The News Feed made Facebook an actual social network. In turn, the News Feed became synonymous with social media.
Twitter’s feed was chronological, so you could tweet out a ton of links to content and get consistent clicks from your followers. Facebook’s algorithm was incredibly friendly to “link posts” that sent users to news or blog articles.
Stories let Snapchat users post a series of snaps that would last for 24 hours, and it was an immediate hit.
Stories were so absurd on LinkedIn that the company is shutting it down by the end of this month).
TikTok’s success has often been attributed to its algorithm, which is very good at predicting the type of video you’ll like. But TikTok is also so successful because it plays on the same part of our brain that makes gambling so addictive. random reinforcement
As our research showed earlier this year, people will continue to consume content in an array of different formats—from blog posts to YouTube to podcasts to good old-fashioned memes.
Document-based questions have long been a staple of social studies classrooms
Since the human brain is essentially wired to recognize patterns, computational thinking—somewhat paradoxically—doesn’t necessarily require the use of computers at all.
In a 2006 paper for the Association for Computing Machinery, computer scientist Jeanette Wing wrote a definition of computational thinking that used terms native her field—even when she was citing everyday examples. Thus, a student preparing her backpack for the day is “prefetching and caching.” Finding the shortest line at the supermarket is “performance modeling.” And performing a cost-benefit analysis on whether it makes more sense to rent versus buy is running an “online algorithm.” “Computational thinking will have become ingrained in everyone’s lives when words like algorithm and precondition are part of everyone’s vocabulary,” she writes.
three main steps:
Looking at the data: Deciding what’s worth including in the final data set, and what should be left out. What are the different tools that can help manipulate this data—from GIS tools to pen and paper?
Looking for patterns: Typically, this involves shifting to greater levels of abstraction—or conversely, getting more granular.
Decomposition: What’s a trend versus what’s an outlier to the trend? Where do things correlate, and where can you find causal inference?
It requires that companies become what we call digital masters. Digital masters cultivate two capabilities: digital capability, which enables them to use innovative technologies to improve elements of the business, and leadership capability, which enables them to envision and drive organizational change in systematic and profitable ways. Together, these two capabilities allow a company to transform digital technology into business advantage.
We found that the elements of leadership capability have endured, but new elements of digital capability have come to the fore.
While strong leadership capability is even more essential than ever, its core elements — vision, engagement, and governance — are not fundamentally changed, though they are informed by recent innovations. The elements of digital capability, on the other hand, have been more profoundly altered by the rapid technological advances of recent years.
Experience design: Customer experience has become the ultimate battleground for many companies and brands.
Customer intelligence: Integrating customer data across silos and understanding customer behavior
Emotional engagement: Emotional connections with customers are as essential as technology in creating compelling customer experiences.
As ever, well-managed operations are essential to converting revenue into profit, but now we’re seeing a shift in the focus of digital transformation in this arena.
Core process automation: Amazon’s distribution centers deliver inventory to workers rather than sending workers to collect inventory. Rio Tinto, an Australian mining company, uses autonomous trucks, trains, and drilling machinery so that it can shift workers to less dangerous tasks, leading to higher productivity and better safety.
Connected and dynamic operations: Thanks to the growing availability of cheap sensors, cloud infrastructure, and machine learning, concepts such as Industry 4.0, digital threads, and digital twins have become a reality. Digital threads connecting machines, models, and processes provide a single source of truth to manage, optimize, and enhance processes from requirements definition through maintenance.
Data-driven decision-making: from backward-looking reports to real-time data. Now, connected devices, new machine learning algorithms, smarter experimentation, and plentiful data enable more-informed decisions.
Transforming Employee Experience
Augmentation: Warnings that robots will replace humans have given way to a more nuanced and productive discussion.
Workers in Huntington Ingalls Industries’ shipyard use augmented reality to help build giant complex vessels such as aircraft carriers and submarines. They can “see” where to route wires or pipes or what is behind a wall before they start drilling into it.
Future-readying: providing employees with the skills they need to keep up with the pace of change. In the past few years, this has given rise to new models of managing learning and development in organizations, led by a new kind of chief learning officer, whom we call the transformer CLO
Flexforcing: To respond to fast-paced digital opportunities and threats, companies also need to build agility into their talent sourcing systems. As automation and AI applications take over tasks once performed by humans, some companies are multiskilling employees to make the organization more agile.
Transforming Business Models
three elements supporting business model transformation: digital enhancements, information-based service extensions, and multisided platforms.
Blogging was supposed to be an extraordinary way for everyone to have a voice. Yet at least in my experience, students have little to no experience with blogs or any kind of “feed” that isn’t generated for them algorithmically.
A minimal-data practice will enable several AI-driven industries — including cyber security, which is my own area of focus — to become more efficient, accessible, independent, and disruptive.
1. AI has a compute addiction. The growing fear is that new advancements in experimental AI research, which frequently require formidable datasets supported by an appropriate compute infrastructure, might be stemmed due to compute and memory constraints, not to mention the financial and environmental costs of higher compute needs.
MIT researchers estimated that “three years of algorithmic improvement is equivalent to a 10 times increase in computing power.”
SkySilk, a Web infrastructure company based outside of Los Angeles, is now hosting Parler, SkySilk’s chief executive, Kevin Matossian, confirmed to NPR in an interview.
Matossian refused to comment on the terms of the arrangement, or under what conditions SkySilk will do business with Parler, which was heavily used by the rioters in connection with the violent storming of the U.S. Capitol on Jan. 6.
In new content guidelines released by Parler on Monday, the company says it will deploy a “privacy-preserving process” using an algorithm and human moderators to police content that threatens or incites violence.
Additionally, Parler says there will be a “trolling filter” in which content that attacks someone based on race, sex, sexual orientation or religion will be covered up. Yet those who want to view the content will be allowed by clicking through the filter.
Mark Meckler, one of the early creators of the Tea Party movement and now Parler’s interim CEO, said the platform has been rebuilt on independent technology and is “not reliant on so-called ‘Big Tech’ for its operations.”
Indeed, Parler has turned to Web infrastructure companies that have welcomed extremist and hate-filled websites, including Epik, a firm based outside of Seattle that supports Parler’s domain, as well as sites including InfoWars, BitChute and Patriots.win, previously known as The Donald.
Besides the algorithms that contribute to this truth decay, there is something equally as powerful that contributes to it as well. That other contributor is our confirmation bias.
“At its core, Media Literacy (ML) is made up of several specific competencies, such as the abilities to access, analyze, evaluate, and communicate media messages in a variety of forms. Experts and organizations typically define media literacy using this or similar collections of competencies, which in the past two decades have evolved to focus more on the active construction of media and participation in the information ecosystem.”
Huguet, A.; Kavanagh, J.; Baker, G.; Blumenthal, M. (2018). Exploring Media Literacy Education as a Tool for Mitigating Truth Decay. Rand Corporation.
Data: Anything represented in digital form, including non-executing knowledge stored in digital form.
Information: The momentary extraction of structure from data that modifies the perspective to the interpreter by creating new data or insight. Information only exists at the time of active data interpretation. Information creates the context that reveals discontinuities between what is known and what is new, triggering the need for learning.
Knowledge: Rules, algorithms, interpreters (such as pattern recognizers) or other mechanisms, including those that exist in the human brain (regardless of our ability to describe those mechanisms) that transform data into information. Knowledge may be changed by its interaction with information.
Wisdom: Specialized knowledge that acts to filter/active the knowledge that is best used to extract the appropriate information from data. Like, knowledge, wisdom may also be changed by the experience of its use through positive or negative reinforcement.
Eye tracking technology – Projects information at driver’s level of sight based on driver’s eye position, eliminating a potential mismatch between the projected image when the driver moves their head
Advanced optics – Advanced optical design techniques provide expanded field-of-view (beyond 10 by 4 degrees) for virtual image distance of 10m or greater; detects pedestrians and objects through enhanced low light and nighttime view; tilted virtual image planes adjust visibility of objects in the driver’s field of view; embedded camera system allows discrete monitoring for the driver’s eye location.
AI navigation accuracy – AI-driven AR navigation technology detects and provides multi-color 3D navigation graphics that adjust with moving vehicle’s surroundings, displaying information like lane markers and GPS arrows where turns will occur and sudden changes such as collisions or cyclists in one’s path
Vibration control – Panasonic’s proprietary camera image stability algorithm enables AR icons to lock onto the driving environment regardless of the bumpiness of the road
Real-time situational awareness – Driving environment updates occur in real-time; ADAS, AI, AR environment information updates in less than 300 milliseconds
3D imaging radar – Sensor-captured full 180-degree forward vision up to 90 meters and across approximately three traffic lanes
Compact size – Efficient compact packaging to fit any vehicle configuration
4K resolution – Crisp, bright 4K resolution using advanced laser and holography technology, with static near-field cluster information and far-field image plane for AR graphic overlay
Stiegler discovered philosophy in prison for robbery and was mentored by Derrida. His 3-volume Technics and Time, evoking Heidegger’s Being and Time, takes up the grammatological rather than deconstructive path taken by Derrida in the 1970s. Stiegler’s research on intergenerational care, phamakology, and algorithmic governance continue with his colleagues at the IRI in Paris and around the world. I first met Bernard when he visited Madison in 2015, and I gave him a tour of DesignLab. At the suggestion of collaborator Ana Vujanovic, we reached out to him and were collaborating on a lecture performance over the past year or so. I had tickets and hotel reserved to Paris when COVID struck. Disappointed, we Zoomed and discussed how to proceed and possible workshops, still being pursued with IRI. He passed away last summer, due to cancer. In this 2-hour interview with Zero Books, Stiegler discusses Marx and Greenspan on the proletarianization of intellect achieved by IT, his rejection of defunding the police, and COVID and the positions taken to it by Zizek and Agamben. Throughout the interview, Bernard’s patient passion and clarity of thought shine through. “Making a Mouk” is a short, accessible text; https://www.dropbox.com/…/Bernard_Stiegler_Making_a… https://www.youtube.com/watch?v=rd-9LPVilmM