Is Artificial Intelligence the next big thing in Hollywood?

Movies have captured the imagination of people ever since they came into the limelight. Right from the first motion picture in the late 1880s to the upcoming latest sci-fi blockbuster, cinema has become a medium of love, joy, and passion for all movie lovers. Almost every country across the globe has its own movie industry. The film industry of USA has the distinction of being the biggest, most famous, and most recognizable industry in the world today. It is none other than Hollywood.

Hollywood is universally adored by moviegoers due to its ability to churn out high-quality cinema with the best technology. Computer-generated imagery and visual effects were always a hallmark of Hollywood. The industry is well-known for using the best technicians and equipment to create breath-taking cinema.

Over the last decade, Hollywood has been increasingly investing and experimenting with innovative technologies such as Artificial Intelligence (AI). AI and machine learning algorithms can achieve tasks quickly and at scale. They are capable of accomplishing a variety of things in real-time that require dedicated teams of people. If used effectively, these new age technologies can bring out the best edits, the best performances, and the most advanced visual effects possible today.

Artificial Intelligence – The Game Changer

Movie production studios in Hollywood are nowadays looking to hire engineers who are good at training AI-based deep learning and machine learning algorithms that could do a visual effects specialist’s work. For example, work such as making a digital character look realistic, or smoothing out an effect, etc. can be done easily by intelligent algorithms. Advanced algorithms have the capability to automatically render advanced visual effects. AI, therefore, helps creative artists focus their efforts in doing other important work than waste their valuable time in meticulously editing an effect.

Last year’s superhero blockbuster, Avenger’s Infinity War, had American actor Josh Brolin play the titular character of the super villain, Thanos. The visual effects of Thanos on-screen appearance was done so well that it looked very real and life-like. How the visual effects team accomplished this feat was by using an AI algorithm to track Brolin’s facial expressions which included minute details such as his wrinkles, and then using another algorithm to map his face renders on to the body of Thanos. Using machine learning algorithms, the whole process could be done in real-time. If this was done without AI, it would have taken at least a few weeks for the team to get the same results using face mapping and swapping technology.

Disney recently came up with robot acrobats that could perform as well as human acrobats. The robot acrobats could be edited using AI and made to look like their human counterparts. Thus, the actors could focus on parts of the job which is less dangerous.

In the early 2000s, Peter Jackson’s Lord of the Rings series used AI-driven software to generate the huge armies seen in all three movies. In 2016, 20th Century Fox partnered with IBM Research to develop a movie trailer using AI for the movie “Morgan”. IBM’s AI cognitive system, Watson, was used to create a horror movie trailer that kept audiences on the edge of their seats.

Apart from the editing and performance sides of the filmmaking process, AI may soon be used to decide whether a film is to be made or not. According to Variety, a Belgian company called Scriptbook has come up with AI-based algorithms that can analyse a screenplay and predict whether or not a movie will be commercially successful. This will help production studios to make calculated decisions on which movies need to be made.

Using AI for Hyper-personalized User Targeting

Movie studios typically spend over a third of their budget on marketing their movies. If they could spend their marketing money wisely by targeting the right audience, it would make a more efficient utilization of their budget. Using high-end AI tools, it’s possible to deliver hyper-personalized content to customers as per their preferences.

American movie production company, Fox, formed a partnership with Google Cloud to develop Merlin, an AI-based learning program.  The main objective of Merlin is to analyse trailers and identify the basic patterns in audiences’ preferences for different types of movies. Online streaming giant, Netflix, uses AI for program recommendations for its subscribers and also to generate targeted mini trailers when a user clicks or does a mouse-hover on a show to consider watching it.

This kind of a personalized relationship wasn’t available with traditional Hollywood companies. Most of these companies’ products were removed or have been cut off from direct customer relationships. Studios don’t know exactly who is watching their movies and TV shows. This situation is however slowly changing. Subscription-based movie ticketing service, MoviePass, is trying to emulate a Netflix style data-driven model around the theatrical movie business.

Online video streaming coupled with AI, enables movie production companies to build direct-to-consumer relationships on a long-term basis. This will be a powerful aspect of Hollywood in the near future. Disney is one of the big movie production companies that have understood the immense value that this model brings. It has, in fact, scrapped its deal with Netflix and launching its own subscription-based video on-demand service next year.

Conclusion

AI and machine learning are yet to be widely adopted by movie companies around the world. This is due to the fact that they don’t fully understand them. Filmmakers who do understand AI have already started using elements of machine learning and deep learning in very specific areas such as editing and performance.

The movie industry in Hollywood and other parts of the world is growing at a fast pace year after year. According to Box office Mojo, the number of movies released in Hollywood last year totalled to a whopping 873. This number is only going to keep increasing in the future. Therefore, it’s important for movie studios to cater to every moviegoer by truly understanding their needs and expectations. Success is going to knock at the door of those companies who are able to achieve this feat. This is where technologies like AI will play a crucial role in building robust customer relationships.

How AI and Machine Learning Can Win Elections

Elections are the time when the people of a country are bestowed with the power to choose the next government that will govern their nation. The period leading up to the elections is packed with massive campaigning activities by all political parties. Every voter has his/her own ideologies and expectations that they would like to see a candidate fulfill. The main objective of political parties is to influence or sway the mind of the voter to vote for their respective candidates. The general techniques used by politicians to achieve this objective is by meeting voters in person, through mass media advertising, public rallies, social media campaigns, etc. This has been the case with political elections throughout modern history.

In recent years, technology has changed the whole approach drastically. Politicians are now relying on technological advancements such as analysing Big Data to connect and engage better with voters. Former US President Barack Obama’s campaign team used Big Data Analytics to maximize the effectiveness of his email campaigns, which resulted in the raising of a whopping US$ 1billion of campaign money.

Along with Big Data, the next technologies that are going to have a huge impact in election campaigns and political life are Artificial Intelligence and Machine Learning.

Engaging Voters Using AI

AI and machine learning can be used to engage voters in election campaigns and help them be more informed about important political issues happening in the country. Based on statistical techniques, machine learning algorithms can automatically identify patterns in data. By analyzing the online behaviour of voters which includes their data consumption patterns, relationships, and social media patterns, unique psychographic and behavioural user profiles could be created. Targeted advertising campaigns could then be sent to each voter based on their individual psychology. This helps in persuading voters to vote for the party that meets their expectations.

Apart from using intelligent algorithms, autonomous bots can also be used to spread information on a large scale. Bots are automated programs that can be programmed to run certain tasks over the internet. They can also be employed to detect fake news and misinformation. Whenever fake news is detected, they could issue a warning that the information is incorrect, thereby stopping it from influencing the voter.

AI for the Benefit of the Voter

AI can be used to gather information about the general voice of the voter. Using micro-targeting campaigns, it can help educate voters on various political issues and help them make up their minds about candidates. It can also be used to deliver information with respect to the interest areas of each voter. If a voter is interested in the country’s economic situation, an AI tool can be used to help the voter find out what each party has to say about this topic. This kind of personalization can help voters be more informed about each political party.

Can AI be misused?

The answer is yes. By knowing the behavioural and psychographic profiles of voters, AI can be used to send political messages that are insincere and fake. Personalized messages that highlight a different side of a particular argument can be sent to each individual voter. Every voter gets a different version of the candidate that is in line with their expectations. This, in turn, helps the candidate in garnering a general opinion that is in his/her favour.

In the 2016 US Presidential Elections, researchers from the University of Washington found that automated social media bots were used to increase the Twitter traffic for pro-Trump hashtags. They were roughly double the activity of his rival, Hillary Clinton. Political consulting firm, Cambridge Analytica (now defunct), was accused of helping Trump win the election by promoting anti-Hillary content among voters. The company acquired access to the data of over 87 million Facebook users and used machine learning to put together their psychological profiles. Facebook’s own targeted advertising system was leveraged to display content and ads at users with respect to their psychological profiles.

During the UK general elections in 2017, swarms of political bots were used to spread fake news on social media. These bots were used to target candidates who were more likely to vote for a particular political party. Through negative messages, the bots were successful in swaying the minds of voters and prevented them from turning up to vote on the day of the elections. In the final 3 months of the year, Twitter suspended more than 58 million malicious automated bot accounts.

Another scary AI and machine learning development that first raised a concern during the US midterm elections in 2018 is deepfakes. Deepfakes are audio or video generated by AI which shows someone saying or doing something that they did not say or do. For example, a fake video of Trump could be created where he talks about intensifying the trade war with China. Such videos look very genuine and can be successfully used to influence voters.

The Road Ahead

According to research from Borrell Associates, digital ad spending in politics reached a staggering $1.8 billion in 2018. The number is only going to drastically increase in the coming years. Artificial Intelligence has the capability to target millions of voters via digital ads with each person getting their own personalized message, according to their psychological profile.

AI and machine learning are ushering in a new era of politics. They are far too valuable to be ignored by politicians. It’s only a matter of time before these technologies are increasingly used by political parties to win elections in countries across the globe. Politicians should, however, adhere to certain ethics while employing AI for their campaigning activities. These innovative technologies shouldn’t be misused to spread wrong and incorrect information. They should be used to help voters make informed decisions on candidates for the benefit of the whole nation and to support democracy.

Essential Tools for DevOps Professionals

DevOps is an emerging professional movement in the IT industry which facilitates a collaborative work relationship between the software development and the IT operations teams. Implementing it is very beneficial to companies that have frequent software releases. The main goal of the DevOps process is to increase the stability, reliability, and flexibility of the production environment. This, in turn, increases the business productivity and value through a continuous delivery of products and services.

DevOps professionals need to regularly update themselves with all the latest set of tools that can help in process automation, information sharing, deployment time reduction, and continuous deployment. Keeping this in mind, here are the most essential tools that every DevOps engineer should master:

  1. Git and GitHub (Version Control)

Version control tools are used by software teams to manage and change source code over time. Git is the most popular version control tool that enables DevOps engineers to track the progress of their development work.

To use Git effectively, it needs to be integrated with the DevOps workflow. For enabling this integration, repositories need to be hosted for team members who can push their work. GitHub is currently the best online Git hosting repository. It allows the creation of unlimited public reports and has an increasing number of project management features.  Users can access public repositories for free, whereas private repositories come with various paid plans.

  1. Jenkins (Configuration Integration)

Jenkins is one of the more popular continuous integration and delivery tools available in the DevOps domain today. It is an open-source, extensible, and distributed continuous integration/continuous deployment (CI/CD) server that allows the automation of different stages of the delivery pipeline.

A cross-platform Java application, Jenkins can be configured and run by its built-in browser interface. Using Jenkins, engineers can iterate and deploy new code quickly, and in that way, development teams can measure the success of each step of the development pipeline.

It has a built-in GUI (Graphical User Interface) tool for easy updates and requires little maintenance. Jenkins offers more than 1000 plugins and integrates with almost all DevOps tools.

  1. Docker (Containerization Platform)

Since its launch in 2013, Docker has been the number one container platform in the DevOps space. As it’s an open platform, Docker makes it easier for sysadmins and developers to push code from development to production without using clashing or different environments during the entire application lifecycle. Docker enables distributed development by automating the deployment of apps. Applications are isolated into separate containers, so they become portable and more secure.

The Docker containerization platform uses containers to encapsulate all the requirements that an application might have, including system libraries, dependencies, and tools. These containers are cross-platform, lightweight, and isolated for security.

  1. Kubernetes (Container Orchestration)

For DevOps engineers, Kubernetes is the default tool for container orchestration. It helps them in keeping track of containers and automates their deployment and redeployment.

A container orchestration platform may not be required if there are only a few containers. If resources need to be scaled and hundreds of containers need to be managed, Kubernetes allows engineers to automate the whole process. With Kubernetes, containerized apps needn’t be tied to a single machine. They can instead be deployed to a cluster of computers.

  1. Chef, Ansible, Puppet (Configuration Management)

Chef, Ansible, and Puppet are the most widely used configuration management tools by DevOps professionals.

Chef

A systems and cloud infrastructure framework, Chef, enables DevOps professionals to automate the building, deployment, and management of infrastructure through short, repeatable scripts called “recipes”. Its key value proposition is its ability to describe infrastructure in a domain specific language (Ruby) and to configure and maintain it with code.

With Chef, DevOps engineers get a granular control over their infrastructure with a human-readable language, integrate with a wide variety of systems such as Azure, Azure, etc., and create “recipes” that describe particular configurations.

Chef saves an enormous amount of time and money for organizations and businesses that need to quickly reconfigure and deploy new infrastructure components.

Ansible

Ansible is a simple and easy to use configuration management tool. It is one of the most important tools in the DevOps pipeline since it helps engineers in automating cloud configuration, provisioning, and application deployment.

Ansible has a unique feature called agentless architecture. Without the use of configuration files for simple tasks, such as triggering updates and reboots, it can be directly run from the command line. As a result, it is a secure and lightweight solution for configuration management automation.

Puppet

Puppet is a cross-platform configuration management platform similar to Chef and Ansible. It is mostly preferred by DevOps engineers due to the simplicity of its declarative programming language and gentle learning curve. It provides them with the tools necessary to enforce security policies, develop effective security frameworks, and securely scale infrastructure.

Puppet automates infrastructure management and helps in the secure and fast delivery of software. For small projects, Puppet provides developers with an open-source tool. For larger infrastructure, it is available as an enterprise package called Puppet Enterprise with different pricing options for different requirements. Puppet Enterprise can enable the management of multiple teams and thousands of resources. Puppet has more than 5,000 modules and can integrate with many popular DevOps tools.

  1. Sonarqube (Continuous Inspection)

SonarQube is a continuous inspection tool that assists in managing code quality. It has the capability to analyze code in about 20 different programming languages. SonarQube offers visual reporting on and across projects. To analyze metric evolution, it enables engineers to replay the past code.

  1. Nagios (Continuous Monitoring)

Nagios is an open source and free DevOps monitoring tool. It enables DevOps engineers to find and fix problems by monitoring their software infrastructure. They can keep records of outages, failures, and events using Nagios. It also helps in forecasting outages and errors and detecting security threats through its reports and graphs.

Conclusion

DevOps is a continuously evolving field. New tools and systems are expected to arrive and replace the current ones in the future. When that happens, DevOps engineers should be proactive in taking the onus of keeping themselves abreast and well-versed with the latest available tools. The more knowledge they garner, the better is their chances of making it big in the DevOps world.

Comparison of Amazon Web Services, Microsoft Azure, and Google Cloud Platform

Cloud Computing is an on-demand delivery of computer system resources such as storage, applications, computing power, and other IT resources over the internet. It’s called cloud computing because users can access these system resources from any region through the “cloud” which is the internet. All user files and applications are stored on a network of remote servers instead of local servers.

Today, any internet-savvy individual makes use of cloud computing on a daily basis. Activities such as document editing, watching movies, listening to music, playing games, storing and accessing data, emails, etc. make significant use of cloud computing. On the other hand, companies use cloud computing services for developing new applications, data storage and analysis, on-demand software delivery, website and blog hosting, and video and audio streaming.

Cloud computing services can be classified into the following:

  1. Software as a Service (SaaS) – It involves the delivery of software applications over the internet on a subscription basis.
  2. Infrastructure as a Service (IaaS) –As the name suggests, the whole IT infrastructure is made available over the network. This includes servers for developing applications, underlying operating systems, services for deploying development tools, databases, etc.
  3. Platform as a Service (PaaS) – It caters to software developers to create web or mobile apps easily and quickly. PaaS provides an on-demand environment for developing, testing, managing, and delivering software applications.

In the cloud computing market, there are three main platform providers – Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP).  AWS is the market leader among the three, while Azure and GCP are growing consistently. All three platforms have their own features that would match an application developer’s requirements.

Amazon Web Services (AWS) – The AWS platform has the first mover advantage among its competitors. Hence, it offers almost every feature in the cloud computing industry. The platform enables easy access to a ton of features for application developers such as data storage, computing power, or any other functionality. It has many products under various categories. AWS also offers other tools such as management tools, developer tools, mobile services, and applications services.

Microsoft Azure – Azure is similar to AWS and offers a variety of products and solutions for app developers. The Azure platform offers good processing and computing power. It is capable of deploying and managing virtual machines at scale. Azure can also run large-scale “parallel batch computing” – a unique feature that it shares with AWS over the Google Cloud Platform.

Google Cloud Platform (GCP) – Google was one of the last companies to enter the cloud computing market. Through its vast number of services and offerings, it quickly rose to the third position among the top cloud computing providers. GCP’s App Engine product is quite popular among enterprise mobile app developers as it allows the creation of applications without dealing with the server in an agile manner. The GCP platform offers high-level computing, networking, storage, and databases. As GCP is relatively new to the market, it has a fewer services than AWS and Azure.

AWS, Azure, and GCP – A Career-Focused Comparison

For professionals who are looking to chalk out a career in the cloud computing industry, knowing all three platforms is a huge plus. Let’s take a look at the following categories for comparing these platforms.

Learnability:

All three platforms are quite easy to learn.

AWS has a simplified approach when it comes to launching servers on the cloud. It comes with proper documentation along with online training material. Professionals, who have worked in Windows technologies such as Windows Server, SQL server, and .net, may find it easier to learn Azure than AWS. As AWS and Azure have a lot of services, learning everything in one shot might get confusing. It’s always recommended to learn one service at a time. GCP would be easy to pick up for engineers who are familiar with Google applications and solutions.

Best Opportunities:

There are a variety of career options for software professionals in the cloud computing industry. The most popular roles are Cloud DevOps Engineer, Cloud Infrastructure Engineer, Cloud SysOps Engineer, Cloud Architect, and Cloud SME for specific services such as storage and networking. According to a survey by PayScale, the average salary for certified cloud computing professionals globally is $83,000 per annum.

If a basic job search is done for each of these platforms on Indeed.com, the results are as follows:

  1. AWS – 44693 available jobs
  2. Azure – 11658 available jobs
  3. GCP – 8960 available jobs

AWS is the market leader in cloud computing and is being used by majority of the companies globally. Professionals who are quite proficient in it are in high demand. In fact, the number of AWS Certified Solution Architects in the world is less than 5000. The above number is a clear representation of the immense job opportunities that are available for AWS professionals.

Azure and GCP are growing fast and gaining on the dominance of AWS. Azure is an enterprise-focused platform when compared to AWS which is customer-focused. Azure certified professionals are more likely to get enterprise roles with fat paychecks. GCP is still picking up and job opportunities are bound to increase in the next two to five years.

Versatility:

Amazon started offering AWS in 2006 and since then has built a comprehensive suite of cloud services. Its enterprise-friendly offerings appeal well to its core audience of application developers.  AWS also has advantages such as flexibility and openness.

Microsoft Azure is already being widely used in many organizations. Azure can link well with primary Microsoft on-premise systems like the Windows Server, System Center, and Active Directory. Azure is particularly strong in offering PaaS capabilities.

Rather than positioning itself as a strategic cloud partner like AWS and Azure, GCP is focused on proving itself on smaller, innovative projects at large organizations. Google’s TensorFlow framework and internal AI expertise are key selling points when it comes to GCP. With its focus on innovative features in machine learning, and using a Cloud Spanner distributed database along with a BigQuery analytics engine, GCP is poised to give tough competition to AWS and Azure.

Conclusion

Cloud Computing has triggered a revolution in the IT industry. It has become a go-to factor for application implementation and hosting for all companies, whether big or small. According to a Gartner Survey Report, the market for public cloud is predicted to reach around $411 billion in 2020. This is bound to generate a wide range of job opportunities in this field. So, if you are planning to start a career in this domain, you are on the right track. Getting a cloud computing certification in this field will definitely help in learning and developing your skills. Become a cloud computing expert and join the elite group of highly paid IT professionals in the world.

Career Opportunities for DevOps Professionals in India

The word DevOps is an amalgamation of the terms development and operations. In the world of Information Technology (IT), it is defined as a modern strategy used by software companies for combating issues related to software delivery. DevOps bridges the gap between the development and operations teams through a shared or collaborative approach to the tasks performed by them. It enables companies with the ability to develop and release features quickly with continuous feedback. This technique enhances the overall communication efficiency among teams and increases the software delivery speed.

A Brief History of DevOps

In the past, IT companies followed two main methodologies when it came to software development: Waterfall and Agile. The waterfall method followed a linear design approach where the development was divided into various phases. The agile method followed a non-linear model where each feature was built and tested throughout the development cycle. It was better than the waterfall approach as it reduced risk and improved efficiency. Although the agile method enhanced development, the delivery process followed a waterfall approach. As a result, the delivery cycle time increased as developers needed to start debugging from the beginning if a problem was discovered during deployment.   

This was when the term “DevOps” was formulated. DevOps combined development and operations along with the principles associated with agile methodology, which could increase the overall delivery efficiency and lower unnecessary bottlenecks. It has benefits such as overcoming all pitfalls of the waterfall model, reduced lead time between software fixes, and reduced failure.

Role and Expectations of a DevOps Professional

DevOps Professionals are expected to have a complete understanding of the Software Development Lifecycle (SDLC) and should have knowledge or experience in using automation tools for developing digital pipelines. They need to work with both developers and the IT operations staff to oversee product code releases. They should also be able to manage and handle IT infrastructure as per the supported software code dedicated to hybrid clouds or multi-tenant environments.

A DevOps engineer needs to have a good grasp of agile methodology principles and the interconnection between teams and company goals. He or she should be an excellent sysadmin, have good programming know-how, and have experience with testing and deployment. Communication skills are a must as they need to work closely with developers.

Some of the other skills that DevOps engineers require are:

  • – Knowledge of at least one cloud platform (AWS, Azure, GCP)
  • – Good hands-on knowledge of Configuration Management and Deployment tools like – Chef, Ansible, Puppet, etc.
  • – Proficiency in Git workflows and scripting

The Indian Career Landscape

DevOps is an evolving field in the software industry. It’s already in high demand in countries all over the world. Many companies in India are increasingly on the lookout for engineers who could take on a DevOps role and willingly go outside the comfort zone of their current role. As long as the desire to learn new tools is inherent, a DevOps engineer can get started as DBA, a sysadmin, a developer, QA, etc. and get involved with other teams in an organization.

DevOps engineers are one among the highest paid people in the IT industry. It’s also one of the most difficult tech jobs to fill. A DevOps practitioner may be called a DevOps Architect, Automation Engineer, Automation Architect, System Engineer, Release Manager, Developer-Tester, Integration Specialist, and a Security Engineer. The popular DevOps job roles for which one can apply are:

  • – Application Developer
  • – Security Engineer
  • – Integration Specialist
  • – System Admin
  • – Automation Engineer

India has been a go-to-market for global IT MNCs in the last ten years. It has seen a lot of investment in new and existing projects, along with the setting up of development centres in all parts of the country. This has resulted in the creation of a wide number of job opportunities, especially in DevOps. With the emergence of exciting new technologies such as Cloud Computing and Artificial Intelligence, DevOps roles are going to be available in plenty.

Apart from foreign investment, India has been a breeding ground for a huge number of technology startups in cities such as Bangalore, Gurgaon, Chennai, and Hyderabad. Almost every startup has a fast-paced work environment. Getting their products out on time will be crucial for their business. DevOps executives will be highly sought-after by these firms for accelerating their product delivery process.  

According to a report by MarketsandMarkets, the DevOps market size is expected to grow up to $10.31 Billion by 2023, due to the increasing need for high quality fast application delivery. Thus, the career opportunities for DevOps engineers are immense. To successfully venture into a DevOps role, software engineers need to have passion, dedication, and the eagerness to cross train beyond their existing job profiles.

DevOps will be a challenging, high-impact, and critical role for every organization in the future and India will be at the forefront of it.

Artificial Intelligence and Machine Learning – Our ‘Technological Reality’

Introduction to Artificial Intelligence

The term “Artificial Intelligence” dates back to 1956 coined by a Stanford canvasser John McCarthy, who defined the key task of AI as a sub-field of computer science. To call a machine intelligent, it must produce a response that is indistinguishable from a human response. AI is making a computer to think, i.e. learning and decision making. Hence AI is the development of mental faculties through the use of computational model.

Over the next few years, we are about to witness a massive transformation in the world we live in by improvements in Artificial Intelligence. More fields are being influenced by AI, and our society is part of that change.

What is Artificial Intelligence?

AI the study of building computers with qualities of the human mind. Since the development of digital computers in the 1940s, it has been demonstrated that computers can be programmed to carry out complex tasks. Artificial Intelligence is the ability of a digital computer or controlled machines to perform tasks commonly associated with an intelligent being.

AI can be embodied in a physical machine such as a robot or in a virtual machine such as a program. In both cases the character required is “intelligence”.

History of Artificial Intelligence

A discovery that influenced much of the early development of AI was made by Norbert Wiener. He was one of the first to theorize that all intelligent behavior was the result of feedback mechanisms. One of the results of the intensified research in AI was a novel program called The General Problem Solver, developed by Newell and Simon in 1957. It was an extension of Wiener’s feedback principle and capable of solving a greater extent of commonsense problems. Early AI research in the 1950s explored topics like problem solving and symbolic methods. This early work paved the way for the automation and formal reasoning that we see in computers today, including decision support systems and smart search that can be designed to complement and enhance human abilities.

The history of AI began in antiquity with stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen as exhibited in the writings of Pamela McCorduck who wrote that AI began with “an ancient wish to forge the gods”. Classical philosophers attempted to describe the process of human thinking as the mechanical manipulation of symbols which culminated in the invention of programmable digital computer in 1940s. In the 1950s-1970s early work with Neural networks stirred excitement. During 1980s-2010 Machine Learning became popular. Currently, the surge in interest in AI is driven by breakthroughs in Deep learning.

What is AI technology?

AI is achieved by analyzing how the human brain works while solving an issue and then using that analytical problem-solving technique to build complex algorithms to perform similar tasks. AI is an automated decision-making system, which continuously learns, adapts, suggests and takes actions automatically. AI is a branch of computer science that focuses on creation of intelligence machines that work and react like humans. AI includes programming computers for certain traits such as:

  • – Knowledge
  • – Reasoning
  • – Problem solving
  • – Perception
  • – Learning
  • – Planning
  • – Manipulation

Ultimate AI would be a recreation of human thought process i.e. a man made machine with human intellectual abilities. AI robot or computer gathers facts about a situation through sensors or human input. Then it compares this information to stored data and decides what the information signifies. It then runs various possible actions and predicts which action will be most successful based on the collected information.

There are two types of AI: based on priori modeling and not based on priori modeling. Priori model AI involves most of the service which is programmed and coded with a definite purpose in mind. Machines therefore take information and explores a predefined decision tree. Programmers need to code the patterns to define real-world objects to make the AI program understand . Most of today’s systems use AI priori modeling.

In AI which is not based on priori modeling –  An algorithm with innate preferences encoded determines the behavior. Based on active perception or interaction, machines learn the same way a baby does. Doing, trying and taking feedback. This model is still in its infancy. Compared to the priori model this is difficult to define since two robots trained differently will react differently. Philosophy, mathematics, economics, neuroscience, psychology and control theory are mixed in right proportion to make AI technology functional. These cognitive tasks include:

  • – Natural language processing for communication with humans
  • – Knowledge representation to store information effectively & efficiently
  • – Automated reasoning to retrieve & answer questions using the stored information
  • – Machine learning to adapt to new circumstances

How does Artificial Intelligence work?

AI works by combining large amounts of information with quick, iterative processing and quick algorithms allowing the software to learn routinely from patterns or features in the data. Expert system is also known as knowledge based systems. These systems rely on a basic set of rules for solving specific problems and are capable of learning. The laws are defined for the system by experts and then implemented using if-then rules. AI is not really machine learning but used interchangeably. AI is a broader concept, while machine learning is the most common application of AI. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering.

To know whether or not a machine is intelligent, we can run the Turing test in which a machine converses with a person via a computer. At the end if the person does not come to know that he has conversed with a machine or a person then the machine passes the test and is declared “intelligent”. But this test is not mathematically possible due to the person involved and his criteria.

Goal of AI is to make computers more useful by letting them take over tasks from humans and to understand principles of human intelligence.

Next Steps

Experts predict that within the next decade AI will outperform humans in relatively simple tasks. More complicated tasks are on the horizon. AI as a technology is expected to be mastered by 2050. The military explains the intent of AI is to reduce current workload and minimize the number of tedious tasks performed by humans. In light of this, the smartest approach to AI would be keeping an eye on how technology evolves, taking advantage of the improvements it brings to our lives and not getting too nervous of machines overtaking humans. The world’s first trillionaires are going to come from somebody who masters AI and applies it in ways we never thought of.

While Hollywood movies and science fiction novels depict AI as human like robots that take over world, the reality is far from that. Artificial Intelligence can be a great ally to move humanity forward. You can explore the required knowledge base required to master Artificial Intelligence, here.