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After years in limited beta with a long waitlist, OpenAI just announced that its GPT-3 API is now generally available to developers. So far, some upstarts with early access have been using GPT-3 to create and sell unique "personalities" for your NFT. But now that the API is truly open to all, the reality is that anyone can generate a GPT-3 powered "personality" by feeding it a few example texts. Whether OpenAI allows your particular use case to remain live is another story. Last summer, the Chronicle published a heartbreaking tale about a man who used a GPT-3 powered service to bring his dead fiancé back to life based on their texts.

This is a port from Python to Javascript of the neural network implementation to describe the inner workings of backpropagation. You can run it in a browser console. Just copy all the code and paste it in the console. Otherwise it can run in node with the command $ node app/neural-network-test.js

Great news for those who like attending conferences -- they hold annual AI World Conference and Expo https://aitrends.com/. His vast experience in AI and applying machine learning to weather forecasting and defence industries allow him to write useful articles and books on the topic http://machinelearningmastery.com/blog/. Future is closer than you think, Algorithmia will prove it http://blog.algorithmia.com/. By the way, it is an application of AI itself. So check out what a machine's prepared for you to read https://aitopics.org/search.

The digitization of business is taking hold. From machine learning, artificial intelligence (AI) and the Internet of Things (IoT) to blockchain, cloud and networks, digital technologies are opening the door to new, more efficient and intelligent ways of operating. And nowhere is this more evident than in procurement. Machine learning and Artificial Intelligence have made their way into procurement applications and are fueling smarter, faster decisions. A new breed of cognitive technologies promises to push things even further.

In the very popular show Silicon Valley, one of my favorite characters Jian Yang creates a Deep learning application which accurately predicts if a food item is a hot dog or not, pretty funny stuff, So I thought of using google's open source TensorFlow library to create my very own Hot dog classification program. So without further chit chat let's start classifying some hotdogs. For this project I've used: First we have to set up docker, since I'm using debian it was pretty straight forward. Let's launch that docker instance shall we: Since we're classifying if an article is a hotdog or not, we're going to need 1 folder and 2 subfolders, the main folder is going to be called images and within that folder we will create 2 subfolders named hotdogs and random. Now we need around 100 images of hotdogs in the hotdogs folder and 100 random images of things that aren't hotdogs, I used Fatkun image downloader from chrome extension store for this purpose, but a point to be noted is that TensorFlow only handles JPEG images and using PNG's can run you into a lot of trouble, after you're done populating the directories with images of hotdogs and random things in their respective folders we have to download and retrain the Inception V3 net by google, since we don't have the required time nor resources to train our own CNN.

My dad got me my first computer for Christmas when I was 11 years old. It was a Texas Instruments 99-4a. He must have gotten it second hand because it had no box or any form of storage like a tape drive. I had been obsessed with the movie War Games, so my first program on the TI was designed to replicate the computer in the movie which uttered the famous line "shall we play a game." My program gave static responses to a huge variety of programmed questions, and mostly followed the script from the movie.

With the recent Facebook & Neuralink announcements, brain-computer interfaces are the big news this month. I wanted to explain this concept to my parents and kids in a quick summary, and then decided to write this post up here for comments. As I see it, there are broadly 2 main drivers for this. A few years ago, watching the movie Avatar with my son was a fascinating experience for its many novel concepts. Like genetically engineering a Na'vi body with the mind of a remotely located human to ultimately help colonize Pandora.

There's a real buzz in the industry around artificial intelligence, machine learning, automated network monitoring and user and entity behaviour analytics (UEBA) at the moment. Artificial intelligence (AI) is not a new concept, but it appears that the mere mention of it conjures up an immediate element of fear amongst many. The reality is that AI and skilled individuals can be combined to create the most important aspect in an organisation's defence in the war on cybercrime and here's why. More on User and Entity Behaviour Analytics (UEBA) UEBA uses advanced analytics to baseline network activity to identify malicious behaviour from external sources, as well as insider threats. It does this by automatically learning what is normal based on typical activity and then, using proprietary algorithms, assigns risk scores to potential malicious behaviour.

The study involved a self-learning artificial intelligence system, GloVe. It has the ability to read and interpret online text. "We replicate these using a widely used, purely statistical machine-learning model--namely, the GloVe word embedding--trained on a corpus of text from the Web," reads the study. But when researchers offered GloVe a list of white and black-sounding names, the robot went absolutely racist! The intelligent system identified names that are common among white people as "pleasant", while African-American names were categorized as "unpleasant" words. "Our results indicate that language itself contains recoverable and accurate imprints of our historic biases," the researchers told RNS.