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Samsung Ai Deepfake

Latest Samsung AI can produce animated deepfake with a single image

Samsung’s AI tech is amazingly creepy and will make our deepfake problem worse as the Samsung engineers from the Samsung AI Center and the Skolkovo Institute of Science and Technology Moscow developed an AI technique using algorithms. It has the ability to transform an image into an animoji like video with face making speech expressions, with the only difference that it would not be exactly like your video. It uses AI to morph the face of the person and get all expressions right, which makes it difficult to recognise if its a video of the person or a morphed one. This requires deepfake to possess huge data sets of images in order to create a realistic forgery.

Artificial intelligence system used by Samsung is different from other modern-day technologies as it does not use 3D modelling and hence, can generate a fake clip which is directly proportional to the number of images.

According to researchers, working on this technology claim that it can be used for a host of applications which includes video games, film and TV and can be applied to paintings also. Though Samsung’s AI is cool, it comes with its own banes. It is sometimes used to morph the pictures of celebrities and other people for doing anti-social activities and may result in an increasing number of crimes.

This new approach provides a decent improvement on past work by teaching the neural network how to manipulate the existing landmark facial features into a more realistic-looking moving video. This knowledge can then be used to be deployed on a few pictures or maybe a single picture of someone, whom the AI has never seen before. The system uses a convolution neural network, a type of neural network based on biological processes in the animal visual cortex. It’s particularly adept at processing stacks of images and recognising what is there in them, that is, the “convolution” essentially recognises and extracts parts of those images.

This technique manages to solve the complexities existing in the artificially generated talking heads system, which is basically dealing with the increasing complexity of the heads, along with our ability to be able to easily spot a fake head. Nowadays, software for making deepfakes is available free of cost and it creates fully convincing lifelike videos. We have to just remember that seeing is no more believing. The research has been published on the pre-print server arXiv.org.

Though this technology looks pretty cool, I strongly think that this technology should not be made available for the public as it can lead to many fake videos and fake news. What do you think about this technology? Tell us with a short a quick comment

Kismet robot at MIT Museum

Researchers use magnetic properties for improving artificial intelligence systems

A group of researchers and experts from Purdue University have developed a method to integrate magnets with networks similar to brain for programming and teaching devices such as robots, self-driving cars.

Kaushik Roy, professor of electrical and computer engineering at Purdue University said that the stochastic neural networks developed by them attempts to copy some of the activities performed by the human brain and compute them with the help of a network of neurons and synapses. This helps to distinguish between several objects and make inferences about them besides storing information.

This was announced in the German Physical Sciences Conference and the report has been published in the Frontiers in Neuroscience.

The dynamics of the switching processes of nano-magnets resemble the electrical dynamics of the neurons to a large extent. Switching behavior is also exhibited by magnetic junction devices. Stochastic neural networks have random variations built inside them through stochastic weights.

Researchers have suggested a new stochastic training algorithm with the help of spike timing dependent plasticity for the synapses named as Stochastic-STDP. It has been tried on a rat’s hippocampus. The magnet’s intrinsic stochastic behavior was used to alter the various states of magnetization which are stochastically based on the algorithm for understanding various object representations.

The synaptic weights which have been trained and encoded in the magnetization states of the magnets are used for making inference. The use of high energy barrier magnets come to a great advantage as it allows compact stochastic primitives and makes the device eligible for being a stable memory element which permits data retention. Roy who also leads Brain-inspired Computing Enabling Autonomous Intelligence of Purdue University said that the magnet technology which has been developed is highly energy efficient. The network comprising of neurons and synapses makes optimal use of the memory and energy available similar to the computations done by brain.

These networks resembling the brain can also be used for solving many other problems such as graph coloring, travelling salesman problem and optimization problems in combinatorics. The travelling salesman is a very good problem involving optimization of algorithms. It involves traversing locations in the minimal amount of resources available. It was first defined by Irish mathematician W.R Hamilton and British mathematician Thomas Kirkman. It is an example of a NP hard problem where the smaller component problems will be as complex as the main problem in the minimum case.

The work is aligned with the celebration of Giant Leaps of Purdue University which acknowledges the advancements made in artificial intelligence by the university.

Tesla Autopilot Engaged in Model X

Tesla announces self-driving chips along with a ride-sharing network of driverless cars

Tesla has revealed its plans of launching a cab sharing network consisting of only self-driven cars. Tesla held an ‘Autonomy Day’ for promoting the driverless system to its investors. Apart from this, they also revealed the “Full Self Driving” computer chip which is the best chip right now according to its CEO Elon Musk.

This self-driving machine has been in the discussion for years now and the company also declared that the previously named “Autopilot Hardware 3.0” is in production and it has been already installed in the Model S and X vehicles. Pete Bannon, Head of Tesla Autopilot Division said during the presentation that the hardware developed by them possesses an improvement factor of 21 in fps processing against the previous model.

The chip production is being handled by Samsung in Austin, Texas. The CEO is optimistic about Tesla that the software which will have everything needed to conquer the self-driven system will be completed by the end of this year. He also explained that this version will need a bit of attention of a driver but he says it will stop by Q2 2020. After this Tesla will start working on a fully driverless system and Musk is sure that this will be released by the end of next year. Tesla has planned to launch a Robotaxi network by providing an update to its present mobile application.

The Tesla owners can also add their cars and earn money from the app or they can choose any random Tesla car to pick or leave them in their destination. Musk studied very intensely in economics about the fully self-driven car from both the Tesla perspective and Tesla’s owner’s perspective. Now Tesla’s vehicle’s hardware is developed as per his studies and what he presumes to achieve the goal of the fully self-driven car by giving updates to the software. He also approximated that the values of the cars based on self-driven networks are of $200,000 minimum whereas Tesla is selling cars having values of the maximum range of $50,000. He also roughly calculated that these cars can be seen everywhere by the end of next year.

Musk also said that buying any self-driven cars other than Tesla can be a loss to the buyers because there will be no fully self-driven car other than this. Tesla convinced very well by giving their presentation on how fully self-driven system is more safer than normal cars. The self-driving space is getting quite competitive as companies such as Google, Uber have made decent moves already.

Cryptate of potassium cation

Researchers identify state of matter to possess both solid and liquid state

A team of researchers from the University of Edinburgh, Scotland used latest models of artificial intelligence (AI) to simulate what could happen if the element potassium was subjected to very high amounts of pressure, ranging from 200,000 to 400,000 times of atmospheric pressure along with heat between 400 – 800 Kelvin.

The atoms of potassium like most of the other metals behave in an ordered manner under normal circumstances. But the team of physicists identified that in case of extreme conditions, they get arranged in complex orders, the atomic core lined up in a cylindrical way, arranged in a ‘X’ shape and four chains alongside it.

The two different arrangements are called as the “host-guest structures”, the co-author of the paper, Andreas Hermann told. The resultant substance is the formation of two intertwined and interlinked lattices which is very unusual.

The study has been published in the journal PNAS in this week and it is reported that in these conditions, potassium atoms possessed an arrangement which is called as the chain-melted state where one lattice known as ‘guest’ lattice dissolved to form liquid while the ‘host’ lattice remained as a solid form. On heating this, the guest atoms melt while the atoms belonging to the host lattice remain crystalline.

The reason behind this is that the host lattice possesses a much stronger bond and thus it remains in the solid state. This is why, a larger amount of energy is needed to melt it. On the other hand the ‘guest’ lattice has a much weaker bond, so it dissolves to turn to a liquid. The two structures, ‘host’ and ‘guest’ lattice comprising of 80% and 20% composition are similar to each other on an atomic level. The only difference between them is on their arrangement in the lattice.

When this substance is observed by the human eyes, it would appear as solid block of potassium which is dissolving to liquid as well as forming a solid structure simultaneously. The team described it as a ‘sponge-like’ material which can soak up the liquid. It can be understood as the sponge which soaks itself up, when it is a liquid and then again reforms itself in the solid state. As the host is 80% and the guest is 20%, the material is always in this state.

This was made possible by AI models and then the findings were tested over a large number of theoretical samples. As a result, the machine learning model can also determine the behaviour of the other elements in similar extreme conditions.

Godfathers of AI

The Godfathers of AI receive the prestigious Turing Award

The 2018 Turing Award, acknowledged as the “Nobel Prize of computing” has been awarded to a trio of researchers who have set the foundations for the current success in artificial intelligence.

The term artificial intelligence merely refers to the intelligence that is demonstrated by the computers. Artificial intelligence is renowned for its cycles of boom and bust, and the issue of hype is as old as the field itself. When the research fails to meet the inflated expectations, it generates a freeze in the funding and interest known as an “AI winter”. It was at the tail end of one such winter in the late 1980s that Bengio, Hinton, and LeCun began exchanging ideas and working on interconnected problems. These included neural networks. They are computer programs made from connected digital neurons that have become a key building block for contemporary and modern AI.

Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, who are often referred to as the ‘godfathers of AI’ have been recognized with the $1 million annual prize for their work evolving the AI subfield of deep learning. The techniques the trio developed in the 1990s and 2000s assisted huge breakthroughs in tasks like computer vision and speech recognition. Their work fortifies the current proliferation of AI technologies, from self-driving cars to automated medical diagnoses.

All three have ever since taken up prominent places in the Artificial intelligence research ecosystem, by straddling with the academic world and the tech industry. Hinton splits his time between Google and the University of Toronto; Bengio is an associate professor at the University of Montreal and has started an AI company called Element AI, while LeCun is Facebook’s chief AI scientist and an instructor and professor at NYU.

Google’s head of AI, Jeff Dean also praised the trio’s achievements. “Deep neural networks are accountable for some of the greatest advances in modern computer science,” said Dean in a statement.

Let us hope that people like this trio improve AI and all of us use AI in the right way. Tell us your view on AI and what do you think about the future of AI with a quick comment.

Machine Learning and Artificial Intelligence

The Future of Computing and Artificial Intelligence

The advent of computers propelled society and spurred into action a lot of businesses and industries that would otherwise have been impossible. Computers and computer technology changed the way humans worked and functioned. The very first computer was a behemoth. Today, everything that you need is available on the computer through the internet and even the most complex computations can be carried out by these modern wonders. But, what does the future have in store for these computers? Are there any more frontiers that these brilliant systems need to face and overcome, and as a result, become even more powerful computationally? Let’s find out.

Computers are already ubiquitous, but, they possess the ability to enhance their presence even more. Computers are on desks and countertops, in bags and pockets, but, soon, they might be a part of everything imaginable. Yes, everything conceivable, even the tiniest of devices and commodities, might soon have computers embedded within them. There is an increasing demand for products that can carry out the computations that they need on their own and this has led to the need for devices that have computers embedded in them.

Some people perceive the final goal of computers to be that of ensuring that computers are inextricable from society and human life. Computers would be so involved in every process that the very thought of its non-existence would make life impossible! The aim is to ensure that computers are entangled with human life in such a way that computers would be indistinguishable from human life.

The notion going around that the future of computing is limited to Artificial Intelligence is incorrect. Artificial Intelligence is still burgeoning and is expected to be an essential part of day-to-day life, but, the future of computing is not limited to just Artificial Intelligence, there is much more to computing.

Artificial Intelligence


Image Source: Mike MacKenzie (Flickr)

The first challenge involved with Artificial Intelligence is the definition of intelligence. Intelligence has long been considered to be subjective and each person’s interpretation and definition of intelligence is generally different. Is a machine intelligent if it is able to communicate? Is it intelligent if it is able to learn? Or, is it intelligent if it can merely solve problems? One way to measure intelligence is through processing power. Machines can potentially gain computing powers that are greater than the processing power of all human beings put together. This situation is termed as the Singularity. The term was coined by John von Neumann and refers to the point when human affairs would cease, and computers would completely take over.

Potential threats

The low-end repetitive tasks have already been replaced by computing systems that automate it. The need for automation has made it imperative that we develop the computing potentials of computers. But, this growth has already caused a lot of concerns. There are several concerns over whether machines would someday surpass human intelligence. Currently, human intelligence, based on processing power, is considered to be 100,000 times more than that of the most powerful processing computer. But, it is estimated that computers can reach the processing power of humans within the next decade, at the rate of growth they are showing. By 2050, it is estimated that they would have processing power that is more than the sum total of all human brains combined. That is a scary thought indeed.

So, computers can take away our jobs and surpass our intelligence, what does that suggest? Is the world doomed? Not quite. Although one cannot quite rule out world domination by super intelligent computers and artificial intelligence, it is unlikely to happen due to the complexity brake. Scientists argue that computers and systems cannot grow to gain such super intelligence because as the processing power grows, the complexity increases and the complexity would reach a level that would restrain the growth of intelligence. Even if computers somehow broke free from the shackles of the complexity break, it is hypothesized that there would be enough ‘obedient’ intelligent systems that would keep the ‘misbehaving’ ones in check.

Let’s now get back to defining artificial intelligence. Since intelligence cannot be quantifiable, a generalist definition of artificial intelligence would be “machines and computers that carry out computations and tasks that would otherwise be carried out by humans”. This definition clearly does not capture the whole picture, but, is a reasonable attempt at defining A.I.

Evolution of Artificial Intelligence

The evolution of artificial intelligence can be viewed with regards to the forms of problems that it intended on solving. The early works in artificial intelligence focused on formal tasks, such as game playing and theorem proving. Checkers playing programs and chess playing algorithms were the earliest works in the field. Theorem provers such as the ‘The Logic Theorist’ aimed at solving theorems.


Image Source: Pixabay

A.I. then began to be implemented in solving common sense reasoning problems. A.I. was used to simulate the human ability to make presumptions about everyday activities. Reasoning about physical objects and their relationships, and about actions and their consequences were the problems that came under this field.

When A.I. began to grow, it started being built for perception problems. These problems were especially difficult because they involve analog data such as speech and vision.

A.I. is now being used for natural language understanding and natural language processing. Humans possess the ability to communicate through expressions and languages. Perceiving the languages and various constructs of the languages and being able to process them, are all problems that A.I. was created to solve. These fields are still not fully developed, but, systems that carry out natural language processing with a high degree of accuracy are already in place.

A.I. is now being used in tasks where human experts are needed. These tasks, such as engineering design, scientific discovery, medical diagnosis and financial planning are being carried out using artificial intelligence with a great degree of accuracy and precision. Expert systems are computer systems that use inferences and knowledge bases to mimic the decision making ability of human experts.

Domains of Artificial Intelligence

Artificial intelligence consists of countless domains. It would be highly improbable to list all of them. Here are some of the interesting domains of Artificial Intelligence:


Heuristic techniques involve finding approximate solutions to problems instead of accurate ones. Heuristic techniques are generally used when the time for computing the solutions is restricted or when it is computationally too expensive to look for the global optimal solution.

Statistical reasoning

This form of reasoning involves using probabilities and probabilistic theorems to determine most likely occurrences. Logical rules based on probabilities are created to determine what event is more likely to occur, given certain conditions.

Natural language understanding and processing

Natural language understanding involves the interpretation of natural language spoken by humans. Natural language processing, on the other hand, involves both interpretation and generation of natural language spoken by humans.

Expert systems

Expert systems are involved in carrying out tasks that would otherwise require human experts. These systems use inferences, which, are generally if-then rules, and the knowledge of previous problems and inferences derived from these previous problems, to provide solutions to the problem at hand.

Fuzzy logic systems

Fuzzy logic systems remove the notion of crisp boundaries and use fuzzy sets instead of crisp sets. In fuzzy logic, everything is viewed in terms of degree of membership. The hotness of a day is generally not quantifiable, but, fuzzy logic aims to quantify such occurrences using the concept of membership degrees. A temperature of 45°C, which, most people would perceive to be hot, could, therefore, belong to a set called ‘Cold’ to a certain degree (a small membership value) and to a set called ‘Hot’ to a certain degree (large membership value).

Genetic algorithms

Genetic algorithms aim to mimic the biological behavior of human beings and animals to solve problems. Artificial Neural Networks have been widely used in the computation. These networks consist of nodes that are intricately interconnected just like the human neural networks. Outputs at each layer of a network is propagated to the next layer. This system is used to solve a lot of complex problems. Ant colony optimization is one of the areas of genetic algorithms, where actual ant colonies are mimicked to solve problems such as finding the shortest paths.

Evaluation of artificial intelligence: Turing test

Turing test


Turing Test Illustration. Image Source: Wikipedia

Alan Turing hypothesized that a computer can achieve intelligence equivalent to that of a human being, or at least, intelligence that is indistinguishable from human intelligence if it could successfully lead a human being to believe that the machine was human. The Turing test is renowned for being the basis for classifying machine as either intelligent or not intelligent.

The Turing test involves a human evaluator who is responsible for determining which of the two entities he is talking to is actually human. The evaluator is allowed to ask a series of questions to carry out this classification. These questions are asked to both the human being and the machine. If the human evaluator fails to determine which one is human, and which one is a machine, based on the responses, then, the machine is said to have successfully fooled the evaluator and is deemed to be intelligent.

The Turing test is said to have set the benchmark for evaluating systems that may be intelligent.

The CEO of SoftBank, which, is a Japanese conglomerate, Masayoshi Son, once said, “I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it will be our partner”. It is absolutely imperative that we tread carefully in matters pertaining to artificial intelligence. It is important to ensure that humans do not lose control over artificial intelligence. The result of losing control can be catastrophic. There is a need to impose regulations that prevent detrimental outcomes due to the development of artificial intelligence. The field of artificial intelligence has immense capabilities and should be allowed to grow, under regulated circumstances. Artificial intelligence need not have sinister outcomes. It is the responsibility of humanity to monitor A.I. and let it grow.

Read More:

  1. A Technical Overview of AI & ML (NLP, Computer Vision, Reinforcement Learning) in 2018 & Trends for 2019
  2. Bill Gates: These breakthrough technologies are going to profoundly change the world
How Will Self Driving Cars Change the Way People Live

How Will Self-Driving Cars Change the Way People Live?

Cars have changed our lives for the better in all sorts of unforeseen ways. Indeed they bestowed upon us with a lot of freedom but in return levied upon us heavy costs. Drivers commuting daily generally count their lucky stars before going out for the day. Accidents on road, pollution, congestion, etc. are the heavy costs that all drivers have to pay at least once in their lifetime. Hence, comes the new era of self- driving cars. These can change the whole scenario of driving. According to WHO, cars are the leading cause of death from ages 15-29. 20 million to 50 million people get injured due to these accidents. Self-driving cars can reduce these numbers drastically. Nowadays due to our smartphone generation, we are constantly distracted. Self-driving cars would let drivers ride even while using our gadgets behind the wheel.

Are self-driving cars safe?

Google has already launched videos in 2009 and 2014 about helping people with disabilities to drive. Blind and elderly people could easily drive in cars now. How much easier it would make their lives!

Waymo, the self-driving car remains the clear leader of such cars on U.S. streets. Now there square measure dozens of autonomous vehicle firms testing on U.S. streets. Over subsequent few months, Waymo’s fleet began driving regarding twenty 5000 self-driven miles per day, or 1,000,000 miles per month.

In the last year 2018, Waymo hit another major milestone of eight million self-driven miles as its new electrical panther I-Paces vehicle hit the streets.

Google's Waymo self driving car

Google’s Waymo Self-driving Car. Image Source: smoothgroover22 (Flickr)

The next most skilled firms, Uber and gram Cruise, square measure still many million miles behind Waymo.

That doesn’t embody miles driven within the semi-autonomous modes that several cars currently supply, like Tesla’s Autopilot, that square measure a lot of driver-assistance systems than true self-driving vehicles.

In the previous few years, the best strides taken within the self-driving business are by ride-hailing firms, WHO square measure devoting Associate in the nursing exceptional quantity of your time and cash to develop their own proprietary technologies and, in several cases, giving members of the general public rides in their vehicles.

In 2017, Lyft’s business executive foretold that at intervals 5 years, all their vehicles are going to be autonomous.

At a news conference in March 2018, wherever Waymo’s business executive John Krafcik proclaimed its ride-hailing program. Krafcik claimed that the corporations are going to be creating a minimum of 1,000,000 journeys per day by 2020.

Autonomous cars could take away certain jobs of taxi-drivers, truckers, etc. But on the bright side, it will keep us safer on the road as so many new companies are setting out on the road, quite literally, to bring this gear shift on the way.

Benefits of self-driving cars

With car crashes increasing every day on the road, self- driving cars are making their mark on the road more in the very near future.

Listed below are some of the many self-driving cars’ benefits:-

  1. Road safety

According to the government, drivers pose a threat in 94% of crashes. So instead of that, self-driving cars can reduce driver error. It can potentially reduce risks and dangers considerably, securing human lives and reducing accidents.

road safety self driving cars

Image Source: Frank Derks (Flickr)

Drivers behind the wheel are often drunk, drugged, unbelted, speeding or distracted. This’ll be the greatest promise to reduce the repercussions for such incidents.

  1. Independent driving

Disabled people like the blind can now lead self-sufficient lives however they want to. Senior citizens can also be greatly benefited from this. Car-pooling of such vehicles can also reduce great costs providing such services to every household.

  1. Cost-effective

With medical bills we are having to pay off with increasing pollution and car crashes and other injuries, self-driving cars serve as a boon in cutting off so much of medical expenses and hospital bills. Not to mention the amount of work time we would lose if we were bed-ridden. On top of that the cost of repairing the damaged vehicle. Hence, these cars are helping us save up so much and also reduce the costs of insurance well over time.

  1. Increase productivity

Self-driving cars can enable us to watch movies, create presentations or even write documents while driving. We don’t even have to worry about parking because the vehicle parks itself upon reaching a particular destination, be it an airport or a shopping mall. We could do our jobs and respond to emails without the worry of paying attention to the road or crashing or running red lights.

  1. Reduce road traffic

Automated vehicles could reduce the mistakes caused by drivers on roads which leads to road congestion and thereby an increase in traffic. Intelligent vehicles as such are programmed to maintain a safe and consistent distance between two vehicles. This reduces the number of stop-and-go traffics that increase confusion and congestion.

  1. Greener future

When traffic jams are reduced and the roads are considerably less congested, it would mean no more idling of vehicles on the road emitting greenhouse gases. It would save so much of fuel and would pave the way for a less-polluted greener and cleaner future.

Greater demand for electric vehicles might be felt and even car-sharing. When electric, i.e, battery-powered vehicles are shared, the cost of battery usage could be shared too. Hence the vehicle can be used for more hours of the day because that would mean more commuting at less cost. This will increase the economic appeal of self-driving cars.

  1. Save more space

Self-driving vehicles could pave the way for a smarter future in the sense that they will affect in the building of cities and the infrastructure of smarter designs of roads and parking spaces. In our day to day parking, it creates a lot of hassle in selecting a parking spot, checking the space in between the vehicles and then parking our vehicle with adequate space between them. Self-driving cars will be programmed to negate such hassle and calculate the distance by themselves and then park accordingly. Estimations point out that this can be done saving 15% less space which will result in saving way more space for more efficient commuting and parking in all urban areas all around the globe.

  1. Saving time

Each driver in urban areas is estimated to spend around 27 minutes extra in commuting in the US. With self-driving cars out on the road, this could save well up to an hour, if not more each day. The time which they can devote to their well-being and even boost the economy and other benefits that lead to a more efficient life.

These are some of the numerous self-driving cars’ benefits.

Self-driving cars future

While talking about the future of these wonderful cars, there are countless wonders and expectations and possibilities to come true. Johann Jungwirth of Volkswagen says that restaurants might cover the cost of travel for certain customers to boost up their sales. Fancy ones could advertise these vehicles as part of their meals too. In-vehicle advertising would sky-rocket taking the help of ride-hailing networks such as Ola, Uber, etc. riders can choose between expensive ones or cheaper along with the offers that come with them.

Other avenues of comfort will be opened up to people such as letting us work out in the gym with our trainer for a quick while on our way to work or call a make-up artist or a hairdresser on our way home.

Toyota has already designed e-Palette vehicles which are kits that can be delivered to offices, salons, parlors, shops, etc. These vehicles will modify our entire social lives by merging groups of people with similar interests while assigning rides, which means much more fun while even commuting! Dating apps could take up this advantage and pair up people with similar interests out for a ride on the way to the date or even long drives. Self-driving vehicles might also serve as a mobile hotel while out for a trip. Spacious ones, of course.

Wrapping UP!

From the sci-fi movies featuring robot cars, who knew they would become a reality someday? In the past five years, driving has become smarter and smarter with each new innovative design to the existing models. From Bluetooth to remote lock and now driverless cars! Keeping human safety the utmost priority these vehicles will venture out on the road in greater numbers making our lives easier and our traveling hassle free.

No doubt, every new technology, and invention come with its own pros and cons but these vehicles are mostly targeted in making our lives easier, connecting more people socially and professionally. Once these self-driving vehicles are economically affordable to the regular folks, these will sweep over our roads in no time.

In answer to the question will these vehicles change our lives for the better? Yes, they will. Commute will become cheaper and less of a drag. People will no longer have to worry about traffic or finding a parking spot. Like most technologies, these vehicles will surely channel the best in us and pave the way for a promising future ahead.

Alan Turing Aged 16

Breaking the Enigma: Alan Turing and the Turing Machine

Very rarely do you find individuals that change the course of history purely through sheer brilliance. But, when you do find such individuals it is imperative to celebrate them. These individuals tend to produce magnificence through brilliance so effortlessly and with such fluency that it is hard not to remain awestruck. Alan Turing was one such individual. Alan Turing is the man who formulated many of the theoretical concepts that have enabled modern computation. He is widely regarded as the father of computer science, and, rightfully so.

About Alan Turing

Alan Turing was an English mathematician and cryptanalyst whose work in breaking the Enigma code and work in the development of computer science gained him a lot of recognition. He is also credited as being the father of Artificial Intelligence, which, is an exciting and promising field. He studied at King’s College, Cambridge and was widely considered to possess brilliance that was far beyond his age.


Turing Machine

Turing machine is a hypothetical simplistic machine that was created by Alan Turing as a solution to the decision problem. The simplicity of the Turing machine made it a phenomenon. The field of computer science was still in its initial stages and was a burgeoning field and the role of the Turing machine in its growth cannot be overstated. The contribution of the Turing machine was immense in the development and acceptance of computer science.

A Turing machine consists of an infinitely long memory tape that contains input characters or symbols. Upon reading a character on the memory tape, the state of the machine could change. There were operations such as read, write or modify that could be carried out on the tape. There also existed a set of rules that defined what would happen for a particular state and input symbol. These rules specified the effect of the character read from the tape on the current state. Each memory tape had a head. The head was responsible for carrying out either the operation of read, or, that of write. This head could also be moved either to the left or to the right.

This seems simple, right? This is where the beauty of the system lies. Without being overly complicated, the Turing machine had the potential of solving any problem. Alan Turing stated that this machine could solve any computational problem, given enough time and memory. This simplistic machine could solve everything!


An Enigma decryption machine called a “bombe.” This machine, made by National Cash Register of Dayton, Ohio, eliminated all possible encryptions from intercepted messages until it arrived at the correct solution. (U.S. Air Force photo)

Therefore, the Turing machine provided the basis for computational studies. In terms of solving computational problems, there is no computer more powerful than the Turing machine. Any computer that is considered to be as powerful as the Turing machine is considered to be Turing complete. Every modern computing system is considered to be Turing complete.

To answer the decision problem, Turing proposed a puzzle that is renowned as the halting problem. This problem aims to figure out whether there exists an algorithm that can determine if the machine will keep running, or, halt, given the input tape and description of the Turing machine.

This halting problem can be used to determine whether computer programs would halt or continue perpetually. Alan Turing was successful in proving that the halting problem was in fact unsolvable. Consider a Turing machine that takes the description of a program and certain inputs for its memory tape. Also, consider the output for this machine to be either Yes if the program halts or No if the program fails to cease. Now consider another Turing machine that is built on top of this machine. Now, if the output of the first Turing machine is Yes, which means that the program halts, then, we make the second machine run in an infinite loop. If the output of the first Turing machine is No, which means the program does not halt, then, we make the second machine output No and halt.

If you have followed carefully, you will notice that the second Turing machine does the exact opposite of the first one. Consider the combination of these machines as a single unified machine. Let’s call this machine, Alan. Now if we pass the description of the unified machine, Alan, as input to the same machine, Alan, then we are asking the system to evaluate itself. If the first Turing machine in Alan outputs Yes, the second machine goes into an infinite loop and it does not halt. If the first machine outputs No, the second machine halts.

So, the first machine cannot determine the solution to this problem. It results in a paradox. Since Turing proved that all computational problems can be solved on the Turing machine, the fact that this problem cannot be solved on it means that the problem does not have a solution. He, therefore, proved through contradiction that the halting problem is not solvable. By showing that the halting problem was not solvable, he proved that the decision problem had no solutions.

You may ask, what is the decision problem? Decision problem was put forth by David Hilbert. The problem asks for an algorithm that takes a sentence of first-order logic as input and produces either a Yes or a No according to whether the statement is universally valid or not.

Alonzo Church also proved that the decision problem had no solutions through his lambda calculus. This led to the formation of the Church-Turing thesis, which, says that lambda calculus and Turing machines are capable enough to solve all computational problems that have solutions.

Breaking the Enigma

Turing played a pivotal part in the Second World War in deciphering German codes that were created through the Enigma machine. This machine scrambled any text and transmitted the scrambled text. This scrambling was carried out by the Germans to ensure that nobody intercepts and understands their messages. The scrambling was not random, it was determined by the rotors of the Enigma machine, which meant that it could be descrambled on another Enigma machine. Thus, the Germans would transmit messages encrypted by one Enigma machine and receive messages decrypted by the receiving Enigma machine. The challenge for Turing was to crack the Enigma and understand the rotor configurations.

Alan Turing came up with the Bombe, which, was an enhancement of the Polish code breaking system that aimed to crack Enigma codes. Bombe predicted rotor settings of the Enigma machine more effectively than any other system. The Germans would periodically update the Enigma machine and add rotors to ensure that codes were not being encrypted by anyone else. Turing and his colleagues worked tirelessly to break this code and were appreciated for potentially reducing the duration of the war by several years.

Turing Test

Perhaps, the most promising contribution of Alan Turing was his contribution to Artificial Intelligence. Turing hypothesized that a computer can achieve intelligence equivalent to that of a human being, or at least, intelligence that is indistinguishable from human intelligence if it could successfully lead a human being to believe that the machine was human. The Turing test is renowned for being the basis for classifying machine as either intelligent or not intelligent.

The Turing test involves a human evaluator who is responsible for determining which of the two entities he is talking to is actually human. The evaluator is allowed to ask a series of questions to carry out this classification. These questions are asked to both the human being and the machine. If the human evaluator fails to determine which one is human, and which one is a machine, based on the responses, then, the machine is said to have successfully fooled the evaluator and is deemed to be intelligent.

Controversy regarding Alan Turing

Sadly, the greatness of this brilliant individual was marred with controversy. Alan Turing was homosexual in a time when homosexuality in the United Kingdom was illegal. When he was convicted of these charges, he was given the option of either imprisonment or chemical castration. He chose chemical castration so that he could continue his academic work. As a result, he spent much of his post-war life unable to conform to the changes that resulted in him due to chemical castration. He, unfortunately, took his own life at a very young age of 41.

Artificial Intelligence

The gift of Artificial Intelligence was provided to us by Alan Turing. Artificial Intelligence has shown immense potential and could soon be incorporated into everyday systems. Although one could say that Artificial Intelligence is still only a burgeoning field, the accomplishments of Artificial Intelligence are already so significant that it would be foolish to not consider it to be a powerful aspect of society in the not so distant future.

The genius, Alan Turing once said, “We can only see a short distance ahead, but we can see plenty there that needs to be done”. Alan Turing had tremendous dedication towards his work. He was even willing to undergo chemical castration to be able to continue with his academic work. That was the dedication that the man possessed.

There probably are not enough adjectives to describe the brilliance that Alan Turing possessed. Then again, it probably would be an injustice to the great man to express appreciation towards his intelligence through mere words. The potential the man possessed could not be, nay, should not be described through mere words constrained by the vocabulary. It is tragic how his story unfolded, but, his contributions to the field of computer science and artificial intelligence will forever live on. He was truly a rare gem.