AI Chatbots and Recognition Technology: How Do The Machines Learn?

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AI chatbots and recognition technology are seeping into every segment of society. It is making new waves everywhere. This intelligent technology is making to every corner of our lives, right from our homes, businesses to our relationships.

Now it is not just about home experiences and trending gadgets anymore. The current market has already seen chatbot therapists, chatbot educators, chatbot lawyers, and chatbot customer service representatives. Let us see to what extent AI chatbots and Recognition Technology, is impacting our lives.

AI Chatbot Success

As AI technologies proliferate, they are becoming integral for businesses globally. They are giving businesses a competitive edge over others. A strategically designed and implemented chatbot can work wonders for businesses worldwide.

AI chatbots and recognition technology is a brilliant way to outsource manual work and non-judgmental work. This technology saves up time, effort, and money. With AI in place, businesses can concentrate and invest in skilled work.

It also substantially reduces staff workload. As per the Grand View Research, the chatbot market is expected to hit a whopping $1.25 Billion by the year 2025 at a CAGR of 24.3%.

As digital transactions are becoming the standard norm of purchasing goods and services, leading eCommerce firms are using AI to enhance their customer loyalty and brand competitiveness. Some of the leading e-Commerce brands using AI technology include eBay, Alibaba, Amazon, ASOS, and JD.com.

As per an Oracle survey, 80% of businesses want chatbots by 2020. Companies such as Nitro Café, Sephora, 1–800 Flowers, Coca Cola, Snap Travel, and Marriott have started seeing returns. Here are a few AI chatbot success stories.

Nitro Café: Nitro Café’s messenger chatbot designed for direct payments, easy ordering, and instant 2-way communication has led to an increase in Nitro Café’s sales by 20%.

Sephora: Sephora’s facebook messenger chatbot has increased its makeover appointments by 11%.

ASOS: ASOS’s Messenger chatbots helped reach 3.5x more people, increased returns by 250% and increased its number of orders by 300%.

1–800 Flowers: 1–800 Flowers reported that 70% of its messenger orders were derived from new customers.

Uses of AI Recognition Technology
  1. Voice Recognition Technology

Voice recognition technology has revolutionized our lives in multiple ways. It is already being used in live subtitling on television, for offline note making systems or offline speech to text conversion and in dictation tools for the legal and medical profession.

Virtual assistants such as Amazon’s Alexa, Google’s Google Home, and Apple’s HomePod use voice recognition technology. These virtual assistants can control your smart home.

They can control thermostats, TVs, garage doors, lights, fans, locks, sprinklers, and switches. They can also play music, make calls, send texts, help you watch the footage from your security cameras, let you listen to audio books, make food orders, create alarms and reminders for you and give you News information.

You can also browse the internet for information about gazillion things as per your discretion. You can do all of this with just your voice.

With “OK Google” and “Hey Siri” making it to our smartphones, voice recognition technology has largely impacted the way we function.

With the help of voice recognition technology, you can also solve crimes, secure your bank accounts, and buy products and services.

  1. Facial Recognition Technology

For long now AI facial recognition technology has been associated with the security sector. However, today, you can see its active expansion into other industries such as marketing, retail, and health.

Some of the common uses of AI facial recognition technology include unlocking of phones, prevention of retail crimes, smarter advertising, helping the blind, finding missing persons and pets, protection of law enforcement, facilitating forensic investigations, identifying people on social media platforms, diagnosing diseases, tracking attendance at school, college and workplace, facilitate secure transactions, validate identities at ATMs and control access to sensitive areas.

AI-based recognition technology has also revolutionized the photography industry. An example of the same would be Accent AI 2.0, which is an AI recognition technology implemented in Luminar 3.

It features object and facial recognition technology that helps photographers to improve different part of the photo instantly, for instance, make the sky more expressive by applying a brighter color or replace portrait background.

Chris Burkard — a well-known photographer and artist, has in-length appreciated the fascinating and diverse use of the distinguishing AI facial recognition technology in the field of photography. He thinks the AI recognition technology has amplified accuracy and acts as a significant support system for an artist’s creativity.

Pioneering applications such as AiCure and ePAT are dramatically improving the health care setting. While AiCure uses facial recognition technology to improve medication adherence practices on a mobile device, ePAT can detect facial nuances associated with pain and help in prudent pain management.

AI chatbots and recognition technology have become decidedly mainstream. This radical technology is here to stay and evolve.

Source: https://chatbotsmagazine.com/ai-chatbots-and-recognition-technology-how-do-the-machines-learn-b458545e505b

From Crawling to Sprinting: Advances in Natural Language Processing

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Natural language processing (NLP) is one of the fastest evolving branches in machine learning and among the most fundamental. It has applications in diplomacy, aviation, big data sentiment analysis, language translation, customer service, healthcare, policing and criminal justice, and countless other industries.

NLP is the reason we’ve been able to move from CTRL-F searches for single words or phrases to conversational interactions about the contents and meanings of long documents. We can now ask computers questions and have them answer.

Algorithmia hosts more than 8,000 individual models, many of which are NLP models and complete tasks such as sentence parsing, text extraction and classification, as well as translation and language identification.

Allen Institute for AI NLP Models on Algorithmia

The Allen Institute for Artificial Intelligence (Ai2), is a non-profit created by Microsoft co-founder Paul Allen. Since its founding in 2013, Ai2 has worked to advance the state of AI research, especially in natural language applications. We are pleased to announce that we have worked with the producers of AllenNLP—one of the leading NLP libraries—to make their state-of-the-art models available with a simple API call in the Algorithmia AI Layer.

Among the algorithms new to the platform are:

Machine Comprehension: Input a body of text and a question based on it and get back the answer (strictly a substring of the original body of text).

Textual Entailment: Determine whether one statement follows logically from another

Semantic role labeling: Determine “who” did “what” to “whom” in a body of text

These and other algorithms are based on a collection of pre-trained models that are published on the AllenNLP website.

Algorithmia provides an easy-to-use interface for getting answers out of these models. The underlying AllenNLP models provide a more verbose output, which is aimed at researchers who need to understand the models and debug their performance—this additional information is returned if you simply set debug=True.

The Ins and Outs of the AllenNLP Models

Machine Comprehension: Create natural-language interfaces to extract information from text documents.

This algorithm provides the state-of-the-art ability to answer a question based on a piece of text. It takes in a passage of text and a question based on that passage, and returns a substring of the passage that is guessed to be the correct answer.

This model could feature into the backend of a chatbot or provide customer support based on a user’s manual. It could also be used to extract structured data from textual documents, such as a collection of doctors’ reports could be turned into a table that says (for every report) the patient’s concern, what the patient should do, and when they should schedule a follow-up appointment.

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Entailment: This algorithm provides state-of-the-art natural language reasoning. It takes in a premise, expressed in natural language, and a hypothesis that may or may not follow up from. It determines whether the hypothesis follows from the premise, contradicts the premise, or is unrelated. The following is an example:

Input

The input JSON blob should have the following fields:

premise: a descriptive piece of text

hypothesis: a statement that may or may not follow from the premise of the text

Any additional fields will pass through into the AllenNLP model.

Output

The following output field will always be present:

contradiction: Probability that the hypothesis contradicts the premise

entailment: Probability that the hypothesis follows from the premise

neutral: Probability that the hypothesis is independent from the premise

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Semantic role labeling: This algorithm provides state-of-the-art natural language reasoning—decomposing a sentence into a structured representation of the relationships it describes.

The concept of this algorithm is considering a verb and the entities involved in it as its arguments (like logical predicates). The arguments describe who or what does the action of this verb, to whom or what it is done, etc.

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NLP Moving Forward

NLP applications are rife in everyday life, and applications will only continue to expand and improve because the possibilities of a computer understanding written and spoken human language and executing on it are endless.

 

Source: https://blog.algorithmia.com/from-crawling-to-sprinting-advances-in-natural-language-processing/