Singularity explained simply: will our technological growth soon become uncontrollable? – Screen Shot
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Singularity explained simply: will our technological growth soon become uncontrollable?

In a new essay published in The International Journal of Astrobiology, Joseph Gale from The Hebrew University of Jerusalem and co-authors raised awareness of what recent advances in artificial intelligence (AI) could mean for the future of humanity and robots. The study focuses more specifically on pattern recognition and self learning while also presenting a fundamental shift between super intelligence’s relationship with humans. The futurist Ray Kurzweil predicted that the singularity would occur in 2045, but Gale believes this event may be more imminent, especially with the advent of quantum computing. What is singularity exactly, and what does it mean for humanity?

What is the singularity?

The term ‘the singularity’ has different definitions depending on who you ask, and it often overlaps with ideas like transhumanism. However, broadly speaking, the singularity is the hypothetical future creation of superintelligent machines. Superintelligence is defined as a technologically-created cognitive capacity far beyond what is currently possible for humans, and should the singularity occur, technology will in turn advance beyond our ability to foresee or control its outcomes. Basically, the singularity will be the time when the abilities of a computer overtake the abilities of the human brain—it’s a little concerning, I know.

As we know, a human brain is ‘wired’ differently to a computer, and this may be the reason as to why certain tasks are simple for us but challenging for today’s AI. The size of the brain or the number of neurons it contains doesn’t equate to higher intelligence either. For example, whales and elephants have double the number of neurons in their brains compared to humans, and yet, they are not more intelligent than us.

When the singularity occurs, which should come down to if and when we let it due to our current power over the situation, the human race may very well undergo its decline. As theoretical physicist Stephen Hawking once predicted, and told the BBC, “The development of full artificial intelligence could spell the end of the human race.”

Hawking came to this response based on the technology he used to communicate because of the impacts of the motor neuron disease that he lived with, which involved a basic form of AI. According to Kurzweil’s book The Singularity Is Near, humans may soon be fully replaced by AI or some hybrid form of humans and machines.

American writer Lev Grossman explained this prospect in Time magazine by saying that “Their rate of development would also continue to increase, because they would take over their own development from their slower-thinking human creators. Imagine a computer scientist that was itself a superintelligent computer. It would work incredibly quickly. It could draw on huge amounts of data effortlessly. It wouldn’t even take breaks…”

Future posed an interesting experiment on ‘supercomputers to superintelligence’ by proposing that we ask our elders whether they ever dared think that one day in the future (meaning now), everyone would be posting and sharing images and information about one another on a social network called Facebook. Or, if they ever imagined that they would soon be able to receive answers to every and any question from a mysterious entity called Google. Chances are that they would probably answer negatively, and who would blame them?

The thing is that very few would have imagined the future that is now, even if assumptions were made on technologies becoming widespread or how they would fundamentally change society. But here we are, and what we might now idealise of our very futures, may turn out to be exaggerated versions of those ideas, or nothing like them at all.

Changes of any kind, in hindsight, always actualise as dramatic, and this is most definitely the case with technology. These sort of dramatic shifts in thinking are what is called singularity, which originally derived from mathematics and describes a point which we are incapable of deciphering its exact properties, or where the equations make no sense and have no sense of direction. Now the term creates a point that could completely change the way we view, as well as function, as human beings.

Singularity and AI regeneration

Because of the potentially approaching singularity, AI will essentially improve itself once it learns how to, and will do so over and over again without our help. Humans will remain biological machines, but if this superintelligent AI were to be kept on a tight leash, humans would be able to use it to their advantage still, meaning that we could use the advancement produced by this technology to expose and discover the wonders of what we haven’t been able to discover in our world yet, and beyond.

Truthfully, the singularity of some spectrum is most definitely due to arrive, it has already within the gaming world and professional fields like health care. That being said, some humans may struggle with the reality of such a time arriving, and some may ignore it altogether (while still using a mobile phone or calculator, ignorantly). While both of these approaches will most definitely remain disastrously behind, others will realise that the path ahead relies on the increasing collaboration with humankind and computers. I argue that the dawn of singularity is here, possibly that it arrived decades ago, and that only in hindsight will we actualise this point in time as dramatic.


AI planners in Minecraft might be leading the way we design future cities

Advancements in artificial intelligence (AI) are leading us towards a very likely future where machines reason, learn and act intelligently like we do—but not as we do. For other than obvious reasons, this will change the way we live and transform how we physically live as well as what our cities look like on a grand scale. Using the interior world of games for inspiration, the game Minecraft in particular has driven AI researchers to look at the way its players strategically invent their worlds.

What is Minecraft?

Minecraft is a game developed in Java (a class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible) by Mojang Studios. It is the best selling game of all time. The purpose of the game is to simply build a procedurally-generated 3D world with infinite terrain with the option to craft your own tools and to explore and survive while doing so.

The game noticed the innovative design skills that were being produced by gamers while playing, and launched the annual competition called the Generative Design in Minecraft (GDMC) in 2018. This competition, which is first and foremost just for fun asks participants to build an AI system that can generate entire Minecraft villages by themselves. Pretty straightforward, right? Write an algorithm that can create its own settlement, easy.

As the competitions run on, it is becoming increasingly clear that the techniques explored by competitors are suited to ways in which real cities could be designed in the future. The challenge of building these complexes are based on very real problems that we come across when planning a city’s existing geometric space on earth. The settlements take climate and human needs into consideration such as roads, facilitated mobility accessibility, stairs and the list goes on—beyond that, culture and identity come into play too.

Minecraft GDMC specifics

Naturally, there are functional requirements when it comes to building an entirely AI-generated structure. Entrants can take on a new or existing map within the Minecraft universe, and then modify it using the algorithm designed. To do this, GDMC relies on a popular save game editor for the Java version of Minecraft called MCEdit, which allows the use of filters or commands that apply specific behaviours over chosen sections of your Minecraft map.

You’re not totally alone though, there is a sample bot here to get you going that uses Binary Space Partitioning to break up lots, which recursively divides a scene using hyperplanes into two until the partitioning satisfies one or more requirements. It can be viewed as a generalisation of structures to create three-dimensional scenes composed of polygons. Bit by bit my dears, Rome wasn’t built in a day as they say.

Once you’ve implemented your instructions, the filter or generator needs to run for no more than ten minutes to build its structure onto three test maps selected by the organisers. You will then be scored from one to ten on four categories; adaptation (does it respect the environment?), functionality (how easy is it to move within and around the space? Is it protective?), evocative narrative (is there a sense of story to the people who may live in the structure?), and aesthetics (is it appealing to look at?).

Real life AI implementation

Jasper Wijnands and his colleagues from the University of Melbourne in Australia are exploring the use of generative adversarial networks (GANs) to do a style transfer on images from Google Street View. Style transfers are mainly used to reproduce one image into the style of another, but instead of a style Wijnands programmed his AI system to recognise how public health data varies visually from city block to city block.

For example, where neighbourhoods had positive public health, he asked his software to reproduce similarities from the healthier neighbourhoods onto the less healthy neighbourhoods. City planners could then use where the system applied the transformations and be guided by the information noticed to improve the urban environment.

There are many other researchers analysing the potential benefits of the use of AI-generated suggestions and programmes when it comes to urban planning and construction, but they all have the same elements in mind that Minecraft does. The data collected using systems like these also have a ripple effect within design, such as improvement in cartography and behavioural understanding.

Arnaud Grignard and his colleagues at the MIT Media Lab use agent-based simulation to explore possible designs for busy public spaces. One project included redrawing the boundaries of New York boroughs based on the similarities between neighbourhoods, resulting in the concentric rings around central Manhattan. As commented on by the AI practitioners Topos, this is evidence that geography reasserts itself as the organising principle. Although humans have already started looking into ways new technologies can help us improve our homes, there is still much to learn from what we miss when designing, and AI could help us gain a more informed and granular understanding.