Correcting Cognitive Bias with Nanocognition, Machine Ethics Interfaces, and an Ethics of Reality

Along with the potential future possibility of changing our perceptual apparatus through nanocognition (nanorobot-aided cognition), comes an increased awareness of the many ways in which we are currently biased due to evolution and sociality.

There is the level of basic biology where nature’s evolutionary requirements filter, order, and hierarchialize the overwhelming amount of input data before it is routed to our cognitive circuits. Likewise, culture and society put a lens on our perception from an individual and group dynamics perspective in the form of attunement to power relations, social conditioning, status-garnering, mate selection, and gender-performing.

With the creation of machine ethics interfaces, we could have the ability to adjust for these built-in biases. It could be possible to choose different kinds of perceptual realities, and this then implies that there should be a philosophical consideration of an Ethics of Reality. An ethics of reality can address questions like: even if we can obtain access to some sort of objective external reality, is it more ethical to see raw reality the way we do now with evolutionary biases or is it more ethical to see a bias-corrected version? One imaginable result is the construction of a transhumanist viewpoint that it is unethical to experience raw reality because it is inhumane, unproductive, or perceptually harmful.

YouTube Video: Machine Ethics Interfaces

Nanocognition Series:

Ethics of Perception and Nanocognition (Nanorobot-aided Cognition)

Killer Apps of Cognitive Nanorobotics

 

Antifragile: Build Open Resilient Systems

Nicholas Taleb Nassim’s latest book, Antifragile: Things that Gain from Disorder (2012) is a nice continuation and development of his oeuvre. The main point in his first mainstream book, Fooled by Randomness (2001), was that humans are not good at thinking statistically, and therefore to improve our lives and ability to act in the world, we need stories or heuristics that package accurate underlying statistical information. Black Swan (2007) made us aware that black swans (seemingly rare events (if you have never seen a black swan, you incorrectly think that they do not exist)) can happen much more frequently than we can estimate. Therefore, we should organize our lives to minimize exposure to negative black swans (events with unlimited downside risk like stock market crashes) and maximize our exposure to positive black swans (events with unlimited upside like investing in startups (with a small portion of total assets)). In the future, there could be a Black Swan App to help us respond to life’s events in real-time with bias correction, heightened rationality, and statistical accuracy.

In Antifragile, Taleb continues to articulate his unique world view and winds it into a proposal for how to better lead our lives as individuals and societies. Fragility is organic systems that aim for stability and avoid change, thereby becoming brittle, weak, and breakable as a result. Antifragility (like Derrida’s autoimmunity) on the other hand, describes systems that are open to mistakes and quickly learn from and incorporate errors, thus becoming resilient and vibrant with the ability to adapt and survive (like Silicon Valley’s mantra to ‘fail early and learn fast’). For better vigor and survival, organic systems (like living organisms, humans, and societies) should develop their antifragility.

Another way of understanding antifragility is that when you have a capability, it means that you are able to handle new situations that arise in the same domain, effectively handling situations that arise that are up to 10% outside the bounds of situations you have seen before in that domain. For Taleb, success is determined more by tinkering and harnessing the disorder and chaos in a system (the variance or antifragility) than applying pure intellect. This is how the industrial revolution happened, and how technologies develop that drive science. Antifragile systems are those that gain from randomness or uncertainty (statistically, pulling a probability distribution’s mean higher with more upside long-tail instances).

Fragility/antifragility applies only in the case of organic systems, not inorganic systems (like our computers (at present)). Organic systems need stressors to grow, thrive, and survive. Taleb’s colorful example distinguishes between a cat and a washing machine.

Singularity Summit 2012: Image Recognition, Analogy, Big Health Data, and Bias Reduction

The seventh Singularity Summit was held in San Francisco, California on October 13-14, 2012. As in other years, there were about 600 attendees, although this year’s conference program included both general-interest science and singularity-related topics. Singularity in this sense denotes a technological singularity - a potential future moment when smarter-than-human intelligence may arise. The conference was organized by the Singularity Institute, who focuses on researching safe artificial intelligence architectures. The key themes of the conference are summarized below. Overall the conference material could be characterized as incrementalism within the space of traditional singularity-related work and faster-moving advances coming in other fields such as image recognition, big health data, synthetic biology, crowdsourcing, and biosensors.

Key Themes:

• Singularity Thought Leadership
• Big Data Artificial Intelligence: Image Recognition
• Era of Big Health Data
• Improving Cognition: Bias Reduction and Analogies
• Singularity Predictions

Singularity Thought Leadership
Singularity thought leader Vernor Vinge, who coined the term technological singularity, provided an interesting perspective. Already since at least 2000, he has been referring to the idea of computing-enabled matter and the wireless Internet-of-things as Digital Gaia. He noted that 5% of objects worldwide are already embedded with microprocessors, and it could be scary as reality ‘wakes up’ further, especially as we are unable to control other phenomena we have created such as financial markets. He was pessimistic regarding privacy, suggesting that Brin’s traditional counterproposal to surveillance, sousveillance, is not necessarily better. More positively, he discussed the framing of computers as a neo-neocortex for the brain, extreme UIs to provide convenient and unobtrusive cognitive support, other intelligence amplification techniques, and how we have been unconsciously prepping many of our environments for robotic operations. There has also been the rise of an important resource in crowdsourcing as the network (the Internet plus potentially 7 billion Turing-test passing agents) filters optimal resources to specific cognitive tasks (like protein folding analysis).

Big Data Artificial Intelligence: Image Recognition
Peter Norvig continued in his usual vein of discussing what has been important in resolving contemporary problems in artificial intelligence. In machine translation (interestingly a Searlean Chinese room), the key was using large online data corpuses and straightforward machine learning algorithms (The Unreasonable Effectiveness of Data). In more recent work, his lab at Google has been able to recognize pictures of cats. In this digital vision processing advance (announced in June 2012 (article, paper)), the key was creating neural networks for machine learning that used hierarchical representation and problem solving, and again large online data corpuses (10 million images scanned by 16,000 computers) and straightforward learning algorithms.

Era of Big Health Data 
Three speakers presented innovations in the era of big health data, a sector which is generating data faster than any other and starting to use more sophisticated artificial intelligence techniques. Carl Zimmer pointed out that new viruses are continuing to develop and spread, and that this is expected to persist. Encouragingly, new viruses are genetically sequenced increasingly rapidly, but it still takes time breed up vaccines. A faster means of vaccine production could possibly come from newer techniques in synthetic biology and nanotechnology such as those from Angela Belcher’s lab.  Linda Avey discussed Curious, Inc, a personal data discovery platform in beta launch that looks for correlations across big health data streams (more information). John Wilbanks discussed the pyrrhic notion of privacy provided by traditional models as we move to a cloud-based big health data era (for example, only a few data points are needed to identify an individual and medical records may have ~500,000). Some health regulatory innovations include an updated version of HIPAA privacy policies, a portable consent for granting the use of personalized genomic data, and a network where patients may connect directly with researchers.

Improving Cognition: Bias Reduction and Analogies (QS’ing Your Thinking) 
A perennial theme in the singularity community is improving thinking and cognition, for example through bias reduction. Nobel Prize winner Daniel Kahneman spoke remotely on his work regarding fast and slow thinking. We have two thinking modes, fast (blink intuitions) and slow (more deliberative logical) thinking, both of which are indispensable and potentially problematic. Across all thinking is a strong inherent loss aversion, and this helps to generate a bias towards optimism. Steven Pinker also spoke about the theme of bias, indirectly. In recent work, he found that there has been a persistent decline in violence over the multi-century history of time, possibly mostly due to increases in affluence and literacy/knowledge. This may seem counter to popular media accounts which, guided by short-term interests, help to create an area of societal cognitive bias. Other research regarding cognitive enhancement and the processes of intelligence was Melanie Mitchell’s claim that analogies are a key attribute of intelligence. The practice of using analogies in new and appropriate ways could be a means of identifying intelligence, perhaps superior to other mechanisms such as general-purpose problem solving, question-answering, or Turing test-passing as the traditional proxies for intelligence.

Singularity Predictions 
Another persistent theme in the singularity community is sharpening analysis, predictions, and context around the moment when there might be greater-than-human intelligence. Singularity movement leader Ray Kurzweil made his usual optimistic remarks accompanied by slides with exponentiating curves of technology cost/functionality improvements, but did not confirm or update his long-standing prediction of a technological singularity circa 2045 [1]. Stuart Armstrong pointed out how predictions are usually 15-25 years out, and that this is true every year. In an analysis of the Singularity Institute’s database of 257 singularity predictions from 1950 forward, there is no convergence of time in estimates ranging from 2020-2080. Vernor Vinge encourages the consideration of a wide range of scenarios and methods including ‘What if the Singularity Doesn’t Happen.’ The singularity prediction problem might be improved by widening the possibility space, for example perhaps it less useful to focus on intelligence as the exclusive element for the moment of innovation, speciation, or progress beyond human-level; other dimensions such as emotional intelligence, empathy, creativity, or a composite thereof could be considered.

Reference
1. Kurzweil, R. The Singularity is Near; Penguin Group: New York, NY, USA, 2006; pp. 299-367.

Is responsibility-taking freeing or not?

In Greek philosophy, there is the notion of taking the responsibility for shaping and defining yourself as an individual. This concerned all aspects of life, both external (e.g.; social, political, economic), and internal, (e.g.; personal life, health). One philosophical view bemoans that this notion disappeared after the Greeks, with external forces shaping nearly every detail of the individual, first in the classical era by the church, and now in the modern era, by science and other experts, and culture.

Responsibility abdication is paradoxically freeing
However, cultural hypnosis is not sufficient to explain why people are not taking more responsibility now in an era where the information and tools afforded by technology are allowing greater responsibility-taking. The opposite occurs, instead of taking new responsibility, it is just better outsourced. Part of the reason is laziness, or more respectably and thermodynamically, entropy the tendency towards low-energy states.

Trusting outsourced solutions to care for responsibilities might seem to increase dependency, but it paradoxically leads to more freedom. It is actually freeing not to take responsibility. Abdicating responsibility has the higher benefits and lower costs of controlling rather than owning assets. However, one danger is that the outsourcing becomes too derivative, and through lack of oversight, later enslaves the originator, morally or otherwise.

Black Swan thinking – there’s an app for that!

As mobile apps increasingly mediate human interaction with the outside world, possibly eventually becoming a full buffer layer, there should be an app for Black Swan thinking, or more broadly, for bias reduction.

A Black Swan is an event that is rare, has extreme impact, and is retrospectively (but not prospectively) predictive. As humans with story-based not statistics-based evolutionary-relic perceptual systems, we should think more black swannishly or at least have mechanisms for minimizing exposure to downside black swans (e.g.; stock market crashes, terrorist attacks, health situations), and maximizing exposure to upside black swans (e.g.; startup investments, knowledge, parties).

Antibias App: a decision-making tool based on personal bias
The Antibias App, an on-board bias reduction coach (an extension to the Siri 2.0 personal virtual coach), could improve human perception by allowing randomness to be seen, statistics-based thinking, and a focus on the unknown (antiknowledge) as opposed to the known.

The Antibias App could list the top 5-10 bias areas (e.g.; confirmation bias, decision-making, belief, and behavioral biases, social biases, and memory errors and biases) with your personalized score for each one and a composite score as applied to different contexts (e.g.; personal, professional, political, economic). Even determining personalized biases is valuable; this could be accomplished through automated data collection, sentiment analysis of social media droppings, and online tests.

An advanced feature of the Antibas App could be a click-through to see the top three pro/con arguments on any issue and where different composite bias scores lie (e.g.; your own, your social network, your professional peers, your neighborhood, your nation state, etc.).

The Antibias App could be viewed in different modes such as story mode, statistics mode, graphics mode, and data visualization mode. The meta goal of the Antibias App is to increase liberty and choice by opening up more ways of thinking about bias and improved action-taking as a result.