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5 common misconceptions about AI

Ever wondered what your life would be without those perky machines lying around which sometimes/most times replaced a significant part of your daily routine? In Terminology fancied by Scientists, we call them AI (Artificial Intelligence,) and in plain layman or lazy man terms that is us, we fancy calling them machines and bots.

Let’s define the exact meaning of AI in terms of science because I hate disappointing aspiring scientists out there who don’t take puns lightly. For those that do, welcome to the fraternity of loose and lost minds. Let’s get down to business, shall we?

Definition: Artificial Intelligence or machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind such as "learning" and "problem-solving.”

Isn’t it evident I copied the above definition from Wikipedia? And did your natural intelligence decipher the meaning of the definition stated above?

Let me introduce you to the lazy man definition of Artificial Intelligence. Like all engineering scholars, I will take the absolute pleasure of dismantling the words and assembling it together again.

Artificial – Non-Human, something that can’t breathe air or respond to a feeling. 

Intelligence – the ability to display intellect, sound reasoning, judgment, and a ready wit.

Put the two words together and voila! Artificially intelligent machines are capable of displaying or mimicking human intellect, sound reasoning, and judgment towards it's surrounding.

Now that we got the definition of AI out of the way, look around you, what do you see? What’s in your hands? Do you not spot a single electronic device or bots?

Things or machines work a lot differently in this era. You must be awestruck of the skyrocketing shiny monuments. The big bird moving 33,000ft above your head carrying humans from one country to another, hospitals treating the diseased and the ill with technology your mind can’t fathom.

Fast cars, microwave and yes, we no longer communicate using crows or pigeons we have cell phones!

Don’t be surprised if I reveal that these are the necessity and an extension to our lives. And no, we cannot live without them anymore.

Our purpose of life has changed drastically, growing crops and putting food on the table isn’t what give us lines on the forehead. We built replacement models that take care of that too. We are living in a fast lane where technology, eventually, will slingshot us to the moon or another planet.

With such a drastic rise in AI and the current trend where all companies want a piece of it, there are some misconceptions about AI as well. With this blog, I try to debunk the misconceptions highlighting both the positive and negative aspects of artificial intelligence.

“If these machines are handling even the simplest of tasks, what are people going to do? Is it the destruction of jobs?”

Fret not. If there is technological advancement, there are always career opportunities as it is the human mind that does the ‘thinking.’ You are the master of your creation.

In fact, in 2020 there will be 2.3 million new jobs available thanks to AI, which results in less muscle power and more brainpower.

“Can Artificial Intelligence solve any/all problems?“

This question is debatable, while AI is designed to assist and make our jobs easier, it cannot save a human being from rubbing off cancers and illness.

Human intelligence hasn’t discovered a way to program the bots to predict or diagnose illness proactively. One must remember, bots act on what is fed/programmed by humans.

“Is AI infallible?“

If you thought it was, then I have slightly bad news. Humans are in a common misconception assuming the machines are no less than perfection and display little to no mistake. The non-sentient systems are trained by us, data selected and curated by us, and human tendency is to make mistakes and learn from them.

Artificial Intelligence is just as good as the training data used, which is created by humans. Any mistake with the training data will reflect on the performance of the system and the technology will be compromised. Ensuring you use a high-quality training dataset is critical to the success of the AI system.

Speaking of data being compromised, during the 2016 presidential election campaign, we witnessed the information of US citizens being evaluated by gaining access to their social media accounts. To proactively block their social media feeds with ads that will prove to be of interest. Therefore, stealing away the votes from the opposition.

We call this “data/information manipulation.” Sadly, the downside of Artificial Intelligence.

“AI must be expensive.”

Well, implementing a fully automated system doesn’t come easy and doesn’t come cheap. But depending on the needs and goals of the organization, it may be entirely possible to adopt AI and get the desired results without breaking your treasure chest.

The key is for each business to figure out what they want and apply AI as needed, for their unique goals and company scale. If businesses can workout their scalability and incorporate the right Artificial intelligence, it can be economical in the long run.

“Will Artificial Intelligence be the end of humanity?”

We are a work in progress, standing at the foyer of technological advancements with a long way to go. But, much like the misconception about robots replacing humans in the workforce, the question is more of smoke in the mirror.

The AI in its current level is not fully capable of self-conscious and decision making. Don’t let Star Trek, Iron Man and Terminator movies fool you into believing bots will lose their nuts (literally and hypothetically) and foreshadow the destruction of humanity. On the flip side, it is the natural disasters the bots are being designed to protect us from.

Oh, look what’s in every body’s hand, it’s what we call a cell phone. A device primarily designed to communicate with people that are at a greater distance.

Communication takes place using microwaves, very different from sand waves. Look closely and you’ll see people doing weird things using their fingers on the cell phone and a weird thing hanging from their ears going through to the same device. Yes, these devices are their partners for life.

Here we are, say Konnichiwa to the lady, don’t touch her! She’s just a hologram.

Welcome to the National Museum of Emerging Science and Innovation simply known as the Miraikan (future museum) where obsessiveness over technology has led us to build a museum for itself.

There’s Asimo, the Honda robot and, what you’re looking at isn’t another piece of asteroid that struck earth years ago, it is Geo-Cosmos. A high-resolution globe displaying near real-time events of global weather patterns, ocean temperatures, and vegetation covering across geographic locations.

You must be contemplating why has mankind reached such level of advancement? Let’s go back to the last question “Will AI be the end of humanity?”

The seismometer, a device that responds and records the ground motions, earthquake, and volcanic eruptions. There are a lot of countries that have lost far too many lives to even comprehend the tragic events of active earthquakes.

This device is a way to predict and bring citizens of Japan to safe grounds. Artificial Intelligence will not be the end of humanity, it can, in fact, be the opposite and could be an answer to humanity’s biggest natural calamities and disasters.

The human mind is something to behold, from its complex neural nerves in the brain to the nerves connecting to every part of the body to achieve motor functions. To replicate or clone it using artificial chips and wires is nearly impossible in the current era but the determination we hold and our adamant nature drives us to dream, the dream of one day successfully cloning the human consciousness into nuts and bolts of a bot.

One day to look at the stars and send bots for space exploration. To look for a suitable second home in an event of space disasters that humans have no control over. And, why send bots into deep space and not humans to add a feather to the hat of achievement?

Simply because we breathe, we starve, and our very own nervous system advertently detects the brutal nature of space above the earth. In this case, Artificial Intelligence and robots are in fact helping humans explore the possibilities of life in outer space. Which is against the misconception that AI will be the end of humanity.

So, there we have it, all the major misconceptions about artificial intelligence and what the reality is. End of the day, it all comes down to how we incorporate artificial intelligence and what we use it for.

If used in the right way, there will be a revolution in the way humans work. Which makes it important for all of us to work on educating people about artificial intelligence and using it to make the world a better place.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

ML has several applications in various fields such as

  • Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.

Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.

Drone Revolution | Blog | Bridged.co

It’s a bird, it’s a plane… Oh, wait it’s a Drone!

Also known as Unmanned Aerial Vehicles (UAVs), drones have no human pilot onboard and are controlled by either a person with a remote control/smartphone on the ground or autonomously via a computer program.

These devices are already popular in various industries like Defense, Film making and Photography and are gaining popularity in fields like Farming, Atmospheric research, and Disaster relief. But even after so much innovation and experimentation, we have not explored the full capacity of data gained from drones.

We at Bridged AI are aware of this fact and are contributing to this revolution by helping the drone companies in perfecting their models by providing them with curated training data.

Impact of Drones

Drones inspecting power lines

Drones are being used by companies like GE to inspect their infrastructure, including power lines and pipelines. They can be used by companies and service organizations to provide instant surveillance in multiple locations instantly.

Surveillance by drones

They can be used for tasks like patrolling borders, tracking storms, and monitoring security. Drones are already being used by some defense services.

Border patrolling
Drones surveying farms

In agriculture, drones are used by farmers to analyze their farms for keeping a check on yield, unwanted plants or any other significant changes the crops go through.

Drones at their best

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones use sensors such as electro-optical, stereo-optical, and LiDAR to perceive and absorb the environment or objects in some way.

Computer Vision

Computer Vision is concerned with the automatic extraction, analysis, and understanding of useful information from one or more drone images.

Deep Learning

Deep learning is a specialized method of information processing and a subset of machine learning that uses neural networks and huge amounts of data for decision-making.

DJI’s Drone

Drones with Artificial Intelligence

The term Artificial intelligence is now routinely used in the Drone industry.

The goal of drones and artificial intelligence is to make efficient use of large data sets as automated and seamless as possible.

A large amount of data nowadays is collected by drones in different forms.

This amount of data is very difficult to handle, and proper tools and techniques are required to turn the data to a usable form.

Combination of drones with AI has turned out to be very astounding and indispensable.

AI describes the capability of machines that can perform sophisticated tasks which have characteristics of human intelligence and includes things like reasoning, problem-solving, planning and learning.

Future with Drones and AI

In just a few years, drones have influenced and redefined a variety of industries.

When on the one hand the business tycoons believe that automated drones are the future, on the other hand, many people are threatened by the possibility of this technology becoming wayward. This belief is inspired by many sci-fi movies like The Terminator, Blade Runner and recently Avengers: The Age of Ultron.

What happens when a robot develops a brain of its own? What happens if they realize their ascendancy? What happens if they start thinking of humans as an inferior race? What if they take up arms?!

“We do not have long to act,” Elon Musk, Stephen Hawking, and 114 other specialists wrote. “Once this Pandora’s box is opened, it will be hard to close.”

Having said that, it is the inherent nature of humans to explore and invent. The possibilities that AI-powered drones bring along are too charming and exciting to let go.

At Bridged AI we are not only working on the goal of utilising AI-powered drone data but also helping other AI companies by creating curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.

Development of artificial intelligence - a brief history | Blog | Bridged.co

The Three Laws of Robotics — Handbook of Robotics, 56th Edition, 2058 A.D.
1. First Law — A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. Second Law — A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. Third Law — A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Ever since Isaac Asimov penned down these fictional rules governing the behavior of intelligent robots — in 1942 — humanity has become fixated with the idea of making intelligent machines. After British mathematician Alan Turing devised the Turing Test as a benchmark for machines to be considered sufficiently smart, the term artificial intelligence was coined in 1956 at a summer conference in Dartmouth University, USA for the first time. Prominent scientists and researchers debated the best approaches to creating AI, favoring one that begins by teaching a computer the rules governing human behavior — using reason and logic to process available information.

There was plenty of hype and excitement about AI and several countries started funding research as well. Two decades in, the progress made did not deliver on the initial enthusiasm or have a major real-world implementation. Millions had been spent with nothing to show for it, and the promise of AI failed to become anything more substantial than programs learning to play chess and checkers. Funding for AI research was cut down heavily, and we had what was called an AI Winter which stalled further breakthroughs for several years.

Gary Kasparov vs IBM Deep blue | Blog | Bridged.co

Programmers then focused on smaller specialized tasks for AI to learn to solve. The reduced scale of ambition brought success back to the field. Researchers stopped trying to build artificial general intelligence that would implement human learning techniques and focused on solving particular problems. In 1997, for example, IBM supercomputer Deep Blue played and won against the then world chess champion Gary Kasparov. The achievement was still met with caution, as it showcased success only in a highly specialized problem with clear rules using more or less just a smart search algorithm.

The turn of the century changed the AI status quo for the better. A fundamental shift in approach was brought in that moved away from pre-programming a computer with rules of intelligent behavior, to training a computer to recognize patterns and relationships in data — machine learning. Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information similar to how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning.

Representation of neural networks | Blog | Bridged.co

Different types of machine learning approaches were developed at this time:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

Along with better-written algorithms, several other factors helped accelerate progress:

Exponential improvements in computing capability with the development of Graphical Processing Units (GPUs) and Tensor Processing Units have reduced training times and enabled implementation of more complex algorithms.

Data repositories for AI systems | Blog | Bridged.co

The availability of massive amounts of data today has also contributed to sharpening machine learning algorithms. The first significant phase of data creation happened with the spread of the internet, with large scale creation of documents and transactions. The next big leap was with the universal adoption of smartphones generating tons of disorganized data — images, music, videos, and docs. We have another phase of data explosion today with cloud networks and smart devices constantly collecting and storing digital information. With so much data available to train neural networks on potential scores of use-cases, significant milestones can be surpassed, and we are now witnessing the result of decades of optimistic strides.

  • Google has built autonomous cars.
  • Microsoft used machine learning to capture human movement in the development of Kinect for Xbox 360.
  • IBM’s Watson defeated previous winners on the television show Jeopardy! where contestants need to come up with general knowledge questions based on given clues.
  • Apple’s Siri, Amazon’s Alexa, Google Voice Assistant, Microsoft’s Cortana, etc. are well-equipped conversational AI assistants that process language and perform tasks based on voice commands.
Developments in AI | Blog | Bridged.co
  • AI is becoming capable of learning from scratch the best strategies and gameplay to defeat human players in multiple games — Chinese board game Go by Google DeepMind’s AlphaGo, computer game DotA 2 by OpenAI are two prolific instances.
  • Alibaba language processing AI outscored top contestants in a reading and comprehension test conducted by Stanford University.
  • And most recently, Google Duplex has learned to use human-sounding speech almost flawlessly to make appointments over the phone for the user.
  • We have even created a Chatbot (called Eugene Goostman) that passed the Turing Test, 64 years after it was first proposed.

All the above examples are path-breaking in each field, but they also show the kind of specialized results that we have managed to attain. In addition, such achievements were realized only by organizations which have access to the best resources — finance, talent, hardware, and data. Building a humanoid bot which can be taught any task using a general artificial intelligence algorithm is still some distance away, but we are taking the right steps in that direction.

Bridged's service offerings | Blog | Bridged.co

Bridged is helping companies realize their dream of developing AI bots and apps by taking care of their training data requirements. We create curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.