“go on
Google and type ‘anything that’s not an elephant.’ What do you get? Tons of
pictures of elephants. But IBM Watson knows those subtle differences. That’s called machine intelligence ”.
AI , Machine learning and Deep
learning are core structure of all software development & invention now a
days . Staring from Google ‘s yearly event to Apple product launch event every
CEO emphasize their capability and
research on these fields and how their
devices are going to make peoples life
easy with smart devices having Machine learning capability .One next big thing is going to be the rise of the
AI-powered digital assistants embedded in personal devices. Rapid improvements
in key underlying technologies -- voice recognition and natural language
processing – are making these “smart” assistants more capable of letting us use
our various devices just by talking to them. The promise of these assistants,
ranging from Apple's Siri and Google's Assistant to the newcomer, Samsung's Bixby is that someday we will each have our own personal, always-listening
AI which can respond to any wish and command, like Tony Stark’s Jarvis in the
movie Iron Man. It’s a future vision of computing coming into reality.
Apple Siri:
For a long period Apple siri
was the only AI capable personal assistant available to common people & no doubt that that was
a huge point of interest of iphone, but apple didn’t invent Siri
The history :
Siri was not entirely developed
by Apple, but instead sprung out of a huge AI initiative started in 2003 that
was funded by the U.S. Defense Department's Defense Advanced Research Projects
Agency (DARPA) and run by SRI International, a research entity affiliated with
Stanford University until the 1970s. The intent was to come up with something
that could help military personnel with office work and making decisions. The
result of this project was called Cognitive Assistant that Learns and Organizes
(CALO), an artificially intelligent assistant that could learn from its users
and the vast amounts of data available to it. Not only could it be used to do
things like schedule meetings and organize all the necessary documents for
meeting participants, but it could even make decisions. For instance, if
someone backed out of a meeting, CALO could assess whether they were vital
enough to warrant cancelling and rescheduling. Another SRI International
project called Vanguard created a prototype assistant for a smartphone, but one
with nowhere near CALO's capabilities. Several SRI employees created a startup
to marry the ideas from both projects. Alumni from companies such as NASA,
Apple and Google also worked for the new company, and their work led to Siri
Assistant for iPhone 3GS . Initially Siri was a 3rd party app in apple
store and later apple bough the company &
Siri became integral part of apple’s AI journey.
Siri is kind of a virtual
assistant who listens to your requests and performs actions accordingly.
Rather than doing most of its work on your phone's processor, Siri communicates
with server in cloud to interpret your requests and retrieve the
information you need. Since most of Siri's brain exists on remote servers
accessed by many people, the more people using it, the more it's supposed to
learn from everyone else, too. Unlike a search engine that returns long
raw lists of links related to keywords you select, Siri is designed to
interpret your request, hone in on what it thinks you want, and perform actions
to give you a more limited but more correct amount of data or services in
return. Siri understands context. And she still goes to servers in the cloud to
retrieve answers via third party services, albeit a smaller set of them than
before. Anything related to mathematical computation or scientific fact is
likely to come from Wolfram|Alpha. Information related to businesses like
restaurants or retail stores is likely to come from Zomato. Weather info comes
from Apple's built-in Weather app, powered by Yahoo. And movie time listings,
reviews and other movie information would likely come from IMDB. Any request
Siri doesn't understand will cause her to ask you for more information to
clarify, or to ask you if you want her to look it up on the Web. She uses your
phone's GPS to retrieve and return information relevant to your current
location.
Google Assistant :
Google’s motive of “Mobile First
to AI First” was quite evident from how the ‘hardware’ event (Google yearly event , 2nd October held in 2017) was actually
all about ‘software’ called Google Assistant. The Assistant is voice-enabled
artificial intelligence (AI) software that bundles machine learning, the Google
Knowledge Graph, and voice and image recognition natural language processing
(NLP) to build a “personal Google for each and every user”.
Google’s AI research group,
DeepMind wants to turn AI into a
personal helpdesk agent for every one by utilizing over 70 billion facts about
people, places and things which are fed into its Knowledge Graph, the database
is, of course, powered by years of search queries made by people all over the
world . Company sees the Assistant as a chatbot connected to TVs, speakers,
etc., capable of holding a “two-way conversation”. Google is offering open SDKs
for developers to build conversational AI experiences, like ordering groceries
or playing a game.
Even if your friend texts you to
meet up at a new restaurant, you can just say ‘navigate there’, the assistant
is smart enough to figure out the meaning . This is truly amazing and only possible
through research progress in NLP , machine learning and Deep Learning field.
How it works:
Google opened up the Google Assistant platform for developers in December 2016 and currently, the platform supports
building out Conversation Actions for the Google Home device. It is widely
expected that the same Actions will eventually be available across Google’s
other devices and applications.
api.ai
is a developer of human–computer interaction technologies based on natural
language conversations. It provides conversational user experience platform
enabling brand-unique, natural language interactions for devices, applications,
and services . It is acquired by Google in September 2016, it provides tools to developers
building apps (“Actions”) for the Google Assistant virtual assistant.
Conversation workflow management:
Agents: Agents are best described as NLU (Natural Language Understanding)
modules. These can be included in your app, product, or service and transforms
natural user requests into actionable data.
This transformation occurs when a user input matches one of the intents
inside your agent. Intents are the predefined or
developer-defined components of agents that process a user’s request.
Agents can also be designed to manage a conversation flow in a specific
way. This can be done with the help of contexts, intent priorities, slot filling, responsibilities,
and fulfilment via webhook (For more
reference: https://dialogflow.com/docs/agents)
Machine
Learning: allows an agent to
understand user inputs in natural language and convert them into structured
data, extracting relevant parameters The agent learns from the data user
provide in it as well as from the language models developed by API.AI. Based on
this data, it builds a model (algorithm) for making decisions on which intent
should be triggered by a user input and what data needs to be extracted. The
model adjusts dynamically according to the changes made in agent and in the
API.AI platform. To make sure that the model is improving, the agent needs to
constantly be trained on real conversation logs.
Intent : Mapping
between what a user says and what action should be taken by software.
Entity : Entities are
powerful tools used for extracting parameter values from natural language inputs. Any important data you
want to get from a user's request, will have a corresponding entity
The Future:
The IT giants -- such as
Google, Amazon, Apple, and Microsoft -- have all invested heavily in voice
technology. Analyst Gartner estimated by
end of 2018 , 30% of our interactions with technology will be
through 'conversations' with smart machines.
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