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ARE MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE THE SAME?

The convergence of Big Data with Artificial Intelligence has emerged as the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities.

Artificial Intelligence and Machine Learning are two very hot buzzwords right now, and often seem to be used interchangeably. Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world. For both, the real value to enterprises depends on data.

Despite this, Artificial Intelligence and Machine Learning are not quite the same thing, but the perception that they are can sometimes lead to some confusion.

Artificial intelligence (AI) is the intelligence exhibited by machines. 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".

Machine Learning (ML) is a class of algorithms that automates analytical model building and gives computers the ability to learn without being explicitly programmed. Using algorithms that iteratively learn from data Machine Learning allows computers to find hidden insights without being explicitly programmed.

AI & ML:
HOW TO USE THEM

Artificial Intelligence and Machine Learning are of key importance to drive enterprise strategies.

Every day, new examples are coming out of new problems being solved and old markets being disrupted by what is collectively called “AI”. Broadly speaking, Artificial Intelligence can support three important business needs:

Automating business processes
Gaining insight through data analysis
Engaging with customers and employees

The teknowlogy PAC INNOVATION RADAR shows Reply at the top of AI-related Services in 2020

data
science

It refers to the techniques where a human supported by a machine is trying to extract information and insights from data. This includes predictive models on the higher level.

machine
learning

It is the science of creating algorithms and programs able to learn on their own on the basis of heterogeneous data sources such as systems, things and humans.

artificial intelligence

It is the study of how to create intelligent agents. In practice, it is how to program a computer to behave and perform a task as an intelligent agent (say, a person) would.

AI AND ML…

… they make results when you have a problem in which defining all the possible conditions and rules that are describing that a particular problem requires an infinite amount of time. An algorithm can understand how to define all those infinite rules from data.

AI is about intelligent machines that self-write the infinite set of rules to automate a task from data.
DATA IS THE PROBLEM TO SOLVE AND TO START WITH IN AN AI PROJECT.

AI starts from different sources of data: Historical, User Generated and Real Time. To train a deep neural network you need real Big Data.

Without real Big Data you will not have results with AI.
ONLY REAL BIG DATA CAN FUEL AND TRAIN A DEEP NEURAL NETWORK.

If you only have 10 examples of something, it’s going to be hard to make deep learning work. Any business that has tens or hundreds of thousands of customer interactions has enough scale to start thinking about using these sorts of things.

«Deep learning takes at least 100,000 examples» - Google Brain chief, Jeff Dean.
CONVERSATIONAL BOTS ARE EASY TO SETUP.

Our people can easily do it in a couple of days during an hackathon. But since a bot is hiding complexity, and the fantasies of customers about them are many, it is really hard to meet expectations without experience and the required skills.

Chatbots are easy to setup, but complex to deliver at the level of customer expectation (and fantasy).
AI TAKES A LOT OF PATIENCE BECAUSE TRAINING A NEURAL NETWORK PROPERLY REQUIRES TIME.

If a customer wants a bot or a smart app without the proper time for training it we run the risk of a disappointing result. And since AI & ML take time, the customers need to understand the sooner they start, the better it is.

The early adopters of AI and Machine Learning in analytics will gain a huge first-mover advantage.

THE POWER OF CONVERSATION

We have entered a promising new era of computing, where advances in machine learning and artificial intelligence are creating a resurgence of interest in conversational interfaces and natural language processing.
This boosts potential for conversation as the new mode of interaction with technology.

Conversational Systems are intelligent machines capable of understanding language and conducting a written or verbal conversation with a user. Their objective is to provide informed answers, assistance, help in direct channel interaction and possibly in real time.

The adoption of Conversational Systems is aimed at improving Customer Experience by steering Human-Machine Interaction.

DIVE INTO THE WORLD OF CONVERSATIONAL INTERFACES

Chatbots, virtual and voice assistants, and robots are a powerful communication asset, an expression of the brand’s personality and a tool to create emotional bonds with customers and employees.

The Reply conversational-first approach would allow for a holistic, top-down perspective of a customer's entire digital landscape, embracing the next digital revolution, enhancing brand safety and dramatically boosting efficiency and customer satisfaction.

Discover more about the power of conversation

THE RISE OF CONVERSATIONAL INTERFACES

More and more often people have to communicate with machines – and vice versa. But the change from "screen design" to "voice design" holds many pitfalls.

Reply's Voice Machine Interfaces initiative offers companies one-stop expertise and consulting to implement a Voice First strategy in a meaningful way.

Discover more about latest trends in the field of Voice Machine Interfaces

WHAT ARE CONVERSATIONAL SYSTEMS?

Conversational systems are designed for conducting a conversation via auditory or textual methods, convincingly simulating how a human would behave and taking advantage of sophisticated Natural Language Processing and Understanding capabilities.

Reply framework Robotics for Customers introduced a Human centered design approach for conversational interfaces, aimed at creating “experience systems”, by humanizing processes and exploiting advanced technology. Besides, the Personality by design approach is the methodology aimed at shaping Bot personality, thus enhancing design of interaction styles, by humanizing Customer touchpoints and augmenting user experience.

Download Brochure about Conversational Systems and Reply’s Solutions

A NEW CUSTOMER-ORIENTED COMMUNICATION CHANNEL THAT USES MACHINE LEARNING TOOLS

Conversational interfaces, based on voice interaction or chat, are rapidly spreading in common use. Messaging platforms allow interaction with chatbot while smart speakers (Amazon Echo, Google Home, etc.) have quickly spread today.

Reply develops conversational agents in the field of customer care and personal assistant. In the world of customer care, there are several applications of chatbots to increase the effectiveness of the help desk services. Personal assistants provide support in daily tasks.

Read more about Chatbots

CONVERSATIONAL COMMERCE

E-commerce and retail trade continue to develop. In the past, ongoing development was often driven primarily by technological progress, but this is no longer the case, as the focus nowadays is often placed on the customers and their changing behaviour when it comes to buying.

Input/output devices for voice-supported shopping are already available and can be exchanged as and when required: the smart watch on the wrist, voice recognition in the car or even specialised voice assistants like Amazon’s Alexa or Google Home.
If natural language cannot be used due to, for example, the current situation or the surroundings, informal written orders can also be placed via messengers such as WhatsApp or Telegram. Based on SAP Hybris as a Service (YaaS), Reply Voice Commerce provides a package that can be used to communicate with various services on the in/output devices via a generic interface.

Read more about Reply Voice Commerce

CONVERSATIONAL SYSTEMS IN THE AUTOMOTIVE INDUSTRY

One of the first cases where the Robotics for Customers approach has faced Chatbots has been in the automotive industry, where online assistants have been conceived for product presentation and catalogue configuration.

The way people shop for cars has changed markedly

For the automotive sector, Reply is offering a chatbot solution to instantaneously and fully automatically handles customer interactions dealing with a wide range of topics such as car configuration, customer survey, after-sales service.

Chatbots have the potential to convincingly mimic human actors and even pass the Turing test

CUSTOMER CARE AUTOMATION IN THE UTILITIES SECTOR

Reply has developed a new customer care service using the chatbot technologies for a leading multi-utility company. In addition to its ability to interact with a human speaker, the chatbot implements an end-to-end process that identifies and extracts the information required by the user from back-end systems, supplying this information in real time within the context of a conversation carried out in natural language. The interaction between the user and the chatbot is immediate and does not require login procedures, resulting in a marked improvement of the service offered to the customer.

The New Challenge for Contact Centre Automation

PERSONALITY BY DESIGN

In recent decades, we have witnessed the emergency of an increasingly robotic society and the growth of complex artificial intelligence. We are in a world where the conversation is the interface and the personality is the new User Experience.

Conversations with bots follow the same rules as communication between people

PILLAR

prediction & prescription

While Data Science produces Insights, Machine Learning produces Predictions. C-level executives should think about applied Machine Learning in the upcoming stages of prediction and prescription.

PILLAR

recommendation

Recommendation Systems deal with a particular form of Intelligent Information Filtering, aimed to extracting value by finding similarities among users and/or items and generating a ranked list of proposal tailored to an end-user’s preferences.

PILLAR

recognition

Image and video Recognition is a great task for developing and testing Machine Learning approaches. The field of Natural Language Processing is shifting from statistical methods to neural network methods.

PREDICTION & PRESCRIPTION

Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future—for example, by helping credit-risk officers at banks to assess which customers are most likely to default or by enabling telcos to anticipate which customers are especially prone to “churn” in the near term (exhibit).

Prescription is the most advanced stage of Machine Learning, because it is, after all, not enough just to predict what customers are going to do; only by understanding why they are going to do it can companies encourage or deter that behavior in the future. Technically, today’s machine-learning algorithms, aided by human translators, can already do this.

Some samples of Reply’s experience: Machine Learning for Fraud Fighting and Insurance Fraud Detection via Unsupervised Learning Methods

RECOMMENDATION

From an organization perspective, a Recommendation System can be built on a small amount of data, which can be as diverse as an organization may hold in specific business domains.

Reply introduced a unified development strategy for Recommendation Systems which allows different kinds of services to be developed and implemented in a straightforward manner, from prototypes to production environments. The data processing engine is provided by advanced analytics, typically built using AI-driven and Machine Learning capabilities. Download Brochure about Recommendation Systems and Reply’s Solutions

Some samples of Reply’s experience: Bringing Recommendation Engines in Production Environments and The Banca Mediolanum Case

RECOGNITION

Image and Video Recognition

Reply adopts innovative Deep Learning techniques for the recognition of images and videos. These techniques, based on neural networks (eg Convolutional Neural Network) allow the use of networks pre-trained on general datasets, or the creation of customized networks on specific datasets. It is so possible to realize recognition engines that allow the identification of specific objects and / or features in videos and images, and the characterization of the sentiment of facial expressions, too.

Reply already developed projects in these field, including the recognition of sentiment in the customer care field, augmented reality (recognition of specific objects to guide the application logic), visual verification of anomalies, the counting of objects on the shelves, etc.

Written and Spoken Language Recognition

Language identification is a Machine Learning technique that allows not only the conversion between voice and text, but also the understanding of the meaning of the text itself. Deep Learning, and specifically frameworks like TensorFlow, are used today to create sophisticated learning models.

Reply has gained the skills for the integration of smart speakers and the know-how necessary for the realization of language recognition models based on the most advanced platforms. Semantic search engines together with machine learning algorithms support the identification of the most relevant results to the requests.

Featured Business Case: Natural Language Processing across the Automotive Value Chain

Featured Whitepaper: Voice Interaction Gets Contextual

Best Practice: The rise of Conversational Interfaces

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NEW INTERFACES,ZERO INTERFACES

Which new interface technologies can we expect to see coming to prominence in our ‘new normal’?

The AI-enhanced Customer Experience

Reply’s data-driven research has tracked and measured the most compelling trends in the field of Artificial Intelligence for Marketing

Best Practice

EDGE AI: DEVELOPMENTS AND APPLICATIONS

Executing ArtificiaI Intelligence and Machine Learning directly on Edge devices paves the way for a new era of Intelligent and Autonomous things.

Reply Echoes

Dive into the world of conversational interfaces

AI TRENDSTO WATCH OUT FOR

A data-driven study to discover Artificial Intelligence trends, figures and business opportunities.

Three times Best in Class provider in AI-Related Services

The teknowlogy PAC INNOVATION RADAR shows Reply at the top of AI-related Services in Germany 2020

Intelligence... inspired by Humans, powered by Machines.

Can technology ever truly replace a present and attentive human mind?

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ARE MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE THE SAME?

data science

machine learning

artificial intelligence

LESSON WE HAVE LEARNT

AI AND ML…

AI is about intelligent machines that self-write the infinite set of rules to automate a task from data.

DATA IS THE PROBLEM TO SOLVE AND TO START WITH IN AN AI PROJECT.

Without real Big Data you will not have results with AI.

ONLY REAL BIG DATA CAN FUEL AND TRAIN A DEEP NEURAL NETWORK.

«Deep learning takes at least 100,000 examples» - Google Brain chief, Jeff Dean.

CONVERSATIONAL BOTS ARE EASY TO SETUP.

Chatbots are easy to setup, but complex to deliver at the level of customer expectation (and fantasy).

AI TAKES A LOT OF PATIENCE BECAUSE TRAINING A NEURAL NETWORK PROPERLY REQUIRES TIME.

The early adopters of AI and Machine Learning in analytics will gain a huge first-mover advantage.

REPLY OFFERS ARTIFICIAL INTELLIGENCE & MACHINE LEARNING SOLUTIONS TAILORED FOR YOUR BUSINESS

THE POWER OF CONVERSATION

The adoption of Conversational Systems is aimed at improving Customer Experience by steering Human-Machine Interaction.

DIVE INTO THE WORLD OF CONVERSATIONAL INTERFACES

THE RISE OF CONVERSATIONAL INTERFACES

WHAT ARE CONVERSATIONAL SYSTEMS?

A NEW CUSTOMER-ORIENTED COMMUNICATION CHANNEL THAT USES MACHINE LEARNING TOOLS

CONVERSATIONAL COMMERCE

CONVERSATIONAL SYSTEMS IN THE AUTOMOTIVE INDUSTRY

CUSTOMER CARE AUTOMATION IN THE UTILITIES SECTOR

PERSONALITY BY DESIGN

PILLAR

prediction & prescription

PILLAR

recommendation

PILLAR

recognition

PREDICTION & PRESCRIPTION

PREDICTION & PRESCRIPTION

RECOMMENDATION

RECOMMENDATION

RECOGNITION

Image and Video Recognition

Written and Spoken Language Recognition

Customer Contact

DIGITAL CUSTOMER CARE TRANSFORMATION

Vodafone leveraged Reply to accelerate the development of TOBi globally and demonstrate the value of digital customer contact channels through a data-driven approach.

Find out more FROM Reply's survey on current developments in Artifical Intelligence!

NEW INTERFACES,
ZERO INTERFACES

Which new interface technologies can we expect to see
coming to prominence in our ‘new normal’?

The AI-enhanced
Customer Experience

Reply’s data-driven research has tracked and measured the most
compelling trends in the field of Artificial Intelligence for Marketing

AI TRENDS
TO WATCH OUT FOR

A data-driven study to discover Artificial Intelligence trends,
figures and business opportunities.

The teknowlogy PAC INNOVATION RADAR shows Reply at the top
of AI-related Services in Germany 2020

Intelligence...
inspired by Humans, powered by Machines.

data
science

machine
learning