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Can AI develop a sense of right and wrong?

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Can artificial intelligence learn the moral values of human societies? Can an AI system make decisions in situations where it must weigh and balance between damage and benefits to different people or groups of people? Can AI develop a sense of right and wrong? In short, will artificial intelligence have a conscience?

This question might sound irrelevant when considering today’s AI systems, which are only capable of accomplishing very narrow tasks. But as science continues to break new grounds, artificial intelligence is gradually finding its way into broader domains. We’re already seeing AI algorithms applied to areas where the boundaries of good and bad decisions are not clearly defined, such as criminal justice and job application processing.

In the future, we expect AI to care for the elderly, teach our children, and perform many other tasks that require moral human judgement. And then, the question of conscience and conscientiousness in AI will become even more critical.

With these questions in mind, I went in search of a book (or books) that explained how humans develop conscience and give an idea of whether what we know about the brain provides a roadmap for conscientious AI.

A friend suggested Conscience: The Origins of Moral Intuitionby Dr. Patricia Churchland, neuroscientist, philosopher, and professor emerita at the University of California, San Diego. Dr. Churchland’s book, and a conversation I had with her after reading Conscience, taught me a lot about the extent and limits of brain science. Conscience shows us how far we’ve come to understand the relation between the brain’s physical structure and workings and the moral sense in humans. But it also shows us how much more we must go to truly understand how humans make moral decisions.

It is a very accessible read for anyone who is interested in exploring the biological background of human conscience and reflect on the intersection of AI and conscience.

Here’s a very quick rundown of what Conscience tells us about the development of moral intuition in the human brain. With the mind being the main blueprint for AI, better knowledge of conscience can tell us a lot about what it would take for AI to learn the moral norms of human societies.

The learning system

“Conscience is an individual’s judgment about what is normally right or wrong, typically, but not always, reflecting some standard of a group to which the individual feels attached,” Churchland writes in her book.

But how did humans develop the ability to understand to adopt these rights and wrongs? To answer that question, Dr. Churchland takes us back through time, when our first warm-blooded ancestors made their apparition.

Birds and mammals are endotherms: their bodies have mechanisms to preserve their heat. In contrast, in reptiles, fish, and insects, cold-blooded organisms, the body adapts to the temperature of the environment.

The great benefit of endothermy is the capability to gather food at night and to survive colder climates. The tradeoff: endothermic bodies need a lot more food to survive. This requirement led to a series of evolutionary steps in the brains of warm-blooded creatures that made them smarter. Most notable among them is the development of the cortex in the mammalian brain.

The cortex can integrate diverse signals and pull out abstract representation of events and things that are relevant to survival and reproduction. The cortex learns, integrates, revises, recalls, and keeps on learning.

The cortex allows mammals to be much more flexible to changes in weather and landscape, as opposed to insects and fish, who are very dependent on stability in their environmental conditions.

But again, learning capabilities come with a tradeoff: mammals are born helpless and vulnerable. Unlike snakes, turtles, and insects, which hit the ground running and are fully functional when they break their eggshells, mammals need time to learn and develop their survival skills.

And this is why they depend on each other for survival.

The development of social behavior

Chimpanzee
The development of complex cortical structures in the brain gave rise to social behavior in mammals

The brains of all living beings have a reward and punishment system that makes sure they do things that support their survival and the survival of their genes. The brains of mammals repurposed this function to adapt for sociality.

“In the evolution of the mammalian brain, feelings of pleasure and pain supporting self-survival were supplemented and repurposed to motivate affiliative behavior,” Churchland writes. “Self-love extended into a related but new sphere: other-love.”

The main beneficiary of this change are the offspring. Evolution has triggered changes in the circuitry of the brains of mammals to reward care for babies. Mothers, and in some species both parents, go to great lengths to protect and feed their offspring, often at a great disadvantage to themselves.

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In Conscience, Churchland describes experiments on the biochemical reactions of the brains of different mammals reward social behavior, including care for offspring.

“Mammalian sociality is qualitatively different from that seen in other social animals that lack a cortex, such as bees, termites, and fish,” Churchland writes. “It is more flexible, less reflexive, and more sensitive to contingencies in the environment and thus sensitive to evidence. It is sensitive to long-term as well as short-term considerations. The social brain of mammals enables them to navigate the social world, for knowing what others intend or expect.”

Human social behavior

mammal cortex
The human brain has the largest and most complicated cortex among mammals

The brains of humans have the largest and most complex cortex in mammals. The brain of homo sapiens, our species, is three times as large as that of chimpanzees, with whom we shared a common ancestor 5-8 million years ago.

The larger brain naturally makes us much smarter but also has higher energy requirements. So how did we come to pay the calorie bill? “Learning to cook food over fire was quite likely the crucial behavioral change that allowed hominin brains to expand well beyond chimpanzee brains, and to expand rather quickly in evolutionary time,” Churchland writes.

With the body’s energy needs supplied, hominins eventually became able to do more complex things, including the development of richer social behaviors and structures.

So the complex behavior we see in our species today, including the adherence to moral norms and rules, started off as a struggle for survival and the need to meet energy constraints.

“Energy constrains might not be stylish and philosophical, but they are as real as rain,” Churchland writes in Conscience.

Our genetic evolution favored social behavior. Moral norms emerged as practical solutions to our needs. And we humans, like every other living being, are subject to the laws of evolution, which Churchland describes as “a blind process that, without any goal, fiddles around with the structure already in place.” The structure of our brain is the result of countless experiments and adjustments.

“Between them, the circuitry supporting sociality and self-care, and the circuitry for internalizing social norms, create what we call conscience,” Churchland writes. “In this sense your conscience is a brain construct, whereby your instincts for caring, for self and others, are channeled into specific behaviors through development, imitation, and learning.”

This is a very sensitive topic and complicated, and despite all the advances in brain science, many of the mysteries of the human mind and behavior remain unlocked.

“The dominant role of energy requirements in the ancient origin of human morality does not mean that decency and honesty must be cheapened. Nor does it mean that they are not real. These virtues remain entirely admirable and worthy to us social humans, regardless of their humble origins. They are an essential part of what makes us the humans we are,” Churchland writes.

Artificial intelligence and conscience

Robot Hand Bulb
Source: Depositphotos

In Conscience, Churchland discusses many other topics, including the role of reinforcement learning in the development of social behavior and the human cortex’s far-reaching capacity to learn by experience, to reflect on counterfactual situations, develop models of the world, draw analogies from similar patterns and much more.

Basically, we use the same reward system that allowed our ancestors to survive, and draw on the complexity of our layered cortex to make very complicated decisions in social settings.

“Moral norms emerge in the context of social tension, and they are anchored by the biological substrate. Learning social practices relies on the brain’s system of positive and negative reward, but also on the brain’s capacity for problem solving,” Churchland writes.

After reading Conscience, I had many questions in mind about the role of conscience in AI. Would conscience be an inevitable byproduct of human-level AI? If energy and physical constraints pushed us to develop social norms and conscientious behavior, would there be a similar requirement for AI? Does physical experience and sensory input from the world play a crucial role in the development of intelligence?

Fortunately, I had the chance to discuss these topics with Dr. Churchland after reading Conscience.

Is physical experience a requirement for the development of conscience in AI?

Patricia churchland
Neurophilosopher Patricia Churchland (Source: Patricia Churchland)

What is evident from Dr. Churchland’s book (and other research on biological neural networks), physical experience and constraints play an important role in the development of intelligence, and by extension conscience, in humans and animals.

But today, when we speak of artificial intelligence, we mostly talk about software architectures such as artificial neural networks. Today’s AI is mostly disembodied lines of code that run on computers and servers and process data obtained by other means. Will physical experience and constraints be a requirement for the development of truly intelligent AI that can also appreciate and adhere to the moral rules and norms of human society?

“It’s hard to know how flexible behavior can be when the anatomy of the machine is very different from the anatomy of the brain,” Dr. Churchland said in our conversation. “In the case of biological systems, the reward system, the system for reinforcement learning is absolutely crucial. Feelings of positive and negative reward are essential for organisms to learn about the environment. That may not be true in the case of artificial neural networks. We just don’t know.”

She also pointed out that we still don’t know how brains think. “In the event that we were to understand that, we might not need to replicate absolutely every feature of the biological brain in the artificial brain in order to get some of the same behavior,” she added.

Churchland reminded that while initially, the AI community largely dismissed neural networks, they eventually turned out to be quite effective when their computational requirements were met. And while current neural networks have limited intelligence in comparison to the human brain, we might be in for surprises in the future.

“One of the things we do know at this stage is that mammals with cortex and with reward system and subcortical structures can learn things and generalize without a huge amount of data,” she said. “At the moment, an artificial neural network might be very good at classifying faces by hopeless at classifying mammals. That could just be a numbers problem.

“If you’re an engineer and you’re trying to get some effect, try all kinds of things. Maybe you do have to have something like emotions and maybe you can build that into your artificial neural network.”

Do we need to replicate the subtle physical differences of the brain in AI?

One of my takeaways from Conscience was that humans generally align themselves with the social norms of their society, they also challenge them at times. And the unique physical structure of each human brain, the genes we inherit from our parents and the later experiences that we acquire through our lives make for the subtle differences that allow us to come up with new norms and ideas and sometimes defy what was previously established as rule and law.

But one of the much-touted features of AI is its uniform reproducibility. When you create an AI algorithm, you can replicate it countless times and deploy it in as many devices and machines as you want. They will all be identical to the last parametric values of their neural networks. Now, the question is, when all AIs are equal, will they remain static in their social behavior and lack the subtle differences that drive the dynamics of social and behavioral progress in human societies?

“Until we have a much richer understanding of how biological brains work, it’s really hard to answer that question,” Churchland said. “We know that in order to get a complicated result out of a neural network, the network doesn’t have to have wet stuff, it doesn’t have to have mitochondria and ribosomes and proteins and membranes. How much else does it not have to have? We don’t know.

“Without data, you’re just another person with an opinion, and I have no data that would tell me that you’ve got to mimic certain specific circuitry in the reinforcement learning system in order to have an intelligent network.

“Engineers will try and see what works.”

We have yet to learn much about human conscience, and even more about if and how it would apply to highly intelligent machines. “We do not know precisely what the brain does as it learns to balance in a headstand. But over time, we get the hang of it,” Churchland writes in Conscience. “To an even greater degree, we do not know what the brain does as it learns to find balance in a socially complicated world.”

But as we continue to observe and learn the secrets of the brain, hopefully we will be better equipped to create AI that serves the good of all humanity.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.

Published October 7, 2020 — 11:00 UTC

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Here integrates what3words’ super simple address system into its in-car API

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Geocoding startup what3words — which chunks the world into 3mx3m squares, giving each a unique three-word label to simplify location sharing — has nabbed another in-vehicle integration, via a partnership with Here Technologies.

The pair said today that OEMs using Here’s navigation platform can include what3words as an in-car nav feature directly through the Here Search API, instead of needing to integrate itself. Existing users of the platform will be able to be given access to what3word’s addressing tech via an update.

Here says its map data services can be found in 150 million vehicles worldwide at this point.

It’s by no means the first such integration for what3words which has found cars to be a natural fit for its simplified, ‘rolls-off-the-tongue’ addressing system. The 2013-founded startup inked a partnership with Ford last year, for example. It also counts Daimler as an investor.

Letting drivers speak or type three words to input a location into their car’s GPS system has clear benefits vs requiring they correctly specify a full address. what3words also pinpoints a more specific location than a typical postcode — and works for destinations that don’t have a street address (the start of a hiking trial or specific lay-by; a particular entrance for a campus etc).

what3words further notes that its tech has been adopted by global car companies, logistics providers and mobility apps, including Mercedes-Benz, Tata Motors, DB Schenker, Hermes and Cabify.

In recent years the novel addressing system has also found favor with Airbnb as a way of simplifying location sharing for less traditional types of stays.

Commenting on its latest partnership in a statement, what3words CEO and co-founder, Chris Sheldrick, said: “We are seeing increasing demand from automakers and mobility services. Now that we are embedded in Here, we can enable our address system simply and easily in both new and legacy vehicles.”

“Automotive OEMs and Tier 1 suppliers can now provide the what3words service to their customers through the Here Search API instead of having to integrate it themselves,” added Jørgen Behrens, SVP and chief product officer at Here Technologies in another supporting statement. “This will allow drivers to navigate easily in dense, urban environments with non-standard addressing schemes or seamlessly get to any location, be it a local pub or a trailhead.”

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Smartphone shipments rebound to hit an all-time high in India

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Smartphone shipments reached an all-time high in India in the quarter that ended in September this year as the world’s second largest handset market remained fully open during the period after initial lockdowns due to the coronavirus, according to a new report.

About 50 million smartphones shipped in India in Q3 2020, a new quarterly record for the country where about 17.3 million smartphone units shipped in Q2 (during two-thirds of the period much of the country was under lockdown) and 33.5 million units shipped in Q1 this year, research firm Canalys said on Thursday.

Xiaomi, which assumed the No.1 smartphone spot in India in late 2018, continues to maintain its dominance in the country. It commanded 26.1% of the smartphone market in India, exceeding Samsung’s 20.4%, Vivo’s 17.6%, and Realme’s 17.4%, the marketing research firm said.

Image Credits: Canalys /

But the market, which was severely disrupted by the coronavirus, is set to see some more shifts. Research firm Counterpoint said last week that Samsung had regained the top spot in India in the quarter that ended in September. (Counterpoint plans to share the full report later this month.)

According to Counterpoint, Samsung has benefited from its recent aggressive push into online sales and from the rising anti-China sentiments in India.

The geo-political tension between India and China has incentivised many consumers in India to opt for local brands or those with headquarters based in U.S. and South Korea. And local smartphone firms, which lost the market to Chinese giants (that command more than 80% of the market today) five years ago, are planning a come back.

Indian brand Micromax, which once ruled the market, said this month that it is gearing up to launch a new smartphone sub-brand called “In.” Rahul Sharma, the head of Micromax, said the company is investing $67.9 million in the new smartphone brand.

In a video he posted on Twitter last week, Sharma said Chinese smartphone makers killed the local smartphone brands but it was now time to fight back. “Our endeavour is to bring India on the global smartphone map again with ‘in’ mobiles,” he said in a statement.

India also recently approved applications from 16 smartphone and other electronics companies for a $6.65 billion incentives program under New Delhi’s federal plan to boost domestic smartphone production over the next five years. Foxconn (and two other Apple contract partners), Samsung, Micromax, and Lava (also an Indian brand) are among the companies that will be permitted to avail the incentives.

Missing from the list are Chinese smartphone makers such as Oppo, Vivo, OnePlus and Realme.

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What exactly is an algorithm and how does it work?

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The world of computing is full of buzzwords: AI, supercomputers, machine learning, the cloud, quantum computing and more. One word in particular is used throughout computing – algorithm.

In the most general sense, an algorithm is a series of instructions telling a computer how to transform a set of facts about the world into useful information. The facts are data, and the useful information is knowledge for people, instructions for machines or input for yet another algorithm. There are many common examples of algorithms, from sorting sets of numbers to finding routes through maps to displaying information on a screen.

[Read: What audience intelligence data tells us about the 2020 US presidential election]

To get a feel for the concept of algorithms, think about getting dressed in the morning. Few people give it a second thought. But how would you write down your process or tell a 5-year-old your approach? Answering these questions in a detailed way yields an algorithm.

Input

When you get dressed in the morning, what information do you need? First and foremost, you need to know what clothes are available to you in your closet. Then you might consider what the temperature is, what the weather forecast is for the day, what season it is and maybe some personal preferences.To a computer, input is the information needed to make decisions.

All of this can be represented in data, which is essentially simple collections of numbers or words. For example, temperature is a number, and a weather forecast might be “rainy” or “sunshine.”

Transformation

Next comes the heart of an algorithm – computation. Computations involve arithmetic, decision-making and repetition.

So, how does this apply to getting dressed? You make decisions by doing some math on those input quantities. Whether you put on a jacket might depend on the temperature, and which jacket you choose might depend on the forecast. To a computer, part of our getting-dressed algorithm would look like “if it is below 50 degrees and it is raining, then pick the rain jacket and a long-sleeved shirt to wear underneath it.”

After picking your clothes, you then need to put them on. This is a key part of our algorithm. To a computer a repetition can be expressed like “for each piece of clothing, put it on.”

Output

Finally, the last step of an algorithm is output – expressing the answer. To a computer, output is usually more data, just like input. It allows computers to string algorithms together in complex fashions to produce more algorithms. However, output can also involve presenting information, for example putting words on a screen, producing auditory cues or some other form of communication.

So after getting dressed you step out into the world, ready for the elements and the gazes of the people around you. Maybe you even take a selfie and put it on Instagram to strut your stuff.

Machine learning

Sometimes it’s too complicated to spell out a decision-making process. A special category of algorithms, machine learning algorithms, try to “learn” based on a set of past decision-making examples. Machine learning is commonplace for things like recommendations, predictions and looking up information.

For our getting-dressed example, a machine learning algorithm would be the equivalent of your remembering past decisions about what to wear, knowing how comfortable you feel wearing each item, and maybe which selfies got the most likes, and using that information to make better choices.

So, an algorithm is the process a computer uses to transform input data into output data. A simple concept, and yet every piece of technology that you touch involves many algorithms. Maybe the next time you grab your phone, see a Hollywood movie or check your email, you can ponder what sort of complex set of algorithms is behind the scenes.


This article is republished from The Conversation by Jory Denny, Assistant Professor of Computer Science, University of Richmond under a Creative Commons license. Read the original article.

Published October 22, 2020 — 10:00 UTC

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