If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As a recent graduate of the Flatiron School's Data Science Bootcamp, I've been inundated with advice on how to ace technical interviews. A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. This series of posts is me sharing with the world how I would explain all the machine learning topics I come across on a regular basis...to my grandma. Some get a bit in-depth, others less so, but all I believe are useful to a non-Data Scientist. In the upcoming parts of this series, I'll be going over: To summarize, an algorithm is the mathematical life force behind a model.
How many qubits are needed to out-perform conventional computers, how to protect a quantum computer from the effects of decoherence and how to design more than 1000 qubits fault-tolerant large scale quantum computers, these are the three basic questions we want to deal in this article. Qubit technologies, qubit quality, qubit count, qubit connectivity and qubit architectures are the five key areas of quantum computing are discussed. Earlier we have discussed 7 Core Qubit Technologies for Quantum Computing, 7 Key Requirements for Quantum Computing. Spin-orbit Coupling Qubits for Quantum Computing and AI, Quantum Computing Algorithms for Artificial Intelligence, Quantum Computing and Artificial Intelligence, Quantum Computing with Many World Interpretation Scopes and Challenges and Quantum Computer with Superconductivity at Room Temperature. Here, we will focus on practical issues related to designing large-scale quantum computers.
You just built your neural network and notice that it performs incredibly well on the training set, but not nearly as good on the test set. This is a sign of overfitting. Your neural network has a very high variance and it cannot generalize well to data it has not been trained on. Getting more data is sometimes impossible, and other times very expensive. Therefore, regularization is a common method to reduce overfitting and consequently improve the model's performance.
Employee turn-over (also known as "employee churn") is a costly problem for companies. The true cost of replacing an employee can often be quite large. A study by the Center for American Progress found that companies typically pay about one-fifth of an employee's salary to replace that employee, and the cost can significantly increase if executives or highest-paid employees are to be replaced. In other words, the cost of replacing employees for most employers remains significant. This is due to the amount of time spent to interview and find a replacement, sign-on bonuses, and the loss of productivity for several months while the new employee gets accustomed to the new role.
In my previous blog post, I introduced the "four pillars of trust" for automated decisions. The key takeaway was that explainability and transparency refer to the entire analytical process. Here, too, the analytical platform must guarantee transparency. The good news is that algorithms are not that dark. Although we cannot derive easily understandable sets of rules, we can – regardless of the concrete procedure – investigate the decisive factors in the algorithmic decision.
You can use NSFW JS to identify indecent content without having files ever leave the client's machine, even defensively if you can't control the content being delivered. Ever see something that when you go to bed, those same images are there when you close your eyes? And I'm not talking about one of those good dreams, like Nicolas Cage or something, but the kind that if you have it on your screen when the boss walks by, you'll be looking for a new job. User input can be disgusting. One of my friends made their own online store back in the day, and it allowed for negative quantities.
As the amount of data created daily increases (already at 2.5 Quadrillion bytes a day allegedly ) ML techniques are allowing us to cluster, organise and appropriate this data into actionable information. This is especially true in the realm of Cyber Security. Don't be scared of the term Machine Learning, it really just means a computer that can learn to do something without being explicitly programmed for that task. The process typically involves training the machine to do a task (i.e. Let's have a quick look at some of the ways we encounter ML every day in Cyber Security.
Machine Learning is the foundation for today's insights on customer, products, costs and revenues which learns from the data provided to its algorithms. Some of the most common examples of machine learning are Netflix's algorithms to give movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend products based on other customers bought before. Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables.
To avoid the paper being thrown in the bin we provide this with a large, negative reward, say -1, and because the teacher is pleased with it being placed in the bin this nets a large positive reward, 1. To avoid the outcome where it continually gets passed around the room, we set the reward for all other actions to be a small, negative value, say -0.04. If we set this as a positive or null number then the model may let the paper go round and round as it would be better to gain small positives than risk getting close to the negative outcome.
Now we have the probability that each data point belongs to each cluster. If we need hard cluster assignments, we can just choose for each data point to belong to the cluster with the highest probability. But the nice thing about EM is that we can embrace the fuzziness of the cluster membership. We can look at a data point and consider the fact that while it most likely belongs to Cluster B, it's also quite likely to belong to Cluster D. This also takes into account the fact that there may not be clear cut boundaries between our clusters. These groups consist of overlapping multi-dimensional distributions, so drawing clear cut lines might not always be the best solution.