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7 Artificial Intelligence-based movie characters that are now a reality

7 Artificial Intelligence-based movie characters that are now a reality

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Rashmi Jain
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March 1, 2017
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12 min read
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“Artificial Intelligence (AI) is the science of how to get machines to do the things they do in the movies.”- Astro Teller

Do you remember HAL 9000- the know-all machine, Baymax- the personal healthcare robot, Ava- the human looking robot, and WALL-E- the cleaning robot? I am sure you do. After all, they are famous fictional AI characters that made every sci-fi aficionado go nuts growing up.

Apperceptive, self-aware robots are closer to becoming a reality than you think.

Now, what exactly is AI?

Artificial Intelligence (AI) is defined as the ability of a machine or a computer program to think, learn, and act like a human being.The bottom-line of AI is to develop systems that exceed or at least equal human intelligence.

Sci-fi movies and TV shows have shown us multiple visions of how the future is going to be. The Jetsons, Ex Machina or Star Wars…they all had a unique take on what life would be like years later.

So, how real are these fictional characters? (Ignore the oxymoron) Where are we with the technology?

This article is sort of a brief history of AI with some fictional AI characters and their real counterparts to tell you how far we come on this amazing journey.

History of AI

We really can’t have history without some Greek bits thrown in. And unsurprisingly, the roots of AI can be traced back to Greek mythology. As the American author Pamela McCorduck writes, AI began with “an ancient wish to forge the gods.”

Greek myths aboutHephaestus, the blacksmith who manufactured mechanical servants, and the bronze man Talos, and the construction of mechanical toys and models such as those made by Archytas of Tarentum, Daedalus, and Hero are proof.

Alan Turing is widely credited for being one of the first people to come up with the idea of machines that think. He was a British mathematician and WWII code-breaker who created the Turing test to determine a machine’s ability to “think” like a human. Turing test is still used today.

His ideas were mocked at the time but they triggered an interest in theconcept, and the term “artificial intelligence” entered public consciousness in the mid- 1950s, after Alan Turing died.

The field of AI research was formally founded in a workshop conducted by IBM at Dartmouth College during 1956. AI has flourished a lot since then.

Some fictional characters that are reality

The following is a list of some fictional AI characters and their real counterparts with the features.

HAL 9000 versus IBM Watson

Remember the iconic scene of the movie, “2001: A Space Odyssey” when HAL refuses to open the pod bay doors saying, “I’m sorry, Dave. I’m afraid I can’t do that.” If you don’t remember, then take a lookthe clip below:

The movie “2001: A Space Odyssey” gave one of the world’s best representations of AI in the form of HAL 9000.

HAL stands for Heuristically Programmed Algorithmic Computer. It is a sentient computer (or artificial general intelligence) says Wikipedia. And it was the on-board computer on the spaceship called Discovery 1.

It was designed to control the systems on the Discovery 1 spaceship and to interact with the astronaut crew of the spaceship. Along with maintaining all the systems on Discovery, it is capable of many functions such as speech recognition, lip reading, emotional interpretation, facial recognition, expressing emotions, and chess.

HAL is a projection of what a future AI computer would be like from a mid-1960s perspective.

The closest real counterpart to HAL 9000 that we can think of today isIBM Watson. It is a supercomputer that combines AI and analytical software. Watson was named after IBM’s first CEO, Thomas J. Watson. Watson secured the first position in Jeopardy in 2011, after beating former winners Brad Rutter and Ken Jennings.

It is a “question answering” machine that is built on technologies such as advanced natural language processing, machine learning, automated reasoning, information retrieval, and much more.

According to IBM, “The goal is to have computers start to interact in natural human terms across a range of applications and processes, understanding the questions that humans ask and providing answers that humans can understand and justify.”

Its applications in cognitive computing technology are almost endless. It can perform text mining and complex analytics on large volumes of unstructured data.

Unlike HAL, it is working peacefullywith humans in various fields such as R&D Departments of companies as Coca-Cola and Proctor and Gamble to come with new product ideas. Apart from this, it is being used in healthcare industries where it is helping oncologists find new treatment methods for cancer. Watson is also used as a chatbot to provide the conversation inchildren’s toys.

Terminator versus Atlas robots

One of the most recognizable movie entrances of all time is attributed to the appearance of ArnoldSchwarzenegger in the movieTerminator as the killer robot, T-800.

T-800, the Terminator robot, has living tissue over a metal endoskeleton. It was programmed to kill on behalf of Skynet.

Skynet, the creator of T-800, is another interesting character in the movie. It is a neural networks-based artificially intelligent system that has taken over the world’s’ all computers to destroy the human race.

Skynet gained self-awareness and its creators tried to deactivate it after realizing the extent of its abilities. Skynet, for self-preservation, concluded that all of humanity would attempt to destroy it.

There are no AIs being developed yet which have self-awareness and all that are there are programmed to help mankind. Although, an exception to this is amilitary robot.

Atlas is a robot developed by the US military unit Darpa. It is a bipedal model developed by Boston Dynamics which is designed for various search and rescue activities.

A video of a new version of Atlas was released in Feb 2016. The new version canoperate outdoors and indoors. It is capable of walking over a wide range of terrains, including snow.

Currently, there are no killer robots but there is a campaign going on to stop them from ever being produced, and the United Nations has said that no weapon should be ever operated without human control.

C-3PO versus Pepper

Luke: “Do you understand anything they’re saying?”
C-3PO: “Oh, yes, Master Luke! Remember that I am fluent in over six million forms of communication.”

C-3PO or See-Threepio is a humanoid robot from the Star Wars series who appears in the original Star Wars films, the prequel, and sequel trilogy. It is played by Anthony Daniels in all the seven Star Wars movies. The intent of his design was to assist in etiquette, translations, and customs so that the meetings of different cultures can run smoothly. He keeps boasting about his fluency.

In real life too, companion robots are starting to take off.

Pepper is a humanoid robot designed by Aldebaran Robotics and SoftBank. It was introduced at a conference on June 5, 2014, and was first showcased in Softbank mobile phone stores in Japan.

Pepper is not designed as a functional robot for domestic use. Instead, Pepper is made with the intent of “making people happy,” to enhance their lives, facilitate relationships, and have fun with people. The creators of Pepper are optimistic that independent developers will develop new uses and content for Pepper.

Pepper is claimed to be the first humanoid robot which is “capable of recognizing the principal human emotions and adapting his behavior to the mood of his interlocutor.”

WALL-E versus Roomba

WALL-E is thetitle character of the animated science fiction movie of the same name. He is left to clean up after humanity leaves Planet Earth in a mess.

In the movie, WALL-E is the only robot of his kind who is still functioning on Earth. WALL-E stands for Waste Allocation Loader Lift: Earth Class. He is a small mobile compactor box with all-terrain treads, three-fingered shovel hands, binocular eyes, and retractable solar cells for power.

Arobot that is closely related to WALL-E is Roomba, the autonomous robotic vacuum cleaner though it is not half as cute as WALL-E.

Roomba is a series of vacuum cleaner robots sold by iRobot. It was first introduced in September 2002. It sold over 10 million units worldwide as of February 2014. Roomba has a set of basic sensors that enable it to perform tasks.

Some of its features include direction change upon encountering obstacles, detection of dirty spots on the floor, and sensing steep drops to keep it from falling down the stairs. It has two wheels that allow 360° movements.

It takes itself back to its docking station to charge once the cleaning is done.

Ava versus Geminoid

Ava is a humanoid robot with artificial intelligence shown in the movie Ex Machina. Ava has a human-looking face but a robotic body. She is an android.

Ava has the power to repair herself with parts from other androids. Atthe end of the movie, she uses their artificial skin to take on the full appearance of a human woman.

Ava gains so much intelligence that she leaves her friend, Caleb trapped inside, ignoring his screams, and escapes to the outside world. This is the kind of AI that people fear the most, but we are far away from gaining the intelligence and cleverness that Ava had.

People are experimenting with making robots that look like humans.

A geminoid is a real person-based android. It behaves and appears just like its source human. Hiroshi Ishiguro, a robotic engineer made a robotic clone of himself.

Hiroshi Ishiguro used silicon rubber to represent the skin. Recently, cosmetic company L’Oreal teamed up with a bio-engineering start-up called Organovo to 3D print human skin. This will potentially make even more lifelike androids possible.

Prof. Chetan Dube who is the chief executive of the software firm IPsoft, has also developed a virtual assistant called Amelia. He believes “Amelia will be given human form indistinguishable from the real thing at some point this decade.”

Johnny Cab versus Google self-driving car

The movie Total Recall begins in the year 2084, where a construction worker Douglas Quaid (Arnold Schwarzenegger) is having troubling dreams about the planet Mars and a mysterious woman there. In a series of events, Quaid goes to Mars where he jumps into a taxi called“Johnny Cab.”

The taxi is driver-less and to give it a feel like it has a driver, the taxi has a showy robot figure named Johnny which interacts with the commuters. Johnny ends up being reduced to a pile of wires.

Google announced in August 2012 that itsself-driving car completed over 300,000 autonomous-driving accident-free miles. In May 2014, a new prototype of its driverless car was revealed. It was fully autonomous and had no steering wheel, gas pedal, or brake pedal.

According to Google’s own accident reports, its test cars have been involved in 14 collisions, of which 13 were due to the fault of other drivers. But in 2016, the car’s software caused a crash for the first time. Alphabet announced in December 2016 that the self-driving car technology would be under a new company called Waymo.

Baymax versus RIBA II

Remember the oscar winning movie Big Hero 6? I’m sure you do.

The story begins in the futuristic city of San Fransokyo, where Hiro Hamada, a 14-year-old robotic genius, lives with his elder brother Tadashi. Tadashi builds an inflatable robot medical assistant named Baymax.

Don Hall, the co-director of the movie said, “Baymax views the world from one perspective — he just wants to help people; he sees Hiro as his patient.”

In a series of events, Baymax sacrifices himself to save Hiro’s and Abigail’s (another character in the movie) lives. Later, Hiro finds his healthcare chip and creates a new Baymax.

In Japan, the elderly population in need ofnursing care reached an astounding 5.69 million in2015. So, Japan needs new approaches to assist care-giving personnel. One of the most arduous tasks for such personnel is lifting a patient from the floor onto a wheelchair.

In 2009, the RIKEN-TRI Collaboration Center for Human-Interactive Robot Research (RTC), a joint project established in 2007 and located at the Nagoya Science Park in central Japan, displayed a robot called RIBA designed to assist carers in the above-mentioned task.

RIBA stands for Robot for Interactive Body Assistance. RIBA was capable of lifting a patient from a bed onto a wheelchair and back. Although it marked a new course in the development of such care-giving robots. Some functional limitations have prevented its direct commercialization.

RTC’s new robot, RIBA-II has overcome these limitations with added functionalities and power.

Summary

Soon a time will come when we won’t need to read a novel or watch a movie to be teleported to a world of robots. Even then, let’s keep these fictional stories in mind as we stride into the future.

AI is here already and it will only get smarter with time. The greatest myth about AI is that it will be same as our own intelligence with the same desires such as greed, hunger for power, jealousy, and much more.

Read more on How Artificial Intelligence is rapidly changing everything around you!

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Rashmi Jain
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March 1, 2017
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12 min read
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

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How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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