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) …
Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. This is very useful since most real-world problems typically do not have millions of labeled data points to train such complex models. We'll take a look at what transfer learning is, how it works, why and when you it should be used. Additionally, we'll cover the different approaches of transfer learning and provide you with some resources on already pre-trained models.
Abstract: Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations? For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers.
Nowadays, we have huge amounts of data in almost every application we use - listening to music on Spotify, browsing friend's images on Instagram, or maybe watching an new trailer on YouTube. There is always data being transmitted from the servers to you. This wouldn't be a problem for a single user. But imagine handling thousands, if not millions, of requests with large data at the same time. These streams of data have to be reduced somehow in order for us to be physically able to provide them to users - this is where data compression kicks in.
If you are following technology news, you have likely already read about how AI programs trained with reinforcement learning beat human players in board games like Go and chess, as well as video games. As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it's the right approach for a given problem. If we simplify the concept, at its foundation, reinforcement learning is a type of machine learning that has the potential to solve toughdecision-making problems. Reinforcement learning is a type of machine learning in whicha computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment.
We can easily use the widely available libraries available in Python and R to build models!" I have lost count of the number of times I've heard this from amateur data scientists. This fallacy is all too common and has created a false expectation among aspiring data science professionals. Let's get this out of the way right now – you need to understand the mathematics behind machine learning algorithms to become a data scientist. There is no way around it. It is an intrinsic part of a data scientist's role and every recruiter and experienced machine learning professional will vouch for this.
In light of the fact that Siri was released back in 2011, it seemed to not have bothered Apple much at all that it was assigning Siri such a coy, stereotypically feminine response that it was allowed to stay in the program for close to eight years. As the UNESCO report points out, "Siri's'female' obsequiousness -- and the servility expressed by so many other digital assistants projected as young women -- provides a powerful illustration of gender biases coded into technology products."
A branch of machine learning called deep learning has helped computers surpass humans at well-defined visual tasks like reading medical scans, but as the technology expands into interpreting videos and real-world events, the models are getting larger and more computationally intensive. By one estimate, training a video-recognition model can take up to 50 times more data and eight times more processing power than training an image-classification model. That's a problem as demand for processing power to train deep learning models continues to rise exponentially and concerns about AI's massive carbon footprint grow. Running large video-recognition models on low-power mobile devices, where many AI applications are heading, also remains a challenge. Song Han, an assistant professor at MIT's Department of Electrical Engineering and Computer Science (EECS), is tackling the problem by designing more efficient deep learning models.
This is a quick transcript of the interview of Peter Norvig by Lex Fridman. I find this interview so interesting and revealing, that I decided to take on the task of making a transcript of the interview published in YouTube. Lex Friedman: The following is a conversation with Peter Norvig. A Modern Approach", and educated and inspired a whole generation of researchers, including myself, to get into the field of Artificial Intelligence. This is the Artificial Intelligence podcast. Lex Fridman: Most researchers in the AI community, including myself, own all three editions, red green and blue, of the "Artificial intelligence, a modern approach", the field defining textbook. As many people are aware that you wrote with Stuart Russell, how is the book changed, and how have you changed in relation to it from the first edition to the second, to the third, and now fourth edition as you work on it? Peter Norvig: Yeah so it's been a lot of years, a lot of changes. One of the things changing from the first, to maybe the second, or third, was just the rise of computing power, right? So, I think in the First Edition we said: "here's predicate logic but that only goes so far because pretty soon you have millions of short little medical expressions and they can possibly fit in memory, so we're gonna use first-order logic that's more concise." And then we quickly realized: "Oh, predicate logic is pretty nice because there are really fast Sat solvers, and other things, and look there's only millions of expressions and that fits easily into memory, or maybe even billions fit into memory now.
Autumn is as good a season to learn natural language processing as any other, and why not do so with quality, free online courses? This is a collection of just such free, quality online NLP courses, from such esteemed institutions of learning as Stanford, Oxford, University of Washington, and UC Berkeley. There are also offerings from independent sources like Yandex Data School, and even a short practical course on spaCy by one of its creators and co-founder of the company which steers its development. So whether you are looking for theoretical or practical, or are a beginner or an advanced learner, the content included herein won't fail on living up to the promise of being 10 free top notch natural language processing courses. So dig in and learn NLP today.
Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. They are, therefore, often called the perfect black-box ML techniques. We can get highly accurate predictions from them after we train them with large datasets, but we have little hope of understanding the internal features and representations of the data that a model uses to classify a particular image into a category.