How algorithms impact our browsing behavior? browsing history? What is the connection between social media algorithms and fake news? Are there topic-detection algorithms as they are community-detection ones?
How can I change the content of a [Google] search return? Can I?
CRUZ, J. D., BOTHOREL, C., & POULET, F. (2014). Community Detection and Visualization in Social Networks: Integrating Structural and Semantic Information. ACM Transactions On Intelligent Systems & Technology, 5(1), 1-26. doi:10.1145/2542182.2542193
The W2T (Wisdom Web of Things) methodology considers the information organization and management from the perspective of Web services, which contributes to a deep understanding of online phenomena such as users’ behaviors and comments in e-commerce platforms and online social networks. (https://link.springer.com/chapter/10.1007/978-3-319-44198-6_10)
ethics of algorithm
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679
Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come.
Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.
Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.
Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learningand/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.
Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement. For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.
Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.
Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans.
Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate power among a small elite if we don’t take steps to stop it.
Ordinary people may not understand artificial intelligence and biotechnology in any detail, but they can sense that the future is passing them by. In 1938 the common man’s condition in the Soviet Union, Germany, or the United States may have been grim, but he was constantly told that he was the most important thing in the world, and that he was the future (provided, of course, that he was an “ordinary man,” rather than, say, a Jew or a woman).
n 2018 the common person feels increasingly irrelevant. Lots of mysterious terms are bandied about excitedly in ted Talks, at government think tanks, and at high-tech conferences—globalization, blockchain, genetic engineering, AI, machine learning—and common people, both men and women, may well suspect that none of these terms is about them.
Fears of machines pushing people out of the job market are, of course, nothing new, and in the past such fears proved to be unfounded. But artificial intelligence is different from the old machines. In the past, machines competed with humans mainly in manual skills. Now they are beginning to compete with us in cognitive skills.
Israel is a leader in the field of surveillance technology, and has created in the occupied West Bank a working prototype for a total-surveillance regime. Already today whenever Palestinians make a phone call, post something on Facebook, or travel from one city to another, they are likely to be monitored by Israeli microphones, cameras, drones, or spy software. Algorithms analyze the gathered data, helping the Israeli security forces pinpoint and neutralize what they consider to be potential threats.
The conflict between democracy and dictatorship is actually a conflict between two different data-processing systems. AI may swing the advantage toward the latter.
As we rely more on Google for answers, our ability to locate information independently diminishes. Already today, “truth” is defined by the top results of a Google search. This process has likewise affected our physical abilities, such as navigating space.
So what should we do?
For starters, we need to place a much higher priority on understanding how the human mind works—particularly how our own wisdom and compassion can be cultivated.
Sejnowski, T. J. (2018). The Deep Learning Revolution. Cambridge, MA: The MIT Press.
How deep learning―from Google Translate to driverless cars to personal cognitive assistants―is changing our lives and transforming every sector of the economy.
The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Buzzwords like “deep learning” and “neural networks” are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies.
Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution(out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence.
Machine learning is a very large field and goes way back. Originally, people were calling it “pattern recognition,” but the algorithms became much broader and much more sophisticated mathematically. Within machine learning are neural networks inspired by the brain, and then deep learning. Deep learning algorithms have a particular architecture with many layers that flow through the network. So basically, deep learning is one part of machine learning and machine learning is one part of AI.
December 2012 at the NIPS meeting, which is the biggest AI conference. There, [computer scientist] Geoff Hinton and two of his graduate students showed you could take a very large dataset called ImageNet, with 10,000 categories and 10 million images, and reduce the classification error by 20 percent using deep learning.Traditionally on that dataset, error decreases by less than 1 percent in one year. In one year, 20 years of research was bypassed. That really opened the floodgates.
The inspiration for deep learning really comes from neuroscience.
AlphaGo, the program that beat the Go champion included not just a model of the cortex, but also a model of a part of the brain called the basal ganglia, which is important for making a sequence of decisions to meet a goal. There’s an algorithm there called temporal differences, developed back in the ‘80s by Richard Sutton, that, when coupled with deep learning, is capable of very sophisticated plays that no human has ever seen before.
there’s a convergence occurring between AI and human intelligence. As we learn more and more about how the brain works, that’s going to reflect back in AI. But at the same time, they’re actually creating a whole theory of learning that can be applied to understanding the brain and allowing us to analyze the thousands of neurons and how their activities are coming out. So there’s this feedback loop between neuroscience and AI
We would all go to the mat for women’s rights, gay rights, or pretty much any rights other than gun rights. We lived, for the most part, in big cities in blue states.
When Barack Obama came into the picture, we loved him with the delirium of crushed-out teenagers, perhaps less for his policies than for being the kind of person who also listens to NPR. We loved Hillary Clinton with the fraught resignation of a daughter’s love for her mother. We loved her even if we didn’t like her. We were liberals, after all. We were family.
Words like “mansplaining” and “gaslighting” were suddenly in heavy rotation, often invoked with such elasticity as to render them nearly meaningless. Similarly, the term “woke,” which originated in black activism, was being now used to draw a bright line between those on the right side of things and those on the wrong side of things.
From the Black Guys on Bloggingheads, YouTube’s algorithms bounced me along a path of similarly unapologetic thought criminals: the neuroscientist Sam Harris and his Waking Up podcast; Christina Hoff Sommers, aka “The Factual Feminist”; the comedian turned YouTube interviewer Dave Rubin; the counter-extremist activist Maajid Nawaz; and a cantankerous and then little-known Canadian psychology professor named Jordan Peterson, who railed against authoritarianism on both the left and right but reserved special disdain for postmodernism, which he believed was eroding rational thought on campuses and elsewhere.
the sudden national obsession with female endangerment on college campuses struck me much the same way it had in the early 1990s: well-intended but ultimately infantilizing to women and essentially unfeminist.
Weinstein and his wife, the evolutionary biologist Heather Heying, who also taught at Evergreen, would eventually leave the school and go on to become core members of the “intellectual dark web.”
Weinstein talked about intellectual “feebleness” in academia and in the media, about the demise of nuance, about still considering himself a progressive despite his feeling that the far left was no better at offering practical solutions to the world’s problems than the far right.
an American Enterprise Institute video of Sommers, the Factual Feminist, in conversation with the scholar and social critic Camille Paglia — “My generation fought for the freedom for women to risk getting raped!” I watched yet another video in which Paglia sat by herself and expounded volcanically about the patriarchal history of art (she was all for it).
James Baldwin’s line, “I love America more than any other country in the world, and, exactly for this reason, I insist on the right to criticize her perpetually
Jordan Peterson Twelve Rules for Life: An Antidote for Chaos, is a sort of New and Improved Testament for the purpose-lacking young person (often but not always male) for whom tough-love directives like “clean up your room!” go down a lot easier when dispensed with a Jungian, evo-psych panache.
Quillette, a new online magazine that billed itself as “a platform for free thought”
the more honest we are about what we think, the more we’re alone with our thoughts. Just as you can’t fight Trumpism with tribalism, you can’t fight tribalism with a tribe.
Understanding what sources to trust is a basic tenet of media literacy education.
Think about how this might play out in communities where the “liberal media” is viewed with disdain as an untrustworthy source of information…or in those where science is seen as contradicting the knowledge of religious people…or where degrees are viewed as a weapon of the elite to justify oppression of working people. Needless to say, not everyone agrees on what makes a trusted source.
Students are also encouraged to reflect on economic and political incentives that might bias reporting. Follow the money, they are told. Now watch what happens when they are given a list of names of major power players in the East Coast news media whose names are all clearly Jewish. Welcome to an opening for anti-Semitic ideology.
In the United States, we believe that worthy people lift themselves up by their bootstraps. This is our idea of freedom. To take away the power of individuals to control their own destiny is viewed as anti-American by so much of this country. You are your own master.
Children are indoctrinated into this cultural logic early, even as their parents restrict their mobility and limit their access to social situations. But when it comes to information, they are taught that they are the sole proprietors of knowledge. All they have to do is “do the research” for themselves and they will know better than anyone what is real.
Many marginalized groups are justifiably angry about the ways in which their stories have been dismissed by mainstream media for decades.It took five days for major news outlets to cover Ferguson. It took months and a lot of celebrities for journalists to start discussing the Dakota Pipeline. But feeling marginalized from news media isn’t just about people of color.
Keep in mind that anti-vaxxers aren’t arguing that vaccinations definitively cause autism. They are arguing that we don’t know. They are arguing that experts are forcing children to be vaccinated against their will, which sounds like oppression. What they want is choice — the choice to not vaccinate. And they want information about the risks of vaccination, which they feel are not being given to them. In essence, they are doing what we taught them to do: questioning information sources and raising doubts about the incentives of those who are pushing a single message. Doubt has become tool.
Addressing so-called fake news is going to require a lot more than labeling. It’s going to require a cultural change about how we make sense of information, whom we trust, and how we understand our own role in grappling with information. Quick and easy solutions may make the controversy go away, but they won’t address the underlying problems.
boyd, danah. (2014). It’s Complicated: The Social Lives of Networked Teens (1 edition). New Haven: Yale University Press.
p. 8 networked publics are publics that are reconstructed by networked technologies. they are both space and imagined community.
p. 11 affordances: persistence, visibility, spreadability, searchability.
p. technological determinism both utopian and dystopian
p. 30 adults misinterpret teens online self-expression.
p. 31 taken out of context. Joshua Meyrowitz about Stokely Charmichael.
p. 43 as teens have embraced a plethora of social environment and helped co-create the norms that underpin them, a wide range of practices has emerged. teens have grown sophisticated with how they manage contexts and present themselves in order to be read by their intended audience.
p. 54 privacy. p. 59 Privacy is a complex concept without a clear definition. Supreme Court Justice Brandeis: the right to be let alone, but also ‘measure of th access others have to you through information, attention, and physical proximity.’
control over access and visibility
p. 65 social steganography. hiding messages in plain sight
p. 69 subtweeting. encoding content
p. 70 living with surveillance . Foucault Discipline and Punish
p. 77 addition. what makes teens obsessed w social media.
p. 81 Ivan Goldberg coined the term internet addiction disorder. jokingly
p. 89 the decision to introduce programmed activities and limit unstructured time is not unwarranted; research has shown a correlation between boredom and deviance.
My interview with Myra, a middle-class white fifteen-year-old from Iowa, turned funny and sad when “lack of time” became a verbal trick in response to every question. From learning Czech to trakc, from orchestra to work in a nursery, she told me that her mother organized “98%” of her daily routine. Myra did not like all of these activities, but her mother thought they were important.
Myra noted that her mother meant well, but she was exhausted and felt socially disconnected because she did not have time to connect with friends outside of class.
p. 100 danger
are sexual predators lurking everywhere
p. 128 bullying. is social media amplifying meanness and cruelty.
p. 131 defining bullying in a digital era. p. 131 Dan Olweus narrowed in the 70s bulling to three components: aggression, repetition and imbalance on power. p. 152 SM has not radically altered the dynamics of bullying, but it has made these dynamics more visible to more people. we must use this visibility not to justify increased punishment, but to help youth who are actually crying out for attention.
p. 153 inequality. can SM resolve social divisions?
p. 176 literacy. are today’s youth digital natives? p. 178 Barlow and Rushkoff p. 179 Prensky. p. 180 youth need new literacies. p. 181 youth must become media literate. when they engage with media–either as consumers or producers–they need to have the skills to ask questions about the construction and dissemination of particular media artifacts. what biases are embedded in the artifact? how did the creator intend for an audience to interpret the artifact, and what are the consequences of that interpretation.
p. 183 the politics of algorithms (see also these IMS blog entrieshttp://blog.stcloudstate.edu/ims?s=algorithms) Wikipedia and google are fundamentally different sites. p. 186 Eli Pariser, The Filter Bubble: the personalization algorithms produce social divisions that undermine any ability to crate an informed public. Harvard’s Berkman Center have shown, search engines like Google shape the quality of information experienced by youth.
p. 192 digital inequality. p. 194 (bottom) 195 Eszter Hargittai: there are signifficant difference in media literacy and technical skills even within age cohorts. teens technological skills are strongly correlated with socio-economic status. Hargittai argues that many youth, far from being digital natives, are quite digitally naive.
p. 195 Dmitry Epstein: when society frames the digital divide as a problem of access, we see government and industry as the responsible party for the addressing the issue. If DD as skills issue, we place the onus on learning how to manage on individuals and families.
p. 196 beyond digital natives
Palfrey, J., & Gasser, U. (2008). Born Digital: Understanding the First Generation of Digital Natives (1 edition). New York: Basic Books.
John Palfrey, Urs Gasser: Born Digital
Digital Natives share a common global culture that is defined not by age, strictly, but by certain attributes and experience related to how they interact with information technologies, information itself, one another, and other people and institutions. Those who were not “born digital’ can be just as connected, if not more so, than their younger counterparts. And not everyone born since, say 1982, happens to be a digital native.” (see also http://blog.stcloudstate.edu/ims/2018/04/15/no-millennials-gen-z-gen-x/
p. 197. digital native rhetoric is worse than inaccurate: it is dangerous
many of the media literacy skills needed to be digitally savvy require a level of engagement that goes far beyond what the average teen pick up hanging out with friends on FB or Twitter. Technical skills, such as the ability to build online spaces requires active cultivation. Why some search queries return some content before others. Why social media push young people to learn how to to build their own systems, versus simply using a social media platforms. teens social status and position alone do not determine how fluent or informed they are via-a-vis technology.
Falsehoods are spread due to biases in the brain, society, and computer algorithms (Ciampaglia & Menczer, 2018). A combined problem is “information overload and limited attention contribute to a degradation of the market’s discriminative power” (Qiu, Oliveira, Shirazi, Flammini, & Menczer, 2017). Falsehoods spread quickly in the US through social media because this has become Americans’ preferred way to read the news (59%) in the 21st century (Mitchell, Gottfried, Barthel, & Sheer, 2016). While a mature critical reader may recognize a hoax disguised as news, there are those who share it intentionally. A 2016 US poll revealed that 23% of American adults had shared misinformation unwittingly or on purpose; this poll reported high to moderate confidence in one’s ability to identify fake news with only 15% not very confident (Barthel, Mitchell, & Holcomb, 2016).
Hoaxy® takes it one step further and shows you who is spreading or debunking a hoax or disinformation on Twitter.
It will be eons before AI thinks with a limbic brain, let alone has consciousness
AI programmes themselves generate additional computer programming code to fine-tune their algorithms—without the need for an army of computer programmers. In AI speak, this is now often referred to as “machine learning”.
An AI programme “catastrophically forgets” the learnings from its first set of data and would have to be retrained from scratch with new data. The website futurism.com says a completely new set of algorithms would have to be written for a programme that has mastered face recognition, if it is now also expected to recognize emotions. Data on emotions would have to be manually relabelled and then fed into this completely different algorithm for the altered programme to have any use. The original facial recognition programme would have “catastrophically forgotten” the things it learnt about facial recognition as it takes on new code for recognizing emotions. According to the website, this is because computer programmes cannot understand the underlying logic that they have been coded with.
Irina Higgins, a senior researcher at Google DeepMind, has recently announced that she and her team have begun to crack the code on “catastrophic forgetting”.
As far as I am concerned, this limbic thinking is “catastrophic thinking” which is the only true antipode to AI’s “catastrophic forgetting”. It will be eons before AI thinks with a limbic brain, let alone has consciousness.
Stephen Hawking warns artificial intelligence could end mankind
By Rory Cellan-JonesTechnology correspondent,2 December 2014
Between the “dumb” fixed algorithms and true AI lies the problematic halfway house we’ve already entered with scarcely a thought and almost no debate, much less agreement as to aims, ethics, safety, best practice. If the algorithms around us are not yet intelligent, meaning able to independently say “that calculation/course of action doesn’t look right: I’ll do it again”, they are nonetheless starting to learn from their environments. And once an algorithm is learning, we no longer know to any degree of certainty what its rules and parameters are. At which point we can’t be certain of how it will interact with other algorithms, the physical world, or us. Where the “dumb” fixed algorithms – complex, opaque and inured to real time monitoring as they can be – are in principle predictable and interrogable, these ones are not. After a time in the wild, we no longer know what they are: they have the potential to become erratic. We might be tempted to call these “frankenalgos” – though Mary Shelley couldn’t have made this up.
Twenty years ago, George Dyson anticipated much of what is happening today in his classic book Darwin Among the Machines. The problem, he tells me, is that we’re building systems that are beyond our intellectual means to control. We believe that if a system is deterministic (acting according to fixed rules, this being the definition of an algorithm) it is predictable – and that what is predictable can be controlled. Both assumptions turn out to be wrong.“It’s proceeding on its own, in little bits and pieces,” he says. “What I was obsessed with 20 years ago that has completely taken over the world today are multicellular, metazoan digital organisms, the same way we see in biology, where you have all these pieces of code running on people’s iPhones, and collectively it acts like one multicellular organism.“There’s this old law called Ashby’s law that says a control system has to be as complex as the system it’s controlling, and we’re running into that at full speed now, with this huge push to build self-driving cars where the software has to have a complete model of everything, and almost by definition we’re not going to understand it. Because any model that we understand is gonna do the thing like run into a fire truck ’cause we forgot to put in the fire truck.”
Walsh believes this makes it more, not less, important that the public learn about programming, because the more alienated we become from it, the more it seems like magic beyond our ability to affect. When shown the definition of “algorithm” given earlier in this piece, he found it incomplete, commenting: “I would suggest the problem is that algorithm now means any large, complex decision making software system and the larger environment in which it is embedded, which makes them even more unpredictable.” A chilling thought indeed. Accordingly, he believes ethics to be the new frontier in tech, foreseeing “a golden age for philosophy” – a view with which Eugene Spafford of Purdue University, a cybersecurity expert, concurs. Where there are choices to be made, that’s where ethics comes in.
our existing system of tort law, which requires proof of intention or negligence, will need to be rethought. A dog is not held legally responsible for biting you; its owner might be, but only if the dog’s action is thought foreseeable.
As we wait for a technological answer to the problem of soaring algorithmic entanglement, there are precautions we can take. Paul Wilmott, a British expert in quantitative analysis and vocal critic of high frequency trading on the stock market, wryly suggests “learning to shoot, make jam and knit”
The venerable Association for Computing Machinery has updated its code of ethics along the lines of medicine’s Hippocratic oath, to instruct computing professionals to do no harm and consider the wider impacts of their work.