The knowledge gap is a bit more than a gap We have no doubt moved closer to being able to produce an artificial general intelligence but as it stands we are still far off. However, the fact still remains that we are designing and producing narrow AIs. Of course, the improvements to hardware over the years also impact significantly on what we can get computers to do. Mainly the advancements have come in the methods and programs used to train neural networks the acceptance that networks with more hidden nodes are more accurate and the emergence of programming languages and libraries conducive to handling large data loads and statistical processing. Neural network built in R using nnet and visualized using: Training the network is done by modifying the weightings. The output layer also has its own biases and weightings. The output from the activation function are then passed on, in this case to the output layer. The hidden nodes then take the sum of their inputs and pass it to an activation function (just some form of mathematical operation). Each hidden node also has a bias with an associated weighting. Each input variable is connected to every node in the hidden layer and each connection has an associated weighting (how much that input affects that node). So then what’s changed?Įxample of a neural network showing input nodes for multiple input variables. But even these AI models have existed for a while in some capacity, since the 1960s in fact (MADALINE was created and affectionately named at Stanford University to remove echoes over telephone lines). Training the network basically boils down to tuning the mathematical operations such that the output at the end conforms to the output you would expect as determined by your training data. As the values are passed between nodes, some mathematical operations are conducted on them and the value at the end is the output. The recent spotlight on machine learning and AI though is, as I’m sure you’ve heard, solidly fixed on ‘neural networks’, termed as such because they were vaguely inspired by the biological system of the same name (bonus points if you can guess which one).Įssentially an artificial neural network (ANN) is a set of input, hidden and output nodes wherein numeric values are passed from the input nodes through the hidden nodes to the output nodes. It corrects your texts, sorts your spam and recognizes your voice (‘Hey Siri’). Machine learning has been around for a while. Machine learning, being a paradigm in AI design that uses statistical techniques to allow an AI to iteratively improve at a task it is being taught. Well as it happens, these AIs exist thanks to, among other things, significant advancements in the field of machine learning. The advent of “Neural Nets” is upon us… and has been for some time So, if AI is already making AI, then where is Skynet and the T-100s? In fact, Google’s AutoML AI was responsible for the creation of a daughter AI, NASNet, which outperforms anything we have made ourselves in the field of computer vision and object recognition.Elon Musk’s OpenAI has bested a professional human player in DOTA, a video game which is a far departure from the usual test benches for AI frameworks, like chess.Google has produced AlphaGo an AI capable of beating the world’s top Go players in a game with so many board states (2.08 x 10 170) that it requires intuition and experience to be good rather than simply raw processing power. “But how can that possibly be?” I hear you ask, when we’ve already seen the creation of AIs capable of outperforming humans in tasks we once thought too computationally time-intensive for them to handle: So, with all the hype around AI recently, have we reached the tipping point? The idea that the creation of artificial super-intelligence will lead to an unstoppable cascade of technological advancements because no doubt a computer intelligence smarter than us can in turn make another computer intelligence smarter than it and so on. Here we have (under slightly more violent circumstances than would be ideal) what is generally thought of as the technological singularity.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |