion, hot and cold, eluded the struggling species, with nearly 30% still without proper food, clothing and shelter. Lifting humanity from despair remains to be an energy problem, something not many are focused on.

The simplest of processes, fusing two Hydrogen atoms into one of Helium and releasing an abundance of energy, still remains outside the grasp of the engineers. Recent news from the famous defense contractor was encouraging but the path to practical implementation still seems too long. And the crooks who raised false hope on cold fusion seem to have gone away. Tactical conversion of Sun's power - solar and wind - still seem too expensive and rather cumbersome. And, if the energy secretary ever goes to school and perhaps learn something, he may learn that fossil fuel is not the answer either.

The answer appears to be tantalizingly close in fusion. The template is readily available and a generation of great technologists stand ready to convert dreams into practical applications. What is missing is imagination, something that cannot be taught or bought. Perhaps, we need a bit of luck.

The code

Humans and their cousins, the apes, have been exceptional in facial recognition. Their life depended on it for the ability to recognize a friend from a foe was crucial for survival. Recent research from Caltech (1) appears to get closer to the neural code for face recognition. The trick appears to be specialization with singular neurons, focused on specific features. The idea appears to validate recent advances in deep learning for image recognition and it could provide further impetus to the acceleration of artificial intelligence.

The idea that single neurons encode specific features has been tantalizing for deep learning enthusiasts. It allows scalability in deep neural networks with increasing specialization in layers. The single feature specificity at the neuron level and its ability to make binary decisions provide further evidence of micro compartmentalization and voting based decision-making. Complementarity, communication and cooperation appear to dominate in goal seeking and that has implications for future research in computerized image recognition and more broadly, artificial intelligence.

The neural code developed by primates, an efficient and massively parallel processed algorithm that helped them survive and evolve, could be replicatable. Hopefully, those who get hold of this will use it wisely.

(1) https://www.scientificamerican.com/article/how-we-save-face-researchers-crack-the-brains-facial-recognition-code/