Soundtrack: https://melodysheep.bandcamp.com/album/the-human-future-original-soundtrack Patreon: http://patreon.com/melodysheep Change is coming. Humanity is entering a turbulent new era, unprecedented in both Earth and Human history. To survive the coming centuries and fulfill our potential as a species, we will have to overcome the biggest challenges we have ever faced, from extreme climate change, to rogue A.I., to the inevitable death of the sun itself.
The headlines make our chances look bleak. But when you look at our history and our tenacity, it’s clear that humanity is uniquely empowered to rise to the challenges we face.
If we succeed, our potential is cosmic in scale. Incredible prosperity is within our reach. Being optimistic is not only justified, it’s a powerful weapon in the fight for a higher future. – Story, visual effects, music & Sound by melodysheep (John D. Boswell) Narrated by Will Crowley.
Soundtrack coming soon to all major music platforms.
Thank you to Protocol Labs for sponsoring this video: protocol.ai.
Artificial Intelligence makes art, knows more than many humans and works faster than they do. But will people accept AI-controlled social robots working in the service industry or entertaining those in need of care?
What does a robot need to have to be accepted as a social partner by a human being? Does it need a face? Should the machine understand — or even show — emotions?
The psychologist, neurologist and philosopher Agnieszka Wykowska, currently researching at the Italian Institute of Technology in Genoa, says: “We tend to humanize everything. We even see faces in car hoods. This is further reinforced whenever a robot demonstrates humanlike behavior.
In a care home for the elderly in Rendsburg, the film shows what sort of relationship forms between residents and robots. Hannes Eilers from the Kiel University of Applied Sciences is carrying out tests there with robots for health insurance companies. The robots sing with the elderly people, play games or demonstrate physio exercises. The one thing they’re not allowed to do with them is pray. The systems there function autonomously. This means they can’t access an AI server, so they abide by data protection laws.
But AI servers are already controlling much of our communication. They don’t just suggest what we should read, eat or buy next: ‘chatbots’ also serve as personal contacts. At the Massachusetts Institute of Technology (MIT) in Boston, the scientist Hossein Rahnama is working on perfecting the appearance and communication skills of chatbots like these. His view: “We now have access to such immense computing power and data that we can create a digital version of every person. Before too long, we can even make them sentient.
In future, will we be able to tell the difference between a flesh-and-blood human, and their digital clone?
Ticking clocks and flashing fireflies that start out of sync will fall into sync, a tendency that has been observed for centuries. A discovery two decades ago therefore came as a surprise: the dynamics of identical coupled oscillators can also be asynchronous. The ability to fall in and out of sync, a behavior dubbed a chimera state, is generic to identical coupled oscillators and requires only that the coupling is nonlocal. Now Yasuhiro Yamada and Kensuke Inaba of NTT Basic Research Laboratories in Japan show that this behavior can be analyzed using a lattice model (the XY model) developed to understand antiferromagnetism [1]. Besides a pleasing correspondence, Yamada and Inaba say that their finding offers a path to study the partial synchronization of neurons that underlie brain function and dysfunction.
The chimera states of a system are typically analyzed by looking at how the relative phases of the coupled oscillators fall in and out of sync. But that approach struggles to describe the system when the system contains distantly separated pockets of synchrony or when there are nontrivial configurations of the oscillators, such as twisted or spiral waves. It also requires knowledge of the network’s structure and the oscillators’ equations of motion.
In seeking an alternative approach, Yamada and Inaba turned to a two-dimensional lattice model used to tackle phase transitions in 2D condensed-matter systems. A crucial ingredient in that model is a topological defect called a vortex. Yamada and Inaba found that they could embody the asynchronous dynamics of pairs of oscillators by formulating the problem in terms of an analogous quantity that they call pseudovorticity, whose absence indicates synchrony and whose presence indicates asynchrony. Their calculations show that their pseudo-vorticity-containing lattice model can successfully recover the chimera state behavior of a simulated neural network made up of 200 model oscillators of a type commonly used to study brain activity.
So if I understand this…send a bot to catch a bot?
Israeli tech start-up Cyabra, which worked with Elon Musk to evaluate the presence of bots on Twitter, released a new tool that can detect AI-generated text, images and profiles.
(Reuters) – Microsoft is planning to start selling a new version of Databricks software that helps customers make AI apps for their businesses, The Information reported on Thursday, citing people with direct knowledge of the plan.
Databricks – a data analytics platform that uses artificial intelligence, which Microsoft would sell through its Azure cloud-server unit – helps companies make AI models from scratch or repurpose open-source models as an alternative to licensing OpenAI’s proprietary ones, the report added.
Microsoft and Databricks did not immediately respond to a Reuters request for comment.
Conference presentation of “Process Physics, Time and Consciousness: Nature as an internally meaningful, habit-establishing process.” As presented at the Whitehead Psychology Nexus Workshop Conference held in Fontareches, France, March 27-30th, 2015 (with some minor adjustments). For full published paper, see: https://tinyurl.com/yc9r6kys (date of publication: October 18, 2017).
Abstract:
Process Physics, Time and Consciousness: Nature as an internally meaningful, habit-establishing process.
Author: Jeroen B. J. van Dijk, Eindhoven, The Netherlands.
Ever since Einstein’s arrival at the forefront of science, mainstream physics likes to think of nature as a giant 4-dimensional spacetime continuum in which all of eternity exists all at once – in one timeless block universe. Accordingly, much to the dismay of more process-minded researchers, the experience of an ongoing present moment is typically branded as illusory. Mainstream physics is having a hard time, though, to provide a well-founded defense for this illusoriness of time. This is because physics, as an empirical science, is itself utterly dependent on experience to begin with. Moreover, if nature were indeed purely physical – as contemporary mainstream physics wants us to believe – it’s quite difficult to see how it could ever be able to give rise to something so explicitly non-physical like conscious experience. On top of this, the argument of time’s illusoriness becomes even more doubtful in view of the extra-ordinary level of sophistication that would be required for our conscious experience to achieve such an utterly convincing, but – physically speaking – pointless illusion. It’s because of problems like these that process thought has persistently objected against this ‘eternalism’ of mainstream physics. Just recently, physicist Lee Smolin even brought up some other major arguments against this timeless picture in his controversial 2013 book ‘Time Reborn’. And although he passionately argues that physics should take an entirely different direction, he admits that he has no readily available roadmap to success. Fortunately, however, over the last 15 years or so, a neo-Whiteheadian, ‘neurobiologically inspired’ way of doing foundational physics, namely Reg Cahill’s Process Physics, has been making its appearance on the scene. Process Physics aims to model the universe as an initially orderless and uniform process plenum by setting up a stochastic, self-referential modeling of nature. In Process Physics, all self-referential and initially noisy activity patterns are “mutually in-formative” as they are actively making a meaningful difference to (i.e. “in-forming”) each other. Due to this mutual in-formativeness, the stochastic activity patterns will act as “start-up seeds” that become engaged in self-renewing update iterations. In this way, the system starts to evolve from its initial featurelessness to then “branch out” to higher and higher levels of complexity – all this according to the same basic principles as a naturally evolving neural network. Because of this “neuromorphic” behaviour, the process system can be thought of as habit-bound with a potential for creative novelty and open-ended evolution. Furthermore, threedimensionality, gravitational and relativistic effects, nonlocality, and classical behaviour are spontaneously emergent within the system. Also, the system’s constantly renewing activity patterns bring along an inherent present moment effect, thereby reintroducing time as the system’s ongoing change. As a final point, subjectivity – in the form of mutual informativeness – is a naturally evolving, innate feature, not a coincidental, later-arriving side-effect.
In AI development, success or failure lies significantly in a data science team’s ability to handle edge cases, or those rare occurrences in how an ML model reacts to data that cause inconsistencies and interrupt the usability of an AI tool. This is especially crucial now as generative AI, now newly-democratized, takes center stage. Along with increased awareness comes new AI strategy demands from business leaders who now see it as both a competitive advantage and as a game changer.
As OpenAI and Google battle for artificial intelligence supremacy in the West, a parallel contest is happening in China.
Dozens of young Chinese startups as well as local tech giants Alibaba, Tencent and Baidu are developing machine-learning models to power chatbots similar to OpenAI’s ChatGPT. Five Chinese startups, including MiniMax and LangBoat, stand out from the startup pack when it comes to funding they’ve raised and their founders’ experience in the AI field.
