View the full animation from 1971 to 2019 below.
The Moore's Law has been one of the major engine of growth over the last 50 years. As we reach the atomic scale, Richard Feynman will be proven wrong, there won't be any room left at the bottom. We'll need to move into other dimensions. The 3rd will do for now. But how many does the brain use? Answering this question would probably open new doors to AI and growth.
Over the last few months, I have tried to listen and understand as much as possible about the new and sudden epidemic of Covid-19.
Why such a dissonance between the not so frightening statistics and the unbelievable over reactions around the world?
Why dissenting doctors and Nobel prize winners are silenced with such ferocity?
Conversely, why other doctors and scientists defend thesis which are manifestly not only wrong but proven wrong?
As a scientist myself and a statistician, I found it amazing how suddenly, the scientific method and the validation system it requires is thrown out the window and plays second fiddle to political expediency.
From the beginning, I have been critical of China, as clearly overwhelming evidence was showing that they "let the bug out of the lab", but at the same time, could not understand the reluctance of other scientists to condemn what happened in Wuhan. It is only with insight that the global picture came into view. The responsibility of scientists around the globe for not only helping build the laboratory, but sending and supporting in Wuhan, research that could not be officially conducted in other countries.
This video put the pieces together.
But as it makes clear, the problem we have now is not a pandemic. It is a full scale social experiment which is being conducted on a grand scale.
From the early times of Edward Bernays, we have learnt that society works according to rules which are complex and although based on human psychology are on a different scale and fundamentally distinct. And slowly through the years we have learned to use them to manipulate the masses.
The end of independent journalism, the use of "choc doctrine", of advertising methods not to "convince" people but to "prime" them to receive a message. (This is one of the great misunderstanding of how advertising really works. It does not intend to "sell" anything to you, only speak one word at a time to your subconscious.) All this put together has generated the world in which we live today. A world of mirrors where almost nothing of what people believe is true.
We ridicule the Middle age, the inquisition and its obscurantism without realizing that we have done very little progress since and almost all of it is veneer thin and prone to be scratched off in an instant.
I post this video, not because I believe what it says or to convince anyone but because it contains true statements. Even if "everything" is not necessarily true it is good "food for thought". As Plato said, the truth is a fleeting reality that no human mind can ever expect to reach. But intelligence is what gives us the ability to think, question and doubt, and eventually progress. This is but a feeble candle in the night but this is our one hope for a better future. In this, I share the ending optimism of this documentary.
Online advertising has changed drastically over the last few years, moving from cookies which people could easily erase to device ID number which of course are unique. This evolution has allowed Facebook to refine its Audience Network advertising model with better targeting and specific clusters.
This of course presuppose that device ID number are both linked to Individual Identities but somehow not private, a legal oxymoron. This is what Apple is planning to change with the release of its next iOS platform following the iPhone 12 later this year which will require consent from users... for every application!
This is a master stroke from Apple to protect privacy and a mortal danger for Facebook, endangering the essence of its business model. But who said the 5 giants would coexist very long in the current ecosystem?
The article below is from Zero Hedge
Typically, it is media companies that cower in fear any time Facebook changes its new algorithm, terrified it could result in a catastrophic drop in ad revenues. Now, it's Facebook's turn to be at the mercy of its most popular platform, in this case Apple's iOS operating system, whose new upcoming version iOS 14 could lead to a more than 50% drop in Facebook's Audience Network advertising business, the company admitted in a blog post today.
While previously Facebook had warned that iOS 14 could impact its advertising business, only today did the company outline just how profound that impact could be. The Facebook Audience Network allows mobile software developers to provide in-app advertisements targeted to users based on Facebook’s data.
In today's post-cookie world, advertisers use a unique device ID number called the IDFA to better target ads and estimate their effectiveness CNBC explains. However, in iOS 14, each app that wants to use these identifiers will ask users to opt in to tracking when the app is first launched. Facebook said its apps will not collect IDFA information on iOS 14.
The impact of the revision could be tremendous: according to Facebook, more than 1 billion people see at least one Audience Network ad every month, although many of those are probably using Android phones and will not be affected by the change. While Facebook derives virtually all revenue from advertising, it’s not known what percentage is attributable to the Audience Network versus ads on Facebook and other properties.
As CNBC adds, in Facebook's blog post it outlined steps it will take to ensure its advertising business is in compliance with Apple’s requirements. These steps will limit how effectively Facebook and its advertisers can target ads to iPhone and iPad users.
“We know this may severely impact publishers’ ability to monetize through Audience Network on iOS 14, and, despite our best efforts, may render Audience Network so ineffective on iOS 14 that it may not make sense to offer it on iOS14 in the future,” Facebook said in a blog post.
“While it’s difficult to quantify the impact to publishers and developers at this point with so many unknowns, in testing we’ve seen more than a 50% drop in Audience Network publisher revenue when personalization was removed from mobile ad install campaigns,” Facebook said. “In reality, the impact to Audience Network on iOS 14 may be much more, so we are working on short-and long-term strategies to support publishers through these changes.”
Apple has not said when iOS 14 will launch, but it’s expected to roll out this year.
A great article from Harvard Business
Review on how to integrate AI in a company. This should be compulsory reading at many board meetings. AI is a moving target so understanding what exactly it can do for your organization may be illusive. But understanding how to integrate it within a company is not only possible but a necessity. Within a short few years the gap between companies who have done it successfully and the rest will be unbridgeable.
Too many business leaders still believe that AI is just another ‘plug and play’ incremental technological investment. In reality, gaining a competitive advantage through AI requires organizational transformation of the kind exemplified by companies leading in this era: Google, Haier, Apple, Zappos, and Siemens. These companies don’t just have better technology — they have transformed the way they do business so that human resources can be augmented with machine powers.
How do they do it? To find out, we conducted a multistage study over five years, beginning with a survey of senior managers and executives, followed by interviews and surveys across a wide range of industries to identify technology implementation strategies and barriers, and in-depth studies of five leading organizations. Our key takeaway is counterintuitive. Competing in the age of AI is not about being technology-driven per se — it’s a question of new organizational structures that use technology to bring out the best in people. The secret to making this work, we learned, is the business model itself, where machines and humans are integrated to complement each other. Machines do repetitive and automated tasks and will always be more precise and faster. However, those uniquely human skills of creativity, care, intuition, adaptability, and innovation are increasingly imperative to success. These human skills cannot be “botsourced,” a term we use to characterize when a business process traditionally carried out by humans is delegated to an automated process like a robot or an algorithm.
From our research we have developed a four-layer framework that shows organizational leaders how they can create a human-centric organization with super-human intelligence. The four layers are not “steps,” which would imply a sequential progression. The four layers of intentionality, integration, implementation, and indication (the Four I model) must be stacked all together, or else the use of AI will fail to deliver a sustainable competitive advantage. Here’s how it works.
The first layer of the Four I model is intentionality of purpose, beyond the mere pursuit of profits. An intentional organization knows why it matters to the world, not just its shareholders. A good example of intentionality in the use of AI comes from Siemens, which evolved from a shareholder-profit-maximizing power generation and transmission company into a leading provider of electrification, automation, and digitalization solutions with energy-efficient, resource-saving technologies driven by AI and the Internet of Things (IoT) in service to society. This cultural shift toward a higher human-centric purpose impacted not just marketing and product design but also the strategic decision to, as Scott D. Anthony, Alasdair Trotter, and Evan I. Schwartz wrote for HBR, “divest its core oil and gas business and redeploy the capital to its Digital Industries unit and Smart Infrastructure business focused on energy efficiency, renewable power storage, distributed power, and electric vehicle mobility.” While financial performance and shareholder value will always be important, creating human-centered, technology-powered organizations will actually drive financial performance in the age of AI.
To that end, Siemens is launching a combination of hardware and software that enables AI throughout its Totally Integrated Automation (TIA) architecture, an approach that aligns Siemens’ mission with its AI strategy. The TIA architecture uses AI as a bridge that spans from corporate headquarters out to industrial end users. Siemens’ proprietary “MindSphere” is a cloud-based IoT operating platform that reaches into Siemens’ industrial user-operated controller and field device products. The MindSphere’s neural processing unit module allows human users to benefit from Siemens’ in-house AI capabilities, while also enabling human users to impart their own experience to train the machines. According to Siemens Factory Automation specialist Colm Gavin, “With artificial intelligence we are able to train, recognize, and adjust to allow more flexible machinery. Because, do we want 10 machines to package 10 different types of products, or a tool that accommodates different packages and different sizes and automatically adjusts to the new format?” Smarter machinery with TIA architecture leverages AI to advance the company’s intentionality, while increasing flexibility, quality, efficiency, and cost-effectiveness for its end users.
Alternatively, a negative example of the relationship between intentionality and AI is illustrated by recent issues confronting Facebook. Facebook’s mission, “to give people the power to build community and bring the world closer together,” sounds noble. Yet recent use of its AI has raised concerns from advertisers and civil rights groups alike. The social media giant has struggled to align its mission with its use of AI that seems to have the opposite effect: Facebook’s content “feed” is driven by algorithms that prioritize inflammatory, misleading, and socially divisive content. Facebook’s use of AI seems to drive social division, which is antithetical to its purpose as a social media company, and is having financial consequences. Because its algorithms have promoted disinformation, violence, and incendiary content, major advertisers are now cutting ties with Facebook, dealing a strong blow to the company that derives 98% of its income from ad revenue. Some of the largest brands in the world, including Coca-Cola and Unilever, pulled advertisements from Facebook for promoting content antithetical to their brand’s values, resulting in a one-day drop of 8.3% in market value, or $56 billion.
The second layer of the Four I model is integration of human and AI resources across the organization. To lead in the technology era, companies must shift away from silos to organizational structures with flexible teams that integrate people horizontally and vertically, from product creation to strategic decision making. As one executive we spoke with explained, before the AI shift, it was necessary for workers to have deep knowledge of a narrow area. Today, deep analytical content can come from AI. What is needed is the ability of workers to synthesize information, which means collaborating across functions and working in cross-functional teams. To foster innovation and adaptability, organizations need to transition from rigid hierarchies to flexible, agile, and flatter structures. Google, Haier, and Zappos may have differences in their organizational structures, but the common elements are flatness and fluidity. The recommended structure is more like a playground for smart, talented people to generate customer-centric products. Employees have fluid roles in cross-functional teams around problems as opposed to individual roles and responsibilities. These teams spontaneously form when problems arise, then dissolve when the work is done, reallocating human resources as needed.
The other side of this — which can easily be forgotten — is that human and AI teams should also be structured in an integrated manner. This allows humans to transcend their ordinary cognitive limitations, without placing unreasonable reliance on a robot to perform human tasks that require high degrees of care and skill. An example comes from the medical context, where AI offers tremendous potential not as a substitute for, but as a supplement to, physician-driven care. Recent research in the journal Nature found that, “good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone.” This means high-stakes, highly-skilled human decision-making can benefit from AI so long as it is integrated properly within the human decision-making context.
The third layer of the Four I model is implementation. Implementation requires engaging human talent, tolerating risk, and incentivizing cross-functional coordination. An executive at a large pharmaceutical we spoke with said, “you have to get people to believe in the technology.” We saw this in another of the companies we spoke with when we learned that despite having integrated AI, managers were modifying the output values from the algorithm to fit their own expectations. Others in the same company would simply follow the old decision-making routine, altogether ignoring the data provided by algorithms. Therefore, human behavior is central to implementing AI.
Top performing companies spent significant time communicating with employees and educating them, so that the human talent understood how machines made their jobs easier, not obsolete. To build trust in AI, it is imperative for leaders to communicate their vision transparently, explaining the goal, the changes needed, how it will be rolled out, and over what timeline. Beyond communication, leaders can inoculate their workforce against fear of AI by arranging for visits to other companies that have undergone similar transformations, providing a model for workers to see with their own eyes how the technology is used.
We saw many approaches to this in our research. Pilot projects where technology is rolled out in a limited scope give workers some ownership over the adoption process. Giving workers an opportunity to tinker with the technology before a final adoption decision is made eases the transition. Financial services firm Capital One even created an internal training institute called Capital One University that offers professional training programs to promote a broader understanding of analytics throughout the organization’s culture.
The fourth layer of the model is indication or performance measurement. Ultimately, success and progress need to be measured, and leading companies have moved from traditional productivity measures to aspirational metrics. Using the right indicators can drive improvements and help a business focus on what they deem important. Aspirational metrics that incentivize innovation and creativity encourage employees to exercise those uniquely human traits. The lesson is to be careful what you measure. Monitoring the wrong performance indicator has a strong tendency to lead to the proverbial tail wagging the dog. Humans are clever, and if incentives are not properly aligned with intelligently designed performance metrics, human workers will resort to lazy, clever, and cynical hacks to game the system, maximizing the appearance of performance under one measure while actually failing to deliver the output that management was actually hoping for when they implemented that measure.
Most companies use KPIs, but in our research we saw that successful companies more often used Objectives and Key Indicators (OKRs). What we learned was that KPIs by themselves don’t encompass strategic and ambitious goals needed in the age of AI and they don’t motivate to reach for the sky. The goal of OKRs is to precisely define how to achieve ambitious objectives where failure is imminently possible, through concrete, measurable specifications. They encourage creative, novel, and aspirational performance by showing progress toward a goal even if the goal itself is unattainable. Google famously started using OKRs in 1999; a change some even credit as a critical element of Google’s success. At Google, OKRs have helped develop transparency. Everybody knows the company’s goals, what everyone is doing, how they have done in the past, the trajectory they are on, and how they are getting to where they want to go.
Our research shows that AI is so much more than just the latest incremental improvement in existing technology, however deploying it effectively takes leadership and coordination across all sectors of a company. Unlocking the full potential of an organization’s human resources by adopting AI strategically requires revisiting the very structure of the company and how it measures its progress toward fulfilling its mission. These issues are core issues to the identity of a company and modifications here are fraught with insecurity and risk, but this is a risk needed to compete in the age of AI. Intentionality, integration, implementation, and indication must be layered in order to create a human-centric enterprise governed by super-human intelligence. Achieving this requires talent at all levels to have systems-thinking, understand how the work being done meshes with that of others elsewhere in the organization, how it meets customer needs, and how it impacts the company’s strategy and financial picture. By following the Four I model, companies can unlock super-human intelligence without losing the human touch.
We were surprised to discover how few organizations have unlocked this secret. But we were encouraged by the progress of the ones that had. With this model, we hope, more companies can create the conditions for realizing super-human intelligence and performance, delivering sustainable competitive advantages in the age of AI.
A catchy tittle but in fact just a translation of the previous video without the jargon. In other words: AGI is here!
