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) …
Six of the eight largest public cloud providers worldwide–Alibaba, Baidu, Google, IBM, Microsoft and Tencent–have been investing heavily in quantum computing research and development (R&D). AWS, by far the largest cloud provider, had been completely absent from the quantum computing discussion until this week. The holy grail for cloud providers is to find a hardware solution that accelerates machine learning and artificial intelligence by orders of magnitude. There are two ways to improve machine learning at scale: build specialized architectures using today's design tools or find a completely different path. Quantum computing is everyone's big bet for the completely different path.
I was recently messing around with the new TensorFlow.js Since I can only do things with JS, I was glad to hear about this becoming available. From my brief experimentation, I have found the API to be extremely easy to use, given one has some basic Machine Learning concepts under one's belt. I devised a simple experiment which I didn't particularly expect to be fruitful, but if I was able to get a functioning model it would be a proof of concept for handling actual datasets. As I suspected, the results were bad for predicting new examples, but I still think my efforts were productive enough to be worth sharing, and I definitely learned some things along the way.
AI is better than humans in a lot of things. What size is the cylinder that is left of the brown metal thing that is left of the big sphere? Any 6-year-old could answer this pretty easily, yet these kinds of questions are just out of the scope of traditional deep learning models. Deep learning models are pretty good at understanding relationships between inputs and outputs, but that's about as far as it goes. Whether it's supervised learning or reinforcement learning, the input and desired output are clearly defined and easy for the model to understand.
A new image recognition algorithm uses the way humans see things for inspiration. The context: When humans look at a new image of something, we identify what it is based on a collection of recognizable features. We might identify the species of a bird, for example, by the contour of its beak, the colors of its plume, and the shape of its feet. A neural network, however, simply looks for pixel patterns across the entire image without discriminating between the actual bird and its background. This makes the neural network more vulnerable to mistakes and makes it harder for humans to diagnose them.
Most of us want digital privacy, and most of us also want autocorrection that works, speech to text that is accurate, and smart systems that find all our selfies with Serena, or surface the most important emails we need right now. But are those two imperatives in direct opposition? According to some tech analysts and AI experts, they are. Especially those who are experiencing huge issues with iPhone's autocorrection capability in Apple's latest mobile operating system upgrade, iOS 13. "It's way worse on my iPhone," says veteran industry observer Robert Scoble, chief strategy officer at Infinite Retina. "And I've tried several things to fix it, including deleting all the settings and deleting all the history and trying to reboot everything ... I'm seeing a lot of bugs in the spellchecker where it's putting capitalization where it doesn't need to go, where it's switching words a lot more often than it used to. Apple's iOS 13's spellcheck was so bad Scoble ran a Twitter poll, asking his 400,000 followers whether they had similar issues. Twitter polls are hardly scientific, of course. But there's a broad range of people who are claiming that Apple's recent software release has been a big backward step in terms of autocorrection. "iOS 13 got significantly worse for me," says mobile entrepreneur Albert Renshaw, CEO at Apps4Life. "I've been learning French with Duolingo and have been typing in French every day for a little over a year in that app, but have never had an issue with it affecting my autocorrect.