Name Lernout & Hauspie fighting for customers. Much later

Name – Mehul Singhal

Registration Number – 16BCE0823

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Software lab – 1

 

Artificial
intelligent Home assistance: –

 

Abstract:

A virtual assistant is a software agent that
can perform tasks or services for an individual. Sometimes the term
“chatbot” is used to refer to virtual assistants generally or
specifically those accessed by online chat (or in some cases online chat
programs that are for entertainment and not useful purposes).

 

As of 2017, the capabilities and usage of
virtual assistants is expanding rapidly, with new products entering the market.
An online poll in May 2017 found the most widely used in the US were Apple’s
Siri (34%), Google Assistant (19%), Amazon Alexa (6%), and Microsoft Cortana
(4%). Apple and Google have large installed bases of users on smartphones.
Microsoft has a large installed base of Windows-based personal computers,
smartphones and smart speakers. Alexa has a large install base for smart
speakers.

 

History:

 

The first tool enabled to perform digital
speech recognition was the IBM Shoebox, presented to the general public during
the 1962 Seattle World’s Fair after its initial market launch in 1961. This
early computer, developed almost 20 years before the introduction of the first
IBM Personal Computer in 1981, was able to recognize 16 spoken words and the
digits 0 to 9. The next milestone in the development of voice recognition
technology was achieved in the 1970s at the Carnegie Mellon University in
Pittsburgh, Pennsylvania with substantial support of the United States Department
of Defense and its DARPA agency. Their tool “Harpy” mastered about
1000 words, the vocabulary of a three-year-old. About ten years later the same
group of scientists developed a system that could analyze not only individual
words but entire word sequences enabled by a Hidden Markov Model.  Thus, the earliest virtual assistants, which
applied speech recognition software were automated attendant and medical
digital dictation software. In the 1990s digital speech recognition technology
became a feature of the personal computer with Microsoft, IBM, Philips and
Lernout & Hauspie fighting for customers. Much later the market launch of
the first smartphone IBM Simon in 1994 laid the foundation for smart virtual
assistants as we know them today. The first modern digital virtual assistant
installed on a smartphone was Siri, which was introduced as a feature of the
iPhone 4S on October 4, 2011.  Apple Inc.
developed Siri following the 2010 acquisition of Siri Inc., a spin-off of SRI
International, which is a research institute financed by DARPA and the United
States Department of Defense.

 

About
Virtual assistants: –

 

A virtual assistant (VA) is a person who
provides support services to other businesses from a remote location. The term
originated in the 1990s as the ability to work virtually due to technology
improvements, such as high speed Internet, document sharing, and and other
advancements, made working remotely a reality.

 

Virtual assistants are especially in demand by
solo-preneurs and online businesses that need help, but don’t want to bring on
staff in their location.

 

However, many small and mid-size businesses use
virtual support, especially in specific tasks such as social media management.

 

Theoretically, a VA can do anything any other
support staff does, except bring the coffee. (Although when home-delivery
coffee is created, the VA will be able to do that too!). However, virtual
support duties are not limited to clerical work. Many VAs provide marketing,
web design and other services. A basic list of services include:

 

Calendar management

Email management

Social media management

Appointment setting

Marketing and PR

Research

Writing

Graphic creation

Website management

Bookkeeping

Customer support

Project management

Travel booking

Customer service

 

Some virtual assistants specialize in a
specific skill set. For example, a marketing or PR virtual assistant only does
marketing or PR work. Other virtual assistants do a variety of duties, but
within a specific industry.

 

Artificial intelligence (AI, also machine
intelligence, MI) is intelligence displayed by machines, in contrast with the
natural intelligence (NI) displayed by humans and other animals. In computer
science AI research is defined as the study of “intelligent agents”:
any device that perceives its environment and takes actions that maximize its
chance of success at some goal. Colloquially, the term “artificial
intelligence” is applied when a machine mimics “cognitive”
functions that humans associate with other human minds, such as “learning”
and “problem solving”. See glossary of artificial intelligence.

 

The scope of AI is disputed: as machines become
increasingly capable, tasks considered as requiring “intelligence”
are often removed from the definition, a phenomenon known as the AI effect,
leading to the quip “AI is whatever hasn’t been done yet.” For
instance, optical character recognition is frequently excluded from
“artificial intelligence”, having become a routine technology.
Capabilities generally classified as AI as of 2017 include successfully
understanding human speech, competing at a high level in strategic game systems
(such as chess and Go), autonomous cars, intelligent routing in content
delivery networks, military simulations, and interpreting complex data,
including images and videos.

 

Artificial intelligence was founded as an
academic discipline in 1956, and in the years since has experienced several
waves of optimism, followed by disappointment and the loss of funding (known as
an “AI winter”), followed by new approaches, success and renewed
funding. For most of its history, AI research has been divided into subfields
that often fail to communicate with each other. These sub-fields are based on
technical considerations, such as particular goals (e.g. “robotics”
or “machine learning”), the use of particular tools
(“logic” or “neural networks”), or deep philosophical
differences. Subfields have also been based on social factors (particular
institutions or the work of particular researchers).

 

The traditional problems (or goals) of AI
research include reasoning, knowledge, planning, learning, natural language
processing, perception and the ability to move and manipulate objects. General
intelligence is among the field’s long-term goals. Approaches include
statistical methods, computational intelligence, and traditional symbolic AI.
Many tools are used in AI, including versions of search and mathematical
optimization, neural networks and methods based on statistics, probability and
economics. The AI field draws upon computer science, mathematics, psychology,
linguistics, philosophy, neuroscience, artificial psychology and many others.

 

The field was founded on the claim that human
intelligence “can be so precisely described that a machine can be made to
simulate it”. This raises philosophical arguments about the nature of the
mind and the ethics of creating artificial beings endowed with human-like
intelligence, issues which have been explored by myth, fiction and philosophy
since antiquity. Some people also consider AI a danger to humanity if it
progresses unabatedly. Others believe that AI, unlike previous technological
revolutions, will create a risk of mass unemployment.

 

In the twenty-first century, AI techniques have
experienced a resurgence following concurrent advances in computer power, large
amounts of data, and theoretical understanding; and AI techniques have become
an essential part of the technology industry, helping to solve many challenging
problems in computer science.

 

Existing
Work: –

APPLE
SIRI

Bio: A voice-driven
assistant that talks back to you–invoked by long-pressing the iPhone or iPad
home button–and proactively recommends actions to take. Recently took up
residence on Apple TV and Apple Watch.

 

Notable skills: Easy
to access on Apple devices. Understands natural human language. Knowledgeable
about news, weather, sports, movies, directions, and local businesses.
Well-versed in what to watch on TV. Knows how to control some smart home
appliances.

Character flaws: Doesn’t
know how to communicate with most other apps and services. Not always as fast
as some assistants.

Level of humanity: Can’t
hold an extended conversation, but cracks wise when given the chance. Female
voice doesn’t sound overly robotic.

Outlook: Siri paved
the way for modern speech-based assistants, but hasn’t gotten significantly
smarter over the past few years. The lack of an open API means you can’t open a
song in Spotify, add a task to Wunderlist, or post a message in Slack, even as
tie-ins with other apps become table stakes among other virtual assistants.
Apple must figure out these types of integrations for Siri to stay relevant;
maybe we’ll hear news about them at next month’s WWDC keynote.

 

 

GOOGLE VOICE SEARCH/GOOGLE NOW

Bio: Voice
assistant powered by the world’s largest search engine. Also digs through your
email and search history to help you out. Lives on Android devices, iOS, and
Chrome.

 

Notable skills: Fast.
Uncommonly accurate with directions. Eerily adept at mining your personal data
for flights, packages, reservations, and other useful info. Has some capacity to speak with
third-party apps for
certain tasks, including notes, messages, and music playback.

Character flaws: Attempts
at proactivity can sometimes be a nuisance (e.g., sports scores for teams you
don’t care about, directions home from familiar places). No hands in the smart
home business. Third-party app integrations seem to have stalled.

Level of humanity: None.
Averse to conversation and doesn’t even have a name, aside from “Google.”

Outlook: Google’s
vast troves of personal data and search engine knowledge should in theory allow
it to dominate the AI business, yet Google hasn’t quite figured out how to turn
those advantages into an assistant that truly gets you. For now, Google Now and
voice search are capable rivals to Siri, but haven’t reached the next level.

AMAZON ALEXA

Bio: Voice-activated
assistant that lives on Amazon audio gear (Echo, Echo Dot, Tap) and Fire TV
boxes and is making its way to other connected devices such as alarm clocks and
pet feeders.

 

Notable skills: Streams
music and reads news from multiple sources. Provides weather, traffic, and
other info, and controls a growing number of smart home devices. Allows voice
purchases for Amazon Prime items and even lets you order a pizza. Open API lets
any app or service tie into it.

Character flaws: Housebound
with no smartphone integration. May make you wonder if you’re nothing but a
receptacle for Amazon goods and services.

Level of humanity: Employs
a touch of banter with tricky questions, but is quick to guide you back to
business. (“Alexa, what should I do with my life?” “You should write that
novel. Amazon Kindle Self-Publishing will help you when you’re done.”)

Outlook: Apple and
Google should be terrified of Alexa, which is quickly gaining developer
momentum and is now leaping onto new, non-Amazon devices. Still, Amazon doesn’t
have its own smartphone platform–anymore–which means Siri and Google’s assistant have an
advantage on the one device that matters most to people.

 

 

MICROSOFT CORTANA

Bio: Voice- and
text-based virtual assistant that’s available on Windows, iOS, and Android.
Combines proactive knowledge with answers to queries. Might someday help stop aliens from extinguishing
all intergalactic life.

 

Notable skills: Handles
reminders and calendar appointments, tracks packages, sets alarms, and taps
into Bing for sports, weather, and other information. Hooks into some Windows
apps, and has recently started talking to other bots in Skype.

Character flaws: Feels
most at home on Windows, the platform that app developers–and, arguably,
users–care the least about. Has fewer capabilities and is harder to access on
iOS and Android.

Level of humanity: Loves
jokes, especially corny ones, and has a long list of wisecracks at
hand for
generic questions. Will also quote Shakespeare.

Outlook: After
years as an also-ran behind Siri and Google, Cortana has become a much more
ambitious chatbot. Microsoft wants its virtual assistant to serve as a master intelligence for all
kinds of other bots,
guiding you through travel plans, meetings, to-do lists, and more, and to be
deeply integrated with other Microsoft products such as Office. The goal is toredefine computing in the post-PC
era, but it’s too
early to tell whether the company will succeed.

 

 

FACEBOOK
M

Bio: Part
artificial intelligence, part human-powered service, still in development. M is
to be a text-based assistant within Facebook Messenger that helps get things
done.

 

Notable skills: Attempts
to do anything you might ask it to do.

Character flaws: Doesn’t
actually exist as a consumer product, and is a long way from getting there. Only a small number of people in San
Francisco have access.

Level of humanity: Extremely
high, as M relies in large part on real humans to answer queries. The
hope, according to Wired, is that thousands of these helpers will
train M to work on its own over time.

Outlook: At the
moment, M is little more than vaporware. But given Facebook’s interest in chatbots as a whole, don’t count out M’s eventual arrival as a
superintelligence.

SOUNDHOUND HOUND

Bio: Voice
assistant app for iOS and Android. A related service called Houndify will let
third-party developers add voice to their own devices and services.

 

Notable skills: Impressive
understanding of complex requests such as “Show me coffee shops within five
miles that aren’t Starbucks.” Ties into some third-party services such as Yelp,
Uber, and Expedia.

Character flaws: Connections
to third-party apps are limited, and no shortcut exists to open the app on iOS
and Android.

Level of humanity: Isn’t
much for idle chitchat, but knows how to respond to follow-up questions after
an initial query.

Outlook: One gets
the feeling that Hound’s mobile apps are just a showcase for the Houndify
service that SoundHound is hoping to sell to other companies. If it succeeds,
you probably won’t even recognize it’s there.

 

VIV

Bio: Virtual
assistant from the inventors of Siri. Not available yet, but intended to run on
all kinds of computing devices.

 

Notable skills: Viv’s
claim to fame is that it can interpret complex questions such as “Will it be
warmer than 70 degrees near the Golden Gate Bridge after 5 p.m. the day after
tomorrow?” Tie-ins with third-party apps like Venmo are in the works.

Character flaws: Little
proof that it actually functions as advertised outside of prepared demos.

Level of humanity: Appears
to favor visuals and actual information over descriptive feedback. Capacity for
banter is unclear.

Outlook: Viv has
received plenty of hype from the tech press, as its natural language skills
make for an impressive demo. But until the startup announces when it’ll launch
and on what devices, it’s best to remain a teeny bit skeptical of its
world-changing claims.

OZLO

Bio: An AI whose
sole purpose, at least for now, is to help you find things to eat and drink.
Available to a limited number of early sign-ups.

 

Notable skills: Finds
and internalizes data from multiple sources such as Yelp and Foursquare,
pulling it all into slick informational cards. Tries to be conversational by
offering and understanding follow-up questions, such as “which ones are open
now?” and “what’s on the menu?”

Character flaws: Limited
utility, at least until Ozlo’s makers start adding more capabilities. Heavy
reliance on users to train the AI.

Level of humanity: Appears
to avoid human pleasantries beyond a brief greeting by name.

Outlook: Ozlo
wouldn’t be much different from the long list of other single-purpose chatbots
if it wasn’t promising to be something bigger. Its ability to string together
different data sources in a single query is unique, but it’s unclear if the app
can fulfill the potential that its creators are promising. And unless Ozlo has
a business plan that involves more than just a downloadable app, it may have
trouble getting the training data it apparently needs to succeed.

 

 

X.AI

Bio: One of several
single-purpose virtual assistants. Exists solely via email to schedule meetings
on your behalf.

 

Notable skills: Knows
your schedule and preferences, handles the legwork of corresponding with other
parties.

Character flaws: Relies
heavily on humans to verify the vast majority of calendar data from emails that
the virtual assistant, “Amy,” generates, according to Bloomberg.

Level of humanity: Unsurprisingly,
has been praised for its humanlike capabilities and tone.

Outlook: Highly
focused intelligent agents like X.ai will be great if they ever become smart
enough to operate autonomously. Then again, people who don’t mind the
appearance of having an assistant for meetings might be able to afford real
assistants.

SPEAKTOIT ASSISTANT.AI

Bio: One of many
Siri knockoffs, for lack of a more charitable term. A search for “Siri” in the
app store brings up plenty of others, such as Voice Commands, Voice Secretary,
and Assistant.

 

Notable skills: Little
to speak of beyond Siri, but it can learn custom voice commands to activate its
existing list of skills.

Character flaws: Not
as useful as the virtual assistant your phone comes with, and not as easily
accessible.

Level of humanity: Sounds
pretty robotic, but presents itself as a drawing of a human secretary, whose
gender and appearance are customizable.

Outlook: Some of
these Siri-alikes seem like holdovers from when not all iPhone models supported
Apple’s own virtual assistant and needed a stand-in. In any case, their makers
may have realized the idea isn’t a winning one. SpeakToIt, for instance, has
pivoted to a set of tools that help developers make their own
chatbots.

 

Algorithms:

 

Decision Trees: A decision tree is a
decision support tool that uses a tree-like graph or model of decisions and
their possible consequences, including chance-event outcomes, resource costs,
and utility. Take a look at the image to get a sense of how it looks like.

Decision
Tree

From a business decision point
of view, a decision tree is the minimum number of yes/no questions that one has
to ask, to assess the probability of making a correct decision, most of the
time. As a method, it allows you to approach the problem in a structured and
systematic way to arrive at a logical conclusion.

2. Naive Bayes Classification: Naive Bayes classifiers
are a family of simple probabilistic classifiers based on applying Bayes’
theorem with strong (naive) independence assumptions between the features. The
featured image is the equation?—?with P(A|B) is posterior probability, P(B|A)
is likelihood, P(A) is class prior probability, and P(B) is predictor prior
probability.

 

Naive
Bayes Classification

Some of real world examples
are:

·       
To mark an email as spam or not
spam

·       
Classify a news article about
technology, politics, or sports

·       
Check a piece of text
expressing positive emotions, or negative emotions?

·       
Used for face recognition
software.

 

3. Ordinary Least Squares Regression: If you know statistics,
you probably have heard of linear regression before. Least squares is a method
for performing linear regression. You can think of linear regression as the
task of fitting a straight line through a set of points. There are multiple
possible strategies to do this, and “ordinary least squares” strategy go like
this?—?You can draw a line, and then for each of the data points, measure the
vertical distance between the point and the line, and add these up; the fitted
line would be the one where this sum of distances is as small as possible.

 

Ordinary
Least Squares Regression

Linear refers the kind of model
you are using to fit the data, while least squares refers to the kind of error
metric you are minimizing over.

4. Logistic Regression: Logistic regression is a
powerful statistical way of modeling a binomial outcome with one or more
explanatory variables. It measures the relationship between the categorical
dependent variable and one or more independent variables by estimating
probabilities using a logistic function, which is the cumulative logistic
distribution.

 

Logistic
Regression

In general, regressions can be
used in real-world applications such as:

·       
Credit Scoring

·       
Measuring the success rates of
marketing campaigns

·       
Predicting the revenues of a
certain product

·       
Is there going to be an
earthquake on a particular day?

5. Support Vector Machines: SVM is binary
classification algorithm. Given a set of points of 2 types in N dimensional
place, SVM generates a (N?—?1) dimensional hyperplane to separate those points
into 2 groups. Say you have some points of 2 types in a paper which are
linearly separable. SVM will find a straight line which separates those points
into 2 types and situated as far as possible from all those points.

 

 

Support
Vector Machine

In terms of scale, some of the
biggest problems that have been solved using SVMs (with suitably modified
implementations) are display advertising, human splice site recognition,
image-based gender detection, large-scale image classification

Pseudo
code: –

For an agent learning from experience to act on
some environment:

1.  
Try something (from a list
of available actions for the current situation)

2.  
If the outcome is good, try
it more frequently in the future when in the same situation

3.  
Otherwise, try it less
frequently in the future when in the same situation

4.  
Go to 1

 

Future
of AI : –

 

Technology moves at breakneck speed, and we now have more power in our
pockets than we had in our homes in the 1990s. Artificial intelligence (AI) has
been a fascinating concept of science fiction for decades, but many researchers
think we’re finally getting close to making AI a reality. NPR notes that in the
last few years, scientists have made breakthroughs in “machine learning,” using
neural networks, which mimic the processes of real neurons.

 

This is a type of “deep learning” that allows machines to process
information for themselves on a very sophisticated level, allowing them to
perform complex functions like facial recognition. Big data is speeding up the
AI development process, and we may be seeing more integration of AI technology
in our everyday lives relatively soon. While much of this technology is still
fairly rudimentary at the moment, we can expect sophisticated AI to one day
significantly impact our everyday lives. Here are 6 ways AI might affect us in
the future.

 

What
I conveyed and what you achieved as conclusion: –

Since AI is a very big and humongous topic I
would like to do more research and learn about current trends.