You can change your Windows Password if forget the current password

1st step: go to run then type this code lusrmgr.msc and then enter.

2nd step: open a new window then click users folder 

3rd step: The you can see your windows username 









4th step: follow my image instruction

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What we can learn about effective meaningful and diverse organizations

Posted by Beryl Nelson, Software Engineering Manager

By becoming more conscious of our own stereotypes and biases, and making use of the insights revealed by the research on bias and stereotype threat, unconscious decision making, and cognitive illusions, each of us can bring more to our work and create diverse, innovative, and meaningful organizations.

Since 2009, I’ve been reading literature about the challenges and successes in making diverse teams effective, and speaking about this research. My goal is to help everyone understand more about unconscious decision-making and other barriers to inclusion, and through knowledge, combat these effects.

A short summary:

• A team that is heterogeneous in meaningful ways is good for innovation, and good for business.
• There are many challenges to making such teams effective, such as unconscious decision making, stereotype threat, and other cognitive illusions.
• There is repeatable quantitative research which shows ways to combat some of these effects.
• The barriers to effectiveness may seem overwhelming, but there is hope! Meaningful change is possible, and some examples of successful change are cited below.

In a bit more detail:

1. Diversity is good for innovation and business. There is a correlation between financial success and the diversity of leadership teams, as shown in research by Catalyst, McKinsey and Cedric Herring. Further, research shows a strong correlation between having women on teams and innovation; concluding that there is a strong correlation between the presence of women and the social skills required to get ideas percolating into the open.
2. We all make decisions unconsciously, influenced by our implicit associations. As an example of these effects, a large proportion of CEOs are taller than the average population and height is strongly correlated with financial and career success. It’s long been argued that women and underrepresented minorities are not represented in CEO leadership because there aren’t enough qualified individuals in the labor pool. This “pipeline issue” argument can’t be made for short and average-height people, however. Simple, repeatable tests measure, via response time and error rate, the implicit associations we have between concepts. These associations are created as an adaptive response, but we must understand our own implicit biases in order to make better decisions.
3. Stereotype threat plays a role in preventing people from being fully effective. The low representation of women and minorities in Science has long been the source of a troubling question: is this an indication of a difference in innate ability (see Ben Barre’s response to Lawrence Summers’ remarks), or the result of some other effect? Claude Steele and his colleagues elegantly showed that two groups of people can have similar or opposite reactions, depending on the way a situation is presented. These and other experiments show that stereotype threat can compromise the performance of the subject of a stereotype, if he or she knows about the stereotype and cares about it.
4. Change is possible. The above and other challenges may make it seem nearly impossible to create a diverse and highly functioning organization, but dramatic change can be made. Take, for example, the discovery of biased decision making and effective changes made via the use of data in the MIT Science Faculty Study, or the amazing changes at Harvey Mudd college, which not only increased participation of women as Computer Science majors from 12% to 40% in five years, but also increased the total number of CS majors from 25 to 30 per year to 70 CS graduates in the class of 2014.

If you’re interested in learning more, watch the video about the data on diversity below. You can read the full research in the November issue of Communications of the Association of Computing Machinery. You can read even more using the full bibliography.

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When can Quantum Annealing win

Posted by Hartmut Neven, Director of Engineering

During the last two years, the Google Quantum AI team has made progress in understanding the physics governing quantum annealers. We recently applied these new insights to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes.

We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 10⁸ times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called Quantum Monte Carlo. This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 10⁸.

Time to find the optimal solution with 99% probability for different problem sizes. We compare Simulated Annealing (SA), Quantum Monte Carlo (QMC) and D-Wave 2X. Shown are the 50, 75 and 85 percentiles over a set of 100 instances. We observed a speedup of many orders of magnitude for the D-Wave 2X quantum annealer for this optimization problem characterized by rugged energy landscapes. For such problems quantum tunneling is a useful computational resource to traverse tall and narrow energy barriers.

While these results are intriguing and very encouraging, there is more work ahead to turn quantum enhanced optimization into a practical technology. The design of next generation annealers must facilitate the embedding of problems of practical relevance. For instance, we would like to increase the density and control precision of the connections between the qubits as well as their coherence. Another enhancement we wish to engineer is to support the representation not only of quadratic optimization, but of higher order optimization as well. This necessitates that not only pairs of qubits can interact directly but also larger sets of qubits. Our quantum hardware group is working on these improvements which will make it easier for users to input hard optimization problems. For higher-order optimization problems, rugged energy landscapes will become typical. Problems with such landscapes stand to benefit from quantum optimization because quantum tunneling makes it easier to traverse tall and narrow energy barriers.

We should note that there are algorithms, such as techniques based on cluster finding, that can exploit the sparse qubit connectivity in the current generation of D-Wave processors and still solve our proof-of-principle problems faster than the current quantum hardware. But due to the denser connectivity of next generation annealers, we expect those methods will become ineffective. Also, in our experience we find that lean stochastic local search techniques such as simulated annealing are often the most competitive for hard problems with little structure to exploit. Therefore, we regard simulated annealing as a generic classical competition that quantum annealing needs to beat. We are optimistic that the significant runtime gains we have found will carry over to commercially relevant problems as they occur in tasks relevant to machine intelligence.

For details please refer to http://arxiv.org/abs/1512.02206.

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