a universal heart with a wonderful a.i. mind
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a Universal Heart with
a Wonderful A.I. Mind
The A.rtificial I.ntelligence Explosion & the Singularity
The Sharing Economy, an enabler of Abundance
The Rise of Altruism Economy, the 4th Sector
Food for thought (conclusions)
Technological singularity seems plausible and recent advancements in machine learning and AI suggest the intelligent explosion event is within reach in this century.
An arms race of narrow AI entities will happen in the framework of todays traditional economy. Strong intelligence or AGI will eventually emerge followed by an explosion of intelligence.
New globalization processes driven by technology are fueling the sharing economy, as well as the 4th sector where public, non-profit, social and mission oriented enterprises are converging.
The 4th sector is poised to grow and thrive; mission driven enterprises will have more resources enabling them to play a key role shaping the right path for AI evolution.
good and bad AI entities will coexist in the context of traditional and new economy environments (self-interest vs altruistic economies)
We, humans, as a species, can succeed managing the risks of a super intelligence event as we did in the past overcoming other technology threats.
A Universal Heart with a Wonderful Mind.
The Intelligence Explosion Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever.
Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an intelligence explosion, and the intelligence of man would be left far behind.
Thus the first ultraintelligent machine is the last invention that man need ever make.
I.J. Good, 1965
CPS = Calculations per Second per $1000 cost computer
Human brain at 1016 CPS Entire human race at 1026 CPS Given historical data:
In 2023 we reach human brain capacity with a $1000 computer
In 2049 we reach human race capacity with a $1000 computer
Human vs Computer
* Ray Kurzweil. The Singularity is near.
The Path to A.I. Why believe there will be AI? Evolution got here, dumbly?
(2nd law of thermodynamics: energy dissipation as a driver of evolution*)
We can get there too. *Jeremy England, new thermodynamic theory explains life evolution http://bigthink.com/ideafeed/mit-physicist-proposes-new-meaning-of-life
Different Paths to AI Direct programming: Really hard. Brain emulation: Interesting. Machine Learning: looks promising. Simulated evolution: self improving systems.
A point of time in future when humanity wont be able to keep up with artificial technology developments.
Nonbiological intelligence will have access to its own design and will be able to improve itself in an increasingly rapid redesign cycle.
Singularity = self improving A.I.
Singularity: cant see the forest for the trees
Q: But how can it be the case that we can reliably predict the overall progression of these technologies if we cannot even predict the outcome of a single project?
R. Kurzweil: Predicting which company or product will succeed is indeed very difficult, if not impossible. The same difficulty occurs in predicting which technical design or standard will prevail. For example, how will the wireless-communication protocols Wimax, CDMA, and 3G fare over the next several years? However, as I argue extensively in the book, we find remarkably precise and predictable exponential trends when assessing the overall effectiveness (as measured in a variety of ways) of information technologies. And as I mentioned above, information technology will ultimately underlie everything of value.
Q: But how can that be? R. Kurzweil: We see examples in other areas of science of very smooth and reliable outcomes resulting from the
interaction of a great many unpredictable events. Consider that predicting the path of a single molecule in a gas is essentially impossible, but predicting the properties of the entire gascomprised of a great many chaotically interacting moleculescan be done very reliably through the laws of thermodynamics. Analogously, it is not possible to reliably predict the results of a specific project or company, but the overall capabilities of information technology, comprised of many chaotic activities, can nonetheless be dependably anticipated through what I call the law of accelerating returns.
Technology adoption cycle
e.g. smartphones Its difficult to estimate penetration of individual players (no one predicted android or iphone)
But we can predict with accuracy overall smartphone penetration in market.
And everything else h/t Horace Dediu
Technology drivers: Moores law
Exponential evolution& Moores law: From vaccuum lamps to transistors to molecular electronics (quantum computing spintronics, the quest for the spin transistor)
There are limits to the exponential growth inherent in each paradigm. Moores law was not the first paradigm to bring exponential growth to computing (it was
5th). Vaccuum lamps were there before, overtaken by transistors.
Each time we can see the end of the road for a paradigm, it creates research quest for the pressure to create the next one. Thats happening now with Moores law, even though we are still about fifteen years away from the end of our ability to shrink transistors on a flat integrated circuit (i.e More than Moore MtM technologies)
Were making dramatic progress in creating the sixth paradigm, which is three-dimensional molecular computing
The next tech paradigm:MtM - More than Moore
6 tech paradigms
Quantum computing: timeline 2013
Coherent superposition of an ensemble of approximately 3 billion qubits for 39 minutes at room temperature. The previous record was 2 seconds.
Documents leaked by Edward Snowden confirm the Penetrating Hard Targets project, by which the National Security Agency seeks to develop a quantum computing capability for cryptography purposes.
Scientists transfer data by quantum teleportation over a distance of 10 feet (3.048 meters) with zero percent error rate, a vital step towards a quantum internet.
2015 Optically addressable nuclear spins in a solid with a six-hour coherence time. Quantum information encoded by simple electrical pulses. Quantum error detection code using a square lattice of four superconducting qubits
Evidence of an Explosion of Intelligence
Intelligence Explosion: Evidence and Import
Luke Muehlhauser, Anna Salamon. Machine Intelligence Research Institute https://intelligence.org/files/IE-EI.pdf
The best answer to the question, Will computers ever be as smart as humans?
is probably Yes, but only briefly
- Vernor Vinge
From data to A.I.A natural evolution
Atari gamesno longer for humans
G o o g l e sDeep Mind AI group Atari reinforcement deep machine learning model is a recent example. The model, based on a convolutional neural network, interprets raw pixels from Ataris Arcade games of the 80ies. The machine learns to play the games and outputs an estimation of future rewards. It beats a human expert on three of the six games tested and outperforms any previous approach.
Intelligence Accelerators I 1. More than Moore (MtM) Hardware technology (quantum computing, spintronics)
2. Better & more efficient algorithms. IBMs Deep Blue played chess at the level of world champion Garry Kasparov in 1997 using about 1.5 trillion instructions per second (TIPS), but a program called Deep Junior did it in 2003 using only 0.015 TIPS.
Thus, the computational efficiency of the chess algorithms increased by a factor of 100 in only six years (Richards and Shaw 2004).
Intelligence Accelerators II 3. Big Data & Analytics
The greatest leaps forward in speech recognition and translation software have come not from faster hardware or smarter hand-coded algorithms, but from access to massive data sets of human-transcribed and human-translated words (Halevy, Norvig, and Pereira 2009).
Datasets are expected to increase greatly in size in the coming decades, and several technologies promise to actually outpace Kryders law (Kryder and Kim 2009), which states that magnetic disk storage density doubles approximately every 18 months (Walter 2005).
Intelligence Accelerators III 4. Progress in psychology and neuroscience.
Cognitive scientists have uncovered many of the brains algorithms that contribute to human intelligence (Trappenberg 2009; Ashby and Helie 2011).
Methods like neural networks (imported from neuroscience) and reinforcement learning (inspired by behaviorist psychology) have already resulted in significant AI progress, and experts expect this insight-transfer from neuroscience to AI to continue and accelerate (Van der Velde 2010; Schierwagen 2011; Floreano and Mattiussi 2008; de Garis et al. 2010; Krichmar and Wagatsuma 2011).
5. Accelerated crowd sourced science efforts.
Finally, new collaborative tools, open source projects and other corporate driven initiatives as Google Scholar are already yielding results such as the Polymath Project, which is rapidly and colla