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A Universal Heart with a Wonderful A.I. Mind

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The Artificial Intelligence Explosion & the Singularity
The Sharing Economy, an enabler of Abundance
The Rise of Altruism Economy, the 4th Sector

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 today’s traditional economy. Strong intelligence or AGI will eventually emerge followed by an explosion of intelligence.
New globalization processes driven by technology are fuelling the sharing economy, as well as the 4th sector where public, non-profit, social and mission oriented enterprises are converging.

Published in: Technology
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  • ML is what powers Speech to Text / Voice Recognition and Computer Translation. Both of these are at the same 80% solution and have stagnated despite having Trillion of data points in the form or speech interactions and translated documents. If you are betting on ML for "singularity" in the next 100 years you are betting that the deceleration of progress is going to happen and that is contrary to the evidence. Strong Intelligence is not eventual. There are a few of them in existence today. The Challenge with Strong intelligence is like a Human Baby vs a puppy. If you judge a human baby at 8 months vs a puppy at 8 months you will quickly conclude that humans are inferior. SI as a take over the world is not something you will manage. If I wanted to take my not ready for singularity AI and have it to trillions of dollars of damage to the world economy I could. I don't know how to profit from that, so I don't. But if I were a terrorist, I would make the economic impact of 9/11 look like a missed earnings at Apple. Slide3: The Hockey stick doesn't work. See the first point from previous slide. ML learns less from each future data point. As far as I know I have the only AI that writes its own code. That is a Unicorn. ML will be a LONG LONG time before it writes code, and the code would be of a type that was not based on ML, since ML is basically unchanged in the past 10 year. Slide 4 See above. Slide 5 Computer Neurons and Computer calculations are not an analog. It takes nearly zero calculations for you to recognize your mom's voice, or in the "Stranger things" the sound of your son's breathing. The computer calculation to do that is huge. You can't calculate the cost of a human analog. If you could you could simply use all of the power of AWS or Google AppEngine and be bound only by economics, not software. Slide 6: Evolution didn't create a path to Natural Intelligence. The virus is the most successful "living" thing on the planet. Intelligence is counter to evolution. Ants, and Emergent systems are the success story of nature. Humans that are working hard to destroy our selves are the anti-thesis to that. Slide 7: Direct Programming is not really hard. It is easy that is why we have done it for so long. Brain emulation is what ML currently is, and doesn't work. Mind Simulation (what I do) Is very different. Simulated Evolution is ML and doesn't work for entirely different reasons, it throws away data to make more efficient systems at a sacrifice to accuracy. The deck goes down hill really fast from here. Slide 18: We are no closer to quantum computing than cold fusion, and other than for a few types of cryptography and traveling salesman problems quantum computing has no application for AI. Slide 24: Atari AI is not doing AI. it is doing outcome pruning. Which is useful but is 20 year old tech that nobody cares about since we proved 15 years ago it doesn't help. (PS Nobody is impressed with baking soda and vinegar volcanoes at science fairs) Slide 25 See 18 points Slide 26 People made good decisions with less data, More data doesn't accelerated AI it slows it down. I appreciate that you used many of the same Randall Munroe images I use, but you missed the point of them in almost every case. This deck does a lot of disservice to the AI community, especially to those looking to make smart investments in the space.
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A Universal Heart with a Wonderful A.I. Mind

  1. 1. 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 @efernandez
  2. 2. 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 today’s 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.
  3. 3. 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
  4. 4. Criteria: CPS = Calculations per Second per $1000 cost computer Threshold: •  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.
  5. 5. 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
  6. 6. Different Paths to AI • Direct programming: Really hard. • Brain emulation: Interesting. • Machine Learning: looks promising. • Simulated evolution: self improving systems.
  7. 7. Singularity is: A point of time in future when humanity won’t 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.
  8. 8. Singularity: can’t 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 gas—comprised of a great many chaotically interacting molecules—can 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.”
  9. 9. Technology adoption cycle
  10. 10. e.g. smartphones It’s difficult to estimate penetration of individual players (no one predicted android or iphone) But we can predict with accuracy overall smartphone penetration in market. h/t Asymco.com
  11. 11. And everything else h/t Horace Dediu Asymco.com
  12. 12. Technology drivers: Moore’s law
  13. 13. Exponential evolution
 & Moore’s 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. •  Moore’s 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. That’s happening now with Moore’s 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) •  We’re making dramatic progress in creating the sixth paradigm, which is three-dimensional molecular computing
  14. 14. The next tech paradigm:
 MtM - More than Moore
  15. 15. 6 tech paradigms
  16. 16. Quantum computing: timeline2013 •  Coherent superposition of an ensemble of approximately 3 billion qubits for 39 minutes at room temperature. The previous record was 2 seconds.[155] 2014 •  Documents leaked by Edward Snowden confirm the Penetrating Hard Targets project,[156] by which the National Security Agency seeks to develop a quantum computing capability for cryptography purposes.[157][158][159] •  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.[160][161] 2015 •  Optically addressable nuclear spins in a solid with a six-hour coherence time.[162] •  Quantum information encoded by simple electrical pulses.[163] •  Quantum error detection code using a square lattice of four superconducting qubits[164]
  17. 17. 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
  18. 18. From data to A.I.
 A natural evolution
  19. 19. Atari games
 no longer for humans G o o g l e ’ s Deep 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 Atari’s 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.
  20. 20. Intelligence Accelerators I 1. More than Moore (MtM) Hardware technology (quantum computing, spintronics) 2. Better & more efficient algorithms. IBM’s 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).
  21. 21. Intelligence Accelerators II 3. Big Data & Analytics (Massive datasets). 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 “Kryder’s law” (Kryder and Kim 2009), which states that magnetic disk storage density doubles approximately every 18 months (Walter 2005).
  22. 22. Intelligence Accelerators III 4. Progress in psychology and neuroscience. Cognitive scientists have uncovered many of the brain’s 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 collaboratively solving open problems in mathematics (Nielsen 2011).
  23. 23. Intelligence Accelerators IV 6. Economic incentives As the capacities of “narrow AI” programs approach the capacities of humans in more domains (Koza 2010), there will be increasing demand to replace human workers with cheaper, more reliable machine workers (Hanson 2008, 1998; Kaas et al. 2010; Brynjolfsson and McAfee 2011). First-mover incentives. AI could make a small group more powerful— a case of “bringing a gun to a knife fight.” A “winner takes all” scenario. Thus, political and private actors who realize that AI is within reach may devote substantial resources to developing AI, provoking an AI arms race (Gubrud 1997).
  24. 24. Heavy development of narrow AI will lead to strong AI, eventually leading also to ‘bad’ AI or ‘misused’ AI in the same way other technologies were and are wrongfully used. 
 The 2004 Madrid train bombers, who killed 191 people and wounding 1,800, downloaded their bomb-making instructions from the Internet. After the 1995 Oklahoma City bombing, anonymous UseNet posts criticized the construction of the bomb, and offered suggestions on how to overcome the failure of the bomb to do its maximum intended damage. ‘Good’ & ‘Bad’ A.I.
  25. 25. The emerging 4th sector of the economy 
 Social & mission driven enterprises and the AI arms race. Agent Smith Clone 1: It is purpose that created us. Agent Smith Clone 2: Purpose that connects us. Agent Smith Clone 3: Purpose that pulls us. Agent Smith Clone 4: That guides us. Agent Smith Clone 5: That drives us. Agent Smith Clone 6: It is purpose that defines us. Agent Smith Clone 7: Purpose that binds us.  -  From the movie: The Matrix Revolutions Any entity & system exists for a reason, a purpose, and an objective to meet* * There may be a self-programmed system capable of redefining its own objectives through learning processes
  26. 26. The 4th Sector: social economy
  27. 27. ‘hybrid’ enterprises
  28. 28. Toms’ Shoes: the ‘one for one’ business model concept blends footwear sales with donation, making the buyer a benefactor for impoverished children each time a pair of shoes is sold. The company has given away more than 20M shoes, became a global brand with global presence and extended its businesses into other categories such as eyewear, bags and coffee. Given its growth and notoriety, Toms is probably the poster child of ‘conscious capitalism’ of our times.
  29. 29. The Sharing Economy: enabler of Abundance •  Sharing economy players create aggressive low end disruptions (lowest price) in each and every industry. •  Untap value from idle resources increasing the offer in market •  Enable access to products & services on demand (Fremium & Subscription) •  Change economy paradigm: from transactional ownership to on demand access •  EaaS: Everything as a Service will eventually lead to abundance and ultimately, foster altruism. •  Abundance & Altruism form the basis of new economy (4th sector)
  30. 30. The sharing economy has already created $17 Bn companies, with 60.000 employees and 10 Unicorns (startups with value above $1Bn).
  31. 31. Clash of Economies / Clash of (narrow) A.I. 
 (the invisible hand is left behind)
 *h/t Graciela GarciaAn Universal Heart* with a Wonderful (A.I.) Mind
  32. 32. 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 today’s 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.
  33. 33. Afterthoughts* •  These are issues determining the future of the human race •  We have entered the exponential technology age, this is a fact making AI a self-fulfilling prophecy. •  Who will benefit from it it’s still to be seen. •  It’s about fulfilling basic needs of people but, more importantly, providing sense of purpose in life (Maslow’s hierarchy of needs re- visited) •  Wealth distribution, equity and contribution to society will be key drivers in a new context. Politicians (& our political systems) are not ready for this. •  There is a need for a philosophical framework around the new technology paradigm, given the impact in humans (and humanity). •  Artificial Intelligence is a major step forward in technology. We need to develop policies and mechanisms to ensure the human race at large benefits from it. (*h/t Antonio Mansilla. )

  34. 34. Acknowledgements (in progress): Turing 1950-1951 Good 1959,65,70,82 Von Neumann 1966 Minsky 1984 Solomonoff 1985 Vinge 1993 Kurzweil 2005 Yudkowsky 2008 Nilsson 2009 Chalmers 2010 Hutter 2012 to my father, Eduardo Fernandez who inspired me to write during endless hours together at the hospital (where he still was recovering by the time this was posted) …and for making me who I am @efernandez Ed Fernandez Fundacion Tatiana Perez de Guzman UIMP Universidad Menendez Pelayo Albert Cortina Miquel-Angel Serra Alvaro Matud Graciela Garcia Maite Garcia Martina Fernandez Antonio Mansilla Alberto Buendia Peter J.M. Simons Alvaro Plaza Roberto Fernandez

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