Artificial intelligence marketing (AIM) is a form of direct marketing leveraging database marketing techniques as well as AI concept and model such as machine learning and Bayesian Network. The main difference resides in the reasoning part which suggests it is performed by computer and algorithm instead of human
Interview: Max Tegmark on Superintelligent AI, Cosmic Apocalypse, and Life 3.0
Ask Max Tegmark why people should read his new book and get involved in the discussion about artificial intelligence, and you get a weighty answer. Forget about book sales, this is about cosmic destiny: The fate of the universe may well be determined by the decisions made “here on our little planet during our lifetime,” he says.
In his book, Life 3.0: Being Human in the Age of Artificial Intelligence, Tegmark first explains how today’s AI research will likely lead to the creation of a superintelligent AI, then goes further to explore the possible futures that could result from this creation. It’s not all doom and gloom. But in his worst case scenario, humanity goes extinct and is replaced with AI that has plenty of intelligence, but no consciousness. If all the wonders of the cosmos carry on without a conscious mind to appreciate them, the universe will be rendered a meaningless “waste of space,” Tegmark argues.
Ignore today’s small step-by-step advances in artificial intelligence, such as the boosting capacities of cars and trucks to drive themselves. Waiting in the wings could be a groundbreaking growth: a machine that knows itself as well as its environments, and that might take in and also procedure massive amounts of data in genuine time. Maybe sent out on harmful objectives, into space or combat. In addition to driving individuals about, it could be able to prepare, clean, do washing– as well as keep people business when other people typically aren’t close by.
A specifically innovative set of machines might replace humans at essentially all tasks. That would certainly conserve humankind from workaday drudgery, however it would certainly also tremble several social foundations. A life of no job and also just play may end up being a dystopia.
Aware makers would certainly also increase uncomfortable lawful and moral problems. Would certainly a mindful machine be a “individual” under regulation and also be responsible if its actions injure a person, or if something fails? To think of a more frightening circumstance, might these devices rebel versus people and desire to remove us entirely? If yes, they represent the culmination of evolution.
As a teacher of electric engineering and also computer technology who operates in machine learning and quantum theory, I could say that scientists are divided on whether these sorts of hyperaware makers will ever exist. There’s additionally question regarding whether devices might or must be called “aware” in the way we think of human beings, and even some animals, as conscious. Several of the questions relate to modern technology; others concern exactly what consciousness in fact is.
Is Understanding Sufficient? The majority of computer system researchers think that awareness is a characteristic that will emerge as modern technology develops. Some think that consciousness includes approving new info, saving and fetching old information as well as cognitive handling of everything right into assumptions and activities. If that’s right, then one day makers will without a doubt be the supreme awareness. They’ll have the ability to gather even more information than a human, store more than many collections, accessibility huge databases in nanoseconds as well as compute all of it into choices extra complicated, and yet much more logical, than anyone ever could.
On the other hand, there are physicists and also theorists who say there’s something extra about human habits that can not be computed by an equipment. Creative thinking, as an example, and the feeling of liberty individuals possess don’t appear to find from reasoning or estimations.
Yet these are not the only views of what awareness is, or whether makers could ever achieve it.
Quantum Sights One more viewpoint on awareness originates from quantum theory, which is the inmost concept of physics. Inning accordance with the orthodox Copenhagen Interpretation, consciousness and also the physical world are complementary facets of the same fact. When a person observes, or experiments on, some aspect of the physical world, that individual’s mindful communication triggers noticeable change. Because it takes consciousness as a provided as well as no attempt is made to acquire it from physics, the Copenhagen Analysis might be called the “big-C” sight of awareness, where it is a point that exists on its own– although it needs minds to become actual. This sight was popular with the leaders of quantum theory such as Niels Bohr, Werner Heisenberg and Erwin Schrodinger.
The communication in between consciousness and also matter brings about mysteries that continue to be unsettled after 80 years of debate. A widely known instance of this is the paradox of Schrodinger’s pet cat, in which a feline is positioned in a scenario that leads to it being similarly most likely to survive or pass away– and also the act of observation itself is exactly what makes the outcome specific.
The opposing sight is that consciousness arises from biology, just as biology itself emerges from chemistry which, in turn, emerges from physics. We call this less large concept of consciousness “little-C.” It agrees with the neuroscientists’ view that the procedures of the mind are identical to states as well as procedures of the mind. It also agrees with a much more recent analysis of quantum theory inspired by an effort to rid it of mysteries, the Many Worlds Interpretation, in which viewers belong of the mathematics of physics.
Philosophers of scientific research believe that these modern quantum physics views of consciousness have parallels in old approach. Big-C resembles the concept of mind in Vedanta– in which consciousness is the essential basis of reality, on the same level with the physical world.
Little-C, in contrast, is rather much like Buddhism. Although the Buddha chose not to resolve the concern of the nature of awareness, his fans declared that mind and consciousness develop from emptiness or nothingness.
Big-C and also Scientific Exploration Researchers are also exploring whether consciousness is always a computational procedure. Some scholars have argued that the innovative minute is not at the end of a purposeful calculation. For example, dreams or visions are supposed to have motivated Elias Howe’s 1845 design of the modern stitching maker, and August Kekule’s exploration of the framework of benzene in 1862.
A dramatic item of evidence in favor of big-C awareness existing all on its own is the life of self-taught Indian mathematician Srinivasa Ramanujan, who passed away in 1920 at the age of 32. His notebook, which was lost and neglected for about 50 years and released just in 1988, consists of a number of thousand formulas, without evidence in various areas of math, that were well in advance of their time. Moreover, the methods whereby he located the solutions remain elusive. He himself declared that they were exposed to him by a goddess while he was asleep.
The idea of big-C awareness increases the questions of exactly how it relates to matter, and exactly how matter as well as mind equally affect each other. Consciousness alone can not make physical modifications to the world, but probably it could transform the possibilities in the evolution of quantum processes. The act of observation could freeze as well as influence atoms’ movements, as Cornell physicists confirmed in 2015. This might quite possibly be a description of just how matter as well as mind connect.
Mind and also Self-Organizing Equipments It is possible that the phenomenon of awareness needs a self-organizing system, like the mind’s physical framework. If so, then existing machines will certainly lose.
Scholars don’t know if adaptive self-organizing equipments can be created to be as advanced as the human mind; we lack a mathematical concept of calculation for systems like that. Perhaps it’s true that just organic machines can be sufficiently imaginative and adaptable. However then that suggests people need to– or soon will– begin working with engineering brand-new organic structures that are, or could end up being, aware.
I have studied emotional intelligence as a hobby for a long time. Until recently, I believed emotional intelligence to remain one of the core advantages of us humans after artificial intelligence has taken over all tasks requiring memorization and logic.
During the past few years, I’ve focused my studies on emotionally intelligent algorithms, as it is the business of my startup, Inbot.
The more I have researched them, the more convinced I have become that people are no longer ahead of AI at emotional intelligence.
Yuval Noah Harari writes in his best-selling book Homo Deus that humans are essentially a collection of biological algorithms shaped by millions of years of evolution. He continues to claim that there is no reason to think that non-organic algorithms couldn’t replicate and surpass everything that organic algorithms can do.
Let that sink in for a moment. Our emotions and feelings are organic algorithms that respond to our environment. Algorithms, that are shaped by our cultural history, upbringing and life experiences. And they can be reverse engineered.
If we agree with Dr. Harari, who is a professor at the Hebrew University of Jerusalem, and Dr. Tegmark, who is a professor at MIT in Boston, computers will eventually become better at manipulating human emotions than humans themselves.
People are generally not emotionally intelligent
In real life situations, we are actually pretty bad at emotional intelligence.
Most of us are ignorant about even the most basic emotional triggers we set off in others. We end up in pointless fights, dismiss good arguments because they go against our biases, and judge people based on stereotypes.
We don’t understand the effects of cultural context, family upbringing or the current personal life situation of our discussion partner.
We rarely try to put ourselves in the other person’s position. We don’t try to understand their reasoning if it goes against our worldview. We don’t want to challenge our biases or prejudices.
Online, the situation is much worse. We draw hasty and often mistaken conclusions from comments by people we don’t know at all, and lash at them if we believe their point goes against our biases.
Lastly, we have an evolutionary trait of seeing life as the “survival of the fittest”. This predisposes us from taking advantage of others, to focus on boosting our egos, and to put ourselves on a pedestal.
The most successful people often lie to gain advantage, manipulate to get ahead, and deceive to hide their wrongdoings. It’s about winning at all costs, causing a lot of emotional damage on the way.
AI is advancing rapidly at emotional intelligence
While us humans continue to struggle to understand each other, emotionally intelligent AI has advanced rapidly.
Cameras in phones are ubiquitous and omnipresent, and face-tracking software is already advanced enough to analyze the smallest details of our facial expressions. The most advanced ones can even tell apart faked emotions from real ones.
In addition, voice recognition and natural language processing algorithms are getting better at figuring out our sentiment and emotional state from the audio.
The technologies to analyze emotional responses from faces and voice are already way beyond the skills of an average human, and in many areas exceed the abilities of even the most skilled humans.
The advances in this field are currently almost solely driven by commercial interests and human greed.
Media and entertainment companies need our attention and engagement to make money. Companies like Facebook and YouTube have a large number of engineers working to create ever better ways to addict us to their content.
These algorithms are designed to pull our emotional triggers to keep us entertained. And they have become very, very good at it.
Some of the core developers of these algorithms have gotten scared of the power technology has on us, and say our minds can be hijacked.
Big data gives an edge to emotionally intelligent AIs
Unlike people, AI can leverage your whole online history, which in most cases is more information than anybody can remember about any of their friends.
Some of the most advanced machine learning algorithms developed at Facebook and Google have already been applied on a treasure trove of data from billions of people.
These algorithms already know what your desires, biases and emotional triggers are, based on your communication, friends and cultural context. In many areas, they understand you better than you know yourself.
The progress of algorithms has gone so far that Facebook and Google are now accused of creating filter bubbles that can effect public opinion, rapidly change political landscapes and sway elections.
These algorithms are getting so complex that they are becoming impossible to fully control by humans. Facebook’s chief of security Alex Stamosrecently tweeted that journalists are unfairly accusing them for manipulation, when in reality there are no solutions available that wouldn’t lead to someone accusing them of bias.
The future of emotional artificial intelligence
People have a lot of biases, which cloud our judgment. We see the world as we wish it to be, not as it is. Algorithms today, being made by people, incorporate some hints of our biases too. But if we wanted to remove such biases, it would be relatively easy to do.
As artificial intelligence gets better at manipulating us, I see a future where people happily submit their lives to the algorithms. We can already see it in practice. Just look around yourself in public — almost everyone is glued to the their smartphones.
Today, people touch their phones on average 2,617 times a day.
We are approaching an era, when artificial intelligence uses humans as organic robots to realize its goals. To make that happen, thousands of engineers are already building an API to humans.
I look forward to debating this interesting topic with you. Please comment and share!
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How will artificial intelligence transform marketing over the coming years? Columnist Daniel Faggella dives into the results from a survey exploring the major trends and opportunities in AI for marketers.
Artificial intelligence has been making headlines over the last 12 months in domains like health care, finance, face recognition and more. Marketing, however, doesn’t seem to be getting the same kind of coverage, despite major developments in the application of AI to marketing analytics and business intelligence.
Five or 10 years ago, only the world’s savviest, most heavily funded companies had a serious foothold in artificial intelligence marketing tech. As we enter 2017, there are hundreds of AI marketing companies all over the world (including some that have gone public, like RocketFuel). These companies are making AI and machine learning accessible to large corporations and SMBs (small and medium-sized businesses) alike, opening new opportunities for smarter marketing decisions and approaches.
Below, I’ve highlighted three major trends that impact the theme of “Intelligent Content.”
Recommendation and personalization predicted to be greatest profit opportunity
While most of our executives voted “Search” as the AI marketing tool with the highest profit potential today, “Recommendation and Personalization” topped the list for ROI potential in the coming five years.
While search requires users to express their intent in text (or speech), recommendation pulls from myriad points of data and behavior — often bringing a user to a) what they were truly looking for, or b) what the advertiser wanted them to find.
The implications of recommendation in content marketing are numerous. Below I’ll list just a few:
First, recommendation engines help serve the content most likely to engage readers. In the past, this was done with simple text analysis or tools like elastic search. The “recommended” content was better than a random guess, but it was by no means truly optimized for user engagement.
Companies like Boomtrain and Liftigniter are developing technologies to tailor content to individual visitors, displaying material most likely to keep them on the site based on their previous engagements, purchases, clicks and more.
Second, programmatic advertising (like that used on giant platforms like Facebook and Google AdWords) is often used to drive users directly to content before seeing a product page or being asked to book an appointment. Many ad networks (Facebook included) don’t allow for direct lead generation and instead prefer to engage users with the right content first before looking for a conversion.
Ad networks are partial to keeping user experience high in addition to driving engagement on ads, which is a delicate balance. Companies that can leverage these programmatic platforms to target the right prospects with the right content are the most likely to win.
Third, we see entire content marketing platforms at the heart of business models. One such example is Houzz.com, a site that hosts millions of articles and photo albums about home improvement and decoration. This content ecosystem links to and references millions of home goods products (from throw rugs to couches and more), and “recommendation” drives the entire experience.
Houzz is one of the best current examples of “intelligence content” directly tying to sales, and I suspect that in the coming five years, we’ll see elements of their business model become much more prevalent.
Intelligent content might be content that makes itself
Content generation is a complex machine learning problem, and until recently, it’s been relegated to big-budget media firms working in quantitatively oriented domains (namely sports and finance). Yahoo Finance uses natural language generation (NLG) to turn information about stocks and bonds into coherent, human-readable articles, saving time for Yahoo’s writers so that they can complete more important and creative tasks.
NLG is now being used in a vast number of business applications including compliance, insurance and more — and a quick visit to the “solutions” page at Narrative Science shows a plethora of use cases and case studies for machine-written content.
While domains like finance and compliance involve strict, formulaic transformations of cold data into readable text, executives in the field are excited about its profit potential, too. Rather than simply saving costs on human writers, intelligent content generation will alter existing content (and/or create new content) to help driving marketing goals. As Laura Pressman, manager of Automated Insights, explained in our survey:
Content generation has high profit potential in the coming five years. Personalization and segmentation can be achieved through altering the content text to speak to certain groups of people, across different platforms, highlighting unique and targeted features that are most important to each specific segment.
B2C companies may have an advantage in intelligent content
When we polled our batch of executives about the most meaningful applications of artificial intelligence in marketing, we didn’t want to leave out their opinions about which businesses or industries would be most able to take advantage of AI’s advancements in marketing.
“Industry” didn’t seem to have much to do with the predicted success that a company might have with AI marketing tech. Much more important was the way the company did business and sold products. For a business to take advantage of AI, the most important traits (as predicted by our batch of executives) include:
Data collection: Ability to quantify customer touch points across all marketing activities.
Transaction volume: Reaching the marketing “goal” more often helps to train marketing algorithms and provide better predictions and recommendations.
Uniformity: Businesses that pool their marketing and sales data into a single stream are more likely to succeed in applying AI.
The above three qualities repeated themselves again and again in our survey responses, along with strong predictions that “Digital Media” companies and “E-commerce/Consumer Retail” companies would be most poised to take advantage of AI in marketing. As Lisa Burton, chief data officer of AdMass, explained in the survey:
Advertisers and e-commerce businesses have the highest potential gain from machine learning because of the ease of measurement and quick feedback needed to train and improve machine learning algorithms.
While B2C and retail companies seem to have an edge on “quantifiability” and attribution to sale, some of our respondents also hinted at the strong opportunity in B2B. Leveraging the many content and interaction touch points in a B2B sale will aid greatly in “cracking the code” on B2B marketing attribution, which is undoubtedly valuable.
In the coming five years, it may be possible that attribution and recommendation take off quickly in retail, while adoption in services and B2B sectors will provide more of an “ahead of the curve” advantage in industries where tech adoption is slower.
5-year trends in artificially intelligent marketing
Robots are likely to replace 50 percent of all jobs in the next decade, according to Kai-Fu Lee, founder of venture capital firm Sinovation Ventures and a top voice on tech in China .
Artificial intelligence (AI) is the wave of the future, the influential technologist told CNBC, calling it the “singular thing that will be larger than all of human tech revolutions added together, including electricity, [the] industrial revolution, internet, mobile internet — because AI is pervasive.”
“It is the decision engine that will replace people,” Lee said, adding that AI capabilities far exceed those of humanity.
For example, he said, companies in which his firm has invested can accomplish feats such as recognizing 3 million faces at the same time, or dispersing loans in eight seconds.
“These are things that are superhuman, and we think this will be in every industry, will probably replace 50 percent of human jobs, create a huge amount of wealth for mankind and wipe out poverty,” Lee said, later adding that he expected that displacement to occur in the next 10 years.
Lee’s views on technology are widely followed in part because he previously headed Google China before founding his venture capital firm. He has also held executive posts at many other major tech companies, including Apple and Microsoft, and he has celebrity status in the industry and in China — with millions of social media followers on multiple platforms.
Speaking with CNBC from the Global Mobile Internet Conference in Beijing, Lee tackled the big question: Will human still have a place in the world as machines grow more intelligent?
The answer, he said, is that nothing can replace human-to-human interaction.
“Touching one’s heart with your heart is something that machines, I believe, will never be good at,” he said, explaining that service jobs should be considered “first-class” employment.
And while all of this change is happening, traditional companies like banks, insurance firms and hospitals, are simply moving to slow, Lee said. And it’s too bad, he added, because they “possess the biggest treasure” in the form of troves of data.
“Because AI is about taking data into insight and decision, so I anticipate [the] internet sector, entrepreneurial sector, to continue to grow and in many cases displace and even wipe out traditional companies in China,” he said.
Chinese tech tycoons like Tencent and Alibaba , for instance, have forged the way in mobile payments — it’s become pervasive for consumers to pay for everything from rent to transportation through mobile wallets and transfers. Meanwhile, China’s giant state-owned banks are just barely starting to catch on, Lee said.
It’s tempting to think that in order to be a valuable team player, you should say “yes” to every request and task that is asked of you. People who say yes to everything have a lot of speed. They’re always doing stuff but never getting anything done. Why? Because they don’t think in terms of velocity. Understanding the difference between speed and velocity will change how you work.
I once worked for someone who offered me the opportunity to work on a new project nearly every day. These projects were not the quick ones, where you spend 15 minutes and crank out a solution. They were crap work. And there were strings: my boss wanted to be informed about everything, and there was no way I’d get credit for anything.
I remember my response: “That sounds amazing, but it’s not for me. I’m busy enough.”
Saying no to your boss, especially as often as I did, was thought to be risky to your career. I was the new kid, which is why I was getting all of these shit jobs thrown at me.
The diversity of skill sets needed to accomplish them would have made me look bad (perhaps the subtle point of this initiation). Furthermore, my already heavy workload would have gotten heavier with projects that didn’t move me forward. This was my first introduction to busywork.
My well-intentioned colleagues were surprised. “You’re not going to get anywhere with that attitude,” they’d pull me aside to tell me. The problem was that I wasn’t going to get anywhere by saying yes to a lot of jobs that consumed a lot of time, were not the reason I was hired, and left me no time to develop the craft of programming computers, which is what I wanted to do.
I had turned down a job offer for three times what I was being paid at this job because I wanted to work with the best people in the world on a very particular skill — a skill I couldn’t get anywhere but at an intelligence agency. Anything that got in the way of honing that craft was the enemy.
Over my first seven years, I’d barely leave my desk, working 12- to 16-hour days for six days a week. Working that hard with incredible people was amazing and motivating. I’ve never learned so much in such a short period of time.
“The difference between successful people and very successful people is that very successful people say ‘no’ to almost everything.”
— Warren Buffett
Certainly, offers of work are good problems to have. A lot of people struggle to find work, and here I was, a few weeks out of university, saying no to my boss. But saying yes to everything is a quick road to mediocrity. I took a two-thirds pay cut to work for the government so I could work with incredibly smart people on a very narrow skill (think cyber). I was willing to go all in. So no, I wasn’t going say yes to things that didn’t help me hone the craft I’d given up so much to work on.
“Instead of asking how many tasks you can tackle given your working hours,” writes Morten Hansen in Great at Work, “ask how many you can ditch given what you must do to excel.” I did what I needed to do to keep my job. As John Stuart Mill said, “as few as you can, as many as you must.”
Doing more isn’t always moving you ahead. To see why, let’s go back to first-year physics.
The Difference Between Speed and Velocity
Velocity and speed are different things. Speed is the distance traveled over time. I can run around in circles with a lot of speed and cover several miles that way, but I’m not getting anywhere. Velocity measures displacement. It’s direction-aware.
Think of it this way: I want to get from New York to L.A. Speed is flying circles around Manhattan, and velocity is hopping on a direct flight from JFK to LAX.
“People think focus means saying yes to the thing you’ve got to focus on. But that’s not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully. I’m actually as proud of the things we haven’t done as the things I have done. Innovation is saying ‘no’ to 1,000 things.”
— Steve Jobs
When you’re at work, you need to know what you need to do to keep your job. You need to know the table stakes. Then you need to distinguish between tasks that offer a lot of speed and those that offer velocity.
Here are three ways you can increase your velocity:
To the extent possible, ruthlessly shave away the unnecessary tasks, priorities, meetings, and BS. Put all your effort into the projects that really matter.
Don’t rely on your willpower to say no; instead, create systems that help you fend off distractions. I have two friends who were about the same weight several years ago. Around that time, one of them was diagnosed with celiac (gluten intolerance). He immediately started to lose weight after changing his diet. Upon seeing this, my other friend decided that he, too, would go on a diet to lose weight. Because they both ate out a lot, they both were frequently in situations where they would have to make healthy choices. The person with celiac developed “automatic behavior“; he had to avoid gluten if he wanted to stay healthy and pain-free. The other person, however, had to keep making positive choices and ended up falling down after a few weeks and reverting to his previous eating habits. Another example: One of my management principles was “no meeting mornings.” This rule allowed the team to work, uninterrupted, on the most important things. Of course, there were exceptions to this rule, but the default was that each day you had a three-hour chunk of time when you were at your best to really move the needle.
And finally, do as I did, and say “no” to your boss. The best way I found to frame this reply was actually the same technique that negotiation expert Chris Voss mentioned in a recent podcast episode: simply ask, “how am I supposed to do that?” given all the other stuff on your plate. Explain that saying no means that you’re going to be better at the tasks that are most important to your job, and tie those tasks to your boss’s performance.
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Eight Ways to Say No With Grace and Style
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