Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.

A drunk man reportedly ran into an armless K5 robot in the Knightscope parking lot in Mountain View, California and met his match. The April incident occurred after 41-year-old Jason Sylvain tipped over the 300-pound robot. Unfortunately, when the roving security robot found itself off-balance, the K5 called the police and signaled for help. The company spokesman Stacy Dean Stephens said that members of the robot company Knightscope -- which developed the robot that appears similar to the iconic Star Wars Droid R2D2 -- came out and detained Sylvain as the police came. Robo-Cops Are Now A Reality! Silicon Valley Gets KnightScope K5 Patrolling Robot… https://t.co/BiytOrxpPU

Those fretfully debating artificial intelligence (AI) might best start by appraising the half dozen general pathways under exploration in laboratories around the world. While these general approaches overlap, they offer distinct implications for what characteristics emerging, synthetic minds might display, including (for example) whether it will be easy or hard to instill human-style ethical values. Most problematic may be those efforts taking place in secret. The "Moore's Law crossing" argument is appraised, in light of discoveries that brain computation may involve much more than just synapses. Will efforts to develop Sympathetic Robotics tweak compassion from humans long before automatons are truly self-aware? It is argued that most foreseeable problems might be dealt with the same way that human versions of oppression and error are best addressed -- via reciprocal accountability. For this to happen, there should be diversity of types, designs and minds, interacting under fair competition in a generally open environment. As varied concepts from science fiction are reified by rapidly advancing technology, some trends are viewed worriedly by our smartest peers. Portions of the intelligencia -- typified by Google's Ray Kurzweil [1] -- foresee AI, or Artificial General Intelligence (AGI) as likely to bring good news, perhaps even transcendence for members of the Olde Race of bio-organic humanity 1.0. Others, such as Stephen Hawking and Francis Fukuyama, warn that the arrival of sapient, or supersapient machinery may bring an end to our species -- or at least its relevance on the cosmic stage -- a potentiality evoked in many a lurid Hollywood film. Swedish philosopher Nicholas Bostrom, in Superintelligence [2], suggests that even advanced AIs who obey their initial, human defined goals will likely generate "instrumental subgoals" such as self-preservation, cognitive enhancement, and resource acquisition. In one nightmare scenario, Bostrom posits an AI that -- ordered to "make paperclips" -- proceeds to overcome all obstacles and transform the solar system into paper clips. A variant on this theme makes up the grand arc in the famed "three laws" robotic series by science fiction author Isaac Asimov [3]. Taking middle ground, SpaceX/Tesla entrepreneur Elon Musk has joined with YCombinator founder Sam Altman to establish OpenAI [4], an endeavor that aims to keep artificial intelligence research -- and its products -- accountable by maximizing transparency and accountability. As one who has promoted those two key words for a quarter of a century, I wholly approve [5].

Artificial Intelligence systems are becoming the new warriors in disease diagnosis and can even accurately predict when you are going to die. Scientists at the University Of Adelaide in Australia have developed an Artificial Intelligence system that can accurately predict a human's life expectancy. The system was developed through research that examined the CT scan of 48 patients. Looking at the scans, the deep learning algorithms gave a'diagnosis' of whether the patient would die within 5 years. The prediction has a 69% accuracy rate, a score'similar' to the accuracy of human doctors.

A major part of the strategy should include machine learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today. If you want to stay ahead of the game, then you cannot afford to wait for that to happen. Your digital business needs to move towards automation now while ML technology is developing rapidly. Machine learning algorithms learn from huge amounts of structured and unstructured data, e.g.

Just 200 face cells are required to identify a face, the biologists say. After discovering how its features are encoded, the biologists were able to reconstruct the faces a monkey was looking at just by monitoring the pattern in which its face cells were firing. The finding needs to be confirmed in other laboratories. But, if correct, it could help understand how the brain encodes all seen objects, as well as suggesting new approaches to artificial vision. "Cracking the code for faces would definitely be a big deal," said Brad Duchaine, an expert on face recognition at Dartmouth.

This may be for three different reasons. Because it takes a long time for gradient descent to shrink "incorrect" large

Cybersecurity and artificial intelligence (AI) are the future of state IT, according to New Jersey Chief Technology Officer Dave Weinstein. While working in the executive branch to help modernize and secure critical systems, Weinstein admits that the state is generally still in a "fact-finding, data-gathering mode." While some argue that AI and machine learning still fall into the "exciting, but how can we use it?" "We need to start thinking about [automation] not just as at a technical level, but from a strategic perspective. How are we going to embrace this or scale it across the enterprise?" he said.

Machine Learning is a complex area of computing for those with deep technical knowledge and the ability to translate between business requirements, large data sets and how computing systems develop. At least, that is how it has been since first discussed in 1959, and continued until today. Over the next few years, it's been predicted that Machine Learning will become business as usual. Initially, companies will use machine learning to automate certain functions, such as pattern recognition, and improve efficiencies. Over time, Machine Learning will expand to automate more of the analytics steps involved within jobs.

Sören Schwertfeger finished his postdoctorate research on autonomous robots in Germany, and seemed set to go to Europe or the United States, where artificial intelligence was pioneered and established. Instead, he went to China. "You couldn't have started a lab like mine elsewhere," Mr. Schwertfeger said. The balance of power in technology is shifting. China, which for years watched enviously as the West invented the software and the chips powering today's digital age, has become a major player in artificial intelligence, what some think may be the most important technology of the future.