The theory that consciousness is simply an emergent property of complex systems has been gaining adepts lately.
The idea may be originally due to Giulio Tononi, from the University of Wisconsin in Madison. Tononi argued that a system that exhibits consciousness must be able to store and process large amounts of information and must have some internal structure that cannot be divided into independent parts. In other words, consciousness is a result of the intrinsic complexity of the internal organization of an information processing system, complexity that cannot be broken into parts. A good overview of the theory has been recently published in the Philosophical Transactions of the Royal Society.
The theory has been gaining adepts, such as Max Tegmark, from MIT, who argues that consciousness is simply a state of matter. Tegmark suggests that consciousness arises out of particular arrangements of matter, and there may exist varying degrees of consciousness. Tegmark believes current day computers may be approaching the threshold of higher consciousness.
Historically, consciousness has been extremely difficult to explain because it is essential a totally subjective phenomenon. It is impossible to assess objectively whether an animal or artificial agent (or even a human, for that matter) is conscious or not, since, ultimately, one has to rely on the word of the agent whose consciousness we are trying to assert. Tononi and Tegmark theories may, eventually, shed some light on this obscure phenomenon.
Last February, Google auctioned a number of computer generated paintings, raising $84,000 for the Gray Area Foundation for the Arts, a San Francisco nonprofit institution devoted to the convergence of art and technology.
The auction took place during a two day event, which also included a symposium about the technology used to generate the paintings.
These paintings were generated using a technology dubbed inceptionism, which uses internal representations of neural networks trained using deep learning to derive abstract images, with styles that remind us of different visual art styles. The painting are the results of a project dubbed DeepDream, which can be used by anyone to make their own artworks.
This kind of artwork is probably going to become more common, as more people get interested and more computers “decide” to become artists….
Both the University of London and NYU are now offering courses on computer generated art.
A recent report on The Economist about Facebook makes clear that the ever-present social network is now more, much more, than simply the sixth most valuable company on Earth and the (virtual) place where humanity spends a significant fraction of its time.
What started simply as a social network, doomed to perish (many believed) as many other social networks, turned into “one great empire with a vast population, immense wealth, a charismatic leader, and mind-boggling reach and influence“, according to The Economist.
But, more relevant to the topic of this blog, is the fact that Facebook has amassed immense knowledge and created the tools necessary to explore it, in the process making enormous sums of money from targeted advertising.
As artificial intelligence, machine learning and data analytics advance, companies like Facebook and Google can explore better and better the troves of data they have, in a process that may end up with the engines behind these companies becoming truly intelligent and, who knows, even conscious. Maybe one day Facebook will become not just the place to meet friends, but a friend. The investments made on chatbot and virtual reality technologies certainly show that we have not yet seen all the social network can do.
The Economist reports, in a recent article, that artificial intelligence and machine learning experts are in extremely high demand, even among the already highly-wanted computer scientists.
The Economist reports that faculty, graduate students and practitioners of AI and machine learning are being harvested, in large numbers, by big-name companies, such as Google, Microsoft, Facebook, Uber, and Tesla.
The demand for this type of skills is a consequence of the rise of intelligent and adaptive systems, advanced user interfaces, and autonomous agents, that will mark the next decades in computing.
Machine learning conferences, such as Neural Information Processing Systems (NIPS), once a tranquil backwater meeting place for domain experts, are now hunting grounds for companies looking to hire the best talents in the domain.
The Human Brain Project (HBP), a flagship project of the European Union, has just released the initial versions of its six Information and Communications Technology (ICT) platforms to users worldwide.
The six HBP Platforms are:
- Brain Simulation
- High Performance Computing
- Medical Informatics
- Neuromorphic Computing
These platforms enable researchers to use the tools developed by the Human Brain Project to search and analyse neuroscience data, simulate brain sections, run complex simulations, searching of real data to understand similarities and differences among brain diseases, access computer systems that emulate brain microcircuits, and test virtual models of the brain by connecting them to simulated robot bodies and environments.
All the Platforms can be accessed via the HBP Collaboratory, a web portal where users can also find additional information.
A very interesting new book by Nick Bostrom, Superintelligence, addresses the questions that will be raised by the appearance of Artificial Intelligence (AI) Systems that are vastly smarter than humans.
So far, researchers have concentrated their efforts on the development of artificial intelligence systems that are as intelligent as humans, the so called strong AI. This is a tall order and it may yet take many years until we reach that point.
However, as Nick Bostrom points out in this book, there is no reason to believe that, once developed, strong AI systems would remain approximately as intelligent as humans. Once an AI system with human-level intelligence comes into existence, it will certainly be able to improve itself rapidly past that level.
As Irving John Good, a statistician that worked with Alan Turing at Bletchley Park, pointed out, a smarter than human machine is probably the last invention man will ever need to create. After that, such a machine can invent all sorts of new technologies, including the ones related with AI.
Nick Bostrom describes in a very clear and convincing way how a superintelligence may develop out of research in AI and neurosciences, using a number of different paths that may include whole brain emulation, strong artificial intelligence or highly connected communities of human brains.
A recent article in Venture Beat points out that top management jobs, up to the level of CEO, may soon be taken over by robots.
There has been an ongoing discussion about which jobs are safer from being taken over by computers. Many candidates have been put forward, like lawyer, doctor, manager or driver. The fast developments of Artificial Intelligence have made these predictions look ridiculous, usually just a few years after their are issued.
The fact of the matter is that we have no idea which jobs will be safer from being taken over by computers.
Relevant to the case in point, what do CEOs do? According to Venture Beat, they ” allocate resources, build the culture, oversee and deliver the company’s performance, be the face of the company, and juggle with everyday compromises. For most CEOs, developing objective, data-driven decisions is advantageous for the success of the company.”
Clearly, many of these tasks require analytical minds and data analysis capabilities which are, by now, more likely to be mastered by artificially intelligent agents than by humans.
It seems to be safe to say that more creative jobs will be safer, for a longer time, from being done by computers. The difficulty lies in defining what, exactly, is creativity. And, once that is done, computers will be able to have it, too…