Yasuhiro Kanakog, the lead author of a study, states that morality is an important but mysterious part of what makes us human. The study in very young children indicates that it may be intrinsic. Humans may have developed behavioural tendencies toward moral behaviour during the course of evolution, specifically the punishment of antisocial behaviour may have evolved as an element of human cooperation.

A new study wanted to discover whether morality is learned

Researchers may have determined whether or not morality is a learned behavior or if it is intrinsic.

philosophy

Philosophy

personaldevelopment

Personal Development

Determining The Origin Of Our Moral Compass

“Abundance is not about providing everyone on this planet with a life of luxury—rather it's about providing all with a life of possibility.”

We live in turbulent times. When we glance at the headlines, it is enough to put anybody on edge. However, a quick look at history shows that progress continues through the good and the bad times. Global living standards will continue to improve regardless of the horrors in the headlines. For example, the 20th century saw advancement and tragedy. The 1918 influenza epidemic killed fifty million people, and World War II killed another sixty million. Yet, this period also saw infant mortality decrease by 90 per cent and maternal mortality decrease by 99 per cent.

The future is brighter than the media portrays

Underlying influences tend to push us towards a pessimistic view of the future. <ul><li>The amygdala is always looking for threats in our environment. When triggered, it starts the fight or flight response. But modern society threats are probabilistic. For example, the economy could crash, or there could be a terrorist attack.</li><li>Information we receive. Media outlets know that good news doesn’t sell as well as threatening news, so we are constantly bombarded with fearful images and possible situations that feed the amygdala and prevent us from seeing the future objectively.</li></ul>

Why we have an overly pessimistic view of the future

The world consists of complex systems where changes in one area can impact another area. For example, population changes in one species affect living and survival conditions for others.  While some systems may seem to exacerbate current problems, they also present great opportunities because they create momentum and positive benefits in others.

Solving complex problems together

Contrary to what many believe, big businesses contribute more to alleviating global poverty. <ul><li>They develop cheaper products for people at the bottom of the income pyramid - around four billion people. This is a huge potential market and the opportunity to make a profit while raising the standard of living for the poorest citizens.</li><li>Technophilanthropists have a global vision and business and political connections to get things moving forward, such as Bill Gates, founder of the Gates Foundation.</li></ul>

Big business can raise standards of living among the world's poorest people

Access to education is a major global problem. Those students with access to education follow an outdated framework that does not provide students with the skills needed to handle the problems of the twenty-first century. ICT could offer a solution to address both issues of quality and delivery. For example, Sugata Mitra ran experiments on self-directed learning in Indian slums. He found that by just providing a computer terminal and internet access, groups of young children could teach themselves.

Traditional methods of education are outdated and in need of a re-think

A stigma is attached to failure, and many people will do anything to avoid it. This can prevent innovation.  Failure should be viewed as part of a longer learning curve and an important stepping stone toward innovation.

A successful future depends on overcoming the fear of failure

Nanotechnology allows us to create new materials leading to major advances in many areas. Nanotechnology involves building things on an atomic scale and create anocomposites, even tiny programmable assemblers, called nanomachines. These nanomachines can build other nanomaterials and create greater efficiency in the technology. Examples include nanocomposites much stronger than steel produced at a fraction of the cost. Lifesaver is a water filter that can make water safe to drink without chemical or energy-intensive methods.

Technological advances

Access to biotechnology is multiplying the potential to find innovative solutions to food, energy and other global problems. <ul><li>Advances in genetic engineering provide solutions that can feed an ever-increasing population. </li><li>Biotechnology is also used for other applications such as developing strains of algae as a biofuel source.</li></ul>

Biotechnology provides solutions to global problems

The internet and social media spread has helped people share and discuss societal problems and provide tools to find solutions.  Access to information is a significant catalyst in this process, for example, Facebook, Twitter and YouTube.

Global connectivity is accelerating the sharing of information

<ul><li>Advances in solar energy technology can further innovation and lower production prices. New Energy Technologies has recently found a way to turn an ordinary window into a solar panel by using the world’s smallest organic solar cell that performs ten times better than current commercial models.</li><li>More efficient methods of growing food, like urban farming, hydroponics and vertical farming, can reduce the need for arable land.</li></ul>

Other advances that point to an abundance

Abundance shows practical solutions to humanity’s most significant concerns such as lack of quality food, water, education, healthcare, and freedom. The author shows us how exponential technologies can help us improve our quality of life to achieve abundance.

The authors document how four forces--exponential technologies, the DIY innovator, the Technophilanthropist, and the Rising Billion--are conspiring to solve our biggest problems. "Abundance" establishes hard targets for change and lays out a strategic roadmap for governments, industry and entrepreneurs, giving us plenty of reason for optimism.

technologyandthefuture

Technology & The Future

creativity

Creativity

books

Books

artsandculture

Society, Arts & Culture

The New York Times bestselling “manifesto for the future that is grounded in practical solutions addressing the world’s most pressing concerns: overpopulation, food, water, energy, education, health care and freedom” (The Wall Street Journal). Since the dawn of humanity, a privileged few have lived in stark contrast to the hardscrabble majority. Conventional wisdom says this gap cannot be closed. But it is closing—and fast. In Abundance, space entrepreneur turned innovation pioneer Peter H. Diamandis and award-winning science writer Steven Kotler document how progress in artificial intelligence, robotics, digital manufacturing synthetic biology, and other exponentially growing technologies will enable us to make greater gains in the next two decades than we have in the previous 200 years. We will soon have the ability to meet and exceed the basic needs of every person on the planet. Abundance for all is within our grasp. Breaking down human needs by category—water, food, energy, healthcare, education, freedom—Diamandis and Kotler introduce us to innovators and industry captains making tremendous strides in each area. “Not only is Abundance a riveting page-turner…but it’s a book that gives us a future worth fighting for” (The Christian Science Monitor).

Peter H. Diamandis is an accomplished writer whose work has resonated with readers around the world. Peter H. Diamandis has published several books that explore a range of topics, from fiction to nonfiction, and has received critical acclaim for the ability to weave compelling narratives. Peter H. Diamandis's writing style is known for its depth and authenticity, and has earned quite a loyal following of readers.

Peter H. Diamandis

Steven Kotler

Abundance

Mental immunity is a psychological theory that is also known as cognitive immunology.People with a healthy mental immune system are more likely to detect misinformation. A strong cognitive immune system can also help spot bad ideas at an earlier stage, so you may avoid wasting time, energy, or money.

Mental immunity

The mental immune system is believed to function in a similar way to the body’s physical immune system.<ul><li>The purpose of the physical system of immune cells is to detect pathogens including bacteria and viruses, so that they can be eradicated from our bloodstream and organ systems before they have a chance to cause damage.</li><li>Similarly, a healthy mental immune system will detect harmful or incorrect information that enters our mind, so that it can be recognised as such and then promptly rejected.</li><li>Mental immunity research has deep roots dating back to the 1950s. </li></ul>

A mental immune system

<ul><li>Build awareness of misinformation. Remember that misinformation is common, and train yourself to immediately interrogate the information or data you are presented with.</li><li>Develop healthy meta-beliefs. A meta-belief is a belief that one holds following a thorough reasoning process or cognitive interrogation to check the validity of the belief.</li><li>Practice self-reflection. If this process of reflection indicates that you are drawn to the same news sources, or solely rely on influencers or social media platforms for your updates, your information diet may not be varied enough.</li></ul>

How to strengthen your mental immune system

Every day, a new video goes “viral”, and an “infectious” idea starts spreading. Mental immunity is a psychological theory that is also known as cognitive immunology.

Understand yourself and those around you.

psychology

Psychology

problemsolving

Problem Solving

How developing mental immunity can protect us from bad ideas

Robots don’t have personalities, ideas, thoughts, or desires. Any human-like qualities we could attribute to a machine or its output are merely reflections of ourselves or its programmers.That doesn’t sit well with the mainstream perceptions of AI. Thousands of articles promise a near-future where human creators are either displaced or forced to work in tandem with machines.

Robots Are What We Make Them

Most of what AI does is prestidigitation. That’s because there’s only a handful of things a typical deep learning system can actually do. Much like a real-world magician, developers create incredible programs out of some fairly basic algorithmic foundations.The only difference between a disappearing coin trick and what David Copperfield does is scale. There is no more or less “real magic” involved in the former’s illusions and the latter’s.

The Perception Of AI

A few years back, a former Microsoft intern named Nabil Hossain and a pair of Microsoft AI researchers developed a machine learning system to generate humorous headlines from existing news articles.The big idea was that the AI would make microedits by changing a single word in a serious headline to make it a funny one.

Making AI Funny

The AI picks a noun or verb from a headline and replaces it with a word that can be objectively quantified as humorous.So here’s the simple answer to the question of whether AI can be funny or not: If you get to define what is and isn’t funny, sure. AI can be just as funny as you decide it is or isn’t.

The Machine Workings Of Humor

Jokes can be reduced to formulas. Just about anything can be reduced to a formula, but “funny” isn’t a thing. It’s a perception. Just like you can’t hand me an ounce of satisfaction or purchase a mile’s worth of courage, you can’t quantitatively produce funniness in a lab.

Funny is a Perception

AI can’t be funny. Funny as interpreted by the recipient of a joke is subjective. And funny as an intended construct requires intent.Just like fashion, being funny is as complex as the people you surround yourself with. What a group of AI devs might find funny or fashionable will likely differ from the tastes at Fashion Week in a Comedy Central writer’s room. And both the individual and public perception of what’s humorous is constantly changing and evolving.

The Bottomline

Can an AI be funny? What does funny mean? Who gets to decide? Luckily for all of us, Microsoft went ahead and answered these questions on our behalf.

computerscience

Computer Science

entertainment

Entertainment

artificialintelligence

Artificial Intelligence

Codifying humanity: Can humor be reduced to an algorithm?

Across industries, value chains are facing increasing uncertainty from climatic anomalies, market volatility, and the COVID-19 pandemic, among other factors. Industries as diverse as agriculture, oil and gas, and mining face essentially the same problem: they need the ability to both run with increased efficiency and recover quickly from unforeseen or unexpected challenges.  If companies simply increase production levels, they’ll inevitably run into bottlenecks—and if failures occur that worsen those bottlenecks, the entire network can slow down and become less resilient.

Value Chains In 2021

Increasingly sophisticated artificial intelligence (AI) technologies—such as advanced analytics-based forecasting, digital-twin supply-chain simulations, and supply-chain optimization tools—can help companies improve the resilience-efficiency balance of their value chains.

Enter AI

Coping with the highly unpredictable dynamic in an intrinsically uncertain environment can be difficult. The probabilistic nature of value chains must be accounted for in any strategic and operational initiative. Further complicating matters, processes must be continually updated and people reskilled. AI technologies can effectively deal with complexity, helping organizations keep up with changes, mitigate risk, and create value.

Opportunities And Challenges In Uncertainty

Value-chain owners looking to increase efficiency and resilience must tackle the following tasks: <ul><li>Identifying constraints in the value chain and how they shift over time.</li><li>Determining the optimal value-chain setup under variable conditions.</li><li>Selecting the best way to schedule capacity across the end-to-end value chain.</li><li>Adopting the best value-maximizing production strategy and setting up the value chain to support this strategy.</li><li>Identifying which value chains are exposed to risk from factors such as the COVID-19 crisis or difficult geographies.</li></ul>

The Main Challenges

The next generation of end-to-end planning processes will be fundamentally different from today’s. AI can help balance efficiency and resilience by incorporating uncertainty into realistic simulations of reality to help organizations evaluate scenarios and identify risks. AI can also find optimal plans for different time horizons. In doing so, it can recommend operational decisions that balance cost with revenue and provide real-time end-to-end visibility to better anticipate and react to supply-chain disruption.

Integrated Planning: Simulation, Optimization, And Real-Time Monitoring

Simulations are descriptive analytics tools. In a simulation, a digital twin is constructed with uncertainty measures and operational inputs. Next, a number of simulations of the future are performed using this digital twin and a summary is presented to the user for analysis. The end result—a set of key performance indicators (KPIs) describing current challenges—helps companies build resilience in the current environment.

Simulations Of Reality

Optimization models are prescriptive analytics tools. The main output is an optimal execution plan for the current environment, in which decisions are made accounting for the end-to-end impact. Given operational considerations, an optimization model finds the optimal plan. These considerations capture the current operational conditions, a detailed view of how the supply network functions, and can include uncertainties about the future.

Optimization For Execution

Most companies have a control tower serving as an operating nerve center. However, fully digital end-to-end control towers can increase resilience by expanding the scope of real-time monitoring to anticipate and respond to upcoming or potential supply-chain disruption. In this way, AI allows for enhanced end-to-end planning, giving historically isolated functions the benefit of full transparency.

Real-Time Supply-Chain Monitoring

Enhancing end-to-end decision making with AI requires companies to think in terms of the concrete answers they want to obtain. For example, do you want a step-by-step action plan to help run your value chain? Is the goal to produce a go-no-go decision for investment based on normal conditions? Or are you primarily looking to ease communication and centralize decision making?

Enhancing Value-Chain Resilience With A Threefold Approach

Define which challenges each digital solution should address, who should be the primary user, and which process should benefit from the enabled technology.

Creating Value With AI: Value-Driven Solution Design

Redesign or enhance the business processes expected to benefit from new AI-enabled solutions. Improve data governance to facilitate high-quality data for enabling these AI solutions. Bring in expected end users to codesign AI solutions while upskilling them with the required technological capabilities.

Creating Value With AI: Enhanced Business Process And Capability Building

Begin by making the relevant data available. Ensure close collaboration among data scientists, operators, and stakeholders while building AI-enabled solutions. Develop user interfaces and user experiences (UI and UX) to support processes and integrate solutions into the organizational IT landscape.

Creating Value With AI: Seamless Technological Implementation

Deploying the right AI capabilities to look deeply into an organization’s value chain can enable teams to quickly pivot while balancing efficiency and resilience. Armed with the right processes, organizations can create and adjust plans for the entire organization while keeping everyone aligned on risks and rewards. Operationally, AI capabilities can enable fact-based discussions across the entire value chain, which not only affects business processes but also results in better alignment across different business units.

AI Capabilities Put To Use

Value chains are facing increased uncertainty. A threefold approach underpinned by artificial intelligence can help companies adapt to rapidly changing markets and operational challenges.

Discover the world of business and economics.

economics

Economics

business

Business

startup

Startups

Building value-chain resilience with AI

Businesses with large amounts of unstructured data will find it difficult to simplify their operations or make informed decisions. But well-defined and structured data provides consistent data processing. Data annotation is critical for Artificial Intelligence (AI) and Machine Learning (ML) implementations.

Data annotation is unavoidable in any organisation

Data annotation is the process of labelling data available in various formats like text, video or images so that machines can use it. When preparing data for a Machine Learning model, poor quality annotations can result in poor model performance. With the help of correct tools and approaches, labelled data sets are needed to find repeating patterns in annotated data. When the machine algorithm processes sufficient annotated data, it can detect similar patterns when receiving additional unannotated data.

Data annotation defined

<ul><li>Text annotations. A metadata tag is used to show up properties of a dataset. The data uses tags that emphasise criteria such as keywords, sentences, or different feelings in text, such as "angry", to inform the computer on how to differentiate meaning or emotion behind words.</li><li>Video annotations. Adding metadata to unlabeled video files to develop and train an ML system.</li><li>Image annotations. Classifying photos in a dataset to train an ML model.</li><li>Content annotations. Monitoring and applying a pre-determined set of rules and standards to user-generated contributions to assess if a message is allowed.</li></ul>

Different types of data annotation

Data annotation is a groundbreaking data analysis tool that could help businesses take their operations to new levels. But annotations are time-consuming and complex and requires a highly skilled workplace that can turn out huge volumes of annotated data. This means that outsourcing to a top data labelling firm can result in considerable advantages in the form of dependable AI engines.

Outsourcing annotation services

Businesses with large amounts of unstructured data will always struggle to simplify their operations and make informed decisions. Having well-defined, structured data, on the other hand, would…

What You Should Know About Data Annotation

Organizations today are often faced with a challenge: How do we move forward even if we don’t have all of the answers yet? A data engineer can have an impact across the application, from application performance to the semantics and meaning of the data flowing across the system.
AI team members must be curious and humble enough to acknowledge that they don’t have all the answers and identify who can reach across different boundaries within a system to track down an answer.

Accept that no one has all the answers

A common refrain for many organizations, and government organizations, in particular, is that building an AI-ready workforce is particularly challenging because it is impossible to match <a href="https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html" target="_self">the salaries offered by large, private-sector</a> companies such as Amazon, Google, and Microsoft. Salary discrepancies between industry and government are unlikely to change any time soon, however.&nbsp; Many individuals <a href="https://hbr.org/2018/11/9-out-of-10-people-are-willing-to-earn-less-money-to-do-more-meaningful-work" target="_self">choose meaningful work over a larger salary</a>.

Draw talent into your problems

For many organizations, the workforce needs to depend on where they are in adopting, deploying, and maintaining AI. Organizations just starting out on their AI journey may have a large set of data that they have been collecting over the years, and they are now trying to identify what predictions they can make from it. Leaders need to move away from checklist-driven hiring practices and focus more on skills that showcase a candidate’s ability to work on a team, feel comfortable with ambiguity, and move forward in a rapidly changing environment.&nbsp;

Match your workforce needs to your development needs

Too often when organizations seek to hire talent in the AI space, they assume they should focus on a couple of schools. In our experience, robust, secure, scalable, and human-centred AI systems are ones that incorporate varied perspectives and data. AI systems learn from examples, so it helps to have a diverse team that can bring different lenses to a problem and identify appropriate datasets to train the AI system on.&nbsp; It is therefore critical to consider who can help translate across these different roles or how teams invest time in developing shared language over time.

Focus on hiring and supporting diverse talent.

AI technologies are evolving so quickly that any specific requirements might soon be overcome by advancement. For that reason, organizations looking to adopt AI need to grow a culture of learning. On the hiring side, that also means looking for people with a sense of curiosity.&nbsp;In the early days of adopting any new technology, and AI, in particular, it is often more helpful to have individuals with the curiosity and willingness to try things that are outside their traditional bounds to figure out solutions to problems.&nbsp;

Help your talent learn how to learn

Organizations also need to focus on helping existing employees learn how to learn. The expectation cannot be that everyone will be able to easily add learning on top of days filled with back-to-back meetings and never-ending lists for deliverables. Organization leaders have to think about ways they can create the structure to enable learning behaviours for individuals and teams.

New Ways To Think

To help individuals learn how to learn, managers can ask themselves the following questions:<ul><li>What is our shared vision for leveraging AI? What outcomes are we hoping to achieve?</li><li>How am I creating opportunities for people to learn and grow? How am I establishing psychological safety to encourage risk-taking?</li><li>Am I there and present when my team members have questions about where to go next? Who else can provide guidance?</li><li>How do I help my team see things they haven’t previously seen, ask new questions, or curate a set of resources?</li></ul>

Enabling Learning

Organizations today are working to assemble teams that can make bespoke pieces of AI, leverage them towards specific outcomes, and endlessly tune system components to arrive at assured AI systems, able to be deployed in a variety of different environments.&nbsp;To develop such systems, organizations and leaders will have to take action to develop a workforce that has the necessary skillsets, mindsets, and an array of experiences.&nbsp;Unfortunately, there is no perfected recipe and we at the SEI are trying to navigate the growth of our workforce to support our AI engineering portfolio.&nbsp;&nbsp;

The Starting Point for an AI-Ready Workforce

corporateculture

Corporate Culture

5 Ways to Start Growing an AI-Ready Workforce

Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory.  For AI to make a sizable contribution to a company’s bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily.Achieving such scale requires a highly efficient AI production line, where every AI team quickly churns out dozens of race-ready, risk-compliant, reliable models.

AI At Scale

As AI has matured, so too have roles, processes, and technologies designed to drive its success at scale. Specialized roles such as data engineer and machine learning engineer have emerged to offer skills vital for achieving scale.A rapidly expanding stack of technologies and services has enabled teams to move from a manual and development-focused approach to one that’s more automated, modular, and fit to address the entire AI life cycle, from managing incoming data to monitoring and fixing live applications.

The Rising Bar for AI

A best-in-class framework for ways of working, often called MLOps (machine learning operations), now can enable organizations to take advantage of these advances and create a standard, company-wide AI “factory” capable of achieving scale. Since MLOps is relatively new and still evolving, definitions of what it encompasses within the AI life cycle can vary.One needs to examine the potential improvements from four essential angles: productivity and speed, reliability, risk, and talent acquisition and retention.

Business impact of MLOps

Moving AI solutions from idea to implementation takes nine months to more than a year, making it difficult to keep up with changing market dynamics. Even after years of investment, leaders often tell us that their organizations aren’t moving any faster.Companies applying MLOps can go from idea to a live solution in just two to 12 weeks without increasing headcount or technical debt, reducing time to value and freeing teams to scale AI faster.

Increasing productivity and speed to embed organization-wide AI

Achieving productivity and speed requires streamlining and automating processes, as well as building reusable assets and components, managed closely for quality and risk, so that engineers spend more time putting components together instead of building everything from scratch.

Automation Of Processes

Another critical element for speed and productivity improvements is developing modular components, such as data pipelines and generic models that are easily customizable for use across different AI projects. By building a central AI platform and modular premade components on top, the company was able to industrialize a base AI solution that could rapidly be tailored to account for different drug combinations in each market.

Modular Components

Organizations often invest significant time and money in developing AI solutions only to find that the business stops using nearly 80 percent of them because they no longer provide value—and no one can figure out why that’s the case or how to fix them. In contrast, we find that companies using comprehensive MLOps practices shelve 30 percent fewer models and increase the value they realize from their AI work by as much as 60 percent

Enhancing reliability to ensure 24/7 operation of AI solutions

While a robust risk-management program driven by legal, risk, and AI professionals must underlie any company’s AI program, many of the measures for managing these risks rely on the practices used by AI teams. MLOps bakes comprehensive risk-mitigation measures into the AI application life cycle.Reusable components, replete with documentation on their structure, use, and risk considerations, also limit the probability of errors and allow for component updates to cascade through AI applications that leverage them.

Reducing risk to ensure regulatory compliance and trust at scale

The availability of technical talent is one of the biggest bottlenecks for scaling AI and analytics in general. When deployed well, MLOps can serve as part of the proposition to attract and retain critical talent. Most technical talent gets excited about doing cutting-edge work with the best tools that allow them to focus on challenging analytics problems and seeing the impact of their work in production.

Better talent retention and acquisition for implementing AI at scale

Implementing MLOps requires significant cultural shifts to loosen firmly rooted, siloed ways of working and focus teams on creating a factory-like environment around AI development and management. Building an MLOps capability will materially shift how data scientists, engineers, and technologists work as they move from bespoke builds to a more industrialized production approach. As a result, CEOs play a critical role in three key areas: setting aspirations, facilitating shared goals and accountability, and investing in talent.

The Role Of Leaders In MLOps

As in any technology transformation, CEOs can break down organizational barriers by vocalizing company values and their expectations that teams will rapidly develop, deliver, and maintain systems that generate sustainable value. CEOs should be clear that AI systems operate at the level of other business-critical systems that must run 24/7 and drive business value daily. While vision setting is key, it pays to get specific on what’s expected.

Setting a clear aspiration for impact and productivity

Among the key performance metrics CEOs can champion are the following:<ul><li>the percentage of models built that are deployed and delivering value, with an expectation of 90 percent of models in production having real business impact</li><li>the total impact and ROI from AI as a measurement of true scalability</li><li>near-real-time identification of model degradation and risks, including shifts in underlying data (particularly important in regulated industries).</li></ul>

Key Performance Metrics

One of the fundamental litmus tests for impact is the degree to which goals are shared across business leaders and the respective AI, data, and IT teams. Ideally, the majority of goals for AI and data teams should be in service of business leaders’ goals. Conversely, business leaders should be able to articulate what value they expect from AI and how it will come to fruition.

Ensuring shared goals and joint accountability

Newer roles needed on AI teams have emerged, like that of the machine learning engineer who is skilled in turning AI models into enterprise-grade production systems that run reliably. To build out its ML engineering team, a North American retailer combined existing expertise of internal IT developers who understood and could effectively navigate the organization’s systems with new external hires who brought broad experience in MLOps from different industries.

Investing in upskilling existing AI talent and new roles

softwareengineering

Software Engineering

Scaling AI like a tech native: The CEO’s role

“A basic or citizen’s income is not an alternative to a negative income tax. It is simply another way to introduce a negative income tax if it is accompanied with a positive income tax with no exemption. A basic income of a thousand units with a 20 percent rate on earned income is equivalent to a negative income tax with an exemption of five thousand units and a 20 percent rate below and above five thousand units.” — Milton Friedman, 1976 Nobel Memorial Prize in Economic Sciences

Basic or citizen's income

Imagine a group of 5 people. They have an income distribution of $10, $20, $30, $50, and $100. Someone gets the BIG idea of everyone putting 40% of their money into a hat, and dividing the result…

The Cost of Universal Basic Income is the Net Transfer Amount, Not the Gross Price Tag

Futurology, as practiced by the likes of, Google’s Chief Futurist, Ray Kurzweil is typically based on technological determinism, a reductionist theory that presumes a society’s technology drives the development of its social structure and cultural values.But the reality is more complex. Technology is just part of a broader spectrum of human activity, and social change is driven by society rather than machines. We have the agency to act independently and make free choices.

Futurology And Human Activity

The path of innovation and its social consequences are almost entirely shaped by society as a result of numerous social factors such as culture, politics, regulatory mechanisms, and economic arrangements.&nbsp;

What Influences Innovation

The logical conclusion of the industrial revolution and the metrics of capitalism is that anything that can be measured or based on rules will be automated. This means that the only jobs available in the future will be the ones that machines can’t do.&nbsp;We are not preparing society for this future. Our education systems, driven as they are by the $multi-billion measurement industry are still training kids for a world that doesn’t exist. Learning how to code isn’t going to help that much when machines are coding themselves.

Automation and AI

“A basic income is an income unconditionally granted to all on an individual basis, without means test or work requirement.”Taxing automation, even if feasible, delays the inevitable whereas a universal basic income addresses the cliff before we rush headlong over it taking capitalism with us.

Universal Basic Income

We should welcome robots doing more tasks for us, freeing us up to engage in other, more fulfilling activities and work.And with all the talk of automation, there is a justified concern that the private corporations who own the robots will accumulate most of the wealth.

Taxing Automation

Post-capitalism is a buzzword that describes a utopia where capitalism has gracefully declined and delivered us to a world of collaborative production as part of a social movement.The existing super-structures of society just aren’t going to give up without a fight and hand over the status quo that easily.

Post-Capitalism / Progressive Mutualism

A lot of my work, in both public speaking and private consulting, is rooted in foresight and anticipatory research. Unlike “futurology” I’m less interested in predicting the future than anticipating…

Determining The Origin Of Our Moral Compass

Abundance

How developing mental immunity can protect us from bad ideas

Codifying humanity: Can humor be reduced to an algorithm?

Building value-chain resilience with AI

Chelsea Jones