The best answer to “Will computers ever be as smart as humans?” is probably “Yes, but only briefly”
Vernor Vinge coined this descriptive question-answer posted in an IEEE spectrum essay almost a decade ago.
Artificial Intelligence, as opposed to what we have seen so far, radio, TV, PC, smartphones, or the internet, can’t be tracked as a single technology in adoption process, it is too pervasive and has way too many different manifestations. Internet can be measured in terms of penetration, by the number of connections or traffic, AI can’t.
However, there is a conspicuous set of drivers or Intelligence accelerators behind AI evolutionary path (previous work by Luke Muehlhauser, Anna Salamon; Machine Intelligence Research Institute)
Here are six intelligence accelerators. Beyond those technology based enablers and drivers, the economic incentives will eventually create an arms race of AI systems, a clash of AI Clans.
Early signs of the AI arms race are already here: Google and Facebook, among others have been protagonists of the machine learning open-sourcing frenzy lately, starting a race to scale and achieve network effects by releasing deep learning tools to the public.
1. More than Moore (MtM) Hardware: quantum computing, spintronics and related technologies. A new computing paradigm.
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).
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).
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 perhaps 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 problems in mathematics (Nielsen 2011).
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. Once AI looks to be within reach, political and private actors will see substantial advantages in building AI first.
AI could make a small group more powerful than the traditional superpowers — a case of “bringing a gun to a knife fight”.
The race to AI may even be a “winner take all” scenario. Thus, political and private actors who realize that AI is within reach may devote substantial resources to developing AI as quickly as possible, provoking an AI arms race (Gubrud 1997).
An AI arms race will eventually happen amid social and economic changes. Changes that have already started.
More importantly, there are signs of structural shifts in the economy related to the core principles of the system itself. As poverty reduces and wealth redistributes gradually, the economy starts shifting from a for-profit (self-interest) model into an altruistic based one.
Non-for profit, social enterprises, public and government are all converging in what is called the 4th sector of the economy.
(continuation to: The Second Arms Race: AI and An End to Moore’s Law to be continued: The emerging 4th sector of the economy: Social & mission driven enterprises and the AI arms race)
