Whoever becomes the leader in Artificial Super Intelligence (ASI) will become the ruler of the world.
Russian President Vladimir Putin’s
“Data is the Driver behind ASI”
McKinsey Global Institute
The answer revolves around data. Data is food for AI and the 2000s witnessed the creation of larger and better datasets than ever. People developed large corpus for text analysis, huge data sets for images, and video processing. Which improved the accuracy of AI algorithms tremendously.
“The new spring in AI is the most significant development in computing in my lifetime. Every month, there are stunning new applications and transformative new techniques. But such powerful tools also bring with them new questions and responsibilities.”
“By the time we get to the 2040s, we’ll be able to multiply human intelligence a billionfold. That will be a profound change that’s singular in nature. Computers are going to keep getting smaller and smaller. Ultimately, they will go inside our bodies and brains and make us healthier, make us smarter.”
Ray Kurzweil, Futurist
Contrary to popular belief, ASI is far from reality. In fact, it may take anything between 15–20 years (best case scenario) to centuries to achieve it! In this article, we shall be looking at the road to achieve ASI, some current exciting projects and the dangers ASI possess.
Artificial Super Intelligence is driving massive shifts across the globe, and every day more questions arise. The narrative in the media focuses heavily on an “ASI” arms race”, with the US and China as the key players. But there is more to the story. The US and China are certainly central figures but they are not the only ones in the race, and the finish line and what characterizes a “winner” is still unclear. There is no doubt, however, that ASI will have a generational impact; for example, PwC estimates that ASI could increase global GDP by $15.7 trillion by 2030.
More: WE Forum AI Policy
My journey with Data and Segmentation from Database Marketing (1994) to Artificial Super Intelligence (2020)
To understand the past history and future value of Artificial Superintelligence in Marketing and Sales, follow me on my journey.
First time I get in touch with Data and Segmentation was 1987 during my MBA Master Thesis in Marketing at University of Münster, Institute of Marketing from Prof. Dr. Heribert Meffert, about “Customer oriented Databases in B2B Capital Goods Marketing. Requirements and Applications in Direct Communications”.
Then my following journey with Data and Segmentation started:
-Database Marketing (1994-2000)
-Multichannel Customer Management (2008-2010)
-Social Business, Social CRM and Cloud (2011-2012)
-Retail & Consumer Products (2013-2016)
–Artificial Super Intelligence and Superintelligence2525 (2016-
Let’s take a closer Look at the phases:
Database Marketing (1994-2000)
Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.
The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built.
There are two main types of marketing databases, 1) Consumer databases, and 2) business databases. Consumer databases are primarily geared towards companies that sell to consumers, often abbreviated as [business-to-consumer] (B2C) or BtoC. Business marketing databases are often much more advanced in the information that they can provide. This is mainly because business databases aren’t restricted by the same privacy laws as consumer databases.
The “database” is usually name, address, and transaction history details from internal sales or delivery systems, or a bought-in compiled “list” from another organization, which has captured that information from its customers. Typical sources of compiled lists are charity donation forms, application forms for any free product or contest, product warranty cards, subscription forms, and credit application forms.
More: Database Marketing
Customer-relationship management (CRM) is an approach to manage a company’s interaction with current and potential customers. It uses data analysis about customers’ history with a company to improve business relationships with customers, specifically focusing on customer retentionand ultimately driving sales growth.
One important aspect of the CRM approach is the systems of CRM that compile data from a range of different communication channels, including a company’s website, telephone, email, live chat, marketing materials and more recently, social media. Through the CRM approach and the systems used to facilitate it, businesses learn more about their target audiences and how to best cater to their needs.
Multichannel Customer Management (2008-2010)
Multichannel marketing is the blending of different distribution and promotional channels for the purpose of Marketing. Distribution channels range from a retail storefront, a website, or a mail-order catalogue.
Multichannel marketing is about choice. The objective of the companies doing the marketing is to make it easy for a consumer to buy from them in whatever way is most appropriate.
To be effective, multichannel marketing needs to be supported by good supply chain management systems, so that the details and prices of goods on offer are consistent across the different channels. It might also be supported by detailed analysis of the return on investment from each different channel, measured in terms of customer response and conversion of sales. The contribution each channel delivers to sales can be assessed via Marketing Mix Modeling or via attribution modelling. Some companies target certain channels at different demographic segments of the market or at different socio-economic groups of consumers.
Multichannel marketing allows the retail merchant to reach its prospective or current customer in a channel of his/ her liking.
More: Multichannel Marketing
Social Business, Social CRM and Cloud (2011-2012)
Social Business Model
Organizations that have adopted the social business model utilize social media tools and social networking behavioral standards across functional areas for communicating and engaging with external audiences, including customers, prospective customers, prospective employees, suppliers, and partners.
Combining social networking etiquette (being helpful, transparent and authentic) with business engagement on LinkedIn(for one-to-one interaction), Twitter (for immediacy) and Facebook (for content sharing) more fully involves employees in the organization and increases customer intimacy and trust.
More: Social Business Model
Social CRM (customer relationship management) is use of social media services, techniques and technology to enable organizations to engage with their customers.[better source needed]
Greenberg (2009, p. 34) defines SCRM as: ‘A philosophy and a business strategy, supported by a technology platform, business rules, workflow, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It’s the company’s programmatic response to the customer’s control of the conversation’.
In essence, SCRM is the fusion of social media and customer relationship management. It combines the conceptual elements of social media and CRM: social networks, communication technology, communities, strategy, customer value, and relationships.
More: Social CRM
Cloud computing makes computer system resources, especially storage and computing power, available on demand without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Large clouds, predominant today, often have functions distributed over multiple locations from central servers. If the connection to the user is relatively close, it may be designated an Edge server.
Clouds may be limited to a single organization (enterprise clouds,) be available to many organizations (public cloud,) or a combination of both (hybrid cloud.) The largest public cloud is Amazon AWS.
Cloud computing relies on sharing of resources to achieve coherence and economies of scale.
More: Cloud Computing
Retail & Consumer Products (2013-2016)
Retail is the process of selling consumer goods or services to customers through multiple channels of distribution to earn a profit. Retailers satisfy demand identified through a supply chain. The term “retailer” is typically applied where a service provider fills the small orders of a large number of individuals, who are end-users, rather than large orders of a small number of wholesale, corporate or government clientele. Shopping generally refers to the act of buying products. Sometimes this is done to obtain final goods, including necessities such as food and clothing; sometimes it takes place as a recreational activity. Recreational shopping often involves window shopping and browsing: it does not always result in a purchase.
Artificial Super Intelligence and Superintelligence2525 (2016-
Artificial superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines “superintelligence” as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Artificial superintelligence is a term referring to the time when the capability of computers will surpass humans. “Artificial intelligence,” which has been much used since the 1970s, refers to the ability of computers to mimic human thought. Artificial superintelligence goes a step beyond, and posits a world in which a computer’s cognitive ability is superior to a human’s.
More: 23. My publications, presentations, awards and memberships
23. My publications, presentations, awards and memberships
The 3 Ways Artifical Intelligence will change Target Marketing
In many ways, artificial intelligence (AI) is already influencing digital marketing in general, and target marketing in particular. But the truth is, there is so much more to come – so many more changes and improvements that AI will surely bring to content marketing.
In this blog post, I’m going to explore some of these changes in order to try to understand what the future holds – read on to discover the 3 ways that artificial intelligence will change content marketing.
More Target Personalized Content
One of AI’s main functions is its ability to analyse huge amounts of data – and interpret them. That is an incredible feature and something that can have huge effects on content marketing and even marketing in general.
One of these effects is that it will help content marketers understand exactly who they’re targeting. Not in a creepy way, but rather in a way that many consumers expect: a Salesforce study, for example, found that 76% of consumers expect companies to understand their needs and expectations.
So, how exactly will artificial intelligence help us create this type of content?
It’s all about the data and segmentation: AI can absorb huge amounts of data and help you segment it easily.
When it comes to audiences, AI can help you understand who exactly forms your audience, what platforms they use predominantly, what other content they read, what types of content they prefer, and so on.
Lucy—Marketing’s Cognitive Companion
An expert at research, segmentation and planning, Lucy analyzes structured and unstructured data and empowers marketers to do far more in so much less time.
One of the ways that AI is already heavily impacting content marketing is with AI marketing assistants – like IBM Watson’s Lucy.
Lucy is an incredibly powerful tool that marketers can use for research, segmentation and planning – and it’s so powerful that it can do more in a minute than an entire team of marketers can achieve in months.
So, how exactly does an AI marketing assistant like Lucy work?
To start with, Lucy can absorb and analyse literally all of the data your company owns, or that has commissioned or licensed. What’s more, once it absorbs all of this data, you can ask it any question you might have, no matter how complex, and it will find the answer for you:
Which regions should I first target?
What mix of content should I create for my audience for maximum results?
What are my competitors up to?
What are the main personality traits of my audience?
These are questions that companies need to answer in order to put together a strategy that works. But finding these answers is not exactly easy when you don’t have a tool like Lucy on your side – gathering and interpreting these vast amounts of data would be a difficult, if not almost impossible task without help.
And the possibilities of marketing assistants like Lucy don’t end here:
You can create clear and complex segments of your target audience so that you can create highly personalized content
Plan your content marketing (and other marketing) strategies by seeing how different strategies would work and what results you can expect
Systems like Lucy will have a huge impact on content marketing as they become more affordable and more popular. They will help companies better understand their audience and their data in general and what’s more, they will help marketers put together more effective strategies as well as help them understand what types of outcomes they can expect.
More: Lucy on IBM Marketplace
More: Equals3 AI
Will AI Take Over Content Writing Jobs Completely?
Unsurprisingly, when it comes to technology this powerful, it’s easy to get a little scared: will it take my job from me? Will all content be written by machines in a few years?
If AI can analyse this much data and take human-like decisions based on this data – and even arguably better decisions, since a powerful machine can hold so much more knowledge than any human realistically can – can’t it also write the content, maybe just as well or even better than a human can?
In the world of journalism, it’s already happened – as far back as 2015. Back then, the Associated Press published a short financial news story: “Apple tops Street IQ forecasts”. The piece could very well have been written by a real human – but if you read until the end, you would see that it was actually “generated” by Automated Insights; in other words, it was written (or generated, if you will) by a so-called “robot journalist”.
Associated Press aren’t the only ones who experimented with robot journalism (or automated journalism) either; numerous top publications use it, such as Washington Post, who published as many as 850 articles generated by their robot journalist over the course of one year.
That might sound gloomy to many, but it doesn’t necessarily mean that AI is taking over any and all content writing jobs. Rather, this type of AI technology can be used to free up your time and give you all the data you need so that you can focus on creating better content. It can be used to automate the little tasks – like Associated Press’s news story about Apple’s quarterly earnings – while humans can focus on writing the more complex and sensible content.
Conclusion: Will AI Take Over Content Marketing Jobs?
Clearly, AI will rapidly become an even bigger part of digital marketing. It already is taking over numerous aspects of our lives and even threatening some jobs, but how much will it really affect content marketing?
While no one can predict the future, the present does tell us that AI will have a real impact on content marketing – from creating incredibly detailed marketing strategies based on huge amounts of data to generating content automatically, marketers will start using AI more and more to help them every step of the way.
How artificial intelligence is shaking up the job market
The World Economic Forum’s The Future of Jobs 2018 aims to base this debate on facts rather than speculation. By tracking the acceleration of technological change as it gives rise to new job roles, occupations and industries, the report evaluates the changing contours of work in the Fourth Industrial Revolution.
One of the primary drivers of change identified is the role of emerging technologies, such as artificial intelligence (AI) and automation. The report seeks to shed more light on the role of new technologies in the labour market, and to bring more clarity to the debate about how AI could both create and limit economic opportunity. With 575 million members globally, LinkedIn’s platform provides a unique vantage point into global labour-market developments, enabling us to support the Forum’s examination of the trends that will shape the future of work.
Our analysis uncovered two concurrent trends: the continued rise of tech jobs and skills, and, in parallel, a growth in what we call “human-centric” jobs and skills. That is, those that depend on intrinsically human qualities.
Tech jobs like software engineers and data analysts, along with technical skills such as cloud computing, mobile application development, software testing and AI, are on the rise in most industries and across all regions. But a number of highly “automatable” jobs fall into the top 10 most declining occupations – ie, jobs that have seen the largest decreases in share of hiring over the past five years. These occupations include administrative assistants, customer service representatives, accountants and electrical/mechanical technicians, many of which depend on more repetitive tasks.
Three growing trends
The impact of AI is not just theoretical any more; it’s very much part of our present. So we took a closer look at how the growing presence of AI skills in the workforce is impacting different industries and job functions globally. Our research into emerging skills around the world shed light on a number of growing trends:
AI skills are among the fastest-growing skills on LinkedIn, and saw a 190% increase from 2015 to 2017. When we talk about “AI skills”, we’re referring to the skills needed to create artificial intelligence technologies, which include expertise in areas like neural networks, deep learning and machine learning, as well as actual “tools” such as Weka and Scikit-Learn. LinkedIn data shows that all types of technical AI skills are growing at a rapid pace around the world while we see AI skills growing in every industry, our data also shows that industries with more AI skills present among their workforce are also the fastest-changing industries. If we consider “change” to be a proxy for innovation, then this indicates that the presence of AI skills correlates strongly with innovation within an industry. It also means there’s an opportunity for many industries to invest more heavily in their AI capabilities.
As the recent report makes clear, the anticipated impact of AI on the labour market fits neither of the polarized narratives that tend to hog headlines. It’s estimated that by 2025, the amount of work done by machines will jump from 29% to more than 50% – but that this rapid shift will be accompanied by new labour-market demands that may result in more, rather than fewer, jobs. As the report notes, these predictions “[provide] grounds for both optimism and caution”.
While AI is unlikely to replace human workers, uncertainty remains regarding what types of jobs will be created, how permanent they will be, and what kind of training they may require. Preparing the workforce for these changes will depend on a data-driven approach to understanding the trends that are shaping the future of the labour market, and a commitment to investing in lifelong learning opportunities that can help workers adapt to rapid economic shifts.
As the world continues to invest in AI technologies, we’ll continue to assess their externalities and impact on the workforce, especially as they connect to opportunities for more effective reskilling and education initiatives. As new skills emerge, governments, educational institutions and employers should consider how they can most effectively develop learning programmes that equip people with the skills they will need to keep up with the modern economy.
More: AI and Job Market
Why countries need to work together on AI
Given the sweeping societal considerations and monetary benefits, it is not surprising that governments around the world are taking steps to win in the digital race. Countries as small as Kenya and as large as China have created or are working to create formal national AI frameworks that tackle the important questions AI raises for society, the economy and government.
None of the US, Israel and Russia have a formal national AI policy yet. Private sector companies such as Google, Amazon and Apple and the US department of defence are driving the bulk of AI investment in the United States. Though Israel does not have a specific policy, it is keenly focused on AI and has seen the number of AI start-ups triple since 2014. Russian President Vladimir Putin’s assertion that “whoever becomes the leader in this sphere will become the ruler of the world” was interpreted as a declaration of Russia’s investment in AI, and thus Russia is often viewed as a leader in the field. However, the country’s estimated annual spend is only roughly $12.5 million, and it has no official national strategy.
Learning from one another
Which countries are approaching AI most effectively, and to what degree is there opportunity for greater international collaboration? It may be too early to tell; however, when analyzing the best practices of existing national AI policies, there is much that can be learned. These are the specific areas to consider.
Data. From self-driving vehicles to smart cities, data is the driver behind AI. According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. A Brookings Institute report notes that “in this regard, the United States has an advantage over China. Global ratings on data openness show that US ranks eighth overall in the world, compared to 93rd for China”. But right now, innovation in the United States is limited without a national strategy that answers questions about protocol and ownership. France and Denmark, on the other hand, are opening government data. France is hosting troves of centrally collected public and private data that it plans to make available as part of its strategy. Conversely, by taking a restrictive position on issues of data collection (as indicated by the implementation of General Data Protection Regulation), the EU is putting manufacturers and software designers at a disadvantage while balancing the demand for privacy.
Talent. The demand for AI talent far outweighs the available supply. According to a study by Element AI, there are only 22,000 PhD-educated AI researchers in the world. As a result, almost every nation’s strategy addresses talent development. Canada’s AI strategy is distinct in that it primarily focuses on research and talent strategy. The country boasts AI degree programmes and is building a $127 million research facility in Toronto. Companies like Facebook and my own company, Uptake, are investing in Canada to access this talent pool.
Legal. A whole host of legal questions swirl around AI. Estonia has been a leader in addressing legal questions related to AI: the country is developing a bill for AI liability that will be ready in March 2019. The government hopes the legal framework will attract investors by providing a simple, comprehensive guideline to enable the broad use of AI systems. Issues that arise are being tackled early, giving the country an advantage and serving as a roadmap for others.
Inclusion. One of the great promises of AI is its potential for improving quality of life. But without the right planning and oversight, we risk exacerbating problems of inequality or marginalizing groups of people. As an example, India’s AI strategy is focused on leveraging the technology not only for economic growth, but also for social inclusion. The approach is called #AIforAll and outlines a strategy that aims to empower Indians with the skills to find the quality jobs, invest in research, and scale Indian-made AI solutions to the rest of the developing world. India is not the first country to incorporate AI and inclusion. Canada and France, for example, recently announced a task force to develop an international study group on inclusive and ethical AI.
We have a unique opportunity at this juncture in history to make decisions that will have worldwide, lasting impacts not only on our national economies but on society as a whole. While each country must consider its specific needs, there is early indication – and early promise – that a global framework may actually create more innovation and allow us to solve more complex and pressing global issues. I am encouraged by the opportunity for collaboration and believe that many of the questions we are grappling with today will be answered if we take the time to learn from one another.
How long until the first machine reaches superintelligence?
Not shockingly, opinions vary wildly and this is a heated debate among scientists and thinkers. Many, like professor Vernor Vinge, scientist Ben Goertzel, Sun Microsystems co-founder Bill Joy, or, most famously, inventor and futurist Ray Kurzweil, agree with machine learning expert Jeremy Howard when he puts up this graph during a TED Talk:
Those people subscribe to the belief that this is happening soon—that exponential growth is at work and machine learning, though only slowly creeping up on us now, will blow right past us within the next few decades.
So when do the experts think we’ll reach ASI?
Müller and Bostrom also asked the experts how likely they think it is that we’ll reach ASI A) within two years of reaching AGI (i.e. an almost-immediate intelligence explosion), and B) within 30 years. The results:4
The median answer put a rapid (2 year) AGI → ASI transition at only a 10% likelihood, but a longer transition of 30 years or less at a 75% likelihood.
We don’t know from this data the length of this transition the median participant would have put at a 50% likelihood, but for ballpark purposes, based on the two answers above, let’s estimate that they’d have said 20 years. So the median opinion—the one right in the center of the world of AI experts—believes the most realistic guess for when we’ll hit the ASI tripwire is [the 2040 prediction for AGI + our estimated prediction of a 20-year transition from AGI to ASI] = 2060.
Of course, all of the above statistics are speculative, and they’re only representative of the center opinion of the AI expert community, but it tells us that a large portion of the people who know the most about this topic would agree that 2060 is a very reasonable estimate for the arrival of potentially world-altering ASI. Only 45 years from now.
Nick Bostrom describes three ways a superintelligent AI system could function:
As an oracle, which answers nearly any question posed to it with accuracy, including complex questions that humans cannot easily answer—i.e. How can I manufacture a more efficient car engine? Google is a primitive type of oracle.
As a genie, which executes any high-level command it’s given—Use a molecular assembler to build a new and more efficient kind of car engine—and then awaits its next command.
As a sovereign, which is assigned a broad and open-ended pursuit and allowed to operate in the world freely, making its own decisions about how best to proceed—Invent a faster, cheaper, and safer way than cars for humans to privately transport themselves.