Natural language processing seems very popular today but is it actually any good and useful?
When was your last encounter with a good product taking advantage of this technology?
It gives suggestion on reply message. I use it a lot.
NLP can be use in informational retrieval such as organizing things into cluster of topics via LDA.
I've met someone with the mindset that AI is a fad and that what have it done so far? I disagree, it may be over hype but I see that person using Siri. I use Hello Google and I consider that to be NLP not the traditional statistic NLP but the deep learning NLP kind.
Also those website translator such as google translator and old school babel translate helped me a lot when I was trying to search for things that were in Japanese. There were a few famous Korean and Chinese novels that are machine translated and it's decent enough to read if you're really into that novel and willing to ignore the quality.
Interesting to hear that - I actually find Gmail's autoreply feature to be extremely annoying. When it's not suggesting outright inappropriate responses (once had it suggest "Ok! see you there!" on a thread about a funeral a colleague was attending), they just seem trite and useless.
I also don't like this feature. I've used it only perhaps a handful of times since it was introduced, but every other time it has actually cost me the time it took to read and mentally reject the suggestion.
I really need to poke around the settings and see if it can be disabled...
They recently updated it and it is much more relevant in my case. (More business than personal)
Keep in mind features like this are built around the needs of the people who make it. Outlook is sort of a snapshot of the needs of a Microsoft employee circa 1996. GMail is a little better, but is still of reflection of the company.
This can be used to great effect in companies. Essentially, we deploy a search engine for tribal knowledge in a company. Overall, we see significant improvements company wide. Reduced turnover and in turn improved knowledge retention
So have you already started analyzing word patterns and finding different accounts people have on the same service and also correlating them between multiple services?
People do leave much more information about themselves on the internet than they realize.
It didn't work for me. Took a long time then returned the same sentence. Next attempt on a different sentence seemed to just freeze up with no progress bar.
Almost any google search you do or any siri question you ask takes advantage of the NLP research that has been produced in academia over the last decades.
Theoretical research of these subjects it's not like an open-source project used in industry: "who's using this tool to do x, y and z?". The output of NLP research is the combination of thousands of various papers that, all combined, makes something like a siri search possible.
Siri isn't very strong, it can answer (very) basic questions and resorts to web searches when it can't answer. I find it occasionally mishearing me or completely parsing my question incorrectly. I recall a particularly bad one: "set a timer for 3 minutes and 15 seconds" and somehow it interpreted it as an arithmetic question "the answer is 18."
Alexa and particularly Google Assistant are much better, the latter retaining context between questions e.g: "who is the 44th president of the US" followed by "who is his wife?" is a neat trick.
No way. Hello Google is miles better. Google got the upper hand on AI currently. You can see it with their camera AI technology Pixel 3 is top dog in camera even though it have one len.
But Apple just took 2 famous Google AI researcher. So it seems that Apple is trying to catch up.
Oh really? Okay, I just read how two Google's top AI researchers went over to Apple so I just assume their problem was lack of AI expertise. But I can see your point since Apple isn't a software company that hoard massive amount of data like Google is doing.
We use NLP / sentiment analysis at Last10K.com to highlight positive and negative remarks inside lengthy annual and quarterly reports of publicly-traded companies in the US. Here's an example of a report filed yesterday:
Translation is a NLP problem... Isn't it? I've used Google translate to read non-english websites and found it quite useful. Is text to speech a NLP problem? Today's TTS products are actually listenable these days.
A lot of students (including me) use Google Translate, and less frequently deepl.com (deepl is better, but less known), to do foreign language assignments. Oftentimes, one can write the initial sentence in English, run it through deepl, and make a few fix-ups before turning in the assignment.
* Swype keyboard which uses your spelling corrections as a way to better figure out how you use a swype keyboard
* https://flexibits.com/fantastical and many other calendar apps do NLP stuff to pull out meeting info. Microsoft, Google and Facebook do this kind of stuff pretty well (Disclosure: I worked on such features at Microsoft)
Happy to give more examples if people are interested in working in this space.
Past NLP researcher chiming in here. Google Home and Alexa use pretty sophisticated speech to text software to decouple your voice from background noise, and then translate our speech to text, and then from text to program triggers that ie turn off your lights. It may sound trivial but the Home work so well it really is kind of magical.
The Chatbot stuff is more questionable, especially the ones based on deep neural nets ( again I've published in this domain so I looked at it pretty closely ).
Twitter/FB/Douyin/Google all use NLP to tune their feed or give you search results.
I work as a PM for Lang.ai. We develop unsupervised NLP technology that helps companies understand (by clustering topics and tags) thousands of texts from virtually any language without the need of a training phase. This is currently helping -from call centers to chatbots- to discover the new things that people is talking about and learning about it automatically.
As @anthony_doan mentioned, this is something that is actively being used by consumers and companies.
I'm the founder at Akia (https://akia.ai). We're using NLP to help hotels with the number of inbound they receive from guests who are coming on or staying on property.
Though I'm not personally a regular user, from what I've observed, the AI is able to very, very efficiently resolve a significant number of requests because a lot of the messaging is the same: "what's the wifi?" "bring me towels" "can i get my car?" etc.
The application of NLP for a niche industry has felt particularly appropriate for a small startup where in contrast, doing generic NLP really feels like a boil the ocean type strategy that makes more sense to those who have the resources (google/apple).
Custom Voice Commands is an iPhone and Android smartphone app that uses NLP to allow you to tag photos, videos, notes and web pages with questions or phrases. For example, you can tag a bunch of photos with the phrase "show me my favorite lunch photos" and every time you speak that phrase you'll see a slideshow of those photos.
Photo Recall is an Amazon Alexa and Google Assistant app that lets you retrieve Unsplash photos by voice plus photo slideshows created with Custom Voice Commands can be viewed from your Amazon Echo Show or Google Home Hub (or via the Google Assistant app).
All voice recognition and text prediction is a result of nlp. Every time you search on google, or say “hey Siri/Alexa”. Every time you call an automated service and it asks you why you’re calling and connects you to the right department. When you type a text message and it completes a word and predicts the next one. It affects what ads you get shown. It lets companies automatically block negative reviews with sentiment analysis (not always used for good). Or to quickly find where supplies are needed in emergencies by parsing twitter feeds in all languages. Etc.
If you own a smartphone, you interact with nlp technologies dozens of times per day. So yeah, it’s good and useful.
Text classification is widely used in almost any major website, for example for spam detection, putting things into categories, detecting inappropriate content, ...
I suppose spell checking (and then grammar too) has been the most ubiquitous use of NLP to date. Unless I'm missing something even more pervasive (software).
Another shameless plug. My company's product extracts conversations from Slack to automatically generate polished FAQs for the purpose of answering repeat questions. We also use this to give teams visibility into how much time is being spent responding and what types of questions they're getting.
We are building Zoho Writer, a full featured word processor for the browser (https://www.zoho.com/writer/zia.html), and we use NLP (Deep Learning) for suggesting grammar corrections & writing tips to users.
Translation services, Unbabel [0] is the only one I have experience with (they went through YC a few years back). And I noticed a couple of days ago they're hiring developers in Lisbon.
They are a great service to restaurants and use NLP to automate a lot of service integration such as between the embedded POS system and delivery companies.
Interested as well, but I keep feeling nlp is relatively less stable and mature compared to vision, thus maybe only used in niche ways rather than full fledged products, other than obvious ones like echo and siri of course
facebook, they are scanning your chats trying to figure out what is your political party, social status and needs so they can sell you more stuff, more expensive and more often.
Most search engine products, be it job search, Airbnb, e-commerce, Google, etc are going to have a lot of opportunities and use for NLP to understand queries and content.
There are a decent number of companies that use NLP voice recognition when you call their support lines. So when you call, instead of pressing a number on your key pad to move through the directory you can speak it.
In that same vein, I've seen companies that have "chat support" use NLP bots to get a feel for the type of question a person is looking for before handing off to a human support person.
I've also seen AI personal assistants that handle meeting schedules etc like https://x.ai/
It gives suggestion on reply message. I use it a lot.
NLP can be use in informational retrieval such as organizing things into cluster of topics via LDA.
I've met someone with the mindset that AI is a fad and that what have it done so far? I disagree, it may be over hype but I see that person using Siri. I use Hello Google and I consider that to be NLP not the traditional statistic NLP but the deep learning NLP kind.
Also those website translator such as google translator and old school babel translate helped me a lot when I was trying to search for things that were in Japanese. There were a few famous Korean and Chinese novels that are machine translated and it's decent enough to read if you're really into that novel and willing to ignore the quality.