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Virtual Assistants - Alexa, Siri, Google Assistant

Siri was introduced as a feature of the iPhone 4S in 2010. While it could only answer simple questions such as “What’s today’s weather like?” and “Who is Barack Obama?”, users praised the potential of the new voice assistant. Quite a feat for that time for a virtual assistant.

Expectations were high, and Siri fell short. Users complained about inaccurate responses to simple questions or commands. If Siri didn’t know the answer to a question, she’d crack a bad joke, which can seem like an unacceptable excuse for not having the ability to answer a question.

While Apple made improvements to its voice assistant, it wasn’t able to meet a lot of high expectations, and that frustrated users.

Alexa

Three years later, Amazon introduced its own voice assistant named Alexa, and it was instantly pitted against Apple’s alternative. Users observed that Alexa was quicker with responses, and was answering more questions right than wrong. Alexa fell short next to Siri when it comes to the fluidity and flow of requests and conversations. Siri could respond to commands better, and it had no problems understanding multiple sentence structures that conveyed the same message.

In 2016, Google came out with an answer to Siri and Alexa in the form of Google Assistant. It became the gold standard for how natural language processing (NLP) should be implemented with a voice assistant. The drawback of Google Home was that it didn’t have the broad integrations that Alexa had with Amazon’s devices.

These three voice assistants are the most popular in the market and each of them has their own strengths and weakness. But, how exactly do they stand against each other? 

The main tests we will conduct for these voice assistants are commands, conversation flow, music requests, home automation, and technology. MKBHD and Undecided with Matt Farell have given us interesting demonstrations and questions that can be used to test each of these three voice assistants. Let’s compare them using the following parameters:

Commands

Voice assistants started off as devices that could answer simple questions such as the time and the weather. Accuracy of response is key here and speed is an additional bonus.

What’s the weather?

Siri, Alexa, and Google Home have no problem answering this. Google tends to have a slight delay in its response generally, but nothing that could test a user’s patience.

How far away is London?

Siri and Google answered this right in miles as the crow flies, while Alexa provided an inaccurate response, or the answer to a different London (there are 29 places in the world called London). 

Conversation Flow 

When humans have conversations, the talking points build naturally and flow from one topic to another seamlessly. For a voice assistant, understanding context while having a conversation is key. 

Conversation

The following questions were asked one after the other to each voice assistant separately.

Who is the 45th President of the United States?

All three voice assistants provide the right answer. Siri cites the source and asks users if they’d like more information.

Where is he from?

When asked immediately after the previous question, Siri and Google fail. Alexa seems to handle context better than its two competitors.

Music

Since all voice assistants communicate with speakers, they need to understand song, artist and album requests. But before we get into their ability to play a track on-demand, its important to note that each voice assistant only plays music from a select set of streaming services. Alexa wins here as it plays from most major services. Google works only with Google Play Music, YouTube Music, Spotify, and Deezer. And Siri, not surprisingly, only plays from Apple Music.

Play Get Lucky by Daft Punk

Simple task. No losers here.

Play the song that goes “like the legend of the phoenix”

Alexa fails here while Siri and Google Assistant get it right.

Home Automation

Home automation refers to command-based control over home appliances such as fans, den lights, television, heaters, etc. Here’s how the voice assistants fared with the following two questions.

Turn off the den lights

All assistants successfully turned the lights off. 

Set the room temperature to 70 F

Google Assistant and Siri got this right, while Alexa adjusted the room temperature to a value between 65 and 70. 

Technology

Siri primarily works on Natural Language Processing (NLP) integrated with Machine Learning (ML), and voice recognition. Alexa operates on similar tech such as Automated Speech Recognition (ASR), and Natural Language Understanding (NLU). The technology isn’t too different from google either, its voice assistant employs NLP and ML.

Yes, the three voice assistants use ML and NLP to understand what the user is saying and to make suggestions or respond to the user’s language input. While the primary technology is the same or at least similar, the end result is what separates the three. As observed in the tasks assigned to them earlier, certain aspects of each voice assistant’s tech, such as the ability to understand speech patterns and words,  give them an advantage and a disadvantage.

Conclusion

The aim isn’t to be diplomatic, but there isn’t exactly a winner among the three. All the voice assistants can, for the most part, do the same things. Alexa has the largest home-integration options among the three, while Google Assistant and Siri are a lot more natural to talk to. 

Virtual Assistants

If you’re big on home automation and having wide music streaming options, Alexa is the voice assistant for you.

If you find yourself comfortable with Google’s streaming services such as Google Music and Youtube, Google Assistant is a smart pick. It also comes with a formidable range of home automation.

And finally, if your household is equipped with Apple’s products, it’s a no brainer to pick Siri, who’s device also has the best speakers among the three. Siri also has an advantage concerning privacy, as it encrypts all data, unlike its competitors that use it for targetted ad campaigns.

As a consumer, your goal is to see which one of these fits your requirement and aligns with what you’re looking for from a voice assistant.

Development of artificial intelligence - a brief history | Blog | Bridged.co

The Three Laws of Robotics — Handbook of Robotics, 56th Edition, 2058 A.D.
1. First Law — A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. Second Law — A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. Third Law — A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots — in 1942 — humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior — using reason and logic to process available information.

There was plenty of hype and excitement about AI and several countries started funding research as well. Two decades in, the progress made did not deliver on the initial enthusiasm or have a major real-world implementation. Millions had been spent with nothing to show for it, and the promise of AI failed to become anything more substantial than programs learning to play chess and checkers. Funding for AI research was cut down heavily, and we had what was called an AI Winter which stalled further breakthroughs for several years.

Gary Kasparov vs IBM Deep blue | Blog | Bridged.co

Programmers then focused on smaller specialized tasks for AI to learn to solve. The reduced scale of ambition brought success back to the field. Researchers stopped trying to build artificial general intelligence that would implement human learning techniques and focused on solving particular problems. In 1997, for example, IBM supercomputer Deep Blue played and won against the then world chess champion Gary Kasparov. The achievement was still met with caution, as it showcased success only in a highly specialized problem with clear rules using more or less just a smart search algorithm.

The turn of the century changed the AI status quo for the better. A fundamental shift in approach was brought in that moved away from pre-programming a computer with rules of intelligent behavior, to training a computer to recognize patterns and relationships in data — machine learning. Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information similar to how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning.

Representation of neural networks | Blog | Bridged.co

Different types of machine learning approaches were developed at this time:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

Along with better-written algorithms, several other factors helped accelerate progress:

Exponential improvements in computing capability with the development of Graphical Processing Units (GPUs) and Tensor Processing Units have reduced training times and enabled implementation of more complex algorithms.

Data repositories for AI systems | Blog | Bridged.co

The availability of massive amounts of data today has also contributed to sharpening machine learning algorithms. The first significant phase of data creation happened with the spread of the internet, with large scale creation of documents and transactions. The next big leap was with the universal adoption of smartphones generating tons of disorganized data — images, music, videos, and docs. We have another phase of data explosion today with cloud networks and smart devices constantly collecting and storing digital information. With so much data available to train neural networks on potential scores of use-cases, significant milestones can be surpassed, and we are now witnessing the result of decades of optimistic strides.

  • Google has built autonomous cars.
  • Microsoft used machine learning to capture human movement in the development of Kinect for Xbox 360.
  • IBM’s Watson defeated previous winners on the television show Jeopardy! where contestants need to come up with general knowledge questions based on given clues.
  • Apple’s Siri, Amazon’s Alexa, Google Voice Assistant, Microsoft’s Cortana, etc. are well-equipped conversational AI assistants that process language and perform tasks based on voice commands.
Developments in AI | Blog | Bridged.co
  • AI is becoming capable of learning from scratch the best strategies and gameplay to defeat human players in multiple games — Chinese board game Go by Google DeepMind’s AlphaGo, computer game DotA 2 by OpenAI are two prolific instances.
  • Alibaba language processing AI outscored top contestants in a reading and comprehension test conducted by Stanford University.
  • And most recently, Google Duplex has learned to use human-sounding speech almost flawlessly to make appointments over the phone for the user.
  • We have even created a Chatbot (called Eugene Goostman) that passed the Turing Test, 64 years after it was first proposed.

All the above examples are path-breaking in each field, but they also show the kind of specialized results that we have managed to attain. In addition, such achievements were realized only by organizations which have access to the best resources — finance, talent, hardware, and data. Building a humanoid bot which can be taught any task using a general artificial intelligence algorithm is still some distance away, but we are taking the right steps in that direction.

Bridged's service offerings | Blog | Bridged.co

Bridged is helping companies realize their dream of developing AI bots and apps by taking care of their training data requirements. We create curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.


NLP in AI and the realization of futuristic robots

How a well-trained conversational AI can empower your business

When the most valuable asset in the world is data, the most powerful tool you can have is the ability to process exabytes of information that data has to offer, and productively so. As we begin to produce gigabytes of digital data every day, De Toekomst — The Future — is with those that can effectively utilize this space, or more appropriately, the cloud. And it is precisely here that Artificial Intelligence is making its mark.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. And the sphere where AI is making its presence felt like a real and tangible entity is when it has a voice of its own. Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

AI has grown to become our personal assistant helping us with tasks at our behest, literally. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana and Google Voice Assistant are only a few examples of AI systems integrating themselves seamlessly into our daily lives and routine. They help us plan our schedules, carry out functions without us having to push a single button, inform us of the latest developments, all the while learning more about our preferences and customizing themselves for us just by listening. With our permission, AI can become our best help.

Leading voice assistants | Blog | Bridged.co

How businesses are leveraging the AI assistant

Equipped with the knowledge of human communication, AI bots can potentially be used in any field that involves language to derive fast, intelligent, and useful insights which can then be transformed into follow-up actions tailored for each customer. Companies have realized the benefits of this incredibly powerful service and have begun utilizing them to gain significant market advantages. We will now talk about a few major applications of the conversational AI, and how we at Bridged are helping companies realize their ambitions for the AI-driven future.

Voice Control and Assistance

Voice control and assistance | Blog | Bridged.co

Performing basic tasks — reading messages, checking notifications, news updates, changing settings, operating connected devices, speech-to-text services.

Planning and Scheduling — setting up meetings, calendar events, automated replies, navigation, online assistance, payments.

Personalization and Security — compiling playlists, product suggestions, mood-based ambiance control, surveillance, and security.

Bridged.co Services: Voice Recognition, Speech Synthesis, Search Relevance.

Chat-bots

Chatbots training | Blog | Bridged.co

An ever-present agent ready to listen to your needs complaints and thoughts, and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.

Bridged.co Services: Chat-bot Training, Virtual Assistant Training, NLP.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

The ability to monitor end-user opinions of a brand or product and gain an understanding of the same on a large scale is clutch in any competitive scenario. Customer retention has become a zero-sum game and sentiment analysis stands at the center of this marketing field. Armed with NLP and machine learning, AI can listen to the scores of available user opinions across multiple platforms be it social media or community forums or even personal blogs. Accurate analyses of brand value at scale provided by accurate AI are invaluable to businesses.

Bridged.co Services: Brand Sentiment Analysis, E-commerce Recommendations, User Content Support.

Customer Service

Customer service | Blog | Bridged.co

AI is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. AI can be put up to a) responding to common queries, b) as a first layer of gathering service request info and routine troubleshooting, c) integrating with the resolution system, learning from successful cases, and suggesting or implementing final calls. AI makes the whole system faster and more efficient.

Bridged.co Services: Chat-bot Training, Sentiment Analysis, User Content Support.

Translate languages as you speak

The need for a multi-language translation book or for a local guide to communicating your need in a tongue you don’t speak is reduced with the advent of live translation by conversation bots that speak your message out loud, as and when you call on them right from your phones and smart devices.

Bridged.co Services: NLP, Voice Recognition, Speech Analysis.

Real-time Transcription

You can count on AI to take down notes for when you are in meetings or need to parse audio or video clips, or just want to pen down your thoughts. Transcription of speech to text is a very common application and finds use in several business tasks.

Bridged.co Services: Audio/Video Transcription, NLP, Voice Recognition.

We are at a very exciting juncture in the development of AI technology. New machine learning techniques including deep learning applied to NLP processes have made it possible to stretch the boundaries of what can be built using AI bots.