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) …
Experienced machine learning experts will know about the challenge's complexity and rightfully question the results' validity. At the same time, submissions like this Notebook illustrate how the Titanic competition's leaderboard can be forged effortlessly; A top-performing model can be created by collecting and including the publicly accessible list of survivors. Clearly, such overfit models only work for one very specific use case and are virtually useless for predicting outcomes in any other situation (not to mention the ethics of cheating). So, how can we make sure we have trained or are provided with a model that we can actually use in production? How can machine learning systems be deployed without likely ensuing disaster?
If you want to get into your doctor's bad books, turn up to your next appointment having preemptively diagnosed yourself via Google. If there's one thing that annoys healthcare professionals, it's patients thinking that computers can do their job as well as them. Some physicians even have signages in their waiting rooms stating that. Algorithms aren't going to replace our doctor any time soon. However, there's a lot of machine learning in healthcare that can help him diagnose you faster and more efficiently.
Differentiating service offerings in a remarkably competitive market such as communications is incredibly difficult. One route, a relevant understanding and early adoption of emerging technologies that can provide that niche. Platform providers globally have been claiming that they have leveraged the power of artificial intelligence and machine learning but have, in some cases, delivered little. Certainly, there are some innovators out there that are utilising artificial intelligence tools to disrupt and enhance existing processes, but there are also a host of vendors that use the term as a form of, pardon the pun, artificial smokescreen to cover their own lack of imagination or ingenuity. Disguising intelligent routing or process automation with a guise of true AI may fool some but many customers will see through this as a shiny gimmick.
Specialty re/insurer Canopius has partnered with technology start-up Arturo on artificial intelligence (AI) and deep-learning property analytics. The integration of Arturo's AI-powered technology will enable Canopius to gain access to the physical property characteristic and predictive analytics using the latest satellite, aerial, and ground-level data. As a result, Canopius will be able to make more informed and differentiated pricing decisions at the point of underwriting. Canopius chief digital officer Marek Shafer said: "Arturo's, AI-powered image analytics capability is hugely impressive. Canopius is excited to be harnessing this pioneering technology, which will help to fine-tune our risk selection process and improve point-of-sale underwriting."
Companies know that employee turnover is expensive and disruptive. And they know that retaining their best and brightest employees helps them not only save money but also preserve competitive advantages and protect intellectual capital. Most retention efforts, however, rely on two retrospective tools. First, exit interviews are conducted to better understand why people chose to leave, though by this point, it is usually too late to keep them. Second, annual employee surveys are used to assess engagement.
In this post, you'll learn the main recipe to convert a pretrained TensorFlow model in a pretrained PyTorch model, in just a few hours. We'll take the example of a simple architecture like OpenAI GPT-2 Doing such a conversion assumes a good familiarity with both TensorFlow and PyTorch but it's also one of the best ways to get to know better both frameworks! The first step is to retrieve the TensorFlow code and a pretrained checkpoint. Let's get them from OpenAI GPT-2 official repository: TensorFlow checkpoints are usually composed of three files named XXX.ckpt.data-YYY A trained NLP model should also be provided with a vocabulary to associate the tokens to the embeddings indices (here encoder.json
Mathematically, this is why we need to understand partial derivatives, since they allow us to compute the relationship between components of the neural network and the cost function. And as should be obvious, we want to minimize the cost function. When we know what affects it, we can effectively change the relevant weights and biases to minimize the cost function. If you are not a math student or have not studied calculus, this is not at all clear. So let me try to make it more clear. The squished'd' is the partial derivative sign.
A Statistical Model is the use of statistics to build a representation of the data and then conduct analysis to infer any relationships between variables or discover insights. Machine Learning is the use of mathematical and or statistical models to obtain a general understanding of the data to make predictions. While some may think there is no harm, a true "Data Scientist" must understand the distinction between the two. Statistical modelling is a method of mathematically approximating the world. Statistical models contain variables that can be used to explain relationships between other variables.