The neurosciences are often making headlines with their research into the human brain. Their progress in this area provokes a discussion upon which many hopes and concerns hang: Will it one day be possible to fully reproduce a brain using technology? It’s no surprise that computer processing capacity continues to evolve and far exceed that of humans but in many areas, the human brain remains superior. But is this set to change sometime soon?
All of these questions relate to the area of artificial intelligence (AI). One idea behind AI research is to try and technically replicate the brain and its functions using computer science, neurology, psychology, and linguistics. In many ways, the approach of AI research reveals a lot about how we view ourselves as humans as well as about our understanding of the word ‘intelligence’.
At the moment, forms of artificial intelligence that have their own will and behave autonomously still only exist in works of fiction. But there is no doubt that there are many areas of life where AI technology already plays a central role, although it’s not always immediately noticeable. There are many out there that do not actually know what it is or exactly how artificial intelligence works. AI helps make better diagnoses and treatment plans, it means market forecasts are much more accurate, and it also makes Google’s search algorithms more and more dynamic. AI technology is behind every digital assistant like Cortana and Siri, helping develop self-driving cars, as well as helping computers select new employees. At the moment in the US, AI is being used to write legal documents. In the last few decades, AI research has achieved a lot in many sectors.
Such new developments are also very relevant for search engines and online marketing. Having a basic understanding of AI technology is, therefore, very beneficial from an SEO point of view: what is artificial intelligence and how does it work? What are the aims of the research and what are the current sectors that it can be applied? What are the prospects and risks associated with it? And finally, what impact do these developments have on the world of online marketing and SEO?
Artificial intelligence can be defined as a branch of computer science, whose goal is to create a technological equivalent to human intelligence. It is with this goal in mind that many computer scientists work with experts from across a variety of fields. But exactly what ‘intelligence’ actually is and what way you can reproduce it using technology – needless to say, there are many theories and methodical approaches dedicated to this line of research.
An exact definition of artificial intelligence is impossible given how hard it is to even define the word ‘intelligence’. Exactly what intelligence entails or involves is something that humans themselves cannot even agree on – so you can imagine that this debate is even more enhanced when it comes to the issue of machines. Should the machine primarily be optimized towards rationality? Or are other human characteristics like intention, intuition, and learning ability also important? It could also be argued that social competencies, empathy, and a sense of responsibility all play a role as well. At the end of the day, the question is whether the technology should primarily engender rational characteristics or artificial humanity?
Should the machine be built in a way that is identical to a human brain? This approach to simulation aims to achieve a complete replication of the brain functions. Or should the machine only have the appearance of a person or only be similar to it in when it comes to the final result? This phenomenological approach is centered on what humans actually gain from artificial intelligence – regardless of which technical process is behind it.
Defining artificial intelligence has always been difficult. In 1950 the mathematician Alan Turing developed a test which was designed to make AI measurable: the ‘Turing Test’ aimed, through a series of questions, to determine whether a machine was still recognizable as such. If the answers given by a computer are impossible to differentiate from those given by a human, then the computer is said to be ‘artificially intelligent’. But this definition does little to help current AI technology as these days is primarily being developed for very technical fields of activity. This means that AI is less focused on mastering the art of human communication and more so on carrying out highly specialized tasks in an efficient manner. For these technologies, a reduced or limited version of the Turing test is used. For example, if a certain part of a technical system features identical characteristics to a human, e.g., in the case of a medical diagnosis or a game of chess, then we are referring in this case to an artificially intelligent system. As a result, there are two definitions of artificial intelligence: a ‘strong’ and a ‘weak’ one.
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SecureSimple24/7 SupportThe ‘strong’ definition of artificial intelligence refers to an intelligence which, with its diverse capabilities, is in a position to replace humans. This universal approach that a machine can act as a humman has existed since the Enlightenment but currently remains fictional.
There are various dimensions to intelligence: cognitive, sensory motor, emotional, and social. The majority of the current applications of artificial intelligence are in the area of cognitive intelligence, i.e., logic, planning, problem-solving, self-sufficiency, and individual perspective formation.
The vision is that one day AI can develop an autonomous consciousness and its own free will. It is with this long-term goal that AI research touches on the likes of very traditional subject areas like philosophy and brings up countless ethical and legal questions. For this reason, law philosophers insist that there needs to be binding legislation for artificially intelligent beings. The legal capacity of intelligent machines is still very unclear.
On the other hand, the ‘weak’ definition of artificial intelligence is one where the development and application of artificial intelligence take place in clearly defined, marked out sectors. This is the position that artificial intelligence finds itself in at this moment in time. Nearly all of the current uses of artificial intelligence can be defined as ‘weak’ but also undoubtedly specialized, AI. A good example of this is the development of self-driving cars, medical diagnoses, and intelligent search algorithms.
Over the last few years, research has made groundbreaking success in the area of weak AI. The development of intelligent systems in the individual sectors has shown itself as not just immensely practical but also as less harmful, ethically speaking, as the research in superintelligence. The sectors where artificial intelligence is applied are extremely varied. Areas that are experiencing particular success at the moment are in medicine, finance, transport, marketing, and, of course, also online. At this stage, it is almost inevitable that AI technologies of this kind will continue to expand their influence in all aspects of our lives.
How does one even begin describing the operating principles of artificial intelligence? AI is only ever as good as the nature of its technical representation of knowledge. For this, there are two fundamental methodical approaches: the symbolic approach and neuronal approach.
The classic approach for artificial intelligence is symbolic AI. This touches on the idea that human thought can be reconstructed from a logically superior conceptual level, regardless of concrete experiences (top down approach). Knowledge is represented by abstract symbols, including written and spoken language. Through symbol manipulation, machines learn to recognize, understand, and use these symbols on the basis of algorithms. The intelligent system retrieves its information from so-called expert systems. Here the symbols and information are sorted in a specific way – mostly through logical ‘if-then’ relationships. What happens here is that the symbols and information are sorted in a specific way – mostly in logical ‘if-then’ relationships. The intelligent system can access these knowledge databases and then compare the information within with its own input.
Classic uses of symbolic KI are word processing and speech recognition but also other logical disciplines like playing a game of chess. Symbolic AI works based on set rules, and with increasing computing power, can solve more and more complex problems. This allowed IBM’s Deep Blue, with the help of symbolic KI, to win in a game of chess against the world champion at that time, Garry Kasparov.
The structure of an expert system is done on the basis of files that are equipped with processing rules. Here’s an example:
Based on such logical links, an expert system can imitate the cognitive behavior of humans. Expert systems are nearly always limited to a specialist, e.g., a specific branch of medicine.
The performance of symbolic AI goes up and down based on the quality of the expert system. Originally developers had hoped that the more progressive the technology, the more capable an expert system would be. This would then mean that the dream of strong artificial intelligence was somehow within reach. However, the limitations of symbolic AI are getting clearer and clearer. This is to do with the fact that regardless of how complex the expert system is, the symbolic AI remains proportionally inflexible. With some exceptions, variation or unspecified facts, the strictly rule-based system can be difficult to handle. On top of this, the symbolic AI has only a very limited capacity to acquire and gain independent knowledge.
Too rigid and not dynamic enough: the technology didn’t seem to be able to meet the very high expectations that led to the so-called ‘AI winter’ in the mid-1970s. This lasted until well into the 1980s as nearly all financial support fell away dramatically. It was during this deep lull that the technology went in a revolutionarily new direction; the development of self-learning systems. And so the work on artificial neuronal networks revived AI research.
It was Geoffrey Hinton and his two colleagues who revived neuronal AI research in 1986 and with it the research field of artificial intelligence. The further development of the backpropagation algorithm created the basics for Deep Learning, with which nearly all AI operates these days. It’s thanks to this learning algorithm that deep neuronal networks can continually learn and grow by themselves – and now overcome challenges where symbolic AI once failed.
Neuronal artificial intelligence (aka. connectionist or sub symbolic AI) has moved away from the principle of symbolic representation of knowledge. Similar to what happens to the human brain, the knowledge is instead split up into tiny functional units, artificial neurons, which then connect to become ever growing groups (bottom up approach). This results in a diverse and branched network of artificial neurons.
Neuronal AI attempts to replicate the operating principle as precisely as possible and to artificially simulate its neuronal networks. Unlike with the symbolic AI, the neuronal network is ‘trained’ – for example, in robotics this is done with sensory motor data. From these experiences, the AI generates an ever growing knowledge base. And this is exactly where the big breakthrough was – while in the grand scheme of things this training requires a lot of time, the system is now in a position to learn independently. This is what we now call ‘self-learning systems’ or ‘machine learning’. This has made neuronal artificial intelligence into very dynamic, adaptable, and versatile systems, some of which are no longer completely comprehensible for humans.
The structure of artificial neuronal networks nearly always follows the same principles:
An overwhelming majority of the most recent AI successes can be attributed to neuronal networks. Under the heading Deep Learning, we group the extraordinary achievements of self-learning systems in innovation research. This applies to everything from speech and writing recognition to self-driving cars. It was thanks to deep neuronal networks that allowed Google’s DeepMinds AlphaGo to defeat the South Korean professional Go player Lee Sedol in 2016. Go is recognized globally as being one of the most complex strategic board games.
On the other hand, Google’s Inception, originally a system for image recognition, creates striking dream images. They created a lot of viral hype in 2015 under the hashtag #DeepDreams. This ‘side effect’ of the system was only discovered by its developers coincidentally. They wanted to find out exactly how the artificial intelligence that they had created actually worked.
From blind optimism about progress to a simple refusal to acknowledge technology – intelligent technology provokes every range of emotion and reaction. This can be primarily attributed to there being both positive and negative future projections about how these technologies will change our lives. What are the prospects and risks associated with artificial intelligence? Here we have compiled the most important points of view from both AI enthusiasts and skeptics.
There is a whole range of advantages and prospects when it comes to artificial intelligence. The most important advantages are undoubtedly in the world of work, where it can be highly efficient and dramatically improve economic prospects.
“AI today is advancing the diagnosis of disease, finding cures, developing renewable clean energy, helping to clean up the environment, providing high-quality education to people all over the world, helping the disabled (including providing Hawking’s voice) and contributing in a myriad of other ways. We have the opportunity in the decades ahead to make major strides in addressing the grand challenges of humanity
. AI will be the pivotal technology in achieving this progress.”
- American author, computer scientist, inventor and futurist Ray Kurzweil writing in TIME magazine in December 2014; Source: time.com/3641921/dont-fear-artificial-intelligence/
Proponents of the new technology refer primarily to the prospects offered by artificial intelligence:
However, there are also some prominent experts, like physicist Stephen Hawking or the aforementioned Silicon Valley icon Elon Musk, who warn about the risks of artificial intelligence. These critical voices also happen to have the support of larger organizations and initiatives. For example: the Future of Life Institute (FLI) regularly mobilizes renowned critics to help call for a responsible approach to technology.
“The pace of progress in artificial intelligence (I'm not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast - it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most. This is not a case of crying wolf about something I don't understand.”
- Elon Musk, Tesla founder, and AI investor, during an interview in 2014 (Source: uk.businessinsider.com/elon-musk-killer-robots-will-be-here-within-five-years-2014-11)
Here are some of the risks of artificial intelligence:
How does artificial intelligence work in the digital world? Firstly it must be pointed out that for the ordinary person, artificial intelligence online is hardly visible. There are many companies who are very quick to distance themselves from the term, even if it is the case that their products actually need artificial intelligence to function. As big as the fascination with AI might be, the associated negative connotations are also quite prominent. Consumers are often skeptical of AI technology in everyday life. On top of this, it is not always easy to categorize a technological performance as ‘intelligent’; the fluid nature of how such technology is applied as well as the differing definitions of artificial intelligence can often lead to a sort of confusion surrounding such matters.
However, it is pretty much inevitable that we will get more and more used to the idea and application of AIs. There are so many areas in the online world where AI technologies play a decisive role. The list of active AI technologies and programs is long and getting longer. Google dominates this market with its innovations that are miles ahead of everyone else’s – apparently as many as 2-3 years ahead of other IT companies. But just how is artificial intelligence being integrated into well-known search algorithms and SEO? Below you’ll find some examples of the groundbreaking technology and software in this sector.
Artificial intelligence should not be confused with the Semantic Web. While the roots of the Semantic Web originated in AI research, today the two sectors have nearly nothing to do with one another.
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The innovations of self-learning systems are bringing about big changes in the online world. With the purchase of the company DeepMind in 2014, Google showed that its search algorithm will further specialize itself in artificial intelligence. Google is constantly buying start-ups in the area of AI research – just like the British companies Vision Factory and Dark Blue Labs – and integrating them into its DeepMind team.
Up till now, the Google AI initiative that has had the biggest influence has been the intelligent algorithm RankBrain. Highly efficient in the recognition of new search queries, in 2015 it was globally implemented Google’s search algorithm. Since then it has become one of the three most important ranking factors, alongside links and content. RankBrain’s specialty is converting search queries into mathematical entities. This makes it easier to recognize the intention behind the search query. However, it is unknown how the artificial intelligence behind this actually works.
The influence that AI has on Google search cannot be overstated. SEO expert Mark Traphagen quotes its CEO Sundar Pichai: “We are now witnessing a new shift in computing: the move from a mobile-first to an AI-first world. We want to build a personal Google for every user.” A personal Google for everyone means the complete individualization of online search using artificial intelligence. This is an immense challenge for SEO.
The artificial intelligence from RankBrain orders search queries by converting its known data into hypotheses and generalizations and applying these to the respective input. As it is constantly being fed with new data, this means that its conduct is always changing. This means that Google is not working with weekly updates performed by people, instead, they are working increasingly with real time calculations from self-learning systems. Given that the algorithms were already only partly understandable, this now makes the dynamic and personalized AI SEOs even more difficult.
The role of artificial intelligence in SEO is outlined below under the respective headings. There is one thing that you need to bear in mind when it comes to SEO: everyday AI is gaining new knowledge, relating to the quality of a website, from user experiences and signals. These are then applied to future rankings. Google knows where a user clicks, which links they use, how long they spend on a page, and how likely they are to react to an advertisement. The following points could be helpful when it comes to SEO:
1. User signals are highly relevant: It’s no longer all about clicks. What is now also crucial is things like time spent on page and, according to some studies, social signals. Four things are truly decisive:
2. Semantics versus keywording: RankBrain was originally developed in order better understand longer and previously unknown search queries. The result is that Google is getting better and better at interpreting searches in everyday language and the respective intentions of these searches. This shows that with Google, semantic phrases are becoming more and more important, whereas classic keywording is losing importance. Hence, when it comes to achieving a good ranking, it is the quality of the website’s content and its relevance to the user, which is becoming more decisive.
3. Google recognizes user satisfaction: Google evaluates user signals in order to rate the quality of a website more precisely than was done by search algorithms prior to RankBrain. For this reason, it makes sense to focus on a greater user friendliness. The key to such improved ‘usability’ lies in comprehensible texts and intelligent link building. The loading speed of a page is also decisive for user satisfaction and the length of their stay on the page. The navigation menu is crucial as well – both users and any AI need to be able to find their way around easily. In short, it is not just flawless content but also technical perfection that is demanded.
4. Promote inter-divisional online marketing: The larger a company is, the more it will invest in their online presence, the bigger their team of online marketers, SEO people, social media specialists, usability managers, and so on. If you want to be able to react effectively to innovations in the field of artificial intelligence, then all of these parties must all be pulling in the same direction.
While the rankings are getting more flexible, the good news is that this hasn’t made too much of an impact on search engine optimization. It has been a long time since pure keywording has been the only standard for successful SEO. In the last few years, the industry has made user satisfaction one of the main focuses; at the end of the day, the target audience should want to visit the website frequently.
What is the role of artificial intelligence when it comes to rankings? The answer is, it doesn’t differ all that much from the classic algorithms. AI-supported algorithms do not necessarily work any differently, however, they are more efficient and precise because they register and take in more of what is actually relevant for internet users. Existing SEO strategies should not be discarded, they should be enhanced and optimized with greater expertise.
One thing that’s left to say is that in 2016, Silicon Valley’s big five (Google, Amazon, Facebook, IBM, and Microsoft) decided to combine their artificial intelligence research.. This news should immediately cause some alarm for data privacy conscious users, as these companies have access to the majority of global data. But above all, this cooperation is primarily committed to the development of common ethical guidelines when it comes to the overall issue of artificial intelligence. The necessity of common ethical guidelines in the area of artificial intelligence cannot be denied. Without a doubt, developing AI technology in a way that is universally beneficial should be the central focus for our society in the coming years and decades.
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