Its very easy to misread and overestimate achievements in artificial intelligence.
But mastering human language requires much more than replicating human-like voices or producing well-formed sentences.
It requires commonsense, understanding of context and creativity, none of which current AI trends possess.
It’s free, every week, in your inbox.
These advances have enabled many services to provide real-time transcription services to their users.
There are plenty of uses for AI-powered speech-to-text.
YouTube uses deep learning to provide automated close captioning.
Speech synthesis
The flip-side of the speech-to-text is speech synthesis.
But it is nonetheless an integral part of many applications that interacts with humans in their own language.
Like speech-to-text, speech synthesis has existed for quite a long time.
I remember seeing computerized speech synthesis for the first time at a laboratory in the 90s.
The blind also using the technology to read text they cant see.
Now, with the help of neural networks, synthesizing human voice has become less cumbersome.
If it doesnt, the generator corrects its sample and re-runs it through the classifier.
The two networks repeat the process until they are able to generate samples that sound natural.
There are several websites that enable you to synthesize your own voice using neural networks.
There are many good uses for this technology.
AI speech synthesis also has its evil uses.
They can then use the meaning behind those words to perform a certain action.
But NLP is a very broad field and can involve many different skills.
At its simplest form, NLP will help computers perform commands given to them through text commands.
Smart speakers and smartphone AI assistants use NLP to process users commands.
An interesting use of NLP is Gmails Smart Reply feature.
Google examines the content of an email and presents suggestions for answers.
Current NLP is really only good at understanding sentences that have very clear meanings.
This field too has benefited from advances in AI, particularly in deep learning.
The basic levels of NLG have some very interesting uses.
For instance, NLG canturn charts and spreadsheets into textual descriptions.
AI assistants such as Siri and Alexa also use NLG to generate responses to queries.
Gmails autocomplete feature uses NLG in a very interesting way.
The suggestion takes into consideration the general topic of your letter, which means theres NLP involved too.
Some publications are usingAI to write basic news reports.
Another interesting case study is Googles Duplex.
Googles AI assistant puts both the capabilities and the limits of artificial intelligences grasp of human language.
These are domains where the problem space is limited and predictable.
But Duplex doesnt understand the context of its conversations.
It is merely converting human language to computer commands and computer output into human language.
It wont be able to carry out meaningful conversations about abstract topics, which can take unpredictable directions.
To be true, Google Translate has improved immensely.
But AI-powered translation has its own limits, which I also experience on a regular basis.
Neural networks translate different languages using a mechanical, statistical process.
In other words, theyre mapping based on mathematical values, not translating the meaning of the words.
They do research into the background of the topic before making decisions on words.
To be clear, AI translation has plenty of very practical uses.
I use it frequently to speed my work when translating from French to English.
Its almost perfect when translating simple, factual sentences.
But dont expect AI to replace professional translators any time soon.
Deep learning doesnt understand human language.
But thats not anytime soon.
Youll be better positioned to understand its capabilities and limits.
Looks can be deceiving.
