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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.

what is content moderation and why companies need it

Content Moderation refers to the practice of flagging user-generated submissions based on a set of guidelines in order to determine whether the submission can be used or not in the related media.  These rules decide what’s acceptable and what isn’t to promote the generation of content that falls within its conditions. This process represents the importance of curbing the output of inappropriate content which could harm the involved viewers. Unacceptable content is always removed based on their offensiveness, inappropriateness, or their lack of usability.

Why do we need content moderation?

In an era in which information online has the potential to cause havoc and influence young minds, there is a need to moderate the content which can be accessed by people belonging to a range of age-groups. For example, online communities which are commonly used by children need to be constantly monitored for suspicious and dangerous activities such as bullying, sexual grooming behavior, abusive language, etc. When content isn’t moderated carefully and effectively, the risk of the platform turning into a breeding ground for the content which falls outside the community’s guidelines increases.

Content moderation comes with a lot of benefits such as:

  • Protection of the brand and its users
    Having a team of content moderators allows the brand’s reputation to remain intact even if users upload undesirable content. It also protects the users from being the victims of content which could be termed abusive or inappropriate.
  • Understanding of viewers/users
    Pattern recognition is a common advantage of content moderation. This can be used by the content moderators to understand the type of users which access the platform they are governing. Promotions can be planned accordingly and marketing campaigns can be created based on such recognizable patterns and statistics.
  • Increase of traffic and search engine rankings
    Content generated by the community can help to fuel traffic because users would use other internet media to direct their potential audience to their online content. When such content is moderated, it attracts more traffic because it allows users to understand the type of content which they can expect on the platform/website. This can provide a big boost to the platform’s influence over internet users. Also, search engines thrive on this because of increased user interaction.

How do content moderation systems work?

Content moderation can work in a variety of methods and each of them holds their pros and cons. Based on the characteristics of the community, the content can be moderated in the following ways:

Pre-moderation

In this type of moderation, the users first upload their content after which a screening process takes place. Only once the content passes the platform’s guidelines is it allowed to be made public. This method allows the final public upload to be free from anything that’s undesirable or which could be deemed offensive by a majority of viewers.

The problem with pre-moderation is the fact that users could be left unsatisfied because it delays their content from going public. Another disadvantage is the high cost of operation involved in maintaining a team of moderators dedicated to ensuring top quality public content. If the number of user submissions increases, the workload of the moderators also increases and that could stall a significant portion of the content from going public.

If the quality of the content cannot be compromised under any circumstances, this method of moderation is extremely effective.

Post-moderation

This moderation technique is extremely useful when instant uploading and a quicker pace of public content generation is important. Content by the user will be displayed on the platform immediately after it is created, but it would still be screened by a content moderator after which it would either be allowed to remain or removed.

This method has the advantage of promoting real-time content and active conversations. Most people prefer their content online as soon as possible and post moderation allows this. In addition to this, any content which is inconsistent with the guidelines can be removed in a timely manner.

The flaws and disadvantages of this method include legal obligations of the website operator and difficulties for moderators to keep up with all the user content which has been uploaded. The number of views a piece of content receives can have an impact on the platform and if the content strays away from the platform’s guidelines, it can prove to be costly. Considering the fact that such hurdles exist, the content moderation and review process should be completed within a quick time slot.

Reactive moderation

In this case, users get to flag and react to the content which is displayed to them. If the members deem the content to be offensive or undesirable, they can react accordingly to it. This makes the members of the community responsible for reporting the content which they come across. A report button is usually present next to any public piece of content and users can use this option to flag anything which falls outside the community’s guidelines.

This system is extremely effective when it aids a pre-moderation or a post-moderation setup. It allows the platform to identify inappropriate content which the community moderators might’ve missed out on. It also reduces the burden on community moderators and theoretically, it allows the platform to dodge any claims of their responsibility for the user-uploaded content.

On the other hand, this style of moderation may not make sense if the quality of the content is extremely crucial to the reputation of the company. Interestingly, certain countries have laws which legally protect platforms that encourage/adopt reactive moderation.

AI Content Moderation

Community moderators can take the help of artificial intelligence inspired content moderation as a tool to implement the guidelines of the platform. Automated moderation is commonly used to block the occurrences of banned words and phrases. IP bans can also be established using such a tool.

Current shortcomings of content moderation

Content moderators are bestowed with the important responsibility of cleaning up all content which represents the worst which humanity has to offer. A lot of user-generated content is extremely harmful to the general public (especially children) and due to this, content moderation becomes the process which protects every platform’s community. Here are some of the shortcomings experienced by modern content moderation:

  • Content moderation comes with certain dangers such as continuously exposing content moderators to undesirable and inappropriate content. This can have a negative psychological impact but thankfully, companies have found a way to replace them with AI moderators. While this solves the earlier issue, it makes the moderation process more secretive.
  • Content moderation presently has its fair share of inconsistencies. For example, an AI content moderation setup can detect nudity better than hate speech, while the public could argue that the latter has more significant consequences. Also, in most platforms, profiles of public figures tend to be given more leniency compared to everyday users.
  • Content Moderation has been observed to have a disproportionately negative influence on members of marginalized communities. The rules surrounding what is offensive and what isn’t aren’t generally very clear on these platforms, and users can have their accounts banned temporarily or permanently if they are found to have indulged in such activity.
  • Continuing from the last statement, the appeals process in most platforms is broken. Users might end up getting banned for actions they could rightfully justify and it could take a long period of time before the ban is revoked. This is a special area in which content moderation has failed or needs to improve.

Conclusion

While the topic of content moderation comes with its achievements and failures, it completely makes sense for companies and platforms to invest in this. If the content moderation process is implemented in a manner which is scalable, it can allow the platform to become the source of a large volume of information, generated by its users. Not only can the platform enjoy the opportunity to publish a lot of content, but it can also be moderated to ensure the protection of its users from malicious and undesirable content.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – 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.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

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.

ML has several applications in various fields such as

  • Customer Service: ML 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. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

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.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised 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 like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.

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.