Monthly Archives: September 2020

Sophia. Source: https://www.hansonrobotics.com/press/

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce.

Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life.

This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie.

When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background. I am a researcher and a communicator; and, I consider myself a happy person who loves to learn and solve problems through simple and creative ideas. My thoughts on AI may sound different, but Im happy to discuss them.

Humans are the most advanced form of AI that we may know to exit. My understanding is that the only thing that differentiates humans and Artificial Intelligence is the capability to reproduce. While humans have this ability to multiply through male and female union and transfer their abilities through tiny cells, machines lack that function. Transfer of cells to a newborn is no different from the transfer of data to a machine. Its breathtaking that how a tiny cell in a human body has all the necessary information of not only that particular individual but also their ancestry.

Allow me to give an introduction to the recorded history of AI. Before that, I would like to take a moment to share with you my recent achievement that I feel proud to have accomplished. I finished a course in AI from Algebra University in Croatia in July. I could attend this course through a generous initiative and bursary from Humber College (Toronto). Such initiatives help intellectually curious minds like me to learn. I would also like to express that the views expressed are my own understanding and judgment.

What is AI?

AI is a branch of computer science that is based on computer programming like several other coding programs. What differentiates Artificial Intelligence, however, is its aim that is to mimic human behavior. And this is where things become fascinating as we develop artificial beings.

Origins

I have divided the origins of AI into three phases so that I can explain it better and you dont miss on the sequence of incidents that led to the step by step development of AI.

Phase 1

AI is not a recent concept. Scientists were already brainstorming about it and discussing the thinking capabilities of machines even before the term Artificial Intelligence was coined.

I would like to start from 1950 with Alan Turing, a British intellectual who brought WW II to an end by decoding German messages. Turing released a paper in the October of 1950 Computing Machinery and Intelligence that can be considered as among the first hints to thinking machines. Turing starts the paper thus: I propose to consider the question, Can machines think?. Turings work was also the beginning of Natural Language Processing (NLP). The 21st-century mortals can relate it with the invention of Apples Siri. The A.M. Turing Award is considered the Nobel of computing. The life and death of Turing are unusual in their own way. I will leave it at that but if you are interested in delving deeper, here is one article by The New York Times.

Five years later, in 1955, John McCarthy, an Assistant Professor of Mathematics at Dartmouth College, and his team proposed a research project in which they used the term Artificial Intelligence, for the first time.

McCarthy explained the proposal saying, The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. He continued, An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

It started with a few simple logical thoughts that germinated into a whole new branch of computer science in the coming decades. AI can also be related to the concept of Associationism that is traced back to Aristotle from 300 BC. But, discussing that in detail will be outside the scope of this article.

It was in 1958 that we saw the first model replicating the brains neuron system. This was the year when psychologist Frank Rosenblatt developed a program called Perceptron. Rosenblatt wrote in his article, Stories about the creation of machines having human qualities have long been fascinating province in the realm of science fiction. Yet we are now about to witness the birth of such a machine a machine capable of perceiving, recognizing, and identifying its surroundings without any human training or control.

A New York Times article published in 1958 introduced the invention to the general public saying, The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

My investigation in one of the papers of Rosenblatt hints that even in the 1940s scientists talked about artificial neurons. Notice in the Reference section of Rosenblatts paper published in 1958. It lists Warren S. McCulloch and Walter H. Pitts paper of 1943. If you are interested in more details, I would suggest an article published in Medium.

The first AI conference took place in 1959. However, by this time, the leads in Artificial Intelligence had already exhausted the computing capabilities of the time. It is, therefore, no surprise that not much could be achieved in AI in the next decade.

Thankfully, the IT industry was catching up quickly and preparing the ground for stronger computers. Gordon Moore, the co-founder of Intel, made a few predictions in his article in 1965. Moore predicted a huge growth of integrated circuits, more components per chip, and reduced costs. Integrated circuits will lead to such wonders as home computers or at least terminals connected to a central computerautomatic controls for automobiles, and personal portable communications equipment, Moore predicted. Although scientists had been toiling hard to launch the Internet, it was not until the late 1960s that the invention started showing some promises. On October 29, 1969, ARPAnet delivered its first message: a node-to-node communication from one computer to another, notes History.com.

With the Internet in the public domain, computer companies had a reason to accelerate their own developments. In 1971, Intel introduced its first chip. It was a huge breakthrough. Intel impressively compared the size and computing abilities of the new hardware saying, This revolutionary microprocessor, the size of a little fingernail, delivered the same computing power as the first electronic computer built in 1946, which filled an entire room.

Around the 1970s more popular versions of languages came in use, for instance, C and SQL. I mention these two as I remember when I did my Diploma in Network-Centered Computing in 2002, the advanced versions of these languages were still alive and kicking. Britannica has a list of computer programming languages if you care to read more on when the different languages came into being.

These advancements created a perfect amalgamation of resources to trigger the next phase in AI.

Phase 2

In the late 1970s, we see another AI enthusiast coming in the scene with several research papers on AI. Geoffrey Hinton, a Canadian researcher, had confidence in Rosenblatts work on Perceptron. He resolved an inherent problem with Rosenblatts model that was made up of a single layer perceptron. To be fair to Rosenblatt, he was well aware of the limitations of this approach he just didnt know how to learn multiple layers of features efficiently, Hinton noted in his paper in 2006.

This multi-layer approach can be referred to as a Deep Neural Network.

Another scientist, Yann LeCun, who studied under Hinton and worked with him, was making strides in AI, especially Deep Learning (DL, explained later in the article) and Backpropagation Learning (BL). BL can be referred to as machines learning from their mistakes or learning from trial and error.

Similar to Phase 1, the developments of Phase 2 end here due to very limited computing power and insufficient data. This was around the late 1990s. As the Internet was fairly recent, there was not much data available to feed the machines.

Phase 3

In the early 21st-century, the computer processing speed entered a new level. In 2011, IBMs Watson defeated its human competitors in the game of Jeopardy. Watson was quite impressive in its performance. On September 30, 2012, Hinton and his team released the object recognition program called Alexnet and tested it on Imagenet. The success rate was above 75 percent, which was not achieved by any such machine before. This object recognition sent ripples across the industry. By 2018, image recognition programming became 97% accurate! In other words, computers were recognizing objects more accurately than humans.

In 2015, Tesla introduced its self-driving AI car. The company boasts its autopilot technology on its web site saying, All new Tesla cars come standard with advanced hardware capable of providing Autopilot features today, and full self-driving capabilities in the futurethrough software updates designed to improve functionality over time.

Go enthusiasts will also remember the 2016 incident when Google-owned DeepMinds AlphaGo defeated the human Go world-champion Lee Se-dol. This incident came at least a decade too soon. We know that Go is considered one of the most complex games in human history. And, AI could learn it in just 3 days, to a level to beat a world champion who, I would assume must have spent decades to achieve that proficiency!

The next phase shall be to work on Singularity. Singularity can be understood as machines building better machines, all by themselves. In 1993, scientist Vernor Vinge published an essay in which he wrote, Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended. Scientists are already working on the concept of technological singularity. If these achievements can be used in a controlled way, these can help several industries, for instance, healthcare, automobile, and oil exploration.

I would also like to add here that Canadian universities are contributing significantly to developments in Artificial Intelligence. Along with Hinton and LeCun, I would like to mention Richard Sutton. Sutton, Professor at the University of Alberta, is of the view that advancements in the singularity can be expected around 2040. This makes me feel that when AI will no longer need human help, it will be a kind of specie in and of itself.

To get to the next phase, however, we would need more computer power to achieve the goals of tomorrow.

Now that we have some background on the genesis of AI and some information on the experts who nourished this advancement all these years, it is time to understand a few key terms of AI. By the way, if you ask me, every scientist who is behind these developments is a new topic in themselves. I have tried to put a good number of researched sources in the article to generate your interest and support your knowledge in AI.

Big Data

With the Internet of Things (IoT), we are saving tons of data every second from every corner of the world. Consider, for instance, Google. It seems that it starts tracking our intentions as soon as we type the first alphabet on our keyboard. Now think for a second how much data is generated from all the internet users from all over the World. Its already making predictions of our likes, dislikes, actionseverything.

The concept of big data is important as that makes the memory of Artificial Intelligence. Its like a parent sharing their experience with their child. If the child can learn from that experience, they develop cognizant abilities and venture into making their own judgments and decisions. Similarly, big data is the human experience that is shared with machines and they develop on that experience. This can be supervised as well as unsupervised learning.

Symbolic Reasoning and Machine Learning

The basics of all processes are some mathematical patterns. I think that this is because math is something that is certain and easy to understand for all humans. 2 + 2 will always be 4 unless there is something we havent figured out in the equation.

Symbolic reasoning is the traditional method of getting work done through machines. According to Pathmind, to build a symbolic reasoning system, first humans must learn the rules by which two phenomena relate, and then hard-code those relationships into a static program. Symbolic reasoning in AI is also known as the Good Old Fashioned AI (GOFAI).

Machine Learning (ML) refers to the activity where we feed big data to machines and they identify patterns and understand the data by themselves. The outcomes are not as predicted as here machines are not programmed to specific outcomes. Its like a human brain where we are free to develop our own thoughts. A video by ColdFusion explains ML thus: ML systems analyze vast amounts of data and learn from their past mistakes. The result is an algorithm that completes its task effectively. ML works well with supervised learning.

Here I would like to make a quick tangent for all those creative individuals who need some motivation. I feel that all inventions were born out of creativity. Of course, creativity comes with some basic understanding and knowledge. Out of more than 7 billion brains, somewhere someone is thinking out of the box, verifying their thoughts, and trying to communicate their ideas. Creativity is vital for success. This may also explain why some of the most important inventions took place in a garage (Google and Microsoft). Take, for instance, a small creative tool like a pizza cutter. Someone must have thought about it. Every time I use it, I marvel how convenient and efficient it is to slice a pizza without disturbing the toppings with that running cutter. Always stay creative and avoid preconceived ideas and stereotypes.

Alright, back to the topic!

Deep Learning

Deep Learning (DL) is a subset of ML. This technology attempts to mimic the activity of neurons in our brain using matrix mathematics, explains ColdFusion. I found this article that describes DL well. With better computers and big data, it is now possible to venture into DL. Better computers provide the muscle and the big data provides the experience to a neuron network. Together, they help a machine think and execute tasks just like a human would do. I would suggest reading this paper titled Deep Leaning by LeCun, Bengio, and Hinton (2015) for a deeper perspective on DL.

The ability of DL makes it a perfect companion for unsupervised learning. As big data is mostly unlabelled, DL processes it to identify patterns and make predictions. This not only saves a lot of time but also generates results that are completely new to a human brain. DL offers another benefit it can work offline; meaning, for instance, a self-driving car. It can take instantaneous decisions while on the road.

What next?

I think that the most important future development will be AI coding AI to perfection, all by itself.

Neural nets designing neural nets have already started. Early signs of self-production are in vision. Google has already created programs that can produce its own codes. This is called Automatic Machine Learning or AutoML. Sundar Pichai, CEO of Google and Alphabet, shared the experiment in his blog. Today, designing neural nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and engineers. Thats why weve created an approach called AutoML, showing that its possible for neural nets to design neural nets, said Pichai (2017).

Full AI capabilities will also trigger several other programs like fully-automated self-driving cars, full-service assistance in sectors like health care and hospitality.

Among the several useful programs of AI, ColdFusion has identified the five most impressive ones in terms of image outputs. These are AI generating an image from a text (Plug and Play Generative Networks: Conditional Iterative Generation of Images in Latent Space), AI reading lip movements from a video with 95% accuracy (LipNet), Artificial Intelligence creating new images from just a few inputs (Pix2Pix), AI improving the pixels of an image (Google Brains Pixel Recursive Super Resolution), and AI adding color to b/w photos and videos (Let There Be Color). In the future, these technologies can be used for more advanced functions like law enforcement et cetera.

AI can already generate images of non-existing humans and add sound and body movements to the videos of individuals! In the coming years, these tools can be used for gaming purposes, or maybe fully capable multi-dimensional assistance like the one we see in the movie Iron Man. Of course, all these developments would require new AI laws to avoid misuse; however, that is a topic for another discussion.

Humans are advanced AI

Artificial Intelligence is getting so good at mimicking humans that it seems that humans themselves are some sort of AI. The way Artificial Intelligence learns from data, retains information, and then develops analytical, problem solving, and judgment capabilities are no different from a parent nurturing their child with their experience (data) and then the child remembering the knowledge and using their own judgments to make decisions.

We may want to remember here that there are a lot of things that even humans have not figured out with all their technology. A lot of things are still hidden from us in plain sight. For instance, we still dont know about all the living species in the Amazon rain forest. Astrology and astronomy are two other fields where, I think, very little is known. Air, water, land, and celestial bodies control human behavior, and science has evidence for this. All this hints that we as humans are not in total control of ourselves. This feels similar to AI, which so far requires external intervention, like from humans, to develop it.

I think that our past has answers to a lot of questions that may unravel our future. Take for example the Great Pyramid at Giza, Egypt, which we still marvel for its mathematical accuracy and alignment with the earths equator as well as the movements of celestial bodies. By the way, we could compare the measurements only because we have already reached a level to know the numbers relating to the equator.

Also, think of Indias knowledge of astrology. It has so many diagrams of planetary movements that are believed to impact human behavior. These sketches have survived several thousand years. One of Indias languages, Vedic, is considered more than 4,000 years old, perhaps one of the oldest in human history. This was actually a question asked from IBM Watson during the 2011 Jeopardy competition. Understanding the literature in this language might unlock a wealth of information.

I feel that with the kind of technology we have in AI, we should put some of it at work to unearth our wisdom from the past. It is a possibility that if we overlook it, we may waste resources by reinventing the wheel.

See more here:

The world of Artificial... - The American Bazaar

Dir. Mona Fastvold. US. 2020. 98 mins.

It would be easy to sell The World to Come as the female Brokeback Mountain, but that would be to traduce the richness, singularity and command of Mona Fastvolds beautifully executed and acted drama. The story of female friendship blossoming into passionate love in a severe 1850s American rural setting, this is an austere but lyrical piece underwritten by a complex grasp of emotional and psychological nuance, and a second feature of striking command by Norwegian-born director Mona Fastvold, following up her 2014 debut The Sleepwalker (she has also collaborated as writer on Brady Corbets features).

Understatement and interiority are the watchwords for a film which uses suggestion and period language very subtly

Scripted with heightened literary cadences by Ron Hansen and Jim Shepard, the film is well crafted in every respect, and marks an acting career high for Katherine Waterston, as well as a fine showcase for the ever more impressive Vanessa Kirby. Fastvolds maturely satisfying piece, picked up internationally by Sony Pictures, should find acclaim on the festival circuit, and upmarket distributors will hopefully find a way to highlight its appeal to discerning audiences on the big screen, where its stark elegance will truly flourish.

The film is framed with handwritten date captions as a diary kept in the 1850s in rural upstate New York by Abigail (Waterstone), the young wife of farmer Dyer (Casey Affleck). Their relationship lies under the shadow of the recent death of their young daughter, and grief along with the normal rigours of life in the remote countryside is keeping them emotionally apart, with the thoughtful Abigail and the gentle but taciturn Dyer unable to communicate their feelings, as seems par for the course in a rural marriage at this period. One day, however, Abigail exchanges glances with a new neighbour, Tallie (Kirby), in a subtle hint of what could be classified love at first sight. When Tallie pays a neighbourly visit, the two instantly bond, exchanging confidences, with Abigails reserve gradually conquered up by Tallies candour and ironic knowingness about womens domestic lot something she is familiar with, being married to the possessive Finney (Christopher Abbott).

Working over the seasons, beginning with a descent into a harshly forbidding winter, Fastvold teases out the shifts in the characters lives, at first establishing a tone of pensive reserve, then setting a note of heightened peril (mortality, after all, really means something in this environment), notably in an extraordinary blizzard sequence. As the action enters another year, warmth comes into the two womens lives; at last their slow-simmering romance catches fire in tentative declarations followed by a first kiss, and the fond words, You smell like a biscuit. There are flashes of overt sexual content, but used extremely sparingly and telegraphically towards the end, while Fastvold shows the meaning of Abigails passion in subtle touches like a moment where she lies back on a table, fully dressed, in a quiet swoon of rapture.

Acted with finely calibrated subtlety, the film uses close-ups sparingly but to resonant effect, contrasting the cautiousness with which Abigail reveals her self and the warmer, more openly expressive face of Tallie. Waterstone and Kirby pull off something very finely balanced, conveying the enormity of their characters emotions while speaking a stylised, formal, sometimes playful language: the script will be music to lovers of 19th-century American writing (Hawthorne, Emily Dickinson, Edith Wharton). As the two husbands, Affleck and Abbott contrast sharply both playing deeply enclosed, solemn men, but of different emotional literacy, one with a capacity for moral generosity, the other shockingly without.

Understatement and interiority are the watchwords for a film which uses suggestion and period language very subtly. Poetry plays a part in the central relationship, but theres a poetic ring to the prose too, both in the dialogue and in Abigails journal (both screenwriters are novelists, Ron Hansen having explored this period in The Assassination of Jesse James by the Coward Robert Ford, the film of which starred Casey Affleck as Ford). This is also very much a film about the power and necessity of writing, as suggested by a line that compares ink to fire: a good servant and a hard master.

Ink on paper is also sometimes suggested by the look of the winter sequences, colours bled to monochrome. Shot on 16mm by Andr Chemetoff, the film at once captures the look of period photography and establishes a feeling of contemporary realism, with no alienating sense of historical distance. The grainy texture of the images, combined with Jean Vincent Puzoss meticulous design, somewhat recalls the American period films (Meeks Cutoff, First Cow) of Kelly Reichardt, with something of the severe grace of Terence Daviess best work.

There is also a distinctive score by David Blumberg, foregrounding woodwinds - notably in the blizzard sequence, which has a feel of free jazz without being incongruous for the period (improvising legend Peter Brtzmann is featured on bass clarinet). The closing song, featuring singer Josephine Foster, catches the period feel perfectly over manuscript-style end credits.

Production companies: Seachange Media, Killer Films, Hype Films

International sales: Charades, sales@charades.eu

Producers: Casey Affleck, Whitaker Lader, Pamela Koffler, David Hinojosa, Margarethe Baillou

Screenplay: Ron Hansen, Jim Shepard

Based on the story by Jim Shepard

Cinematography: Andre Chemetoff

Editor: David Jancso

Production design: Jean Vincent Puzos

Music: David Blumberg

Main cast: Katherine Waterston, Vanessa Kirby, Casey Affleck, Christopher Abbott

Read the original here:

'The World To Come': Review | Reviews - Screen International

ALLEN PARK, Mich. The ball looked it like it could have been intercepted easily. Jeff Okudah was in perfect position in the end zone. He read everything right. He was where he was supposed to be. It didnt matter.

Not even close.

Matthew Stafford put the ball where only his receiver, Marvin Hall, could catch it. It was a window so small realistically only the football could have fit through for the play to work. You could say this is only one play in a training camp and might not be indicative of how Stafford played in practice throughout August.

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Except this wasnt a singularity. It happened to Amani Oruwariye against Kenny Golladay. It happened to Jahlani Tavai and ended up in the hands of Marvin Jones. Combine that with Staffords arm strength -- which remains among the best in the league -- and theres reason to think the 12-year veteran might be on the cusp of a season in which he fulfills the potential thats surrounded him since he was drafted, both in his physical abilities and his knowledge of exactly where to throw the ball and when.

Hes a wizard, man, said backup quarterback Chase Daniel, who has known Stafford since high school. Its impressive. His recall of plays, a photographic memory, all that stuff you want in a quarterback. Its impressive and makes you want to work harder and its why hes been one of the best quarterbacks in the league going on 12 years now.

It isnt a practice thing, either. Hes done it during games, too -- either with the help of Calvin Johnson earlier in his career or throws that make you wonder how he pulled it off the past few seasons, including a pass through three Kansas City defenders for a touchdown to Golladay in Week 4 last season.

I wish more people could appreciate it, backup quarterback David Blough said.

At the time, Blough still was learning about his new teammate. A rookie out of Purdue who was traded to Detroit from Cleveland at the roster cuts deadline, Blough only watched Stafford from afar on television and what he remembered of him growing up just outside Dallas himself when Stafford was in high school.

The next day, in the quarterback meeting room, Blough got to see a small bit of Staffords personality. He almost shrugged it off as hes just doing his job although Blough said you might get a wink from him as hes saying it.

This always has been who Stafford is -- from top-rated high school recruit to top-rated college quarterback and then the No. 1 pick in the 2009 draft. Hes thrown a 5,000-yard season and holds a bevy of fastest-to NFL records.

Hes led 28 fourth-quarter comebacks, tied with Brett Favre for No. 11 in history. Hes No. 18 in all-time passing yards, with 41,025, and if he has at least a 4,000-yard season hell pass Dan Fouts and Drew Bledsoe to be No. 16 all-time. His 256 touchdowns are No. 19 all-time and hes 35 touchdown passes away from moving into the top 15.

He is also, at age 32, perhaps playing better than he ever has. Before he suffered broken bones in his back last season, sending him to injured reserve, he was playing at a Pro Bowl level in the first year in Darrell Bevells offense, completing 64.3 percent of his passes for 2,499 yards, 19 touchdowns and five interceptions.

Had he played a full season, he might have reached 5,000 yards for the second time. While hes played in other offenses before -- becoming prolific in Scott Linehans Air Raid offense early in his career and then more efficient in the Jim Caldwell/Jim Bob Cooter system for five years after that -- its possible Bevells offense fits him better than the others.

It meshes a mix of play-action and focus on the run game with enough attempts at bigger, explosive plays that take advantage of Staffords arm and the skills of Golladay and Jones to win contested catches.

When were out there at quarterback, were empowered to throw, Blough said. Take shots, take shots, take shots. [Bevell] keeps calling them and I think Matthew feels encouraged by that and confident.

While it appears he has mastery over Bevells system, and Stafford is reaching a point in his career where almost any offense is going to be something he picks up quick, Bevell has noticed some small, subtle changes entering another season with Stafford, something that could make a great quarterback even better.

He might be even a little bit quicker on some of the decisions hes making, Bevell said. We really have put an emphasis on his speed. Starting with last year when we got here and how your feet correspond to the plays, I think hes done a nice job with that.

I mean, hes just a special talent in terms of throwing the football. It just looks so effortless. He can just flick it, and the balls flying out of his hands. Hes always been impressive that way.

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Its something his teammates have known and his coaches have learned as theyve worked with him. Its something the public has understood in fits and starts, but if Stafford can stay healthy in 2020 and manage his team through an abnormal season in a global pandemic, its possible he might be able to do one thing that could get him more recognition.

Win the Lions first since division title sine 1993, when Stafford was 5 years old.

Excerpt from:

What's the magic behind Matthew Stafford's mastery of the Lions' offense? - Detroit Lions Blog- ESPN - ESPN

Can you already hear Morgan Freemans sonorous voice as if this was another episode of Through the Wormhole?

Astrophysicists have figured out a way to traverse a (hypothetical) wormhole that defies the usual thinking that wormholes (if they exist) would either take longer to get through than the rest of space or be microscopic. These wormholes just have to warp the rules of physics which is totally fine since they would exist in the realm of quantum physics. Freaky things could happen when you go quantum. If wormholes do exist, some of them might be large enough for a spacecraft to not only fit through, but get from this part of the universe to wherever else in the universe in one piece.

"Larger wormholes are possible with aspecial type of dark sector,a type of matter that interactsonly gravitationally with our own matter. The usual dark matter is an example.However, the one we assumed involves a dark sector that consists of an extradimensional geometry,"Princeton astrophysicist Juan Maldacena and grad student Alexey Milekhin told SYFY WIRE.Theyrecently performed a new study that reads like a scientific dissection of what exactly happened to John Crichtons spaceship when it zoomed through a wormhole in Farscape.

"This type of larger wormhole isbased on therealization that a five-dimensional spacetime could be describing physics at lowerenergies than the ones we usually explore, but that it would have escaped detection because it couples with our matter only through gravity," Maldacena and Milekhinsaid."In fact, its physics issimilar to adding many strongly interacting massless fields to the known physics,and for this reason it can give rise to the required negative energy."

While the existence of wormholes has never been proven, you could defend theories that they could exist deep in the quantum realm. The problem is, even if they do exist, they are thought to be infinitesimal. Hypothetical wormholes would also take so long to get across that youd basically be a space fossil by the time you got to the other end. Maldacena and Milekhin have found a theoretical way for a wormhole thatcould get you across the universe in seconds and manage not to crush your spacecraft. At least it would seem like seconds to you. To everyone else on Earth, it could be ten thousand years. Scary thought.

"Usually whenpeople discuss wormholes, they have in mind 'short'wormholes: the ones forwhich the travel time would be almost instantaneous even for a distant observer.We think that such wormholes are inconsistent with the basic principles of relativity," the scientists said. "The ones we considered are 'long': for a distant observed the path alongnormal space-time is shorter than through the wormhole.There is a time-dilation factor because the extreme gravity makes travel time very short for the traveller. For an outsider, the time it takes is much longer, so we have consistency with the principles of relativity, which forbid travel faster than the speed of light."

Fortraversable wormholesto exist, but the vacuum of space would have to be cold and flat to actually allow for what they theorize. Space is already cold. Just pretend that its flat for the sake of imagining Maldacena and Milekhin's brainchild of a wormhole.

"These wormholes are big, the gravitational forces will be rather small. So, if they were in empty flat space,they would not be hazardous. We chose their size to be big enough so that theywould be safe from large gravitational forces," they said.

Negative energy would also have to exist in a traversable wormhole. Physics forbids such a thing from being a reality. In quantum physics, the concept of this exotic energy is explained by Stephen Hawking as the absence of energy from two pieces of matter being closer together as opposed to being far apart, because energy needs to be burned so they can be separated despite gravitational force struggling to pull them back together. Fermions, which include subatomic particles such as electrons, protons, and neutrons (with the exception that they would need to be massless), would enter one end and travel in circles. They would come out exactly where they went in, which suggests that the modification of energy in the vacuum can make it negative.

"Early theorized wormholes were not traversable; an observer going through a wormhole encounters a singularity before reaching the toher side, which is related ot the fact that positive energy tends to attract matter and light," the scientists said."This is whyspacetime shrinks at the singularity of a black hole. Negative energy prevents this. The main problem is that the particular type of negative energy that is needed is not possible in classical physics, and in quantum physics it is only possible in some limited amounts and for special circumstances.

Say you make it to a gaping wormhole ready to take you...nobody knows where. What would it feel like to travel through it? Probably not unlike Space Mountain, if you ask Maldacena and Milekhin. In their study, they described these wormholes as "the ultimate roller coaster."

The only thing a spaceship pilot would need to do, unlike Farscapes Crichton, who totally lost control, is get the ship in sync with the tidal forces of the wormhole so they could be in the right position to take off. These are the forces that will push and pull an object away from another object depending on the difference in the objects strength of gravity, and that gravity would power the spaceship through.This is whyit would basically end upflying itself. But there are still obstacles.

"The problem is that every object which enters the wormhole will be acceleratedto very high energies," the scientists said."It means that a wormhole must be kept extremely cleanto be safe for human travel. In particular, even the pervasive cosmic microwaveradiation, which has very low energy, would be boosted to high energies andbecome dangerous for the wormhole traveler."

So maybe this will never happen. Wormholes may never actually be proven to exist. Even if they dont, it's wild to think about the way quantum physics could even allow for a wormhole that you could coast right through.

Read more:

If you flew your spaceship through a wormhole, could you make it out alive? Maybe... - SYFY WIRE