One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Amazon's Black Friday Week event has kicked off with a great range of discounts on everything from Amazon devices and Nintendo Switches to smartwatches and speakers, placing the best tech at your fingertips for less. Although Black Friday itself isn't officially until November 27, there are plenty of smart home gadget savings to take advantage of already, with the Echo Dot (3rd generation) now £18.99 and home security from Eufy reduced by 25 per cent. If you've been curious about smart lights, but don't know where to start, it's worth taking note of the deals on Philips Hue Starter Kits this Black Friday week, with up to 40 per cent off on Amazon. However, we recommend signing up to Amazon's Prime subscription service for a free trial to take full advantage of their Black Friday Week as you'll receive free next-day delivery and access to some exclusive offers. The Hue Starter Kits on sale all include three bulbs (screw, cap or spot) and the Philips Hue Bridge, which is an internet-connected central hub.
A year after it started testing the feature, Amazon is finally bringing workout tracking to Echo Buds. The company told CNBC it's rolling out the function over the next couple of days. According to Amazon, Echo Buds can monitor the duration of a workout, estimate the number of calories you burn, act as a step counter and measure how fast or far you walk or run. You can start, pause and end workouts using Alexa commands, and ask for details about your pace. The Echo Buds workout tracker may not be as robust as dedicated fitness devices, including Amazon's own Halo.
Ever since IBM unveiled Cloud Pak for Data as a cloud-native integrated set of analytics and AI platform, we've been wondering when IBM would take the next step and announce a full-blown managed cloud service. It's now starting to happen as IBM is rolling out IBM Cloud Pak for Data as a Service. Roll back the tape to last spring when we reviewed IBM Cloud Satellite; we noted that IBM's primary cloud message has been about multi-cloud, or at least cloud-agnostic. Propelled by Red Hat OpenShift, IBM carved out such a strategy for this managed Kubernetes environment where you could deploy open source software yourself on the hardware or public cloud of your choice or choose IBM to run a managed OpenShift service for you in the IBM Cloud. That is now getting repeated with Cloud Pak for Data.
Today we will talk about how to create a chatbot with Python. Natural language processing (NLP) is one of the most promising fields of artificial intelligence that uses natural languages to enable human interactions with machines. There are two main approaches to NLP: – rule-based methods, – statistical methods, i.e., methods related to machine learning. There are several exciting Python libraries for NLP, such as Natural Language Toolkit (NLTK), spaCy, TextBlob, etc. A chatbot is a computer software able to interact with humans using a natural language. They usually rely on machine learning, especially on NLP.
Google Assistant already works with Hue and other smart lights, but functionality has been limited to turning them on and off, using them with alarms and a few other features. Now, you can schedule lights and other electric devices to turn on and off at specific and even general times, as Android Police and Reddit users have noted. The feature works via Google's "Scheduled Actions" feature. That allows you to say "Hey Google, turn on the lights at 7 AM," for example. You can set times for the current day or any other day over the next week by saying "Hey Google, turn on my coffee maker at 8 AM tomorrow," or "run my sprinkler in a week at 5 PM," for example.
Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. We are moving towards a world enhanced by these recent upcoming technologies. It's the most exciting time to be in this career field! The global Artificial Intelligence market is expected to grow to $400 billion by the year 2025. From Startups to big organizations, all want to join the AI and ML bandwagon to acquire cutting edge technology.
It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
You might have an easier time deciding on a Google Assistant smart display this Black Friday. Lenovo's Smart Clock is on sale now at Best Buy for $35, an all-time low and less than half its original $80 asking price. You can get it for the same price at Walmart. And if you don't need a touchscreen, the Smart Clock Essential is on sale for $25 (half its usual price) at Best Buy and Walmart. Both smart screens are very focused, and that's mostly a good thing.
Natural language processing (NLP) has taken great strides recently--but how much does AI understand of what it reads? Less than we thought, according to researchers at USC's Department of Computer Science. In a recent paper Assistant Professor Xiang Ren and Ph.D. student Yuchen Lin found that despite advances, AI still doesn't have the common sense needed to generate plausible sentences. "Current machine text-generation models can write an article that may be convincing to many humans, but they're basically mimicking what they have seen in the training phase," said Lin. "Our goal in this paper is to study the problem of whether current state-of-the-art text-generation models can write sentences to describe natural scenarios in our everyday lives." Specifically, Ren and Lin tested the models' ability to reason and showed there is a large gap between current text generation models and human performance.
As the pandemic reaches new heights, with nearly 12 million cases and 260,000 deaths recorded in the U.S. to date, a glimmer of hope is on the horizon. Moderna and pharmaceutical giant Pfizer, which are developing vaccines to fight the virus, have released preliminary data suggesting their vaccines are around 95% effective. Manufacturing and distribution is expected to ramp up as soon as the companies seek and receive approval from the U.S. Food and Drug Administration. Representatives from Moderna and Pfizer say the first doses could be available as early as December. But even if the majority of Americans agree to vaccination, the pandemic won't come to a sudden end.