Glossary

Algorithm

Etymology and history of algorithm

The concept of algorithms has a long history that involves the invention of numerals, mathematics, and computers. The word algorithm itself has an interesting story of origin that traces back to Khwārezm, also spelled Khorezm, also called Chorasmia an oasis region in Central Asia along the Amu Darya. Mohammad ibn Musa al-Khwarizmi (780–850), Latinized as Algoritmi, was a Persian mathematician, astronomer, and geographer during the Abbasid Caliphate, a scholar in the House of Wisdom in Baghdad.

In the 12th century, Latin translations of his work on the Indian numerals introduced the decimal number system to the Western world. Al-Khwarizmi’s The Compendious Book on Calculation by Completion and Balancing resented the first systematic solu- tion of linear and quadratic equations in Arabic. He is often considered one of the fathers of algebra.

Some words reflect the importance of al-Khwarizmi’s contributions to mathematics. “Algebra” is derived from al-jabr, one of the two operations he used to solve quadratic equations. Algorism and algorithm stem from Algoritmi, the Latin form of his name.

Definition of an algorithm

“a procedure for solving a mathematical problem (as of finding the greatest common divisor) in a finite number of steps that frequently involves repetition of an operation”

—Merriam Webster

The term algorithm means a set of rules or steps that need to be followed to calculate or run a problem-solving operation. For example, consider cooking a new recipe. To do so, you need to read through the instructions and execute them in a step-by-step manner. Only if you do so without missing or avoiding any of the steps will you be able to cook the new dish perfectly.

Simply put, an algorithm is the sequence of steps that is language independent and is defined to enable a machine to execute an operation.

Characteristics of an Algorithm

There may be many written instructions to cook the recipe. However, you wouldn’t use anything you lay your hands on. You will use only the standard recipe to cook that dish. Similarly, not all written instructions for a machine is an algorithm. In order to qualify as an algorithm, it must have the following characteristics.

  • An algorithm needs to be clear – every step should be unambiguous and lead to only one meaning.
  • The inputs should be well-defined.
  • It must be finite.
  • It must be simple and practical so that it can the operations can be executed using the available resources.
  • It must be language agnostic – the instructions can be implemented in any language yet the output must remain the same.

How to Design an Algorithm?

In order to design an algorithm, you need to consider a few questions.

  • What is the problem that you want to solve using the algorithm?
  • What are the limitations of the problem that you need to consider?
  • What is the input you need to solve the problem?
  • What is the output you expect when the problem is solved?
  • What is the solution to the problem considering the limitations?

Using the listed parameters you can begin writing the algorithm. Let’s take the example of multiplying two numbers and printing the product.

Let’s consider the above mentioned pre-requisites in relation to the example.

  • What is the problem that you want to solve using the algorithm?

Multiply two numbers and print the product.

  • What are the limitations of the problem that you need to consider?

The numbers must contain only digits and not any other characters.

  • What is the input you need to solve the problem?

Two numbers to be multiplied.

  • What is the output you expect when the problem is solved?

The product of the two numbers taken as the input.

  • What is the solution to the problem considering the limitations?

The solution consists of multiplying the 2 numbers. It can be done with the help of ‘*’ operator, or bit-wise, or any other method.

Now let’s design the algorithm using these pre-requisites: 

Algorithm to multiply 2 numbers and print their product:

START

Declare 2 integer variables num1, num2 and num3.

Take the two numbers, to be multiplied, as inputs in variables num1, and num2 respectively.

Declare an integer variable product to store the resultant product of the 2 numbers.

Multiply the 2 numbers and store the result in the variable product.

Print the value of variable product

END

Once you have completed writing the algorithm, it’s time to test it by implementing it.

Algorithms in Brainalyzed Insight Platform

Brainalyzed Insight is an artificial swarm intelligence (ASI) platform that uses swam intelligence to process data and help make accurate predictions or solve a problem. The platform uses genetic algorithm to perform and execute these operations.

Genetic algorithm

A genetic algorithm is a search heuristic which rides on Charles Darwin’s theory of natural evolution and selection. The algorithm closely mimics the process of natural selection where the fittest individuals are selected to propagate the species. In the case of the ASI platform, the algorithm selects the optimal brains to make accurate predictions regarding the problem you want to solve.

Brainalyzed Insight uses genetic algorithms since they are robust. Unlike traditional AI, the algorithms do not easily break due to slight changes in input or the presence of data noise. This enables us to generate high-quality solutions for optimization and search problems for our customers

Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) may be a fast-emerging field that aims at building “thinking machines”. These are general-purpose systems with intelligence that’s like the human. Though this was initially the goal of AI, the mainstream of AI research somehow turned towards domain-dependent and problem-specific solutions. Hence it’s now become necessary to use a special term to ask the research that culminates within the original goal of AI. AGI is alternatively referred to as strong AI or human-level AI.

Can AI ever achieve general intelligence?

Artificial intelligence systems, especially artificial general intelligence systems are designed with the human brain as their reference. Since we ourselves don’t have the great knowledge of our brains and its functioning, it’s hard to model it and replicate it working. However, the creation of algorithms which will replicate the complex computational abilities of the human brain is theoretically possible, as suggested by the Church-Turing thesis, which states — in simple words — that given infinite time and memory, any quite problem are often solved algorithmically. This is sensible since deep learning and other subsets of AI are basically a function of memory, and having infinite (or an outsized enough amount of) memory can mean that problems of the very best possible levels of complexity are often solved using algorithms.

How far are we from artificial general intelligence?

Although it’d be theoretically possible to duplicate the functioning of a person’s brain, it’s not practicable as of now. Thus, capability-wise, we are leaps and bounds faraway from achieving artificial general intelligence. However, time-wise, the rapid rate at which AI is developing new capabilities means we’d be get on the brink of the inflection point when the AI research community surprises us with the event of artificial general intelligence. And experts have predicted the event of AI to be achieved as early as by 2030, the expected emergence of AGI or the singularity by the year 2060.

Thus, although in terms of capability, we are far away from achieving artificial general intelligence, the exponential advancement of AI research could culminate into the invention of artificial general intelligence within our lifetime or by the top of this century. Whether the event of AGI are going to be beneficial for humanity or not remains up for debate and speculation. So is that the exact estimate on the time it’ll deem the emergence of the primary real-world AGI application. But one thing is needless to say — the event of AGI will trigger a series of events and irreversible changes (good or bad) which will reshape the planet and life as we all know it, forever.

Artificial Intelligence

What is artificial intelligence?

Artificial intelligence or commonly known as AI is a term that blankets an entire branch of computer science focused on the thinking and learning capabilities of machines. Depending on the kind of data and information they are fed, the kind of training that they are put through, AI learns to make better decisions. This kind of ability to both learn and apply that learning to practical usage, is similar to the way human beings learn and apply that knowledge in the real world.

This means that machines can now accomplish tasks that were once possible only by a human mind. Some of these tasks include

  • Problem solving
  • Interpreting visual cues
  • Speech recognition/Natural language processing

These tasks are accomplished with the help of complex algorithms. These algorithms or intelligent programs can be run on various types of hardware or software. This results in a huge variety of use cases that AI solves for, making the subject of AI more difficult to understand.

Why is artificial intelligence important?

AI automates monotonous learning.

However, this should not be confused with automation driven by hardware or automation of manual tasks. Instead, this kind of repetitive learning implies that AI can perform high velocity computerized tasks in a reliable manner. To enable the layer to AI to perform this kind of automation requires human interference.

For example, if you are use a smart phone or any modern smart device or app, you would have experienced this kind of artificial intelligence at play. How does Amazon know when my stock of grocery is expiring? This is a result of complex algorithms that Amazon uses to trawl through the data that we create when we use the tool or software. This way it is able to make personalized recommendations, in turn enhancing our digital experience.

AI adds a layer of intelligence to products and services.

Seldom will AI be sold as an independent service or application. Instead, AI will work as a layer or a platform that enhances existing features in a product or existing suit of products. For instance, Google Assistant is not an independent application. Instead this AI capability by Google has been added as a feature to its products.

When large amount of data is combined as automation and conversational platforms, it eventually improves the technology that we use.

AI learns through progressive algorithms.

One of the unique aspects of AI is to find structure in data this results in the algorithm acquiring a new skill. Once this happens, the algorithm becomes a predictor. For example, based on the kind of things we shop on Amazon, the algorithm is able to find structure and using this information, it is able to recommend what product we need to buy next. Every time there is new data, the model adapts, learns, and is able to predict outcomes better. If the outcome or answer is not quite right, the model adjusts itself through relearning or new data. This technique is called back propagation.

AI analyzes deeper data and achieves accuracy

To train deep learning models, huge amounts of data is required. Artificial intelligence is able to analyze these large amounts of data deeply through neural networks. For instance, it is possible to build a fraud detection program with more than five hidden layers. All this has become possible today due to the power of computers and big data. The more data you feed the model, the faster it learns, and more accurate is the outcome. For example, the AI of Google, Facebook, Amazon, or any tech giant are all based on deep learning. They are able to predict and personalize the experience for customers accurately. This has been possible only because of continued use of these tools and apps.

How does artificial intelligence work?

Similar to human intelligence that works by processing huge amount of data, artificial intelligence works by processing data through algorithms. These algorithms, as mentioned earlier, adjust themselves based on past experiences and new data, so that they improve their accuracy.

In order to simulate artificial intelligence, machines are provided with the ability to perceive the environment around them (through data, in most cases), identify patterns in this environment, and finally learn from these patterns and continuously experiential memory. These three steps are constantly repeated until the machine has sufficient data to make predictions confidently. And what makes AI so remarkable is its capability for speed, accuracy, and endurance.

What are the applications of artificial intelligence?

How to use artificial intelligence?

There are numerous applications of AI. Some of the more popular use cases include:

Natural language

Machines can recognize natural human language. A common usage is chatbots where companies use them for customer service, marketing, sales, etc. The chatbot is able to recognize what the customer or user is saying and is able to respond appropriately.

Artificial neural networks (ANN)

Neural systems attempt to reproduce the kind of connections that happen in the human brain. This kind of simulation helps predict future events based on historical events or data. Such systems learn to perform tasks without being programmed with any specific rules. For example, image recognition where the machine is able to differentiate between whether the image ‘is a fish’ or ‘is not a fish’.

Expert systems

These are computer applications that solve complex problems in a specific domain. And they do this at the level of exceptional human expertise. They are highly reliable and responsive. For example, they are used to detect frauds, airline scheduling, or stock market trading, just to name a few.

Robotics

Robotics is one aspect of AI that focuses on creating intelligent robots. It is mainly comprised of electrical and mechanical engineering, and computer science specializing in the design, construction, and application of robots. The aim of this branch of AI is to free manpower from doing monotonous tasks. The key difference between other AI programs and robotics is that most AI programs run in a computer simulated world while robots are meant to operate in the real world.

Gaming systems

Gaming systems are programs designed to manipulate strategic games such as Chess or Go. Machines are trained to think of all possible positions or ways to play the game effectively against a human being. In the past, these programs have been able to beat the best of the human opponents.

Artificial Narrow Intelligence (ANI)

What is Artificial Narrow Intelligence?

Artificial narrow intelligence (ANI) is also known as weak AI or simply narrow AI. It is one of the types of artificial intelligence we have been able to successfully create and implement to date. Narrow AI is designed to perform singular tasks such as speech recognition, voice recognition, etc. It is quite efficient in accomplishing singular tasks.

Though these machines may be intelligent, they can only operate within a very narrow framework and their ability is limited to a single task. Hence they are referred to as weak AI. It is cannot mimic human intelligence and can simulate human behaviour based on constrained rules and within the context they are programmed for. They can learn and be taught only specific tasks.

Artificial Neural Network (ANN)

What is artificial neural network?

An artificial neural network or ANN is a mathematical model that processes information and identifies nonlinear relationships between various pieces of data. It is modelled on the human nervous system, the neuron structure, and how it processes information. One of the popular uses of ANN is for classification of data. For example, you can train the model on a set of various cat breeds. It can then classify new images one of the cat breeds using a statistical score on how closely the new image matches the ones the model was trained on.

Popular examples of the use of neural networks include self-driving cars, image classification, stock selectionand stock market prediction, etc.

How does a artificial neural network learn?

One of the characteristics of artificial neural network is its ability to learn quickly. This makes it a powerful tool that can be used for various tasks.

Similar to the human brain, the smallest and most important unit of an artificial neural network is the neuron. Neurons are connected to each other and are capable of great processing potential. The artificial neural is similar to that of a real neuron and has input and output connections. These inlets and outlets simulate the behaviour of the synapses in the human brain. In this way, information is passed between the artificial neurons.

Each of these connections have ‘weights’. This means that the value sent to each connection if multiplied by its factor. In this context, weights simulate the number of neurotransmitters passed among human neurons. If the connection is important, it will have a higher weight value than those connections which are not important.

Since numerous values can get into one neuron, each neuron is defined with a ‘input function’. Input values from all weighted connections are summarized using the ‘weighted sum function’. This value is then passed on to the ‘activation function’. The role of this function is to calculate and check whether the signal should be set to the output of a neuron.

Systems that can learn are highly adaptable. This is the case with the artificial neural network as well. They can adapt and modify their architecture in order to learn faster. An artificial neural network can change weights of connections based on the input and the output we desire.

How to use artificial neural network?

Autoencoders

Autoencoders are artificial neural networks based on the observation that each layer should be pre-trained using unsupervised learning algorithm for better initial weights. They are rarely used in practical applications.

Convolutional Neural Networks

ConvNets derive their name from the “convolution” operator. The primary purpose of this neural network is to extract features from the input image.

Recurrent Neural Networks

Recurrent Neural Networks (RNNs) can be trained for sequence generation by processing real data sequences one step at a time and predicting what comes next.

Artificial Narrow Intelligence (ANI)

What is Artificial Narrow Intelligence?

Artificial narrow intelligence (ANI) is also known as weak AI or simply narrow AI. It is one of the types of artificial intelligence we have been able to successfully create and implement to date. Narrow AI is designed to perform singular tasks such as speech recognition, voice recognition, etc. It is quite efficient in accomplishing singular tasks.

Though these machines may be intelligent, they can only operate within a very narrow framework and their ability is limited to a single task. Hence they are referred to as weak AI. It is cannot mimic human intelligence and can simulate human behaviour based on constrained rules and within the context they are programmed for. They can learn and be taught only specific tasks.

Artificial swarm intelligence

It is a computer program that coordinates many individual technological systems to work together as a group. It mimics the behavioral structure of animals that work in swarms to arrive at an optimal solution.

What is swarm intelligence?

Swarm intelligence refers to the collective intelligence of a group of species. Natural scientists have been studying the behavior of social animals and insects to understand their ability to solve complex problems efficiently. Though these insects or animals are not so sophisticated individually, they are remarkable as a group or swarm when interacting with each other or their environment. None of the individuals is giving orders and yet each of the individuals in the swarm seem to know precisely what needs to be done.

The key to this lies in the fact that each individual follows a small set of rules related to its local environment. These rules help these social insects or animals solve complex problems as a group, a feat they cannot achieve individually. Let’s look at a classic example to help demonstrate this principle.

Characteristics of swarm intelligence applications

A typical swarm intelligence system has a few common characteristics.

  • A swarm is made up of many individuals.
  • These individuals are identical to one another.
  • The interactions between the individuals are based on simple rules that enable them to find information from the surrounding area. This interaction is exchanged between individuals or with the environment.
  • Because of such an interaction, the group behavior of the individual is self-organized. There are no leaders or hierarchy, enabling the group to make swift decisions.
  • Agility is key to the swarm intelligence system because it directly impacts the survival capability of the group.

It has been noted that in social animals there are no leaders. Every individual in the group works for the benefit and welfare of the group. With no leaders, there is no need for permissions. And each individual works based on the information received from the closest individual in proximity or collectively.

It isn’t a surprise then that these individuals do have knowledge of the bigger plan. For example, there are two kinds of information are gets shared among bees. They are information about food and threat. When bees find a good source of nectar, they perform a waggle dance and signal to others the newly found source of nectar. Similarly, when looking for a new place to relocate the hive, the bees perform a different waggle dance to signal the newfound place to the other bees. Even information of threat is communicated in a similar way as a group.

As mentioned earlier, since there are no leaders, individuals of a social group do not require to take permissions or orders from anyone. This means, there is no hierarchy in the group. Every individual does what they are required to do in an organized manner. There is a clear understanding of what each individual is required to do within each context.

One of the key reasons for the absence of hierarchy in such social groups is the need for speed and agility. And this is crucial for the survival of the group.

How to use artificial swarm intelligence?

By studying the behavior of social insects and animals, several companies have implemented these patterns to derive optimal results from their business. Some of the noteworthy mentions of companies that turned to artificial swarm intelligence include Southwest Airlines, Unilever, McGraw-Hill, Capital One etc. Such companies have developed effective ways to organize and schedule factory equipment, divide tasks efficiently among their employees and workers, organize people, and even come up with strategies for their business development.

Some of the ways in which you can use artificial swarm intelligence include

  • Detecting instances of fraud in banking sector through behavior prediction
  • Improving customer service
  • Automating workloads for effective workforce management
  • Preventing IT outages and security intrusions
  • Predicting performance of the business of employees
  • Analyzing data to draw meaningful insights
  • Enhancing marketing efforts

Backpropagation

What is backpropagation?

It is short for backward propagation of errors. It is a method in which neural networks are trained. In this method, the system’s first output is compared to the desired output and then it is adjusted until the difference in between the actual output and the desired output is reduced or becomes minimal.

Backpropagation was invented in the 1970s as a general optimization method. It was used to perform automatic differentiation of complex nested functions. It was only after 1986 when Rumelhart, Hinton, and Williams published a paper titled “Learning Representations by Back-Propagating Errors,” the importance of the backpropagation algorithm began to be appreciated by the machine learning community.

Big data

What is big data?

Big data is the combination of structured and unstructured data that organizations collect. It can be used to mine information and largely used in AI and ML projects. The diverse sets of information that is collected keeps growing at ever-increasing rates. The data is collected from various sources and is available in multiple formats. For example, the New York Stock Exchange generates about one terabyte of new trade data per day.

Characteristics of big data

Volume

The term big data is itself associated with to ‘huge’ or ‘large’ size. And the size of a data set is directly proportional to the value that one can derive from the data. Volume of data is one of the determinants of whether or not a particular data is Big data.

Variety

Variety refers to the heterogeneous nature of the sources of both structured and unstructured data. Data can be obtained from various sources that are not restricted to spreadsheets and databases. For example, emails, photos, videos, PDFs, audio files, etc. are considered as data.

Velocity

Velocity refers to the speed in which data is generated and processed. This determines the real potential of the data itself. When it comes big data, the flow of data is continuous and fast.

Variability

Variability refers to the inconsistency that is characteristic of data – structured, semi structured, and unstructured.

How is big data used?

To be able to effectively use big data, you need to draw insights from it – this means sifting to large volumes of data and filtering out the ones that matter the most to you. This is a mammoth task if it has to be done manually. However, today we have the kind of hardware or software that can process, store, and analyze vast amounts of information.

Companies today can take advantage of cloud computing that makes the whole process of processing big data easy and least expensive. Data centers or server farms help in distributing batches of data that can be processed over multiple servers. This in turn helps in scaling the project as required. This is achieved using tools such as Apache Hadoop, MapReduce, etc. They are the closest scalable alternatives to SQL-based database systems that have been developed.

Much of the processing of big data and its analysis is focused on finding patterns that we can draw insights from. For businesses this means mining large quantities of data for information that can drive their operations and revenue forward. The uses of big data is not limited by industry or function within an organization.

How is big data used in AI and ML?

In the last few years, there has been an enormous growth in the field of big data or big data analytics. Some of the top industries benefitting from big data include

 

  •  Banking
  •  Healthcare 
  •  Energy
  •  Technology
  •  Consumer
  •  Manufacturing

AI is the technology that is expected to reduce the overall workload of humans and empower us to make smarter decisions. By pairing big data with AI, we can not only crunch data at extremely high speed but also draw powerful insights with respect to the business decisions or functions where AI and big data are implemented. By marrying big data and AI, organizations can identify the customer’s interests and realize their revenue goals in the shortest possible time

Black box AI

Black box AI can be defined as any kind of artificial intelligence whose operations are not explainable to the user. Blackbox approach to AI evokes distrust and a general lack of acceptability of the technology. Parallelly there’s a rising focus for legal and privacy aspects that will have a direct impact on AI and ML technology. For example, the European General Data Protection Regulations (GDPR) will make it difficult for businesses to take a Blackbox approach.

Bot

What is a bot?

A bot is short for robot. It is an autonomous program that has the capability to interact with other computer systems, programs, or human users. These are usually supervised directly or indirectly by humans.

What are the different types of bots?

Bots can be broadly classified into good bots and bad bots though bots can be sorted based on various other categories. Chatbots, crawlers, transaction bots, information bots, and entertainment bots fall under the category of good bots while, hackers, scrapers, spammers, impersonators, fall under the category of good bots.

Chatbots

Chatbots are bots that are designed to carry out a specific task – to converse with humans. They fall under the category of narrow artificial intelligence. They usually carry a personality close to that of humans. For example, ELIZA is a chatbot that runs on simple question-answer based script. It automatically generates responses to questions. Another example is Cleverbot is a more advanced bot that uses AI to learn from past interactions

Crawlers

Crawlers are bots that continuously run to fetch data from various APIs or websites. They are also designed to carry out a specific task and hence fall under the category of narrow AI. The most common example of crawlers is search engine spiders such as Googlebot and bingbot that extract data to build a searchable index. Other crawlers monitor other systems for change such as the Pricing Assistant which monitors ecommerce websites for price changes and Alerbot which monitors websites for server uptime, website errors, bugs and performance issues.

Transactional bots

Transactional bots are bots that interact with external platforms to complete a specific transaction by moving data from one system to another. Since these bots can interact with any endpoint which has an API, the solutions you can build using these bots are limitless. For example, Birdly is a Slackbot, which can be activated using specific commands to retrieve specific data.

Information bots

As the name says, information bots help in providing ‘information’ in the form of push notifications. It’s predominantly used in the news and media industry. For example, Techcrunch’s news recommendation bot pushes notifications with personalized news via Facebook or Telegram.

Entertainment bot

Entertainment bots are also known as art bots that are appreciated for their aesthetic quality. For example, RealHumanPraise takes positive movie reviews from Rotten Tomatoes, replaces actors with Fox News personalities, and tweets every 2 minutes.

Hackers

Hacker bots are designed to distribute malware, deceive individual people, attack websites, and sometimes entire networks. These bots scout for security vulnerabilities and exploit them. They can create denial of service (DDos) attacks. Google has said that 180% more sites were hacked in 2015 vs 2014.

Fun fact: Networks of infected computers are known as ‘botnets’.

Scrapers

Scraper bots are designed to ‘scrape’ and steal content such as email address, images, etc. from websites. The content is then reused and published. These pages are then monetized through paid advertising.   

Spammers

Spammers or spam bots are designed to post irrelevant promotional content and drive traffic to the spammer’s website. A classic example of spam bots are forum or blog comments with links to the spammer’s website. However, the volume of spambots has decreased over the years as search engines have made this tactics unprofitable.

Impersonators

Impersonators are intended to copy regular user behavior, making them difficult to recognize. Impersonators likewise include propaganda bots that are intended to influence political opinion somehow. Countries like Turkey, Mexico, and different countries have utilized Twitter impersonator bots for these reasons.

How to make or build a bot?

Choose the purpose and goal

Consider the bot sort of a member of your team that carries out multiple tasks round the clock without human intervention. Just like an employee has certain task that they are responsible for and need to carry out, your bot should also be assigned tasks. Hence, first you need to identify tasks that can be automated, problems that can be solved without human intervention, and assign them to bots.

However, you need to manage your expectations as a chatbot maker. Not all tasks can be completed by bots. For example, bots are not capable of filing taxes or closing business deals. At least, not yet.

“Ultimately, the purpose of a bot is to provide a service people actually want to use — time and time again,” says HubSpot. “No bot is meant to do everything, so when you set out to create your own, think of an existing problem that it can fix in a more efficient way.”

Choose a conversational design suite

Building bots are often fun and useful if you think about conversational design before building. Conversational design allows you to automate interactions between your company and your customers via voice or text. This is often a sophisticated process that involves many codes and other complex activities.

Measure the success of your bot

Once you’ve implemented your bot need to use analytics to measure its success. Find out, for instance , the amount of leads your bots generate on various platforms. Alternatively, send your customers a questionnaire and ask them whether their interaction with your bot was a smooth, pleasant experience. Their answers will provide you with valuable insights. Based on the feedback and insights, you can tweak your bots for enhanced bot performance and user experience.

Chatbots

What is a chatbot?

A chatbot is often defined as an AI-based computer virus that simulates human conversations. They’re also referred to as digital assistants that understand human capabilities. Bots interpret and process the user requests and provide prompt relevant answers.

Bots can through voice also as text and may be deployed across websites, applications, and messaging channels like Facebook Messenger, Twitter, or Whatsapp.

How do chatbots work?

Chatbots work by analyzing and identifying the intent of the user’s request to extract relevant information, which is the most vital task of a chatbot. Once the analysis is completed appropriate response is delivered to the user. The chatbots work by adopting three classification methods.

Pattern matching

Bots utilize pattern matches to group the text and it produces an appropriate response from the clients. AI terminology (AIML) may be a standard structured model of those patterns. A bot is in a position to urge the proper answer within the related pattern. The bots react to anything relating it to the correlate patterns.

Natural language understanding (NLU)

NLU is the ability of the chatbot to understand a person’s language. It’s the method of converting text into structured data for a machine to understand and learn from. NLU follows three specific concepts.

  • Entities – An entity is an idea to the chatbot. For instance, a refund system in your e-commerce chatbot.
  • Context – When an NLP algorithm identifies the request and it’s no historical context of the conversation, it cannot recall the request to offer a response to the user.
  • Expectations – Bot must be ready to fulfill the customer expectations once they make an invitation or ask a question.

Natural language processing (NLP)

NLP bots are designed to convert the text or speech inputs of the user into structured data. The information is further used to choose a relevant answer.

Natural Language Processing (NLP) comprises of the below steps:

  • Tokenization – The NLP filters a set of words within the set of tokens.
  • Sentiment Analysis – The bot interprets user responses by mapping them to emotions.
  • Normalization – It checks the typographical errors that can alter the meaning of the user query.
  • Entity Recognition – The bot looks for various categories of data required.
  • Dependency Parsing – The chatbot searches for common phrases that users use to convey information.

Classification

What is Classification?

In artificial intelligence, classification is a method of grouping observations into categories in a systematic manner. It has its uses in machine learning and data science such as to

  • Identify shared characteristics of certain classes
  • Compare characteristics to the data that you’re trying to classify
  • Use the estimation to identify the likelihood of the observation belonging to the particular class.

Why is Classification Important?

There are many practical business applications for machine learning classification. For instance, if you would like to predict whether or not an individual will default a loan, you would like to work out if that person belongs to at least one of two classes with similar characteristics: the defaulter class or the non-defaulter class. This classification helps you understand how likely the person is to become a defaulter, and helps you adjust your risk assessment accordingly.

Classification problems aren’t limited to binary cases – multiclass problems have three or more possible classes. For instance, you’ll want to predict which of 5 (or even more) marketing channels will achieve the very best return on investment supported historical customer behavior in order that you’ll optimize your marketing budget to specialize in the foremost effective channels.

Classification + Brainalyzed Insight

Brainalyzed Insight’s autoML platform has numerous classification algorithms that can automatically whether or not the target variable is a categorical variable. It weighs the variable to see if it is suitable for classification or regression. 

Brainalyzed Insight has various features that allow you to analyze the performance of classification models for binary and multiclass problems. Our platform also addresses the Blackbox problem by explaining what factors led to the classification of variables.

Clustering

What is clustering?

Clustering is a machine learning algorithm technique that enables machines to group similar data into larger data categories.

Types of Clustering

Centroid-based Clustering

Centroid-based clustering organizes the information and data into non-hierarchical clusters, in contrast to hierarchical clustering defined below. k-means is that the most widely-used centroid-based clustering algorithm. 

Density-based Clustering

Density-based clustering connects areas of high example density into clusters. this enables for arbitrary-shaped distributions as long as dense areas are often connected. These algorithms have difficulty with data of varying densities and high dimensions. Further, by design, these algorithms don’t assign outliers to clusters.

Distribution-based Clustering

This clustering approach assumes data consists of distributions, like Gaussian distributions. As the distance from the distribution’s center increases, the probability that some extent belongs to the distribution decreases. The bands show a decrease in probability. once you don’t know the sort of distribution in your data, you ought to use a special algorithm.

Hierarchical Clustering

Hierarchical clustering creates a tree of clusters. Hierarchical clustering, not surprisingly, is compatible with hierarchical data, like taxonomies. Additionally, another advantage is that any number of clusters are often chosen by cutting the tree at the proper level.

What are the uses of clustering?

Clustering has a many uses each of which vary from one industry to another. Some common applications for clustering include the following:

  • Market segmentation
  • Social network analysis
  • Search result grouping
  • Medical imaging
  • Image segmentation
  • Anomaly detection

Data science

What is data science?

Data science continues to evolve together of the foremost promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they need to advance past the normal skills of analyzing large amounts of knowledge, data processing, and programming skills. so as to uncover useful intelligence for his or her organizations, data scientists must master the complete spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the method.

What does a data scientist do?

In the past decade, data scientists became necessary assets and are present in most organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of complex quantitative algorithms to arrange and synthesize large amounts of data to answer questions and drive strategy in their organization. this is often including the experience in communication and leadership needed to deliver tangible results to varied stakeholders across a corporation or business.

Data scientists need to be curious and result oriented. They must possess exceptional industry-specific knowledge and communication skills that allow them to elucidate highly technical results to their non-technical counterparts. They must have a robust quantitative background in statistics, algebra, and programming knowledge with focuses in data warehousing, mining, and modeling to create and analyze algorithms.

They must be familiar in one or more of these skills:

  • R
  • Python
  • Apache Hadoop
  • MapReduce
  • Apache Spark
  • NoSQL databases
  • Cloud computing
  • D3
  • Apache Pig
  • Tableau
  • iPython notebooks
  • GitHub

Deep learning

What is deep learning?

Deep learning is a machine learning technique that teaches computers/machines to try to do what comes naturally to humans: learn by example. Deep learning may be a key technology behind driverless cars, enabling them to acknowledge a stop sign or to differentiate a pedestrian from a lamp post. it’s the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting much attention lately and permanently reason. It’s achieving results that weren’t possible before.

In deep learning, the machine learns to perform classification tasks directly from images, text, or sound. These deep learning models are able to do state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by employing a large set of labeled data and neural network architectures that contain many layers.

How does deep learning work?

Most deep learning methods use neural network architectures, which is why deep learning models are often mentioned as deep neural networks.

The term “deep” usually refers to the number of hidden layers within the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.

Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the info without the necessity for manual feature extraction.

One of the foremost popular sorts of deep neural networks is understood as convolutional neural networks (CNN or ConvNet). A CNN convolves learned features with the input file, and uses 2D convolutional layers, making this architecture compatible with processing 2D data, like images.

CNNs eliminate the necessity for manual feature extraction, so you are doing not got to identify features to classify images. It extracts features directly from images. The relevant features aren’t pre-trained; they’re learned while the network trains on a set of images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks like object classification.

CNNs learn to detect different features of a picture using tens or many hidden layers. With every hidden layer, the complexity of the learned image features increases. For instance, the primary hidden layer could find out how to detect edges, and therefore the last learns the way to detect more complex shapes specifically catered to the form of the thing we try to acknowledge.

Why deep learning matters?

Deep learning is important because it can achieve recognition accuracy at greater levels than ever before. This helps consumer electronics meet user expectations, and it’s crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the purpose where deep learning outperforms humans in some tasks like classifying objects in images.

While deep learning was first theorized within the 1980s, there are two main reasons it’s only recently become useful:

Deep learning requires large amounts of labeled data. For instance, driverless car development requires many images and thousands of hours of video.

Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that’s efficient for deep learning. When combined with clusters or cloud computing, this permits development teams to scale back training time for a deep learning network from weeks to hours or less.

Examples of Deep Learning at Work

Deep learning applications are utilized in industries from automated driving to medical devices.

Automated Driving

Automotive researchers are using deep learning to automatically detect objects like stop signs and traffic lights. Additionally, deep learning is employed to detect pedestrians, which helps decrease accidents.

Aerospace and Defense

Deep learning is employed to spot objects from satellites that locate areas of interest and identify safe or unsafe zones for troops.

Medical Research

Deep learning is used to automatically detect cancer cells. Teams at UCLA built a complicated microscope that yields a high-dimensional data set to train a deep learning application to accurately identify cancer cells.

Industrial Automation

Deep learning enhances the safety of workers around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines.

Electronics

Deep learning is getting used in automated hearing and speech translation. For instance, home assistance devices that answer your voice and know your preferences are powered by deep learning applications.

Explainable AI

What is explainable AI?

Artificial intelligence is enabling advanced decision-making capabilities. The deep layers of a neural network can mimic the functions of a human mind. When human beings make a decision; they are able to explain how they arrived at it – thought process, the observations, logical thinking, that went into making that decision.

Basic machine algorithms such as decision trees can be easily explained by following the path of the tree. However, complex artificial intelligence or machine learning algorithms that have deep layers are incomprehensible. Data scientists find it difficult to explain why their algorithm gave a particular outcome. Due to a lack of comprehensibility, the end-user finds it equally difficult to trust the machine’s decisions.

What is explainability in AI?

Explainability has multiple facets to it. We usually assume explainability as the ability of an AI model to explain the process behind a decision or output. But it isn’t just that! Explainability comprises of the individual models and the larger systems in which they are incorporated. It refers to whether a model’s outcome can be interpreted and whether the entire process and intention around the model can be accounted for.

Therefore, explainability should encompass three key aspects that the AI platform or system should explain:

  • The intent behind how the AI system impacts the users
  • The data sources that are used and how the outcomes are audited
  • How the data inputs result in specific outputs

Why is explainability important in AI?

Explainability is driven by the lack of transparency – what is commonly known as the Blackbox problem. Blackbox approach to AI evokes distrust and a general lack of acceptability of the technology. Parallelly there’s a rising focus for legal and privacy aspects that will have a direct impact on AI and ML technology. For example, the European General Data Protection Regulations (GDPR) will make it difficult for businesses to take a Blackbox approach.

Another example is IBM Watson. The world was astounded when it beat the best human players in a game of jeopardy. However, when IBM began marketing their AI technology to hospitals to help detect cancer, it saw a lot of resistance. It was alright if Watson couldn’t provide the reasoning behind a move that allowed it to win the game. But diagnosing cancer wasn’t a game and couldn’t be taken as lightly. Neither the doctors nor the patients were able to trust the technology because it lacked the ability to provide reasons for the results. Even if its results were the same as that of the doctor’s it couldn’t provide a diagnosis.

Another hotly debated topic is the use of AI for military purposes. Advocates of the lethal autonomous weapon systems (LAWS) claim that using AI will cause less collateral damage. However, despite the availability of large volumes of training data that will help the LAWS distinguish a civilian from a combatant or a non-target from a target, it is highly risky to leave the decision to AI.

One of the AI projects of the CIA is AI-enabled drones. The extent of explanation of the AI software for the selection of targets is only 95%. That 5% is left to chance and leaves room open for a lot of controversy and debate on racism, bias, or stereotype issues.

Methods to develop Explainable AI systems

There are two sets of techniques that are used to develop explainable AI (XAI).

Post-hoc methods

Post-hoc is Latin for ‘after this’, a method in which AI models are built normally and explainability is incorporated only during the testing phase.

LIME

Local Interpretable Model-Agnostic Explanations (LIME) is a prominent post-hoc technique. It works by influencing the features within a single prediction instance. For example, the technique perturbs features of the pixels of an image of a cat to define which pixel segments contribute the most to the AI model’s classification of that image. Perhaps, the model classified the image of the cat as a dog because the cat had dropping ears.

In 2015, it was reported that Google Photos labeled images of a black developer and his black friend as “gorillas”. In such a scenario, LIME could be used to mitigate this kind of bias by having a human operator override such biased decisions by evaluating the reasons provided by the algorithm. There are other post-hoc techniques such as Layer-wise Relevance Propagation (LRP) that can also be used to solve such classification errors caused by bias.

Ante-Hoc Methods

Ante-hoc techniques entail baking explainability into a model from the beginning.

BDL

Bayesian deep learning (BDL) is a popular ante-hoc technique. It is a great way to add uncertainty handling to the AI model. It helps gauge a neural network’s level of uncertainty about its predictions. By leveraging the hierarchical representation power of deep learning, the architecture can model complex tasks. The idea of BDL is straight forward – instead of having to learn point estimates for each parameter, we learn the parameters of gaussian for each parameter during forward propagation. To learn the parameters, backpropagation is used to make the parameters differentiable.

Bayesian Neural Networks can be trained by beginning with flat gaussian distributions for the trainable parameters of the neural networks. For each batch during the training loop, sample the weights according to the current distributions, forward pass with the weights, then back propagate the loss to the parameter. The advantage of this approach is, that for each output we also get the prediction certainty, which will help the user to judge whether to trust the result.

Reversed time attention model, alternatively known as RETAIN, is a model developed by researchers at Georgia-Tech to help doctors understand the AI software’s prediction.

General Intelligence

What is AGI?

AGI (Artificial General Intelligence) is the term used to describe real intelligent systems. It is also known as strong AI. Real intelligent systems possess the ability to think generally, are capable of making decisions regardless of any previous training. These decisions are made based on what they’ve learned on their own. It is often really difficult to style such systems using the technology of today.

Is AGI possible?

AI researchers and scientists haven’t yet achieved strong AI. To succeed, they need to find out how to form what we can call machine consciousness – programming a full set of cognitive abilities. Machines will be required to take experiential learning to the subsequent level, not just improving efficiency on singular tasks, but gaining the intelligence to use experiential knowledge to a wider range of various problems.

Strong AI uses a theory of mind AI framework, which refers to the ability of a machine to discern needs, emotions, beliefs and thought processes of other intelligent entitles. This theory isn’t about replication or simulation, it’s about training machines to really understand humans.

The immense challenge of achieving strong AI isn’t surprising once you consider that the human brain is the model based on which general intelligence will be created. Researchers are struggling to duplicate basic human functions of sight and movement due to the limited knowledge we have about the functionality of the human brain.

Fujitsu-built K, one among the fastest supercomputers, is a notable attempt at achieving strong AI. However, considering it took 40 minutes to simulate one second of neural activity, it’s difficult to estimate whether or not strong AI is going to be achieved in the near future. As image and face recognition technology advances, it’s likely we’ll see an improvement within the ability of machines to find out and see.

Machine learning (ML)

What is machine learning?

Machine learning, also known as ML is the process of automating time-consuming and repetitive tasks within the development cycle of an ML model. This leaves room for your team to build ML models at scale without compromising on quality.

Traditional ML model development is resource and time-intensive and requires significant domain knowledge on the part of your tech team. However, with ML, you can accelerate the time taken to build production-ready ML models with greater ease and efficiency. Does that mean, everyone can automate AI and leverage it for their business purposes without having to worry about hiring data scientists or analysts? The answer is not that simple.

While the real purpose of technology is to automate tasks and make life as easy as possible for humans, you need to understand that it is not possible to automate everything. This means you will need human minds working in conjunction with artificial intelligence to build state of the art AI models for your business. Finding and retaining data scientists is one of the biggest challenges in AI adoption. However, with the help of machine learning, you can empower your business teams to build AI models with ease. This also allows data scientists to focus on more productive and complex tasks.

Creating a class of citizen data scientists

Machine learning is creating a new class of citizen data scientists. Gartner defines a citizen data scientist as someone who augments data discovery and simplifies data science. Though their primary job function is outside the field of statistics and analytics, they create AI models that use advanced predictive capabilities. Think of them as power users who are capable of performing moderately sophisticated analytical tasks that would otherwise require a lot more expertise. They play a complementary role to data scientists.

Key steps to machine learning

Preparing data

Each machine learning algorithm works differently and has different data requirements. The machine learning platform can transform raw data into a structured format based on the requirements of the algorithm so that the algorithm’s performance is optimal.

Feature engineering

It is the process of modifying data so that the algorithms can work better. The machine learning platform should be able to engineer new features from various types of feature such as text, numerals, etc. It should be able to generate only those features that are of value depending on the characteristics of the data. We at Brainalyzed believe that feature engineering is expensive and time-consuming and doesn’t quite add sufficient value to the overall ML process.

Selecting the right algorithm

There are plenty of algorithms available today. However, it is impractical to explore every algorithm on the data you have. An ML platform will select and run on only those algorithms that are appropriate or suitable for your data.

Training ML models

Training an ML model on your data is a standard step in the ML process. A good AI platform knows what features to include in the training and what to weed out. By using a method called hyperparameter tuning, the platform can tune the most important hyperparameters for each algorithm as part of the training.

Easy-to-understand insights

Machine learning has proven its capability in various ways. It can make accurate predictions but at the cost of complexity. It isn’t just enough if an ML model is accurate and quick. It should also be able to translate the predictions in a human-friendly way. The outcomes and predictions should also be trustworthy. A good ML platform can explain the predictions that it can justify and are also easy for humans to interpret.

Ease of deployment

It is one thing to build a great predictive model but if you do not have the necessary infrastructure to implement the model in the production setting, it is a waste of time for your analysts. You need to opt for an ML platform that lets you build models that are ready for deployment.

Monitoring and management

Business requirements are constantly changing. The AI models you build should be able to keep up with the changing business needs. You need to keep them up to date with the newest trends as well. With an ML platform, you will be able to compare predictions with actual results and update the AI model with the latest information. It will also be able to identify when a model’s performance is deteriorating and notify you.

Narrow Intelligence

Artificial narrow intelligence (ANI), also known as weak AI or narrow AI, is the ability of an artificial entity to accomplish specific tasks. The current applications of AI fall within the realm of narrow intelligence. Narrow AI is designed to accomplish a single goal or perform singular tasks. For example, face recognition, speech recognition/voice assistants, driving a car, or searching the web are accomplished using narrow AI. It is extremely intelligent at completing the precise task it’s programmed to try to accomplish.

Though these machines seem intelligent, they operate under a ‘narrow’ set of rules and limitations. Hence it is usually known as weak AI. Narrow AI doesn’t mimic or replicate human intelligence, it merely simulates human behavior within a narrow range of parameters and contexts.

Narrow AI has experienced numerous breakthroughs within the last decade, powered by achievements in machine learning and deep learning. For instance, AI systems today are utilized in medicine to diagnose cancer and other diseases with extreme accuracy through the replication of human-like cognition and reasoning.

Narrow AI’s machine intelligence comes from the utilization of natural language processing (NLP) to perform tasks. It can either be reactive or have limited memory. Reactive AI is incredibly basic; it has no memory or data storage capabilities, emulating the human mind’s ability to reply to different sorts of stimuli without prior experience. Limited memory AI is more advanced. It is equipped with data storage and learning capabilities that help machines to use historical data to make decisions.

Natural language processing (NLP)

What is NLP?

Natural language processing (NLP) is a field of AI in which computers process, understand, and derive meaning from human. By using NLP, businesses can organize and structure data to perform tasks such as automatic summarization, translation, sentiment analysis, speech recognition, data categorization, etc.

What is NLP used for?

NLP is used for applications such as

  • Language translation applications like Google Translate
  • Word Processors like Microsoft Word and Grammarly that employ NLP to see grammatical accuracy of texts.
  • Interactive Voice Response (IVR) applications utilized in call centers to reply to certain users’ requests.
  • Personal assistant applications like OK Google, Siri, Cortana, and Alexa.

What are some of the NLP tools?

This is frequently asked question about which NLP library to choose. Based on our experience with building NLP applications, here are five NLP tools that can be used

  • CoreNLP from Stanford group
  • NLTK, the most widely-mentioned NLP library for Python
  • TextBlob, a user-friendly and intuitive NLTK interface
  • Gensim, a library for document similarity analysis
  • SpaCy, an industrial-strength NLP library built for performance

These five Python NLP libraries are not the only range of tools available. However, they are the backbone of NLP domain.

Neural networks

Neural networks are a set of algorithms, modeled after the human brain. That are designed and trained to recognize patterns in data. They can interpret sensory data through a kind of machine perception, labelling, or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

Strong AI

Strong Artificial Intelligence (AI) is a form of machine intelligence that is equal to human intelligence. Some of the key characteristics of Strong AI include the ability to reason, solve puzzles, make judgments, plan, learn, and communicate. It should also have consciousness, objective thoughts, self-awareness, sentience, and sapience.

Strong AI is also known as True Intelligence or Artificial General Intelligence (AGI).